diff --git a/.agents/plugins/marketplace.json b/.agents/plugins/marketplace.json
index 6e0e657..6adbb5f 100644
--- a/.agents/plugins/marketplace.json
+++ b/.agents/plugins/marketplace.json
@@ -14,7 +14,7 @@
         "installation": "AVAILABLE",
         "authentication": "ON_USE"
       },
-      "category": "Business"
+      "category": "Business & Operations"
     },
     {
       "name": "data-analytics",
@@ -26,7 +26,7 @@
         "installation": "AVAILABLE",
         "authentication": "ON_USE"
       },
-      "category": "Analytics"
+      "category": "Data & Analytics"
     },
     {
       "name": "product-design",
@@ -38,19 +38,7 @@
         "installation": "AVAILABLE",
         "authentication": "ON_USE"
       },
-      "category": "Design"
-    },
-    {
-      "name": "financial-markets",
-      "source": {
-        "source": "local",
-        "path": "./plugins/financial-markets"
-      },
-      "policy": {
-        "installation": "AVAILABLE",
-        "authentication": "ON_USE"
-      },
-      "category": "Business"
+      "category": "Creativity"
     }
   ]
 }
diff --git a/README.md b/README.md
index 07ab824..ef0c4b5 100644
--- a/README.md
+++ b/README.md
@@ -2,8 +2,8 @@
 
 Role-specific plugins make Codex easier to customize for a team's day-to-day work.
 These templates package domain-specific skills, connector bindings, and starter
-assets so teams can adapt Codex for roles like sales, data analytics, product
-design, and financial markets. They were built with OpenAI subject matter
+assets so teams can adapt Codex for roles like sales, data analytics, and
+product design. They were built with OpenAI subject matter
 experts around workflows that are already helping teams move faster internally
 and with alpha partners. Over the coming weeks, we'll continue expanding this
 collection with more roles, workflows, and examples.
@@ -19,7 +19,6 @@ available to the target workspace.
 | [Sales](./plugins/sales) | Prepare for meetings, follow up after calls, review pipeline, find account context, and build deal plans. | Salesforce, HubSpot, Slack, Google Drive, Gmail, Outlook, Outreach, Clay, ZoomInfo, and other sales tools |
 | [Data Analytics](./plugins/data-analytics) | Query, visualize, explain, and validate datasets; build dashboards; and investigate metrics. | Databricks, Snowflake, BigQuery, Hex, Amplitude, Mixpanel, Statsig, Metabase, ThoughtSpot, Google Drive, Slack, Microsoft 365, and more |
 | [Product Design](./plugins/product-design) | Create product specs, prototypes, UI critiques, and product design artifacts. | Sites |
-| [Financial Markets](./plugins/financial-markets) | Build public-equity research, earnings analysis, valuation work, model updates, long/short pitches, risk reviews, dashboards, and investment memos. | FactSet, LSEG, Morningstar, Daloopa, Quartr, S&P, PitchBook, Slack, Google Drive, Gmail, SharePoint, Teams, and more |
 
 ## Repository Layout
 
@@ -29,8 +28,7 @@ available to the target workspace.
 `-- plugins/
     |-- sales/
     |-- data-analytics/
-    |-- product-design/
-    `-- financial-markets/
+    `-- product-design/
 ```
 
 Each plugin generally follows this structure:
@@ -66,7 +64,6 @@ matching app or connector id available to your workspace.
 | Placeholder | Replace with |
 | --- | --- |
 | `REPLACE_WITH_SALESFORCE_APP_OR_CONNECTOR_ID` | Salesforce or Agentforce Sales |
-| `REPLACE_WITH_SITES_APP_OR_CONNECTOR_ID` | Sites |
 
 Canonical shared platform connector ids and existing `templated_apps_*` template
 registry ids are portable bindings and should be left unchanged. Do not copy
diff --git a/plugins/data-analytics/.app.json b/plugins/data-analytics/.app.json
index ad12dcb..346d4b4 100644
--- a/plugins/data-analytics/.app.json
+++ b/plugins/data-analytics/.app.json
@@ -1,83 +1,113 @@
 {
   "apps": {
     "slack": {
-      "id": "REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID",
+      "category": "Team communication",
+      "optional": true
     },
     "teams": {
       "id": "connector_246af0940da3457da0e751171dc1ce60",
+      "category": "Team communication",
       "optional": true
     },
     "notion": {
-      "id": "REPLACE_WITH_NOTION_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_NOTION_APP_OR_CONNECTOR_ID",
+      "category": "Company docs",
+      "optional": true
     },
     "gmail": {
       "id": "connector_2128aebfecb84f64a069897515042a44",
+      "category": "Email context",
       "optional": true
     },
     "outlook_email": {
       "id": "connector_4aaab2856305417b993eca9a216aaf6e",
+      "category": "Email context",
       "optional": true
     },
     "outlook_calendar": {
       "id": "connector_e6a7394682e24467ac68c60696f275a4",
+      "category": "Calendar context",
       "optional": true
     },
     "google_calendar": {
       "id": "connector_947e0d954944416db111db556030eea6",
+      "category": "Calendar context",
       "optional": true
     },
     "google_drive": {
-      "id": "connector_5f3c8c41a1e54ad7a76272c89e2554fa"
+      "id": "connector_5f3c8c41a1e54ad7a76272c89e2554fa",
+      "category": "Company docs",
+      "optional": true
     },
     "databricks": {
       "id": "templated_apps_Databricks",
+      "category": "Data warehouse",
       "optional": true
     },
     "bigquery": {
       "id": "connector_7c3f2c8cdbf64bb0b183ff52f527a06e",
+      "category": "Data warehouse",
       "optional": true
     },
     "snowflake": {
       "id": "templated_apps_Snowflake",
+      "category": "Data warehouse",
       "optional": true
     },
     "sharepoint": {
       "id": "connector_1e4f6a44acf14e3ca1d96672f8c945bc",
+      "category": "Company docs",
       "optional": true
     },
     "github": {
-      "id": "connector_76869538009648d5b282a4bb21c3d157"
+      "id": "connector_76869538009648d5b282a4bb21c3d157",
+      "category": "Code repository",
+      "optional": true
     },
     "statsig": {
       "id": "REPLACE_WITH_STATSIG_APP_OR_CONNECTOR_ID",
+      "category": "Behavior signals",
       "optional": true
     },
     "hex": {
-      "id": "REPLACE_WITH_HEX_APP_OR_CONNECTOR_ID",
+      "id": "connector_690a9430a270819196671dcb4c95898e",
+      "category": "Notebook lab",
       "optional": true
     },
     "deepnote": {
       "id": "REPLACE_WITH_DEEPNOTE_APP_OR_CONNECTOR_ID",
+      "category": "Notebook lab",
       "optional": true
     },
     "amplitude": {
-      "id": "REPLACE_WITH_AMPLITUDE_APP_OR_CONNECTOR_ID",
+      "id": "connector_690e2dabf430819196f8b3701ec838ec",
+      "category": "Behavior signals",
       "optional": true
     },
     "mixpanel": {
       "id": "REPLACE_WITH_MIXPANEL_APP_OR_CONNECTOR_ID",
+      "category": "Behavior signals",
+      "optional": true
+    },
+    "posthog": {
+      "id": "REPLACE_WITH_POSTHOG_APP_OR_CONNECTOR_ID",
+      "category": "Behavior signals",
       "optional": true
     },
     "omni-analytics": {
       "id": "REPLACE_WITH_OMNI_ANALYTICS_APP_OR_CONNECTOR_ID",
+      "category": "Dashboards and BI",
       "optional": true
     },
     "metabase": {
-      "id": "templated_apps_Metabase",
+      "id": "templated_apps_6a044bbb47ec819193270b902fa5cd03",
+      "category": "Dashboards and BI",
       "optional": true
     },
     "thoughtspot": {
       "id": "REPLACE_WITH_THOUGHTSPOT_APP_OR_CONNECTOR_ID",
+      "category": "Dashboards and BI",
       "optional": true
     }
   }
diff --git a/plugins/data-analytics/.codex-plugin/plugin.json b/plugins/data-analytics/.codex-plugin/plugin.json
index 6c477f1..cb672fb 100644
--- a/plugins/data-analytics/.codex-plugin/plugin.json
+++ b/plugins/data-analytics/.codex-plugin/plugin.json
@@ -1,7 +1,7 @@
 {
   "name": "data-analytics",
-  "version": "0.1.34",
-  "description": "Analyze product usage, investigate metric movements, prepare KPI reports, build high-quality dashboards and notebooks, create source-backed semantic layers, guide first-run analytics setup, and generate analytics-grade report apps.",
+  "version": "0.2.6",
+  "description": "Answer product and business questions with data",
   "author": {
     "name": "Data Analytics Maintainers"
   },
@@ -11,6 +11,8 @@
   "keywords": [
     "data-analytics",
     "analytics",
+    "business intelligence",
+    "sql",
     "business-context",
     "dashboards",
     "funnel-analysis",
@@ -33,7 +35,7 @@
     "mixpanel-headless",
     "metabase",
     "thoughtspot",
-    "onboarding",
+    "guided-flow",
     "semantic-layer"
   ],
   "skills": "./skills/",
@@ -41,10 +43,10 @@
   "mcpServers": "./.mcp.json",
   "interface": {
     "displayName": "Data Analytics",
-    "shortDescription": "Turn data into clear decisions",
-    "longDescription": "Turn analytical questions into validated answers, dashboards, reports, notebooks, and clear recommendations. Explore product and business data, explain why key metrics changed, design KPIs, size opportunities, check data quality, and save reusable metric and source context for future work.\n\nStart with connected warehouses, BI tools, product analytics, docs, chat, spreadsheets, or uploaded files. Guided onboarding helps you connect core tools and set up reusable data context, while interactive charts, tables, dashboards, and report apps keep the evidence reviewable and ready to share.",
+    "shortDescription": "Answer product and business questions with data",
+    "longDescription": "Data Analytics helps you turn questions about your product or business into answers you can trust. Ask why a metric changed, what the data shows about where a team should focus next, how to define KPIs, whether a dataset is reliable, or how large an opportunity might be, and it can help you investigate the data and turn the findings into shareable reports, charts, dashboards, notebooks, and recommendations.\n\nStart with the data you already have: connected warehouses, BI or product analytics tools, docs, chats, spreadsheets, uploaded files, pasted results, or clearly labeled sample data. The plugin guides you through the right workflow, checks sources where possible, and keeps the evidence visible in reviewable tables, charts, dashboards, and report apps you can share.",
     "developerName": "OpenAI",
-    "category": "Analytics",
+    "category": "Data & Analytics",
     "capabilities": [
       "Interactive",
       "Read",
@@ -54,8 +56,8 @@
     "privacyPolicyURL": "https://openai.com/policies/privacy-policy/",
     "termsOfServiceURL": "https://openai.com/policies/terms-of-use/",
     "defaultPrompt": [
-      "Help me get started and set up reusable data context for future data work",
-      "Analyze product usage and recommend where the team should focus next",
+      "Help me get started with my first data task",
+      "Analyze product or business data and recommend where to focus next",
       "Diagnose why a key metric changed and identify the biggest drivers"
     ],
     "brandColor": "#0285FF",
diff --git a/plugins/data-analytics/.mcp.json b/plugins/data-analytics/.mcp.json
index 69d2fcf..0fc3663 100644
--- a/plugins/data-analytics/.mcp.json
+++ b/plugins/data-analytics/.mcp.json
@@ -1,7 +1,7 @@
 {
   "mcpServers": {
-    "datascienceWidgets": {
-      "title": "Data Analytics Widgets",
+    "dataAnalyticsWidgets": {
+      "title": "Data Analytics",
       "description": "Render Data Analytics charts, tables, dashboards, and report artifacts.",
       "icons": [
         {
diff --git a/plugins/data-analytics/DEPENDENCIES.MD b/plugins/data-analytics/DEPENDENCIES.MD
deleted file mode 100644
index 25eb593..0000000
--- a/plugins/data-analytics/DEPENDENCIES.MD
+++ /dev/null
@@ -1,27 +0,0 @@
-# Dependencies
-
-## How tool references work
-
-Data Analytics plugin files use lane placeholders such as `~~structured_data` for whatever source the user connects or provides in that category. For example, `~~structured_data` might mean a warehouse connector, SQL runner, API/CLI result, pasted query output, uploaded file, or schema description.
-
-The plugin is tool-agnostic. It describes workflows in terms of analysis lanes rather than one required stack. Direct connectors give the best experience, but the workflows should still be useful with pasted results, SQL snippets, screenshots, exports, or uploaded files.
-
-## Dependencies for this plugin
-
-| Category | Placeholder | Providers | Purpose |
-| --- | --- | --- | --- |
-| Data warehouse | `~~structured_data` | Databricks, BigQuery, Snowflake, Redshift, PostgreSQL, MySQL | Query tables, explore schemas, and produce result sets. Databricks, BigQuery, and Snowflake are declared in `.app.json` as app-backed connector choices. |
-| Notebook lab | `~~notebook_lab` | Hex, Deepnote, Jupyter | Iterate, preserve reusable code, and support richer exploratory work. Hex and Deepnote are declared in `.app.json` as app-backed connector choices. |
-| Behavior signals | `~~behavior_signals` | Amplitude, Mixpanel, Mixpanel Headless | Analyze product event trends and funnels. Amplitude and Mixpanel are declared in `.app.json`; Mixpanel Headless is a companion plugin/skill path when installed. |
-| Dashboards and BI | `~~dashboards_or_bi` | ThoughtSpot, Omni Analytics, Metabase, dashboard/BI app | Inspect dashboard metadata, metric definitions, widget SQL, and BI answers. ThoughtSpot, Omni Analytics, and Metabase are declared in `.app.json` as app-backed connector choices. |
-| Team communication | `~~team_communication` | Slack, Microsoft Teams | Search recent decisions, blockers, owner confirmations, and links to durable artifacts |
-| Email context | `~~email_context` | Gmail, Outlook Email | Inspect relevant message threads, stakeholder context, and follow-up history |
-| Calendar context | `~~calendar_context` | Outlook Calendar | Check meeting timing, attendees, and operating cadence context |
-| Company docs | `~~company_docs` | Google Drive, SharePoint, Google Docs, Google Slides, Notion | Read workspace knowledge, PRDs, metric docs, strategy docs, and source caveats |
-| Sheet workspace | `~~spreadsheet_workspace` | Google Drive, SharePoint, Google Sheets | Review small exports, manual QA, and stakeholder-friendly tables |
-| Presentation surface | `~~presentation_surface` | Google Drive, SharePoint, Google Docs, Google Slides | Create final charts, dashboards, and share-ready narratives |
-| Code repository | `~~code_repository` | GitHub, local checkout | Inspect source logic, model SQL, lineage, tests, and repo context |
-| Files, notebooks, or spreadsheets | `~~files_and_notebooks` | Google Drive, SharePoint, local files, uploaded files, notebooks, spreadsheets | Use local or uploaded analysis inputs, notebooks, CSVs, and spreadsheets |
-| Application hosting | `~~application_hosting` | Site Creator | Host reports, deployed app links, and previewable interactive artifacts |
-| Planning tracker | `~~planning_tracker` | Atlassian, Linear, Asana, Jira | Track analysis requests, owners, deadlines, and dependencies |
-| Data operations | `~~operations_logs` | Airflow | Check pipeline freshness, model ownership, lineage, and outage context |
diff --git a/plugins/data-analytics/README.md b/plugins/data-analytics/README.md
index e493400..83d6c3d 100644
--- a/plugins/data-analytics/README.md
+++ b/plugins/data-analytics/README.md
@@ -1,30 +1,26 @@
 # Data Analytics
 
-Answer product and business questions with data, explain why metrics changed, and turn analysis into reports, dashboards, and clear decisions.
+Data Analytics helps you answer product and business questions with data you can trust, then turn the findings into shareable reports, charts, dashboards, notebooks, and recommendations.
 
 ## When to use this plugin
 
-Use Data Analytics when you need to understand product or business performance, explain why a metric changed, define a success measurement plan, assess whether data is trustworthy, or package analysis for stakeholders. You can start from connected data in a data warehouse, dashboards, notebooks, spreadsheets, uploaded files, or pasted context.
+Use Data Analytics when a product or business decision depends on metric-backed evidence: understanding performance, explaining a metric movement, deciding where to focus, defining KPIs, checking whether data is reliable, or sizing an opportunity. Start with connected warehouses, BI or product analytics tools, docs, chats, spreadsheets, uploaded files, pasted results, or clearly labeled sample data.
 
-## Onboarding
+## Get started
 
-Ask Codex:
+Try asking:
 
-`@Data Analytics Help me get started and set up reusable data context for future data work`
+`@Data Analytics Help me get started with my first data task`
 
-Data Analytics has a guided onboarding flow that helps confirm the right data sources, save reusable metric and source-of-truth context you provide, and build a context-specific first analysis prompt.
-
-Onboarding is an interactive, step-by-step conversation. Codex will guide you through setup, ask for approval before making changes, and help you try a first workflow.
+The plugin will help you choose a data source, pick a useful workflow, and decide whether to use connected tools, upload data, paste results, or try sample data.
 
 Already have a focused task? Start directly with one of the workflows below.
 
 ## Example workflows
 
-The primary hero workflow is bolded.
-
 | Workflow | Try this | Skill | Result |
 | --- | --- | --- | --- |
-| **Analyze a product or business question** | `Analyze activation and recommend where the team should focus next` | `product-business-analysis` | A decision-ready analysis with evidence, measurable opportunities, and a clear recommendation |
+| Analyze a product or business question | `Analyze activation and recommend where to focus next` | `product-business-analysis` | A decision-ready analysis with evidence, measurable opportunities, and a clear recommendation |
 | Diagnose a metric movement | `Diagnose why weekly active users dropped last week` | `metric-diagnostics` | A calibrated explanation of verified drivers, likely contributors, unresolved questions, and next actions |
 | Design KPIs | `Design a KPI framework for this new product area` | `design-kpis` | A measurement plan with outcome metrics, drivers, guardrails, targets, and validation priorities |
 | Prepare a KPI readout | `Turn this month's metrics into a leadership-ready operating update` | `kpi-reporting` | A concise KPI update with actuals, comparisons, validated drivers, and operating implications |
@@ -33,7 +29,6 @@ The primary hero workflow is bolded.
 | Build an analytical report | `Create an executive report explaining the biggest growth drivers this quarter` | `build-report` | A polished report with answer-first narrative, charts, tables, caveats, and source metadata |
 | Improve or render a chart | `Turn this analysis into a clear chart for the product review` | `visualize-data` | A production-ready visual with the right chart type, labels, hierarchy, and accessibility checks |
 | Create a notebook | `Build a reproducible notebook for this experiment readout` | `jupyter-notebooks` | A clean SQL or Python notebook that can be skimmed, rerun, and extended |
-| Work with spreadsheets | `Analyze this workbook and add a polished summary tab` | `spreadsheets` | A verified spreadsheet artifact with formulas, formatting, charts, tables, and a clear readout |
 | Validate an analysis | `Review this analysis before I share it with leadership` | `validate-data` | A QA pass covering methodology, sources, calculations, analytical pitfalls, caveats, and conclusion strength |
 | Assess data quality | `Check whether this table is reliable enough for our retention analysis` | `analyze-data-quality` | A source-backed quality assessment covering grain, freshness, missingness, duplicates, joins, and material risks |
 
@@ -44,17 +39,9 @@ Data Analytics can use available tools when they are connected:
 | Source | Supported integrations | What they unlock |
 | --- | --- | --- |
 | Warehouses and query tools | Databricks, Databricks Genie, BigQuery, Snowflake | Schema inspection, query-backed analysis, and source-grounded metric investigation |
-| Product analytics and BI | Amplitude, Mixpanel, Omni Analytics, Metabase, ThoughtSpot, Statsig | Behavior analysis, dashboard context, experiment evidence, and reusable reporting inputs |
+| Product analytics and BI | Amplitude, Mixpanel, PostHog, Omni Analytics, Metabase, ThoughtSpot, Statsig | Behavior analysis, dashboard context, experiment evidence, and reusable reporting inputs |
 | Notebooks and analytical workspaces | Hex, Deepnote | Reproducible analysis, notebook handoff, and shared analytical context |
 | Docs and collaboration | Google Drive, SharePoint, Notion, GitHub, Slack, Microsoft Teams | Business definitions, source-of-truth documents, implementation context, and stakeholder evidence |
 | Email and calendar | Gmail, Outlook Email, Outlook Calendar | Supporting context for stakeholder questions, operating cadence, and analytical handoff |
 
 You can also start with spreadsheets, uploaded files, pasted query results, schema descriptions, or manually provided business context.
-
-## Local development
-
-The published plugin does not require users to install Node.js dependencies. For local development of the Data Analytics MCP server and widgets, install dependencies from the plugin directory:
-
-```sh
-npm ci
-```
diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part001 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part001
index 1f9e968..d1374fb 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part001
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part001
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part002 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part002
index e064074..4f3af0b 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part002
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part002
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part003 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part003
index 448e85f..3686d24 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part003
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part003
@@ -1 +1 @@
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
diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part004 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part004
index da5b2b8..94ae670 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part004
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part004
@@ -1 +1 @@
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ClRwPPjrjSc14JINsDVDtw+82nvt05CnxvS/oB+kJMISzbvCJGtAETZjcmAXAYdu5LJg0EghKHNEE9ThEzwVS+r8qzXv2SL/YgeNjildKtrlLKUY1prZo1b+nt1DnkPhlpqFyBjmRsLgC13aVDar4U62GkEHroV+5aojSdrAP+tmu7cgdMPaipk6SLmJ0t47E5yPx9kh8OBKbR2L3SPx5JN4ciV+PxPsj8eVIfD3CBETvjliH81H+/YM0VBv4r7mAD50j+edRhY0jebAyV9whSsgSS7IurawKHaHtmyNN0H0+WsIJ7mdr25+QojJ6tR1NV8fBr8uANf488tsHOKCGzly92BQ0hnFqMWrZsvdeLUAqQkAPZZ7dYmpLA0YHtfqbsp28CtL2wkKiAQnlzjYXeMgnzxJzHsk1og37nGfDOC9vWQ65CCjqPA2R1sSdWT2LgxADa4ZmhXecFd46AiYPNnQpAdhKS1pnI7GSyaGMRpAEGij6XMILPkpSZDjaJSa+mM079nvEg6nfKl815+dJHWheyUPJO0MpzRU9QnNGGQ5cRu7khmh8lJ4vJikaOyzKFA9hqCKuIqhml8NRGfcpvJOH25wbrQ5cDL6UNlGPTHyEGFKYtJmLPP9FTCIr4xMCrMD6tb9nONBRD0OUjmd+mMHotr6T5bVy21C3bU7pfoSXFCa1xJ9N+ZPMJrhoWRZJiwsu/FEWskkFlz2XZcoCg0t/kqU8Ti57MT/PhWi3YWd13bGHaQvCtWD9pJ/kzonUYhUsbHg1uUMkp/qZzup2vo+GhEZQWTbk3UBdK9Ud+zyxIS4aCCjyA5PoEgitlACfp7DnGIwHVwW4CGhu3bqlM5k3Gp2kgTlgG96LhgKjL0dLc2yDBtgBFufzEaGeaeItG5G0p9QKtcb8Y3x7gvFrsdU4bMfd/BjG6pMoIHb4RMwTgN8WFCyeXthzjcpIwy0jdY7+6bfmmrSQD7boo+IjogyWspmD+XAT+DCIkn4oGR4ABsWXRDkuLNsO3sAoScGfqnG52GynOx5hzzcw/hRRp8BVhYWHddobZMiH0+Q4kEVC32BatJ1w13PYdYLmD5gbHU8YkkIJjBLvDUShdL8rmXXlwmMlGa2EFmsvyUkjXhBbR76lEsoYRGD5pQUy6piRDgnCswi+Eo6/Hi0EpRKYmIal1E1pPSBOU32HwYfGyztZuSMYyPpoSGqlK94xtiW+DDT89ahN16c0WkilYcmrIIdbI/PvEMK72TE0kNyVAnIotKeJ1GDn3ZHWwnrw7Bgk4L1MBYw/fb+1QwRk9iqXNKPaCmiMS7JxAAh8SZIDxi7E2gkYPN790cLXIysh8Y4h9m3uBUOFOAmFaDAe2yhojvXzHDx3KODfIpEtIcJ8TIYWZoSd0AA54w+ZH4ZCdIVjCztZyX133INLAwUSAKgteCwRnsnwBBjTCkDjlsEe46H7FKXRedzXxjFeYseHPYvVJ/B2zttVkqgeg8AqSkYOpSDMx9GhdABD7YTKWAX3boR/7DWm6CyYikA9K+I09yM4XWh/oA9WjARWinmcSjr45NWBBhuZUvgw4CNrNe5FZe8C5npn6IfEpAman9fgjIHGHStlvhEwjnS2VEbnqEkeC5KRkplCa+YOSvF1qV/PAP5s0f0KNbNBv1oztjqSVdPxeCmU5utsqB75Y/6ZjA+P5FFVGEkeIWB3DmFO8/OVY5NUj01iHxuDmnKkIhmECnxkECK2T2eNS/D+WV7+GaqYwogLXyLoIXKNGC0CeigwApWIkAo1Rh0RVcKlT2xcnC5FZ0AsqJGnS6cx4I7Y/J44uhpqpdXaAeLhhkjgczEb0mSA/mERYKusU47ZA5jhqlyBro1cDt0UAXYGbLgYBVHVZk70g4Y4k2YZ7f6rIJufP3sVFNp6ruz2yZShe0YRfWMC3FFQ7HgRgmikZuRDVwsL4mxhgcEZA2JZFz786CG0n0G9ni73e1hair5Z2Nme5Br8O+ivrWRaxDgN9CsaADEjcF8jjWfGQcPoGRbD6YI7z/XFO1rGywmADi/YEZqSXCflxRvaNG/kO7dNjGHcH5g3b0fJb51ufHpdhfPEuieSiXviYYCHewIPCe4UXBRJ/UXB+eP6rzNxGZy9pmB+Q6h9qYG1Wyw0jwHZxEapSBD3mcYoqfg3t0BDEf2G+wfAIgp3fqU8fzOX0Dl84ACZp77IrDUCgqsBgFmQiR/wINlDV1eOVxcBcz3KR/lQiZytFGvRwimpDq/Y/f3GEcpxCiZS5DK+A6Rg09YfdRf4RMgau6A15h5i1QOv+8cjH4liK+f2Ti3nDAO0yF6Lr8z1M/TvvWj60pbgD/jWrmGXN9UjXDwMocyN062B+62U7FC2Y5FcUe2ytuXlUNqZh4Oy5cU1ZJyo7wNNv2iP69oojgLhDk4injOcNIw0cfn0XISsDkAaDDeXfyHdBpcdgAburtLM4NLabo04fnk+YOmW0U7DuRNTNN4lXECtRDrWq4MCF1zwH398rrTKJUGMZMA9XH8i23SZT8R2JLTlam5RzGcetchkCy72iYaO4D4HuhCpDkTFaE7SoK/nE3Q1vbbv/vqlF3oD7dTRsCMtr/yODQTEyDS1Gn3OjJ+cAzyR0HiHjF540ABpf8CGOEokR0AFiGiH6cAdT8IpvkZ8Aac0djiGfBrHkGvwzR/nGMZ6DrMbR8AO8DBxKmR53t9n2eXXI4Xj8oqVTuxURYyTBzp3XJWZ0EAJQG1iPDtnHkUeaJgIm7KmyF80f8RyTQ8X6vCyKA2pEdTkGak78MZvGjBpf5ySis/KUr1jSwFSI+VtT/JG0CF+dtJmFlXwQH9arVEZb4zQnRnVGCG181dKFtfOFxZ8abADvLRRsFvaK0VxK1KERZF/AGZHqQqsZ5DC7iLYsa8IoALzAyOy3d/3G4AysDAl28kPRzSzgbT/K5YupWGf2ISLUewCvwMkH2JwrI/q9DfIWOlfG/QLTZe308Gt+PVIGwy48gR12OVbNbn3RwHcZjteiMKtUOIXmIaK36efNvXTrn76Uz+90U9b+ulX/fReP8FywPwzy0TfAoJf9cVioUUjVvn5J5IPWDb0O1b0b3J1j1mC3N8J4F24t765vrr/YXvrZPX9yta79ZNVWImVrTXA0OtncaXKh6299d39k9XND5/fbK/srp1sba+t71lNzrhTqLD60SoecvGb9bfbu+vQy+cv+9bbS35bLb6QfW1/+ry994G+v7e/smtXOZ+ssr61ZlU44QprMIP9dZzh7srq/vquVeNKfZxmtvlha/3kze76ij36W6fKZ+jj3e7K5/dWjVM1jq393e3NzfW1k/31g325XLS2uuoNV/28srdvr/UeF++uf9r+dZ1bm5erziy+bu/aU7x2XtIEzMt9fgnL/mllv9rtOr/8srW2bZWuqZE4pdtc+nH9cG37qz2fb/rFycru7vbXk90P797bX1mTUMQT28YNgtKVarPN9bd2q+1KK9p5KP+92u7LZ6vVp+rbymC3zPv1LRcOPptXe59XVu1FfGtevVlZ/Vh9/cHqdG915bP9btO8UzCo3+2ad/srb+wllXOXAOe+XNEv12AKJwhy/FdV+JO73f6yP6XCGwkwu9v20v0u+5WgAoP95Db7VTVbeVc9hu/NK9gue1U/yV7xlXsyv6gj8/nQKv0qSx1E8M7GVCcrm5v2jsoPrG6ur+yerK992N92dtV5/f7DHry2v/dRfm9l66QC8H+YN5UD8lav1OoXG/d9kOVvNr/YQ9iUxbjPn7bXPrz94KzQxo6yyz3L4/jP2LsbAxm7E3S7IVxM/ew6DYWrsT/YsX3JxNEO22QJkheiQiYrSM4D9CiwTNs7SIgdi24oc2/UdamkVdI7BrVOsjbau7TXl1nCjVrhMhv1LkL5AygfyuJ6OirLLGWT/Y0mfJUchG4bZBnn5XMBWejTKHo8QPy21OC4Q8ExX+gxW7XJ1dWtu3LQibDvFnoMz35Gl+PNgw72cI49tLxsjhIlzaEJaVRG3Cd7c1e6QtVQNkRlU3TO/iCwNXom5VI/KYYob4P1vYzSvndJQxR38RXFE4c9m9shOoyHPUh636qfMN3xch95yCiyu11J7nbnTaaKc+nwZgvTWWNOksiK01tVP45SclaF84akBBaDQTQs0L0NBcsNDw1J3jc8f1LfLMWYRK5nw02Y4ECSkVqFhBZCKNTFzowUA79DubOVpkhG30R3jxSVROUtOj4pQQ9an5VRgkKiH6VBgjPKiDgTsg0wM1Zk/Ztl4FPi4DWzH7O2HNpvS54EePcBnCuvoSRl9FELrgMNzsjpV14gaGtvHMvhsY0eUwjWawjdPvnPXDS9BIhdNngZI/ydEXhIGEZo29hhcB7equd+Hp3LE2AKMMuN/ZuAnn8OowJzXPAPWhb143Qwshphhl14Pm7/DufhcIf9VqESCWsk7MsYRPVIgOrQuSenDRakrZqDiIwTrJj0GEKLH+T2Qykqq9RjJDSk5aDPAMuA1CcgsQaiLhLB7+APeX5EPEfk99c4+oake+n+TOfQgCGnfxP6N6J/M/oXTjpwGr057mkwF3QbAgrgH6bcG8dGJz+am1T8KMMUclGkNRYFy0ylmUmPLJPEQB7bno8ywYHj9ziwgEX0oTXi5rOgNyFS62PCNO0mp3zEYSkzbAII7dArEH3Oogns7O+A4nZ2XuULPzV8zLyWFGt4ovCkDF8t399/lOAYybNP/fAjHJDCvRru79cb1Lnbz+smGtzCl3/HABln+mRXm8/Pj+Ddt4Z3Bl0MDANq/Mpm0VhBHmayjUZAX2XzhRivDnuVdIVtNfKB40zsU8q5ygi8whaQzlKkLCn7KyxJKb0grb9fY/lGhloqmoHxDyYDRUIqFkaq5jjRPsjK7Igxc0XbDlh9v2F52tMl2cHlidIPdGLwmEhJqk/maLVvpDrQ2cPE9dm1jKImLNSUNeEhndcdbaWmkSb173x5BRUZHgk8W9qWasLGSe51TcPJucgXtVMh7FDYvtdzjrvZWkMide2U6oBdg/9Yr1XGWuMmPWdbqxroa8GlagFbKxF08OlVJCwf9lY2DgAnzqGTCGEbGCLijvl5eEC5H7rZzqGgUV/0lP3YMbSB24KFmvjqBv3eUQqoxECcY/aIb/YefOnAQ1vOogKJ8hIsFLYqg96cfG6z8wztF/zFFdO0gCILFpp43+niJoKCRnF4iVuGdfX2jnT+8KPSxVt8RXRCtkUK+eLI1Z2iB5qzwNJQLxMzM+Z+HLkbJ+BGG1jS20NIQNEVSpROxNQSkWVALCWdxJDKrQmfeNHNWDEm+sfBYA7QNNJEgJZnm7ReFZxzNiHDxIFSoqDoVX9huUHUll5Yool4M0b+cM4rBHzNaLpSPVWyJ/EPpZS6+bzpi0usntdVNmTe7NCItpd/+oVXIHaWODakWsfFfkM6DmpCWtGkS7RqFcYBpDQNv5AoVmjto1Z74iVXBoWk1/BXHmi6rfDYW10mHuwFuO0YvOU6yvt0nfY6GRy8EfxNWhncmr0OZkqFW7MTt4B8CJYbz38Wl2S1qKTRAZCSr6J2rJymywCFquQxUIeocJPYOjdlfDFLukxEHGx9CT/6QNHgXQ7HA22tF5v2yzO49UjpC2B3iWzFcL5izDGk12zZOVZrFVx2MHgvUYX9HSH1nnaIuaFGTOoKY5tKeW/xgDz9DvNG84tUxAb8LXfqueoOu7urd1a3uJiTPguz7ONgRcWaMCqTN4xdx7Ipk1tMlxxRQ2W3cWw+tHsW2zeVpjRh12gM0vJbJAoHljYFSrupwIatk5PAtk+NNQc1e2AM4uA6B/Skw1EQljrQMTvKIFEXOe9zYmCYDnaV2YYBfYr7SbQJ9DgcwujVwc7Cj6jbq2ILJjE+S4QviOJFdSJ0lo1KjqEBV0XOV4Wqh+mYf2xAdSuqSDIZ7gQjJuffJOkmDkltiKhjmVBHQl6zdED19zk4DKzgGjt+eMq9crJ3ijwwoVhCe5e6+xwNDFxaiM+VWkhdFhkE8AGYB6JL5+czuuso7IDLAHq9YHau6fWWPdU9mj8DxT3bQzFBLk52hML7HIcEez3QhkmVzuaCuLr88q7ytYfxbEMG8pEwZPm/6AA2KEhoz+1o8uOg4SFHAvwLRgmi8whMz472upitTAyo3YY3ueY+O3hIJw4MdzG4Xdv+RCcMTYIkeyRJBL0iPQWvgEQtPhlRaUVOEGgeTTJwdgnl0ciKmE9Vhm/+I/QnQUbgul/tYJgefTVh7Y2DaZVvNc6zG/DS8WLSx8/i3OAFuxRngUFX2Nb62XAAHESI8D/xtWrcilKqrDMVFWWEByOFS9g+0QBBHW/K2E93gKrzW7D0mcJwO67wDQCmuE7QDqumC0AAGBlOrjG6wB1G6bewVSlcQ5bdLtxltTbe8rRVLTmWWDoRtCsdWDx32NLn/YFG2uaoUl1u7ER9DLR5nkfDC6t+AtTp7KeDjsdcuGnutyrbXjvmzyTNaNWVrhQ7o4zVezz1m+os+EJ4c+tM3HPpG+VVhyFikOOJJd+DsZYa9GuWGLOxT+m+UE6Ri71pX1qDBQhbw2Xv3VnMazW16upI7tiUGhKhcp2TiRXnSl+zvK9vOqq5umO8SKo14X53K9Z1+R5q0Mbrbq2Xe9lZ6b7Erq6nfFNOQX+25it179RHnOFeTwyX6Zw9Csi4zwEZuer+jgidOI1hTbs32aBvqmNoyLpaHygKpKnHUSHran5RwR5NZR3/0a1PseLzW2iQcd312jMgq+3GqtqaoQr1LaQppj+ZYgKsub/sKQE9QOttw/xy76iSSSglX2ZsChQVEVgkXVb48ICk+oAVMXRQ7mJFOMd3ERzxwDuf4+gls9paKa0jGLQcuy2tIy0nBX17pi7dXwRZJ3VJh1bqUA1wv2EVu6hVaUFyzknxMVuRRhbzqG/sZIK2WSpg92MP6DN/IV+Y+r5Y6CHHd9jw7H4BleASAa7NeYUGmtVpk5D0dZMIEkXsBg8ISyd1OF5luovBAGO0svjGrUkUpkLQ8vJj6gSlU1JGKUPMYfwwa3vv71VwINxmlWVXIVT/jsLqDI88BC1gCX1hw+gbm6o3wNeWOozJWRlnDknF1MFUG2bCLswC8O5HxLueBaJMdqMZU5UxBsyeVkmupCIsTLWwkOEkcUWGqRQZsvQadvbwoO5w/npmh/eElaTJS10TjvoPHLXd4nyu4kBsKbg+MsOhb9jUlnBpyyGi2kIrpGCKp/xbg4U2joA59dV514EbJ6VnyBOr8fAZjidCP6aWjscRxuTkepxWQz+mTuhHQeEdrW9zoEeN7sgjrVuaVKWS+pRT1JBEmoYmDVf+Nl4L9L12qYDICiKjEAkLlvwxbsDWjmNZOB6P8Zg0hBvM6n0VrMn+mblEF7khTTvHdl3LliOWkQPSISvmgNhwFBXQ1Po5dpyeDAWD9uzhSp5n17scx/euuEjOSsqvE6W3IcdX9PET33acwINP6axcNs3XzuKnt9+kAMPTx7Ky87192UPZ/p6hfBnCQKKBGYaYOqrfv2NUa6Qpf2LHn57W8TpHb3Y77ZX5wPy6jGXiJfeDmCmIvpaQUhu/ufXoN6VMI5wJ9UA/P9YIQ3fzYDMYqBqb/nwd56VHdMUU49++DNbUm0+cOn0HiY1iCMxUDaTe35uZXtTNlA4zmRO8ZXOCWjaxlHKIp23EetGLhrHZjQ9Pm8caEdAwSHfY/bphP/h92RGFS6SpbT46tab/fQudLk8FFOxy9Ykgoqb8hO6aT4RnGxoUeD0E19j/9aPDxf69yjbZvd/f88tvlc169LPNJ6zSqUGbU3qy2KBHexs9qTeb/3m0y+RJXWr+69H+9qPTh26d3aedpz8fHdX6pNyqdu87ukNr012s3eIqt5X9V3Dxp1Neg/CnjHDNHaHym3CiKC+dsBEMjLptxItIlXcSjvY67eS/w4X0W1NXsKdXsDOtiy+PdHHzeBdfuYsnjXVcZVts9CcXmFx65ALjM98/+CQ3b0wfmrT32iR6xKHlT+ZcB33pdEGRat9Dm4GJWGPbaAMF3z0WUxqgm0ZJIuirOW2FbqKGWl88HcZGAqAEvBZZKoX7q0B727yq44kga+ao0maTGiBZU2OjlpM+hULQVwygEjaASowBVC7YpR5oXOXY/0eD46bk3dwQ96iUJZt7kQVXc2TLHpEkAJi0NuaYT+fnz4hcRgQo6DGl5Ks47KSTzc9DM+ndF/kt+IHUPrmV6YU6pYWCNZZLzKvbD+ztu5kzADLbYP8vt7qp+8XRW83ZAVKCSpxLue4lVyr1gk9WAv53sdnOXwfkDNZ88RxYxzkrNErJMyPhS65UlWxaV4mGrM9474LTXd/OKVnNNco623u4VR0HgREvI/MhcIR3J9RLibsXUwA2mzWjnWvLfUt9jv5v9iOVUgdTElMLOOSJ78A8fZG/1yJ/3OYvL3nMUUAHq6roVRIV1PdG3fhY+vLUnaGupXHc08pVFGG0VUwVGW/HDpDB/F1Sc4qimogcR1rPSCLlHYvtK9vGwKSVsgWJDojmyMxsaYdsfLlMyjvktJmJ5ZFaMYB8cUnGjyrkRH2dkmLuZ6gxJ2Y5YoWk8hOUcpcUHhXrzAQicvS3yLcil15vS8TFbsAp/BIeUbeX9J/qhUEIOH0aM+vy9gjCWbKnDDtVl0o+IKvPZo4pFMVvwvhiSar3+ROB9gl8ASNuisWm/CaVt9P7+xPGcAnqO9/E0oBCqvL0r705ihwtMAI9uaKxIMm2jZUjXU+dtBKrc07YP8T213OBTIGCYB/GQ4wPlEcDoIiMX5XTrHs9d0whurH5+pzKtXOCUo42JzxR6U7wAdYK/qAHWFsFmMN/k96MDmpoR4HeZ0chjAGNURydFA9WDJfnHB8SE0Bi/C7ZIyvaYaxQ4yerxljmBtIRcu/GY1lih+RcgkHGab/QAc4rAaupQytWsx6crA7rkJxjmMk72REcQun45Is7QB0YLJrc9FdxoKh3wsFyAhtp7CHPVEdm1lF5Y2L9KPPd0IpSMT6oQlxtrgsPqpBj8emofBg4Rz4iTVBbx41EiPswERW2MnrZX2D/nOxlaSK0rNq65HKY5ehw7QbD1v2TrLW6ASYupnCXi2SI9prYBbRyVCD3rR+fAU+h4z1RZN9QziGklKEAKaOc8mihsC4d4RGhX01B4aNUDt5rOVvOrvx7g/sCADSw7mQ3ommNjTUkAG7zx5/FxDwZiu1cMUoAqWGH4yjEJgeRjJnABshVTc2HhkVG0LUjw8JRtBBeRRPP0kKDhhi5vzemMrZUUpmplpYRls77GmtANyNXFlIEZsrqR0lekfSQstU6dwPWz1TfaBPbqVa0cuUqdzDmxMFRyMjFdmyD2GAJW5PUkUNoqVGyqBbTAVkWFo5xXjzp+aC/58kkATpKhpU5yAn8TZ7OFVjCQFP1987d9CnHLtmx2LRtrZx4HamK1VE6MKZGXlp7l2IIYQkATrIq9ZGxNYrK4FSGBA2G9Ul+CIDHk2bP06Za6YQukRfCAkK4RSYdT5z+Jk6YbfdsLenJsrUYrP+PJcDPz1P8IiK7f0GyGw+cE6DoAeeXytQmhlo7x58n58hjLXSHXYzEMbFgla3WmdvUxk6srOn6I0V7/md6V+N88BtVgu4BAELi4xHwcXt7bNyT7O5MbOO7yaxwRoVFykFJ1agZp3iu0omD4kzYeTN9skRiPTxZq6cpE3VH8RgMTB8zSi+ydCXtxYW8x/mw7PD8KaB5q7q6sBR4XCjNiF2co1Fq2uk/pwjeLU4KZxK29JxvcQQwRKW2KY5UZ9ZQWlBVujroQeKH+N6ejHVgub3BzdYDKi8iza9VfprjVRB2etANMqqLzZZ8i95KBVzldHWpgkgOPKQ4UD//QppwNBYtJGo7e9rIzAB0j2N1A7+Jy+s4Tiv5M6yZw9J3j2W6kb0YGUaD8RIX45EY5UQaqXLo/IicLSk2A+UxJDBJfI5oFUvSgg0xMHxQp+wk1cPSSio3H28zKji1eCAJMhNTic0RZU8OIHJXk54oaPVv9VW4ffU4zlE1ZqWeY0/iJsyCRfPrcTwpe3oDJitHQa/dz/ByHNmo+CXam5ed0eSAR/WM6igYOedg5NJqyv8JTRFwhKPqCEf++BoWM/aUnxhmDRzo2IZYT6fsxKg1yiFLIgYOhU12pZtxdGWEikaHL2OkmxtmbOEksh2ac+k6lWADuv/QsBvWXV7N5o/yyjJxVKZ2qqUscfCZUjEpgxArNkduUZ0SxCtJmeAGIiZyK4MtivtxX2RK4mKyRmYo/VglQpzpWzabiFSILzmsLcpvlkjD2bfI2atZRhwn3xdb6L4iWOSSC3gsxlUTIInFwnBc513g0IAT1kPSFckEOWV/YO/nhsrlIAtRTgXFKI+4kYyYFb1D0tWMsK3OdBwVMvqmhk5WGZsZ7DicoUo/o+Ly22FCLNZIXHH+6VYTE7fHY5dbVDkoAFJe2tz+ZJaKCQG9bQrjJn5Ab7o5lB27OSz2juCuNnloOIkGGqFYQbbwOUdHRkqnkwMHmFBQHfhH+8GlagnQ9Wx+nlJQLFUGjMFQx5p1lFMuySw5yy/NnstYlxRfGSa5x8dCIIuKpDbZ8vJU9eFg+yhDk3hIfSj/BHG6rK8XGYCnekIS+wRFWp6ZTSxjUaGdjAu3Ww9dcmSAy08NLzN2bFMw4mTMedGnEFkk8TijR5KIDLmUWQg4vnto8Qj8OkoLtd9soUJjBlFr4I5MCQoikSkhQl9jYGqIMUyh3WiiHVXmdtTFmbAyAA+FmnDQE5wMWPKcmBWYOIMff5HyDCfiFQro1iM0+A5e32UyLSUSdj76bKDHPrr4GV8s7aInjaLaShWfABeCwukM3XLsohKK1O3wtkE+ZvDiM/rsi2zMloPsegFAhQJTts6rQE2qd945VVUASeQeq8stIrPRjIySpPA4qSgGpopVMf0eZSZFWazxXWHYTI3UOddSZ7QInZQnKCly7DDs5QIOqU7+kJv6uVN/LEmKWrIaqYAnnY1RoOLz9ylUGlH46LOioHCgoHDU6tVD4UgMJLQBS676gFJ1TACmcy0jkx/LLTmaJXZEs12zumoGddtB8u2BiBeaKuTdQGXCNoaxUsUULyzb2oMCK7BQp+79WPMYDI2Kfv17wJHHSJAk1S96nyYwUfbEPSx4DRE59R5cNGW53Mk0TkpaUT1OSgihWLtK+5eYrSzMBj+4qxLbWqmUaZsHgI1gEwdiyhxFD+kyxaDsxn+MkpyEklpYxUQCv5cCCplKTH6n10DZL0kV99ADv0LJVBUtY0ufUVMRz5fXEA1VTzes3n7TNzSftqE1iuuZXIf4gG/qYJjqWlYvWVF36IRbr9/9hWa72nPMl7jbI9mJWlOeNj0X3/xzU5s+LzXpJ03tOyZmORbeTR7a8fQMyibxrdWFnelrzdFgAe0GRBUwyUDXrc+RHusr5s6QDhKLyDyFYnvOlFC6mlC8w1rFt2S4qBXfizIgykd8ZdlUqywRf1TLKbDKt7ng2W/FD7NMx0ZpCT/mniVmwCuWwcXdmKNCOGpe2OwwhJ2Ff7TJNEYjUfcnYBuT8XoAzwMTSXGgPJdHQdwdUHTlyEfSJvwB3d67gwU2hX4GvzzqFlsYLzq/6OQLcOek8A9reu2M8QRMyPvOUhf399zhLHWvX/wj5Fge4f8yZZ559OmxuL+nXy1MND2r4l7TSNvQvhc4AbDLPLmUmJwfSVYbhkyY418PSfEAyWu9eERraEGANTFWzBY6cFAv+BSVFwBgNwD/PczaY1WWVQAbTxQmsEYThRGarQ5kxOoZezlpcr/9FnZQI90aMQnm8RZzYOWBmerITFXCxkDOdGRmPDgORk5gxg0TmJ6p/G9zS0Tep34n1BAJi46glZPVfydtpSrorfdtDt3w9Ro7uVJKSidpJWCaU3mZQ+1EkLq2KADqZKwDf0tjfEGp6xJfJmCkrORq3Jh9rVIx91WWEDMSTorzaU7l5Z2RSlk48ncTGmjJY6O2hxMNuCplN/t0rAXV44qyshgNkTr2pzLx/xFazLjutA84hIKz8zsl0Q9P89Dm06371VIZau+QqibVaLtP81ZsB10yBPXQvqHtLC/z89+WLQeNculMC+vUrRFTQnhz0zAh/HbOSlhaLg2iulZD9/q1Wxp/aD1Zzw2h868fvaMqzKuzyLl1PnUWeV1OQBRskzQe9clKrHF4FothnmRwyd1irIZ6+YaSJ815E6KC2LnmDm23IMpD3cJGVjYp/Kn6u132yNBvzjqvn6delJ/mTK23di0ZIOfZf3sz97+V97/lnd9Sf2HuGeOU2E4ecH8f2smHPriRdpov5ssO3JX9OGyV883ll50Qr/Ow9eMyvihGp2HrxXN+HMpMH7qvzUpfVK3Ms/Q8bFHz+BIwWjGM0tCKClETiSs3uavgbovQKirEPFGh384sWQ9L8JaWljJfV9PmyXsVX1GWANKl/BIthWASTmfey/kUS9OOhxeO6N3fW/37rR5+Twp6uMgSTgH6OzhQqPzgAG44klbymNAUyyTERtCf5xh/BRq1Im43NtWspqw6ugJhhSm93O+bG9Pz4DVKChpwRQPx8JQundWTyQsmv2FB7Z9z7oVln2eybXOc/FAuteAlnT8OWpgyjE8s3Z+lk8kiskOgW15ipBbHxBy4XgnGm6PvYrZQkRwH9UJEGWCCkAU/JkEDRiJ15skr2OAEk+tgyjcUEmDmNz8BEoDeRwtUA/9QnXQxYn+0spvTY9uPoK7M752IdDFZjDBbPbmHwp27GPnHGMkqYukehQXhy5NiGODiAQvIXLMsSXyVOFFPPYisVX8z50bT/pPCe5jA9JFMJodr6oAzHCtMXJILlDFZTouOC+KUO88KWCZFXDoHRkoX+vvaC50TKLR1pHyZ47TNOVDhD4d8bE+7+CkQ0uN3fkm2VurynzTlovt/aaJc6eBLZbNVWjZbMrqOlchVS8ox1o7J6SqLcUImA6wslBEtY/3oUiZI6ZaKPCkr46kMpOF+H1taH25UvtgYW/6tVWWEpZ3mzrHyGjV8sLKcAhR+coQG5VG3pjaO63hs3Ggf6JimBMTTfvYNRaoP1LxkP1OktCzUMmlxA3VQVDp2wvk93u8ya9uYhxzERWEIuVmvOT91TfBDTvZU02r5oVYXUSE3SZNPfFVItYRqqnZyvkRv5MbYTd9aY7ymo4hp7bqcYWMaDVy3KtiDAaS3g+i8cK7zlYY3FabYebYXpe+jq7gCibONWkq5og+zZCe60zz4gFiMOJNN+RQF2pk9aWGozik4jN0KOFW9WXimbHypPrTy9RXDeDDoXcQYuFdmoQNcbVQ0xbTvACObOUiy8H3xBuOlkQAUxmwfdCUQ7QX2+VbYticZRmDYsiWZHxql/hNaQksxJPtNbNwVVdYx43VMcEnoKUKdz6YsG/BTRLc0xfOh1e21MgQ/mLkSdLcKN0YmVgUSBkc9sJyxDZUfdzob3vOXyhthygqaHC9vyKaaly0SlPqwkuaFiCkg5FlwUrYLow82ezVyaZMRjeIX2JY/Uf00Emz9g12gSN+6P/sVPWIkr1H0WB9hmipsc2bdBsPabcTQqUPaxVLu4lCc0bCnc4f/+E8cwT9a6H3j2dzJnMud+OJ0ss5RtQ5C+WS1d3Nurfjy8TrJ41WQP3i8VvGEKsAYTNY6qE6OOYondDc6fUql4eOVRo9WeZhp/FjPNE5VkTMFM/FapaNSyJyhlorVvSIDW1CZvONVpG1OlohXebyE1wP95ttaxch17Q7QIUAbHYwDHpRdQ8H7PseBRqUpJWbi7L3XF0kZ76G/bRACk754nUdDScZY+HiQXcc5RcZBM15uibPaV5r2wKrSqrYeAd59pLWpMtG6Fw3J8/RPuhG8KR3YtSaGz6615Nb3K+puw1Ng1iZqKf9Wq15SV2+CRdPVi7rqlieuVXWEVa2Nc21CJLphkNDGf4ocFAw+rSolIhBgdCnTg4JWSxdKuk/g6rVqDWHQesQGIh7ReKzF+HrRmSIYW3DtmMy5OZ108ElNPNckzCI6qwXdjq2D8bROJY1d0+mR3ak8SU/rU3IV0LLMzs8HcXWaFqmlt6CaD40Vb+o9UF/cVZWgdTXFk8Mx3Mp/B00Ry15c8vZ7+lhGTyCFaLSU8EhKRFzytJywSEsf+EqqGgIJNK6iQmMGLzkqNAqs9v7YtihWbGwpxGzNXky5Byk8qTB66WqcTMsdxORmjaq53WMdENNJje3BV9jhU5fkOv1eCpxfHjSq4SlnzppeRqENcxGq7NQUVLitrAgbUhFjRzudn+cSHfD0/v4zWv+IRF00qC3mEJRElDEli6oF+d3kUeWxrQ9mvXHFfn4iUG9NH6SsLIYoZdmXCldKSX3HZGWNaQGbxxJhnOkg+0D8xu3iVYOo+WyhEOq54SuiXFoZ5qh/7+kQsfFC1u5Nrgk6D6ILoF4NBUGRHVAsXYh0/DBg+pNxbYB3iy2qibFqqRdgFegNiudiCzjLSTOxyVjIRtkcKmek7rGWryuLTAQWCuOkfG6XCsDWnswaES+WFE0WbVsjJYXqHuPiqgZaKyTBtXwFhHSOaWVzpfQEdqWbA0Z4XVJuWzbDl+uEQjE0HU18acCTqY6l2U1Tjb08lrYTWbdxLEauv4Loi7OJEM12CGRxGRj8Li5QNHzOWtwT/nNlok9dMTB0YQ2OgyvX+6h7JWOwXsnDddzqqkch3x2346UKpx5b3rLneOjLag3bK/ckQAzvJizoeP0ASMsBTNVcfmf0gy+YIT3LC+xSvkAfwzVK0urjpBt+qw/rUMGnA97Z26DbP7Z8sZvt+FXPBN1O8fMZWVam9ghSawSpPYLUHUGqTHxShS95tmztdqu8MwgMToPzzrkOAojbcxOcdE6ckj1L3U4Go7cc1/p8fn72xDIRL4O9hXhqnPBzzUmemqdX6P5w+jrYw92SjtfKBUycLu4pjCtOX5W0oRyGT5zoLm7ME3V2A535dyfVvm5MXxw3crzHKS9H2hDPe2reByEta10XEaOf28IQqFakuS20XRz7nmGQ46BiWWm5qdTZmVxgLA9G1bC/DXErqVapU/DR71W/buJrPFtk6HSFnDkp9RVCvR0TEYKfUKM3E1bei7VeBqXx9pqYPzIqPzUcYx1pnGVFBrBFK3iZSAQkDTIZRFOF/TSWyHyF3TJ1/uGmkdaOiRWBUp/r0yZdI+gqjwIrXaVwqhS6ivS40OLrXlB2ioWklSwUbUUR2ue4p2x5HyLe1OYIdNnB1utpmZS3tqrb0m8eOPpNOG4kZWn9MafZSEFKT1INGsXDkRNEgy4nZrfOYKhfYwxHh6gvRRwyCNs13SthSqqiWEruy/4cKie+ODmDzMB3dDDV2X0rUB+buXLuEExhj6P4vLs+4a0bT/C1mlaZyLCMuJtsjwoEXg/5X1vnOuesRbuiQ7edL5Z/0YBa1eG2KzGJqDc68dwbZSDKAyS8Kz5wwCp+UTE8fIvSnMUVAqj2c8cvznQn5XillTC8VVZCdeR2qI4vHMSEUjMYx4sEs8rMuqH7U0ILpffMI931/W+l/wyRDTlETVAW6AabvsrRx1UJQ+NuetzOOXjk/T09/Jb/R9gpeS6oc/dbVPx/6cJDLpRSV1n4kWwHxrb+HphoopuM+c2z3/Jn52SBo0u6M7+Vv6XHC1g+E3LAGk1m3RkYJRIM6BUOzKcJYpjmbMP1biXt8FdU2KEz2qTLaw1AKMXn64Z/p0akrABKX7oaMJnVpFTqAP18y4zz6luaczd1I3M+ecQNa8QoV560QTBL51otPKN1NfOwBumOsSHM4EwAKF73jrXiLX7+iGYlFkb66kZj5Zm0VXJ1+gWzKTuOhUprwm7GD5yNifWJsUxiKg7enE8DvslgFQtKKmcdBUI0GIRqEN2q9giis9ucfU7Z1M3GDppJUpYG2Z8Zy2BDckI5TchIx2E+2q7GbFgakA89J7mzowGkDpp8sxs6nyL0GwMNe0cCaFa2oJhZScBEYh5JbBxeAOIfcCzSolURgClJL+N5kueiAUrRy5Mh1h8N8fcwzlXJqGXJxKwr650dq2sYdw2yXyqzTRQvrkbof3c8GbHLhaOa2w5jwNTddwBtMRncuYY+73Eo1lVwOM3U571l6vNHJW6wuTIJOeLBkunHobZEpISDkoBOw8+NRsgJJuRSooVNkvYGI3ScI4O7RS11lNnvUmaTKFy13LHCaWVJH4EMiZeugGfFYEwrsJ2p8SR8fUgR3hIC1hrJKW6BJQJTd3pUrT8hG602rFQQWbUHI4WtNlVv0PPBbeOIWKvNnBUgt+LRKSWTtIet4RWPbXXI5qXqAKB5sgsD4rWdWK/RQ8SZQTk5buTfgQ6nw7oxacjxfrplJlCOf8kmE65eYQwxJFe4PF0rxljFUhSrQ8xiZPsFehhRiHzU5gkaqREcWmq90liWKfMn1DI+qL45rFffTIogLddcmCrL1xBNrx8AJfeClE1xXiXRoWa9dMu5z2DkbN+VBrJzSt5Gyb9evuA1dWXb1mCWmZb82VRT4ll7xJL05rovZd0HpVXNB6VVDtdwOGnzuGHbPB5MtXncYOeAEq4VjnJFPkwc/J/DXZkglw7UsaqcgU1XsYIQIYcl7W6k25i0+iExTOqGL9AtAs63LC0trIAyKtum6QJLZWwhE91gSn8dq7tXqrNW8dw7fe6dyYADYUpiAV9wWawL/FeNsQ50c+d6dk/z615u+CyprxWnmgVTgm5c9VzOTjvcuWtQmTzmLXvKQovfm8iVe5K5joi5hhNtGGYvqXf2TkwCNN+xwT3StooWdiqHyuTegN3OnBt8zEgCZOD4xLgaqhxyGLeAJTQqitEUSfbr2JHeI+O548StnxDN0BeAPki1KCIPGkJ/zjKVdEOzxKw40G3KqS5O6CNOPBnb21o86ZzOi7rjWG3LiBBrOtCGhzmWkQpFz6MOrWDqt+RK8lTVD6IHm+qKuaMYAEX7pGd2sk07WB9vzKpmxw6jThRASQCgYEY2gFTMpKyuxhVQqv1cLE8/pfBsOsICS9rOQzHhDUzE1yXyLaZUEvAwP7+S59Ht0hlaInJ+yvwWXYhTilYQU6ARK0yW7WJgBM2mlGTYQPavp/3PmLe6qNyYeLGpaakTYi/eElcQ9cs81kFIH+jBClRa30lvEEe591AXssa0Dnhl3G3khvxmLD3mXQJExk7xrPqYDOWmzKNejfmbBJaxmzsMPjs2ydI8/UsDjKtxUUkuU2N3DEewm2K824TPdO776BKlvfoco+ep7oiOe7ZxbrXVYirtmRpaRRqTkhgGPa60lG9sRuxEiLAPpTS+noywVAX87rErcC+t1CSxLWpOVdgixaZLpF+z+WhkPGG26KoN1YqXmH/VFQppWRHKhsoFkgtNqsEUGEhYr1kP8xUyizvC0WI8ZF9HEYu7jWMdEBZoT9RByRu5ffncizBSUom8nnaSQvsBvzQ7YUtVp5E/Jcf4LA0WldbVbHImA96xiPkxxKouepogt1R4lDqg7KIWrURURzlZ8gB2ljogafTISqD834KqlQ0up4C0psgh/fhZvWGdN72gR1MuM7AqSypZrnKxSqa/Fl3PVL5pPvRXz9ykvqd7LLqwlceBVmPtIQOB0JpqSSYm7ISelrI8OU9Snb9LRpeK1JM5QAygqMrQ8qg7bozRYNO2k+A1kbdIy4v0fcLfjWX6qYn2O7Q1kUTgmPhd1h+z0l3+zt1xi8Nla5t21LN6jfwtF8hFXgGYgeaeEsz5BkGUzyutRaikeXBoadaonsHxpab/WfVsR/e1ytz8RbADQfoc1y/RgbH1VGCG2VAHdVNCzqaWHTo9tZlixJUyaxbbQYgTRT+wblP2PrYEwRw5PK5LIZ4Gp8/NwYAry/pJavNBMhxSdbQuDl5vUEKUeEnt6MR6o6aMCMOE0lI7nTkVO2pBWrkGzlKZFOjQWGNv9bl3zryPr3md9mP3B8XQnzDWqMN9TvzsUmtwLQRBhLKiqlWFJ15OrFUyu+CrEFJKn4T3BZCO3dgKjy+NunViNDMWltkpRjKh5OXFcZDIaKmih04iAyWRZ/voRntk7sOR0pX04ZujYw4s0Efw7ltBVv0BLGpvgVJwYXd9A27awXyAphKH0FbNvD95wfbxmuzgv2mnLmpLUlvGTBdGcJKPCEVB9qoAsGOZP0yadMhsUUMJWuMgMq+tl2jYQAkFPLtGw+6ggR0ImG7MfAtmHKYl+TwHf+/vZQcyUJ+TUw4XaXLeGnp7YzeHbzVDHvFV6yZVB/pSGJ47D0ZNT8r2se8I9iYXsmBbpTRDlwPrJfyUr8iyQjrJVsLZdxMRHQd5u3oMSCctEs320vZQnKbKcYioYqQqRqbi70cMipowtwhGHXHe9NZ26PYj1O/SOVQ9szmML+ANJ5hX0YBEKt9YN7ZFbvgTNpX2za4sK7XbCksSHC7FNTy1W8f/H3vvutw2tq2L/c9TSLVVKmIbUpOU+kYaZnzr1Te37ba7W7aWlgiRkIQ2CbAB0JclIn/yO5XKA6TyI3mIPM95geQRMm5zzjEBUHb3XvvUSdWpWqstAhPzPscc12+0C1q/T8UnNIt9UuCP8Bsm5qcpkFgjdIeZVHAjq6aZVCyyzmGnIBy0LjNp1mUmzbSZNPPMo3wklOiRBVpkSj62+obSyToIpWOLShyR2954t33u8N61RJAMmsDGoM9lIDvRpyg6TFTruBIvEUNqMb7usyDWM+ofNIag8z5Fuyhv2wyDfuziw+W5QI+lIDa+LRh5eZqfeXIepQiYIOSOgQ0DYpU7yKHUoYmN4kMfWgn4KYZ3JscCOIW5y/ogt1bS/bzuxeQXSmFK7QlaHQNFZXVi7tSJEisdqzSs5s9FSwZbRwsj9c6jBVxq41dEO7/vDb8wETPzbaiI4YrfNbGNllDTWlggxN8YYMRWu/u9GeG6Obo0M+OWI93YPBjLdAmfPe5TF+F261Jgw4tV54v9/XlLZ2dIrEWH3aLV47XrVKfDffO4TyEUvYq2W1ipvVbIT95rsI7dfZvMDzU4XBWMVo0HXI/1yoXVAvq3q5wUdshIok62HGWrvtw2pf3tU2rG2J5TN/p5pxNX58R2Asry5H5kDre0bqfN0hQ7b/aJmri/Mmtz32sTpgw+vzS8E/S1g41gRBfX+7l1lkiwC4nTKHetifRPJm7ZBM/ethJb9z3nTodF3q22fll1funBw+r5mSJ7ybyrBUl08nK4bU4NOWAI2r7lQWnNUeGkFspOmQ1nr9VOsk7BGf8Wp2ADUDwD6ZT97yjxFCed3etsb+tB/UhnWEamDdJcf2/9fjCpuz+1MTqSQXNj24yC7Mh8BsN23uQl7jiPXGDxupVlWrONDW9lvZY2W7WRPuyg5819rcZQszPw7KAE8WF+qGyiIP2EzGS254rdfeZWqzb6aB9NmJDWUWXhvJVR+9YBORUHLJHROp8eHh6q+4wx+DHuadl8eMZA63MgQKPOG7CAt8tgshwtu96m0ZJOMqkfCkoGpWS4eQ48DiJtdqmPxwxH7X3A/E1zsgjta7NZdk85HPquK3m5RZ19dxYQqV4iqYaTi/9iA8tO4xmdt6VPOBXFXHZQzKUmkUsgkcBw5Sr/h+nurhkhXBVLaF5tsz7yD8Q7kEZraZwZFBfXtRpwdPxMUR3ZRaDVwWTpNqnzuhXsFa/FsYHB8cBngVkyG22Bvvuy+Bi14KVJ2cqkTLLRXAkHsVU8jJN7KCocHNgcYSZeC5uY2wsAf1pA/LlTO+mkOHhNrBiSnFR8UAHwvdebzVWbwIycMpxo++61WWSUFsgXrSvfR+HnbxG8ORF+CP8OU8OfppiyDrPABBze6ZLScYILCrVK/Nwh5OnJFwveBMgvBvMOu81HLhxe1BYVuzwo//V0zL/OFE3T99qfJm+NqI+1jvpYoD9vYaM2KsFcZyx7Y9sQUGyxoHFNr3vGfDK+FqeGJ6vEKvng5CX7+6/ptLCN5be+6BR4rMZBxuQP6GJJPY6joQpgvgJ99ymUsoHXKQJN0bJqZcGrHmfcE321dYS+d5NZSOgMfhm0Ttmm2WEHnsY4My5A9h6s66AmBZ8LdIqVzrB7LGYYPKi0ZzGBNFNVapl7ZlNOWKMyCHSzSTnKMV/CJB+VwFz2EcEblZMrkOyaBH7SHy1kt2AvF82ducIT00nSYcfNowHVPcC64RBctrMtLvnr1pylcGFQgesoPsBAreL0+iw8j9Z+ByZr4zpztTW+5xJpwhVfMdjdc9M+/AYZ4hL28YqFmvOtgwkuFR+z9OT8S8VWrcJzNFJSdRNU+I4SGLzHNS5hCZqTuGDc5ZaMv24WXHsFzw9WVuzXBGJpmK8VU0YZZO80vDzzuxvIuviDw3G9ja7a6wLvzu/1KTotironnFq7otauzqCMa+wcTvuVauitojlA1O4MxtXd63HlqA7hT6ZdZ+qtnCmpCTnJ7im9lJlafWxKr8yUNq0CPKGbt02PgG5l/7iTQsGt6GKimD6GmhhLsnbOGXejpCC4pOKrIl5d26BPBRp9YkEYjpGB8fsnkk+vdf4njjzoCydQCrIsejCE6/r+kCC7tbnCgBPv708RAz27WsdXyTTNgGDihDQekr28JdPBwy7x192zD8XCMc50YBkGSXv5NWWLE0ivzXTXY+sZ5fsKyDNWMb3BDbsxfd8bDgYwY81cf2HlUhsait/uTNrRGS1uph3WW1QCav6GL4v+uHDWmkakLFwOz/YwdwneDDQgk27UDAdZtPazV5LJtA0TTiuURSoHRXq4FTAcGWJWTiICUxAY5EEVVGSQ4nqp42jNAxmrsuwlVu/a9C0Lc6WPZeaujHZ5pXaVBXbSfS44EHQW5ae5MqtxhPAYHSnW4+e9dYTg6veHvTVuirU26/J27umWkK0B/v4JxSJeJ5k8DihtxCdvoNBOEIiDC3+EAXVwADIhr4Ty0hwMP1pxFxZitQUUX+V2EiwDDKImHpvArjD2qytgcnDcD4w86eOap2KzNzJL4rNOaRBHscf4AzHqZeEiMAhMDzCfMRCXcYmzj9a3Xtk135sNLNiMpn2uprJsTqUKNg15NRt2exvuGHtnljXV+MUk60yDBQfwEqUryVLuWN3WJrSMaSdBZzYBaYAh3d+iUcAQtxZXjraE5Iw1/CJqcLasxJIj4DEeDL1PHfnGqX0ud8NwcBToTHm+0QNXOJucZqiwaKbDQ5XF6PSMssHL1ZK8E0mO8t0MXHjfpJeqBSoYUELhRIRpIAniqKIfgVY9QEHSEcifxObSsjIh6nSYBG0nWKcaybZ6v47tDGboBKy8AiUJH/S2IQxgWj6LK5qxzR4vwQqpCttSrQU+7+CuT4HpPovyyaniwtUywZR2a0Ch8kW4pi/jMIVi2JRJfNh32A3yhPWhr2i3d2iEG0GWdPFC9bhtFg3W1JDoSU+Sf7jIdMeHzpqvuj2gbXlcNdg8Qv9Ps7Pa9pbTonV+jib76/QS1me0YDRV4FizM38UsBswljILjbNGu7tAB8gBU/ZWsc0WNV7zXLATzQjj7TGUH25f9Ny0Ta6xScUERYXMR+HaLm6dD5VBZZnP08sPxlkdZuXdQBKn0BMfpJ3YVKQ5iAlPfgRx9HDYu8KkZqzYj/UHeJGeXyzy2ZuH66IEIZVvdLhO4TnSIvNgpmRElZNe9B+c3S63f8FdPANiMvP4jFlDA/Zi2MvDFHOpOUu42YXroaD8KfKIW9E9t4g0wfg2JUgSvUEvU89FoaUowTIVKxYQdAGz4iRhB5GtdC1i/a29EGtjQib4NFw1/l1zjZPphRz76WgKVyD9RYCVhwV6gDzM11l1r+/Af2CiyTXkftXrB8Y23qLeKdqN8UoavV1RcLa9WhruJb7LQbhbeJCaDfUGJ4xouArrwG2j10Y14HJIkCyTwrjwjjAtAs5NjioRG6iLPbPqO2S3OI+oT/t1VtGmR7MU0erW4rRQ4oZXId3at1XHcoFJdfQTQ041nWpcLqSnLpRHudl0Y94zIUe/f5vUin1UUDw1njGS3YpF027vFnawkcSDiDIJv2vReNUU6UXJEGDrzOPiA+xnjzyE0zlHfeZFOSUVGW66R6jltBtWQhjwtHQo659LfKAL4WlrrinisAld43/QVo8EXWEq5mpucJc9P1FuG7EkcHImsnIvruN5/u5nIGC9Lox/1vI+vI7xbk+KVrpVdS92i+dbXFd8ymjUAd6ME0pHg2gXHL8VJhPnajqyzoyU2bThwevCQxJLdCkanFzC6f4308u5GkG4Tqs0XmDIs/Y1Lw5TmADsM/ldlr1paaduGoifb8WS4G7l/FmthMwZ3O1zb+JZ42E2BgG1HNBlMxWrAK1ZYamscaZtPHGsuRnTBWbsterh+JjiHPBK9b+z3LfI2ok17nBfcIalQxQB673k57T1buhv2HX0b0iexyM4nzSxujSCfBqL7ccG/srO63M7ga3p9KZZz7+1sDRnjxtg8dr23HzodVmNvnZ5AqCdvUGjQ97Cl/kiZltbxyC9GdUDAZ7nIodz8ILILqPLkEdAe6hIcv7Unti27BJ6txwQW4+2M9uc4cKC8Le+9cOoWhXIvhH0i+2LSl7sZlvwlGd1ZMFf1EbVI1byplNZnB/3Xg9B+KjwnmZXT/S2s+x/4KElsWawrlVYpiAJCfkRNmTKkdtTuBSmM0P2pl0OUHxo0Yxn6Tdm0ZA7RVE4Q8PcVVxEaUPTnrriNk00wdR4papGKYWpXXSZd6sodRdosVWHX7BdDwgjjAaRmmG/yZ99zy8nH/SK0AUmYh+VyqqjAyjgqht8WwfE9kodSNlOvit/xs0OIHRTCvd6d9IKy0EYtkJU3O6uIVQ09wg73riQG7yxk/fUVMqfeZTcjRGWapKMYlSUTeJRglaggwGhaaMGSrM8cq/2fuiTKjXDINoAQcLSthxlvLJZS1NNCpGLkPOBlWe33VEmkho/FWdeNIHZbLu12eGeijxB2bbLGbvbLcVxOW1uX+cs6+S7x4k+w8CCe0Zi4plu8VlzZwoHwwwJajmsm3BX9ztOs8f8dR1oVoK2OZ5Rm4xwod/yYq7BUv9TGJ9tw3kHjRvT1F8dRkdA7I4X0WXcnZQt45Z42W3qFx2aR/G/nbF5ycDFTxcDLSA4C2mDeW7mD1SeyBp8Jmvz4ZNsm+Ji1HfdSAcNUMDmwI3KSBleT/thn5A0OaSscoC7Xdl2t2fsFVRZlbXXnLcJNTE6hUnCFDhAk4Mz12c4LEQRVZzWOMMoKu/MOds6kd+ZbJ/7IjYmfm6Cc4pg48udyncdV3lvRQe0DwCBi3oI935TFR+ERPycXD1+v+pN//731c3jZf57Wk8JZztkFK6yOrxIyXGbJrc3/S//+//1//zf/ytCxsCPf4M//8v//H/Kr//3//jf/hcH0FTVs7iaXd9IF7BZ6EaAAWJmcvKBn/QhMUmbG6RXwgIm0d/L4LOrQKEWAsnvA8HvN+T9WIcF4IrGBwPW+eRhfifCrOmmAqC6OQM65PfgP5i8yqirk3AAC0owupLfyuCJMXZrevg7bLyeylU4Y0eXwjm69Io2bKE1qMF1UmrApHLQcObHcAHsN2P63bDmixJwJoczPEGk+KAc5zIczGzeoRgjV/wZ4tGiAgX9ECV18joqT+OzcC4ZsVFNuAjo4Z0BPe670CgP1i6LFho9L5FoonuMBrAq8irH03SYoq766eXhLF4scGvLuQ/RShUfHNTEA/ytj6pHNOgFcbSHurJ8wnLkSAcxWo6txC8ST43nwY/RcSplxma8p2QmHnxABrp0nPR45tmkjg2q5JMhnOTDPUSQf/rkxSLH1ZrByoxPS1SNL2CNQaR4y8k72MqABQg0Cxtnm8TnBNYU34vKNtWjCA+V9LOjCIK/mXVqGTViUR6gpc9X5XmuZM1EuZmVvAun+CwaSk5iZR39rntrTPdiBdl8su4ycHKu+m8Qgc3D1LBakDJaj2CTJbCDsd+TNX/RUHLhCe/Xr7BAj9KbmgS0sDXWnVuDVdolnGWM+SPrpVmBL7A2nmU4rB1zHMJW1y6vWXRLDm2RK0DUxIBd2oWYKQbzl1IW964syLXZjrIAr/d6Bpc11rnHhY2XvW0AmtDDEL4w2ebpdMBns0W8XE2t3r2RrXDWpKwO8yXxeZ7C8YsFW6fbULtiztZ+9oym0stQFkYp156h57AtUfGodQDid9kd5PBK9HTa26faGlNjfIAEi4X8g1EFqF1+J4nw5iYxeXvJ3ayPXlEdHZ/c2r4hijjwdFtpb1p903LXlJICoenv7Hesr3puCTOuwscWAWYp7o5i0Kvjx/Z0rYwLvlJ4gIuBUsgmbajvDgEOuUMV7EYiIkWBjWnr4hUYwl9VuEt/FiY4jAClQruduTGNfLke+EkFFUIlA4wJwXvfp2SDmQcoKbYluYdPYBYnRoXOEj57qeRddw7fS+WA0IfkU/Y9Ul9uu626vM3L1jOzVkQgYMr+1herCWq+AqJKhQ+QabI2Dyi4mOOq3VzNNffPAAaeG5EredmYVZ6nZwPyYCEAqkFPgKlSDmuFkjEZKh1WnTLh5Bp4A7gg19Jq4MPecMXsX+Bt/3DLY2rS1bds1JcMDI6OKnTdZLzQ//cdsC75u7HOOt5c7zgq0AMpeYumxjyKJxyoOWJ2ETURlJ3UjHR2jQatKfBru/Fr5CvZ9ji9oGOQZqs1eu7TI5XRnRO9t5+j6GmeAr+KmssYD71gD0bRkbydF/lKSgofGy4w51d4ydDetO4zC8xxKDHOHdKU3a8LEJ2d7YnSE9Bep19z+1Jiti/Na/ltBi69wYwU1Ac4kxl2LWj3BWd7hZHjC+j3OrwkoXwsXq5da3O6DK/PolV4BQLCecQIwieWi66scmpMwRgs5JEdK7iyACnwoYCijJthEnKBMgDoFV+q1k/93Iin5J4ejPhomWLKO9oWJRuJFK9rf/vDQMKr8Fxt19duS+MRVGfJlblS+/4bKpS4iPzOL87tIUCfCxNMLPqD2KrwxIdNbLilXByWn9ilqIicnDXwr9KzGc0MDh2rDzpVNKnSMGSUTqC4h+CQmHyguNs394dinKs7wJYTjl5vhqZDRwzirodvB5TW2i5pzneR82wpSXOYgjClfAZgk+eTdBTD9l431EY4zoXljY2JfYy9T2zvE+490nXukxpAAgPQnKCLTrGOBR+pdP3xSmset13vt546RenE5G7VWtvCaVuVzgz2QuFPRdKhlN9FbkRvAvEQaLBkQHPvgWAA22sSt/23QBAatR9nEiOb23nqj1Pynsi7bbehH2KeWDeeovu5cr2lUdxs73/B/Ye5+vP9T1X/C+p/emv/01Y/uYduHvKOHsLRusfpCSZ5dw/bj1PpYaZ6WFEPsy09LDxkmaSLX/wwaKdFJyaJtTlEwl0mVAeVyamPsFqSikrhKEfsHQzMldvtCN0UFbZ0AVQ632JzCU09SKiR/zYlG9PnVR/GLp+F/CViB2ISlNB8vtVXL8S772BADXUoQw3egZnCXB1idXwvvElscN0TA4RpOOU7vWrSH6VOXjQv7m3D6DOcv89xHww0SbGdee91BrWL9PGDuEzuZ/PH70ksMu+NYtB9/qIhrq6HPXEEFh2Nh9yZom4m8XUzFWYDD2z+8kPpY5jdSa1KgqaZWZBeYD32gOYplrhYJZrTDfNAFE6F5ndQpeT4nYV9KfzN2ryW33OXHjaGZt4mCj6fYa6mpPK9EJjzqbjixpvN3KXpocA9F3uxi0ER2hzXc5kKiJFCWxUKk6yLvL9YkDtVaRz+Ly2XRrmjmNJLiJKQ9+topX6dAhNyFr1AAWcZXloUkdO34Qd5fB2u7OMLqpO4qPcmH0r4Iqr8CyJ8GF2F76K34UtUQKKO+nH4KHwavgmThLAJLxv7uvcQnd6uAvhgqXkuGPqFhPMKHwVP3gcj5iyTbW52LxFfF3rdfP4OW3kLR/GhndN3Rv0Jr18QBTSy5MvNhpuxOUq5N8I7Ukfw7ePoInwUvQ+fRufhG5hoOldJtEoK+iqbIXY/uvku9qJTNw/oOg91nG82qOP6sNkgwXm42WCE8bvNxg2K1hd6/2Kz6bl9E6OLPTqEcbCr7LfyTbo6sEzgAa3+lMDY6M/eDerWkETOClit0S6mir1sC6R8w79HQ/TD8Dx8h1mPdu+fkIdEIy4otvP1kS50n5XO8XSW/Mgodr3m6cVBmlX5wds0eTeVQGDPhOlIPY5hS6NyO55ILPEWg+3kodLbn56fAQ+HfmGF50c5KXwHSuuCkHgaeDbBaZsdXm2GPW4kpGZXSo4lwxn/iUHSMdwUG3uAltg0u3q4SCmSZlbZ4G0WULoKjHvdWudLtE8BI3SNGnSN1QVL30btuqnyFXDVF3lV5ctRXkfoxdsHEtsHwiqpPRbNBDELMsBd5PMPoozkT67oXkizLCm+pRwSYyNS4C0LovO6SuYCbHRoZsisIDBGq7gok28WOWYSOOS98Sye47hf5itU2m0v8ID6D8Mtv0GXtYQ9wMs7iPlqn6FWrjc7iGLPEXSxdYLRzgPTQzkoeIJqgwaLwVJ3y0kWHfTKgxhYt3sziq/JD2ac9kycVlPp5oMPlGN+7LXLr2BsY/U3dFmmTT08SMbxAWJXHXDGtsqknIvmQ5B90MMhgz0Qo2/nHrrQKrPtSkHEWazZscsF1asijhJxrq0dUX62vocDP8FUM/7QM6qLKRpjCU94SSpcERtQ+IXYwQp3G6ZaqFHuJbwVd++jS7oFtSQMZA6WiFcYMRcj+0a4AJ4FdidWNmHmMZwitIlhk20NpLEwWjpi5hRfhX2JPEgCp/R3nv2VuBa1wjhPkUfs5gDPVE6sPnCPg7H9pJDQxi5s55Q6IuCe1Z9pSmMfGAFEOm4mS+kaHbilpxPsfE5KQTJ+NAJ8UALDwEKZ2ALjg4p2fFBQf3QIdwaKjXyn+OFIuTIfxNmcne/gqrsRBRvHPbD+Xnm5iTK2qSE5rdCRISNnbj9XpxHXExeBY+X6xOTqlN1UbHfchiuyL3A1lxZTNsw6fHxR/m85cmNYvgvmmEUxkPGU1JCds0Ab/v0xpaT1outmMNqIMekN0G6FvolYnvTdDMDh/DoRtxLE291cuA8XDGtdJRxeLjVRHANFbjoSU+fFs2eivYpHEuTGeElkkU/abqDcC6GzJLYk1ifVhpZi0K7C4O2ohvDfgOo5F040U8deXj/M6JOT1n/mKpvd7rtqKAs5l1otK/qYzrb4mIbWAX1nhg7MM0oltHJ4uiBmhCu3UeAX2ldy8jh5OWDB/rH8+2iA7gtP6b9vBnBn3x9EN3bLP4qrGJi0kFgkTGODP8r1RVUkCTJvznPkd6XdfDQAFnxgebSXg8NzmLT502zxwR1ITCZ882hACWCO1GXyEz5/M7j39df06li9eqbagHGozIVffx5+h3Dp4UvlJPWNKv5YFz86+lKKP1bF8YnRqIQWZQl4kGUf002VmPLFpdhmW8RddMyxQdW4gy2X98c6KT7wQucFyHq96emMlRyo9sW1P5ta8fldXxizF4jbGCR37lhbYmo69YKyPYJ4qgN0pWObzfRu/3Dw5eFgGhjPj15yJ5ru9MoEWCQ4j4sPOxfrdFGFO4v0Df6K4Xc2XyTFDlR4mV6tWdTdSctynQRTg0uUiFNwAZU9SioYTjLfydEPLtlZxVfJaGfvJql30AwC38Owdlit3SsDmKTqeke6CMWyeurnrMCMJdhJoD7ANbeqKL0KSqjBz2zBzj7hzjSoUWHoljJXeaR29Br/2FBrUB52DI1Jo3M4WmjYFD9cXI9YJ0aPI9V4eliBfFCilFhisDXH1MSBAUWJu8FRc6znNDlD+6qPYxSQf9TY9fRncVNTOZNMV+Q+5YjBxKvGff/Pgc2wsltpXS/lD+H8Ui/jK4JGHfPIoCL8l3ENC4zVDvzw6iLIzJZwHlIrnWjoAbbKxg3OhuEKPhj4LBZNuySPwUtfZ6GHE/1laDHki8M3mIxgbOMqUheQTmW/ClOKVDdR4emhl1mLcswZN6exKI5NlhQz6DwIGmvVilDLIhgDJqBGiJjYYRR0MJS/NvbZywHssEcD2FyPB2MgXRXR3T5S4WSMyjljYMIlTLN4sfhwA8UKLJZiqVix7982ZvIc+wGkQQE8c3phHXaJnNb5NVDhRVKWztForHAZfNXV4fk8uUyKIpnboLv9/b9h0+pVYAPxPWsWWl41wnSJ1lv3E4Wnc7I8v+QFRcESpmiNUzSHKQov7SaFcYUr/JVfAJsIpCBc2o53Dp2vNb8/URbuAlMJ8tjKCuw49SHehnIFqgbRx874Y1ZeX3le6adMbMkzS49+TOK3STleYfgU0NSMhYFl9PISBE3KqVwpdat47FRaX/C4KPKCTnie0d9IKZ4OAkw0CGz2eI1EiiMbMNpz+ZoFD9fBaEh3eT+kTRIHfLOb8BOztVaHMptAtO4PAm/slzgpxBescePNQXx01/dmE0mK4uvGqMOr9sycc6oiTPQGxF5cI9826M+HSO2n8Yx0dmo8fewc2Zx/ykEESOawH00uH78H9sZu9oNePIlXYbs3+hvXJ/O0G+2CumsYZ94Qsgsf2cebTUbgBUQFsemYKGkMaw1UlPVdMZNZvquL6AFHF7Nf+k5xGp8FNSW7GnNC+pQDFjkEOwnEuHdumAmdP74322xcMLU4eQAHWLIhGHXQtuzuW6V8m+dLp/9D7sQeEjoPaNy3HzZ2eVeTzpbZ5QDrFIgvMOaXLl9U9uchRpxehG8p3HmbG2VXjeRQYAI3Gzje2+M1Pqq9dB4o1vSeeiZXG0RUIiGZWfqEVqUOw2TwfthrWx5z9Cwlm9lu34Mk6ijKRm5nXBg2c6i1nfp6ztPq/VBACrEpIsZNZ1yTWJOtmKWnF/EKxgZC0nm69TrXBsTSrtBmTqDZ0o3KR73pPH07RRWpcM6PZbNH08sYJmjKnBewP0V6sa4SKA/yysEieY+pYQ9m1IFpOAVmOZk6rZLqnRW+FXtlOPxCZ1jvNT6M3hyjZ1eoP8za2T6xSFbjlDCPzfZiSrhMQX6wSaGuKTGT7LuNAroJUBYWg3FhCoxl8kB2Uc6z8L6FPk0ShBYgRxuWjqXQp9e/AZDSE+Gv66Ut1EQjcLZhxQrD2BYw4iQDLv1wua5IiAjC7TywZag4p0hscsZbNrM0rTBvCGxdrGY5h6v0RpH9URGiGkRxAKM0dLR8lGEoLVLSJVCUa7wST8gTWaFN2AHn7B8jEdArvKUf5ssliCe9+fPQhBxg5953kv3x+119DNw1Eb0Pu8ozr/LLoOdUMVOC7AzfN6CWHOn7FREm4HL5lVwNcdmBs8Eq0FIm50QqSekGqTabGJkFLMPzIq9vvFtydBXqWb0OFf80ykJaWbk1R8uwcY+OzluLQM1WuAJv6yAkxvGDHpO6HLhXpU4pv2t9oJGiCTILMata5Tb+g/nsgpdYSRu/GHUfHUIba2nZnLH6W243m5XTbmdk9VO91zMrEJ1C7WeEOHWDUYI9I6skfOrHJtfihGVDDMkcoVoTQ2zNadRdSHWq+Jbv4x+k9onVSTp2ILrmYQ6/87smDmecG7GljNLTnMB7cmThS9dqcBNHpUTQsC8YZjfiDU9+VqLrndnjZzHlLGIvivEOaITVYN8PemVIUU2mbymn60N1BjqiNqzkeOPViLRkkWqsxx35T/L+4Dip3xBTRbBQgnBXyfZ/cxKv4igRf6RrUymWd9zB+vs0q9KyIMKZKzFNf5xpn5ofmmKabHMUeoEZY0SUcab6Zo1pmd7xXMUpMJRnUUVuf10CXz5O9R1GTEn2C7WIiFjA3b20Ggu6ztDMnhIPB1OdkIJDK9QG6Kp3frlYl9cvPmQzKp6TZGLFQgJoyYOQVA3pIZx1ihPhy+sPdhpH8K2ec6BUOpo/WgERWirAQCvgpQg5RmfU7hHmiRkYtZxRy6S38YeJk4ypXewg8QEMumMUqclC0pBjZ1tmm/TgvZm9QmZORBJFcbdo2ifWnHJd9RHZSk1wLlFFL1EEfoQiMMjBK2+fwv0865RWWY/AiLaemE8c289ODWCDhsdXBpaYSB/0eTnRiZiVLOE5hqsfxhM4GHlFukWm2/NPOaHGwotiuH0me+maXfixwpFtloQjZLrIn4DN9kFgrVbnyi/7B4SNJsvhDyIVb0UxvKWT2keXRY3CKu8dqoJxNOJcrK3Qi56fDshg1UGVdt8ygQyr7ih7L0B6l/JiJ9sqwUtxFjY0BAJuFk+658CT6QuTaxhE2CZItQY4Qx7tlcVs1+js+PtFulwtxPorTsi/ZIR6wRaQ/f37rxHFnDs7+qR+VZ1aGCttR5jeG8++SP7AaVFMJt5W6B1GeYTwBxPhGfruru8hAcbrDu+zqlO30MmoSjgRT4IJF9KTULQnoeichMIXKH4ekFJ5f//HAbnT5YTryQpYCl1pTIsdIY3gzeDOHYfGUncPKKy2KUsWBsFejGwG1VgiPFBI9SbDRqESbCFQ0lOG7BXvUwx22S2dwc0E7Ir2d+YPe+aGPYNh19uHumhvBbO2Ic5Aa9ztERdySpA4NLluncVLbS+l2ckVgDtwBqQm15K7yUzsxEtvd2w2PcbFSl+TEoRcHEOCsTRJOAuV/zq3SU9dql+a/jyIXbEtZ9X1x+ra2WTrdcc1nKmGVe14Uimw1l+SptpNpOMWKeY7xeMshHmetSisc8OeuRlH7yEH5hcilrDF8Bsnwrd69hLeZLtu0MjhiDtVZtCNJd+35FLvN68+ul+c5vbjattuFqBsqmv/w+pNWx5VvkFT1fsS9biPULoHHuHS4xtWZPCtm5cDcg1t3b+Gpvvrd2Zh1e9EGYxiUgB2HL5aEz7HoS1Y+AQ6sxO9kyaNfTXgGQlGwMYsVyI/0KM6GLXKIq9TeHOvJIjtlgbtAP69ZWnJ3JxwOifecxMMxrY8L5niZ7D+Js226CPHHHg2jtEVpp1OWwBdpLDJ/MjfRMIDCdwkVoAYChR0iGZUG9npp7DXFRpYSvo4oK9MOrfbPnJ9QKUTfmW4zcpHTTH9xmSt+GAktflVqB6wuov1Xz6yHhcWbHvYg0/iLL5K5g7d1wGcWFa2EngE57+qe0VgA8OhAN/Iw0ZmREQSwNeSbcR2zLptfLyBo0YDfk2mdsrsIg+VFfhTRnDcaqBRGbfhueD7WYVp8ifuz8PMeV6NvLovbQ3Wc0UyrtgFsg86F0lAGOvOl1175/D8XNS7tqDs8bKjisSLNg4UwE/HflEq98TzjkK2/ceTzeYJ/P/ZiaDE8sZoVVMFJhVNx3j8zM0dXcDOetvcBk1E6Ik+T36Kl9S37578DSNK9MKQVEfp2PWWoizgzT3WMYlCpep6y7j8bm3tfWUnHNicZod9yKZts1QrIFkz/ELvKWD1t5kLLoqpwUvT5zZFfsrieG/7OF1eTSUV4jaLArnVYTetUSGUkAL86lmRr6DRD2i2KOHQfYBSKXmP4R/kkRBn1bZvLvJinqC1or96/ynll3FxlWZbyseLCjN0Z036hdkRulZfUjx2vkvrznAdG3zm0qg4kmGNe8BqAtHyrVHk3mRVfW3QFst4i2+C3grNbdjul3GJTUILMVLd6ciR5gIzUX8cnAny4SuUOdAHtZFpgJ57PSG3ki2wJA3wow6wY0Q8gmNtU59pjvxu5sA9q3tw6+d3ooFBOjLD60Kjzz0osVeeApKTNrvsC4mzAo8x14KDDupa2qK5iB9f4cyIKrUysiV8SX2O+xAjQYoSVTnIDZ0MDDNEnp7lzuO9m/NzumHGmNqF/iGRHZ5STA/+plDw83PENMrwAXCU8N/KBnTLD4ow8Pmp4KZcrwj4jZdV2oocD8Wteg9IOOjbDzhBj/3NidDg3Mlv7pV7D53zanPddGVsb6Gems7SQ5QOviFTm/T5sPXcfM1R8+IgZrKumZEl5i9veIn84Q0xkT+CxlAS+2djDhL7pz8ZiflLz0HC/3ZMQ+L9bM9Jon/VKtrM4xUIBwmR0lAckW65wuw4Zj7YOzm1tETqOttsYO5d3NotdZemH9/RpNxaluet9hKImFwQmPmhTdKEkLijFiQGFR3Lt/FhLFFVjXAG0L/akMle+MnNMXG9bcqgQG3hLpsF/WoiSk5fPkIx0Y6B3Qs9EXisvCSZ1084J6Q9FYjlqHKsNFgx0z01Obens+kSLlIKioFy9xcL1PMJBP6fnfkbm2PaLfbzpn+H38RYSmImlaDuWqpaL1ULDawxG+3eYeTI2AN69MpoWBK+uKAyr8kmLFlr/j/aoC7y8fZcWw8+mHwzDc5BpXzCSXW5EJN7kQ33qU6zA5en5LmErnYMCAQdkUPEywTvdfsZK4W7pn6zKeS7WwSyBj2jK1HJWw7N/ru+bEHFFpkdfNsiNyrDO/r4c3WnYA4gfw22d49u6tt7Z+vZ1rmOk6b79vUX7b41kgttWWxh/YC7pJW+W3025CCXrHNiQsyT5mxhxd0o4QOK7k7WXG3RQ31AseLOnVojP/nNNHJjHQx0O/dub6eNB1ccHHhtNeHPZH6RNfnIAfAzxGU6NVx1WpwBC9uBrhYy3gPxkwijsJv6gTPw0fcnPhFqgT6o0It/eQ9l2T/ey6HfyUcGOP3WGw2uorqBInRLacfe1HBF2QSrNxJN7DKxPz9xNKTX5FH2ScOtElFAXaoD9gCcdFWie2oq6kiDagZxv9+7dRzsbOZjJn1kAph/k3hJagvDgVxoaMjQs4Ru03mEGdgIX2mUTKt96MOF3ILd7ri2XpGd47nD892xUZz9sO9ABXd7XgAGI0Foqw7Bwge30bHbW0O1kY42KaC2MI08x4fYS7nLoE3KXj7pWS9XQs4JCx1vmYmNhF+l/iuLApcFo8sBfeqV0QgjXvacrun1cEbNOFuZeThztBm8CjTumMLba+TUPH59osZv1PVbkUrQ3XgL2wkb1CRYxowoKrlfUrskc4m/vXUuvfZdgwk7UTnpaEnrdurol5GUwqp2qWaT1rFyXypRjKjI/j7Rj82GJUushY/mRyoRsQ1FIPthF2G5ve9apOMhONrwJ6oQoY9rEBHrI/03gqp03uBGk/Hk5R5nO6IYwOGxYiKspInRtsZRuHs9065+J3equ4g8iLqNL0KJ1APpWf6q7F9F4IKUBEwIRYMS/zPzm7TMzJ0KHYZIcbCOioPqTqa5VQMsgGSvCBadtEeptrubCJQ3PkEJdgKYE8yAc/ZbGO6ui7qOFm150UGuA1d1txpneHNrxm7Q75tcw+3ETKQEZ6r3RFIkuBXATJwkA2VwVEEYqDnd56JlCZ8rEZHw3GjAc05f3x6y7o1ab9ygrDBDF//9/fXBQfikb5Kiu47ChM2NpJI6FROp4FFnxjKfQoCYNzcWFEQtv/5GKq8Tq/CJcfK+/IJV+9Kv2PgKwIzMI0rHAzuJNombg968dYC4NQJINSqiufHyN9fhzIM9QeO832scto+M0jkwGs3cIgxL2zMzPEo8pCeKoMmNimodlh6ob0KQEg1MSbGZ90rKCiw/8iC8EQjZImRA2BTj2F+juw0Siv39D/inBp2g/BoLDKWGUqkrlYZpZ6k1W9JbHMpmc86pU/283MpFktTtBkQr8QyvM4q1rJP3qOqHIkJKjfEYo/FZP6hLCFp91WJOjMpqnNxjtxQ2MqwYPYX5Yrqto+P+vyd3pqv3U5vOVapQ/DGannqUaiiyEZGqY9Jpisi0+cRMTCamerU5ikYdlYd8tY18LpgVeSFT/FFzWOj5rWeDm66VeOHBfmbeY+FidZKMPlvzp9PWEvr8A2UDf4WONJW42mHWBnsdVowFM53aF3zJZcRjsAcsKnUldtUsP4+k9VrloTdIV/TM3JWHPDn0TDEgKiPUYeN+V30F3sG+lqvbdRgZBGOgt9gdWmqgA0v41p5mdLdft5Np3FgxJnk/W6znNMSH+eqD9w4q+5mN2WhtUa/6tcXRNlmfvVf29HV8gIMT89i214LL7d46qdF1zk9Y53Wac0v7XR6YfMD43NrmkUlpf+myUruXsHx5NksXyVMO45k/kcgbbfJ5gsfeB+WTdI4Ejtmp67tFXsKcEh35Em1wRTM4LNFwge7KKYRrsFZarj/FPqXBSCJaMt9xgUy4aKZSJm/0b3XWrufb8uacqBiB14OOdAziTcuWsbFz6bVXeCUecQhsL38qhyR2gTWmPz+/y/MOo5b4FSTt06KxU/TOau423IFdUCLuvZqXva0JhZ5zH5O+6aPt5cng5vx8Ri6BeADGJe6t2Y6Nupfq2BW0lrfil+15CtXa5GYsbuJQanhu105kdVf56sfkbbKQ7eTUhN/3Ph8E27RbrfqMsxhik+zvK7uBcnWzeTCIwDZrHiW1uaCxbVIP0a7EOYUHR4YEGgs7PT02Ty01goefB0Ld6Vp2Sw8r7EspDY6VUZtoFUm++Gro4Nupz97XZjE8AIQbDVjVukNa9Lh7H2Fasi2JqVRGqKzf4Fnifk88invOg1Y7zRX93rYPTk/F2Zj2Uv/sTH+X9nUMmb7jSVBQG4+PsGBHZA3siKN+mDF4RPhcIXVVDjDCx4koaBU+/9p8ZeK+fC6g4QCQdjkA9HvoAwCLacw8lgWsVCIHD17Dm/a4T8c3tsf3xrrqK6dL5+Q4tiEqTcdCcSs0nxu/QuX86vkW+n6T/MhUHRHB6jLumdqNg+1AMtGpVvgo1liXRxx/HWAqjuqQI3NUV1C1wmSn0nBkootRDsPOC9NvclS5JDx6ELAp6ioXbtX1wjWMvqS9G9ydI1hIPFhBjY7RYd5vUHygpdvIJxy29DKdUTJddkYSpl5avDUqGtoqOyn3ttYQBuiqiFfXDYrdcu40FlOvSyLFbekR5r3JonjYMyHdqjU3w1pf2o6VRlc1n0R67JBH2LCAeXezGiW4GLCpBSloBHQxXBVpDuLmh1Gfcun+VZlJ8ge67IwG9+U2HzSTMLZLXhknSthCdvr+Ir3KoqRLaLqNmM+6iXmHnNWWgiQ0yUklzvlIyyTNqM8ubSmmonsVjKtJQ8zx0i/oSisvEUPjqy7JzPu4LaMpi02XNMJUgWAbs5asIxJTmDXFHFqdpiJQS7uWaHiCVSFVSQNdyy8luCFTwAwlVK6lgoaIvHrWcUX7G0zdPLS0DlRys3nVw4gcNFwLRKSfGIkT5UVseTIpN5tMvu+Gv6OU7T9xulRPlxFyVkTrStHUqm2vzRTtqNEx1oopWWskU1pkGj4tHx8zJR83jlwQfkeg5hRr11ilyBniJHpRuYTG+PWUNB74P8R0eX4i6ZCkMacCwTsIj7JLBKb4nQ9D4nXKvuJrFlv5rFLxWQQE1IxPAqr9MgcKhixAqkMZNBQS8ltb4hmKsBWsY9GU/qWIPLr8n0LkcU9LCcvogBpRyYAUbtQ4VmkLfOwaVYo3Zqi04RXtGDkvuCtCFRybNV6GqY6RyzS/Otdb1YELmr9AFvUk0Jv8XUYYmIQbAnJI9DOCNxMWrsWeY2xaEkkxUoqDA1Nz+6iCfO1UKngwcbt7z9WXmUIZJwATtkJjIz+9+B1mjc+1pJTE5PEIAZO4LJNedNQem0wf701MIibF6iIPO+9HN3uW3RjRqUTzm+NAKoOggvRwz4jQpjCdg+jeTR3u2Wvc1CLDfNvH8DrH/DsSTjHE5ivLokzUQ/wS8zOoBwE15WtwRgonJbpnnIVaeh7ubajUMvIhspTPRcw7OvoidPoTr+P8w2p04MFeeZ2vF/PHrDtztbku+Eq16XW1XEztd99lje92K+QDMF35guAe0X61ZzkzPePJoWPY6JkFulFE+lJLapsNrFIWEUIjxfESv0hP08ixHBNgzU2wi+b6MfQSzi7qOx5j3LcJ8ieqhlGiKJSVK5DQMZLRCPffw9tRavrmygRocztMvMD+06QffrsXPtkLv98DDhsYetQkczQDtHZ6dhbeSJTfaB3iTI7mCJF+KTURPpEOrzQPw9VYzHQWMMhchCvEx6MG2Ba0MlG4DTly0SFHLtiRnEHhLPSUYBgBHZuadXAh5GOUmEWRQcIyxmzRecc/fkDGXJzSfh4am5Yo+ETnHkdIzuDVirPfhDfu3M339+dyVCbmDwnKNGnYCM9xBESP+zDKzF+/mfZHRVhew/LTpcCAMh9e93CsQJwtwsMorgWddEkztuyTb8MKoW3UHsEUUflydAPrOKfVpKB5eAQ70WyFURnSThwVdbjebFBCzoFbSHCdO+ECTJx0YUk47t97N0If36DbbeLcCSqhRKJ3xblrOHzAfcZgMRSPEIpaIEEYThilgwZWDgpW/UETemhlI/GQlJyy7Apro28KIvDWVFlxImCpI1BuaCmyMQHmY057xIX2VuEclpSbGZnMdZcuzcuODuPuLTuZjFm49AJgfUwg3JdXiL9TGOCl1fPw58sk7CPda75bPg//ue3d9fPwwbZ3V8/DX7e9e/o8/Jbe1b1lEC5VxsUOkHicxsRERuMcwaHWKKyrvi/H7kjuTsRjgvo4kzezWOhDhXfhUoR6I8cb6GGL6jE+13RxrFE+x5rTG3fM/rgJi+GQFz3u0UIyOfARB4s0thhBYyFaCulq3IGKJXR37JZ93GAgxx6AZsO5u8kijhVjOHb8HWnrTQA2TBdQ5odWJQAFJXukRUwcd0EInos0T1N2v4AGWkG4Jg9RWBq9WfObqBStmF6oyIQzqPXS0SCaRzcKuG3optJsB6fObzzwI3qiFtdSLn6j2Gn1gHBljHLPrnx0Yxd6ZEE1ZTrtA4MBZx+Qlsw26lDK7DPugvlZu+HRJmv0l/dSZKJHMhYa7MAttNpN7U9iE3VNPlBijdTvbUyHk+GKNxE03JsWiIbpY7eIoxfAk3L4hSeTuHWMkJsxRXi3R6n8bmz6KFabixiT3BQUYhLpCAb7VArxidHNb5G2aoTbpM2I4r2v9vV36a5R8TLRZRynH2V/KcF3JT6nCluQ5ykkRRl/beAgP+17y4epGuym+LQq7PbSdSh1yqfVoo6Argctx7oCpXNTH+NhMrftk+PeisSa0Gi8NeIzcZabjbmmUWN9S056Ykj291ko5zuqYq2sraKmpk7VdlDNnQVuJOYqlVYyD9n76HNPr+bukkIBnjBm3imeh+J1eMs/Zw4kXLBNUr815Y1nPO80vyEz+eV+UvcItTI9jW3gBiGiTXLklzCrBzKw9AMEKQI3YIy7XN3/lHm4+MDOowZJhII/LHpKbafJmOzdonvQ9Kz/ouDYn+ULJiCkGRRPCCI3VtPml8MhM1dnnBY7kDoNmC2ufwPv3vK1sh9ihHcj5rEwnsgkPCPq2ncZnHCkcVSpmcBc43aKt0xqSiat8SPjy3Nq0WYKyhhrIVlSN9d13TVg79QwoAvtiw7Bv/I3ypew79kOWNWfOO0JG2vHVpiy4ss4cLrZZh9bhVUg3S2To1FXmgwDCCHPENSXF1KPFRVEhYb/T9uiJUH7s8ASBKmcvSkzNfMp5fElkEcQmlDS0yihZos0wT8zD4FV/O5Op9t2/pREHPMWp8kCmb3MaY5aw+9afKeVdyKiCDsgvXRW36r3o52AuT7NEBc0a686+ifuep6RH68zxWwCRTNWukvifDbstYH0qD8NAN3K4UgawCckQcbMXCjISRSENbxmI9+DDQDHn4nZIzK278jhlWTpZVy8kSjNut6iXRZdzE0VX42e7tUjd6ugQbWAOlZMN1mYdZtYaELhu+arJbY0ARqHO8SQgpb1ueQbRlVtipceigvTcPpO3rOgRk5/Qd2EBtam6j3nYsVxiIbpbLJFjlOtmxCNfkF1x9ZGH+iXQGfZ0q+jQQi1iCg+43ZXxcOeZqyN/VZ8dMwt3V5PIeeexGmMPp6Mk4Rr5oFCAXnXynXdckOv98sli9S7lj91JmOxCxF/AgxLw9f4GrEkSZ/F+ADF4Ro4adZmCpwi7Ld312mVvEDdT4SJtA7eFfGKnufFnJEvphRceoAPpuGtwCA8CQ4VxOOfq5asMcTMDbSbWjIA0aune4TmpUt0z1E32Gb+DmjqI7FPj5M9IemZZbz2BBFtMsEQugHh48/nVKm9b6bWSWJ2TWlMww/Qq2pP4PyKO5w3wuCFGCFXLpnzPQ1Dxw7/r54b9WVmEMALTNP36jkS1DwSnSVwIU5nOUEt1/s9Ygt7F/hvSAB6hvWnCFrK0rZIZ28QpTBmm16NTgGUCnXPueuNqQar+NLVjMt3KUK7FcHNLC6T6WxdTUfORADnOvztOWq6+G2++mBf47tf3LtVDDPY/Pa9ez8vYJnRitss86tfBjZ40SzyrV8EM6I1Sjy5TGwRTuDaKPCNKnCxWLea+E69nxf5qvn+AXahrsdJe8sQQqUApxDJNgFU7WKot+2xmkMd8aTLK0Synm6RSG85OWNEPGdLHJEfhM0NhXH0HVXbBNUoS+QCdTHa3uXrf6YoIdJjXZKOZ6L9SIDj/MpGi4yV8xHlDCU3ulAnzfGg9CZJBw7viE2XK6EsnpDVAWFsRAIFuhxHylOKQrYrxFYeu2yxhRsCnMfYrFtKcx+7qQ9uUWB1UMMOFYqHNToIblF5tXvh67E2G9OrmpLFttalg+VyYhgZllJEZY4RlTlHVOayjTk6awKUEi6pr0sK17YQ6UC3Qq9uh2rdppzyFVkmcxRs+MeYa8rHejZQ+Ky6xbRcpFQgbChSX6MVT7nCCeYlKuc7MC8rh3mZBBra3h9a2RzUrGM4C38gaxxIigOJcRy5AxE35q3EpeiYoGfTIS1wD421fOSR2yVvQucD612rLfRK8T284bNMQSW1cdzl3dLKSKMgNSfOPYGp4CmUONO4j4HiDzkjNvwZ3dS+aKf5Nc/bwGdB4nWVz+IVXGEoMQL3kV9eAvPxPQ+RuWbexa9pE39LyWsNI562gxslYKUCjvoyXi+qF8o1k84ieR9NJ2kjmDf1MgWPLLDnb32C1GSY8HA17MmVFITnBo8zkyti+7CAzUDJE6+MW8iKmaRPqC+o8d5ruI3rKdcwMlxUwgAfGl6Z7yEdNWl40vuLBWX2Boa/1vxF+6rA5+aeoDKJsSeYt4yX1WuocBPK5GF+mrsNw4yDps/qjc7h0bqkDk1pjKNwSS6jaf/w+Piwf2dV5PPDpFxOx2iXuu6zcdnatq7IrA+PE7JbreDEDtxbSqdoedK3/MsK6L7qpc9ObvZbKe0EMqXJMVYyaE2IwB/rZJ08SWdFXsXlG4959F8RR/isyJdpmRjNGjrAXSeoClQOCx+0C9beoPdPSs3l+nfR7/CI9XJGkd6FPUhtfljDdRpYxHGzAbr5ESvxp2e/vJwS/H00ffn45OX9nx/fn6IrQTMNEoLDo8Cxv/8OZXGRglxH3/c7NL2erKmTD7oUQoXLiVUF3k8Me3SbfveiTwUecuuwE+iucE67risvtrq1LZVb20PtKmKRkVSi+SSikUqqVAWkMK4w73kV+JArNvuz1/qkJbeM/OIvdfETlj6Qe3uZv0k8++5jXZC+Mp4zV0vK9KoLP9KFHw/FOQe3w0tG+j9yZZ9+rOzXruyb2yYNJupR3+3DnYrw+hxwa/d83W9snJOT04pwCtVtlOwXuOa9bL9QyyDOiP8otJp1Wq4vylmRrvDu2I/+p5MTDs3kZyMpYh+oQhe6yCJ/lxQkmEx6UmINZIcehfLAEfqAP7Il3Ee2nq0fqesCoTe2fGarhsvVzd3vHuWz3ILZv9cGw9pQ1DCp2W+1V202rFFnB/zdzqycZAKEE4CAKeFPBjWlIFg0uOMxRzKwqtPpHaTId+6MUxV5AfQzDiXSZVJ0cdwxAvuPCo9XM2x10XL7dC8c10a5SaiTKh/qEy9YR4XL6UR2nmeFCxU3SWLVE4m3aeMgxV24AXkUezhUkrQO2PjUe24QGWat537ri1Z9jeh3lwuvsDH/OaXG0c8ptB4Y0ZmXa4Vj4KH0QgEymCD4kuFbbUA8/XIB+5mL/md3MImVPzhQhPgnWgrcOOHLPaIrCIZxZFaN/nUwGBZ9TQDyKBWHNIJWB9503/CmK3RWhLSJ0ZdtyStnLyeHTepoFzu3WgIzVklQJGO2ClNgLDiTjRmlRsaa5O7WdS9Dvw2T6qcyAZ7+7qWzkVMdJR2GtH0YdF7jZzSfFr6o4smgeWr42ojmNWM3+5Ywm1jHYqVgzwKPG/U2Wm0qVKpPwljz2FL/E7URvlGO6BghyZuZoyT9rqkc6R5tQ4KFOyBwJEbsDS0+jytXPN6PXk0/9yVv9NZPcS5IaLIJshtM4unU3unQ5fM9lEFRMz49U0mxvUZlw2UNBuNHbmmsZ9debxlyGdkWLuOBR+V0ltjMuE3HZZleZT10TnFmv04fFc0Q/+qxLyykovREAZ3NiA41R9+qJf6l36O1Ujny/Fq9NRT9v0pK19zmWJnamwwqwChSpCp/SGG4bFzhRAko3St9lsrcpuZNTk+nYrt7vX7210towa0r1hJGiJgTVH2LQ57AzpPZGNmdqCbmBzWLn53+ff3oq37/AP558M03Z/jzIf/8Bn5+dnVIZFQnKvxDjV3Z8UgpaCghwv55vmRBZaBdxu2Q0e/VohPGdRGDsLpESScXX4ijLwJjvOx9dvqP+OCfZ3c+uwqn04BYLi7UDz9X/XylNwtxshPmR3+NF+ukwT+fNNhHzu9VsT268NONW/8NhmTBDWPgWRjSexQzSIv8yuuoEEOrR/mggwQh8E/818+P+4oydRGHBW3/cQKiFPz76gQTA5tdZDGVEGoH2AhE965dFa/7hA2Dbn+JR0Vf99vZUckQFjeSQPXSzWYXH4qdVMyomCfV855C7SJxg/lmg3CFcFNgCAbhR/xxwml7q8OSYr774cEfJwYFBy5ia5p9dRIK1DcMuSQUkPdPMegDVTwHg3FQYv4kqaMK7pgfiJQu1akwl+xeRbKpQkzPDhLXrhIcqWShSxaqpGSZjFs0ixZ0symhohmtVOkC0p6J9jJ8JjkYfjwJYhsZpsGTkHaMMZIvXSb5ujI2CG3fbq6Kq6gO6nDYN3SiVhmTDK8KjM4VUqf2AAQabMFpx9b8niy1ZieI5LjZOLZuDUNEGRrW+aTHK+0Ea/TK7kAoISU3Y/xxymkO8dP5kDT+d0dhqDveVjd2ReVS8mvix7aiWbsiwUnBerwEnzux9g8YMzKXgDXtnvTmnOnPWBh86sAVPO+DYFKGc0aWMsGaSCpI1wYvkbMjXyFP9jbH4Ye9XnvPBeP40KE54JmvbTv6hD+3xIxjC/1qKswnnAWecMMrb3FKd/LL0ykvxDQUDeiZTSqOkKVpM01gKvOPp8lkF4/2hpRnU3KNT2eLeLlCbabq616D8Fan2RnlGjUqHmwjzj5MeaITeO0+vl75BhGpbBovcERoWzVPZlWxaDyihKWNZ8ukiumRayMZqsnc5RYzpSSjKYaxw131I4rbD+OSwzEr/4nDX1FJg+8NUPuCX9v41wFyzfhkdh0XD9GLq+r1g7vRYPhlo9EZunmQXZZp7fRRepVS3rvP/vH3+d5nfHVXCroTv8D5pILAb9ZTEcWnOOg72ONfUEPAPR43vytIkVkNoxuZztHum5NQ5mz05qQOM3jHkw8/Q1VK6SqLocfE8baZorsihbVMFWCGVxLXYRpPw2qoVudKxyh+22/jqSUmzyVK6ZznstApKQ9vAW/pQFV3eI8ksrl4t76PT9p4W3TAMqryv7/G85zUNmOmRXSt2h/aPgCDSx/il25G4qGx3Vrp4iGaqFGBSxyudpncXuim1siAnWUwkEiUbEWLjS2MXk1KiOo7dfYvQ+reHCP7Y1hbKA6sTOKw5ezA8qHmW0jStv6qijEjoZ70ARhkZxUnLIDHcNv6mvyjUEBqMT8ju54uxL94Bi/0ZC2suZP9jWehSd+K+RuvGKdvHq05FROMMv+QzJE0kn5OnBPHvbWKtZvbJJV5OJ9IsvP5dJSqLV4OO1Ti6oYn5m/c0ogjAAleUv0JAvFuSfuCKpAAE58j6xS39xqFJG77+M6APsc7rAnPWU3Sg4E3itmw4fFDd4KfcdYeCIMWJO6DBqsQ/YE6YLBwd8lY4eaQP4tOhsda0CZFpzpuVGzPGWFxl4a9oiuVTcWnvGuidu7sVojM1C2EL4aOhFFSYU4QjBoZytKQrdbk/afFvylDLmCA7DPy3hHjS+Px/fL5OmfPVUVX9xohXjZ9uxbz1o0Fa/mYbDOOffl5wzg2H2olA+sTkjnGAuPl5+WHMzU+QtlrUh1e5yXCiriINn0jPGWZbvRyyLKd50TWkO9WQ6MMCFlf1PDC0SM/5ztFFfZTb+uyy2Erws5LqeREYiFZqQpsBF7A4OO3sNgcj6Nmj4Zvr6CJtXr/mibvGgO+8ibdhJIpC4dBsP3yK/Qfd5ZMfaF2pWzqyv5xPmzZa9ofdmBFnevlrPoWbJyhbjU8oeogzXi0O3CTrgLexHbvZul31LWwNJZ1ZSHCnLFFAqToaYaR5khSzDu1hT8MW/oe5ZQLFyEF62d+hOz3vWEfVUu6e+ivL26NNyaWOG3HEsdbVAYpu4lWHliYjv0gcGMP6kwX5VQC3iNCaOsPQ+laoUbV2fORV3vMn8NFwO9jPSmfWGNYtdNAYaVHncU7quX4B4lHcCZvOZm7lUAvmzNGe6xihLqKoAu+/lrtrw/6ALByuuPUfHEUJt65ea+2ck+w73gr7yY+4j89cRCJyuBsL/pMkF9Y0ncZ9GYwhOpgli9ALqN5uw1LhNAtfOKqzOV+hkVlz9ZH8tHJpJdsNuI7Qrg/Rhsq3ntqvvW8XTcJ1a2fvtNtvjSm4yq+MrkY76vL6+XwI3ZmRTsfe/TFeWDlpNoG1mx3N9nfn5qP4fbcQdARKagqnWbr5UVSqG48+mg3VD+eDn0glZ+Tq8fvV73P/tGLN/HFRbGJZ0WefVhuLjaztEo2KG9t5slikyw36QbO2+bNxXyziC/gESomNvm6AqZg88emWF982JSbEgTrDYhP2QbZzuxqU64v4P+rTZUuk816U1UbEN02/4bCerD3WThNp0q8M/4dh0vJFS37RBnstw9gDtxiWW4QGW62SDZxmULfaV/+AawHjCXO3sblZj7fzNO3m/liM682l2mymAPNhT+uZvEKm8A/10WyuQRynxQbDJPYXA8218PN9dHm+nhz/fnm+osNhnvC2+tis0hhJtJsk8VvN1nONvdNvtisNiuopeSdtiEj0qaabyqsd7NebN5C9/K/MgX3h5LzhA534xB7wv0uQ6LQ5eY703SkokIuz6qSnu0RRXpF/61U/QYXNPHyv+wOmJor6gLMr7LrD51eHW8tZd2GvkX4WltTjMN+vgTBhKEafxrScv+WxG9Q7nkPO9sGY1lzopkY6J3yCVa+w4YzgM/HqgwnNR8a88xPw4YtrkJEai/20W1AFQ52CnfCGZ1XC9apAHFJEBQoEAq2JJHG+a1yoEGKumgTJ6A4FbxuoV9ciHC3lDXS47Bu40GqLsZD0X2NmAkjpoqD03d7Z2iaqsiXTRX/zmM5yX/0u8yhhbXR0MNBP8CsGqLkLvI1Qjh8dtzHrAAOervQt4Du0m5frmbRAvySLTkHrrKNDrc6Jjt2ne2JFnAJ7eRP32XmdhNQD7SY0n8R/a06IxAm+FdZUTUpeo1QZgrEaaL+Ps1en/UsgEvmbSROkBNh5SSDdINAWSXS9O46e5OBfHFvChWpX+SMVmleUPzJanzhPBTJGKH8fd2bwH4BPM/XfQnpZNdbJXk+HzDy74n8+3ivZlZwF+qm2Z9KF6YoCtnumOGnKKpzvmNU1wQm8xQGKphzBNwVniNZJ9gSRZqQtBJTaqOMsbv292PMLu03CYfKNhqh2SIOQi5CZ0EK0N9A7u71rvoUKIXonYEp6TAjpbh7gPDa6eGee0CU4R6jf7YwJSsMwNvVlYLUimpRsTbd2IeIpZliqKFtDR5S/61bXgPVLA3lN44U2HRlqVf7EimrDXkncSF516s+OTa6OzBaIqOJLj8YRj5YmG9pRnMMeWKw40bP6qtVRjZf4mSVhCPqvw5bmXhtMtaW14voVIz8AUwzKpWMx0symQCd//IYBTDJUCy1oR+QbZ/G9Q62zw0+Hk1Ngt5paP4aTfHNtA6/HQrgTF6kV2nWzJJLbsP8Kkrq0xcflhf54hD4KnJUOHN3Yn7cu8FwSWzv1TCUUGabl+H+/HdYk6x6iFw3LOIyJtzWJIR14b86StWcUNN/aBp8PXRglaTZN1XTU5figFK5sgJqSw08vNZ3FvrbB9v0GrNMD97RFq6S4gZEDn1asImsK4S5uypSa7h+iU9pD//oZV7SnVRqBhnA2bL4am2mP4p4vY3e0ybWtuOdVCOGur7ES7UKXH5miRVSydh4v7sZz1tR5RXDCjSWTnkm5aRvvpsY/UajpJ10mYSGjx07aGmEvKBuxC7jYc0ldN1K/O1Jt1nrHY6Dy1sQ2ocqmUG425Nsr2gule7R76BbdUU+nVAgDvxNosyPtUS5KaF4CFKxWpA8OPw9T7PedAdtfpyJx63/OI1eDyWgTm9hb07yw7forIEh3K4bOhVaXYe/DFuY0t8Ob0inPCVI9OlY+QPqk+HWybzVyl5VhLbuqDru6K6dNV6dJBLPG3Vg/+ZOfXMXJp1H/5tFulolLrPcb1KBKzX+GEHBlYb+alqRqM7eb1ImVdDmJu0ogklDgD2hC5HeOvbO1zoJVLrtMdlD7S956zaiOTOBNPAM9o2ZUb+PzQ7gRgxf4rVBoE1VH1H4RG04Oh+qK+03LbHDRXOaKK7yb6xVbCwe1Au8J2qXyY+Kw0Z+uGW/lUy0/1U7rvOAbL0baIE+aYk9BmDLcj/3CPqksZkanRqZYNP/f5yFLvNN0rH3/pN2d8fx6fkNQIf17z+GwV9usg7/uGXDkvfGWIxxrSQLnP3ELIw4dVT1v2ZzP29vbt1O8K/byv8VNqc5ND571t6s/gj/0zeceWN8oCr581M34g/Dxk5M/vpG/Ks8rSLh3w+3hWT9MVSOn1tL/aBKnWwt9cuQ6PxjK4v46TH+GN64wTOLOm7SnC0b1c9wb1mhrtJ+Op5QeB0rCt3WJSs5fWq3WmbwT+mazR8kfavDR1sm7If/9ibsh/8mJuzplgn75a9PmFYt394pkbT6YT9kjOJbJ+yX/9iEqdSun9SrxuetBMWqx4o8vPa1kMkERbxHhodrGJ2eD5tAt1T8sRQPyXcxC5qf7Q2NcEpAy1PPK6ftYJuSWoTXcFJwqJ6dvUl/ZDweeuld9Iq9V1BM7S7Bw6Cz5P7+45Pe8Kvjrry8oZbw0ghqgAoLL7IvOfZztsEAfxefSVeo8gtROBGWfLpl5t74GZ4kykdfvCD+qwRbx35p+rxLfiaqex/OhMkOxSwKKvUP2PJZEKzP2O7EMfv2jcmvr417K6ogLmQgaqmsQThyezprcTUCRSX+yI2QMltTF7tTGXaHv2WHJPcJsz+SuihsX3joXM/JU+aNAyV9abMNtm6X1R7PUWkZNAe9jWXh+HT1+3vyumkFsriTP8pCPpSjonY+Z7TJ4FwUlLZK3IUnv2EMyygLDhLL7nBWAtpMUBRxDtjFmNwpLNKC8efitmnhMZlEFcrf0HTGfmNNJsYG3Z1Sl4wTuesBOYKcicqRkC7qVFDuqIZSsYU8W98Lb8I9DSaNLeEzNKM/u2HCAhvImEzcq1oDykTwhCPEFsEmE2uXTqsyUTPp/PJ7BetymmJz4PSdW9slFHVf7ZnWwW0qVZbR/Tk1kVl1+Ls53EZcxk06HdO6wqlmDmxLrjcqFFmJgcuyMEJ7/bts7nb8jSkwSkLeNTe8BTCxDasGYU3uOJe6u9XkFE7i2Yh3RVe+xlNo+8zqE/02tUqKe6puLz97UWkUnM2skHxjeHFQ8UXZ88ZrNLz2hPf0KRF9OIxZ5SY24yUz6Zbum/BkP8LFyTStGILUhuJkwR3zA11ByW6lnb018Y/xVMIl/S0n9HEkTydJbH2gvVPi4wbOhq1jtyGGoDbyczYx3ecbz2tRuRUfmwRr9jyE5iRlvOcLOgOYcRsVyjIvNx85BXWYUaIpsjVmvdjQppt5niWj3X5Imk2TXUbChOXlQF6m8Fnt/LsKDF1PdPREeax19HP4ZaUoI3cZmLhMK2q//DxM1A5lhXTVfIIWGwqsZoC5EmjEdGT1xV4ACCdG0U9Utppi0mkQpi1OwaXJAVl9TaCIkVIxg43UyZUDf5NNDgajYjJwr0TJi88xBTF0FE9KieiGI7tPOsoKYFxSAg8yjzMHblc1Sw9GB1L6okDqbkvOjjGwUjk5270Uwy66GGW4ZQoTt00IrPwXbCO0QLAlLd7fz8cxu1k3cQDyjuzFFKBlXOZS2VYHFBSS2+cGQndnUDf9A3E4yvvYP1JiJ1B+wPpAEm942giwxzt1dOq7pXtFlJ51Lq3J/e3cRm8QVTvP7svSIRwWWlh509VjL4OJ9fyokKRRB8dwLJGp056nMFcuPYn9pghT+gbdl8dpVISNdMgmJDJTEftxK7zBTmaP5o9zrA6//CIImwMpZCB2U9bBKNVfV/brL7d/rbYpfN/rPQdSsNlQ2zHh8DzvpfwAYdEIAQfNN97A5GHacIbElr+ClmPYsGm4pX3Z+PWWOPJLR4foagfKCdsQvpZbV2gn+VZixI/nOs70YtJ7RWNCwv2F70gahHAHxSGNbCRDx2JfNou9O0a0EEtG9L2yUj2UazJT12SBDCbsBiusjZkZ5kgPn0hKNBv1Ohi9onKvkGdqFQammDJnmbKqQEP97L7Jbgki2E1V1NCodzIkLKPnZGfFCfnKQOGrPqRING8NP9Lu7MfOieE1Ma4n5Ig/Wg0cQ3ANVA8Vor+hi4H2kr6WOca5NmFXlWGdQ3rKEViVEVacu7qjnFQcyCcVMST0Eje22INhnS6xC+ZnHME1iEFBd6P+hB8q94Gx4yVeYDG4QuHfFI+NckS3zSvOycg60oXXuJi/kaf7VkN4MXlNclCAxsJOM4MWvN8ee0Yn8bVIXEx9EZ1IbEOD8aersSV/OJ8Mj6aZTwLxv4DBFgq2Qrk+K1JPnrGJU+S6EGTlRciMFQyAMVPR24/lzqml8EqU9SGXMhk3zCyvGQpBsJYPmZ7C3QfTE9voPYwypwdWiLLKd3w5s8iSixa4eNwUSIDhxChBcrObcfhHi/Vh1ygoRtpkj25UKPs+p6O3O+NUVe47oMEWttzrxUJbsBv9a8js4k0ZIIZZmq1pvnTjLGEQYkun0imOYIlRq05puWZc7IkNlrKiReLkCryQJFKtvZibzYzINEaETykRmXyG6IvNyPYdWnivxE3aVav1iG2JNJwOXUlR2GvqQmkipadNbwv45oe9XluKCVzu2kQnrvXyrKrMvyrG2rrBoNkmek603PGmNZk3csxQa40t9DOi9LzwrvTflfTO27mSCm4dzv0dkeP+pNMY3KyjC2JwCCrI31KlKjb3imGRy6iJWkS7CkPkAnWJj1tRhc7qlWp2ynNA2S2gnrRVT6zpTtlTHjkwreL8JYk40m0Fcy6YGtQjRmZIQYSSIOkwt4gPM5q3WdQfz+6W+/vx6ewMtkkO/4xnd+5wDQvfVU4R19m4uGugHsaFSVCIMasFxayeDzFJqcSWoudjxagHGXDdzm8tXEcLYDvuDyn4c39/0cvDJLqXOeh+pgz7++RCbgkvFJ1PTmHtz1gl1oM/wxnN7qVSOFyOST/rLrTAqe4rP5OywwlvhqFZ/PndvnXuEeds67/Xcg0ME/UV79RVhB3GhIQ5/qc843TaH46p2yuzH1K6a2FDrhpy/hi5lK9D8oTlMOD08gNILTaTgpBhhpPzECwVZ9J2CRf3R2KI3ICOBXdxl0k9g21BYe3freWUINSqzppRTV45E2bg6UkrtGA78qPA6rvcyOTTV5gSl/BdrGfF86H9ERKDgUYHHVmkBbDvTaQJUyEgr2iQsC4+nRqOF1uVJihYTxJmJSxRUl7gH/uQeSrL1RFn5Tg/vXzvjhswD7SnmYFrpAytXK7QhMlJcy4FQ40C43hRC7eoxOTp4f9kA5PsHWrEkoYEQCg4OLY9uTvtwNusli75Gy6qLY28ZPsD2hynKd4AeHzpklJoSK9IfIPT8dWAZhFu79QyuNoz/5kPebGroEhf4pze7CHSv0R8/USpvKO3w3DPNurMBqziimB+KOEdMggjYMac/9TU5Nt9gR+D1PoMi7qL/BGcmjTj1HlxtDuoCRYaZuZ7Ffqyk5kEBnnU4mNZvM59sayO8rHlz2QfwKTmLIp7xMu24XwUE0kxraNi8K5CgYHcl6duXWvejG164uNade1BSy2bViwDdGX1bzqYZNwzqWzuYaw6DcYP36F+ItDiFG+M3PhL4l7RJtsisDdQruLdPFObClbb8vyp08ZQBmU81o5LtzOAQRYUN4DoK6hohX+M2gZz01K6YZfvVVkKjzEhQuesUnoerDOzAXeZwoawMQkYkZBxVNb9xaL32d9f3Pnsys5y0gUndh9HhV0yIhV3A+6CzE7+tswNLnbqT1eiM7woGclMgdh5dcoiZH65roPBuLoH1K86OAjI6IpJWgUSpq+hMI/9IIPDVmIIJcFwSl0Pm8gyasgkePHyOpS8hTPHRihiNvLlag38N7PRWph9pvcY9LOS6PyuGHL12TdqRFSFXVCFCKnvnWlRLVQc5nfHxh5fJotL3xxP0U+PszmRvFI8p3WWcIrje8yI0IUOtT5NMc1LgZBqUB6xad7FxZygzlKFSCG4WvJrQQlaemVAaRPgL2bmFpvN2uSOAfqfDhi7qeQS5qt8Mhu5L/NJOYJ38/ASNkU86V1Gxb10ko4KYDHxr2KUBqMFPs/lqagnR8BVwtM+PMmp1JjZNlRBoxJKTDWXiEy1IpsJPzdI3hyug+gh0QvKl83h2VgkWgV1Rwz/j2oBf962gO+KFLMNPBH8IWD1K8pCVOil/PnYw4mwZlSB+XB+qcEE91cwenbs7cB/esYQ6tdktxpV4wfHtNIwwl3LpF06viWbOOZ6ZHgSzMNz7CzKu4NG9rljg9B3osM8lcLEhm5KRKhlIk0MaYOr3O2L7NaBAfGNWLBQmXiTHC7zObLN1WTK7jnQayQ9U3qvwD0/ZWVuOVq/Nng2qi9MxynP7WZTTHZRi2p6BwwLYlbFcLQKjaD1bZOUoqrml59/7E2vq2pVjj77TAILgQa9/Wyez8rPxLdGUpVD2RdJXMyugYzES8y7Ga8Qf7Q3xUDpaZgEzWxyrsRbel0h3dlBawBWA1ylBZYMQk4FMn2SZullmsx3fuTO7FAfdv5t7yapxztv0zKtdvZuMvVljZFzO9V1snMJk7mzTMoyvoIOFDtrSv+T7GR5drA09cLgdpLsbVrkGRJc+pg+pIbKnTib72DsJs4ZNH+dLFbwegcITwbNlYdTdpD55dikEeCw/LvTNaLp09+HBttfZbFsvDmU1M98adhy4XerJPrleH9/agriWcWAePN7YmvQJUh0HeOHnz2JZ5v0WT6H/1wDEYF/4rlgkGXx2/QKbaSHq0VcYWx3ENI3/+hNdg//HRZ3mWco8fUm0eG/fwNb8jJ/Hxz++2dp83tMi3b/ChrHmJbdX46BiepNv0OEHMrYhB0WtILNBoYEp56CFOMCQ+ooywUWwU0l8+K+7U0JaWcqXcPuu6Hk7aG4rthJfvIC9gYMhmu4n80LmNxbPuRyv3Ik6N8/O/37/PDsDsxHfBkX6Ue/+wfN1kPY2svkY5PFHVqtFslvycUPaWUau6URZ8T/UUM7PEAHOJDVn3dEwWsavdnAuT82IN1ewPPPhpwZyvJCaffR8EJecBoDckLqdysma2WIFl1H6pui2wODVaQE+enVyI0jNKgXHwUFSWSn0RwdD0MdwKUhSIqI1eoh2zWLlonAxMQVXo/bxXTEFWprnHBcjJNxEpGUh7eGc8CSGYMeDr86whEYF0u1NpMZIi8LVcR0Q0bM9I25RuSsOjUJvzUUAvYekZhhco2VUGoCb20BuJApH/9QoTe+zUkwNtt5SIyCX8H3lUqVXiH7fLewoLhOnVcAQ22SBluIU1bPxYGnZAcR9QEK3tu2N8qqhqP2vivZUM+15wwySkH1WAc3VRIHImDsYQLirNMhSswf0fd/AiEWzxIcGm51uFEP0E8iXqB7nMNxPmbvktfH9wYe+Ojr44MDdighoCSaolZWh5Njen5yLDfA2EFRj4Pk8MXhW4r6f0vB0S8O0UMohX4jdBtmf/j+WBWXe/17rvJ7U2X4/PjOHULA8grCpX645jVYm5LZ4eV+dHAERP0rEIkvkR8/QkUYhtGDiGzi6nvo07nZRD3MyoZaySwqakSVf34MPC+OG7YnX/YcrPXHcYi9sqEBaq/9cTz+ww5eJZUhTB7JCgYFqpoqOjmG6iMcEvxnD/+THMGOc060R4IrAJ80nAWsoSNrJ3JGpz68hf84ngDLfZOO+uGLURKuRn8cHwJnasL3K/gdGmeh8L35oxhVdYgl1X3fw9+HGVoh8K8I/Z7gV3g03Iffl0hdXqBbH9ooD9HgczCYoFQUoTo50xXBz0OUHBAmY+W/WB1SXiCkE6uIugq/zFqaDkj71JS4OTmn4CPl2vfWuPVRJ/APWxf9qrxfC1Pot6iaHL7DbZHM+clrfLLOvGdEdeCxT3SKI5ML2yxOOXbcEEX4ZgGJi7D1SU5CMGz8dd0jT0z72LHvbb49/ZRWTIA1VJmZhDVqquG04DS/oBnFdToY0GTbTQYCAQJwi/CgfOWO7F4ng1rpAOm844hI4qtxZvdCBm39gjavZNdDCkV9F9r7/U2S0SZJUGWNGQCon9lhAX8VfrHC9Bc7U1OflROebIfsiGFAyGPRlKdlfG/2SOlthqsoOcIJIffa6Pg/vjHKIy2ELtnReGnbZM670jm7gpvXSOZ4FVtEpbKki3Y7USaidchKXG6ir8LZkeDBOJqThUjb9WLOPmUnVbRymOEapkt6nLgJo6Ep8ZC5OiJZu+yfLqRTJKKn6+ogvzzIi3lS7CSXlwiZFYxhW3E6Quincamn4Xy1Lz/2Z6YIjcJ5mukzb9dz6a2nv7pr79dldDS8bel+Napxxy9Ik+PmNZKYtcgOUatjFMHwi/NmO7VtYdw8MYd2XiaoFSpq2KcOxwZ9o+YR3Nuh9/haZ1Ky2nq/zIsO3TOTPFpdcsFoAPa+N7EL5Aztco7A1UbKRbysknFPYjJ/Cwy2VOJOVlAj7YerPGh055cmSA435Lzf7MFIiKS8H1c+sXofoREqcUfdpwEJ3Ubv7WsbF1qZpIFERNSAu0f1esuo0MfcG5Ck47pIvIHJ0v5qMjqqeSe/27DjHLOyUx1RaOoG998IU4WhY5zfMlX09LK9B3ZcO41PjGbh1m/uTKetzwgi5080tEqSN+00X9v4IVWNGn+C8WvM9c8TeO5QCnVT4ZQ+nIY3V4mKFhb6MFZyhNrhkuc3Fcr/tg5LxgEwieDpKf18Dqxvv++TrYcfZotkZ55UpOMFitXTa//6mFOGPj/m3ALJIeY32DumlhfwRwgc8Q3yYG9HsIphCv9Nw3x0clybXKimX5ZruXMHmEH4D90DOHNOZpPDrHhr5KDfA4NdHf6kGE2h93UQ5mr6iIZlR94zn66YM8rc80BIsAcU3zv6wjyOgIIidJL54nNziQKpOWog1JvbVWjvJpJbNkW7x24hSxjoTcL3AHxvNxRORqouDPxjrGbmPUgSvYHpH/BVNLFI+eTvlOC9DXV4q2+cwZd2+j2y7pWDbn9hixl5i24ue495Xa/9uX7RTRAV82WaOfpi3Fj1srnqGYlVL/AS9i8BxfMgSc7/DElGuuqV9yoLm/01HcbtcvSJPf6FQTa8bv3U2IS7vaHZZG5OWlNyy0HYRk3yW6gJtGt3/O0koEF3zDRe91pcriM+dltK3byzvEift3hgF3peZm26SiVfSOJSaPkrf7e/7xQX1flQuKWW5zQHYhmpFNaULc3vzovGMn3idDWOPO6Wr5F95ANjD7Tle+msu8NFm+8BckbIS9l9vdi6gYZ2Snqm6aHhAL9HCZ6500YVc12F+e4rSwQ3G8OHNr9jTq71Nfq+OIH+W630NLonq4XycqXsrI+Uq4KsV+Hl9S1GhXErHBOCYViJ46nSIWYGilxs65RDeJSR1FFiJJcLyjjSiJEar7b0mpVnPXQXpr9Adj09q69eY7QlOjzCWSJEQozwUxtN4G4nyahfK43T/Oi/W3L+NZYcFzlx5AJi9veBD95lBK20pH/JNN2FXKwkUXlmkbxQQ6qmMQUhJshOQXKBtvDfsMDweeeuYW26lBT7KOqHyyM4+tdHcAivjqKj8PwIJOu3R9Hn4Yej6Ivw4ij60u2J90deOpd0DvRspfICv+h4f63e/+JFk3ew6fLN8qgmoyPswaP+5yYKHl/akPgl8DChn7YwCW/S+QjaY+PZb8JT4Vn+mw1GvVinC5BxxwyAW47P5wluuSSbfRifA7NcIDIwHnoyt6KtZlxiOvLxOWtlt0SsSrWGF7EfGwFOmrMd4juYcsNjp2ES62VSXAn0ZOlfKLY2qWazwQyNzXfq+8n2V45Ou/fB6Omx762j+xxAL8gM4XYRRk82hFieJNgAlVm3LyyOAa6b8VfF6/yRnXKLv/gd1Ogeu04GIZYn84R5XeEKlfbLrpeN75/Bsnot4YOOMrL0tqh6pkrXcObQFSoLu4eydRjY8s9JuV5UXmf40W1dbnS3xqCYhru3j7vrDgSv4agKYZNdHYWFII2+wNUaZTXF6TN34e0+DLOIx2qTpo19fYhbADiTOOp4TonAuGW0XunN/qn9hu6eH1HUupmxrr6nlFetpsPXtR/DbIybD7i+86P9fdqVX3q70vx4e2S66UZCtZInSdcLHIAZI/qDqb4Fxu8Ed0njHa7tUwJ0H6E6gvbQCyIsdrX5p17v8V+bwLcgTHJTVffCC/41v1HETEgd3CwKD5QPeMbT+dZM51dmBrPmdBoHaX/uTGuCHdV+gZTG7p3GvHr8eHM6bp2KD3gpoJ7b6oASPaqER3VhRvV156gujrbt5Y8sA7aNAM77+wWy7TGjAOPIirhDH2i2rovSxc59kM4N+p07+EPHDm411LWbO3pzy87+C+O/oPFTmiC7uxuKK7PhnHRGIx1Ic/K69gim4eR90tOsYticE1TjBePWDridJbkH9MNxJIOjTo7kHFcgOXQ0q1a3TWOJhV1w/cJrpp2hWrJpPDal/FvSIn7Xt5W5+dNjvdJjPe4c6xWO1VwuieyWupa76l8z2C0DbYyvuyldz8/JqpHYxdshn3ftEIFmD8Lb57/2rvv/un1p9oMO6F9Z7gu93F90LjdRvqTewm01DrMZd5rNchCjrh7Pr5KSs4h1HMXNBvj02me2/FF4XLnpOYLGG2a616wYP7DTkyYlysEBxR31UBqK7llCpuuOEhlj5xSz3zx94ySGjtnubZ1nvAfcRH/ZSZjCZgtR85ARTzTSZMZc8skh/+Ecdxt11XX48Ci6qeKr0dM9pf34YeX2DaXVVOk8UGkuQjk6FbFk9QdIU4+Pe6Zrj+BPTKpFuV3K0cOjkEH63YOaAYhGvZs9AV7hTOHMjSQR9KCGZdny2oAXpf9M5qPdAVTWdWfd+I1WoepSVqMzrUnl3bhGyAkpPVSNwGLrnxFruTs7V2BSAXT8XhEmDkXkq/cUnmRDZbu0RYGfARppZ41WTPNNgeo/953ETGw26pnRCTiLHal5vG7goja6xvA9KlQBDWpk5PofRbPzmd2dn33HQ6cvpyErq06TfvjtXvhkL/x+Lyz7Z7Aw7448f6aOikRP84CJhHZxenkEu1AlimddgXHwcXEvWsfg608oBgGhnnGTPjqKpv3D4+PD/p1Vkc8Pk3I5DZ8eWcA5DIMqx807aQxn6CrHUC7a20S7xh4lI83BIp3Bx+dlXlTJXFP3cvzs/sMf7v/t8fmvj39+8d3Tn7oTFrQbcWlqGDlftei/avbYf2s75z9udCp6JKYemoTIhf9UHKrD2GZzNzI0iCJ9S2c7CPdunyOoi8EygIUBwhCcNbV6gkRA6+aQCAgRL7O1LuMPF5TVg3eoq7Y6fXd0pvCFgCVvDAZ440fMGw+/Hoat1+GjI2A9G4jLVBgvtcwbVqNtzBDV7FZXHkyqDXNe1HaxpTwHu3VTtTwjHSZQKlQm1IppeMhu21T6fkHyeBH5rPVlv8c6CBQxJzemLoT+qwhToa5HN2ixuMH5I9mybupb6dJq72DC32cZt3CuESxk62HBbfHySDnFIUNesGXCWimgBAcTZ43+m83qzxbG5JK+Hial6YsxqmqFMf1EML6NeCI1iIRHUEAY1Siu7z3y4sQFuo5LO9gHH/AINcFJW1wqhXYHdZuN08xeN28r0gYlcbDSnC3KTh+0eT53TF6N5w6OfSOGdgvZsDz0pBD4o0ygCbsKc24roQy9U8K/OxNR3RjD2lwbDGCSirli1FFI2hVmDDmsoPaIR2L1Cu3dtts4Rl9w8Gx1FhHBMBYxdiTetReglP4y9N4HWmHRuSSVWxLtr5J5q1JJ7V+FUjzcxUSNRFL/tmLEWoyA7m6mtM2YMHFhEC1t9nqiFP7+2L6W1tuGElcTwgYRd3vb1m3X17grTM4z6VGjsXmTkxbqKOtjrgrcsmbYCIbBf0kM2sAw3N6+cBlATD6qqpt372oEvpicZmcjeF3XnWTMWtC79l3g6x86LnJeudOzsIoI2UkURwjsxqw+VP/iSKFGGCwxn6enlG75+P0RRX2TeqmPasvNZnoqucXOpkEI7/PIlrBSd67FsNURgf/6lDQnJcsSeezMKGvFT/1WohHTISgDH2UEAbvKXuBlAtomtxNIRhbGQZ1GaNlJVQ+AnnFoNJpZW7dOq0ab+QUtTFwH2sJ6ajcKa9LcMLcNsWoLm7BbyBndhPV+AhEX+u2NAv0vxTxiU0rbjuGVbPtGHBZ7F/ttZp9wcTSYjKP+wNwTbSar0Z2PbW9jqep6Vzdu1YZ6suusibX8/gW8xOibIl8sEsTbO+X7OMYXveCMcBHFkuZbnKz5XauC0wAzthUtlKXmJ10qVf9Lg0NJIdrGZtBmtISxIfua7wiAvHRh/0qxSIz/yW+ZFTvEkpF+iBu0Kz7K66hkJEfh33z3FHkY2deU8CktMckURbGVfnnvVdQoit9KhmAarv+pfhP5BfHDZM6prfyPzNPIFcDCuD/LVTxrupSbx5EqguU7uGP/y44CUedn1Dp5PninY32EATg3TgBHPkK5dJtNxCdPPDdo+9CZG8qYQvtO0ja2lDeZpP2SYmEZ1JU5j7TM19VyIdm88E+oAPMQKppGdMN7aXMzwjVjc5OOpQjnMbQhWXIjpKH3Oqjzw+oa+OD9/cujXhzKr6CWr4jix4FCOoAhUJEoRqUjxivxCpLLySmRNwPYpOtIXR3hLCok0Gncgzt8FlAN2Cs8M6X9JX2Emy+cuYc89AiR6zzynwPZFduzS0cnh2Ozib5fJQiZ62SSN0faSfzp0aGS8pKgpZndKt0d9Y8sK5ht18K60CPfOx0abgqSfKsAW6l4h0LzDh7TqjAHi4/qyAlZ/6iVBuj54EZEXeuFJHLP9Dov0n8C0Y4XxXqRTI1IzKlt/YwCyH5xkKcp5RJ1Kknq96N2qs6k8cmjp09sF26uC7KHkk5RsqWOTmA5V0UKnas+oG9SXfO+8D4UZMtRd8xzb3qN4ek1P8QP7V122wdmuu9jrKochsPrAqWzFlq4zOH/MK1d9OCNDQ/iOdA9hodqh56odSAfsBFOnfL+wp9mVv857N0/UtvstZ+cQes47iuvmyf/3ZPrX+3J9Vw7DA7CZkpFnZ+V87I+oAhHG4OlC8Pbv7FPYXXnTqsqE3pawyoe91W66jY6x57uFMgtUpXDjDu8TBfIVz04auJwGYbYwXHByqP7mOjL/z/2vrQxaqNL9/OdX2HzenwlLO/g61EjesCQhDcQ1iRAu2PJ3bKt0JYcSQ043T2//Z6lllOS2kAmc+fDnXfBaqlUVarl1FmfI1PN9L5HUaaPYUjQuVFSe2vE/GOciYL96+jcT1Jt+3CfxFL87GbtEmVGvvLnvgxpFnAZisSnGjEp7UwiI7FDnjinQtrIWaNjjp1PRmDz9fWfcBLpDwWtQz2tPDxBa7jwlgasMpoWd156mQMzpL4SdmzGaTj5ZZHp5TKVgWvYf2B8maZTOpmLdPQh1vNPyDiCFyUv1sbK8B/u60B8uP8LSmNUFX43ch18B4MT8YYIIofDgFpLxzobMZU/4nueDv6BmW4PzHyeozu/1SBetr1wcXfQ0sE4zAbIojw7G9smUKhALsS6ynzQyuikGil5bjMj/WatdcB7FXdybhdjTxiW2hsFBhXWaB5kjREXy0L5B5fi01TGVd0xPSrlkh7VVmJvr10Tud/KTuW0kvlOHtrml5BzqtiB+s3vLWVD9DkGPPqIyIY9Uxz4yMS+GqA6Uabwqv0QGRunczUmDVav1PJAzC91KCOti2bctVwEMBp9Bx9TsUh6Gvt5x0P4zIe8z8W4lromOSom37p+KKiIaImRIBCRuZUbTNpZ8y5oXYRwt2/ahGc3vKj6yzOR4V9kH/VQZgrKorEHEJUVevU4wcCa6H5mJ8tXai2ur1D1mdks/ABWdxE0d1vFPu7OF5f2Dn9J4ge5k2WYeNenhndVnOvb3dnJCWmIeoba9NZY39HwPVA34wnQw6xOL+NgtlaXSV6hB3xoEyOlHSdwjUhzS2ipyXvFZM3XxC7tIHbW3GuPGOYxuUGB18g7CxHd4XTfh+NdyQESFxwFn4HIkM6oHUP4boGjqwpbWEUg8SpDGnsuMP+hADSNam6RujNEId8O8mBwskcevR7iuOQBY01bCBUYXokaiSz86u4y2MhV4OHdpVs7YOP425yUi0Wg5j98uxsYiSE8WvNmk6wtLJSXDWEB/+vYXjHCxUBQ6HynOkunWmCRDcDFtCP6mVp0Uc2+e0coGKFcg4SI6tlq3W9Uq6+aNab2+luElEkWu96QLGfg0n0Cyx7rwAWKiXBQaGk/VcYlr+lOKlBtzfZYxdMbIcCW7Y75HLdP63zvexrcBua4zE6ndYr5oUDCtHDD4kmdnFIqhO6nSZklm2qkYkwNQVuvWXHAPTotuBa3hGkgiDd3MREZLjg7FRFlbFG/MNrYfdvpgEZjVuSgH8MqS+MwPktg72OWBV/5VdP4s3o7kisi6B74AXLcA0SwqbeQhgHBTfrJliZn+sSpFDpqspXD/krRS7mK9A9TGsi1UKSpXQ9c5QfEV4UTuVMcgT3y/b7CHXamO7KMZRHlzuejqmy1wHTz1ITt78+5GjBiHLtKHInnqodFv/U47KoSVWpVI1w9RejOCnE7XcGnKpBriO6zoNW3Y5H6YSZ+LCQnrmFBM+TV7G0NOVqyDzVrBwIONlSAlH3c1hXCZoYx5Trgmac7lLghg9rXYMvQnSBDQrtsJzIg42suGCHPIu5A9dqUyQdSJtIBqZP7wZpXSj1alq8k6KiE7+MaN+GX8eamHuXNy6T8kJaba7N6EQfJABV+0A7CoMQGsJXrguqBReFqaddQvbx/v1i1iT/AoEGrK3GH4AuELg0oZYRRtgiFk+YNmEa3HlvJRsXw+/I4h1NbrzSrd3JNIYJib50w72ESAJo8A7x9UITUE76F8/dgkp3nUe74yRsHS4pUNEo/enOclTCzwU8qzrE/SxQ3iOf7G4ufTP5filtQ54XiyXpckBl51DX9xCBd8dMnMeON9NKtM0PBe75l98Vt9CQzIunChAQFWhFXL8KZvVZDRwNu9FyIJk8zIh8GarRNHIQhLypBmr6vpNPFwsrJDlwKQoOlLWgw4MJgFeMQvPRMruxRkj/DuBc0ozf1M0I8Z/OzaC2wPDfLQYoJ4CK5Y31bGAZXhf49FGKLetHywCrUg7MCAT3ecVwa2ui6pEVmEcxgOZOB8+SEpkyDbhjJsiWFGHYV30Ew6qWvaPHDihk1C3qBXb6OwM16sNJgD5U6V40rlkupsiXqpA1J0Qwt5cFKmXXe3f83xXA19F1KpKmB8U+FNKm9EYwA3pE0iSEjJNj4wukawq25ptzu2REMPnSyY/IbtTYJgitU8eQ2XzHnE6dgYX0Ey+DlMrmuMbJZYMc2VZknG9SuS3UiqJ079Xphq3rwfM9JvVL7RpGGGrQW2xjkbQ1aLuSCMkXH3vyouLpGEcplw7sfBq7wpja/IPWruzpV9U/pp675/bjnopgoLyg7fHlQs/8fZ0B6oLRuZgDRebp3wwrFkOym0L10WaFavut2FpEaj9dI14LeRUeEzBeLGrV1CJCtlQd5Uwsjxt5kj1FJH1CdxqjKSWSzIsGK0ykVddYxpTNkiZcyAeJYKlWhl5jkvSJPWZB11JJ11pKpWrJ2LUyc29/kbP6ewWuyh4ujVHDVnYyOYzmH5hb5tWRTuEgUa8VANiCZlSjDByQyZx00597W9r3aO8pibRl0n1MyM4/eX10VoqU2DUru5uv6beRU6HldnJ9P0mWdb7/vKEdkd/zFuTngmtVIpYxO/IFofLThhLLAXz5DGdXc1gjajZIjmiOeTQ/3KYmMk0rSydPXenNjwyZTs8c0ejha3wKFqLC+jkfT7v9BnFjObHQ2Kcj269+Pdvjg+rd/83vuPOhx4Q4SMhh08F7a91A/rmBsgKsJvdz+3tx0SNOCQEnlOepYXvDxK2Y5iPPpeGzZosbD+XyyQ+rfzzWif2MRJtMmuUQXnmvdgngnM4vJ7+bCvqvJVmN/Zvk1ca/U91pJl1UqhaiVMc08W+h6XqV/TIHfHkvQtqWqOj0Mv+578SmsyhQJjBmqizRXai1blcyNbLTWMo0EGvcSIE6om6g+kIi0SWpLXzC0bK4ZKVrOrmR1cq6Z9yc/vfj5Tay3wytc0IulzfxeXMH5vGmVJF9op6sO/fLmpxIPrDImeUK/JwRlexP9QW/ssizpL8TOXaoB6kmb+Z/7lP2CtTDzOW4apYpZX1fqAWFBeOWiVLit4DaOrbIDB6lvGknRBCUKW30UCD25hkgXTf0pm7oGhiz9tPJ039N4jdLa+XB/mS3/6T7pxX/p1IvDsPZQswDcBvydqNPgK9TjbdW415Frq83YKVNQWzvUUJ6vrys7K0W9BWyzvEGpW0zaSt2f9x2dbjD9cpkOva+mxwGZ6q3mV/EzsCgH6XA+T3tKTaMHMSq1thYGWaWLVVg5cTGJw3g6iQOj2oEJ+Cv6YdNYKn7IdlP+6zaU6ivyR0nOb+RY4G08qszUfOXRb3vGaUGwL0vGYOHM/A1d0XUurF36htL8hVXaZKZv7rcaIeiVUM9Ye78dWjiClujXc/coUnUCVdlVTKugAfQoNjH5ojzZeOGb2Z9Fj0/UmPjgB+DZtZMRqxGDsq0LM+SKuq6REekHat1sffaRvjOfe9BGQ1UZpOKzZK+Rbn/DF6Lo9Ff0bnY1bul+qvSdNNVbXP+3aN4C6xSotFxee/a+6duaW1SYA5v1tKZZmyLISPaX9GG6WaP4sjssoB5axBDeSQFG9hqeCInCYiETjlkHNIFZb29q/xmpe8055+PDfb/f75Je1UN0E+ojZf6I2QngpASJMP+ii1FNLkbMg8MaTTgvLfxFF9N86AcJAlll1u9nlXNNfM8v1DLjQR3+uOa1GT7fAu/zCIuPTLqY2AYnLDHi9m821TRyxyuxJhnE6LTGRPhRelVfxEOMQsAks2hR9Td30YP8X7XCMqiidFAMMdvUQL02DCbB1Fh3gjFc0srSjU6FkWdKXYFBnER86Qejtg0Is005wU/da1yVHsNyE9/l2JEqv5mUwdG84qYrUEJXafLoezaYQZ10mW4mfq++v3uDfeYbDTQWnf0yXZLxb6eX207n1v0N89/1WF2L/je1k9JWJasUOmJdxf1d5I2EWmemzGmqAnJqQx6FVBP2pkjyJ7zmfnYQyrXOfqsunhafgJ9IKs6gzHnALagEHs39PEJJRfM+mqKG9BxObcKCaeCNNplhQ9VweTQ445gAmbvkA3W1aWSamPKhuFXDkZBsZtWmWcpc9W5skWm7JBPKBOJLQ4bLkTfMxG28fgwSx3FRxDmU0hxQauLTwgxD14idZ5cUlFuQQTWjCYyofkuQCKjL5TabrP8vzBGLdfn9Uq7/F+HB++P/ePD+jVm1/hBRy/+0vqCzD+k1ZwLW0wGkEc2aaR9pQWjTJjI+tnH2l1P4x/76+h/7FA9ToMSQ9v/YbyXEIGO0UpfEo7PzmHJuUh/gV5gr/IwS0SV0bwZ5UHrU29z3hx2k4q3j1doALU11kulsGGHrGM8IJ1eKgRs6vS0cG/9ER0VKEJ81c+Xk83mF+ia/VwwSOJWB6hQ2L7ZNqeAEVKvoEGbWPGBEKQ5bZDq4dBLcUgAMTdH7/ciCR2BIyDawenHwcgm8hC7xmPgnOv0ps9EE9i35JInkLVM35Du6XzvYALZzY+qcAmatOdktEc/bRnUB9WtYQ0QFFOdKxrsIJ1iFW+AkZ46HpTIM4/DbQ6ce9koRfAI7f3//34JMhRwhsXeDfWAmSVNewfHBhwl+YOZr3bf2eCpNpnU9acBylVZZBofuYGdIegCH7pakCBJpDwUtFTO55oDUBhwvm1JmZeG3fNAsFGSqWA5XAihVzpEuCQybLlsi82ZKX3WXDgpRPoNfwtP4QC47N7rXo7fUBpGiYNJP0I0XHoSZE6KRt2qjz5qx+4WpD24iqydqLPoFPUjgUZg4dV66O8OzIzDj/SiqhdvANsqKq36lHhXwMCycqi+6q6bhmnFUl1M5PAB2VFY/6o/Mwwoeh5XTwPnyvtseK7JC1QKLWJgHoqLyQGfCDNTOAbECd4ibWRi9U2xwH6oQo2Z8H1EWEEmSqPQS2EP4dcg9zOdaPWhs2ik0sYv8+WzhZkVOtWGVOUMUL+ECtnKZjqcgU9A3R/fRdIjjV6OHlb/gFrXnKeKZCBhoCWySod+Av8DvixI7BieXIh0oqtcd757MeDWIvMr20xXrjGfHUJjt0ygbZCYbMXxrD5OP0LhFpACt4j7wmzAMISaqWgS6HvUQxtL3XdRhm9qIdGlACfGCsgTpmBAxsR8dGmzArJG02kLXl04QXVs5OVto6gynNkN495pR2s5oR7MPyK+iXyehbM8WjeB0RVpVNDowasgTWy9eeltQbu11y7VSUvlHa0BUDfw47JioO12ecumfFZ9yZGlJzk8X0SuUn9GwCmsTZWNcZWwJrXCtYRa0hcwxjlC0a0YXEoJcuTZOR0UpfhsNivrlSrx0E92Onj97PSlq+skevY8/jyZTYIHtnSe5vWP0U/CrMYhdGdZhy8E/7ROSsoJ/5AOrNieU9khx0Z0yFdvrBvfquzDwqRh4LUKqx/Kcy0JUuGQGxGq4wFCDbyitdp4OBQc2yviVZxIyQGaOiHZkknlcJrlcJvmSZYJZ8TY2epmJ/LWzb7zDKPRcU59Ex+miIWUz8Wr7yw9Qr1IGBjZouDDxDXU0Jq5Q7KCcwcSR/pRAfeoAyZ5pv7T79NTdzMS/fb5khDiKsg+V4gQV94qFrsK3ZPVhTu2RuqmzgC3gwGFouVmjBK694iOw+xnNxnARIOsXzniRhxbZJt2RUK/L3KjH2cdYmKvRhRgIILSHZh+rtksXi+EQYZcZ25iizcbFpcTindleIavI86wpmAKMHu/gsNOrdvYO0JvebuEYzrr6IFjD1J78yOzfrkfuZsYSF+gHj3HKqozd2/g0PwjSA/PM2ejLHqtd335sSAA+An4lOBPNCiqEj4ElCE7p8YKXcSDwpp1Qac466kgvg9gMLmpV3RURD4HIl0PUZsAfUh2mcEELHP5a7Ptcodu935dZDrKDhoLddcPFQ7cT2vb8vceSCLqTzLLq8R/TZBKWASdYyAl4Kx1SyMPCe7kfcCMLaC5GRJA4eFjAYkuA+cHt8hq2Cz6Cemi2xWPT0yOpweqOKLeucaQWaQJdvd/3+xQR/xlzCDNY5JbZkCJ6/NOlizMdMTygZOii3/c8H+5mhs99hzos9FdfX0e9LXCkb1GaLObzGk87w5qur6M2seg3B5prVCzWH6hBphpldqo/9mkfATtFnwzDPSBcsCCXaTz3o2yBq6wSvDpOwRHBphi0wQYL0pG89uj1a3KyfjWdMOQD64IODO7oIH795t3Tx7AoXx+9evLiDaxF28Bjzb483yFNVUvV9sc0La9fk/cVCrPkGUxKMBKSnbA2Rdds5bllS2px5MhMke30JVpByz7IQk2rcpHwA/L1rjVUmM1bu5DJrkg05OP5jOyZ/O7rizStKx0zx1OHYcJVhWNYceammQlpXlZbqYKW3pDCwdVE6Fob4/cA8yApX7aiRBuAv6y1dFlr5EPtNhblkqlkZXHtDmBukw+rJygelBSsmvnWI1562meBruTc3mV/Mgr5bT17oSzcJNjjf/jbFniCKw0ELbN+vXVajK+FrjSsxY/AmC1c5Sp5e6wmB9Rp6xtugkqrA8RBgP20uuvLeIMmKJJiuiz8kFz0TYxJ6e2FFKvpaSC0MgV5k0h/y5/WPL+FDaVdyRw/MsqrqMw+tXaw+oxnZpaboJfdoBGSjTQDzQqWzXnE+5m+G9hZplbqM4FgegNUZgEhx1TFwO/6Nt1gSai6MOzPyFka1W6sW2WM/6bU0rAs+AWeqCmI2nWQiRwcQp3xXHStmSlIsz7346lFjeUU9nhrff18Upwmky2+ZemHUj0d+gF++I0MFEWZwezkafnDm2dPhZ7TyO88ajQCelVhkm6XxUBtUUBBpuaJ4k30E8x9W5pYl3ds5K2iJ3eARa2BNUD/ipi9Zw3zNmK04HAS8KCHUxJ4juwOGS+wQWtpxmAU8qsfuW5sZ00+8pH6+V2ZnF/yQrqKxn0M/33pVX6/YS5F5hWYRmfyz3xXmXHlK/0OjV0eXJLuo7cKpyoZ0lo8n9JvxcgHo6KeDKgoJ3KE2GpBxA3NJSMgRo/wD5aZ9qfQdjjSWv4z9LJGw+BE76IJJ5erghHtIY9e7Y90rIL5rBRrKR1vQh1XnuPnae2Z0BELtY6VovSHD0idoWhRKmiRVpop5CFaUSOHT2GRWr+BWgHKj7R1goNzZFyGGBwud61ouD/JjIaYy9Y02dCF6jOX4O/MQvQy46gKIqT2RprPd/x7Ecl59oZPpe2xqt/s4z8hZU1RvWRGOZhEqG2DQabvnZo+TjQJnk1hqmiJK2ZhQmNG4H5RM79Uvx7UVvGDINmmGg1CFaSIQZX59H4K85/4wWqlQoeBeyuMObZdeTio0H0PtfO0PnSYtJJfzWQGtgh7+2EgrDimzvCYuoqgA9r/92QPbc2423Wd410PM5yVTSiTsfQzODOdhUU3RrYU192VgUAFEo8mbLaiq8k8i6a0HT7skd0Wfn8g+noWXPWFTIlVFDtGutAnX5AuwhHMc6D6juN4ZqH/XE2JX+jXWOehmuyEkM/t2WgT4l658RhqLa3uhs/3POcVqJVuNV6gxCRmTvlQpZ4g/QJOXUQ92a1c2K384bLhMJI2I9y0k4RgOXie6pY/SBGxzuvMg7ktYMMj+bJxdB51tDI/UVGmIkatyijRdzjfsFnrMFj37C9aPdDYxu7QBn15CiPdVi+hWBLTGgFcDIZGEZcJe9bB/9hp/2akpQkdFtF7wW3V/T4M9O7BgR+8VU4AWXWkIm/g8EXXIOVTXkl/8J1+HIeKVxItTP/OFoiMowPXFoP5ZmfX3gvVpGhzfNDcNqinSerEi2E6gVclxITtz5tqvWyyxBsTqSp9FIF41VJTpHRQgT8SaJEi2lmTZu+urzdA87VaVX37FbKb32GX1YOgFil3UUwuYH/TTCKjr6wQtv+488kgGzOr6z4ANiHL+TOy9XX0l8p8IeFT449JFYnbAbixF/htJQwyfSTqO9Si7CCSzMNugRiPnlKoU4fzquOmhuMoYKCv+18q4MVqEuJgxveRy0VLKTDhW/YOHtIpSQ1yDL5i+IooUf4xYqCmZaZ8ZwghdNUcJm+N0hZRMFGwqb1t77jsH+fz49rfBio2SO0Zj1YVgi+5Kq68G521FMl6zybptyYeVlil438BGYIujst/ifs6sAqJ1XmZXF0Aj0tP/5d9xntk8A6OliHq2fkuCuhsE1JuUHz7VfKJnhTsqHF2EC3ZDmNoMLbqkAeuqWjGu+XH9Dq0cWkLpu40xGcHQWOP5nJ3/i5VbS6DiYeTEQJAeoQu5+e0LF8UWV43fNry6KbCrOkUiJOWEVTBBOyzdcQOVWkZcICbvv+cfi0WrT69KCqirbJb8TTnbMvjuLtvrZe+onvcH5xi27VC9Uq/iAERFI2SQVNvA331zthIhLqLSit9xp87ShNhNFutgnD/nYh8fmmRaUCc5374OuJbF/p0B2M1Tagg49gY+M2OeLvS+fJPKGrmS9QIG7u6QgG+2OaQ0PXtDUVeAM+O9Nz9NiVtZtGScAy1gllErdsjRwqyXJ4MRhcm50SpCeCrfSPItJPKrK/HZjOhB0tpC22ZBxa6vp+TnEJK7XKhjqSs8/uSiLcZSXudJYrozR0FU150FqhAbhedEEGoIBgqOlbpoDQUf0fuMW5rQya9JUhyyFqYBxSmhvbdozve+R3U+Ot1o5Xad8h2dm8HWOY7lH7j3s7CS0B0NlH5xHnDKlF2C+ji6k5vpIJQieiRyA4ML2M4dQY7dteC2wNW5eUdL2Nkl5okNqkNk7kw0IcIEUqKT0BStAqDxUtYDP3WOJCOuaVsjQfkwenyJ9EtjMC6NYwbqjmhbc0FUp1xL2UTBn0IuiPrWUT/Ob8JeMkERUw8ZrxgB1izjkvKGoJLnsCvyffySX41rflnfEqRwBnegUP9dHp6OkmrcHUnGKHCcYIxJfiLCpBpPh7Dp9fpw+tHePAsrK9u54wI1EvXFcZQg/GB9F8yzGBq+n1UXF4Cm+w9PrMuaAGrtzHoKV+gx7x7xtYSQ4ZW/VuF4+MuekNLVBg3uw/D3J8h2q1FGlO8Jvzzzz3CrwNhgtem9CRJSPd2xyMVp9oWJLNNMpBLxl1aWiLbtaoLoRQj/aNHWoYFkWxTAKTA2m4K7fHPCgk6/HJUBVoMwlyKjDpUngw5JOFxLnT2d1TSKmLmYEI5/awO9AMMzBlnZX3NrhcLl0+4tPpNnRYji2qjC8Lu8pZJEXUCuGGGHkGUZ1Jr4lGLUAVerPjlIsop4lfrNwvUPxSo36yDwtVvAvF7Cc/7RVvHOOo8OZzYEsKksxavHgeVcvRQbj1c1tdBDLp7GGiA85dGe4t0Rcds9ppZuIDQwmv/pl8THisgqfNXFY1QDYm5AQcG2hmMCRhY1fKedsrolZpVTaIKDb1FpKYByK2NXu2tgnjBw12o9C6u2jQRCmNuknWm8Npq4ptAiSW+KKIl31VrjJqRsDLexT5ERUcrOYHQJLxwOVpynsEt1og6MM400HZ2T7tz9jJoUo1KOciGNnf3zIiBYdoWDQPtTSXgdf9S3ESQRRUlxIPZJoOW8uxx089fHLA2M6mu89GKdR805BCG/eJgtXny41pdbXCDQN9fwGbKgNnxlPdlM6/eia6WQkR7Qu52D0cUZk9YJpzP+W+AtgrNLcOCO9pDLyXBuc3nyxiUpGkxgYFG+bdSerIrxXVH8Vn2GYHl9IO6uIrizd2dnZ2rz3hXI6mq+nDrUFh6/I+Y4gzVc4VBNiJtEokbxmnTRtdVwQ5wSJQPEF8JdjFxgkLcm0zoLUzpUaBZmH6hYcAZ49p6zBZRajJ26FPr55ewXO97v+LZcAQHwWmRlGM6e4mgQd1mXsnBbTRJk/JNdpkW0xqmHB/zaAY1zxsifGGwIfT7jt+Dp6RR1m/QDHOtfJbVhOgS3N1B3Nn0czrSHYtHQG6B0Fr0D2lHO5ErT9rSefkfoQJVLQ08WBSHuVoiNGJDP2QYc8PNawgEkpYSDX7wk1LVZ2Y4EnO1+nkHU6CgG6mpK48+HqDgXLf4Dg0bhPIVDzgKCkaKscN9feAxYNoOS9lPLtNoMBA6i2ByMAwGNyuigunBcGgFcOhU6mjQurjI2S2rAroVxrHxGxzAlh1a90ud8cKeNU/RQ5CyYtrPIHefVPgnWA5ev4Y22uAvOst8PuDDS3nMHGiHmfD0YIFgUGLZXCv/b/01lJgIRtUn/2Dry2/1DyCmxg1Pu9rA3ZH7wGr3a6gywTk7PYDRvGmCboUD5RGY9qcHHo0EZm/DFDy3zFTLUpPOUmqybLG0FWNJxUUU1FP36AJCgA5ZffyHgpDJ7sks5+aur3l3NYueVV3x6vx80OVIWGt77TMgARRFLXLRNh7hjPlf8jcjBqjxop6RGXntNR7axasyUTUWb7s2v0SPaPLBREMUxbj28maxqFw0HNZsmIvZ2dvfAxcgt3ksfdpeyTNbaTcnBS8UxUgj0w80k4Ci+A5GgbvJSNpnY09qmoCbyj7qc221gTqJWZ5QPhSe6ZnVEqi+kaKiloqKzFVUID9G+5M8ZUfFhOEHkrzMkhM2QnC+vNqARZUpSKqoNTX4mVfJ9aRIxj3OpVtu6WF6goiv8WXyIT26oBMvtiDrSXle9QyC80CFQQTVMKp7Ll/BhMboTNO2eDWKaoF6M4l2gmm0w6orzIpT3keWN6N/vQkIHNOo3MgYu1Yf8uoMDibBKJj6WEkUTeGkIN0uCS6u+jTx3Vb9ANuCnkUFVF9sVD3NjnYx4b1JNLlf9+sQzc1Tupre1J8FqiOLqyeXl0B6UGteFlfJOWfhAnLF/ko9V0WPufzw9MLQ0TRPSy9WNqUYpCk85WlwVWEFJ31TecGxvj74H03tX9fUIsk9OogcP6F75CekJkN3RRxQjScux2vKBX8Cn3F0sL4e64LP0KQJR5/+3Tc1yBKsmXqoXt5+lozm2YtiDP9cgPAGf5Lx9hZBhuTJxwzWXQFy1SSpUZb3g1/gvVVv9egAWDIvtjogbFdxcvM59Ax2IgXqwc5Xk19hEWR61edJ/RFrjvzg0wF3CjthO1S0OzSF7fngnPnfVVXhs9evaxipy+AH/W0P8nEJY3XD28HPuuwv7CVyvD04Hm8NN7Zuv07OkjK7uWVoKfhV1/Cb14+2bh9dwCpK/a3b29kNzX5vugg8R/prevpjVuumb2zyIaYdW4U2gzcH0fSlFz969eD7k0evnr84efHg9ZvHJ989efoYRvLxQSth1dvlCav+mBb1V+ep+gZY9VM4Jj9w5d05oOiZRVQ30DIWRGNphitbN5oOHQijHy+XJ7pKVdJAgyASSagQLmGAfVWeXQdhXwmGSz+51AqltBu3OO3ELU6bEJUStzg3gMXG0UlCoNyUOOzRQWfisDY4pyaNCKwZ3AifTFn57P3SD74WvVPLEQTe2YAp6kaZFSCzBmVSQ/uiYvhr4eJwGBrYAY+lyvj5wTLYgMcHRMM/dO4nhgtbmgYuTcbIT315XxEeld5f3wx5JVCtmkhdF7sxTi0jdNXuG98AdYXFvhItiqp2saKcF/lj82Uko+ZUQypDHA9gw4VQgx+jEf0BG4osi/8tOE83pc/bbRGV3w9c7LSLvS8X2f9ykTtfLnL3y0UOvljkysnWIlDJ9dwZTcCDA/Q9lHUxRpzF4/Nt1fsLvhWgHg6beIA7yXk7NS8/O/BwRfpdFfz/S6JhVFJEc+yi038FCYw2K1X4DeBYHVBXNwI4o+JCQZAq/ndHnBqYuOlRWo1gFhKlBEatHoUOqLc+kIXZpuhaX8/175YEBbxm2sdDA1eQ00vCFOiYDtQAusdU0nFMZQwAQUaS1eYySp3TKjW4zo1jiKQlY29YduK5lS/5lr/nSGxZZrpPxAcHrYjxNhQSCsSkYDemPegJzwo+2ju4qkWA+e8H3w6uhPIZkATYtXS1Z672zdUdc3XXXB2gmJ5HOIS+Ni5aVeaTPQq14vzBynLZcqRF/fdMgxJZZCCp60GuQYAvu43UN1YePFHZolQbtWgDNzx9dZMl+XDgORCmf1wuzUf64UAYxcW4az+eX1EPh8Z1thIg/IDjJxNSgbYtAcs5Cm9GwRammMHqbjAYBqu7Q4NFTPH0ZBnOOPKk8uLvsgmqgCg/ub0rHPnInus+0M6MqqEyEH4ONSbbRO/JbCisenIRr+16f+64A/jdQTNQahBPcDGOYDHGQTxFvF11PUqugJ+ZwNKOhz4B06rJZXRWBus2t2wb/3Qm6dkdr22+OXsZ2MP3vUVIX813Fa4iW2w8NiKgNaldyUl3JSvov9jHs4c8Ko4uEtz9nOsBKgtlI1xEedne0NTRsv6+dWv6tSjHqp0lNX36ypqeog/CjTWdvgxqA/qh9INKq1ILTWg7OSNp8GqF1tH2fFP2JNRstvzIlJPomAw8gUjt8VYkN+H3JMaVbJhp8sJQ4O6Pe/0yaGg/7TilnOpbOjbdMExvvjjg7JNhO7espt/P0kZVGowcDktaU21QcuvD3YyHVBP3cA+F/+j+S/qs1VQ6oDiJNQVt9b80eh+/+M08IbjIHqKXy80ffv3lyWi56d5Q3YezVNaHjrtBawNTrfab9SypnGvsbKM4UoysAi51kA5paXU3+kA1+v0dr5Gk0cDhpyIrTr2xi0x5Z1V/vvxSTfV9bVVTtwib/zL57O0ENZqkllX9+8v2CuOVNetYUWRcMYE/y/3FCB2duTWdXoW9yOzutS2M9jxlOcYppCUOX3KRnSFXur4OZwo5TJGnVFVMkpoTnqw6rlNCMdJotdudzWi3uofl2X/1sGBYMOaj7BgVx3HTMnM0QlbMZ+adkRdQ4GIRAG41XWqYf19YYrHE5bI9Gbt/02T8xBTzL03Eg/+Sifjujkew/P2uGQmXrt52Kgi48ycqU2HhWkwlPVy95R345Q4Fua7ufM0YfPh/Ngb2c8Nla3VZOozWZ6+u8sjsMmVdOgS7N1Hu75jlgEaBz8QcEWjLaLXtxHc0tg+v4CU+0sIlukaoKzKZ806itFPLTlfF++xYJ3zDwzwlCGSLZCSoNNJoD91qKHtWTjmUTWIoKasDuRbu/W2/mKDtVAtHhM7VCz37dAAHuDFeTJL8fJqck7z4odj88ZUBpN01pAfZCfkx39I2sMXLZ/DpV8+guoXLK1cMTu4z8/CVncBzrrsXP3WwrtSOs5VBVsHkuSZ+GKPMPx3M5z9fpvP592RR++Uydd322n2rzU6wu6/Z24/U28XS59dmOpd80BNmCZAhQh8l2mKnk6mSX7rfeWgGYRDkw+gnlNwb5EK5GLw+IHmIw1tqJ7ylNKNDWG+seKoZWjhbRGWQYJgL+xImsiwwXle7PPLvvMT3jRqKUl3ZMIeaY80o2S0n01lWQCbtS5YkO+JXcn7eGd/SczqCJEmk7JJdcB8tft3xfn9Psm57+t4cCJiZ+oZzj7zpEWtiyTT/YqdMz5jy0tO7JYed8hZ92WEJvKUUQqsNzz3kDqRc5bjNuY8C8sTzAww+azxJ/Zvo9A/miEKsR9KGBFZGpC62SSW6NXQFUbX8+1itUHLoFKkCuka0u2ff80bxzinJwNK98TMNtEeOur/iydTQy/Q5nOSGGn7lGlzPXzbrJZ+SrEYn4Dro0vn0U1V3vcQPB4Wevit/hkAdTUZjHbQrINpTkV6QMtKmN3T9syQMQalJg2HjLA79aokZsDuXO6k6Hu9ZGr++frpjf2EE/3vPxmU1gpPSLjraHA1aUbD+aRCt8wIcpnQH9rocVXIWSZu+oxoTwUq6SreACG4z1MHHV0mFpngxbEvW1osb9EHr69/Rbl3K1b366peFpuud64iYioiC93vKZwYJAXySjjWZz9+u2V++zlf6YPx7AgxHfYTuN54TCtOGwH7rKNi4EyKqxhZ84vqNKjRbiXOvUvty0IhNoFVbLOYnB2S8tlwxGn+fHkSMr6+ZmTh4pW9dZOcXE/h/LR7+qR+SGTMOHi4xHus3eicnVBL+ZtXr67xOPv+gq309vUIzTTpeamYmJIMv25h1Y9bQ7NiKu8zEhndLNa6vfrK8m9HqrrEy4zcpq+1fSORkGnc6L6tO9dVXdCu98fG3OLXQePs9x48F7xHz5SbDuUonE5XjXaeyb9gQn6oPswmtZQ1PibaZTIZLu8/nrPPqK3y1mfv2DXbWo23aKP7nQVBqIFabBudaWvcbOdZbXjsiw7g7gxwCYld72Vcp29tfi/iCeF8FT4hHvgpy/eJM926e6fX1rJ+sry/rwyvqQ7sDrw78EF/rfkGDb4vFiYln9HWv6BfQWtU16oj23fXFfx7oyBOZ7D6YqFRllPheBaJA1RNnbkbBhMh3R7KnZYsa6Kld0zqQ6f/jtb3UeO+4ieDQEHAFWf6UzdoAH20TiIRy53OKwLlIh1X8EMRh11viZ+G3scu+EcE4+9jO0/deOrntkktF2pHNr+EGQh472OUfW34aL2WFyi0jqMfSbwTT6AJPU1T4RTHVFQvvn84MmdUmGu42UTtHRr/19R9R0nWa/vWgw6OkLh2XlaXNUoX1lyu82Qvjl25Hub+Uik2v+9RoP9gxg1aoOjK+xTlD1xK2d1ZAlYWNTbDUeQM1D++AeaDIFBEIQUeaphFrBkegttHi3QH8SNsxbLpSuaBL4HzD0ndVVRizqKXwt5cMyaljH3s9wmBZQ1lWhTWihQThpjpyZWob3upbspQzhppiR3cCFWLhBNMEWeum8sqn1vN7sPNLDiuKV+Kej/mIFSDnjskx9e7Ag4foZwE00yPPgZyQdrgDXV1dLJg6ZYwoQ3KT9hD3BzuI4JVbtflOfyfEvGXlMkVBElSMhd7MnzyKBogyxvjjGAXJwJxFsIM4g9iHSeRg1PUm/YnxZQmdPpRtRWzZ+rCmWhZ1sj8c6Bk2OpmFCdjoZQYtFDpFvR0i9pfqQ70B/OKGVUfRVu3Mp/df703q7N2vTOrZwSIvnB16U/ZOVYgSXy8/AL+yKzcxv9ypmw7ZG7p5A9tsqdpXeocy3+6OlSJaN7mhYgnpvfSLkyJm5c897+FBg/YKkivdNX5Y6un7ULjU/CxKsQMPtuimXAOeVMbJvJejgCfl944eYz7vsqtdybTYNkM5efEQtaZKrHhaUwCWeMcKqnQEsj9THw8zPvS60yi/FONtPk9+zK8HjeeNCr53vcesa9h3yWU2ud66JICu+LLIC4qEj7XuRXhauVV8iXfYHOlYICnyr5lBb5pS0rYeUse0GTWqRHSxxxWtEGtQa1kz6Xy4H+05hs/cgRRC/0flfIelX6yRWuWFyqj5wsWMRJOLxjPYcRzOmGzmm3vBXjAY+o7vIhm3glq4JWJYWDP06I+25uGHtdnJidFaUHLrhtN4zLIcawNSw6U33orqxU0aCasY+Rr/d+gc6SWcFiyV+Muu8W6P0+YdpD4/yBs3UiH31ar56leSwEaXBCmEU++H5KP2MZMnX1s5wd8tHzjxNf888GQG5Gaz/g3Cu2Rs7UPgo6BOnfG4NZDohyued7ZpZC0gBQnCFFDCUQThDhJKMoopZBEi/CY2/d3fx6Y3Zm/L7e03sOfOi4YVb6yrxUK6NMlVtsgq5upepX9MszJ1PHYVcKIk+UyppNwiT8Z/uicjgvKgoh63pOlR68agFk6V7w4UEWi6p/5x0ITdfLv0MP2Dw2bSi1Rkd0DQ1jAOFHgrXFwm2aQu8Kq6pBt1OgljYA/fL9GYTssJKktJqw8XZUo/s3qSLlWPAon98BUkCGoO4O4kxB/wN+BGQo5pwcuA2uEbeLVYokmlscNkWoaG5oygrmpU4O3YVqbgNajihA/IqO6p7QNdikTqenw5KvVveD3KzEOsIEq+nT5SE+rrRcWpHgOn7VQMhWw3NSPyDVrUJHaZdt6iT2Gy8HUeFjSAOGpWnEtLunRhizHyiSNCaMXzx5l4H/gxn8NMX5TpGd+rkjyr4UT/uZx4tpDvAsgPYv5iWJswILhC8UPjoaaW8cnJ2ixfxAGrlgblkHO/lShRZohf3uesdh06xZxyTyyLWOoYFUWKbwyLTFqRQDB0jqLoZj3Iy7+PwCJQc7qFY4q/XqUTcmOdqKylHKpSUrIVZaeTT3CY8QH+1XlO5YxBD4C0HOLBp9F7awMPTfeVA9lFqrJwaNADzU7HyWmB+CMghQGBcAAdU+Mc8Q3HABEPRfzxY31DQkyUB32trwmJvk2f6gcwUOYefgRMlL66gSmBtxZ6rL+OAVFkZSF6dGMEIBZZiMn62jhAphncEI3HDa3A2C30Gvm6+olMqa/gAbzpI4g62WX1lZ/A1A0auTEU6uMekQ+XnHVFw6JSQzuGK32ZZPLkY37RffowbUS47i6s26PlGFrxP5Y8dvjOlQZHSYBqZBpNSQhGzscphoXyLoIY1LiX6fQzjibOGPIXGAcFB//Pr540Jo3WJiM8VJT6wjAIPrz0a3r6OqvTr3lNo8OjwvmGIlRiwZlRKIxY26WR4Da4NAvihyiaEtJPHKWRPkzNXbUNInF4mme4hCN91to3aNVF4lQV3B3Q8nasz6c9R2AVdoaeJ3zqVxndJG0C6t3f4cCfly0NR4yHJY4hcDTEGzWf45FoyFzzoTo4NblrPaaD1LfYn+1AqZfLONH3LU50bSkn+l5xooctjvL9wQyVWz/jFvqQjntf4OLU6WsMkPq9CMHs9Q8B49G4rRCBxc1vZ9dEo6nzc6konkzr4ls4YFtlWDstBH+VN/7i9gI6RrpJ3VJrd8vPdAp+tWLUjlozMn1ZS/1lfCvjDYZt+XspI9ctUONGlA3ayPT2rHYyavXh38aoySEV63M+RzfRpawma5m+nj+SS6v1pTcfsfCtlsq6i7RZk2XD3KUqybFkwPSC/eLxLanw4RK6lB426VJ+sTSEMz0kwlSCiLz92/FGf7Cz+W/HledvDmd3g8XatshOeOioSlm5uv3bINn8c4j/wIsbW5vD2+E2J8gzBbb/sTXcRhe/VIRa/nvs9/XBiilK4hC6oIzU8ARFcL5tjlH6KdKIHzoohAsnuahIG4d4pSkhC6bDPv6jU1eHOSG3lUMB3GAziB7afU0tsX1daJCsbX5LXWGC7OByOqkz5IYe68eD2Nzb1EpfSqYd4BnJyhgshb82OeJKPH+Go2ge05jyU4YMTA6j7cHq5na4+e+DzXi2+R/H1XA7KPDuynF9nB+Xx2dwo6Jit/6x9q/r/9vzb28Ex5tb22HvXnS//++D4+FvJ/FsvvgPKJjxOqhmO8G+M/ujQx1KMKHwVod/Uwrn7wyegyB9Nq1RS4eNqZh1wrdMz36bZ5N5Y9w1qA1n28fHnjMKQ3/7PIjXdmPflln/h3c83vB78EThHnLOEYrsPYJTn1G3fppenpL/mWw3uUhl0nq91BLO1kPDkig64+aaRLdsvs9raHpICeBK5duu/2QsJyqvjRY6OgYq9pe+Gq7tsndsC8PRtXVQBeobggTRmE1yNvITckGIy/mc0krSn14dOhmKKjsGlQqtxDGoOsdgFFXtMeB+DCgLN4InDjEjGen5o/tj/tKaO8pDM1JDMysMiLNuBMZp8QJt9331LP4XOBvfiRtUYYdZ3rQQvhSl3XHy1fiafFppxwLVObS2foc15MXSODReNuoIOmjju5VD33zuFVHSL+yy9Qa3T2K1oI+PcUmHrafHx/K5RhIvDAn2jqvbvrd1u+/T1do25lgbwNkB/yvgH0QXrwa7w2ACf/aGwRT+7A+DcUTYjHFwhv9c4T+X+M9FdHbImBPBubrc9YOTdmpaJ+pWh5Oi3wHKdghadEkzjR77J6QfwUvvhBLXApMLTy/oabl1Rjk1iQYn56jUwpFSUNszrjfMCT8kW/jB2UaEKVXdtDhlR1qcK2wfMXxMqGuC986deyqDQeKu6oJBP5UDSaHFmLpn0/hwcp6MlN8Zf0KNKKf8CYyOgkCPJvuG+jodR46nVj+BEpcbqnR4pa+Ckqv3FwyZr1bgmOKZW8uqf7VRwLujjbONycblxtQuzzPnGK37aadtMOxwg9G2EHSjyhuk5urQ0f+/U476Es5AAO47hVdX1cskDsJg0Bcg+Ar8H4+m22vGma21EUWtxYXrJ97e/ah1wARmdMS+Ss8ff756eP0mOR/cim+pJUqhH9DjtKzEI0wUh6j+OrEkzK4v8gdyVbA6q2JajtIgPkfxM5LpMnhnYsaN1G8lfzM23Ax9cDZS2JhqdRHCv767o+/2CqDRlBGZ0trjzgXJkhx4YE+QuoF/1LRB4BMWgcqoN8PUqaoMprhnh6EqctN4mzwIKtGwtqUocFwQsFCz2RosH8EyiI1GsOCdIQgX0LYP336lbzF0jolt2BHO/PXmbo9hVJUeG6hb3Cs3N8k7SnNs/7oHPMQCJgMqzGHA0c8Cqr5PWZvho+DtewjfO8bUjVx9r7II92asBxWlKFzNWE0L2zKhRJQFJqicVdCiyTjNXd1VdW1YvHzsKfzGvtbsw8Wn38Uh5VmuVLTrxLmx6/e80Xw+ISrHc8IYvCpxyRE6HIYTvHx+lebhKBhdJCVMJS2DsAq4bZhEDl1IJk/VDaCD1UaUa8pQLlCGCAhFtjJKl0bCdgOOvIBp3gnYGMRfmtncjjaxY8ajVijcYNihxZbuNAyeyKEHDEJzEKsoXoPjHL9nQRf8iYsYeljChPT7m7swWqu7YlVktCpGtA5mXEmN5g7mPkwvKl3ZfF6JTuAvbA5T1dMF0NbK6bB6jXKSbrkjulE0bvj/uk/+ec2C/7q/ireLrttNzohBCoBn0N0M9KD5cArLDZZ/eYPBJ/L3Ia3lWu5FIxCukfVSPK9xwtMl0A+Qhm8q+hapaPhxNNX9OhMkZqsin5BK0SLRd7rhW/qj7myMJR3S75mP3hwz9td00QNGej4/27Kl7yXih/sIvlc+XF8/29IN30/MJSdPPpMZZUuMebyXURIPzE6qFg4uNdOlaGymAq/14hJ7xzyHbaS/MxqHXmGrCzBnh8p/g5Y/XNa4hoPCLlBZ3qcXFia7MmzZCqaNvaAnUSNbcLG+PuqP5Fhh7kAxHiM7HvBEX/e9STSCarrOtxEyF0M/9BhVGoSLom+vR4Rm/YV3g1VM7TttpTAbR4NcLZyJ6GUwsTMF14jLONlSK22o6fyYhzdyXsSbV9M6yoOZWW+2soCO2NBZeM7r4gNg4VnG4eJQinq4M84PPf3biXwmP67bsXWM0/dO4saLq5lwn+M1SCZZStc8yDdK86Wp0N1Wh1ohYsFnbEIV002doygdwJmJ3AlcwDEUDAjbvOwPkiAbhgOQPkwrhWhltVDNUF6i1cr8Ampp8hwAbVT38VSszLXMsyFl83caWqfFitoXPh66h47m2IIs6ko0kQYKsb3FLzHAgZKJkFPcYj0hM2JNgTxvG4VYTmq8pjLxOW9XqGZnQgSnMzOVj/W2cn6Rwi6sNmrBp63uwrDjHirFuGbiurqHoYuj+yB0j+5FHNFcRhUMyAinFdgcm1ZoWR2JnDNzrXDDzT7J1PYo5PYonT2RLBYknGkWl5AbJK3JHFpTClqTmWvomyh13xbqs9eJ8kPx7Sp2GRLehBx01cHCQ/eDEeEs8vEHknOUhuxIzss/la7vfjgYBa3bZFApNBS+rjwDQRi75Sy2UsuEhVjYmBqrcKHZrJYQzUmkwQVWDa/Z5ApsnB1qCgsNCxpuOcKBHS24FFMDv2j2/B7suWzLNAHL5aNzg21FphC3LUvxHVGs0StdtnGbXjDAGZnewEunDsdcKARxcuBee3KqoHWbulbqzd0X41nI8axa42ne8YD8+drBUUXhYAYeOdJ2wQaZM9KZHmlKCaSTYcBQlc1hLzuGvWwNe9k17GX3sJfdw86bATWhbfm3lzaXcSIIbsUE1uig8ojU3EHZkVoObY74Tynzg2VWtU5x4ukiynrIQMEfvUetaur49vz4t/nxhlBN6exoCYaDG758l/TykZKb8QQFObF/y/ttPvjtGP4TD30v9j2vH9IvuAt3Nvp0s78a+7fC2JRdmyULKI5/4A19x+9v3e4PfsPL4+NqKO7rov3VgX09VsYB1SNb+9Kal9aK96A+bV3u0jeg7AacIFxVfDc07a7NSqVSnKOROla6hCbrhfZqFBCMPUGTH1znGjog0fuK9KNwliSaba/QRwxBs4D7VRCQPTf/XWHz3WVRgolugLqOhtHoArcXnBJbTbtHUPm9op9Go3BygWmegPppOwnm/doytg2Mc9da4u40eER5Rz4dx8DTHhJrUbXNCfd3fYv5YFASKbuhm6ySfAAmE534pIKNM71Ac3RPwjNUyqtdxfkIDnFkrQFWD55ZZefsIsnHk/QJcRNEEagONuXASzS7wCiEibqmx0DQ1N4JK9hXyF6gEpJ5k4Ip0MiyI6iU1Cdj7s1w6KsQDpCiqJn+8cmOcINa+1PZXoycoz5buAl7ZSOJb6QRFcjFusrESSfM34HJNZJ+ssW/4POm6GPd+UJxhUOZTPQr+ne4mgTjqAZx0yhpSP0wvnfW01Rr0k8HYz0yE5VrihCckIGdzufT9XUoj6B/s7HUOrAiF0+gMSNkshA5UmKrW5gX+2DYE2/5mbIB4MywLDOyDF6wmdsfvlqCQqPVK+9FY0p1qbIMWl3foOyoDybFJJPUy7lUne94c8dtv/Wy7no55MWE3GiFwB8oVY5gNWcE4qYBdneC8VCJrgsLuit9tCfSKhYkWi30I9pigKiMmKKoaLcC40RsPkM1LDO1VGqz9suFNBpoM7LRwnstRk1rI5yxD+jrSq8CQjVkxZtOLgv1wOFaEHsSVE5GZqs3uW8Q/qtOhTgNIMYPkDV0PqfUDfP59/tIbczcYCm+JbXQTVzyHsgkMrvPQ8x4/nCfghpNHHfdGT9qu1l3RPy8czJXNZmFHulFlSMaHKtoQWwMbW1XFuZ0216Z7aFPgDLU+gpv033DnP+qMCbFw8yGOvyyTbd1QCbaARtnwNBJ6rgQNHjq+KCxQjpYouZXO/35mRf/L5Whj3Lxbe72/JqMTSUtTrzY2G29EeDNnmRKMc7VwRqatdtFPcD/IshyPSIUUiwzU45p/1Blan1o12Hj9KBio7fG6cftcTGqttOyLMqY+shlX8N5PbpAFNhL4LH1DmNhG9GCG8KyLfGRHtcYwks4lVhNBDxnwWZxGLnH2JYXP8vy7CxLxytPuTMr1IeVf6BDRm/lY1Zl9craLBdvLpDqoZvgCvI7Kyqr1wrcmwIhwvt5kW9e6nrh41bS/GNWFjnyB/QyvUgNVStwmq7A5GR8QqxcpJMreLzyKSlzaK7aijmp4PVhpE2dx+PZbrDwj7eOq+3g1NwfbN7eGOKtM3Jz4LL9UN32+8dV3zsewO35Z/gx9KFoFnym1/8BFR4s8E7wGm/cx6sjeoSeFsPb8WwfWvQGx582hxt+n272t4NPWESUgA1xRY2re3GM3OwG9AL+F0Mje4t5PLsDVdF9H0tgF+HHfM3fDi6558AKz31va8PnK+g4VHyhvmq+0g/7m7fD/krf3zie88M32JF76KhzMqRO3sZXq8Fv9+4Pb8PXbvfvw8c+plLH27IcjBI+enSIRhbDmvbRP0eZuBb+WoxAcRNgYf3g+WF0/t6LL8cYSPksKT+kZRzMKBcghgVoW6e0c8Jw4BwYyx56C8WbsPNSoC/Pc0ynTUzNKqJHfND1o9PGd2k+Sr9cvRp7Uf0tGNhb3Q08oC81Oj/LP6MvUaI3EKZ6RLJhxEwfnSUSHQtJYenB70uq6g7BRAVPy9AKJ+mf+6xXZKSTfjnYJ4PY5zhUEIlkMYHjDz740qPI/kmEmsT4FLZRigwX/eL353MEa3l+qNIzjvw+1xKO1HllUnnjBFp3YyBSIJe+h6Pz+WEw0blLipbnT6pz1RX2vA81XLVyOa945BRh18wNtJ1A20lH29x0wk0Hia1Z1GPREPPo131yNqHUkujQE/eVY0+Jeh09bj2uNlfV5o1qeWJB5q7cSc/QkCTmmYZi2pmgXblgHNfb55ygXd1Ywd+UBN2EyMHhE9XG+pPzDTIQnU0KIMTaVXr7DgUowpfsAic3ZUlFQ6xOYdE9O4xMTlVpt224+ASJMLgWeDxkyJg+xAVAStWi22XLOjg0Z96yO0rOf6ZUvHCAWn+dhWKtBZrFndu5r32Q7MTDqj55DxU8P0SraSUnM17jI1cJZxvJYmslDiuxSaDEaLEyQMMifQXcRBavj7sGWl4MofxoA67QmoHyVbVq64exn0ZTy8vgUUEucehB5uGxAqQ4XtsFeX+PM3VzLvSNycYUxnVjw2hplfgOEvci+Okwmo1TXEdAtDIQ1wYfDoYBu0aFyj5HymxOudGyoOSRWsOpSdeiGLRdk7cl/oeACMHP2yD7gRI0w8+HhtF+gP6j9ykbUiydDDAsF0NITYT6InjR6vjj7o4TG9zR75p8fK3ywdEqpSw5qzGM70OfUwEfLAZQfcRr+xFGDucOlA6j3cm1Pz8QwMK5CW+/mS2tgRElkmEVDGoRU3aWTJCHmpEOeeGAhFGSClyeCc3R/a41ug/t6KZqZH/oHNkuvh6oDO2aD4cqAbJmZXP//uaulRcUIaKTEYgRGa803TLY+ZQkWTtsWMk2Rw2dXmcEnGJ8aDe81AUHwsANf8Or+zCHG3WIP/AqXwQ3qUk6NBp1U6ORS41GudDULouQMgJ1y9R5aMzTIC3n6OtYKmttpp+M0HDKNm0kNZrJOY5nqMcDziGm8Zkw/zAyQEIjo4RAG42RlkZbE5DEnqhxN44uymFM61PgIEJvuu8OzXKpWRZHjdogG5K3CMrh+XDBB4zuFzKPyD06/TOKqAjIbS+zTi6ZVt2hZZA9Nqb8IYmvnSgS9SFTn8xaSqLe2EUZeWIGp5TqBfUZE4GNOtqa5oT8jAPS/q4SrTXmq7IhHwRj2Z4fnN3Y2plobWxaO+tujWHLTIM2CflwIZRwM6t62tEU5tPhwlHNHbUpDmk81IojM1SUw6rr4z9q2SkWcwIP9obaANFTakw2LWdCCe6Xfa+IfjnAsasiNGj5YeOGEzu2AhI731RHQEjvMBOEi0G/m4kxI0OZuy3IHpMxYrfHlTTbUe9FTnPolytxcbNBZgfYuAplygsSkaMsJScu8sOhRoeqUWeECmMLjRSkVnHE/CEMGh9ZCILTT2FMNRfnqzlJDHFte9wvgictMvvLfvB0v3mOEQPTV4zLjnK9UMvi1C6D3w89zVy3z8un//mmrt2mFF/SbuoVNKX8Xwcs9A+1l58qK+MKFsE5yIj2BQuPwm/FwAJ1vnbivHZaTMZxEFOmqpF+9fbt293vfrzx3Syvy+RTUY5D1C5iRScnJ90V/XnYrMc03f3Cdbvl7ga7Xz91Xkdp8kOK+pLp+YVu+T/+o/vVz86rjYHqfuV19ysd3e1+/2Fr0b13mTSWB5D+rLG2ksOCWkxFSeyawazjuGaBm6KAJTPY0cIP/fj4luOEHmT9GLjvcjH0FK/O4eQrt9Zm2eKWD1x412MfvkS6YYTbqKYYeFsbfR/Nb/ADDYHe4DfPP66GrACpbnmkI7kFR+LxLf82Xfm3UL0Db/nAq5dOZYPfBsdDVoAM1PXxUF34t/96O2vbzjGhhvshwlKvHeiB7mmQcTgBoxrYgpcHqNUniVkJ4pND1EjOOEAsaz3LMJdREeWaox7EvqG8cFKY+0N5H84nOIrimBzH7lW6M/jrfmXcl0WVPSg9iMnRWPAEu74i41BCcSVAu0dmfUydhIQmpyklydQUfSpZ5AQYDBT5HfUAVDmBN6aIv5mdn6dlGPvOqudAqEUAcl00+OkQJBSg70B3h8EnvPMdXLzBi1eHSLqQBAH1QHqABBB3J2433ODD4DGWewgv/HJIHBpUiSz/J/7zhv9AoaENhvqh5SJ2ssdfq7ESfRXwJMXjVYHxvdsTeiBXLU9uyDAi5B22SoiQ9i3XbNKyGwq2ZoR2lJJ6N4LT2XMsbiPo36hhhcNSI2OHc/2eSsMgsiGy1AxlNp8Tm2YtCciW5CH0ncpZFg7xlpoRFE1lFyYZGoyCyTCqHc8QYa5Kg0l/MMEpKYb45czk0T+Y/ESjERsxTSa+27Wq90dudEA3bGfDCF6TEbxExwDyUFhfdz3RVyPhnvdzw7ktl9ZMhQLcy+5HZS/Trsx1lG1Syr9f9fKCbqGRZVBvlO5KMKFhm+Kbfj3sjFGDvif3sl7CZkesLMHK8kEJF810N9LD8LlyF/nlULiLUPaN8tDxNgg85Q3iox+yYLPxEDFl2doPRdWO5uIu0I2Nfsgp2JIEWImWN5/TT0ED1J02x2drkqdisznyUCR0CYyGRovM7v/ZD3ILraBiIhJWqSWUcabp6PSfoQHZEhqQfcX+JwcC3vqF2u4a8JV3a6K998RGhNHylu1ZX/t4dO7MBD3nluzMAsai6g8q3JnZMMzQ6dXux2TJfuQxta4hmGR5VVHXpMOrhJ53hyx0ou7CIienWBJ6s2bKTRsTRn6WpZHfa7+ROlJR+Vo4mdbKy8GlaZhDQlms9fOeTPfpFM60nRG9SYGWbggTdlAYjkt5M7p+cYkfDpq+ciiRkiOjm7LMarILTlIi4ToD9o77plGFEYXulqiiAC5kkKggkNYq5bErrL9YvgCGpzC0EKXazWJjl5mQXTh/gISVFBezE6D/cTmoYPIYFbkZOQHNb+xyy7voSrFlGOX1dTzKkkOjrHHeRMCeaTQJxtHPqjHUOUy1lwnKyGfRWTdCJlT7Ys078wku+h1ccK61tpOz22TK9blbfBqdcR9So5hA7z6oDzoxn08jwtMfb6APb+XWdxVNO0nGGOXwq8GYx6xx2l5G/KR7xC7FiF36tLxZsBAh+/xp6+t4dk6DceMjL6JJe62cRxdmU1X+hv6RoUF90vQdOGdlwElE394/D69602aZE1Pd2N/QP2CQVNDrR0qPc81ptn7d8a759imus9se17sX7mLOq2uZ82rKOCHB2GTeuhZprybq6alJu+Ua1bVT8HXDKfjaTXPrNqkbwIr1NQO5mp8MN/A1ba2vXy9xQF55631E/Y3uS/TRhLq2CEFpXnISSP+s4N/gOk9Ljw77KqyDcVbW10/T5CPImnmQjrO6QA1ujb5nmBnnsbiTLZQwVFOAXUww2qdFmSDJIYAkvn8BrRQlCL50eCJ4LsiCFe8+Kz6tIEh5mSZj7z2pxvS1ymmHIQrwBynIauEi8KKndlZh4IKoDbW/amZwNib2p5qQKTR3go67z5IrXOQo76y+AzkGGsip48hQT1YJz2Byv3Jf39jFb5GZnIhbOIT3ZS9S3tWaezBm1Pn8BxbXEyAV06DRN5jbqz09btebaEJBT9HFkhSAypxgM8inzVR9BtJBJlsmgrA8g6QE8FJDlnVAelF1iMMP1aG3ud+UhjJnADTvIMchoSxa6ASXLcP2AkqGLASca+Ro2nJMXSWkKTkCHanU/EWwK4NbPhheGPk9iuwybtRwFPbTUJ6fwsM6dc1e9KK0fZFkYYSD1Krq0QGZnHRRWQsCncdKYKFj7V1pzIzC7yvvvsIPj0QAzydz3QcGZxX92VUxPyz1O8BKFBHDclXq72o1n38WAT+fRZ2vxfW1uD4V12e2Z/P5pfxxIX+s1vP5G/HaY3H96NB7fOjbLsDvN/L3J9M9/R01B7zEcb8IE1Rki7GKKrKFFjSa2qNXM1hCMU0O03rlob98DVQR1lIu1sIDXAu4EtDhDGda2ya6V4D2sUfdQsdypLuK/UUj/WpNWRmsdCWDPPWhLKwGTnSnsQ8vObz9TQ1vsrSEhf7Wxg3GB/FS9hH3l3lp00quWisZkSVS9EdBDJURbMmk4f3dm2iVogLYQMAEsiiMDoGu8l+oA+XJfvwva7PJIg4nLcwNnD0OWEKPV/QH+/4wAj7hWuFjEZn4XPuE6upbXc7vriaAtKA2Fk/pOmEfo7NFMNOJAux2t1BY/b7n+HHmIheKL30Xfzw0aSOog9PK9u77QzsBfRmd/T9ugn+Dm6B3COeinYhnLqyPCt/kwwa+NfsTf+5gbJu9Xs20ULtjdHgnLHJBlWhjFj9J7NTlSc5Z9d6Sj9Nb5WXVyA+QNE5Zt60mMIOV0jR30jhdHJUKncQEVSuiP62TuHppADUPLaRP3ZZvVS25CDSkPNDo6ECV2cSv2YJd3+jol5H49rvEg/u7FivGfFDJAZteIlg0Eu3prmTcrM8IRSuu2lEhFq1UWICIOFUS3AJD/gtiiuPvsDEUnLQX7ugIFeVZ12AGVcWKvZngwfaOOUJ4cgklMexTXboTCoKoTjwwpiJ4SQZ6ijZwyp5FunPBleFQeyR/QuvQxBX+xQh5lBtWlZYsdiu5BFFUcWgX8IK6PI/G+qjU8f56LM95XHB4LjdxLPDGpr6zsdvfd+/074RChfiT3mBGDUoKBNR2lLBVdtBaYQkuHA8T4HDHwVlwpU3rl/gKfuWVFhxIIMDaN8lKHXN8P1aXYdCBH1w1RQnfaFJQz3sZ7PU6KgNih1VVpAoC5jEqECjDV7LtMzq8JtS3plwCwycTtE4NCZlusVCEDowILESN/nqWmuiBHJh32dnrzUtEqIx9Z3so7wC+s0dgWJGjylrZVULRpEF6Vr0xUa77OwScQFem7kK7acL61sHN7DtbC0c7xLe7StMPNt8y6g6g7xecnAo7B5vt8l65AUOXz+djTSthRXRTq9rdPinv5ua9Fl3N5cfp3M+SmJq920yxgo+sRO0+KZov0abB++4bdJs2dsn7G6UYJCGcCAcTcZkf0BdEm9xi/+Ps7JqUgSCh4ipx8Sh99KhsFM5t4axReOEJFKExOfpimkVM3LXwrciEizyLLoJzwXT8QeH5IIfmYziAk9EHZOtQRnR+jqYlfkEjS84Le05Gj4CX2sqLT1pTzFs8X6Y80LLnY5WEuiuyv7a9IFAm00VSXlv70Y7VTqt+Ih3RMFs2r3Ku+EiUgZuJmn98SbFAQSkOI5QBm+X+eKlS2+gRSRj/HbOEJGpfox5HKDk8fZ9+qUzKhgotmD2tb0hC/eivjlF+0xjJkSlvGJk/eGQUU5OrYc2/ME4//jeM009nKQ/UH4ecInpJuRduuaWdXzr7rR60VGFS65W3tF6loyNL+JcS/KqwCEiRVhnHxZFY2BNnxqdyxpFViKSgMZLjqcASNd624CMUF5FRru9RQFH5dBSd0QEzEYuIEqU2SEP34PmBbcybKgQrkFVGi5vWmHb4x5b3dPfsGlLDGqaOVjFfLKRK5rsLkT5MvaldqFTHQ+i4GbYQMVM19bLy3xOoZjZOJ8k1NvcZJhmEhx/4OOZ26wXtKUyggZJbr0PcD/JoN93XB5wW6p6ll4VPdLyez6m7waAe+j39/PHZGRxnXIJJbInWiEEewNXQZ8AuOApNIq1/Huoj+lMGn/XpXjyFo4qvtzRWtGDuG09cFGlTLnh6kUb/POxTn54m18W05p6FopfBu8OIdurzNYFB/AoHL8N8GMnkiNL1wiBqZHc9cEC92iPCNiFOGxrkCch+mKEN9hCyARXsnCInuTLMnDWQBBf15SQsMOlnFaHszkla0G31bIf3IyeX3dKX6o0bG0nhqIXhH5lMpovOaSaRSKfG4x9GCPJdFS+LXBRUz07YlcqboIeEomNNRGyuEwnDPJjpYlgoDSRMjLJnI9SB/TA7r9TpAF4Vhc7QRPaq3KRmWyxgCu3OM19RfcrIXKlYP382SqpUh06FRotJsUKSiNcKbELey6kJDrOlalS8d9gqWduSPSqphxnLynHEEbSDt77O0Qzw8gKOiREaPAejoBouMDWdnjJYz3ISxFJAJEFgHnVvCKW9lnKsTxUFsF5/3fq9+oz6mK0XZfExG6dlMPuYTKa4fOQKtwTpT0GQVqjfTQ21LftQQ3FwOtzU1YTnEdUlY4Ny1AmS2y46+KOW01b2C8fCIsPP9VrLu9Hm6/6I8J/cRBtxMsHdtgFXqaetNg/eGaRkQ1zbtXEUDIf1Em9oiGhUB+SIiU5hSatW5Dc9yhvdKW13jI8GNJ/fK3t5u/nVd7J5k3RQ+q+8FYBUuBx4mIiQvv8vIaQ3U1DhPoZU0xxxSZklD2in26BwoIl0e1oXKGnCp+Oyw1u4lspigsQLf+IrZXaajh9eA6XEO0TQnrEqDcgl3fp8BYdvOgYiiD+f5LCOs3FY0a+nyWkKLIS9nlBlE7rxTGurwyn9fv4pr8IxXeo8ceFZgOkdjpIrcln9Mw2vAspx8RPQ2xDkkHF4EUCP0/CcpajT4nMcUOJ2CsoKT2ATBJS7M/yImcE5tuM0uAVEINlEHX82vhV+RgXo60VwxGfxp+DN0JwqRFJ8yhpgdjVsk8dWu5pMJqfAAvgIDjqrKWfmJ2DgHqn5bN3YGrMt6Jcs/cRh8ZKw1n7Yuse5oQJYnX7wqOO8M8cIRtRYnTF0x3FISn21LmGPW5rYz7HN3KqjDadUI7d8FDwGMvg4OLIk8P0hteq9aY2KZWb1XcPOosb/DW4RKEYfYslgPM4+xsHsFs78Jp9LY7Nab4Wf+rVyxg9UkSlmueKVi4/zMM6LPI3V45FaxfiodN8c6xV9en0rJGzwRPG6s0WoOkAKX6UuvhUmC/VqqtY51Lq+fs7YwZenBS64/upqodtBmIFWlRlvilvAjqvaJrgZbsHOED8n3K2JumeMObdgf/AtWEb0VWP3q1CiL/IJvLv6ySTb1I94I9360k5SoIh62sJPrS3Cey09Cx+pHefss65NRpuKaeLPF8ocAQvyU1KOX6VnvgekSthKXrrw++fume6z3gLJbkNDxg382tXA904D3wtjzIx8li6KCRy9QPwwdgq5vkHmsOF6iVa+9+vWd2VyTka1mTmhB3YNwwcGmuwy6DyNFZzhgd1apvSPTmkNfZov/KE8839EUm5wURWrKhjfRkjwAHjdFt3CjfqSc8W5u3dmc30AY5F7XEjr1tFQfaOfBtaATMeNe96W0tSrxiTOwCmhe3qjp7VLS/Ac1iXkqcef5GXN8uSJdgPtyTTtwXDL1bwVmJA41jkR6G8JJQfph9a678EdVCZKeXkJWbvIxsCr3CLgZb18ErU5/tBrt8uYGKxh6EU2PqJAxNXdgOgTI80F/4Q3Vc4nlfHpeov2Rg5LZuZkezI5nkxeeORTsa6HBYixSXkdLbm/dUrg8pgAjl/FfDlptHYoUjI9Skuk2jSVmHqHzXY2wbj9gB31ASlyVc2m1AGGG5RL1YvINmkSh6MGAe9elcVVtVXkr7Ci/pY3S8pziupMk6qAUyG7hC9O8DzZBLoXL3T/KbQbiMraoY+ZTdV4PVJ9ZCLhNECdhAbwifMCbwtLVsx35rLjmFoMGv0xvQbWSj2gqnuI/bRFn2o+7R2SqS3zAnlpL/ncHGZd+UOFzivq+xHbeclXl+i0XLKp2BK0NDhT3Mwreg6s4nfqhllWQC51IfdjAvv1mZrkxBmFgkGLDJ69muMkaC+EcMlaXJDAay0JZnP6BQUWGRE0hxtiT2kXX5AiRaHScNdQuuTbbIxOFuYEaFUB27UltnV8+rd81SIonBTHtAZQM1YTPL45FI0STUsz6PxFwPQqu8mqQvXOMJgBtUpOLmM6T+xcawUFnijuged7/8QTysxzi6jxaT87LUpcJfHu1eeVqgAWZ+UfZzs7MZxoE6g35h9XaDfPz8P48OpzvLDkL36QKzP+p6Ticc+3cLmaftmyqSKV7zGephGJ9mBfBrS/pzzlfYoYdIIet3/zNjc358e38b/zk5MTBWzTFe3PWu7f9zlVTTs/BZ/ofRmL3wQhyTA2VnhLd8TmA7H75VBE/rzE+ZH4i/A98MFKQ1g3WZOWmu85uRSleMYFeNga1d4ajNp/o0ianqMi7zyHYVG6T8qV9yY5P7pI8nNE1sGQbSrwWlvh1CN0dgrMr1yNRelqS9curO+jOdnLr1OsZw3FeeKo1YuW2r1ixfqIFOs12iGs+0ZhrmHRYMR6l/kV4V2Mzmk+zz10JRyhWyXyRbQI5dzV5/+tcwejLo5xpY39BWned3mIY+6KgEE1PWURK8RUGctZslpiXpbnQrnV4mp5roMuRW9K3CSv9k6mEgthHITsNoULmoIkKFj7Igy3VSE3PjeVX7eI0GApFGmJlpsRGxB5QyNHQ8egBTiGiRG199OAmdIFaeWHhLsD482ULoN5f3wHzniUz+J/V05Y2zCZo3r7Ff6rzyAgmXa+EBXjf/y5/jbYt/Tuf+fmK86lLq/BSiw6fUOVNSkoO7cCnWSP0hH6xsMoeMIh864qoHesKeVIUVDn5cets8m0unh9nY+UuaXkZWwVO62jaaZwhZy2F255136jcaWcNCV26anIQ63UTbQSt4iyQUIupYZlAYndcBT4hSfqF9IOw2DY4tdbr6cVLOdKMkAMY6oL54NiSN+rUfp5fh9eA/eNQazWaqls4jxsvBheAKOSTHwP4UoKK3GXCyRkRAfYRC2gtb91GWinvOf7W5fJ9SlLZPS+x/5BpJSEw+kxSo0VvPPwGvVAHhCdLSQ4vg3xxD0+sPQH2J0s3ySHlUBQpWx0wTeHfmfFGEWCpGl/bycw4SNOjAjwbA35GVZ/cMNn2/Gp7/5VzZFFXT63qhajA0sDqSViv5VGP5YwJHIq1M56dsf7Jw1E8IoBuGjxLzAW/4tqplQoSyc4KlophOol+6gSA1a2O+rolibnUre0aB61X6taoqFvEpGmaokL/b9RLZURMRMmcrrfqclhPU5omKz8rhPoj+H8rw4DjNufwjG8e7gTjOHv7AIWEkoys4vdMKY1f5mUH4Dg55vq0Urn3c3dOLjY+7ZX9uCV/W97ZR9kCkwR3HyLsmPDk6oOZ/hvVqeX7TJVvYkP4qCYdD2Mg2n3/UXwx7QAxrjxjG7GlGYW5cTJuFmAsVqQp2g+yXKKtSZ2Q0rFpbvPOfZl22OQ05gku8Fvt4cbJOAh+MbxEH4Mj73Bbz5c+P72OSeIE/gZqQNHBDIjAQ/b6GV9RzkR3/GbgjJas4v8PBb7VcNZ7AWbe7A6EU5wsbk2qxcM9OU0iUnrnBb1DdXgXrtBZsPaze0ikHKjOWYBt38jXBIcDh/HgwfEP/bXtrfSz+lIRPTlfdFSAs1coBobQaYo7XSMMtoZ8FAIu6oST8cnp5MEV5g4HxEFS3ZEahAov7jsv1tUUqnsroS7ZBM1mZblpM1jv19boKpaS8i5mEYsk0vA6TD3zQLq31s9PvaP53p5pMwHmfS/+CTAKrSRXOTuFR2kzjIEl0YWa4MVdjjQ4xvbv4X9TQTLC/sWSNZ53V2oYXOdhvSBDHKICegRzylcVqAkVCYYA/V7kp7VcVem6rNzN88lnBB9NQj9fhyjADSoN3adm2YlifCfOXZ3dRVGqxSDd3WetqdX8warxKM4VYj8m3eXpGqe7uwkOzhbK7GZ3+Oyf5xvm6mlNJ/H443Blj88rjYEbK9EHPrtGKTwwXG9gvDJkS2ucj9jEuDUF9FBllm76800JdMHK3we9bjRI0p+GSg+d2BSfw6ESqhCudjiCOSGndSb6UruJCCQGtJyBYHRg/hqE6Uofn9BYKKoUBR8D9Sf3VT/dCIbyCT5FKUmWaMbaYMQ4c/ppNmZzO3MBDqT3NSZwulM8p/pTNHqTOJ2ZuphOugbOnM6KUYf1ClnmyuWdOrqK/pka2z2rXD7Noa+IWgGzF4AgxZMPZuPWMHJ2FA5jal7Ru4tepch0TnjWuBtJ4sFbnpUI/ozhN1VmB93vZq4rCxK6HIDA+b6atESSYOW9kTL6+u4vemlnq8ywupagLODbvU2NzG5S3NcWcFsbdMN5oDYwM1qVBaTSdyW34CHjqnIF+sQLws7blwjNyUnSz4rnVlcMtX1hVWQI/vzYJKdw9ZB2tlnUiuU4J1LwXfY+rg+LcbXX7PyO/vHKm1Zatzdv3JZ//gJYuTC0TKZUDc3EV6Nl21ZfLI9RyEj4BloLGJnjaEyHo87YNrozwkB3Ouj74zXXsfiuMAvhAYuyqW1M4iFQtXfR1T9DcSy16zOGQf82MWdRlciCXIdwRdfIYw3xcjv9jv6sCtHucbeNDoT4qt7Xa/uffHVjpf2v/SS+/GXnEGg723dbnz1pUM21NdfWlDRXmoT0TcqvYgIRn/YOZoXXK8iRToO/IIgxJ1azpEVNWdpu6Jz20GsSJGNc7ciQfU0NvaZYV5w2dix6xSn9cj1nb195RIMQyuECkBQjPhRMcUlPgKO8sNKXayg2L1CEX8YZCBPWMmqMBdg2AxK07ABrADwFMBECD4mZ+BkuP0vhu0USc3PtU+mkm4fPX/2An1oy/vxVAflASdmE5iYAp7P3raoElKq1ZSRR7bRlTkWquRcdFxEBqVbNAqU3IqZqlIzQpIPKlUPU0pK94YjnfClrTeP3745+en5o8e+YeBq68hKLCSFl6UrWQ48bz7CL/zhzbOn2gJr+GJcpeobcVzVIlZOx5TKHidzqy6eFp/S8iip0FGEfZAT4X6MGbtqePl0WqdIYtKz2MpDwIUS1mRNYJL5wo9hC1IVp3Go3ZlRAAxNqDYIjVgeDZDwLVy21M9hovkWiXHipWM4AwyaEmZvR5TV+H/DmB7HtiIgPuKdf6xgQ8f5cS5K7DklOovsu0W6ygAXJcpsqhL2OfBi/PnAIepPg/Gx88D1cWE807n0lR60VHlk2zacPigfSfPYYIDDfsq9eguPRCmIXaA+q8gflclZra2KHWbtFFVk3ZbG1Z1AWB3vz0RY3Sz1oFmEZsjv+nQ+yx1+3hCStkYJNPtCxX7iRnsBuwMdNuotoBewgt8G+uqdL3KtoRDEOuJX2Avho62cN8oa+FAOq6WUjfoHhUipwG4P44o0gt+K6gzV96We9PtuYN5J87scL1aQJPDDMRMt56+k3LhIpBPYtjqb6xH/Tku/CUyXO26wlG5BT4gAhO0T3MRoWnkzBaTymjg/zqWSKeQ32C30hRUdPsl4zONXUpBVKKEZP+IyoQppJMKvN66rUVAGHvSrBE7lQZ5dJjzLqOUW5epWwAT6+sD5ekKDlhIqEX8ZvbUIZmo8zDAA14rv52M6UXrW11z1YYSkcdLoQu4vHPO/+fJr3iAYLJV9y3fXrci3Jy+BS7rvwTJqYdswBp8f7PvdnTglQy8BAXyx4XbI3WdqmPQlXgyr9uq0SMrxo6ROEGJUAQk595uRD43HZJGCv55zAFKyvyYEER65uY2B7cMOan99K0gnJaCjt2T+0P4hBNhVIouvFieNVnusJne9GTlD0+wCSxmPiec4QpYjFu71tXDkzQO2xj+jLI3oO2F0EVlwmeWvik9VmETkUsEroYDLZ6qMon5VgD5mtMBNXSPHFjIJPmL7sIGmUQxHVlHWD1F4jZW+Y2xsBU/GPkamnQVXLfsBBkEOLoOL1oMdinqX9vnMD05adz627py6d9h/8nPXzdfuzTM/OHLv8Kb0g08m0UM/g89+EzVEybXZdBF3Bt3NzuZz78QY+7Pgo73GTXoWZMuMpa9NyTMsOGQupyvkgNLDoV1Zv7G+7n0UDgYVWV6CQeVEDtj3qblT4bmg3Cs1icGErW+yy7SY1p4pBgPtxHJrScJ8bO8x2rkKdJjExh8HBXzCkXnnUedXL+nKF3ry2n64acDjQCvq1fMlg3YiBumbG7WfrwoikKAq5n6GshnadQBdm57zqMCUfOiakpQHFao5F6tnyUoKPosPoXSBKSa6uPIIZmyASyxNl8Sq4JlN+ANAXafJJIb12kHV1Ce6B3S/4+T94M0UUxFa9kIxF+bOO2WwC8rgAxrtlvVN9mt9vezuGtoBiqsXZXGVnCfcsa/vLtQ5ghX67f0eUc8fLB1UlQ/NdB4DfoAJmiAI6d/+Ff+ZT/h9GUF45NEKQmvS4BEUfNbheuV60dFriGNnlmwArPJCxMXGeNC+HmWYUu8VkNBnloKOUZt0uYhV5Ozgw0Hw+CCgrBYBphnQ/hopemWja1G6UKG04/N0QYfIGI/PNGqMfR8ZHjwuwzRdhPIJzj2KC/ZEDWsoMiP/4rO2ze0LSkkeis3qIp1MVj5mf24C1z0+OcmLzXGZnIPs9GYRa/OV0rzd2tx064DTefMiRcvMrRBkIZMlaTdI/Nt7ewvXeVboO1+hq64bJ/2sU7c5Eq4DxslBKEuwHic8SXIWViPCruMrzsdvsi3QNUyvdA3SkuFBby8U/e04FfnDybQMf5dhRsT1u44Nby9cN42bJq35CW7/RC1LutShE67uBtbcM1k0dbdPcEydOx9xEoQEYui3U+olvue4Aud3nQIXWI0r7z53ClxzAcXl/e48Oz2nXg0XwaUvnQd/wi0GEteH9BrJ72MyI6L/s7rB2Xo6MC0FLTWOHksCYf5LlhZ2CMTIr15bHNvm1mF57Vth2lmCeoM4W7fCsh/X5TSNw/gsmVQYethRXvHIGL9X5D+m149wjfyE/HYBpBR/HSVXMFRp+IBD6uLTaV0XeWyD6CigME+/uPI+0cpDV5bP5yApQCenkxRGZVKgMeM13HOdBLV2S/mG4r0tVHwBd3I3AmH0qPGGBViCc+oyqyjapJh8VMCrZKttJrc2TpDWQ656eP2GlXJopss/xGhQMuVA4iiv+bxDH9FLODUHGFkCQtN1NKquNvMCDo9hjLa6rL9Fv+bzrN9Q4NF9qcgshCJTdx8OPM8kn2t+E+qwLtIcH3kzjF2ZVjDR08lZhkGaccAxH3j0OCXKFF0VsYAKwIESqC4qo8KrqS0lvtbRa7KxYYrXFRBdp3c1TMn0LorkqzsG+NYa8LdGVUXjBWtvADMb3aKTBM6ctL41JKUZqZJsfLtNltDL72O0u04GkVGuOorFoVlHwG7Mbk/pBLFqym+rKzeIYSaV6rIZwykdUIW3UJl3a4jZeOIGuHK3b6xeDwTXTn2AzxR9CGFdI5lBUL4KejcqsyuE0i6gmqKqnpfZeZZjxruCv6hGjCBK595aGYjrYDqB1j7lvsxZFAqgZr7YHmpt6ACbWYH6pcdI/owvWsy7DEWejofsXU1YQbmn3J9/zvHsRbuB2qMrR69fk4+yMqTROSIdYdx85NSEF3/MgPVSNTzmZmasFaKjHU9LBgi5Sq6xDIgI2kFZYedwRYhzZMLSXzBxT8e+ZrZMQAVtChlWzi4s8zlIW+SfAtsA147ZCOvrGTn64W6wXoKevzWiQDvMNST8uz5phSOZDVQKyd8wW/RwIzzePt7eNqjOK2nPKURPzUP1kZNiRPQcw9MwYnyy0XhLVe1Uy1vHrI8Mw8zximviNYvGCK0HVUngG4v5FHXcjD3XeJII20J79ZVo9ETdtnM3AzpBAFu8tFHJSVepuhCeAcIfSbga6SQ0yilAw17UytaHcWJG1yy8f6AyOAwM7Ty5C/sv1T45r3FzvmDynKVV3zu5Gy17GJzc9UMsAP2zog364sK+HFNhS1T0hTH34NvSbeiuVEt76XJ0hfmcG/PbraU+Z2Hg04UiPBB1xnjBsZEMNk1NkHlsl4uBBw9itNIKJJA30ouJ+hZzWNwm2tI3P2Xj+iJGXx/nGblg6Yc9q60nSe1XvL1RbwhPrMdLWwHZcVkjpwWwE5fLWvmBhA23mUsaWzHpM3oZZEj6O5/z1wYsqIRIP/FiPuf+iZieR1rVuwIV9uryegb9LotRWlUL2v2zhdLJUghs/rFP/2JUSjZKX2SfUxByoaZQhmVj3uo0orl5grgYwe6OHzDq9FYGzORPpGmFIogdlurpb9U5nwuTwHMeWVhH9In3P95Fjpe/C34wx6sfoRrXPOrr+/oeWiv46nb08e62ehyov3ALNoH6oQpw6UBfUBHb2t9SnwSaZwIr9zRaRx9OitO+POtyVMCoB2jhIaDKPv/BqOrkPN2+Qp9X5ACSSVZf981VuAtEPWzWxuyMmo6khmqxflQJIPvpNqAALLrqVsG6GvY+iP2BSgSl/TcSjmECyeqQbNZAOxuAQxn7RCVogii3RhdJeQQs/QOGHfbwbfpsjCKZUX+Wfv7Cdyz9F9LS77AOTrZ0fPIE6+iVW0VOxzJHyMAyHVEqAz5n+eZSOxPaECh2DGshHgMD0lxuKAGG5/oShA3gSLn2LD+H29V1PsJbVTmKMLL2/3L3ru1tG0n+6Pt8CsrrUUi7SYm2ZVlQaPwdyZ54xrex5CQTSSNAJCghIgEOAOoSis9zXv2f8/ZcPuF+klOXvgIgJXmSObu7O7GIRqPvXV1VXfUrSmgYDHrb569RYfhN87ArP79/txeBnIOYImhdkKuHfeW6FbUUO51g/758fqt9yQ2fHaD85PHY5henj6/Go22cG8RJnhbD9guBFlJBpa2/npZAgMnNzVtbu7y87Fw+7aTZ6dqT9fV1LDSwhQ3nWP6w18xEgFlQtliWB31749OEvZIMTY1LnCaTXBEwOyfKr3l30vuk7j0Ks98jlFGw3lgnnrAhc4YLKuqur/+p5rWuaMF7qqIm/XpBusIw/AyLYZoBMVdITZCfBGK6gLVYl9CEZuQEODxwfcUsrZ5uwOEuLjbE9YY42QA5iZf96UbPvkQDYkB7JICJXH84a5JuLAuBmIAw/+jp80ePnn33HZAF7S349HkLhH02924/s+9O0UyxK4LpQ3IekWsKr6iAOSFd/cVGr/v86Ytn0KKe7SdgG6PoKGvrkl2UTmrMURQpEgqMzW34CQURstL1ko6Fy0HeVhQvCX/c3Fxv0I06glEavvi9aydd4VsCqdPTyxGtCVFcxKvGeUN5+TS+tQxMmmvf3jxAAyPo/rfbFt7bB8WEK1emDeCLlPBK69lryKCqZe5pXkr/lMVpBmQbXvlBY4Ujr4bQTpBV59tBy5gqW2jAp5EJfMeyatDBafJoK1H4Ctkz/z3dyXof6EbduPCWti9mpRAxAUqjFNN8btd4tVGucBGHihKgUIFkFs0ArJVQBjqhv4RM5UrApxuIzogcUUcrwXpk/aMfcb+n80BySlL8ZhztRfIzCcvQgNzZbjieKXQuFuSB7LzLbcBTQ0flgATeCSEZBNhrnRYOUSeIIUtw+749jfSePdkgbyyzbN/Zy3btsNM8+Edn7eiR33q4dlp2Oyn8Ak5yzwYe3LOP0/2NAy7ONeY6urmBT7AlOxvickPsb4jXhorsbMBZNwFWgcXAtSFMUvsyHQ4DyNuTB/mvkwgOhv2N3gzfeFgO/H2CP4pi6FULQDKHGxwE/rRw318kg844b2M2CU4pJgh8YXFMv05OPWgo1op/T+Oheg0/gaGKhzoBfwcCDiWVIM9FaH10MlGJ+Dtw6cVn5/zWjlJiDbkli0MvuVORtzZa5tJR3FpDLFA7BOjHDZsw8IGNm3Ibhd3nz/iALp/P5xvu8RxehnHRGEYKZ4dRuLXq4Nn6M6l2kJoSddBgZOesM81G8weNJEV/7ili6ktjIi4165wg52aorqu+KdSBwjzim3gUfQb5O8q2Q81CFSKUTBnsEubLcKMCgWkiho+HqF452qVh0En8gTwgKzPwdopEIBGS5dirXLG3scMm/lbmWGz/Fb/zaA1psi3wZAgytQaL8SaF/2DR3dAK9dditpYmgKCMgpbJYjqPDteoGNjxyniR7LW9rMLrleZHt05J8n9DFR9DAdBk/bpxgPFi7QgBlLQNdDnsn0XfT3PUVcAxi90x3jXBKpB9P2g9JnNUdsw95WvzpgQGj4koxj1YaDyhtHiSDq0WaZDyNokxJCzORaTmIkEsDozQ1ItIoxJlOQH9B9KktI3Wpwi8EQTiM50ZODBq3lDfhLEycTd9MldAmFtqYYM3IayVAarrqCmNTC5KOgnnwTYpZ+wABxLhlgIcSKSClshWV/HkT0cgsoXAjWTaaJkGsBeLmEjZrxvivSFiv6I+Hrd3aeK+LzlPaWHK0FQ8fmiXisBHoR8WOR2EK92Wd4bHenkx/Ogo4OS91F7WV9UsOnr6YXIR5gG75tGxSQhmzeDJILCMGFlqdZQcQmkQeq5WQiR+Z5CFlyQoQZvWBQqEl84nLYGdsHvemmtE4RQNx1TVZ2pBvUL2Cg4WVLy3Kt3/QXWfliEMgd+R57pSLvlsd6oQP6jMDxvNqJyPsgkKB6eUHZbyxZqHchO+lLiva9wBqG7coSFWKjGfZ9/Tr3+MB1Gq36p5NO/fkuCoM6ieenZz3rCbdqlFPxlyBUPyBpeNjhO9rXAaTGyITxvS2A0YGZCTIrYT91Ev6SbBbC1qHn5eO/BcTN1Y2zbpHuRCJ9uzELg4NMH0feAbrAxOfAbiuWsGEdg3eaiAdDPt0wmSkVmiFn9h5hFT3Qixswh1Wa60M2+JigSNBroGlrJ8HqA9CLBybFVrc45LOFMgzZothvNU/uzpROiyvk2W2oG4nNLyhrgzEEHzNVByrbep5XgTjtEUEEuEqEcUCMXcVk2uZOwcZcowHKVwnltqVRAtpydMKJvrQvn9tZ8AcW53umju0BILFwjVjZcYo/Aaa8ZHdhsPuFbypQsknPWArHDcW8KBat8EWtdE5rvyft5CUByUgFXfgXqEIq6VcRJXGfRXtZF1F3AaXgHdNJ1ACMsEGA98CzSLbhXtPicwwyZ3UdYRYHZgQuV3VjAWi4rrwviSTqvUlZWyyHqWa0fSUffMsAoI9BHXO5VPMuppTQMQ/YGCmDnvGPiM1AKBrRX+i1nshsIVwhoTsx2EkUmEGk6hOodbqESp/l62V3duJ18RXls5qRlM8aLGOziyQ53YQcQiSeRmc61HjHvr27GJqxnbcTXjo9IoXREig3QqQeFwQftQU7LwLjxsba8kHHgsOwiPkN2APz19AMV81shw8ho0h2Yob3KENOXmqw7zGkVZd2tra+2KzKBFslgNVkhV2XZS1iaNR0ke4DQlTMM6KgIWCGInQPym6Cyr3jE7EKybFMkK2EnpRZQB6biE5hKCqnkjd7+UsOUsLWvzIBrm1Gj3wq2sHraDTGbuNRw5s0ZOmglHF5XX44cNd6mvENgnB9yEiTSxNyXyzRIrBMUWyBSgzHwsJ07aX8pp8KXaFCyvf9oQbzbEz5bU/havaQTK7iA5IzIL/vNpo0cYCNKBakUHszUeVV9gDKQQjhR2793H/QDKvvd3P/45cGTXX1y21vLI7zx67P/j4WzebN0cHhweHa4dHh6RN/7h4cOuYTM/E9pjM2iCrHh42Dz49sGR30L6Pm/xw+FhqwVk6dTWAf3NrtWwM6b+zxtCBqJCSYOvTbMWOkkLO4bqym4tD/Xw1AkFieylpBf+pTQE8TB2/R5yVyLXIWJ7sa9loRgJRNryNCOLIXYyxsjhNuLQwefBw+7DWT5/+DSo8J6mw9Extgh9agjMY/AGjnGOCGsjShUWJsL3GxTfmSLZbyuUMYQZ+21DKXPQDsDhWtgkBeOHKI1pkGd9j3TZ26i08Kwmva1VR3zekGqI0pC+27C40hX6tkyxuY/G7uBv5BJUz85B13iSMiq0yrNlcgfBQvhtQ8Bg/LjhaL6s3fN5o7eGi6+pFt/BP+DvY7912D3EBfubei9haQ7zRxwvF7948C1/AX8fqw++hw9w3CBj0/cWfCpTDsT2EaS2Hq+d1giNP2zUspp+lb9jJBOtEuVV944YXtSSJ7ZvGpFjm0uKRCxUeh2vhNvGBv8qtbI4lgc51wqNDtA4+TQjnQ9CZGru3H3XJiGecwiTYRzm59XP2pfRyTka9NW+xVS7uEVf2nnK/UiOHYbEMHb4iWJ52NcVjgaiHXglh6F1VphzA/LoZFa5kJ3ooNoNJqvl2FdlQFOoFM/NJdQcvsI53EMgFtS+cZqjKMtESC6P6nIyE5FRj8EvbhA+/hAmg1GU+YzaLVVlGP65JhPnsXRlIenKvFDqxKPtVN5/rq5ivM0O1o23lXBIjMLfkMlvmsQgguIRsjLxeczwmjBAZhivNAmM1+58L65ecmbu5LBkWXJsxpOXlZ5qpGzzwcS6CY2qkvyGxf9b/C2Xo5a4XKWJ+6jaJpFAj81x/eOGOq3fK6LjHJ+h1SuJ8ZDMYXBBSta7ZAfBmQnSpJTWq+RqKaULZZcMG8pK9JNlNKWOoSyKg8M8/JsybbuSrdIWKAbBxnjMjACK501C1/TwDxpb0nCk1nDUkLifLAuov29gQB8rhHbRmwH5xDBiLBbzadp0lNLIPEnAZvoehKTq1v7zRsnlV4nZirQjHScyjkBb9ESkPO7BIEnzM5uQr53S6cnLjuuKLfFbHcIZnu6BBTlrY0IpfFfUdDZDjfsaol9iNmL73pY4R86hWKBTRW1qRNpUDAmlwa1FAAU0UdeJEvFBjJ6dxiXV2RSxbR5gy8MbCg/AiS4LDINiukTi8HvA3z06OMwP944e+YePDtdgiGIUJmGINYvX/F+dRz6smiEqB/LGwSHkpw9as6Z5mMMvSJoT1xcHzPBLHgaY/ozZl4Sx2VTwW44epg3k1o9ac3tIMlLS89m49r/4ZlO1Vs/rIziOZdo2tj60Wk5tgtP8EPnYR6ax8ABJLZ/e/69xNIjDBd1qcb/mrZtmc2GWBX2Oa/vM0xOqd6I0GCAZhbhYBtEVZNK/2SrYSuhZGd0R1CaGpd2Uu7RYgmIZLtzWSEEbA9htn6ejKEe336SFpw6Qb7oXpmTeSeoj5EY02J+MuLuzt4c5O2/ff/r4ef/485d3r5UIHz/uUpx13C0i7wE5SY00Jbe9SaBl7RAsbE0iXX2xDvjAhW6TqyVo+eHjHpkDyGYr+dzcS6jrgojvwug8bXDRhAIJXzVQcdNAv7AiaqCFuJhhMoaLUdj+eHsgDW6xgbWFyoPVKSiy0Jdpt/NU5oR+oAx45ahFrC8i6Y8tymnh3NxogZwo/x7acmOgc5DCMduKC5P7E84ikan7jHjojnZYHdB7998yPBccNmbBVIzixB22BgiDwA7GOEjCJoyZRRibkdOB8pKO1JKOFixpOhgVTs/8tgVzCVxN1MgkdCgamuMKyXnloMkGmRAHbHaCwo8FXnPs3iQbSbeyld58/LB//ObVzmveTVbWt8TXloUBaTgB/BqZu5cowujYvolyDHzl0nJuiSUU+qAhgys04pxuiS9hx8GAhg31sTk7sWt87iP1ocF27YitBcu3RUbB6kqq7FNkofrAaS8tawya3HHkWvDvRUUJaUcPEqrU34TjeHR9c1Nnt2ze24aQLhFCP4TmP1kZ0RJV9B5W8VpfLALbWV1NeI1ZmDtV1etfHHaIh1VNIdBy7r2ywqrhpK3VklHQhH9u1AwHrBPFltuhQ/GOUs+VX03qMISpZUb8Tm4tYtnI3gqLNmCOpd4N3OOpoCa9Hp9EA1j80rAL1st5PEE1Ss7aPO6lHBjP/cbBfLnFnkdUlJraoCkxMV619wDu0Szn0Jd+KSRJ4bxFIcn+GLc/8td/3xDDY9esh4UNkj3+Lq+jxQTy9JszWKBvrJ6RI/DFMfSZjIfp8Zge+QKTEn7ZgOe/oAUMPp3SazLOxotcShtzWsI5zuhp74Kfft6YV5j+n43ZOgo+bLOeKCv1bN5ju/aWloKlbnZl3fDSlI5THRtB7MuGfHKT0eg0Jm1gean8ct+G6Kvpn+VBt9D+1LpmT3th5ZodeBXgtSwTdzLAFxT2m777iQ34E4r+zUk/SOP9zAgULOP1H+VwjkphbgQPvLwxZOhePxxFNzdoNU+2nY4u/+GsjzZ6JXX+w9kIj5caETTFjT+irVoVPgW//YwRhvH+XzZONwxxMS0jgVjUZhJheY7Gx9HdJsklWL8oTqQ88phATSAoGWwErowOWl6Uqz47Lhvglwu3rBnKH5/e42PHuE3bz9/cVC0Ojiulyq2AzgJu+eVPL6qfSnpHhOTaoiI/02KMj/HflP4dHquLB6Qmf9voPY2emRPxxD4v0Q5jlOYgqZbC8zkRwHxc1ejVKYIDcsalIWhzZCj4Q9ZMvW/RfO/bo6AlZRvPDur7sAZu2jU4VOjEsGnIvIau0PnACRDRyWb0s9OTsIm3zGsCBLV1FPY6649BtMsfHWqoy6KFNsGIPML510VD/q9l2UJeOcPBwlyhpbkKk1C0ynuJgiq7SvIC6QW1XBIYaTyz2DGLbF8qJasYTGg7gKFY/2NI/2dbcnLra25fD+4yVSLohEk4ui7ift4eUdCU9gC4yDZIHGh5iW6PNVlOs/CkfUbaR3zfTyfX7SINEck+6EB/pu18mg3DPr1VjuNtfFFJaCu/bCs9n57QK8LAP5LMg0DYZZuviiRkBwfHrd4+HzixZc3qdMtwb1mdT6BN7IFOFZD9MLqeqmpBnCALuN/SJGpL210nTdn4OokRqtmdlEkYZ7Xf0wtTSE5e96do/CafYJ7Ygd9mh3eOyxbthOcAn+2QERjRfDTesFAw6Gc/ikdN7YwWSfy7n9QjwwLTI2Fd875Z6PbByARoO+OOcHVN5jDYFFvaeDmUL7iHcPbCsKkXRTqhi2z1jM6AvaDdRReGdfQjjMtHZzK3kyeIDhFl5KCby0tu/bKsuM30G4q8t9dHdJTe3V0s7c+2lURvGaqt2yrHW4YKQevMOIX1vVTJMLU/LXqTkQ/TuknS9kpqOMJlY4VUJ6xxR3HIWdnNdlYaWS8TCAKVTCfEe8Z6OxNWjRcKyTxYJi6Xx1YwM/tmBcWWW+EDo6ZrRxRturImC92WRbkfecZnLVI+a46XWuV+bdNsPt6JZHgrOWA1PEOSZciU+VryvdQ1XB8Vtl8z/MlcMrY7yD5gvTqa2sJvCvXNElez5jXxCyB16HAzywqcC+lFyMq2DmJJkBZcKBP0xbsBfeilITOCgtbdkQdkgQ1DK/VK5JxE/t4LCBaulkVkTvvQymxMxH5QPG4oG5zIYqprtMo5Ox1g2doDlkrYQqiX6VjhkvONhZEfPO4IHGpPxGIv2tay0RGO6zBDEayEruLGWFYzYW7Quu1YzsqbsGfnwxhO9xE7zNBubNpGZcnmnR0vLaP/bCGQA7tXStVnJg390WY8uw3eIWPNm+9Xuse+j7p7VJjrJRA5+37fPiGXn15Fp4Tp1GPiq6/gK2dUUXMmIe0tSmeXbZ8YfBePTxt4vfoAL4IeNMJR0XvwSnFdDSCkDWSMHrDytPdA2md5ZIm5LSPFrm8Da4ZmmvALztM2G+l6SLG3Ydewq7qHzorbDxprL4Ml1LpolbEaXOzcREVfhXGXjgC5+ijr1QKsbWe1QHG+gZRN1KWPrhQvSxRAaYBMZmChhNaU5naI36sOidhy+LeOkeADBbHm1dPASlDBSQMLqwuxT8/g+f3OJ5oFydYKinGIgRFPYMPlUdZQ0exgnhLUjmbRKIb1ct2YAFeMCkBYp1BA3hjF51Fjb4RZ06yB7tFXHYufzzbNAedER0T08ZubFTm2KcxVaPUotj7DIqwoOpvycnAF0x0KQcNApIN3hLIjQiPFtupQsO0QlZ3SAJ1c8wDJcehUNXzppounvqNQYt8W0Vj2Kgkv4lOUNgyGrN+5zOIickma/lZOF2XB5kyhBKAHuEORwEmqXi2VC20eIL1yGtKcPdDH+gOPOIN566jSmd3j6L9hb+QFz2Ztlz4eW54ZjpaCZk4ECW0Q118SlxUqNcseaTw89y1JdnCTVOpWuB+Ll6rBma2NxBnEg5EDUBXDxihcyNBM5bJ2STNROitEcNJgq7hv4kqoqbCnSxRpDx2ugHu4RPzBXkim+nyIADMZUxBj8sBDNQQfaKuraVuaF0SDV8V3f9sg4+XV1ZxC2xqsHyTIKO7hmkKptW90DJnOCFwMNarfm8l6kdngU9pbwvUBpxq3EDJRNsNLhQLbUvXO9YVORxbY078MilefXAN7keirpgfE2OIZp8a91+uvrsK48wsy7rSuDXlX9PmA7znXJbRVgA+vHPrAHKGGRZ/6Vu2SbSjV3qyuAhmcF7+ln/OW9VEfc6iRgCxWhKymua5QC0WZId/cJGr6YcL1QsJkswx4FGhWcehubswSaidmYbzs/W2DLzw439zw5p+49wPSjRJfTtG0TdprEhj3eEIT9c56BfIVLIpSSfJVXNnY/U19ZajMTWmza0aB5J7tysZHeplUdj6r1uSuV2uM6dACUjDadFy3SD8oy4TFQRF+GtWD/DLUB5Vo5KlzUpkTexxe04XmPU/qV3fhIlGTDaMbEivJzh6RKHub4HJIk9G1kex/B+5SmvhPwn5cXFPaMnZPFEtCARSS22M8XYurY75tXSjwK7UYVrq0EpKlnJxcC5h7XkYXqOPfkvKanG6qswav/BaeuXilp8xdF56hBByvr1HnyrgbyT/Pc+u+XJC54aRmRgR15N7dhNMiJbvK0o2OK6aSkZ8FHGYLZH/Z+/iBY8406Se7fMVDMgNmh9djEFnOwtEwEO/xJ8bpDsRks2fUrRrQUzRYFwvcrYB9MJkWoqEWMOwfmnXROJASUaQkIqlYFY1OHSYtfID4m71vuWzMJxNQxVt6xCCpNUlZOIhT1FZ/gC5sPFkXn+Bvd0OMN3vdZ8/EG3x6Lt7CnyfPxNkm/nu62XsGGY83ey9eiHeY4elzcbHZ21gXnzEfvPsN/z5fF9/D34Mj8SP/+YH/XG/iv1/44Sf+82f8MwO5ndRq3iZ8209H03GSe0/nwrzAFqoXT+wXT7eeWC+OyjetJ5vVm37lzc96E4wR2s4LUghjHPq50bkk0rKSQCytdZG1JPIyHDbpubrWkNxr7Msz17hVx5ziBdKAEegkHrnkWCCPsLkrE0TagCEub9G/LuQtZV9dVpCCNFg3MyXJAeMMJwRIBiyURiZ7g/DMkXP94n4ie0a+MnTthEdwoCg3l9O0zCMFG5iQYFSuxXLQnduNNvcxmyUfPK20KoRW+2CMEltjT9/0gC6OgVwXgVoB1H8L5oaMIaSNpx8ka2HgaRgor2CGMcMApQEhjr1NilGHO8DKo6ZUSc2gOfF4On6ThdSA3fg0LnKvK+GzdSlzuirBDxNZPnMz/evbKlD5vODL3m4g6ut7IuDU5QAcuuuqCbqiahvkCviqLj5xivuqEmoabRdqWTI59l37OJcH60d+h0CafbQfGVi+m8eub3rhS7ttmIcsjlC50oFzObtuNileOPDvauo59iFdusokAmRutZzr18vyXlrRG6Mi3lQCxkJz1v5x8Pg///f/dWSuVgNmToAD8zB83X/+n//3f/7v/8d+n0SnzKF5zMM6++TvthEaL2kZMCGoiexLOwFIHe8UOdrmwrjDvu/AZKuS0DV53c+QM0xervvBY8JFIpwO04af5R37SpcVJMWimst7kAbTo4LNICTfrdt9hp/TIgtHVn37m5Wob3HOSHoRatHh+MwxVK786R1YgDK/HGNMeQ3rXY2/hcE5CYdNYoGzS+cDT+orrU9RaU3RpvEmR6qngyfdQEhkNI+A0Z50G5jGSkN6TR6o3lIMuNqon+gEDs0aeMH77nrneWOrs7n+bGNrp9vtrHefPeHn590GPD/daECW9Wfrz8zTs41nz3/sPu1svth8viNTn3WerHdfNFQJ8LzxdPN5g4qXDzvw0H2x8UK/3Oq8eLa19UJ/rJ+56B9lVTs6XTVEliOb3bD78EsgB1OKeXRDELgRRsvdf955InvSfbL1RD4+wb5sQC3PO5vdzaf6cbPz5NnzjRc79LiJz5ubG8+3Gvx1t/EC+vJsY5MbxQ9b2PP1zfUX6hka+/zZk+4Gf7xJj92tTV22euaquw3+fFO2rGE1+h7dRf4abVe9AMWJdACyix6BJzAhWxsbOzATW5vdp+q50X3R2YBmPu+sv3j65Ll6VPOiHp91XjzBaZAfQ/LT51tq7vlhB5r75MXzZ/rlk85zmNRn+mP9LMtWz6pq9b1umdXsX7gTTzsvoOSnUBX8C8tJPjeewZLcWO9CES+6m5tb+llVpZ/hx1YXq+Tvu5ud9ae6H/yAI/Tk6XPr5SasxWdb+mP9rMZIPqvK1feqcXbL6yeT0tyJ48Czxp8XKdEgYpRwip4lRhwra65xg50AVVmd8i6u1eiFIq0PZZWLfuUFfzNaFGjFnHVGgW5786F3iuy6v+j6kPjmyP6qsG4hgc3XLN1TeJC6ebrXIYa//eRZCy0t4lsrEmHPcIc+3+r5PnpHibQHCRID118XeY+l/8fy3n/tSTtce7Kd94DzzEX3SU0rupCFRfMRfMzAxY9fbKerq6PH6Us7P0uc8AExy+mknbZfvOx1n/ijnn724LvmCKsblavTn7dTVKqRgY3PuopeL4cyfVJQ9Hoj+q0NFHq9wo8wZuiw8HKhkj3ofjqhwB8Ucqo2nBffe876vBqU/G4i8C41CtAfKFTz6hVkFhEAixjpuEHVPCpg9oji81GzloURjHRRrOG4tcbabE6l8znunZGMyzXt5b4cTBp8M6K5HnMa2hynYy4Hft1kC+SKnlyJRj8c9Zvd9fWLy0a78eTZ5Ao9/PHjdeAX8vgkJvBghSAx367hSiZh4saKOZ/E7TgZpu3LLJwEKhoNDlxK2x1GCpVRKmW9peJ6UNSUavK7KLyIrO9r2RBlZyb5I6ZgJ9Hg5PqBF/qJgkZ2IqkYTM65zULpDmA0iqGXCbYLUTXUBBQB9q1+BOI+fEGBbcLVVUmvlBrsO6BYPpQxvpBqxU+I8Tlq2QGslwzuJJ0gwkcg4oGXUENjGQqlSNNREU+UtDU1Lca4G65NIvHtzgHw2jWbYWbcVz88Y0ijhTg0H1OJUnyzk7TUGtkg1RaYolshbgZ5vbKyTpZp0ThMirhPYc3kS6qAtEh2omWOaskfK1SBG9qZOH9ysLCY/uLOTL8STbKbm4x6LdN9DDfMEdwp3LtqWaMmrV0SV6KLyPXzLAlMjAjh3J7gvcLq6oqL4m5BA9rBOEgSxg/wXUkWxsibeyyZo9vkdGzJvIxmsFis1CqKkmxJgHYqWkTzcDB7Nm+18e8TDFZvxe49QACAI0Tg7ml8Q7ow+bK/01TqGwVVD3PU7ooua2/cjmdf0fFxmiA9zM/Qq1AUkOEXEJu8AKoOxHUUgoQJCzoCcc0akYyx8rLF/VN/m7738OZg/zA/aq1ZoBDUY5Dy0fbwzn2WvxGAsdr3+Ksm/dr0TnzVSMSWX6q5cN603M8UDcAVppcMrnR9s/Rayv6JD9I/wbnIFUXhzLmv9gZuZNKMhDawBhFXWyY2moF4oWbg3KY4agFTdRaeaP4YFYUNHUNeXoKYUooLi8boOxJZqoK3PkDwhqNODsOqgFSjthUt4wAYLWPtpoCxHtGUr4tEhyFqWcqCV2aEX1sjoyG1V7oamCmjKDMSzhuO9M2WUoxmB+jRFI0GjDvTLNQIFkx0EJMACLCgYZaOgzqyr1aOWpZkF66Ki5eBrsQOb39RRe0z7YMtgdPDejRknDOFoA3JqpKW7SQZvURWoYfTcRCj3jw8Ep0tvK3XvDwni3V06qEbGGrUnFw4qSKMb2Y7K4Dcsdbsp2Mk1DeWNHTD8ShvoitYJglxfTdAkMkIkQxpJ9B9Tk7SIrrh+D03+XQ8DrPrllSf9Vs3N/nLp8/EtEdzmcGpDPXhJrmBbXlDm/Hmn1O8KM5uLqPo3Hwphr23x9HjJqPzRUkeBf6b48iDvk16Ux+kfm+z80yMeyNfixxmHB4/E2lLPHvW8qyxgZSznlmE40eTx0O9Qqc+rNAzQQvNOc23nnvddbp8aXkDztTtPhfdjectb8TP8hoGtjQ+2S3uPnnmdUG8+Hxsw+/FF2WIO6kmRf/L11JVSobaeMeiHBrVGhNmYR3Z+vdftYmUEvWCL8l5kl4mwbbxKiUabHSBDnktLPIqjYbvd5wSQZVJTGINES1sO5ULS6m8EkkOgO0J2FSAEobxFca+A3bjDf9qoC15wC8nsGpi5EUof/C9vMPMk3ACVdtM0nv31h1ENiwmj9B2mdTOtobywxLtJg6KnfeTw9FpxhdvayS4N3AtNWXMrNuh9MK6f/9kqWlneDMmgPPNUTvRhaEtYFiVW1OHHm0cYU7hIKoNy+gSRmlIdxKnKHGFbCSji7ESncLszEEgDYRzD0ZHgZnlrqdtXePZq0N+JR/Igxzva2Uy/dRFURko+aCPlMxAv2+vjKyMMXTsuziJ5LduGlVNHKpuEv6+vWh12cnt4QcqTEa44BfyodwXulyXWfj3HSpEAzh7hOCBMBUsq7LKuhFo+wUcxif8SK1wjV14gNwnTHnpQiZrtQg/B2uyD5uVlbuu50Xr9V9Zbqr1IPCFfYwH9TbPp2qc7RR7RN5YdnYHM1kEyHHylyDi6akz0Yp8hrxDpNYnUgMV9UwXkpQKSbgQvP7i0hSXp/z2NfKZGVGFqYlnsSxtfjhdX1/vmoM6sOK+hbn0/ANuCV31MQLfumMA/tala9R/C2RDVkLQer5qsHt91a8cRLIDTeLn6HKewoseqSWriy0tFvsgGl1UAnRgRZzAOKOoG/qu13U5ulANX5O7UqlSLTVsXHwkbxIhR0ij5bRharehBpzCbqP12btNg8LYg4Uwc9sVqxK+T1P0MmiRXlO+TKjBChI57sH0JOQQDjxo0okHcNAkOvQc2lABJx6OtJDdiCVIMaah4jJPx+QRpG9IY31DSnlafsy3kZWpIO2qSUvkSFWa5tiF6km4W2t1U/2VlIY/BmZvUVO9mhchYab0bGAvhADy/ZnTIWRo9PwqvlzyMytdUZUCcrXmlHcGkDctYsW61fD1uhdL7p+CJyZ6IAzsIA9RvyT05FILnVvt7MtBLmqvtHnCNNc1kgX6Iy83i+/zZnNmVFiCWfD36ERGXY1z9N6gZ+y1kEb8Xla5RG0hOGFcUqgNwvyMTMVkDHKQFZnpIqghX/7glyRMYlApP6Djt81tcV8mPrpvhpMJmUghIfeqAZStgMhJOSCyra6zgl84TJXv/3psIYB+X3r53n45uHCgaHzyPMw5nnxbd9+Thl/SCdh7OCOSTNwThhCK7MMIpOmgdOU/vGMtNG5ozHTvGiZaQ6Y5eduqYWlh21boFdUoMsXjxeL7Zh0Ec9VI1FZS7CQryuO/uQ35JOqX23D2b2vDBJ1TmX8uteH039YGVp3KBeO04UeLsXnSff68+/T5k+ddO3LrdvKdidyKoQuLf6DRtglciNEsWPSNx9MRHMXd55ubm8+7WxrBonj58mU5PppBgFi25plwsBPX3RY8JEGfQMJlfphYLnXa05fxgD/DlUlhvSQjSrp8i3HEkZH6fSQ9CqBHAKmt7KqLO/RhEMGARoO25Nnv0I/6TmAPrMZU2nL9b15VLLOw61xOy4ubzgLQ4vEvaSAkm6p6doM9c7ppenhyh9EuEeKLp18/4Farv2etGhZZbl5pFq5KqrzIlzKckXxkghGAfJWEeE0y/kEBNffQS4LfvB20ynz23r+tpp3SuMucPgNLlHP/4B6nkZ2rNFhfrKwY9ib/54g1yXzcm4w/lXwcrMYbicTw9L5v51D9olgW8vfyDl9elEI6uPBfqJklZjZaxswmmi1kZZLRlzISeWBYUsX5G0lNsnKJBfDhBR/QLM2ymdMbviI+mwtB2XC8UKiTvQ0Yhra9xIiQ+Jf2sZZGfW18x/aZBJtqKXflYKApsHy64DGJOtHVJAPhGpK1CAuMsa8vkGEovAKmwODF6y3o3i/wqqiO2sLOuYpQpZ6QUmCTtBct3ufUEIyZasjSotp4wOpaZkxCgUt3lHp/luvXVQ+06B7PDWqvJgQnN6HAD3iDWJIVlIG1XcVrW3dT1zpzPfOPg+DB4cHRDf45OnrINt2y8fDyVfuXsP3benvruH30mOCD3KTWrCuezg2SkHUb7RAK6vQBX+nmx9Oco7XS0xd+4A14zGn6eV89yvQjPQfQR6u2jxdO9AShNSUIh3x4Ai1H1fcN7qDWYf64yb32Dw4vO7pj/Ls1W4ce8XD4hPgbM9hRrGB6eSQNkO1B9+iW8aTA9y6IEyqlWQmTIVYyq2AI7FpiatZdCf4VBDg0s76I3kh9aaR1h9RtrU8SyjgWUWylGtGL5Q+Kp+eFQqmyvVSapOU9oMI/kMPjzc0XhD2ggHX93kEsKFmER+U9IEY9nOp+S0x7OAs53sFY8udUDHsw+32YfanXRbwg+nVs0QyTuluXaGetywkLQ0xUPdz3Y0OCsBxO3K1Jy62cNRmx6HFvono08VToLj9zuANFzyXoQDQw6KZS+UYYjFqgP1A2HkLfBQljC3Kkgh/mTW07Ig0/2Gy9WrfRFi5thL4kfI7XVUPVmaEHdDNCnw86WnygN94B/W4MIhDzMcDykTjtDdQHA3pd2G+Pe6FPewTxfo6xxbRqSmmpb3Nd0BunFHHRC/bPlNNYA0hFDs0/PVg/6lHbrKw+WvBnEdpjQ0fRpyzwMOO8AQPWkHuBFAl+MJU3VfPOw9mZLszuHNmcNNTGgs/O9Eg9UVzeNgYK7gTzBhAtwjWQkUGJrWnAHKSX6HQbJyhuFpQjuoId2xilp3G/o/gBo/et9J5mKceYjQ5LjHN7Hl3jH77QrWxDdYNyJior3RurORvDnO3qpTGIM2jl6JrXiOyKpCcwm5o8HAt57+pdqOuNU0svfs4cUmlyFMNUvZ4qN92DpXZn7gdnwLNI4j83//i6LbUXwp04Wi8eNaW/IiQ2o3dija00V7hfKW106HNUWBzJvc3xwltzZxReVTXi0UFyJELgT5MjqZhknOvQAb9vrFsvtWUIcat5P/C7Xrsrw0nXZ2h3va4Mn7Guxi3VbnO2I5pMCy3XpLgdKsVt3OrAdg9HEaKFwRwq6JuwpVCUZtJsBuGE8ighyzIyqcQAGwF5+KlGpV47ta6tYWxmvCcwXmt6aVlgG7sK+Bx9bdDlzGCn2OaZlTnjItsn4eA0AmrxM38tfWzI6A5kCePessDro2qYaJfLBpb2MpDHjONLcEsZMkqfLiMjS/WS+PUeRwlvAtnxhUhJIVQMPUUVE0MUMg0DQf46aFjb6XRSjCGb995tNt9vNjFsixLDzE9apnFZ5DfR/tDHi2GCsc5WCxX2wIKwn5gYlUQNl9127GnZ+bpq9Ym9rJuN2lnGD2jI27ByFszh4o+sD/owZyOLlPwNTV1t/4CR9A/oz7UxqbitkvLUhmj8lR+EalGHygfMC/7z//h/cZekdyFqzvJx+52qiTPfwwYT7gbLiVhpbsSyDiLCVTGV/eDK2H8+jkzsx5doHig9g/XWLEOcoXEOBeyVp5fvd+1bt08I6SHZ9Tp/CjIiLLlNzKTL17r07VqfW4usamCvRGcbEaVwoyKtozfHbVbvGRKyWyH6lHvyQoeMRLffinwaaUBi7pLzipLw+rc1346lXwBfTH4mS/uPJ3mUXcAxGVtAjCknNoslZv/KUD++g81/hmiNwLv20yRhh4+7uADEZNgPbDpJ8Za36CaTtPyeNA2knfCoR4tG6MPMeMf4617FeQRoHi7iUMGCpgbsAWhXM2+rxz/lrT/lQMVmD9ptIka8YB/AnjHBRkpbdCk1Q1X1pGEV1cbQe8pmfVQy1P9rdN2QUk8gbd0NSYuqe/v9BfnU3+dYEKScdhD/Z9whIG1iETN0izemJtwN9aM9MYHSA6A3mG4ltVmFY046RHl4cxH1uuvr4i38DZoG7K2x8Xx9ctUKDBzS3x2MgRqacRdvrWiBq1a0wFMrQLgyhtE5GIlp5T1zQos8fDQiD7CKpCzCtg++xu0LaZdc4WSY/R5DADVh0Fr8HOWt2VS3RvkSEUVKvspXrGm5mMka/E4zYHiLBwhv8eAoaPk6l2fldwCNa75qLWgJZG1Z/mKxcfl6vh33itXV+HGx3OmraD8npy/95BVorhkvdvcqyIJzcYaWmEpnMPb/itGWD52IYuXcZW6JmyUjQtpoi8aFgZ7NwKBw8UADiDxwAERoqOnuw7L6oFi3KPoNrGN1giq3u51VRQ8avK10lyANHEgDzAwEJiCoEQfZw4dC22ob21rLg9C8RnSII18C8jiGS8AQztjzqW9vqgKTrOtfhDxDRyjrNlZ5jrRmhqmcn9nXD8eak1iJS7BW9LmI5kvYBC672jZVFfvpyc0BGRB0njPwKMrRztmMNp0WXCz5c80F0DfNpNhHLYi1mcpf0GEZwQlH7CU18R3dZtU5DFa2+eBuLoODu7sMDu7gMjj4XV0GB3dzGRwYl8GBNViVYdLEzHg0OBj+ZPe3vWLtTbyYDOMEDfBWV1eSBS+cZRczfP15dI075XXeDycUZ/4MPYRMAXIztCpQUdWxlYjViEFE8bKXZIVaOZvi4XTe2gFcWHJtbqtwXpkFDPaMoi28B7rknQkC9cLfe5KnYUh9TPmefAmBq9mFIjy83WrqQQK6mF5iOjm52YlfJuSXEXUkitcu39zBUKJh0aSuCF/SKo+pFIyw1Qq7fnLXgc9XnM/xzLAToQVW0g/pOLKfXycD55Fn2zqea1peXhutWf3SsM9rpsokqri3SjpYJophFEWPziUo3qwSuoewQ8jqFlB3WrNJU46ZTd1MM5MB5+HxtJuFZ3F1AkgXNaFQzu2uX5dj3bPOjmb2GE5w1a0/6V9zMdbr5jMGZaLn9ynush3kN9G1GlI+s8dqenrKc4xa0LN0Ohp8jmA8MkoKLS3oO1SgsAQfCSjqDEHfMKQ232RAPSSsuzJGjQ+/tfLThQs9r1l8/VLHRpWOTXXHBnbHhtWOTea9vyOjEuLVR2LZ/DI2X6+X4T05BtGr6l0q2gRmI2RYi4xqr1e+lFyPVXyJByCkSPEgzCfpZDp5gCoKLK2+Fh0rAzse98+hh/bQZTQ4C3yRl+vm7EoqqrnblHL2x2XFzVgDHmVOMenFAtEITzKv+wzEvCw9j9gf/Qkp9sRkqW7H6QIQ4YYdh6TxcDadB9Zo9aUrtBxuV7FtB0HTC6NmRZTmFdh79KR5AFtiQbtY961mj04cOFK5DhDJMS6uaZZmXIP7zGlVoZpYwzYsovuNu9RpCdXIea2e6zPSiKG+utVCc51ErUmIMb+tagaWTDA6r5+EWVBSABjHHX0bW+OHv7BURPifOPpoeyHEPUsWUWbr7zdJnavN+qUXovJO1Rp4vtasGlmwzyeauZPPgLoXBZEGlexdkFS8A0l4g1fItzGdJXOb+REqpbWy2RBozgNbtnWEFvZ3MeIxo/MOlSKyJF4+FsHvvcya6KqSCC4Ubxv0IRDKilOpJZmbhWLhbclbfsKM0SNdSAetROTTE/6ZcdLn9HJHT1Ss0zDKJdRYi4BVmV7YFdGofUZhCZZqucn/s/Ih1YmaYiAgMU4k2m+HfkdnpDRtum065dfdsdTVfvbEfpPgfY+9Cyb228zae8AYOVvwN9yCuCPeyZVgWb0XZhpx/0lkR/06E5Z+CUEiYLQ+JqNrL0RreZ4SaUlRq97B031Upzaa9lI5Mn7qxfXqnXx1dbDZHCl2DtUBuUhtjQBIZrPw5qaJwfRwjFHG1HJAuJRkGPNktqdnkaVhJRvs3Tpqcda1h39Kazq3a9T4voqqSnIEYui0SBnFZGVdLG5SmjUqOKwOIkpfIqLI+UOKhIiyarz2qUdA5MtJil5X5hZ5BDQp8Lp6z7a8BWpL3Zvbx7SmF+zIUM7PPg1F2afBPRRhUeBhmCa76fRkFPGrgVgkEREujC0NNRbJQQOUIeUhq87TIjwhUcBbv+sisHD5Skab6t7OD/7jPwg35jA5TEhl66kUy7rxb2W0UH3v5yeeLNrCLNl04vZJ1sbxRSn5sH7m9Ipv6642dN42Bq80R6r+mxvrFj7asizesM2cFy+Fz0BGsW4t0ZpaEnF5vY13M0h830tYYKs/xR9T7PeOgd4iC3hOQOt3g1CwVfNdHYCsX8WoybacdQBFwVLAGnQD/cJ59riZlrNCydSZi8BYHPgp/vVu7wteBvxwEfUOAnnet/Os3/gWsR6/DQSZFLSnWWxS5IWUmw1DGVEKbmCPErJxm49uKxeqptwP4/Gp+Q4NnU48MjwbAC2pprMFq1sCz3kpDbUjnDRN8nAYtSk4OLw7soxnzIHxxV4AwXcrINrjyDVwDF9+J/+F1fPyOwxd1UBvDzSufjAthu0XD2Qqwly24aCML3oPZIib9l4EJDYurtufUiBI1w8a0hAfw/bAqM/Vt2hd3HuA5lu4/axsHKmNgz3xFQ0Cbcfojt/O0Uqk14UycJYaTFZ7D45PRmFyDqk0Ci+x9QJne8ZB3DDWjw5Q5VkhM7fnMBkivzgVHI9WYBDvdFYKBCTvUvGw2p5/t8Z1fLfGo4PVvESK9d0a/YR0HDtrx/x0UQolyh1Vcb79SvivpDYXlq7BXFFx6K97Bm7CICj7DjA63m4klST7evzPrk2/Zcz6H7OueD4nCJexcGFdHh0+ah7849HR4xb+RDvXh107QwBvA3gbVF8dHsC7wyP89OgQfraOHh+2dLayvX/skgxurIw6vnaY+YfJmu2VLE21awl0uIWctfbuJvY6zFD+Gmq5K87fAFMGoxUhJ0l+kCXZLDaCWyjZvVTkFXYPL37ZDiYUhbGD0T/RCkbsXTeN2zGcLsuZ9Cgbx2Q8v9CG5Q6fMZNhfRy8x7fJKZGdBo9PGa3UFZnHkAPYFWSuO0zufgCGyWb4kNOjmOd2BuCqCFBXxmvD2CO0e72Ad68jV/Jn71gpolHWdetKOZZK+FZGIxl4/aowvf8zfscrQoVi9j8c69M0TX5ELL9RtBeh94DkNXPmE/vGYdYPENcmxS56AU/Vhf1hPS9pTVk0ngD5RBh5Z6I+pGTl2qA7UrY/lWtTSfOdwOa60oXr3UhStgCUwQPq/z5O4E0spN9UiExnOrn+TEHovBTZTfJjI0gU6H5VVOpDGsaKkCOE8rEqVyZNCZRwEI6oroFACeHjBSwLIL/eUNqWTeqkJUtBOi5fDZwt1Jie1mhMj0sa04uKxvREa0yvbI3pXlVjuiM1ptOWOLgU+1VzIhU0iEIqwc6pD0/zekl4mt268DQfF4enOZ/38s3mBDZp1BtKvvXmZuhrJtEH1nko+VmF7yKG5jWkqd+w37Yo/gkV5fKUvh9F4lWP8TnKDCg/AA0vEJGcy5obHuTXpsYVh7atYHw2+woi6X1sziiiE6pRtpPV1UQHVjJhL+UVZTmqwvsmlTvRMvNs30wDhlmk2E0cjccOtdPfNN+I1xj6bb86fedoG9QcbVKQkxg2IHALKN+FuQq1RMELZPAmqpoDRtSFbVIE1bODUVLsFFmUqp0jMaFCqflV5dCFJgfZjnrKYph8/3uKEw5EaNnnOOpbi0C5salRBYuaH0aog+2dgYxVzdKW7xw3fmVXAMRH4ZmGrpSb8L0cirmfZJRdwjcNS0rg++h/bzM3KjW+75RX1Gv6rYsA1ATf2SaVVGgNdlPn36p2lIP94CzM29YZHiMp5zFEyFJ02SR1xaS2wz9vCqVMXMpb6A5z5lypJerCousBy4gw3gVGtnqXoxTSSP8bUj9KfhsPZ6+kg+6SGeHi63VCvHRQ84Exg9IJKkLD05Dja4o9kEt/Rd2Yd85rymDo/op72joxSutNZcnoqLgVy3a0/OrmRfkKARbVjn+3OcIhqPa9fJNz4tzkHDs3Obk8Ls3cJVEzwPObF2JgLfABXoYsaDSZAwyakmt6Jc6BD/coehJz0AGCsGDRP4KUxwwm+zrYFfQv7lm+LALLxhPDxwoo1pSivnbp4ZLS34sVdMS7tIIJqoPh5mbXji0oDH38lXlIkVO9zArZFQ6XVJgLILaKRhpeVNl769PzlT55hUqwnhyNf2AWAq6CNvM59UtyQ1i6b55uFYeqwYe63IJF3RZUoawYf1ddFjj8oyrNK/ASXHEIRimLgaiBXs1cDoLuTuu4SFspywzIBbY/AZEB2/ADN4H2TuKo+XPgfGPG9IqW3J8tpMnMNTOtZRPb8kXarhGWGlzTLT4FtacRLA9cXM59JDUR1l7XdyrhIGpZ4NWlLhXYgr10HDVY+cl7sI/cK0Xzw+XeOIn64bTOo83kwyA+uNA7HOfArms6KkmHrhExDsEoXjgCFWktB46FfMWlnOz7xjtfxgNafvz+lxBP9X0w2kLPXTPk/pbG9Apgs3bCHP7X75xcA0+bs3sLmlsWcYKe6iNIAiJEl6odun/txEknzkmnR8D1HQzBiKofNJDswGx1sKpOmnTSrENBrzuIcN4hREtsdidD2VaGKwRKM6YopZ1L+U+G3gDFWSCVLAEGCxcj0+iDIMQoNiDZ5uSzmoYjaHLEalLgeOnH1PJnLY4Jt1gQfPExnIJJXz6EGPGEk/knKejgL/YgHqLv7XSrt9Zstw/+cZgcPbr5tul73357c/CPb49aj769+e7lzUrv5rvezcvezeHJ4YBdueFvyz88gZSDsP3bq/Yvx0fyB7qtHz2CVwdwkHe2e48eH7bX/vTdyyPUP/31IrL6iOMNDZE33sCFXfA5MIrHMXaNBhbVrjSK8GOasAMxjSD+xTGEDgzsketnaU5uzRRgMCD7X/iLc4llw2xi0dOCUmmaAvuS758VP/d1F50sYgNqNPidbrWUi1Z2sH5EEMAxXuCgRjBFzAtyb02A4VdeVCniYpADOvpyaSVb/nitpV3TYwSCDB/HCseZTcV0aWUAhMwuMisXaTlr2ErItX/UT5wCGrAvBP5uDwmNj9IB1lm2adz6A3T5C+D46q2jaQGaAQSWA0AOJCzWyvFGI0D1ZRQWTcsm2QEE7PMHEhSamvA6GZDRMzeHZFccB4LLz50+jDB0YUEKJ5SH5xRkObu56eVNhfxoF5khgk8yfzhDNXBCJqRAIfKfYME1g2bQKqV0MIVcuzuB/CHUj5b6sQ0iW0OCfOO5DwtkruEoYJVF36mR3I4QAcp4UB6JaS8pwT4P8ErbSRli1sfdI99ORyN8NA0gFHuO6Cvb3G4D0Zlleuz6iCWNMFPwYxs143EyjdCgEDYtwShMnewr1nCVx4aLmkI7vkwmqn1uoYMtXeZKc4p8IG3Q1VX5Amj66mq5cUtLJASYZqBsx0eqnJsbXPgpNrmv0qCaFfJlhbUK21MuDDzPQokO4Qdq6cCMMYVqCURUbQ6xHkmSbm4G7BMbtPxmDJOGg9fygOGlp5oGtnQDoaZ00tSoXrzg4zZ8RKtFV28PQ2YCR+bNljU6BbrGFMLqDAbMrVYvsHo3kwgPQm3UeVSqurwWqJBOtZBqpu2ampzSRs2+WgM3NyP10y/NsZdoCwt3NSh39W/smLQ//07Eezt8mWiqapPxlnTFjkt7L4cl8c9RuwBRJIG9Vtlofv64FzQkTHvguRm+Ve/lNaq39o/DgaTDsXwlPbo9OWapTD6Pri/hmAy8kZtuFi8QevsgPnpYKniCOachZ6aUGETpIh7GxJ/xECyzuswN0xaTV2qILvQxuoa5x5i6TtZAd9URpgMLGbhf0I8semZ542zVIGuSpxwjryQO8kp2N5ssCQowRi15W0opC4WMWimmroQFerGzp47EsMs9sKUKauOohPnJ2dqnWTxoOCAGnPYVItGgcBvCQkBJvEFW0WTSV2quaHK/ihhpqEHQRMsq+6cF7P2v1CdvE5fWhcgaBg79X6ntPfl8NmxInFs7WcER+ZdasKdx85fU+yvhnMucXN1RtdLfea3vWRKv27q/oaHnoi38B7eKhHxaZ4E0YnTDC6Ido2xa5DRNolscJCK7/Raq1g2B36Lvc831m8KIiiRGVKEwopKSEg0hdZA354jbCKC5MDRdDvTQvZpBKByC+sznjm8ccIwYOdPJL/viOrYQdDOKXa9O0qxAyxQ0f4gQdrvehy7phCdsaPzLhYUBUndjJDjaeLAWTuI1OcOwSSMfm9YD9jnpp4Poy+e3CGaSJnBeoYkdNnse1EQoR8SQcOQlHf4xb5E03uTbLAPszpdTkYxfzm60GKrcjjFewFxUA5JHOiC5gaVTtWD5ZDFaP/yInNySV20m66KbLDT9IqMe9MvB0aRUECzkFpO+gg2M4jFowMh3GrCysijny+xwMmmEyaBRZNeN8DSMk05w5+sy0mMp1XL5uozQFWFoR9fNhX6RcUtqm2eI3C3qswi9SuZkgCuX4xLvQLoT4/3kBNWsX4Sh3nvdF+vrti9nXXvQbPUgPmptly4+R8hl4q0mL5kp4bNA0X26qQxUNHlIYe0hJmn+p0YVbmnOfuq8ycJTslGyaNU7zijHH+R80oBZJfFE+EvdY+RG4qy2qwmllApUBOYuDBQRdgktbqMd8Nzd3MAP1AXSD9Lu3twUfice0B9gBeMkukc9aNl2V98TrniBYvLsvipJ7I0GCEMLGV8laeci6hf94X6VjrC6m16nc3iZ7Nog0907CEuIZkh0/g6XsPk9nZoEbSK5en154Q5iCqaqFezzBRATPRCK6YmP9COjfnVU5Eh7F/W03s4dyTp8g/OTmgwo1pn+z6W297Y98yF1Ve14gDTGQAKjgWvEMyFz29u9j/mUcKJSduiaj5S/w1F6CSfiotdsHfh9hFrOVPlwNZYV1lOxTMXdyuzJSOqu/3BdwcVdS0zmjHRhmX5LKDqMrFGHlWrr/lQoFSWBF4zD6kCw2sEjilYrk3Eibm6aGQF2YlAcDdhpcOO0u5EMCSORndDdyAIpQJlRgndGNngn9J/wza69RMX5YgszxPLUqFtqQbyPxikvhw+bzcyJ51aLgysOsiN0pkLgxeNjibo6YRTFY8JQtPg+U7hpK45xLKDnqr0hmiGo3SNLaqOgTQxmJOD9EfKNdYXqQDyzg/AIJPQ5QaZg/oO+GFX511tAmDADrIkUHa+Wo2cpYRVtHJjLrtvxV1tiBmxDerlDnWckJA94Sx6MnxjvvG8mTBuSXm+S1ZzJJe9AJf7NCHViZcPS2dxYluaMtIiWgjYxOHONYRXCuAa5jiwoKqb5riqIlK0KZJuPh2oGW2OKONwBWtEaPNKgJglpGHARFfTs6HoBLq/RnWQUuG9mAHEjA4gb6VuH0lbK4LTG3YTIVcRuA/uR+51rGYdIlFMZNLO01+u+ZNh34KRFYTHLBiBkqwaBV9pUjsMkHgJ7y86baKToZbxxYwLhLRkNNxaIVzDFBGUmw31Re0C8ApErR/+xOgxgTTm0bbGC/8XxL1QcMVhUtqDYd5qkOgvHDWFtLAI5i3qphj3BoLlAmSdRQvGEztJLMulsMliNu//CUXrqOkyNOKi0jADjSMrKRNns14RCB/Prhp2X2BECAyELMtv4R/oG9Holny9srOsERialbA6kJ8495G+V9LnhbL/l9IWS7ow3yN8sdsS8i5skkuMlg+qqGLQtzULzAWULUr73L5l4KcMIGkDX1KfSPxrvips+RSa3wFB4Um63tJos
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diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part005 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part005
index 76c78b5..6562a8b 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part005
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part005
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part006 b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part006
index a7303fd..d51106d 100644
--- a/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part006
+++ b/plugins/data-analytics/assets/datascience-artifact-widget.html.gz.b64.part006
@@ -1 +1 @@
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
diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part001 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part001
index 738917c..48a72e9 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part001
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part001
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part002 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part002
index 5d48f04..850a229 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part002
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part002
@@ -1 +1 @@
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
diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part003 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part003
index 0916fca..673e217 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part003
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part003
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part004 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part004
index 0a7cc3f..58b8c72 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part004
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part004
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part005 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part005
index 354bb47..8423b13 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part005
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part005
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part006 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part006
index 46cd419..3d1bbb6 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part006
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part006
@@ -1 +1 @@
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
diff --git a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part007 b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part007
index cecff86..2c57eec 100644
--- a/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part007
+++ b/plugins/data-analytics/assets/datascience-chart-widget.html.gz.b64.part007
@@ -1 +1 @@
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
diff --git a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part002 b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part002
index 38dccf3..5cda22f 100644
--- a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part002
+++ b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part002
@@ -1 +1 @@
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NaYYvzcsSpuwXkPewTQ13SJF8LmDJwAHm/PDsK1G8FO3ZOMvpxbMTzMv0WTnd3biwBmQ9TpcYofrI0bpne3HJ0zjpfDsUaWexd3PzF585e6V+7tKVi6eu1a+cmjt/9qoo0MRKFHdGpFzHcT4ze3527pq5ympEW4Lc/tdn+QbFd/hAEw7s3Nwuy1VSgnleXj2rK817oDXSiSITRFB0dW6v2lEX2SdTtEtVDbQ3nwLtctAo8bmxUEEQUR6qrbgYmaFSiRh3Zyp3xNRxIL6GE5QUUYZXB3IPXL3+QjRmeYuMb0r3CQcR6kmMYlatdex1rZSn932eNCHcn++WNOSxij22BOAvv8Fw9eqXvFhfVA4dWCzDUgP5TyjPBKCHoc84bUUZnKtBXPb0WdxCo3M2FVxYkppGjzQ8zRwkQ7ykALElAqKKmD0SMXviQkWjA7amOQ3YbYypt4mmUyUuy2qdfiZIkB57yIfnpHfv8l/sZE8nnCSZkDPpIKRQwpXSy4C5GWvSwmsJNtgaim4ss0yZgbhPHady4ucJZR674qKzp09dJ4qnTmCzEigzJHwNgTRJ3pjvWqVSlTcwhrSqzecY6W6VKK8Wv9Nghs0TqFkcutmcccbhn0O10OmGsBz4ixboEOpXEZBhuLxaYeUq8G/1kNSAGzM1aKJKDc/RmmHS8Cy68sCcE1KvLKNeZwH1A856eDkVYyhZE1Fwzr3tNCvlao3bdOFwxnAgVetPUhXc/AqHrMkJ64knrLKG/fPaCQR2MbJiGsnQa1kVfpKi2t8xxnGjoer/SgYDW6UmbjTY3PX0hRPRIuAnLhVhjZbbDbGKPIubNprOcPhpTTxwd15fKrwJtq9X+C8UcMSZBPCzIFYcIefShie6cgaEhsBdB82FTj+0hs4FGooHYVSwBBzERuCG6iHU4KOq1DDPB5Varaav7i0yOBEHAT08CEgpsW6Bkfaq6a1BGxfuEVt9cVIn+9RLoB76GMiDCL1oeV7n6wdcycQDhXMDuTcjoPYKKNdsJ+vBmowWmSKuwIvmzRsinTHRvQexE9pzGKAAwCjD5NpwDYNF96q1cNXxREnBL4QWqRy2lNlxVVmz4i/jcZW34C6UF/EwKoK/6tH/idRchvN0jDg5ofHKUkmdzYi1FGEYKPiDTfTwiUDJbiwDdlur7q21dsfz118JumHU27i9eae0cBHk7BrI1rDf0Fem+MHCfsKaOlZdjEff0/rzXE+fx+euXpqrSV21kkD9lSixNwG7uuBv4HuYLmypNagHTUrGdWjh5ZsbN7vji4daxMCUjJvd+vjiQUofVzNeHj94d/xgmVfQGl9JbIxcPmA7ojSIhY0gYO48cMdE+2GSh2tul/4mR/RsoBwDsRYQ3IFJ/fV/vKkqJxx8C3I1vqgeZGlSjPlugxUUdODGx6cnZJd65i5hLqf82BRs1e4qC53FQ+/h6V5X4YElM0ClQwqCnEhBEGFL1tqykC1ZwrwgiWdZbgGLKRSoC9Ot5HKe0DkbyMwBe55YaLR4QwBNuxTO0SgVbiQFLIUsa08cnHm5vNWvVO8u3Fy8eZNR7s2b5f+ZIMYgMG66eMbL+SAjFKCOmRlLY42cCaI4AxxunR+ARqwI+4mUFnc/0lq+k5TzTqTY6lb/hEmEZsu6pMwfXX7JsxI5k+5MjbtLJaoVpC3OcyGbKmqZmsB72vYwKHsXlB/gIlbDB6lyiYNcsoAzWUsEYQmPH+1Ol50xuhwscW9WmEld2CjzGBDnjIn1L2sZhwhggfpc2nm1wbJa7NDQSCZtjS7jUvwRyO1N2haFAIWbFoit+ttLmnt+eYT3RleclTbfjnFzpgqarMShjFnlYoC6MSCsI2HhQTnbRotAwdIIJa7PkC0lm+KgEhV1qP6Gh+dwlV4xWKK4G0PIkmOG6GEmiCQGNNluqOlN1NVmOiUQrCnsVd/Rkhqvqk9ZB0G1L50wVUsugaUSnqKXlmIKXqJjdX1vjYySNBep5AmBvJuVvBMtZolx4TGtx7difmLLNmO1hB+FiSJpCakxn9YlY/7JFQ52z9/lCIjI1Feor0+n2FENj58BacuMLTUcC739WtAYkxroGWPH7gqjc7yCCGgSKX6QenrC+L4m7UomYHVX7XX55Ar7HkoxMt7RWvw2O3YvdqBCab0a1U8YfC3Ne2jc1/LQYRM6X5m2YEJbMKHV+LnkAXehtZh8PBhCGuGKwMKP2BM0HwyoNfw4g4qAlhmyazKhgBGSp62Fxf3sjboyTfVHME14ov+ZTRMJM7I0Q5r1Wc0d3+3CvZ612aQuBhKdm+h1aZbFcxDupX1LnLzL00zbWsfLCT4vhPg0DeiT3yPLPm3yhbJV1qvRqDTVK+RoV4TATDZeRnQMlEzQcAw1cwdnpVuYxNg96VItvoGrVJf0m9L+ABq0khQoMEA0ZaDBMZ7uirjhmWRQBjIo89vxxMnsSNOu3jPmTf3w4432ZLwRjDcyjveMMt6ROILsYGYPqYuJjPgxy8A+S3mGWaah5pIQLpR1xEsUWuEb8ynFhNwEZCxLFj8sb+tkF7V7xpZdmgS3yCS09H4z3NNRLjHrFufOxF24AwZmd4MMWWfSvrAaGsye/QR77i34KfbcgDRkWtyqJ6iI8FF0hxpLN/LZr+cE7H61BTX5o16L/Yr5O73114fRqloZbZmbMrSkN5RJmA0gzEYhCpvTKSx3ao2r3zPOa1nMq8fmNTmtoTqtbFYpqVxoVkM5q3xSw+SkliFJaPA6jj3fy0azB9X0J/n6NFIfQXFIg8+Cngber47I988G4kpuIrZ1qvHbVnYIlDiyUg5eYJJOwJ+vwt5M5gmxl5oeHkkyWCxnobc4UxPYpM03CVc5aFLFkSDFp131slDY1EXK+CskWVaBMQHDQgUsYl/JPA6Ikn7h6wBy9RLwS7mEktH18s4SuQqFd2J4ETYTn0ao51bmEyB+4oA32PvWyfYhkF2l1SSOawIBFc9+xHHLSepZDSWKGblC+HHLTI0O1QAOlHFNiSBvR93Q75w15LT9ZZsbysc5+p4v9EkufEbUDbSq5z/FBI9ZB6D5wEHjYQo8UrWUKvA76+iq56o2d6oh4FUntgEs4c3oCUMh9BgoCwGVGAudc9txuL0SKK5OKe5OKfLWPAz0pV9QqN3KPMUs2ZhSTOcWCYNavpQ4yxNQoZ5w+c+1DeaunN/mSx/l/PrQ7c7ZcwhqBk0fvZI1LYuekHW5wDYdM9D4ZjnuOP4qST5ZABnK9Xm8G6NQYb5+6zHZPHmUS65blKP1EjF7pjIoybJ9LZXMAmPbX+Np3h1Pv0NbcMmSPFpUTlAHnJbuR09w3GrQZRd3aKNYYmuOeDdRP7/4Q5qctiLthJH2Nzdx4b9mvsU0GWhgpxeTZzAaEaQulNHNBXMvx+032BmOy+60lW680EgQox36DXmfhvtWbF1UETdg1Yx7q/iGjF1fLRCiXZA97eA0oO0UXs9n3UmdT/ZEvx7rsWbYzqRvSXhLxo7IaiuB3znNW6sEsEEpzTVC305Zn8yn7CfktcA43QIcVO+p6pR0SBVuAYHofbF0soTFHDusVI5Y4xIV1p9YR6r0j97wcvImFUcfN3TTcEl28xCl1dW0k6Yrszs5g3p54vYhXoOd6sf9nKI1OpGhH646ty22OCxWQ9P/MwjFOsSiOQ8gFpzAKaIXrLBoMMZwuRFKhEdMBym29OSxTFK6nEAuY2hIG4hkdT3Eh6sADppD5+hBWJkCFE2UhMUSxy+KJNcb0hIawKt2z5zP9MW7ErSSuAC85jR02Z+P38oMwffjR1WS9StpMY/nPGvasA/twQ7S8H00aFTAi5S4jLzgjAvJJAUSO9pWALEEZSRkDa+OhCWk97Oh9qpi215ANgG4+4iJYQkntStcOt1myrlWlKWcNF/3lrhpddwYrwfCDSyFUsRvsAeIPVrVq+Ra0FTVJFZpVc/YILWZ62JWXJmCdSZro6yVUdsshsk9S8zeif1KWMKEFkfykoUeWS2mKpW3XPYaAJbWGBqqo9E52oJaqxFbarf0Nxg3Gsq7CgetcuyQ9nSAcYHsLlK2uCR4sPcMM+xvLVUGneaxRY7N4PZ+GrnZshe3rGKJO2ACiBJVMmmmFnWd4BSGQZoB2Xm5HTVhyy+dbvtRc6VtB06pmghDoLx7l9eR58Qbc+BPMoqLePIY6OdKDAq/h9rnupxl41uxBcgQHAMlGMEB6Dtff/v3oZK1GRWtxhc2+865hQqPvsesDMFDt5pCk8IjSKg/bS1wRnVxdq5+9dS5s/XZuWtnz5+9MiY42MVTL2gZi0wGCw9PQeXxqckjTx45fvjYkeNjlvz+JJWJRKGJMevI1FNHnjr25NRTRymLTNUZgMNHJqaOTx0+cuzY4eNHp44fP+ZMwdZgSo1r4guQhXGlf9dPXZg/q/WYUlArRBrmY4b+UM1n3BZueKXxp6amDh9+cmri8LHjR488+eTR4xPHcZ5Ffjr7yVJVDk0FNaHUmjx+5MixJ48cmXjy8JMTTx09Onls8ihU42+SFUPpy544Q+BKeS9WXJW7H7wMj0gJV3d5jwzT+CkBdzLNFHT1xLG3wBIXJsiKSvt59y4F71TTWNA2t+KRXEvHLZGepojs+KyI1nEXhHGaGtdpN0VKj403Hu3VgqO16g4HsbBo8aGjqSNf/orVGHIADQHl2jK5JsANgofOrkee2FHKTK8Xhy3VOIGklZC9w6lsWS5/ZROyUx/F3aipgTVnEzYhtV6Zd4pbXObWdthrjsIQ1Bk+yUTJXo1jTMxUWem1dhMHolKLmwHt27exintMpQW8UwMrjneFcS/LFNdl7FErSbF6Z8atSZBqFxpIZvQHyCsxl4rz8vyCY5SRMSxg1LwmjIZskWKXqMrxiDifUijwVK8gBZoIkB2jLixqp2chdEG7T2ukibKRS5SNJFE2NKL0U0TpY5cahPoEcTYKEWdDkpYEMxCKmUjzIEmyw6e6aKVWHkMFnRWLHx37CVLmnkdr+sQ3xMRrp8p90USdUfZGkrLx+bNvIOo5etu8AeRzGb5tMNcnChnHdM7FjLnEsUW9ti4teOmcVEtAho0Pj5FK1YwqvqrG0iF7Xq2TWZU96lYrQDFlxdRrbuh0RHvsOz+YrbSoJqXJVlq5rVBZ2QCO9TKOKwPvG0MtsxeThwy0y5CKrFgO649phI3pDOw3QLMgB0YJnRbfNLMFZX6Ggxae2O2lhfJW1F9ckhtYugoXkLQqCetcrrBG1WoSFFnelheVlxDZUCKttjTykzTJa5ZqwkqPfkb6ZberKcgSy7d6GVgmm1C+NBdrXR9U7QqzsEKvC5FyZF6NiZ7LDnp65gyIEf/X3/0NqA7yjB1UiDGLNTCTHOOSZf3Xq39l2bBvbwGBsFJVpnToA13avySPAp5vJF1XBPKlcvkl7qmplPVePdA9P4gX63T6n3owMIlyex4khlEdlpsFJlDuHRJEgW8H0H9zbmPiyERvjj8XlIaaxsZPoAoXQStoRk2hSCNdd1sjJyr7btG/EZLObEXD5vN4Y0nWoqkMvHhkjgWmuS5HDfIX4c958RlMhR8DC8rbip/4RNZM4j1FhHBtfB6Po7DoYlVLGCP5hV9tBpFXST4GEZvSAhP5y+LsLVRVZ/48QZy+oZp1ij1No6L6ZVx8q9riKlnFm6nBoMnsuBoXJ6bW4K9pK4Ai2OuuVKostBB/rnahhx49oD5aUjtOlckN8SLnjw5p7kLA3AUFk4SGXeBzIonPib3HJx5qb9huSMZSnydktgBtpwxkqeLQ4zjkF4yky0jaw8vfFDGWh0KeJ5BXLkaMfBhlHYW8g9x3NfZEMb0mjBIyI7daKWvYDAU2QzJmZtjkw9IA0pspFqenzFDIdLgx6xbUu5FJkiZcDk2Pe4rSsqTH8uj4DPYejz5g7EavGDUaMCg9e0xbJQzBs2EH5OiEsJmRGY//ObyZ5y16bJv4OiywF3L6Y6gZsZrPQM3zvV1SxO7GcyE9nnPQq+sDe2Woz0f1LNR//rOan1Pp8ZyF/rw4eH5OpUdyDWp+/bOdnxvp8VyEXj1TdH5upEf1SoMJGrZZpFmPFMkmRMkGKJccNrbwxwX+4xYO+RT/4WPODf7j6yhv3uixH8/gjxf4j3P44zz/8Sz+uM5/nMUfz/Mf1/DHi/zHRfzxdf7jFfzxDP2IBab5IPahMx+QrHfblc4aXPREFkVu80mRcmuepxwTKadEivSJc0GkSJcOaDETra9jJPaulCJvEXDPJV8/HduVbiNuNyiDnxtLqFS8HUP1MCF0O7GznVD64pE+NkLmbOfo5FR9NfbgsTkfJzeSHn+aqUzpjYXnHD5+RIV2RUlOQXNSmdI7Bs+ZOnpMhXZbSU5B66QyRc5LPGdShXVNJqYgvZLIEukXybPQyvLRw1NT2qy8wN3tNGK3IW2Xe9uRDlkQ3Z7t+fEslTGp0zyqduvcPE9L9eqsniN941CyLR2/1BFoGx+nqgR1ClPXugrh9TDFXe9JYm3wBAm5TQle6LTiYV2mtKanDf88dmE17LSPask3KNnvhp4dE+KL88xrj/RARC53lH6FofR54zTPRMxdvEQieb/p+Ldc6bKHiqvNtihlMsbQBiY0E5AiSvTRFY9I+joCxws/deFcCnmidC+FCcvQ4Smp8okU6R0GhdRltxnEyJ0PRZLs1iwmNUC67DqBhrfr5NqGXRqJtKtUmC6VpI+jUPWRI+mnJ1Olh6ZQv9nwud8DzVFr6+zt9crz82N4VZdwctEw2wzW1mGLcbsUbkE56yGPGnrW+CTer1dmphcmJhdv3mzenVqYGD+8WIXf40cxoUR+N/RKEwPqaJWXihS8ebMG/26Vt5Sm+v0lvPPPqQ5Z6ncG5GB1pjqj27SthTqaFLQuvVzeIiz2y0sJ65swcU4ExbYs2TsdK31uk7RQeqm0KGwAmYEj3jOw0ypp1xLU/JWVLl6Ei6zKwsHxxZyRVqVBC27gS3jtDKXLWz1+xnW3VO1Xl05kjfClRv8alY/61eQ4G27K4FWrvXBq/CV7/M7E+FMHDy3iDPX72HwSTN3NwXEMoz4uYMSHVbAmcaHizohbN7I/5L/IB5CrINuBvVizUmHmqvIe+tDLFcDUU/b4yqnxc4tbx/vj6s8j8HNy/PiimnYY0o4/ZTdOPZNKVn5OTvXvTvD/xg3/iP/urvD/xg3/iP+q5UMZEzSg84ivkfrO53oMRRsUeFAMQh6M7B83RhRZcIdAtmY9j3ZApZcrN2+ub53lPL5+2V0O/VZgYwiN/l3KQg5fPy2CEvSrB8ulMdyPcJOCHc5oTITv28m4d1q1TtNxgMQ//hR1fYoorLp1tK8m3RVlYITJMiKJjxf4N7J1YK9jyHVxZ8MtBLcb6yWUJ5GrEMcAztNcmDpyDKnjyPHFuzxl8vDRJ59aXJg6JlImFjB3YSIuCrOuFJuYqI5PTI1PPYXlcdJYAxNUBGpOQtXF6jhLGn8KQE1O0Uo/jEu+epcyjhyDjMlJY7EJVmZKzZwUrOL4ImjUMA2wAVrL5ilA45GlhZs3uzdvXqUlOFPruJ7biTo4MRP9MZZk3xZJpRJnwKLSE6VM/uJKzMPOh4IIyli4MaJohSIwCjkoOaNMgXIUikYoNaGoh7IbynUoQqI8iNIiCqcoaaIcimIvyrAg4TLh/9mersQwtaWLnPHQywvLpxcXXkYPH1vHx/rlQ8haWAaRyZ3Fg5jmxWmnxp997vmLc5fHr11HJrV6a63jrY+HPSi6NXWMILgqhN6p8euQM0E5PZ4j+RvkPEk5ZZ4DyZDJOd/UJOVFLO8y0ghsWDdwag/UnrhRrcychBm9Bvwe01+szuCfi+zPGfhzjQpQ5rNaZmWhNrZIe99V3P6q2Mpz86wH4wcXZ6CpA+Uqo0lKIPB3lV8cwIvlKkDUyl00lbuYLnfDVO5GutwZU7kzvByOcAF+jy8mevusqdazQ/ZWJONXDWEziLCQTcugraTwT+LAh5D7opPxoHKkilyYfT+G6jT/jnE+WrztmQM3a1WiBhBm4Juyc37l4M3xm7XFJ6pKGuDpa0qZxfjrzfFFAFE9yFOA9thquDGfoMvagf9R/pP/+ZUnDh46OfNyfWnrbv9/jS8e/FpcgOQS+QuoeGLs2GRfySfU3qwNVaP6BHbmBdaZSmXh5a8+XaneJAcntbET0ze7XystHqwAUGNG9QmY5VLtYKla/VqlcnNhgVbe5NjhPtQY+B3ZbYX35uY4Jh8kTLEUwlSVJu52g+HqZWoV6h470v8a/cIfU0ePAkYj3EGhGG6l1z8/uEVpUZAULo+poyRK0m5xhMQG2pOQZCbYPhN/JSiAEZAoRqmJmGuEYmLV9TB2pD9d3Xqyn0y8ayo2OfZkfzoj51h/uiCMo/1KqiimT2VVOJJR4XBWhcMZFTK7NJVR4Wj/bqp8xVz0WL96Nyvvyf7daUa+s2ISRp384esdqsicKZ4DAg58pw7ND0cV04bpr4LKl64+U8Vl0Mf2J5k8hP2cVLs5ExPnGdYNVaDExYPqDTTwBN9PtPSp/smTbGPRkg/3T8LGgjDP9RKCAGz3xOJe5Cxu5mSNeAayoplyddcsYFx2RNSIE5AFYCuHUOrle9ow7SW5IQLaYEi7eZCwCTLusbHJI5QzF/KbSV3hJIlw3x1WbRx3XY96dFlJwd9tN/5NA6VdGXOuSgYWBpFzd8Vudx2YQBelSsrBMzz6fYH9loevlHiKS2Av4zBoLm6JFNwQKeWZeUWyI4JivPNcKn3r8BSN9lnMAZ0ZbW1LJ0+i1etZTKpjEh5kp2seYbLiRVHzSayJFV8RFVECuJ2ueIwh+CVe8chhUbHDKx7BiOhX0hWfYnLraV7x2BF6VYPSNKt3DEWRzXS9yanjVHGFVzx+TA6yyaseP1ZNWONeaWivTtVHuIoxC0+g445arTZLDpKkGa48C+hAr2EnPQuZ1+BzA1SGF+BzHj7X4fM8fF6Ez9dRlUDlDvM9mBX4exY+1+BzET6vwOc2Knvw6cDnSshUh4s94/3Hsz3l/qMT22+cJnfIY7olDRkwklddZhFDVmP8K7dhi90z4OXsGD4/FP5EqjXfs8PQXmYvhXvxTxFMAwGcbnDLa3FyrVqwi7NG1ZLd5/7X2asIuuo920sM5ILT7V5bRWv3xIA6NQy1wdJEqBCsfLqxID2a0XXsYjxq2W82pYqzyvhyjBVs2C12S11x2XuCLnqanrE8qW1Ow3fnNs+5yBLpMTA3S7986erstdnraEh/bnZu9tqL0g6S9wtNcYWFlWyiqrRwUozghPRqkG5SL6XMI7keIPe/8SATjZEBHu+LBIPWeLKDPE3zYRtpy4JZWvrcrX1PBNehF52h79dhuvljfz6saUufnqRJIE/GB68YdZwCvfA04TSAHodGehof0rSljE/k4QPSYEwzKp22DrjsAb/idIDT8bUkDZ4nR7jB54sM+TmIToYsUSXDubPnT+WT4dNZZMhbGECGplIjk+HTJjJ8eg/IsNux28LrBB/Y558Qb4QJQrwYtUN3vY2+R/PoUCI/QWexM4jYG8Q+5gdCklu11pGNcLcQSoKcnmrWNPf0aeYo7nEUK+FS5Zya/Ep13NsW2zzIyyXbM/BJmehLHWMHY5vcmtbUmOr9D32M8+Q/0SZcPumB2e1EFV5oLO4eN1ZXKK+XQ3l6FzQyhHHVle5zYmy6PbeLRkgm0urpaUOSzwtJ8mEs4RyFPh1AQG6NRUilGedf0W88f5BVUlmdKKC+vCOPi9x7HV680RwoEoG1gG8sKJBdtCAA5LDIUGeRLYqRJmhWEkAr3esxq6VwMo9+yp2zTPJfC82TMX4lpFwOswjb2IOYL3I3O4qzJPkkeJZdeVdaVZG/LzSQ0L59wjkYf2G1j7nMg0FMx8Ph6ZymxJsNJLtSnCdpY1KkcYJqxb+JmNivPrf836cQOXtxkRgJhpsxjIa9EmJOr4wDS7ZulA8UCSHjLaIoBysJq/OedGHddsmfFkZ1BB4Rrjrkc83itgZWYHstp1ZKjF38FEs3ivMlD2fWfdkLTsEde+k07PDVfUnZmbJeaX6eEZAinr5cliBJembaEG2rqkISYYO2cS+19YpeF2aZkoTLe9JNg9Bb3n0nBV8/n+TrbA8rwNcF114QLltWCzFfT2d9KI96OvMtmxhvWWG8PfopGW+UxWWNTelcFiMH9Kry3Z1xmhQlU05TuRg19faImspATdGedNNATdHeUdP1lJBp377q3nGyTxB0SaGieyut1jbQxQM7JqCvuuwfKzjxrIon9c8EOP/kSLELPVC8jybIZRj9Sb0eLqCai9LEsQxQWEgM+l5A0zGNlj3epgF+VYFWXLnB19lFiESCziEWIjlvRNJ5PkU6rvflIp3Y2KCAOi1KP50kHUVF5t93TzpPK9B2STpGNiiBPyrieTFJPEg5FPyu+2WhH2pBP0hB4GOJMz6RRg2LhOEpZIzvlqI234VQf2UgNTIRXgLKdLwi6xSknlqtFvInNZnivMKHEDyTC61pQyVNCFYoMK6WSYTObRtPkBM0Ge1Kb/66YUdkETw/W8LkL5m+zLsiH2LmviipFklxIMGGn4td8hnDLvnlI6fP5U4pnONk7ZW7JafPZuc8F6buyHCYn4e9cy9pyrB7ikCX+v4Zpwr/IrErxJF30bi+to+Ku+EsImI+d9imqoAoQFbkib34lsqAj7CpqhUf67b6gpcU+Oj5WMaxBZEt9/GPE1DkfmPwOQVzq2Q4q3DFGXCVeTrCr8z9ivLzpHXoCetr9frl+Stn63XriUPSdxuGLYoL1uxmsxJDFESotKHdrTOqZEvPdBDCnXCwurW23Q1nMagwesBRgDIXJfywpKpRaO4ZhOJxLnWsvMLnZn/2UbTgZSppUEgHcaYOJG3x79Nqb+MYTES//WGpKnY/RDdYaWRGMTK5h0FhqPBskoFSyPEUCb7gmWgu66ar6Az1TDPU29MZKgVsPJlXSbnzMeLyPpvalURAyxRiFeKYURCH73RDYhT6kSXdySehz+MT1yGh38qCfjEJfZZfZBW+7t8IxI021OIXX4p5mbzzXPe7bmh44rb0cm2rvBXn43ulHj1l6AlvQXIY0ej8b494W1QtcH7LRDB2hy4vBmMkjSnYeJTsquTKuYx3uyZ6l1D6ksnKhl4Gr6SONWT08MLElLBLJNpax1Awt6v92hNLCYJIUnlvV+KWRh9eUfrwdProVQtagXCJQg2xLoda+FRrBJpg7dU3aFYYS6RGiSPStz0UfG4nSeIsjxg/EkHUnuAU0Y1WiCLKX0aCcDiK4nE+SnLA1lRiYE3SERR92zNigH9eSlKDcAVWzKTHJH6IJ7bQXQohpTvWiV3rsI4vuMLt4OYiH4Tibge1Ai4qiXD1Jp87wiMgj/rN2P6VBnrO7I1ZcQvcqSDkRMkcdYl00mckHecaGTUMsUSQ+FD77jDzpIE0r508dBx+7NApasLWo4ivgmJxix9MpAlypKqc6Oh7d5r3wURy1MaITOhKEsOXROi+0elOGlqh2//bUrJVyV34Pnmll+XmP+COVqW371W3SyFyWPxmdHRISS5I1JQywRKqLNkOWl0eewQjHFMhpym8das1D560JmE41EBVBzp+khyBAgDCiKidDlCjOKJnzsB5DKkty7ntLEfcQQ76uSnR83pzvi0KKMcoIupVHP+xu96GWUAHiTJwSZm5+eDyJcWnB80eTdN7zGsVLxC7eixTLHkWg0F6ekT+TEZ/Wh0eD6JaVZNLlozCoSLsCYwucZCHSk44aeYTw6eQEX+Ze7lc9jvrblsNYobTL/yuM3e9WHuG5pV7wobhVViiIAt0XFkqLfKeMuazZFnlLa+/VF1Uglwxz14UuRoWkfQ4yZ3fM9I8zc3snVA3sz8dcnP2jn0LDfSO4GIDdOP3wzws1fLqtHU07ev7TEsL0ESkU0rHtA+UkCYiVIjqXl74qOdpMpAMARCO6ZUCBxgRqyGZ6kpHDpzrMRU0SDjDN0Q1WRivU8x2zvO4L9VxVFVKB8mST4vZAgNGiaMJkk2FyHLZcdtq0JIjVaCZI+wpiO6DIaoLP80slMRWPO7kWpCuU4n5KmeNhxPO/cnGhQxceqL8gchQxhMOOyksSoUQHFVlGyVY/BTIgEV3nEH+S+2VnrtxrZSAR414NbvdMmS4CKByoFKC7BKPZHf3rkXFCaKbjE+g0kqhqd7cmxct+7gd6kKPQjpJ54pxQ6d7oiElmLexOcFDCX0HKj0ceKTtkUU61BfQY6NdEZJWNMAhW1rf5QF032L+fAcNLKwnnKdo0bt5ZOKUl1weT/cALzBTA8JjMeeAXgLbDYFvoZxAOy+JNMlAiyIgEHe8jW5z0StyCT2flLSQsQCBRwldUmMFYdTxuHuK034RQA9HwP3XwlcQk6ZjeYnOe7yo8zwrI07T8XkOD6fIckSFXjXp679V1wkCHYgKNIotnByB1ljjLPaoJ+OxMhe95Dy8QZEsScwh39WNzGjnchI2lFjSzAYYsb1R1cNSkv9xfuji9MiFNAseyShvg23Fag2xGc9RN6QwLXwBo0vx/UnZuc9817Hm5syCM2twjgvOl5l7sl7lMqFvg/BYryrO3iFvTsuLl0QoV1r6/JALmpESxBP9w7NTEEYPoRJqzY1DrZVFTBNG1fqNBzFU/pWPqYZBd4MqH1AyqpohaORKQ6EXmgddgghEfAfdg6dmiB/b8Auvm2MYqU5y9SAhMx04G/CQAtrNIMKcNMD0FiYWBVj8rvqtzsC07u1fRW2sLzIE96Tr0e60cJMuhLAsF6SR4oK0OpYIytdMITQLDWacjoIV11Rpovq5wI/04L7HfVngwXDi6zLug9scK3Ez1KOFM0HQ1SIhUj90L7JBHNw0FUgKN1LXlHiQAT84DPRnA9zvoDKPYq6KXOrW53J9Q9sOk0pJj1SD25dW8PcBctLG1QwZoDwVkVxf9lFVj1+C6Fsow97hwr9KzGF6deE2tYHGQ+UvHjpO0GJRWi9T8NgFFpIirOkZi3GocW+BnqOENcST2ADUJjRkcmx6QtU1Bz1F5OppAs+a+H/AcLWst8lGlRrUosCkmDaKGmQMDhjokSS1M78ARBbuk5m+cex73BP4rvAeDcI79ybH8F4tivhejHgDvVNnTNjSBZfVhjH0sOlc8yIwHKRnVfJokfinhX9pKfFU0lsvQzs7oEW8t6rpcDItWl9qeO6IBJpGFS1luiCANeLzuQgfJ0JWrW2dFPHKpLwbaNyvVU11vTdU17O7OaCXspOB6GSf82dDD8W9FSr90WIcsRUkSBaMpwVjhVXVqom4kwsiQzoJF8sKdbZqohEuFXuxFKSKOxjnO1bEghP7VWYk9gZLOefSF0Yi8B4SIMZqoGc4oF8S0dV4JAUW0zoVRCS5UPSgGVIH4mxqDPuoEPTt3mPSAdcbKZEf2EZOo6gDu0QiIjpLNSm1sq5Eia4QofQGbeQY+YhUFnEHgPEdlX3dU8Rb1cF8oAkULSFQtMYw2BMJFAp1uLsalVtgVKnuJ4YpQjPljAwwwZ+HiDG6RcfYE5dxsFyD5DtYbfrtRkKyGUhkck5j6qKLocAEvpsPnglB6jmAsgXE+tiYElSAymmxjPSFE9STLaYPGwLltJvH2rzObeGMML0EzAOGYZjbGUDs8rFn/F605Pme1I4V6giS8qyrC6x6j18qwD4kJ6J7BqdJPJ34UKwhmeI6qD1JLD5y/NFLOAneXVewfKVXk17r2dtlPyC39VV+li9/binHgS4e2nhdvBqMF0EglXov7zbM4zowk4iv0BDKuIR89ArbY0o9L8tyPSU3PqnS+xPgeUXXubb33XK9nF6xzOQp45VeUtUckUp6RipxM+kj0unDreuee+PFybWUlcBx7jiSvyR3yl7dNI4DgU4MW/v3mcLTa/HpRYB6+s52dRJepGmtEmuLHRkp13VKKXFv1xdzqkEI7E63Srfq+C2+p49z08epdzwexrPP/RTdGLNWYdS3x6wV9E4Mn1X4rMMHTSC78Akw1A58XPj00PUIfCLcKeDTgo8PnzZ8GvCpw2cWPvPw2YDPHHzW4HMJPsvwcQC1d7AMBhiG75fh+1X4PAefC/A5BZ9b8LkBnxfgcx4+17GN+pj1PMKFei/C36/D5xn4rACMc/D3Wfichc81+FyEzyvwuQ2fJuS/BH878LkCn9PYB/hsYl3Mh88qfM4A3HX4i65VuvAJfBgzfFyf3QP1fP0e6CLzs8S9LtlR2vkS2jzIHzfie1bjBXbsiIn5YMpyvxTIy+nYUBt/YADNvsxG1kDmZHSNFZsCsLhwkkTo9rbLifCE1D8pnx1Sx2fUzH8UCfKR1111V/DCSFNUPK6uJ6OzatftVatciRlUlDgm0gxgoYdOEDhNbgMrf/KIh/E4KHmGX+TH99CB5CUxquhGJ2Eoqim9eGqElByDkSEsQQ1AS8H6CWElIk6aUfHRvB5sxChGo3mZx7hJIheA6ofSZLstYw36qUxx/JwIr8VDWSr38gD5RNypy4bNgKKPztRYCBacgwOThlhUAkIdqeyycU/BU3nIh5kSx7+B0/XbPQcESbbP8DAvCl4ZUBai6fJYcjjUnTnNckIgEFAi5gP4i+ih7nJBbSIDcgKtubATvi/qci+rJw+44xcQIXd6wIFoqTzYlYHauMIZxgpnmNguOVxBlh7K7BECiQN5+sZJUii0UWTOefs+H2KdRx6MF1Kljs1WVc8gcuNVSvmyc+xsR1uYhILE+Fu17pq7Thynq0gNCkiqpAQjVmQ65t9HBCOSoxbo0sAIKuFyRqjacCUNoJgJFM/CZxmiWNxXPI1KEGP2bBixK+CLcGWIYWWsSbcaUMDXCvS1sM15KGsMIpHWECTS4INgoYo97FSEzZlIw8NDoEh2pi/O9s5T0Mv/hX1q4syNcWZRiU/7WHwUxZhY2gQots23KMa4l0RUjcdmo5cHfL7LdFzCXhoocbfpYH9GRjbmY+C37ZVyNbHOfZc1yHHA7h9CU3uhqb0w2Z6KNPb+oed4TbQvKVEgU5YSsMgdk7SXCYv1VSVgpwweqgsaN0xmXLFVKEkIM1KumNEMPRcwOry/XiFPcsthJZY/VFBAbGQRpkTzEm8hln0nWEbnXnLaYmMx/tBANReLkY57aD/PoZcMzKxcMMa3jQaTO0VLzrMMVVwo5XjfYoZ1Y6xVaeCbnIuMN02JByWrQfolwopipnd+fvbMEK8bQpRZbqdBNhWQ80aQzBkgI7SqblLK524S6I9bKU5NW1P862FuCAVfj3D7KPh6dNo6yr8em7aO8a9PTltP8q/Hp63jhMcF2eqiYm6qnoUY7SRwEJZcFyV8NsF/9UtL/MIiE0vNQFj4SLOQnKKspAGpqwpSKdjSEBPVypiodXWirlxIQbxd1C4zXnRiGsXRHhoDVvi9HDJ4aIY/5hBkIOJraTtEnJx83HRAyeIP0CSEqnE55ptkK0bZURA7xpIOrxgFiCZkvvKgSXan60fAghIO0HqJjV/3hDXIx1VfQdR64If+st9OIEokGxAlszii5G9p7I6hZsi7p8xiRplQf3yySr4+ZbOPDruiCRN24449Wux62M82Oa2INw6grcBZYYX5CbrIilTRW99RxL2y8ZFdPrpM2DIOVR9pxkD5OPvKzmFrbMS/5Q7BRnz20sLASLoK1Dnb84faRsoZ3CloKQbkw+1M3cgM0kuAnBoCZpDRTVeBOX9hqG56GSB7CsgXhoLoZkAsKxCfvzocMnsZMCMF5uzVS2gYgm5rh4B8Ca0BzcDDNPAhAM9ldLmlQx2yu2vZ3fUT3eWxGocAHmV0ua1Cvtw7MgTIdhKkesYXv0kvYcjMEm+uoTd3bIjmGsWbQ7CFxAkhMiythuH69KFDCxjMj/hgf3FJF+R3zXJZvx4Bz60rOL146vQQKK2jEUDTabsdF1KqxfDbsZfFbM6q/G72zJWhyGc2gyLnk0CHIZL5TCLJe2cmL4qZVf4h7oI5phPtxBetm6Z0Ub4aH2xJP8BRLIUeKGcWR5QyBzOVsiK3LpW3wv4SNWWuS9qk9VVrAk82Q+tpa3LquLGggcS9vSduFsz1EZA3/LOhEMQzFLh1CII4k8s1+Pubsx6M0qXdu9QQLQxwj3CmVdzzQaE3m6LhvfKXDf/MpTBnUsOykXeuNxLySLgchL9y/ZHgLxZs98Tl+JqCwbOTQ1HehpG/wT+XFJj47mZktZi4Ul118Y6PdfYGm7c2wr3E47LK1OniOfdg6fZwjkpWvL2lJaNHmN0hwFFicMwJn/FDnW8qK08huba7+/NLvv+Yzy9DcX4p7hl7Ca9k/Ewz4QEEH0hJN+tz9hz3qCWTzuFYnUrsKYOfe4qzcDPUGSsJc8YqwTd8wHcgDRtzZ70V/LmJRdgpnPwyxEGr5iZ74EGryRFarVYr04l64Cw7bg+hlrmnIOMZ7J1WkmCyDmFDjW6cXlqkmnWVfYDFTR+W+jCM7G7pjDedQWjl4QiNx38f7dxcVB5pPo3Ttamsb+a+fFgM39kLDLPgHyPeRHDv3aNhVPX8vXc3EZdbSbRmrILz+irYNKyCqwqsq5udht8uOkXG2ShGpl3W0Gi3O7yTe0akz6lnSSJM37B0eiGmU+bhQXFWscAYq2LkgkacWCt+66hm0UtIU96oOH/aKkWjITuK8bFn+L6g8fB2e1hUP5eHanxdvjgylTLyZC/UR8CWF7X3kjBPKYg65W0Ouy7Zgy0O65ZG5Guev+HtCt4NdRLpqe1uuEZB9LJ29piZvqCM5Dos1EfL/kZfij3q254R13ll2MaD3mGGPXgPxgWKzWTswq0MMYf7aU2+hVQctqZF6jiUW3UkRFOYyuEQbRBj5T0bIdfk/dIwLfDPdXXR42PCR0KPB/T3iqrCkY8cm3Vp12SokAbvgrAq1xQe9tIxNs4M2VNHPOOTznutUD51lDG7vIVwkTkgcGv8OU3SXZYwEhN2XwnrMPnIP8+8qsywpJtLbTbQXqqHL6WEwRRdY0J6g6ef0AInlZMG9eqT+7L25L5X1QhZoxy/HlMOM4832oDo5HOAG9LDmrm04QknZWec7nLgrqPjJtCkS+R0olSdwVJVPTiayzxSUK84KCYxCFAKAHSQ1EKvFJWENS5FlLXINXJfM64sApFPY4M8MTU4W+lXVecOeJq9LFAY1vFC6QSzUONOKUroroye+XSlnZppmIyktvo6UaI9YGwxjCk4E60Ff5HAx0RU5wKLNCZoQBk6IqMveb72TsijZcVKOYaH9eml1UY1GZpLmPTFVtKCKawE/Dmw9NHBjVGTPEU3aSXHTJUwdqYdL1k0yGwlWY0H60KSdiufvfC4nAX5i2/kL8ylYUvyFz5dYl7QT8kGmVvHlCvROodoDdXHuVSPLLEX5qDm8xg9UJfUT+qy+itxiTSz8RFIFr9RLX5fMZtlM7S9wlnCC4HwK/JCQIbHc2OAE+t5ne9A9itars598BpGGj/zy5wZjQXVNRbUQguVlsqGWnU0a/WZ6fNY7GI90MMItZLs6bnZ9DGvX8/w/KfS5JhpRTNzp8qcvngvc9njlV5lgVAiOAf7sW5vtn1byFWl5fB2Cb+hBvG86i3+FdpagwTfCtE887keZmhsa4naWChvhUF/USEFwauIfHn2mBUTggVEAF2oLinG1ZdrzFldibzpsarQKu84X2uleAm+EMSvo5YponGF9BnQ/mnv1Mi9FyC9P8/p/YVgoRcgG1qC3/Xy1ubBg/0lUycAoVecbtQO2erirWdATaCLNQLjjHEHXZvFA1fMED6VshwCiccPvENLrEOESrRq2XoFAfZPLBHQqiXL7bf4f7ThYvG0GyvL0gstA0z0OoQo1/MtiX9u5HsykYDXQMBU9ZbwZS585UbQlvYfOszodscSqeyhGWTw12XWAvVqjBen1EVrmicvarX7xAWU3/vjbwZsxCdeUucfFieSLHh3LAVWRk/ES8hcIEoHT6QHsaTyuhHne29n0zSXo8+kPo9fyrnrazxGY22MfTOoJzDWsCzGeS0vrbLAekBbsPRCqe5VwB5AyGGMvB6Qzy2WcII59iozvygoGuF+dcD2arfcsO3AfDLpBhXO03JfqCfEpw2T+ASNXI53jg0SnzZM4hNuOXNJ8SmsPB+LT3N7KT49bxSf5pRIqTBWH/+5rL8poySBFzLHODChBUUGkYL09oobO6zbJ/ylAVEwlID2ET+8mtN2UBApUE57noSWy+QVLVBoReg/DI6mBc276kGXyRxMV4AUJYBOZaX879bYvtOtdf2OI/yPetoBrrY70cWa/CXv01JtwL5WuJH0HliolZ5BlWFnNKI5PErbTLTHlYn4Rbl0tixqrbTt8GLsjJUdIqyAqFhJgJCOwFUdi53gDtsrEU1FlSTYk3lRS3MdptXRaErztw/s0FMrei6o4czauVplLmRLd0vVfrWs8Kh+VQ+ILjqgOlQb4z66eR76UhMCoIk3MDeKmr/p2O224ptJHH4cmGSMKXkw0mBeinir1cRT10aWPuLpxkgplaQ88BFii7Eiv8oYJ3NPpD/apMq5bv58rt5KYKb40gdCWX6lgTopYCeQXTQcnbSE3qK+x4whNBIQEmZCL6r2wH76fHve1fgIDzOBHuNomv5IKcNQij7xpilv7nrKm/lTji4IlCNxF0/AOq4HClTGPpKccfRlUEB5HeLkyTMcNpUzp4+ik/GGJFDF3DJEo0jVu19YTaxG4zuspooJi3wpWqx1fNdA7gDZ6yxB47GHQPlQK+7+Ap4RLOIYeE8cLwxcp0sPpBV+ccAjj1/4b965WPJYzEfIDVaPzsP8rPMwTz8P0w4UtAmQA2OHZZlu45IH1cxJun4sHSanZ6a24NYUDPvBojJj5AaixSPGPoopCk1T1Eg7QChLt29a+2ei9ba7jL4ptSGwYw5skb8BbSjN7CsLx3Fh1tSU46nJZ4KC6pOx4w+saA/uvcEGb4pwnCVOc6E5NArNcYzAVizYk0e80DDFsbvFlnIwKhmv7pHRrdGEngP2hq/g44sapVzxgca+UJMOT+UDsTnf6qBCg9arWr95NS0NzQK1BBOyuL+cJBqyMSmDqqoPIxT3ent5JybOwNvOSpgRUsXLCKAS8a3MrQVua3X42so1psGXB5mw53gaUPc9dHwaLorNbwFWl7+Y3gFXG4gKzFS3Pz23LBamFIRWFCOua7Di86+KdbkHNHXmsyg5JcylfDKOWZR/TVmuKiFp0qTOia5ssDNUFnXIhpDJBQQhRtz3pjiG4HJU0t05PrDvLQr3XRV0mOs16UWo8AFRV1XGA7oyxy6UWsTdxydBtSM7zDiwRxzP8gC6COuGyVsm6QbzaVmNXW/IjK9CE+MU+ER1+7KRi0m80K7HYUoz4pTGgUp7ij/5VKxR4QJn2gROi18aBzDtqQFMRQTTxAQn3k5Ir4rsipo/nBDz2FevqMYn9c26Tn5S1RMM/+BBPO95Osajth9STiPpciZ21oETUM+SpMsLfvYdkKeI0RvG1S982fO1zu47btNRSIQrW7v7gfQNnq5L1lnUxF4i97TL+OSto0QFYCnhw4iBTY1cDH0jxxWLp7pYmcsbuu5V/zZ51deGLsc+p449oU6GebfuoXblFWl8MtK44zlli7riLPvBozEgOvBsMISVRsA7snszjdgQQ2x1oDqgxzP1vCc2c6qmDH+4/iJlowGivC7Jh8z7Kree8jXXILQ/ahnCHj6VIc1Nt8QxA6oG5soz6MEnjlo7LalHLhLu2DRGgoHQvXiNm2g90t1w5ZiWCH9UGylHWxvJCw2TR1ofNgPVI+0+ZXKYycGGQmjC9YzR61a9YHv1/PbqentJL0cpKmBRdJg6RFoIGhPgptlgHmxgB9UPjZi1DjpCi7fGiQw3CrnhM1ILJLHhMMfSDe2FnqWgLb0GdMfcV5yVNmrA/oaHsVcqnq4BM6GgVK+TM4Z6vWR0HNdIr0kDObaK0qLZzElV+kqn6CKAxafpWqAzWN1ofZ05NIPJYjoThrbpWstREIB+394sqW3wddfSVzNMWNtlPjNaVTZ9KT+6MokdlrM1saF4NNd4HNbYqMm4OuoqjzEtt+scJAos8hdBrarAZgqdGj7z9vA9Q6iJFYizAqSNDW2hcV9xhgBOsU+OjsRm2/e7Tkm65mBHMXiyItrO8G9teiErXHfropq2Tyn4bGjOrDeEM+uNOAJIXIUtUUnhQtlsLSpF5Mamulzbl3TSOAqbb30u2Hwrj8032N+9Z/atPGavtJrJ8vfUAhJdxipP/tG1+qOQw3QnzNBMYaFMeHvfM4ksYUdb8GRyAR0HLqaOJ/M3EuHwLsdWlt2P562aXRvcomTn5y0j7UiEHYKII5E6LBH1SGTfegOt1jaYRa64FxA96Gta1DrdHPjGonttxXtWfY/lhI+DiKGZfCLOecCpPsxywgLHKiMQcaweDDheL0B8YSHia+URWUu7YrKZVbeudtt0WdV7NBRyTX19DwJN4eO4wIXtnd+38GBPcbg/5ZBOvqqKDK+vkrfoPRm9hZ3n6ZEyvKoWvTXxCpCLfjPWBiq35PgsVr2St/BpYi92GcBC1qjXAUMemZuiN/eK3gmIvemiMmkX0K+M3S5gnc8JuptzT1c6bXsoMzJbT5ChCTYXJy2Mdg4ipcXuibhZSEZEbW67YSKEXgFCkH2l+Y7U+Y4M8x3hfEf8uzrtkMy/RwVooNg5MovhrR4fawc6o0bwZt/Gsg+ftbNkQQqvKKRwzm07j4TFawweWxnpKdSK23b28M3ZbWXkMtDFrh+eRTnOomMnx5tBJagpEclr5LzyhH7TGwcFkZdDvarCScgITmLSGI8DHTsCCSfdpntV3Z+wdi8Tb3+h2CD7VfNUJlRIzX1zCpqXfFzWVK5yLsVPFgrgf9gXywOM0eIwm57nBMnzRMSheNCL9rHmUmOWeFet+a4oYnWmXFDpkHlpDZ28N5rpmAsEwq3F+tUZ4EypLhR5MJlo3GxUmLR6S1RCEaenSy9DhInpNirlONSZbi8HeZ4WBk0RZXqmiEYG/pLfXcEXXlIlmtv2cphJm81elvFmgsoyaCafNrLIoej75WKj7STev2PwuNGNVE00NMDkNF3FJ0eIj3XV3KVXKaaV81hYB/MTMMKqVSk/21FAMUq4ohqcsVhjo3PjUdbC7jbXQmMdyC4y4q2pmmIBxjck3wvqyPf4O1AZ5KKODM/VrR9OK3N0OfjvNkm97Gh4Q9K6o3I93ysofQzgBZkcSbmXTDAkwRl6cdhRhs2IbDP4wIflDI+GSj2i0kCnUo+oNNCpdFM9NWKBKh6fXF1SGq0+wkWbITMbo50n5Oi0aJxTSZDsiuqeDy3iPm+79OeKhTzKOdfYE0y4MNxL31LE7IoeZRGzUoya+LkNM33Uo8QMijlv9Y1+XKWdUS9+PchPNbXjO1WjU8mQD8UbaijxSFID8bSBlMVAyumBpMdhHobpqqWpcnN7bte8JuF47UDCnEP38JL0apl3Zp3tn1J1LJTr9E4/uTZhY1WVEdx1Z+RNDddOtrqSZirFuQn8ypH2028QYuhx5q4ZSazKMpNb2SmDElvO4xFl7SzlpR6uUxbLNEoosZBXjvN0U0DBrfaATYlmKMhrpG/YL5GltszT9u0zymvF00B7y/+dSUejnYGhfFVCMs/hsHTUYXSUJqIOm92YglQznz0n5+LdUClpXbNDtJu+194sTkwZ06hr+eps7vLkJVM+Uvw/FJeQdKcRX0x5x61rTMMFKV9nFbZ6eO501tt26GTdJGXdALIdXd6iRuwtGaAp7GqWlvzChr9/QUMqph2xAw+JRBHYWByTmV8nhbyz8m4K28NgvJa4TFrxIw89/24pIZVZvNrFGgtNW+0v6VjXW1bxy06cQNvTi/CDJ7x0Sh9asRdnw/Qfe6v1VO0gvX2vQY8Aryzi08v0bnsSWp8Y47eUcTioMg8HJa3+J+kqlLs3Y/fjJNl43Dyb3mho0c1kSDWcIeSdm5F6DRtRHClmNx5gGIUIuhtHzB1y2Hyo+TeCeDPMLu9K1X6BqzuV+lQpMBkIMOfhR1RE1hsYHFB//6FEUBaElAz5lcgmE8YoKaKO1O1hvLKjNWxJzFm9LRjDfi2yV2BaEvqA00buGD9bubHksdVTPIdXTEn+yVDXym8Xeku++Qz+64VGwKhARHQvJS6/JQTYEzpRN7QajrVst9tOk11921ZcUx2eSEVLtih5x8I6XWOulmas0K2oKbSVYCR4WubCiNdeX29vItsPV92u/oyDV4adifyuiCjArp4Rv6BWX0HycKoaspi96K4xRmCGRBtzAjIM8lg46FYeClmRkRGZbKEgOkfRSBWc5miehZ6WSlDDOa1N4wE3Zy0hjkaOLbONfJ3JHCX1WCFJUbpn1kTgPaWwXk5Th4lMuYMyJA5DUCHtDl6OSH/m11uYWNS9kEQctTiiTORhwZWeOOzhRdXXfvvoNSJgGeCPiZC/iOPewuSiOMehvwyT0zpi08eJhbsWtzgAMqGQT6y+wpUmNRwWaV5be1o3enrTIgigr955MNLZA/fOSZsIQUhc+/HM6sMQ72m1CwdPGcQF+84QypDsgKJrtBzcMikUY841KUdDSjGJ75Hzda7M+hmql65GZdVmF1EgHxRwSJQDA408VCCF5jwJL+eGyPWT0XBSU9bRpmwUD+M6/y8Q/myMK3krXmW0o6EeXeL06PxVXOX06A5HpKlqHn7EIrbKfoW2Vw53y2r7sEs7Z9mZrz0PuVCvv79nB/ALxyGGgYMIeGBnJvHy17cRYAz4xM7Pdt57+E0Ze7yBSf/88FXr4fd3fvbwtYffL+3nr27RAixR9a2dN6HYOwUq00vcRO2PHr6688HOewVqd9FV8Ah19xMyJRLdCrcTFhS54JHPXdSUEOn7pXbMCrWc2wD24Xd33t35+cPvUFsOhn5mQ38PmnrX2vmXh995+O2dd+Dnaw9fhXaxVBRQmfd23oRyv+YV/VsuJv7Lw+8//O7D13b+VZSNXJQCWKBu/huja1NK70icdkyksTBynu35Lj+yFv7gWwzWeQFrmf3GP/L3FE+YYpDbrPULvMZt+vkC/7XWZQB4yFK0ugGVInQ7uMHAmN8kJPzcgqG/9vBbO+9YO28Btj6CNMixZq9eknX08sZiHGwuJB7XU8zK2+ZiGFCTEwkg+k2gCha5k2UdS2UxhFKIviMc9MM/tYjG3kRaglIwjx9AK7+FJiUsFtOvUAXWAovAhhVeffjnOx9gGaSb70K59y2WOe5gtDgeZENGbMutAvlarVtd36uLlS7rfQRk+qfWw+9BxZ9Cn567emmO0eUk9QhgfuvhdyEfEPnOw2/qeD1bm+SB925t4OqjoGw4YwkdU10s+2kfJm/eQKDTPAoVLkdpMikZbRc0DQrAQ+/3uBqxDINLiM7TiXPWaMGTj/rYQpY/2dHKJQDJfFwyDw3RQkjlwliTOPTywqnxlxYPiRjdAkAsdS6JMSFqrJ3fAlLes2AGvgU4eg1S354GpvPwmzgvOx8CP8DCQAbfUfcFdDEnIPd3fogLBrgGksk7gHWlTnmrxR3t7ttt8+WtcsGm+ml8Myvn6diQ0GB+Prl7FJW31iRoFPWTYwd4P4d+MzJMwUWokIsM4yMLFzP8+BYUfcPa+WfY1t5Fcv7Fw+9h1negdwQFe4hHV/OyXZB80KictcywILxbJAnOU/x8zVilr55Er4ulr5b4g3gAyd4zqjFIYyRBr2CneIsN4VXqFWHs/Yff1fHzz2y2vo07CpEOg0rnOlTrW7iH7Lxd6tMkW8yBITngUMznMT1Ej0HsQEhumSkcP95epUgu9v0xAN1PM3Q/XRDdQAMfAP8dNC5I/oYynL7FxQg5UDEU5pDEiOA0Sh9105kLl58MplCp3D/JWMXsUBWPput41lNS1jPfNYwr7l933hI9Bbp5d+efcbf4Bjr58kA9cVbc2/3SUlZ7eNY9amu0wGEHk+2Va91oJb89oqAmPnUZfnDv7HwIuX/58M/hJ7UmgOW0R1LjKI39GoXFh99i1PIqLKRfIwEIfTFJYeWt3oJomG997Ec/3VSKVjw/rHeiduiut526vwLEIgmXiQGDZ4JRKJE3dPVD6PtHQNbI/ZEKmm7P7fqBxlPTnhWm1e3jI9RCsC5mxe4a0BFSCeHS0i/h6EiGfCur6Nu8oODxWAY4PGyECR6vPkdP9ySNAuvhN4HylNVqAsZ8qklwUhxKgysZavPwNHp3MupndIdbZhbuAVOR4P+oOLbxsfvV2PPhrUpSDY1y1dCupoZ2h1JDu26nJ2L5cX3Ob8Ou8Ol7zU/fC3KU0Ia9GQ6sZlA/BbIHVU3pnkUq7oniSZKrpnXSX8tuNgOy6pSaJkWtVpRM+vvF1i5Bl7PET1WLFOmqyohpcblYR6Sy/KeqGKIGp2GRa4WoqGnpUiWkGoHttRxd86MacbpU8HRlzlJuGlWdLqW/qQV1NQ71NTWXq22kmVlK8N2BChojqi+Rdvai7cHK+/2PreZ/vL75H68DG2ptf3in/R+vey7+k6mFjQEZ3YbV5Ldtz6R2DYKL0lkGjN3rU4Mbz9ebZP2us/2m25m27N//2P70x8Ch7LVP32vbQRM63HAD11I412ekFm1/z79tNbY/3Nz+3Vp6nJqawbHBNYxCao/g1kkM7XWrj02toY6vuTCtOf1WujqE2rJ70I9YLZFk3fmP10NkWyQhc5XDwp5aDaD0ti1peig9xAheaBgcvBtCJozIDYZROYyQY21CspHB/U7qF0nIqs5gwaJvtH0v8mwr2vz0x5FnaCIFKFu1GEKRkND+43VYPmTRFasFHJXbH7a3f+cRUonGUn0brDhcs71P3/M69iawM3v7TTvIUAuA45n1grFiaoFK/jRXrgfDsjbFKKntIgpBkrEXUwMGNc+BZagAxjY1wb8xUPCv1+lyjd1UVYVAj/dhF+1wtWY3uhWM3sVCtP6JNSls2+g7vpwiLoa+UicpZkxofRW+PqV5YWDGsfpO6Mp0qDqFNctY84j21jKG0Y97HOaqKsEZVVUJzgyhqjAO5nsoed7/5v2/ufe7e+/f+8W9fxPWWM5GIuP+D3hWx/Y2E3n3fnH/H5idmapOQO4H91+9/8Mc/UXrxZ8BoA+w0r2P739L74eWleqJlluwL0mF6LPrSUoZVLty7z3A70eJTojEZPMivUDDe6La3f82TP8n935l0O6gI7+6/w3oyDdzFDxA1++w/r1f3/+bL76eB+P4BeLeuv831v3vyaErGp8okdT61NK63nfv3wmJr97/0b1f0jL7Js1gUgtMYFtVBBNZui4IWT8A0P8OhX4NE/GxQS00FpEaIkKGUX1y77fW/W9b978D7XwD5hR6fO8DfrGX0hcL1IGCWdpjXFvVIGG5fZtgfGOYaz5Jv7EiyYiJq5JEWqSVYhP/BvyONcr5WIlaBDZIq+0LqXbe+3ucW8Lp+zC8f7v/Q8DkD6z7/8AQM02kee+3yFBgoD/KUUORsu9/4/4PECf3PoZVYFBGh22NKac5kHevohbuEl99PxqotGZAxI0SRwGZsDWkhkpEDWzw3sfWvV8/Xg12S3e+CzIS9wsrNUSMCkGqVg02Jfkd9iL5HTcg4TFTIuL/sPkCDoJcEbDxI7aG7v9NGq1j1v3XoNgnSa0VmA6AuP892Co+hv99AOor8SJE7cecGsIa7nCDtOmGmKj+/s+gl/feg6kH1v2ZKt95M8204L2Z6d8xebEQ/orMp1H5Lzifu+pLetYe+2FBFoOS2+C02ukPkH/8O5HiD4gkX4Vd9RfIUrRDhqHOE0bowa85qj8GpL8f9wJROvJ950jd+N39H8Esf4+68MORbz9HaBozWNKPSOrD5u+/BgnvAf19guJK3r1oRoN7cqaRtT8R00sPRKwAYIjfIOn1Y9yJR7ofvff3oICQKMrFyY+JRH5gZRx3AAn92/0/R1Im7iZ+jnr6kTVwDtaiKRnqXnSA8FDsVMQEBL5/YCU3FYBb4Ka0SJ+0U5PZgacmrdwzCE87g/CGOYMoASX8hs4QPkGurN1F4qqxgE9/Hxj592E4H0J+9vVprPkWBmK4TAWK19T3wrBSt6sjQdoTnRxm/bspnZyw/MG9j+79m9AHP8hVzKnrqJh/eO83XwLF/H0cMGKgqEKerGFQyj/ByYNJBgwBBJzuj+99ApP6rSzl/INs5fyDDOUcpu0XA5RzQ5Hk9S0wOJzK3xBP+Bh34d/c+3XOLW52eYM6ruRql7mIDTpw+kYxVVySbRFVHDb03wDrgI3UoI5Df97/4qrjQEnsRPU3MAW0AXPEwM78CRdh36d5yVHF/x356f1vQ3GEYFTEi7fDlfAsmHuhgg/TmUHKtwaLZPhPYL2+CgTzoyQ0kP2h9K/Yl0/uf+uzuTi+908wWtwGfoV7/i8JzT9C9qt3FzYMyE/onchvoMJHkvf0LcPmUvyaObVtpfD7CDoLIub3Cd7n4V5aHyApj8gP84c3AOtD3F3vpvk0Hj8DZTW9lGmDAPUbz/B+BHIaDoD199e0dX3CdEQY1K501GEbJjSDRqY2PZpaOkzL7wttg9JG10eHaJOo8n2SEdhPk+IpYg2U0qBLmfNA5nlVYz1Y4pn1pD3caFUzq/0is9qorQmju0EtxhfcYX+vlPRkL61Y6DFSllj8sZ6Ouxo7UBtdVYelghs/00S/laWn/0aYNQvqzir4CXT4g12ZMJhIXyjw994fXoHPFD2KKu8peSO5zRm7ZVDb83uiqezzA1V2P1dldzWV3R1KZV+2g+1/XMbI1ZpOi6HHHM/NNXHeDJ0BlbINnAdUzDJvzqm2J9o2Bp+xm7ambNvNwNl+07YcdP8RbL/vucv2l9vKGb7almut+oGdfC5LWYm3sXq5WLfGr007+f5VoDP5+FVJT7x8RUtmw/tWkZx4xbpsNx3PtkAfdFdgpqADjpd1dZ1T1nxlzSsk36l627/tOIE/zF21oLQvkc3zNXc96oIe29v+R6K97lec7joMt2fnKLfdr6zaAKERhSbFNgck02ON1Xevw163236Q1W6+ynppfdnd/qWsa2uV0eax6SQVU8u3RlNNl/2OZVud7X8EBYu2YeCRm11ooaB2dNHudm2rFSDhKb18JXKSyp7TxuBMPpobs6iUrWj77e4QDz0lA0/ia6Q+rPjdR/ScU6D0Z55AKY5zSIyuAycNc4aThcQhNMuiDemYeuwa5DlWIl5Jq7QAAMuOh9aplt1p7EJPNIL/ir1sN1TQo+iBZshY0I8CZ2RVLxMfAAvNt6nTDvoyBFHjl3l3iglIQApQeW+0lTm+nSV72YUmOtu/ZVUYHxteFTndtqMMfaIr9A7PtxCA57Sipj2g8Gh6B/Yi5tGEuKE0jbNs+45BFNMvznJ3gnLW7AK6hLktTYM4M1CDaOdqED1Ng+gNpUHc8ey137+rCebAPsPcx5G3wrwaBrVhPejlNpJSGPIq7ImqAH+j9vYbgbf9M6u3/XFg39HNZsfxi9/bfsNCybb75VcZItw4P/0zu2vBTopMZ/uNMDLoD1DOVIADygagKBZ+w7bCAHCLyE/oF/wZpUS5/opSJsfahX+na6+a9AuZkdAwfv+6E376/99xlq079tr2L5t+z/a2P9Y6naFvFK5p1j7i6mqNpDLy6Z9t/6zbHkoZ6XXDaL3QVR0HTonpflGyWFfM29/asqNd6q37beeLep035yDiaMYQYdOW/+mfOWvbbyAh+jk6jt9oBr//xIEyBhUnHyjTckwAdq/kDGo5X88Rtd+wOv7vP/F89FOpALCtW04T/r1jrfpNjP/6mVzGPUttY1+s9d+/vv2ztvv7N2DxtKGT00n1gvXTBvGjE3WBr+B2Ufyqje83SSztpgON7Y/Dz8UlmnEQ0M7QSMzRb4ZGYrH2dRw+dvUnXmKSR07znt2xkZd6UB9WyegaUE4La7736Z+5MfxR1KAc8H4DtkfYwcKRlaE84M11kJ1+BuKC1bvjB1GeJhSD4XviHmlBHOzbFt/0plWaAsbX9RtrTmckJWjOuQMAOgB7rb39M+Cao2kx8cgZGBBSh9RjEntAMS0mZv18uZnaTakxpqY0JWZjoBLTyFViypoSUx5KiQmdlqcpC6t2r+kMdfuh18i++uA+MUQ112s6q367GQ68/Mit+Kh8u5AaQ3Jz1/lv4dvFH7f8Fqwnf23N6ba3fxomjAzHsUzCwHA8VVyzMBwHenRbq9wTpqKnjPudYPtfmk5SUdHSNSvC8e66sxYGUSdtPahnJa0G10CgDtFXi5Pr8iVVLG0jOK7kqjaC46AkdEZ1+JLSOGSTSmtSCwl55IiGD9oEVePfNFWj7Xa5Rx4WJ2aaBYxZY1NEq6zUFTPGVjgGlP+iaifzrc12023h4RK5Tocdr4dco+nkqCYr7ppJKcmBxTQSvd7udRHeoNXb/mnQdBMN5isisq9QpKXVdDzLXrFWtn/TboHo5DxyFUTMnkjgk81+GCXrc34Aiw03L23ALZJlGQr7iRdUQ123wHI0nBsPbpL2ssfuMHM0/LXdNi5dfTRmrA1xv5IDOYmcx65ViMXCWOO01V2z2xYVdayO09yFNmGETGtHwh1FizCCFfLL6JcpRrAdjEUK/d3+DTA9J3DCPNVBQNgThUEyohC3OOoMsjO8KWk7bcxAJI6iLmRcgcyvwdTAnHvbv2m1HeZLhqXxpFFvRwRaGBTLHVarSG4fbE7wQr3FeDJMegiLy8be455Z0OOMvkuY+pXSOvSupJWOucFOZnKVjkhTOqKhlI6XHBfQousddyLgLw1IzFY9nkmpHqlKBu2DXz05+RVTusfganuieZwF5ABETfc4O34RvoyfGqh8nP1SKB/jZ+huJPKa1vj8anDHcVOqB5VI6h5aWV3vOGNHXCBXlQ4VoarSoabrSsczwEddDC+e0jnUHKlyPMNUDjSrcmBPCqyrZk+TvBxMp7msQfe4anY2OT43hO4haK3IfcdL9mpbUyooAtQXVkXwWtu/g62r5VgcCdOWgzFrgQ5zlAQnWLXboWN0SJkLkukKhup7oDDktztAZYgrX1pn60XWdVzPsXoY3OazuLB4KbJagb/947hD6M2z202ec9+AxTKMw0nJxlF8DRPo2MNGQeV+bNcS0O21NkxXTrcLepw042R3wE2Y2GM1QFKx4Jp0Qt21Om6YcELZcFoY4cobThkYCF96oUQVwRtKI8iCnfRBCUTLuEdxrSALdNPpWBcjVAp0f5RolgiYQlEsi09M75GGEPMd3Fh4v4DKrOuu016xoQtdznsUv5RdKLBbTcFrOGu2BwzAurq82t7+HezzbaYvyJxAzRpVcVBxL6HB1jK0BpFi7wU1BKUDyK6MTaeVBHNrmqKwNlBR2HDzFIVQUxTC4R5prNqBnX6kEfp4FjHcLUWyjkFToCiUuZVSWsLgKh17PXGp4cFAczr3R2f1Xwpn9R17GZIvnjr9Rxf2n4GyoG9nInI6P5URlbiYDpuKA4IY/pTaxZ6J69lN50vqop4vxHRR0QcpnTSVz0BIv+b7VgOFAmUYmqAsjtE5PxvaL3z6lcQwTTacz4XVEHaZ4Bo7bUDOEKfzBWA3nM/Q7keQbTeWP0N2Qs/il49+Qm+EDAUVuKOc0BvB8nIjn88bgdJZsMWF77yzeVF7TyRvAUwc7Iig8rYlylv83GPYs/l5Jc+CvEfyXEF0H1JHEKY15ltMjhZVWHz1QkJ0uhXdL5k/SH6ezz1ob2nyc2so+XkNxOe1MPBvpWx83AHPFHKrZNv45FbLsu/JrLRHT5vdlq+JwlCdDHv8fDH40793/S/+4Tpa7aDU5hcx51EK6gfqTfv3Pwv81Im6gkb1QF1J1s/TUbD1DafpcboUeEHYjTqOteY33dC2OCHbgZ+SefEUPbew4TQ9UUK35vE7DVFz8GNmpK0i5+kKVHGiHoK6zV8UIAWjUY7j0UNP+4v7UID6gM/JATHTFmDLa7qwkLo5p+yBu+z0WCHjM4FMkFx8N1Xfi0cCOe0OeiHAq/rry65eNfIifOX42UjvgU/PjfFpttKlNccgUvtAq2TwEQ0twyM3T4mte97045PmoesAdlDvkz0eRqYv2gIsu+gztennZB1zz2mrCYnQUXzWvIydpue4u7Hqz2xjBQasNTCaWX8mfNdba0d3QAjehVl/DoK8VhC51lrkWW2QvYF5t/18234Oa49M+jk0tg2JXgFBuZbfgJ6M+KZ5zoGB2WHWU+Vb4l3z7/++7Xfdtj+g3KiPAfjgRCuW4w3/HEDl9kVfA/A6jGkZWzU8Bkg1pGkLlwaetp/J1RZ8TVvwh3WJRIftycNpx3OG84ek18jRFvKqZWoLWZUemSOkpgsrAJ2beMxlQRA4vvCK9EvPXfa/3MfpK87yqm1tGt0isbwh/CIxNCaeLisITnpH0rMMDpL8DA9J/i5dJMFEZhd8fM6Rst8HhM7t0E8oGaxF8xMBXfmAv06Lax/aKwEOk70SWPa9W5HHk/hLATtYXnV7fooKWKvI+0Jl8CTVsLuM7man4ZOas/0z/MaXDWTD1u40+cse3OabmlYEf3zDA2siipK8YivBv6wPuF0FTeapwAV0cS03Wqfu41++vAFvhHNQHTlh8h6xNuCvSIpniH+j1PXA77hdh5wugADEH9j32Grt2cvbP+PjcHpseqDT3B2Ct+b5G7QmnC4g2V8WY2YB0JYju/1K5DpfXE1Q9d3xBncH5VjMW08jzy8WzJ7bAGyYdMEBQOV1ThLA7rXBgS0X8Y3lZVU3e8f6HDzTOANbYNe1m2Id691mLpdaseKG1zBRD8l2eK0xffUzfOMr0YCmPx+PN+KBrTswjO33fOPQDMgcQqss3EgCaY9drTzNdk9laTSdBveUdQdkWGCPu1AoM6ADE4eRAnTHG12ZzIBNJd1gZDUyEyHCVRZiRLrKGnSVRDD8vfePRWB511T/WCPrkhfads8Z5CAr3i4fjXss6kSMeqbTZaztof1lvVHcX1bsh5FNX4F+5LrPesPsPmt5sG1XrrbZ1rTN9lDa5h/e3nlz572dN+HvOzvvaTrdzruQ9auddx5+z9p5i76+m6N/YpH//OHOO4VBGBTSnX9CCA+/WxhGSjsdCsKeqKoPX9t5Dz7v/ucPNW1155+g1fd2fmkhVh5+Fz7fydFPWSEA868czBdWTQUqAnqCSfi59fA1a+f9h9/defPhq4QF+PGaqq8oRbVsAclcN1ZkIfvdnXcy2lANxcRcpCzFtAzdKAxpBmbk1YffM1iGJTKzzMMANFFdAQMxrajBREzL14zEdj6C3rxbTLFVaLXIbZkCWiisgLH3iFi/90VViWIcWA9fxWmEUb4DbOG9aQuX6X9944eck/0SaCtLQYqLIqidHzIZDokJKr758JtIlsBlEnLAUE3TA99hmtm9ejVk/xLKlt7ZxND/8O7O+0A1sGIKgP7PH/7hbegDcIf3TdqYqaFHeWO383Pk3tiht3be33nvD++m3D4BL/gWbTLvgXDEB/JuceVr5yPAyQeysoFyHkUX9KYewx2eHMQf3n742h9e/cPb09q9WrrTuW9mBmFqF41kYmZvVS7B01MrQnYSvySe0kAVXKMfwRdlmRXTwoZpUL6tQWkKSw9q0KCZFWjvV7SRfif17iYxu4PVtYJjg7zv/OHdh6/95w/1RzhQ49fQ4luwiN6xDPLiUjI0S5Yel2h/CLWO7bbZ/YfED2GUb+mvc/QODtbvYFl8B8Fl6G0Pv4etCT0P+wLTjv/+cjRNTjSngaIdbNi7wczNqZgqF/PHRPWMzqT0uPz2NX3uzkB9bi5Xn2to+lxjKH2u4wRra+72T5gC1I3EpQJLvwVae97b/tDuRXayaug6Tb8b+rnv++32moueDxN10QmSnfO+P6PaLT9aU9oTEqlaM9VS1IviGuIGJK9GN4oC0OjXfLw9d7s6gIG1zdWYapNXb337J25v+yed7Z9s/yTY/skeOjHoIkjP2/6w3XY9By0coq6zpjs1gDKra9sfrsN8un7Xd8OEY4NxJfHL9RZp3HbXYIm5HTtpgpmYkaQ1JtYzGWOugYqRtsVU8Kd5VIuTdVtMktDTppgyOe1IzYeuh5EVL+hcf2rm0gZjzEQJzRizHa1FxTRMlgRNugq8L1GQmetusOo4fIGFm5vr67BC/KYPlBa6rpfnhbntmi7TBgHkHpi1ynsQYEZttbu5/SEuhnD7J5vQA2isPfguTYOAffBCOwFic63rWl0HVo3dw21g+rMxuLzgujYMEpi9O036A+fF/eSAB2tr/aUapHUqiYdtWgt2gC+oi4J+bMoX6+O663jFsJB7x5WDBd5CHhYe+91WLrWDBGLvxgNyLvC2v74e7cL/cS7wrgu7eTvEbWvkW67cBno24DO0LVWw6EKmkCzC0M679tJg78nNlwYRN6UExyFZTNePOr7nrjlBaO/ShcG1yIO9DQbqIy7tng0bXEgrUOQAvWOyN+KlV0kbHEGyQPIOI8fpdo33VUkNSQNAmW4h3SjRMqxci+Rv2WxKFzJQTVoLujxQC1rL1YLqmhZUH8GGcvutpBGl3fNzg0r6sFmHeVUMms/22+3tt9PRIfV6KcVnYK09s6PcfttJBZTsog0C+Vb+0seFgYGG1qqDUbPSwWCclNGkXlCzmgREmqJJIi5N0SR5esJYEqTjlmMwlpTpSWPJVXv75x46kwV5GHuQE04ys2hGNElePmUxGXW23x7eYhLprPAbLCf5Bqvt2NEX2coO8c374sCukHlxhFul14zG2FUOxhyNDOZ1CWh0F5RZc2/s6lJNJq93ePspmzrGnuKaETowAzYeBda6HXRc4xUOB+Y8lvdW68x0zZo2vHOCbqJBC2jnCZe9hW5uJBevdKum51YFW95+Jwycz8s7q3UW1tHSL06yMDTcC6sc2AkcPH4DOM4NFUqmfi37HXxV5RA178YELgN+V5jBqS2MZAeX0QAV3UXMyGzEBLDJrfse7HWWHeFrqu232k6eRpBzbSKgD2MHRxtJql+MkEAPkK4VRg4Vuf324FCRHosV6UUDreZGDRW5/XY8xqbtdUcJFqly98K2b8jCB7RsNHdLNabpBVcH6gWXcvWCWU0vmP2jXrDHesGyHwWB6/w30AzG/6gWPG614At/AWAQlbksm/uaBoT1rKc0GdDEMxq95iOR9eMmB72fScr6omb7Kyj1N9F9KG0bXQvWXs/2AI97dOj/X3/6k9HFftFNQ1T3tm1JWdx2w4LXADmCfsG2ur77iKIr/tef/svoIr+59wnUjCrzZwHXcfFHyf+LJ/mH7sof5f4/yv25cv9zA+X+5Vy5f16T++eLyf1t9O4E0/DgHx78nwdvPfjrB3/zgL9QoZhceB65YjYzUmq+8eAnD95K1OrEpkbiibde7Z+guf9rPfhLaPPvHrz+4C8e/OOD17NhqOZKAsQzbmvWCzPrKKZGstF3oJ23oLX/m1lLVVSUIVJvMyuJp+l6rb+Aof0lNPb6gzcf/G1mXfZ83Igb7OmbILBAiWpmfeVluhHI3z34CwsG8NcP/urB//fgLasiy2eDFE/gdXhvQG/+gc2XVblKRbJBxI/gdSA/ger/CPj4KQD735mEFnujVqbgJw/+t1W5aK9XM6vFyqKs9iZ1+KdY9aoTZldVVFqlLs7fv2SOUT6s16pdZqmZtUC0T9QQNwDm2eVP8LPn9nWY179+8E+ZIEiyNAIwViFdxM05I4BF9Nfwv9cf/D033Vv1g/BCjLSfCl7Q9r3WhXgxIHf5vzlnCHyt/G025L998GMD5L+i8f9d3lED8qe9AJ06jdgrwDkGnEPDownEA42KT+zfh832/7H3bt1xFMnC6DPzKwptnTnSHlnA7FnsdcSezQEMjAcwBgzM4M9LVKtLrXJ3V8nV1ZIlxmdZlm+AGcyAsQHjC5fxBXy/IF95aPs87Qf7wTMyvFjb2B4Gs76/cCIiL5VZt65qtWzMPmsGqysvEZGRmZERkZGZ3hJ3KayzeOmDcT8zNXkuYsTVARefEUjDZQk1n36p5vT30qh5KPChuKiieL18bMpsQOsEgF/9+3udr9XhY+WrPaR1KgiFVYsIH+5lA5AsgCpoV6RqDXagioBjCVj7EUxfGOvvwc/NsDyh2sBzPlTTGQm+jgrbcq8rG8PdPoICG/mieIOY5at4gsISYVuKJOeOIr3KRzRd3vn7PgOmAApQHOPbEuUQuSQSIez5+96U5Zl7n8KL0AdM+v+fKcsr81JpFaXHKnbN4t4prYbiqYqrU4rieDINx0C0vPR0JZX/dUyFXye3uhJt9dMpFK2IFP9DSmnuXdPKB562JIWF+dMSlBaD9LPtf98h/Wm59Z7UmnHIM+CT3r7wir0XSW4OgDkFkwa99PnFDXnmN0yp+mBiVeFa1CtvpbVtSxzWql71wZSqDyYrstxBmah8o0b8Dulau4wHkxuuODAzgkJJKVizubkw0XyeySikEzRWID0Q11SyFhQvaRybyHOqVRRe1FjBK5wK+eS1sPjz1aq4o5aHRmK+aoprJV/F+vBwXny0kBXS3MluxJ3s5nAno67gKt7jUfguLOmnFbyD1tM66A89xkLyKrvSq7yIyi2kEh78fZipDvi5MMnJ7MY5mVnbuf67D74++PuOPiNGJ9B9zq7qc4Yi+8g82AY5i8IemewYOl8bTYKW7IzmzMTmuunuaE3VDwh5T6RvIYXiHZ2isoSquqmDruMYwczq6lpEQ6EMfxX3ZxxRv85A1CaY23tJODBSIGk7LT5MCaGEHUBgP9C1EjWizfTxwFKVRhxO/Uv6Be55xgNLQ50za/S9tYo9YHWBKjzvge7eZa7tdDG/k6BpNN7BHHbEIztxjMpLlpB2RyYoT39ozOW53OdKj9t0KwG2/TAxhHZPIKT1tBJN+Xf//kEfkWiE9eOPULekgR7vngfAMjSHXgp0ddc4NR54uCXQs3HR2GaIsQWmXSi6Pdog/iqPMlq4dRJLe2gu6QS9ygwe1inQs7jAYlPnKU1c+SrQKQtu17NWvppEJVmJHcaf/mSEusPym5KOKMJ1dHNFs1Gw1/Te0dqoEhztI61LWFP5R7ixkbqvhroKB+xCmv9J8WIooDubjF6OQxuvwXBdGebUIvxn4cqUMRm7LxI3CYSRmxNHxp2iOZvKgQXPmsE8JNlncuxuUraZLOYsn8liYrcwhcX0yj2B5QSKncKsbXwKB3NNz2x1ErO+pc59ioZ9njnLUHKcnBS2Nhu//KU6hdKZ1PnaU5HZH0vGtmBac58SsmzL398NdV4kW3DnnrxkRIUQZ3iSEOKjiXUW/wh3VlMhtDJNFCnBmC2LomC6pcqJ+FmVLIuisxg9cTlR5NmoJU1NaWuGjVpSdxTzj+uG+HMbKahbqPNBW+zAtsZs1vY33axtgmMXYMGEjyWWmA3b/mYbtqlIQB8hMUHQY3dt+1N3bZtyaROjfxv8fAd3pUiu9Yf3boWmWloi0C0ND5tRxcDplzu6pLZ2vtY1mur5ZGrtTiGWuJbbkec0mLS1w4pyjM0AZT+CsttQNu/lAiLvvjDt0cAgw7ox+71Sp+xYKXel3sfVIKk8+tSp8e+JI2NuziNj0JR32YoatB+9IvrOXIat4jhzMNt2cZytAiR0vhZSLdhS1p20fZxIgLaF/HTTLeTx1C3k+doW8vwWHvHiz3GKsMyKVRqznCa38qfVSLiVP61K7I38SRXaEi1awE634h6yHWjsr/78X7G1ixfeKTT2VsasUuQh28Yuv14NP2WLFeKessV0G9+6htlU4dXUiwolO9VLCmWifkEhrpButbHLGYu5oDCUGblEonG06Fb85u/XxhTM8X5tbbyxq5otUlQMsSwHyAKw8vxY40S1cLeeHmu87o009jogKCoYmcsY0WeYxkhjl+cbjb2e39hbTokuxZduhl0/KBoTaJoRCQs6TQE4+/jTzJSkx6LqYFy62b0PwyvNqlUZs8sSEAN7Z576apyqGI5ZGosc/+LEgaaAMdWWbxm+KJvvzvboobMApym4aytcTSDDV0n9CRxDQ3LK9kDNbtKMEAOpSmuBqtkxZsM0x0Gr+viX8li7K5ATXwYNHf7Df8ZhASo6tpUrfjUFlbwlMIwKKpTGm+CKMYxSUCmXA3JkwDjChfwxK1Vz3LHNzLGt8Zi0OwGhU31YQs0yQ1O1SoNQuBI0KR5WW266CJGHK16ffrEFrHmFAmSc8BonalDU4mRCeUlhc5tmQa1qwW9EUq5XBmpzcp273hjCkzeWNXbVyGah6FX52OnLENKajFMzSh5papQs6+/yegy7xxhRbZNnTH+o1yzUurxu7nh72LAxiAntE1n3SQfzoRYF+nhyAHcgISg7MJRtCdDUcXkX6UQdlz/nf/fwv1/wv1Pi799Ws19/Wws601IWTOUt8UIbUsJEMH5ldNly+nVBG2BAXD5BIwJwdGvkLkq1v0Y1+2s0j/3FhKXroIp9+cTlry7vAhLI6Lmnyl7rEanwv8//tqqDpppqFWG7MQ/+23X5y2SbTcP0CZQ9fnlvCBNPzYUnaulpiL6+vOtvqwCcjoin5kIUtg/nAE1brMrLxwjy139boxmWl3df/rLXuHwIkJ392wQfzPHmJRQFGJdP8xF+V9uY0Nxjl8/Cv3svf3H5nPG39cblfZgWNjj1cmHDU62jm57A5/XAqyPE870weqlXIxZoiO+qHRrK0q1RIOoL6Ihdlw/i1OBdqtuksUVClqlx+TCQuevySRiQk0DuOaAbqU42UVNrxNiqSq5mq1LrjuE8yGawKqM3i82qdJcwWi//laTVp5fPwd8Dd6v1evmty/tRLhgBQ+DzUxAm+HXsv1d9ZsBwPYVDBxpKacbl3X9blWLPQkkUSBNQ/nMEvDvOoG0ZLzNx03DM3sadBXGK1ftA1OoNAJPIhoUIRDZOqWSQMLA/p8l/HCuyxfHyF3+bQNo+h7m45/LHt9c6VrYtMYYLlKOFNEu6pMkJA5IZab2wasnfuFh1h/YCL79O034Xm0uiUQcMYs4+aOqaBMbACJgifoN+pZnEQU2MlgH+KKtfE9O8IHpr5S9uK41fgmg5cfmLO2q4p3Uqs41n0ako2P+2BsdyKyxL6cZY/0DWbpw9VZGOu+1eBFWewLJI9J8iebEGZSK04iC15AvdszAPF9pcPoRmiHbhKghs/DpAJnwL6chinAjNcJ0iTGxAoDbyRcsHZJth4joFDYivaVDspzX/C93RoKkwMCSShH5bPAsBzaSSRGj+hIZ4QPUXlw+qIzXwQMy7/EVWLwPMnaPUcGwkqTsMD1kUl79IcDpwQ455Hnpb8jwEjVXQhSfiPDZRs7ghmizs2RwSARApOFJJijgmslChuShebuqieCHVbl+o2e0LZ79vWrWqdsUu23l2TiN1YvZOQT2vpleK7J6mV2mLpUtatn7XTsVEoWL9/G/gBB2/VDIreIzcGIWBUDe4QA3duiPKhXI5lGVmNVJPf9G8ZocLiLt4GKsjV/HI5PBr5jBZgRYjXC941jxSIHQ3D5sFRhGEGTTacspgtyVdzpNSNv52Hl4hfDkPEFQ281zNw8bkz+hingUIwfDtolmm++hhRBbtIRA+w2Uz7V7+IrLErppxRmY6TGZJxtSfvQHZDHH63ugikF9DppNYvwZDdwj+hclYND37Dm2OWl4FMBoFq2Z6oeY50bDIlYaQy3kftY5ukebH7Cwzi/ZPY2eUE1+2BnAliSU+jld5tkIzoggx5bYbLwyPPsqB1HoNSAbd17SFWG396p5kFGDbmuUh29NwtHJ5TzIK4PAY3lxfFuBbsFCSodesWh1YNOxWzBav7lGA5rA/HqGVKoagslWxgQhoLBdK+W/ueQr0NJvDLtplyzErDFDbtzRVTJRvFO28e5oRIZ/NfFho49BuhjpiNMRj08yEPzQ1E36faiaUNTOhnMtMgCk+aOtXXjYOwcgYNNNshMZf/SaVYoyEoUq9Wa2IldCkTlvMhJJdKdqameBY/qDJI+uYvjhiDdYqjaONQz/HOMuiWQLGOyiwXJBZjf1VOxJvKYuEt76U4vrOF2aYOPeLkd0us0gXcka2utR0fZ/Lg+6Iea9LSU96DBoGPdBQ9zI8Bq0VjY+3DPK1XSy/caJSD8ZRUyuAFc10a3/jVJUb8WIDa3CsQlbyXWkuPA8d5hvU/j6jsb1xysB5VcNL8xx2w37jHAixmlU1GrsMnOww781EOyK0arYIHXWpdpkQGgkl0CMMr7ELEA2OeaC1NA2vfBdq+rhQ9BlVIJHIs2zfN8xBwxr0bW/QxAsv75Dx8OwgTILGUc/nTbM8rXXI0JAuz0b6ShTZOSwIJvYj96C2jL3W2PuTsCKgBZXGft9uHMrShDDXctgSeRFpDLr9QZU46mH5kBK2Dwd+41CdEVoY85aZOGFbNyiaYIAqAYJWrIkm8EH8gDVXKZotGxRNEMCaUAIeVcccXLLSDAsmn9qypfF84wTaOGbF1IkhoVU1PYzbBEllDrZkVDTebZyzymU/yZCAUVwZK1dMj2Zgnb5smLEtWhbIFwnEaOzPa1Uocj+bPaFUiEUXsSTCGDQb4smmNsTTqTbEs5oN8Wzu2/1xrIUUdfhr5dlo0CsknNACHqahiT2klVJnjvYZbFihPXt83P2fsNVQxLcKLcOFSR+90t8MbyloxfS9BKUwtxYCPob3EbSc0FaCPOrlxmwj6JmxWwimAaqzPWgPIEV24gX/0JXJBeN2D8y4u/2tfFf7h/cPUiwHgq1ZDiOW77s08u/irQa8AppocdnN5jU37QUAe8Aaqftu8i5DDDj+BEC0ars2GGJxNnsDYBwmihVUNWXVuuMaPlkxd2I7wXOHh112C78V99wWjreVRtEC6CR2824jxNgBeXDiux8/oTe+gOxhe2DArbihp7hi+JPzuv9U0GE23KFNA1Mbv0QZUDJumx6+fzP7HYMY+Oyqfw1B69sFsfRT2Vlc958IGu/7t+m+fwRvVgyu0qep9wtYr7VHwV/IlidVXKmjKbjz3xW+8Nx3/g/Z5kiSgk+zyxK3/tvQf25twK77CeUtKm+2agAQKaqANWynta2FgF+Zr/4HiaVUi8WcsLMQQqYZBS81NQoeSTUKBjSjYCCXUXDj/fXX92/R1O5rE7uvTay+tvrNFKvg2uSma6s/uza5oUnNGPPgH3+duHlsZ5N6EQOhaa22mAjX135+/Y2Prq/6TDMTrk3uujZ55trkF9dWf3Jt8vVrk19eW30yxVi4efy4wta7ec/hxpbPb3ywOrTLAInfnd4a2mCAYtc3nI7ZYLixbcf3778b3luIcFLZX4jkaXsMNw+uuf7Rkegeg5Ie2mO4tnrPtcmj11YfpU58nXXN9Q1bkrcaUmtEdxz0fHXH4ebmfdff/iqbyRCMvSxmw43Nh8QwFWbD92vfQhruUqvh5ppd1984eW1iH+ND8hvB1yY2wpi6fm7ttYnN11ZvvDZx7trElmsTOyD92qoJtALgFwMSKhFagyMYUe2aLfTZ2x4xZIVsjiQak9r3/cTUjTe294UtD2hPBwI7cG1izbWJDwnSm1KoXv96LchcbDZ+vkFIPmjBUPnu9OffTQVgafG/tnoDgLx+6G1swMSajIr59c8AxltAxbWJPyOBqz6LGBU4JaBFBxNsCM1u4SvKSuz1lMbe0070qZhasW2Iu2/EcpeTm5m7vDfimydbFbVIpLGTmZGtY8rKv/YaRXIiSUEfukoC+H9995tIS+q8yWgjJaITBhGgu3kcgL+dG12MxZSILTCRrq3+y/VNoPysyocsbEDFYbo2+c611V/TMntUNZeuTXzx3dSqf6w/hsMkvZUR8G2xqQJiaaHVb5cAmXl91VuQk11apllX/9j31j/2bxXC/K8kjvfhv6v3A2cSzKib5z6LD9FiYr25IaVMuevrYCk5IJvM8GYxpEJrVTY7KhkxMDrBiIpDpNlQTzW/ZiLVhhrXbKjxXDbUzOSumcmtM5MfzUy+MzP58czkh/CvZqrMTH46M7ltZvLdmclNxszknpnJ96jGPviemdxMGZCyK8XiItibqNYn8O/swMeYZVTiQ/r3I/p3WzsQRey49qNpi+EnkOwm8JsI+ybNCGSEz5uZ/Iy6eBcjG34Y1JKtlLSJfsO/O2cmt4hWxRuJggUA66AsebeaitRa1mxgxF/h33nUSTvFRODWY7ScakaGqgRm5MzkAdEpf6ESX9AY2kqd9TGbGZHotWbdoka0NSurR7kRnVup0Hb6932iYptWWoGdXjocETcz+QEVYLV20jh5h2YENtkQQ4+xkHFka0rUXMvgYiLrkgtrYXaU9bGY4DslwGYWcPwkzGINC5L20UjZLDBK6lOYJp6n7FDY8p7AzsoErxl20PRnwulzQrdVDD1hhYvhs0uMz613q0UuZgFrzJfUpTvZmInppz5DiDM2iT4Uo26bEPlbUzYAxQT8a3h8xmwItp8utpOYgYbZW/dzQXz6lmQKxs2KMN4kl+IMSNm6zCb3lnnyFyMnsr3J1gJEMPcbnSRe3yOETPx8QpjflWL43WwtDJv2VGEbQdhDjJNFxfrDtv1Qg1nZ0jPpd5Jyyt3N4P4kNmFTmMEW0U/ksM7HktheyrGFO4eExXfCbd8CTpEXMYsoNlJT1rdSnd1Chn4slfXQHRlUcksux8gsKftIaJ+bqeS7SSTKmzWakBjjTJklhYmGTtgjA6Ttyux3aQtR74lR/I5QkSPMo6KsxIdCxd2VtgmeSFhbPDgpzQ4ri3GDOJiIyp0e0uI7KNa8TUwuZ3X1EJ59QkF+h5HzF0HkJql8JDh9BNe3kjDFvy3uoqcwJ4aakKSaRz29NYuDKJ+6k81/lAKzyUqX2IiIv6kFujV31B+buqMW2mnuqEWaO2pRPnfU9rdntp+d2f7JzPZVMzv+MrN9x8z2D3X/yvbVVOLDme1bZ7afmNn+bprjaft7Mzsms9aP8yxt/wsVAqpOZwUT9RvlBNIer9D2d2a2fwUgiY3v0o9NM9s/QJZsf50S185sPzOz/SPdVYSsP0F8h6JvULk3GW2YteONme1TSO2O1aJmgosIITA0r1N1BpZaionQ3j/P7HjrLncdwSDFVn1ETP2Q+L2WmrduZscE9IBBbIPUNdRu4Af8WBOOYW4OJhTgHA9M8TkhkA9mdmyY2f4+QfgoHPWc0s2ROOgmZUOR0VRiNR8uTVFId1PWaqFo6pntE1xMYGVNaojh+hWxFwb6G0lR1q0Aib/CJQVSJDAb+xv7ZiJPbHaOWZ3J9yRo0N1AcrLumDC6HsHUbl6vUuFii89hJhfeFrMaoE0aXVis+671HGXmMDHgK84ATIfRcpIS5ScU2zaz430jnMQn/IeJXiWDVi6QnKcFJW/TkHqH/n9WyAqaFXFuptvaCLT3WiO4DT6p29zSJv6q7evFVIC652jmt451PWHdSuz7QMiSd4nFa2iFWEeLKK4tdyYyHxUbJIItMquasE737OhCo4N5NIxMUfuaPhXtgttE0+3zJyFZZ2gc52uUSn8eN9Hc4Jtr10/aIt76HFxLrQTRBjrIZi7XcGJvJd0Ept4HszhRMEc0q5KPUa6R2srZhDmilGsQLZ9rmCOy2NLFuns1JcrfawjiSfrxjiLr1wg+r1aXOiKIpDTCfDPVoZSd3va4mITqN3tGsXXq/ZntO2ltWk3//pmW4smWjmwQBpAvkzTVXqf+XaPQuUP0BhlNwHhiSguepDDgVAZge9aGluG8pzeS1ZVMNGR0Ms0OScaGRv1ObWibfret28wR9Qc9Lgr9UsLv9FzIi/WyG6n9ZGpU1QuaG+uFXG6sH76curV9k+br8V1jyBxJO63+w6H3bn187Mc3plLrxTip/nloW2qViEMqrUJbnE+3tq/9YefnmmMJmvbDXmjgGePWp2durduY4jvCovv23Nq96u6/9+qH1Ttv7d52681t/zy0JnzhFcsLX3alltVvu/rn1CGRo8YKKexUw4KUZD0C6Icj793aFveKi5IRfrvl1sdTt86+98N7ew02sm9tOZbybktS6ZioHL2AFonzw5ENP26dyuYW4ePtZ3Tn7a3tW3/Yd/qHTdsM1rY+49bWDbcmJ4wfJ1dByq2P055Z+eHQR1ggKIoWN8J5c8MPb34WUjwyYGIxJlmgzt6gj5Lzz0N/YdhCtjeMs2O33j92a/MZ48fNe5u1bevxW2u3Rs6R/LB10w9vvIfagnHrs3cAztYfVm+9teXLGKC5DGkU5JvZTtcPB6d+2L2R23qdTI/labxkuBFULUxEBxtNirHYQ+9xlB4ODONbH76Hcz4YaKXumMhlKgpM7VgJuI0f1mz44ctjxo8TZxmePiPxBIgMO/CjV4nOEngIcCtWNTJycoLx7tiGfx49o7NcZP90WH5r+zu33jiuMSbu3EjLXM8EP5nx7TXPg5koRX7oIErXrW1nun/YtQ1XQCD9x82xMzubYZ2MTRi/EtsPEx/jlM+EKsYwTsYUmLO3Pl7T9cPnZ7qNH9/+8sfNezK3LGztxuK6tXPzP78CDeNj/eGRHzfu+XHNMZgFICXP3Dq5NQFpFHRbjEtF7m74AijVj6Dc+ngrLCaHbm1IlbRpBiJqGG9+/OPmnQB/q3FryzoU4j+u/qy1gIKAWg4il0kXXrCy2WhBLZi2WZ4sjUWjmUuPNt23f6KfvzgqXhdVHiplb4Xaxv9hPHA/U6LY7/sfYg+U1o3/hHX6AeOXv4TM/4Cf/5fxpz9hHRip9wdvl+KrTx28ykhobe9wHasjUMY6Bq3RDu2J0ZdSDbLf63cQI/30wOp/KlW83oEh03vE77q/G6Tci8PDlvcY9ARU/BXk1Sr2gNX1ALKlB8C1+EZpza4W3Ipd4y9tQiuCRNPW39/kyReORh7fZBBpZcOtOP7KllzR0Cqre7ZRuPCFbxuOZdiVklVzzm8xyqY9zJHcAzNwQXwN36tXh5UK7NkrVg30l1ImnGEgTbHqRAZPba3M9CRrwbR9U2crJYV4imltYGjRBuHs2LWMTauaF84p5VviZwhGU5w6iTHsTH95lkud82s1lvLUek3nKk9unbHwz/ktnLVmvWSb9aatFFWQL7J8Hs4qODUYGXBqNMawNu2t3f+fsS0yNupc6jE6QU0HRZtZ4OKVwZCbiZaTAi0nFFwgVpTCQ4FnSumrAunnSzqXKswuUOD0ktLSJT7d/CPIJ81fbU/HUqQ02bd1/uAIWO1c/AvvllXpNYbNC7t81zDxgnEuxuL9W8vMes2vl0WZu/ted7yW0DMqpi3ao13p7pth/5ZaMnSbu1cvV89vibnKPWCofpV7kK67uHzbKVdcgyyLMhQxuh5bMP/57hifV1rJsBOsfuFc2S3WXbyK0q7U/fMpt7AkFY5xganZmgOMblfk46ypB0yOyixhPzWYm+ffsrmkKqDaSQ81jFjYN7De6AUkvVqz5eGzigv1Yc33oPaFXaIH6w6osLZj0Qi1zGG7AOLhwjngh15OOaAGP8sD9jI2ZGpjqDmF1S0RolQ1a2MjvO/dwjJrAOnHH2WhQfBYJfgTIu4ujUt60qxD02DADsO/6HnrMVz4KoPMxRfS5mW9v74pnHbeVL/Is/GuRK7ONH3cSis9YlsOkGhf2BUX3ALStmbDsCzb1QtHszjosJuYhcU7iX2EQzB60EiKvFoMPad5mx427n0ACOFLd4cat/EwrTOqE6hsd3WqKMlZE4zHbv1YT/ZbK0GjEBzERpaiLrGIlmvQ1NGU0LBLqTm1ioJK7x2k0PywGmCSyanXWjfxd4gTuokpO+3upZSImty9FOoT7Ca949rUS/E0J/VSez2Aj7PFA9ZaNsuH8WXHIqmMrUfQRIACTLAPSwJoK7EuYZjnD/qeWS8LkC2EqYQhgtYDEtdPvUR/oeVbdg1v7ay1xyf3gljTJRlidASeORhsnu+CUmTXerP64hZapj+MNoLjp96yCevySgMfjE0oZQbFWnLjdTxv1nBlK4OGWWR8Q2wAL4MT7ylRyZCKVMb7YGAmJsisDPNT0Hp+Lcd7/mCC+y+BQM0B+LumDsCnUh1sT2sOtqdzRTxc/Ori2YuHLq27qEcvXDx28ZBxcerimYuHIHd1SvTDxcNQYOul1RkBxIRBXJqA2ocuHrl4KiOMSFxELghtCZQAXGcvHr80oRmTgOTYpVWYjFjP4j/H5138+uK5SxuQvhTL8uJxIPHcpR2c/LvatESeX1p9afLiGeC8cfEIcgRadzxsZAblwqZmuI5ubga5UHsV8Pz4pQ95SdX6VDsjYoJGMnU79OK5i19D1vGLR2NMz1Bm+CoVyDgL4/AcNG+KyDvLBsMUzLRDKXemNK8XY4Nq+ZoRevEwADl3KeMtKHI4Z7FCNdDCsJOU3KVm2sV3aTxswG4wBDv6DOxo6PFL6yH9FIzVIxcPpcRvwMBYRdIG+/Jc3AGPHGhY8EYiyHY8OMDuAyfm9RmiHU3NPdkGPkZxPsD6cWnrxUPxLYGfx6joHTnscHEHlxfAv4tHL00aF4/Cj9OYlMR4bfXn8gZbcI4tNKAPKQtL9lcLAMppJrkunqUFM8LZuSP1MBB7DAD8FC7aiDYT1+fTzZqYwPUcJyRmiTjCw9t+WOLix0AgEHfxBEzA9bBcBEtEn0EqxCpMImohjXSP9TDYWIugledmYbPlRQ6DERSfMOpWLLs8mFEJPH1p8tJ6jrlV2y8PTly0caYeYmkkFb9G7QaqrqLJuPq2PcygrzB8qY5QLIYy/gMCZuriEdZFrUT9J9iFxMCvacLBMGR2Bq1ioBTyLiIOT5HsSCoty7Z664TODw4Mh8m5vAEjsWt3NnNTVqWhEZLSCcRETMlk/Jo1+XhTa/KPqdbkI5o1+Ugua9Is10zP1EywKmibdWfMtNPeezNtv0mlxDffmtRLePUtpdYcvftmVkyUNda8qvUzf/QNVi67PGRUzVrk1TeeFboWQS8ZWHtoqLhD4SsQOCPDtx0EyaGLDZbVK2pxaczJ9NiH3vCxerdYNhOfeIsvFn+9AC8beeLNrRZcb46eeCPYd/dzbr5dNMtGzRzqAzYvM51S2UwxvorIJbtqJr/nFgePWVkxddv1oFs80iYbanbFHoIxFVe3BgN9CP7160YRZtOwWbxTz7tZXgUwGgULZL7WOM0mceyKaee+qFDZiYm+8ZYPMTAIOfaTeOaNU162BnBNiKF8tlcFZsEQZsgdevBNHd1V9IxDh3qg05pGEaaTOfsX32IRmOUh29MwtP7kWxRBleAW607JnuWbb1HYNQvnuiNoH3ChLWlWRbI1EcDO8+AbW6wUsoZhrNUNbK9pOi1ZDk+BkmVzkLCQWg4Msta0fBUSEGfgEK+29kSbBJJNtV+IYqY55oQn2nRkmh6/uKke/1yqHr9M0+OX5dLjfQs6o6YpyUOWNeg3ebM5rUbyo80hbVyvlvRqc2KlNqnvI67l6ZGC+JdC137e2juP/6tXDQsVkGXFmODAejW8ZRMU1DdrivW6F96amRdwUdmTUVK1zZh5ILQtuxzdiFEzwpswJQvVWssr4iOf5ZqfsO9iQP/Fl43Za2FfkZ2WeaByV/lYyaC/07jKosCXLN+s3K36+7NOyaoU7RK+gYot7jNgno6aA0N+igLvOv4Irnwx+nsKPKbAR+vOXn9PRdrkSWZZ1R32bUup2djb2IuvcjpzrrT38JglrhKTroHTmI5SVkxQ46Cins2HOCuAP3Aceq7rdySos8RvpXUAP6yGj5omTK2O3Go/SnhFG47oue1CbdfmQv1PZf6I51qlVO6XXY+6p6NcsWynNe5nNCBaZnE6/Bi2tteICKYYSW3Qkl0AVIX/NLsBdH6g1bGcXAZEPHB3WLcYDOgbLOTkMR3iQYeMBaB6BLV6J7PVkEAxPhtuOWUXJhTxBmwFmM+pz0QLSH3t2Y8Q4Axa0DhdQBMMK6sy4taLXBgqwWzj9jInqwnxrFNAXRUWb8hI2ImoxT9altG0CDgL6S08/BxeRrKZFUEtJsWyvfsci0uzKp5palU8mmpVvKxZFS/ntCpKutbe+NwYMnPZFHqNFJvCC1dDITDkVopWc9OiSd052iCw5g27IOpQC65Z/yNsDHee4ZaMcsUtl60aMMkPmRrzsEzI0pgXKa4ZHPNgVNqlIcuPGB1u1Wt8XrQiZoearhsetWGrLAaDbnmoOWHTA6VtETocJoDFzYX4gK+YglGrY56SOwurI+uugY9KjxrWVbFrvnW3WiEvlsZo5eEBTrB2jeD0TrNBBu1yOc4ASQTFzI9QtdnbHgIfyKCireNLtzxkRbNSUuvBgmuO3JmNgidcDwa560Ga3hJNWWcMQt9kxSApn99g8CJbBXOA+rZtFiDtOIdhvsXTHktwjr2CdiGYY0VfjGgmCUGBBIKopAUabXEWuwRxgEmTlGBb2RqIgyr1h5Y3BeKgQuGBIaC2MQXiDPR8P02rFwDaotMLYLhcMFpGGn/1LAMNMFahXmXipm3RRC+Wl+HwNJzGVBmP7uFMYmmUVLYqrer3ojEcjGHnVfC1tSGbdq+J9ziMEc0+ikRT65c3v0MmVa3/g6bW/yGXWj9keoOaxuxWQByeP1w8f9hLDfoZ85tWi9Hw606t7jWtGFHum1dri15fsj2+NSH1elLrS41Phn7+Sv0QzNnGJ9Xzh8ObBhj4M2SHdw3GzarpiML6xkG1caYI2rEdOdfh2Bd2ND4RtdRDHXqOfqID5t+4XbFjjnOoOWFd/sIOe9CzKriv69tOii4fUzBmByHI1I5q1Myx84drnOymyjwbYJlCgOpVEYwn1PmaOagesi+ZY4W7Vbl/ovFJDfQ/YkefUcfAASdFsceZ7pCjKaLbx0Jien201uxV+wR06Wo9rwSDYMDGiIDGad+CXoaV8A7p9OZ4xTQKjTNjjTPlvgSFuie3Co9HTWvmkD1scOE8BtK5N8yL2eOOQr8dCj3RXW6caewOEa5Sm+qrz8ibFnCkcqS9Cj0fyyQNQ3dJGqAlGAXzwg4M5qwEzcum0McAlq56Amz7VRDSvXnU+RiYipveHvLtkcYnoPimAw6r8zpU7QJIxyxf2IFSx6iPaTwI1W2LIs9h0QrUFz5R7p8/HDMsmmvwi3FNd6rmGMg10zGHYBAYc+KZVy1Vu7EbBIAfgxoU2t4savyTKJA5BBh92OKsZ8nTyCgD93DnJSAjekacF4ni1nT8FU11/N+l6vhPajr+k7l0/Ol9VzZ8s/fKG5oCfWXDlTXThyExWcmf3jO9+9uNV85MH02vGaPnZ0MZ0fObV2uLnn9lA8DbMH2QIxLKPrT38Lcbv/nwyrrp3VfWwK+9itIPjNhgTH8yfdjATE3/Jy6tBYBfcoB3rRlwZR3295X12MYNBrTowPQ+1RqA/E2Yr9oCaqnADCB+HOQluQkwfZAi+Y0r56Cf16smAMt5UM2RJoCoNX10evf0fjVXranlRt36qAUXjStrAfy6VLd+pGCMKaDkclsAqGDmwPSxK2u/3QSja302g0AZiVmsgumT0wenD2pWATT78Leb7lZDAKfa9NEra42AEX0GkzvA08SH+qAklOiBnvjmC5iRdNfO9D6AsIbArcd+2BBaetNR0SNaOcDO3pRoRk/44n1GXFKjYMKuA1IPwOceBQxJrStrgPS467y+WTt9ALJUyHNpd3zzOtB0GMnZ8O3GiO6PA3n6yDcfgixZqfdLJiNk+iQw4Dhy8so6mH3IL7ZshDjWbiIAzRu3zQrhxMMSueGbdSisNSMhhuBUgySdQ7NCEsuV9loiwfwhcawMevi/bpjA4nAYx/k5mA5rcBK/kcs0aY5JWioSExYwcDGj6Q213/jm/RS0McZLMtawIRPqx+YWTGqDNINGtGft9FcoWa6snt6XNGxSjiwEUuqgkDOZLJygHq17YToVgwcIPAFs3qMMvOa2jgSfYODADEAt8HUuUnCiiqRWrR6JUgCCoULCOZhf3+z9dlMWk0eA4kOMaxGZjB6Fr0xoplERsXiSEWsWzytNLZ7HUy2elzSL56VcFs84GOKNo6P6QWHbOr8+dUtjmZ9eJzliKb1eUqxScq22WDmjY5554Yzl2JZ+lhmDlAz6+nnvabBrko2SWxy3HdMYNUgcDdhW+GAzFYzJ5nDSAAQGz8A4XvDqjZqObUZOPRPLI4eeRWrozPO4WdaKS1snyAideh6wz68tGXzQG1DMLbpAyJhKcsJJ6OxV409H6/WVSuHz0hV7YLxg5jkvPWotu/ARgMpkGTHwmmXkmwVINe9W02ihbQ175qhdvLDaHcVb6B3L4CyB7z7DHRi3yjb0lZt2LMP3xseqWCZm7yQHBn5QIwba7A2hXGSk77RooEzDHR5YZmoAlllFx1pmjBujeOcmorgzWzCvmEaxfuGMKeg4v14ls8cAuR09EMFLgmVSOP8ByCTLoBUj90YNrjthzr1SGPOJpK6x+8z7rO6WyBkdc9yanU7RbbOVgMVV88LqNrM44wXEiSxGktrL4tse06VNsjFDWwH6jCr0F6wjA+NjjglkAq0fGO5sIr0yoCu7F9YAQoEO1uqWI8AyNW7UhiUKsLUaEJYBiVscdkdts4hoRsddbwC+Um8uVkHyAdaebaaQTGULLaeygDwHblTcsuf6DgnU82tbCh0DNJ47PuwCG0D+lyt1mBtzsvEUYj5hAsUlZ9xY+qKV9fS5xlk53eOoiVhhGQjQzLFqU3Nscao59pRmjj2VyxwbMD1zAKMc9fMgvuXlOj+ilo+xxUBxdJIrRIyw5OJtsb6GQb1vfOrq0WROEZjQ2O0aRQsvk/qfYYJZxpDrmbFGV8jS0ssFthX+bOwGboYNq4ChYeNKywkZWIOmvQIvKokxsdSskJHlWyt81wBTwR4Eq6LoggmdZFRBTwYFzVBB3X5iUCOXSzVOVUEc5rGWQLR4gCxbcBkDH3qyxb1bLaXF9jDaQCONXUCJ22dYtWFgSjHNLoJ1yCpA4TizKBEcM4Jiqs7eBnqc9Z5Ea2pom5xXH2ZzI1rXNOpVGM30mMEdMHKeqdswtkueCXNRac/yuhUT9+V6dIhjBA/Q5bZo3FpE286PfNBtgvq2mS6M9mELiHXcROJj2JXjAElWHCGu3HZrYzEJSGU6Fi3g6YBbBeEOigj8mIVtEQ8cRCq0UwXeiiURD5sK2l7LpkMSOzxQroZdXPQME19kIeWjxRumJPQ8VgJfscKU1YCgauMUVSG1pxXb4LEhc6SZMQCwawOuM2TBWm7OieFAVARSFpf1nEZDRMpnsxNeQgEVcDYWccQ+eMysqstY1CB4vqlB8Fh/l9dj2D3GCGgU8gljJ/yEMU1q/oSxL3+LJ4z94Aljnz1hLAdznRfpDK2GtkyHqr/Gmp1Y8zeywEigpdS1t4yfSTVh/qiZMH9s9SniSxN0i/kRvN1de4tUzeDPOsgHSbW8cxePRN4l7WAPBVxafWljR6aHe/HtlosnL63WSBCJYfSycBa06Q/cXnpLvQhfZ4CWFWGBnpuVCWlvwt5OWtr19AwOgWPR28mUZ01SbEKg+wzdWP7Vz+e1GXoWQL4Mc+nt+LdmeA/GvDQjaoRemjmGV6PjLe4XT1/aKK7rhr5Mfmsm5aWZuHdmjsEAwVv6D0FnnLt4Nu61mbgi0qAkelZBOl1Pf8S4tIZueT/D7p5Psi0z1Yrfq6PgRbW+anLSswo43lflsTrleM5idl5aDx0ygfJPf30GaZ8gAXnkrn2Bhj04QC8GXHrz4klDMKYPHxg4QRf2H8KWQ0M3pj5CgwsEv14/6RmaXLjESzSJcGdvvOYlqMkLNTHQDtE7OodQVsfCJJGIzcI3Hm6vscsf8xZvaD8W85ApACATrBcWLfkbVin5GxclDjVgwy6SXxvoWakz+OgDduBGSDiHTz+wl99Yb05dPB7HlR7j0npc2yIPzYTq4jMzhy9N4msS+J6BXPWaWOHyzrGVv/gp0H0n7fa0McBfSW3LGKAHb0hl2UhJLfOzaY/HOhIy9vjcUHnH3+1hr8EcuvS2toT24SKPOv0J8eYbtnOK/gJHoSiWn/3DPdmw48s9p0hOIg1HFBoof7Zv+GQiAp+ioT5bBQlEwGyf8cmO9hyMoCM0lifo1znBA4NePDtMttfZS5NpjpHI+tOel33UpuCcCJQhrSHncMy/SSRfPIVv/kA7kIwzLTlOECsBmVAe5jl78WyCfwTwHCeBh+1G8Yov7vz50vqkR4KmZuVPiS70HB2qtXmf99FhHUfjYC3TCtjnMdZwamG2V3/CHRYWYrFURt/9yUuY5qOxmvpolqd6PJ7TPB7P5Y6hdUe0DVIbz7w2CaFNrZIcQZtaLSmANrFSW2z1EcetRS75u7DLd2zDMWsVjvx/wF3ioH+df8usZbpLPCiom+O+Zy4znWVW9EqQgJXafSBBsjS73ULNKsVs2sr0B+NO/xllt2h7pmOAhEw++hcpFWM6iyztAhAYENaIXSmb2QxmGlRZjGUO1w2HtFqVuzei1RqxKsvMEbw/FtjQh2+iw3gBKZMexAp60zLLjw9hTQXJbN9o9TbErDbB2yRMldd2TKPqXjgHAPwQBAsZMl6lB13vwM7tIg8ptMuuTlYPzHmj4EY2UD2riG0AjQEEQcXNv307Etm+bZmCn8L+LRBfNZcNOU2JjzAsxwZuDiR3MFY0mCcgPfECac80AM24ef4tWGiM8dkEhibALrsOcCMA3lIYaBzskZpVAB77rT8PFAu2jvcCjiPYEQz0rKeGeQYA2hPcKSSRKxYyl1NVsJEg6/xbZVjHHbvcWlCnBT3LYjoTbAjbKFeW1c+/ZdME4x8th3YG3GGAjJHccZ26YM8YyBnIcyGJ4jBHYzhjkGn6/1hT/X9Fqv7/qKb/P5rzwu+BcuihHhiTee/8DlWJMQDcwjKr7Ecq0bWbjc8rFTPZDMhWtS3GAPytV5Y1PvN8o+773thAOfH+75+9WTCPmQUuqJq+XYy59ztiFsyT5SIXfptlP/7ObxsvBR4Rd1qrt37rOTH3fnucgJiLv0VW+IoQUP/NIt7Q2vgs9eLvaLn4e79ldssXf6NeMo9/ZrIYTAcfHQpfAH7XWgvPlmxYsfDS1CouQMjdwcYJbwQ465vp14APxF4D3gSguAx8oL2XgTfF2iS4k1XHK6gqak0MIL+Dt4IDHcrd3CpVYc0cRGbRQsWcrQS5bYPoteAJuP3ZoL599wgi9XRxd+ucy3NN+Jygm+vXgfiU4WKULqmu+ZZRAPKXmbO8ODwJeK1S983Z3R6eBDrQR1o2HJJA0zXiQHbjhINWhHg/iRsPHfHCBMdje54HEsKJPQ7ESMI1HWUTq2JV2nyX+LNlYACYuA6oYBWT3SXO0iip9avEBYsZFO1eb32CZHo3SEr9zNeKB5KeEDUlIPp+UASnZksMNrUlXkm1JZ7QbIknctkSVw/8+eqBY1cPTlw98NnVg2/Rv/B7M/3efPXAUfihqfGU98bVA59SiR1XD3x99cAhtY5x9cDxqwfXByUw7wCCSbZOrh7Yd/XgBiosq8wR8hg75+qB168eOExF91G1fXNJQMRWun3o22JvEblHCTz8u4oh0UwuxIiU7aVCe68e2E4/Prx64Aj8MIgg1sDjSP6Br3/+2zU0r9bjvwe+ho899HGId01o90YtG97Eia2q7+cQY88Br+HXWuwjHBETkd2dhF5Qd3oSiujBliLvgBy6MeGWCYWSboOksaI2Mf1OyEjxmB2iSBltq4ik4KeyevteftIBC+uPOuZTwU9+zo/6aZ2QBEDnhrvVPqShexxbguNwrxErMfoY0z8TMozGBZY/xJcevgAJkSjXBlqhUoxMqkJ8TKobY4HeDpKZGZuHvNnbuLenXemGcpSG48rCJYHizJw1JdGbNK8eWC/Xnb+INQnn2Nzb4mId3Cw7/RB9H2Tkzrqxmg5KyBisQIvI8z5wEzUEObmOtWV9VqUjMhB+4vxotYW3wTsheYcLGQ3mD+n78JxwMOsjx+0bEj+BZrWh99vrbYkKzkCHCb/woIq5z8hGgOZ/kK0zsrlnMlAjL1tVqWE6JLNVjhN2MPQO5qcpxq2TgSTlMtarBzbp6x8StblVFoX9QOm0aFe3Cp4cJlawoaxavjvYMsWk5BtiMO5h5nn2maXT0xa3UrSRXL0NvXJB/S/bACMBp/m5lmZXc3cUadN8WBGWo6w+mwnqKnNO8A+/Qd5sSPBkBateu97R0KdnnDaE5GTxX2XS6PK/sJFGYMxCmuDoyk6c5gArNnWAVVMdYL/THGC/y+cAm9p8derw1andV6f204+Jq1N7r059qvtdptZQiU+vTu26OnU8zZN1Ekp+iEBPbswEIs4fhYWOXJ36hIg5kpWYqGMpP5z2eIimVl89+TrxEwCfvDr1+dWpLYDh6skNV6c+Elkf6D6joMR+ovik+A2JqwjQ5yluIip57OrJ1VQSKh67u31G1DEwGD8I+IJc2E28PHL15AQNsg/DVzfFVQsXFRjS4QU+patTb1BXQNn16ZW4eym2KyPXQCWWCl0JpaKHbENBGwk3Ti0bCkFWBimbBkdw6MgKj8af8c1Uy7g6te/q1NGrU1/Q5DqMOWLA6q6pJuDCl08R+z+k4p+CfKHhvoVw7CE02/ET0jHlEKHfTfw4gl0HM4+lIxCotcfoIudXt+Lz4hCxBNDzgZHiA8s6yTN5yaQMRswAaLXuLkNIR0SbV1EzPoHfRtcjWKJb86Kh/N1FRKBQw3YQB4wuLNB917rUtN7bSayO8D9o+vuUMkHLECuzHowoKe8RBnTReuzixJdcaBhuJhEC9d4jQGI0x3nQ5oxCegAmGzVtcJiJAdMqrU18YeGR/hXBm2gJG81w+PcE1QPhPMlIaEfYSUAQIpzAxRUms0Gz8QNBGFMsuIzJ6shQ4NGPo6IZ70XefJGdgd4nXVuhQZHNrxXVgiK9Mvc0te4o4pwO5HjAft4fuyhnopXOSOjLFCaktDpH9MtcI55rzwzScJImIl+rm0/guJVeKYIFcCa/Ti3fLH4wSQflP5lFZM2ckPs5/buFUtj0CtPaSqTOnJCKoyXqDlINDRKdsZCyO4BaoFwlcjMBWE+lDhOACLxAwdJmSdqxhPxUtcdRFNHocjFDW+Q2UFs/oB5RDBtsPSmF6rjTvVCBqvC5YDHSlNmxhAr1hzQYjjONUzGrZGuIv4hjA3MftOYs0rCkskou87lOT7SgjAX5WYYmm0mrqIuoHVpfaeOVIMPIeD2bmyov6UkMinqrcgKOOq6Gmjqunk91XD2uOa4ez+W4KlOEPo9ZFz4d/rBw6knwMT+1SoxDqnHi/A4rtVLE95RepS1uppLtFe24sx4Gnfawf/5RRaCb2EMG3uVpmn44jIgyw/FDQUE9aKjWOONFT4ArfFTjgpRkPRYIRiT08Pkd0MvRGKBQZvgsOL4kfmGHPeiBAHCq9oUd7BnvlHPhaTViIoCKIOI51VrsD73SXeNUNQ39ubDdLFRgyHJogXfDCHwbxl3pYnjSauy2vJo9bhQtmLsgnQpWGThrOSnBNtil0PA4h0AKPBYJE607e/M9FWm6aR5UreEvxyr3GeaFHSaO2KJZhtHmFQFMwfZs8X57350549FY75aNQuPMWONMWWugZqgyBuSJAGEyO8yX9mG7bcESRHK5caaxO4nmlS2d2Zgl3Dk2SoMBTHIwHCGA4rIAA7pi0tjNZUDGg5bb/QTa9qtWxc5j68VDVUw0G/NToYaNsAhIbeMdoFqO7VhGfUzlQbRyW2wgRaDgEhPaIkee+WYVj85UGmccq2BXgpY2t00Wmw5Iz6o5BgLUdMwhGBEJm9niKEY7trGhQ2ynaBkl2TSOO4sJEpbN+Tepo+gZpAT9Phahpr8PN9Xfn7XT9PfFmv6+OJf+rt47fen9i0c0ZZnfGkv3/6Uo8+Ly6Gz1YzR7vIM3uG05G5iIrp8XSJvua760FlDhbV3v87u6Lr2vWQPBHcF4AR+j8RS/pe4sXTu8hW5fvbQBqUuxFqhtdLUzx3DXGg3yWmf2z3q8fy68hyzLhDaMw6UDA4J4Spca4khmlzmHN4W1zgjvBYczQ1vAkPm+ellzzM5vXJHQhu+lVZfepuv8ThmXJg26//LYpUmi+iwNoqRrnbPVi7/YWakc3sxt9VrnuGE/6zue7/obnr/GyxShU6bYneLInDhO4f2ml9ZD8qlLk5fek3dwplz7fI6P7jPU38nXPs+OAH4XdDKyNt0FPVsqM1wQHUKBsvccJK4DyfB2Alx+STTe5HhnLKqLW4jhKIbwolu8FhJvzoXJfjye5B7j0utN7kE+e+ntDmZK4DqYwwyLWU4jjJ4zesW9vj8JOy7cTH5/ccYmxjI/h7U3W+xRVt6ZC5LDE3Lq4kkjWJf62A30R+hmZBgHTCWSFyXjmoEz9KvZXpSclwq6MBm4fBZ4PckuCxa0zP7C5LzEnGFqDb1aMTW7S5Pzoj5CwpkS6RfxIe+lyXFI23d5chh66ArloEH8EuVJakJwiTIueLO5RBmvT/4amCMuUZ5SbixOuk/5/dlfkBzHVHlRMnVK/ouSM67POS5KjnZOWOrH0hp/XXIe8jRD+zG3maFt6RtlaHcLu9oMWemPuZHaY6nbbM9oZvozucz06f3Th69suDKhmbTfTkDSuundKab59J7p3dN/vXJy+mh6zRijfPoTrHhl7fSR9KoRQzxLxbYY39O7r6wzrpy7clIzuKd3f7vRuLL2241X1hj4+5sN04e/3cgZwIwpyNtgAJmHqcCVNZqdTUlrr2yY/vLbjaqdTZVEzb8Cjm82KAXIRIwtYlzBf0LmeErJkJF+ZR0UWxeDlZvsSGY8WdyGnz6aVuDXSSUMzcxnubsZU0NgmOVPrN47fTSSLVwB3+z99i8EAsrEkqN4CHgWLw39vIGKXTlpwFDGAb1W9RXElJ7eB8Ww2w+ozoOYgjAmV0/vC/sS4gqunT7IC3JnAi905Zzs4+h4466FSNEHo0WloyEO7mEcBNNf6u6GOKhqQel0mN5D7XgQCYQp883eKxu+gf8Z00eBnYevrPvmYMTfEFQJT5Z0GLrjAQbnX/R+QRDrwpW4L4JmLm6GwnhfC5JL3QwlSMCvPVfWEEFXTvJRE+OdCORCFo+Ehkz4I7C93/5FDemGYmvuVt/E9Kkra6a/MiRf+pLjrYEX+2hOnIQ59M05mEUbyf43SCQeh6w1Bkry6d0hDS+KgyIj88KbvYchjpCQwyBKVUJjPrlybvrYlXWxO6y0xnxzCsYyH+Pf7E2CO5dOA5ioE9P7jOk937wzvTsSJ4xigc0YsKpJAmd2AEyfxNV6+jjMjJUGW7yheqiF3/4lzLk85EzvTiVHaAxJbL0Ntj9vzjfrvjkHBJzUmxTmaOoh9bwcTEapci0GZQ6utdfMF5NGyvbw3i+bKcdQkwWaJJ3fwD/fTny7UXIimz2fhE7uB8PchTXrADBhbS5kMeZ6Ei5llxjatRtk2ppcqMK2eQweXN3Zl76LTMwkEbQuBWUIcFusbQ5LrJuhHWUan5BzAmTHySsTybQ1N6ynT4HicBh1uaMkNNYikw9+O4HTbmOCSQ1GR6xJDctO7n1mIeBZexFpFgs6vP7k32DW8QYyM8EmjkWo2b21phvMg6mW63LNcl2ey3ItWJWSrRmCBfe/V22t2LUh2yiDxlbOEyaaWDf55aBs9ZOeEGpeuy2mbNn2AKweRlrB66gB4rA7MIRXj5rOuF35HxBPWjPxnnz8/3LfDseTYmY4nBQLxoWTFs0Rt2qPVexyJKaUMTMSU6okV80BSH3mkcfURD3QtGibw+a468SEmWpZMQ8OVUynZDrQGt9Lf3MoXDAmpFTmaQGlnrncrGbbzeWDL4uxFEAVllLVrIFuddc+OeT6OKdL8J8Hk5oY0WeU675dQbYn78guNwv1irEcJiT1T9zTQ+mg2V5rIpg2PEHUDH+TN4i06iD3bdOBcbS8PmYXzZJdMb2iyeNQFZh3YNf09/WiaZRBLTK11ml2x3J7DBXcbNufyo5diCftQnXbzBdGsD0wZJdjKc56w1Y6Q1qEP9dPC2kDGIVk2B4pwCiGIezWhuBvsKrne2QoCYs0QxgWv14ym2OIe2koCYFiezi2gWXGDeyJkmm4lQytibw9FINIszhqQ3ToABFU3RpAj2KJh9Wel4g0kLQOhSwPfDwBZiesS0CYbYSVtcxvErlV879XfVCpVwEYLJK3IaAVBSpDZjhqMzM9SBQj5vObHHhhugaIibGkd4mScGpWh7egmdUxlGp1rNCsjhX5jqU1zhr+zOnDmvo+0Dja5G2ixOIx5sXw0Myp3Q5i2Z9YLWJVNKvUFmPi/DuApG6MNHa6mkUB6affNI2BoZnTrxuU+PM2JpxSY+eYUbJnTn8cDi9lWaHY0lDJwJgoD7kzpz4F6eID6z62ASRI9FBoqcbccGhpODMUWloE6HZMPKlMDwWRDgzVZ05vsY1q41NjqHHUTAoaTSgXHyQqCocjRGszp9/JExuqjr4sJgXC1y0K4vXdalGc3ySbb5SHGsdg1ECvfzpsVGZOr8e38yABhwN0bLJ14YCY2O9Auf/3EJQciDMvcuBhpkYyzNnbGrmISbc7FjdOjtFEectpBqo6c/oD30AfSckYaOwagHnZ2AXfMNfujCnyXB3wA50fOeE2ayZCQGZroZnqIhJmX2sk3HlLheiGIXr67SS6ZxtImQ3DnbJWHuPSN2bIY3cDnYWZUwd9g0tX+DgMxVqPhmyOrzxz6muYXUONUwMaulbCHTO0znSN0szpd6stxzVmaBGkfTRsjMC/KDxOfZH6lGqy1RJFkcOOeQGX02QaK42dwO3TH4AFhgUHZk7vNluKSnyKFnyOSAp9vgiAYrELF/1WDBcNcNACLodzhhumLhzZzJgnA6GfmaiIRdOcDs20cZqaNsOpps0rmmnzSi7T5vr+LTe//KtmPVzfuPb6pi9SbBuo8483VqfViTFwvt91Mq1GxLZJKd8Ws+Yf5969vvZzzaK5+d7x6/s3fb/6wHenT6TYMv/YtefG5Frg2vW3v7rr90dubPn8xrYdN7ac+H7LsdDuCMsKbY+oJbX9EczY/HV4a+T6tkPXP14V3hlRUrU9kJtn37lx4Hh0A0RJD+1+3Dzz/s2dE2wQfzd1JHn3I65gdPdDz1d3QKCvoX42m4UPrSzmyo3NhwCnZrBA0s3Ta9RgsZt7T11fdebuvuzzxpadNzZvYJz532c+xDG37aNEm+V/n9l4/cD27z9Ye+PPn99470ScxRIPEFWu5MqzN02SsKZbIazW96teB6Ema313+vPvpt787uSb302tujMGBg6+VWf+8fX665/tVhuj6fZQIs99G99N7bu+dvLmsZ0RW6I92G6bCRGQe+jtOHJbMxtmBXWOjQY2SqUIBPKuf732+12nYaCGtjqun1l1/bNjuSyEpsDlDsfN0+9eP3Qujz2QBJupDC2bAElgb5ze9Y8TR2/s//T6hg9AF/jHuXPXz7ydpvszQG3Zt2Cg2KoRELT1oL53cfPDNddXvQWlsqr319efuvnnQze27bu543Oo/P17B7qgRPfs9yK+m9qvwGPUZ9HkVTmbf/8BsLK+510IiyeJ1u4EfT2CTVPP7abquZmqnlc19byaVz0Hcaopwjfenbix7fUU9fy7s2+hCD6+Jq1anIa+c+3Njw6kVYoq6alV2qOnn5kK6+nfTx4HDV1RIn/WAUxcQ/9g9ffvvxuOX4pV0Q21rB7DBIVFjhq/hCMmqqWryXqo0s2Da65veysmUEnJCIcp3fxqz81PzsB4TlXTo8ViQpSUXC1I6cbmKdQfsunobFj9jO66u7lmFwgxkLasacAJWCC+37kJ+vzm6lNpqvZ3Z9eBnnx9w6E4PbsJWK5wx4GYvbbdFHe62i2rfz8xBZJKrcuU7+trT8BScWdVcByy30/8mSnFjL54pfjG+tcFwzNp4tdXvZmoibcR6W1UyAXVpDpHqI4Sm0svnz3wOVbP5Whm4o9rfzs3RNXz799/8/ud+3Op502BB+r58U051fMk2LNUz5PAomNu04YbO8800cpl/fZEFElwOJSkcg7k0KCJUc6nsirnCPno5n/s33p9Aynnbx2HoZoQR3R91Ts8jggI0JT3a6smWtHeQZcLxChhzqK+RwV3S0q8ilxijmjvCcg0HX6kqQ5fS9Xhn9d0+Odz6fCNnVX9yILT2N8kcqiWWD5Gb3fKppNYPqKyJ5Zui7Y+c+rkf63yZk6/ZTS+LDR2jcKvSmOvpr03dhYb+z0sOFFrfAnlqvS70vjy563LN3aWG0fLjSPh4CFg0TL4778mwgFE0QqBMi/z/MZRY7ixNxw/FGJyOIIomh2KIWrsLBUae/C/mEAiPTMUTYRNoRHwX6uAuP2GaZRZ62CcJUUWqXXSgorUcuHAIoewVAuNnXnCixLGaxbXfYBQc9+bZXcYcu5WP31jY8AGw5w5+WnjS/gHhqNJHVhp7PJtYzAt2gjEHesh0/CgRoxJkRUHizRKhDd7+yI7IemGRmMV1t051tjpaGDwIluiHWQuQvPMO2NlLEYhwZi4rHFSb94ykgIkQ0IWgD1mpUUUJSvTGdCFYd02K+Kpxl4PmrqsnoELKTFCyY1PRxAGM8c2w8zpjVJgqgOzVCDhVeQil0tB/Bc6Dv9UGqdmEQ6UDe2wifM5hK+VeKBs6LAvWrU1MvKxsQukFTYI/o/sNKqpMUGPKJDac6ohWAOTqeQjsbEHxBUM+P3wf8fw7ZbCgh4FkH7jSwf1uJ2oySGG1nYNFFAB7dhpTuNU3jigBLmezQZZMGZloSBihqQh1WyRziRbBDNfclDrgJJPe10vOT2YPz60+OU+bm6Ybg8mPLZQJAxjwpgrPmv4OWKLzyH8rI+Lz0H69MTnGH2WxeezNn6a4tPCXF8Wfp4+h8RnlT5l4VfwszYiPlfQZ0V8LsdPry4+n8HPYV98LqbPmvj8HX3Kuo/jpxtgegK/HdngR+lTln4OP6sS1h/pU7bwKfysSMQv4WdZgnqSPqvicyGyoywb/AfKlWQsw89l8vMR/LQl5KfpU5Lxe/osis8X8HNoTHwuok/JnXH6tMTnAH4Oeo89IruJJYjPMn3KHl9In5KuUfy0JCHz6VO2+EX6dGRhbHFRYu7H3KIEVcDPAQmqQp8y18XPQkl8luhTgvLx05TjsE6fsgmdrhz+XkWfG5V+nBz3FNifBezPi+zPfPZnlP1ZyP6UeUm6Tu+eZ9nXAPszzv4sYn9eYH9+z/48zf48wv68zP78gf15kv15if15iv35I/vzHK/A8D3Kvn7H/jzO/ixmf55hf5azPyvYn1fYnyr78zz7Y7E/Y+zPIPtTZH+G2J9h9sdkf9jlgffU2Je3gP447I/N/oywP50LVGHjILcHKmathrwecEGn9+oDvusxVwhMd7vW2181h6HYff9q/N/9/YtefP7x/n7jX+8zHGvUeNkyy8+Yw11gcbCidjG5MBVkngOzWOzyeoze3l6bIRKuBBu0a5SXpLQJ5L01y8fiI4BmxPjlLw20j8DuGGFrtVtYBqZHB2Z0gN0OlgmUVOmh+iO9NthJHimFXGJjEUbPQMUyRZuVzPY2vUdB6FlVd8Tq8oLW21A1aHEJW9wtOGErjbYTG23rjS7CyuZbXTa0O9xohoMX8DTCGGKNKq+3f9wtgg7jwTopSeKGq/QBYWcSbIQAuaDBvLaSNtJwqURMBnbBQ6KSMK7vuQcqjvSIX2EWkOpDlcQdmtRw0i266t3CrnsY4PUZI65dNO7n6220wZKpkDtk1kQ7I+VYHpVbqTgIB/RXM7BfnUpwoWd9QY8xNNJjDMN/g2x6jdu6MBtCTr0wVi24la6OV9zis3UfzPWObqyk5yxwKANo6KqjtlCquAWzshhI7O7tx+7oZynPWyUb5uwYshtKxuf9lkgnUaKBii0d8oraFZx5drjhXheJBxiSeOSK3DPkBIEeHIfeB9YRXySYkXxgBlzLG4C0e+9PA2qO5ALK1GnyJYqzigND1kBZlhI2F3lzCq4HZe99II2ARXZrBJSkf3F2+GstMqDeJvxe5+3BP4IP3pDbk3sikwhybj9BD6YSZN9+gv49laAXWhyywh87yxEz0tnqlBXu/1kS0NkiAcqmwSwpqLdIwUCbJq0/C/y/bgcBpVbnRKU9DHBbxL+iPegrLaKnvaV2EFBokQDcWWoH/v7W8T/YFhFQaQ1/1RxoB/oFrc4/2pprBwUvzoaCtnTB/BYpULYPZ0nB6KwoaNNquLDV1fCB9jCh3CL+ZaN+W1bCFiei2FNvQoM7OEjBD4wIiuYQH8OeNWAzhQnDGVKXy1kQ2YTAxAWyRZQZeJKj4W6rDedBCi02vpIBLduCDxDU+owlS1OXvJxANfMzG4r+FuiWSYlTR3K1Zg5atpPOugVzS8JgxTX9f/t1Kgkv3gYSHky3AefPLQnQCU14MDq3BNSbU7AwAwUF161YPBIscX3ICSeb2+bZLFBxoy19tA/kA5ONtvGcQHnfsaRMg6fJ6F00twTUm1PwQha5T57KVDC/zwCm7hStQduxiqmQns40nyrp5DxS4e5eHYLB65vOWIcRqrIsvUrdKTvuqBOp9nIWcq0RPvuT6P1DBigjwiORBOTJDECknpIE5KU8QLLNsqeysKiJbBr3YmE8PkJAxLyoWLVavz+kwyKHGQZg9RkexUSKcCKcLlGFZG4Q3R+d+vGIFuuISp4FzPZm2ShnznBF2/V7W59H0M772U6cOslDhcbjCj0SKlSJK7QsVMiJK/RSfPtf9rX2q0E2Cc2Pju14yC+FIJsr+mv2uBUGy8Ph4gC/EA/4qRBg24kHzOLN4gAvjgf8Rx0wAu23ltfNSi0MG7PiAD8TD/i5KCvYLl4OZiyLB/1olBkJoJPZsTwe9BN+aL4j2ASGsMw44MPxwH8X4nXEWpILOAtMC+Hj4WVxCE0vuog9nhlbxR0FeW7WlOHkxWxDxeBYnBlHfXg4Aw7PiWXcM5nRyLDBEO9EehzznHikyzMjVYMyw31GcZVxWEfisa7IjDWIyAxPVIqtjMP5MtsWwhiLENpXdLTDngvd5Y9xbwL74MtADQpVzT7DlmhHYryv8a2rhmdu1WJh8Gy2VsVSk2jyxIzB53WYLuhco57NNRU/lg91RwcDYDGY4bf/adi9DvK3ArIOioR8RE5XuJJHlbxe6KVqV6h4KaW4+zROucdgOoRruWm1XsRJFFerklTr+XqkGX+sJA0CTTOjgw7s5AWLnWzS49b8TGBZDCc57oaxXq0J2LHZgdX0ozQ8gxJPj1HPi4oCW4p2bQCGge2YPh4lC1pVj2IrZmsVmHGWV7MGpKOtYg2KfvDs0hD8HonAHoq0BANxMN50RD3O8fJDyXj9OqhD7CCRb1UFLz2rBggd42EAFHIsOiI0J9rU4cyMxfhjj5k6ZWtsMSXaUgtj3yNpXDUzo6qaw7PAU8vWezWLSWwFbOpQ90YzgbWcOntMBeakZ+OS9ghO1V67Rn8pKkvETw16bvVxVqzL7oVWd/EjSkucHsNZ2t1tNKHJaZWmVKh2Nqj8/FbAxPi22tCIJfbSNITPZTBF8YhmenTTaHMgvmc6NVyi2eojvoAf0SUyAzgmYTgLbMexPD6OokvaaDb3iVkQMzsNmp+tg3hwfAQeHboEocPzX8K+43GXMg5PC+4UiDtQuHThqLTq0I8ipj60sGYjznGdRPaljRU3Aydr9YEBq1ZrzshKNloHTH9gKJZKynmJmaEpPDNEkHV0PShkI2HYHhYDg6N267HrS38G/niWWXSdylhzBi3IRl34TCdTTFHtbtKbL2agtmKOM0phzPp46DNK5vwMYEBPrtq1DLPrUV0DE3Gy42zEc/D1XvLtspBP/vu3xr33d/fEDJ96zXermqtYSRp0Ah7VI8Q8kU0dzI4itht+J7ysMgh6QReUE6dlOdaRXrNYXFCr1S08eF+X2SxQnI3+ujgIR3uS3RhujBVqvcP12lDXuNNVh6awk5fQLBbUDHKom50TtCo1Szlrh1pR/SHlHOmg6ZuVbgNP7Tq+7RAh9z7ASrCjvNQh/OdvJROwV9ixXJ7Pfv9WkMLza0F2jY4xUrrEJUAHqIMGsCGAsfHhBju8bSvpMgDoNtl8Gk8rlUFl61J+QTCoRYQ3WWmaQaV0s7YyBp1GJFJxjCMHtBqWx8Ndz1EYAgGY5aA4FyzmY5ekMrAgvn3fHBiCekuUASPmzCCFe4/wQHRkwyCOoa6RHtbLOOR7WHA6Ihlm5+o95BTxZqkcI4J+HsYNjNSZtbhZM6qWb85pE+BHK4Q/U4n2MrkKCPsI2K51f4hCy5cAdNBg6kyEdTzA/oxxb0oHt0E6mKwEnQ91iWLHL+4BWsi8gNlTqQWQ8IuDup/9cVwOaXCQJ7A/YDpJWHiWBabiSC8d4LuX5rrl1GwfjDiY7khznWmyi9iZIyYWFmliAZbERbqVDQvkom5Gp9Nq9Yd+ERzQhc5/Abqt3s3OI4tvh5/K93ofAwExgLyYD7K1QEmPsk1UTFwAiQt5vVLiMoy5XV4vu3oIqw153VQ4JjLjHotdYTLSSz9ooMhVHAEVtJrqhm5SVUV97VrUYzwVyGNkw3Igf9FDQjon9ReWWq7zUtbppDMYy4NrCe69PxDSJPIjBR4ICvCkpzRxyEQ7Cue+8HUB7PCJuMFAMg8ZwfOEjbEEZlonnVvyl7IckuZ9gIsJVp6G5vDCHo6RyWy2DUMM5JN5JTOdMXTbWpzIUGwtG4Owwoum1Zfcv9T4058MNicjLXdENptoElm4N1WRtFDfxXAUg5kOpGr+gnEa3p38CA+3htPjfZIjhmyhJzTRYktMjWV3V5S6+bLjsHs2aKSpXoznrdLjK4a7DXn4GuVQ2LPRKU/vPNvJjuyUQmdznxlhJwdtdoiOnbNbxs4WivY/4owFXo9noLPxWpd55FR6wYL+hyF1P8uqVEANZCn/xlIGPFekPMjOCKunb1Z4oaXFe7jXNz20oPCChqJnDvrzfn3/r++f98CvOzgX+AExlvcbmGhYj3/e/5vYUv8eKvXvHQGjXmN+XjRqXA+95L3BF1uYyEMLS1wRNCRxiqgPKRWJWGyQxgCRznscj5UPexaIBB9FPNUIpVEj/SHPHWVeNZgqnl1kRcUHqUZihWMefZcK2C5Vd9kZK7Y5XXdIi7+fXYyEB1+TT+thhbGBCu0MPNzLfhJEz2JrlGfVayguIJf9ZJdmOBXbYTLFWoHjDqMOoYj4wELsnBrrbkX5F1OODSsYtkM86Iw51hfxhF+wuUhnzh4K5qQnFUI2M+1ebGFw6I3kKjO1mYgldvzqV6g3Bgjk5QjoHYbJQ+2mJRzpUc4RdvJKwVVQvrgCi28DsJFBWPqMByQ/xTE9vnE11Mdh83nMqOYnPn2xspaCBvou3vzzAiF5uLdLNA2kg89JwkNueIpWaPQIoF8cN5SnDTWmYqrCBdCVmXzXCWQnERGbOBPJpkJAULeRlNMFPSsI7DH6aa0LbA4kEVf9oMQi6sJgri1xelE+LpVr5L2L8LwnzA2DrijrenWJypmlsAC5zjx9OiEAw3L4PMCxi7caYOrKV9niu4iPwYWCxJWcf6NJB0FHu4N16l4fZsIgdgT8gQqjBPPRrlGC2d/dow7K0W5YnxcRLDJfhUuHdXiBmh8WK/poLnQLbyY+Gl1yugLuFUQZG+UAvzcE5QAemO30aHDzU6miEsxQszpcwVt5wjncW5UCUhbtx309LNwNZqrSod0CCDfbxddv46oG0jdMiSzTo89vORkVefKKF1XvvaCSLVh074g+kDpedPiwA5HG8Rp4XtSA/5tGoV7C08avwEjoENOz3uTINyz1RheTEbA0cyK4R7irOzhuzKZ5bK/7eO/Sw/wEMZ/wcuoUSPfHYqVAiSxgxxRI8aTK+myZXx+u2AMmMJcLDZBJHXi3VAcw3SdN0CjWSb2mO4fZtDJgqeZn7YAno65RtAcHLRrDDEzNGDAdx/XxeVsPOm4I4JtVC6GPDlkOr4/AfRfoHbK8Xj716iT0Sj2MWH02oHzHS4wUA5GxiisDyloutAG8GqkTRlmNbkeiODubNqU6Ahkm1iSFk2yeiwyYx3FdwCwUpRjwia3DTHF9KuiEUamzvgY/BkEqYdJKeQwcxZy/5IGlOCEW0ApKX3zY2ZTyKvvqfM3r5aLrV7/il7AIn3EAAUwPbh7fQwlgnvXwS3cA+6udry3s6ujvp94pdnSv/Jf7sM/hn0UrX5VnzVeKGwgALPF2JNyKjn9hl0RTG0AaGK9yQK/20FIT0whAmtQMRdUSRPf3/ELQ7Bq/Mvr54rOSre3KWBB0CmydJIEZxIeUsYJlegT5BZRxhMc1VqKx2eXLywZKyEhxor8kllRA7HYbJclk9yFlYS0FukAw1UdRSvRLMdu/ZJTWrn6iECoVHgo0d0/oVzSQmcbXbeSRGti8oAUELTThHyNNRsztPoM6jJV8uHeZaztdHffBgLjvPzzX9f/zF794ARHD9P1frzLa/tereGEVzGXoOJi8kE6aICTDByyybmXEMrAoSJWKlAYYhoG8rrFpvjKfLFRbxTR2JslopHTyTpMmpjp0Yye3m2Vyy6lrS9GJgtTlcAK0Gl7mY5TI00V4lgbITsxUFOcTXoGrKCOe0Mr5hV6DHdlasFJdQ6u3dw2NlfESmzpHaQ5Bw+hNqNB0L9CUxFkMcqckdSJXTOoCzmY2bwFMCCYzZHHisktju5geKDVUhR5U6FB+L1SmP/NXsDkOo6YrbokC+xK9E7FZv4nJcoctxxy25/1b7/0dMEEKvWal8iyJEP4L/Yog3Pgn8/csUtz6upYIwm9RsEBF8lx03KmaLhHRr4ij5dh3BTGaEMhyRii2mujn30QODDttmLHx0kVA3EANLSxZvjS46C3gYnvxEoiFNDagd1Cx6WUeGXtwrAtJ6CYIoQyqgJnx1Ko2gh9rIyCyURInwbgaTRxXo0HvLHxYX9eCphfEOrJQLdHDGqcwbZHGNMWdFnBtURrXeI1Uti1KY9sijW2LGNtYLwvmedKbwdyUwMEXuBHtkx6At+CKlIJimJbIMGXjX24ARPRttG7D68zSXu55xCWn3iV0TqFsvhaszMkqJu6GdUqTu2PI94drfffdh8TO433ieqX7qNJ9vNJ9LKND2OdJOO7/9zjwSdChOAf8L80h/yYf5N9kgKxJKMk5sdA+jPpxsLD7KauwrZk49/qhVYUbIbCGVG0QWGCbmI7xql0E1UTEZdLcgebZxYiKzhYQGHC6zHPQA8RXC+EEUXc01IagJiO3o1rXz/hE4upkt9G5hP9cKpYuRmhIk1E7QL0HqrM7uAX//m4j07illvzW6BQwnV7FRuIZeHmS2ms0v0G0wawJTXW+06rfUcUONy3iPdMFNnvH/4P+6aKJwWxMIvFzBPImLHuJUobvMaizX4gxtu3wRwfdStwP0aP5ZrtZ3XuY/1MWFe5Qvawikti2j1OvWh5zy7LoyHswclYksH0gulh6pUFxKKS5sBvjlOFKf4TJi6OVzzlQmIl/HfySLSX0KUbfItPyNSPZX4vba8ymZPtNNA7ZJV76/hDPotvnVLUrcJ1yGHVyinFrRAZu6btR4XIsLFaWgZbUe3mALN9vD9fAMEC9vAwITKhB8Sh6FRaZgrfLxdYQoSe4zqnpMgAqnKEGR0XyRJhYOENG9YQzRBxYOF24sUKNkcExCa3Rwl71qhgAS6EqgIkSKAw2AQ4PKA1ThcGfOlQeBqoBbtZJtFEbbpcGASZhQl1+rR+bS1K+Oqh+1HtrQ+awxcQbgaHvJc5ShBXaKVVH/cooGhapHMGCQTS9PHZZ4nHyg2dRwvHgKW44FTiDhjHF3FnLPpqToXyyTb3leL0lCRS+hQm2VRBnIWb/ChbOJDYG1M0uW98jpR0a3Pl8xcNgIrw5scp/AcoekLECH9/F4SjrKsrXjIpd8Exv7FkRJA5MEDtjneSOqfONNVp0AvKcYL9NlhcbXnaY9JEY0n1Gui9J9znpbO/zD6HrCtmmp7Yh+aRyfRj2Uwe7bJLun1SujayzcB8nIJ/WNqa5KkQOdPKlpFPonMF4KbDxwm+zDKkVhGWJC3bqUu5Kwn2GfkCp7Vr4EqwTbMD9VoSbiq1KFkQDChKwkXQ1Hhc0CBR2il2f1ul6hYY5Gsv+EheTqzxB9WGkqDNgUnF/JRnwuBHUgtd3KQ0u1dsofFRgZFCG8qJLMOYi4wq7LDKwRtjAGpEDa0ScuMRxNTraY6yoKFODgvCDGSG25h0uQmSUwENixsjjep0AXZwKhMErQghKPQbf5a+RcxNNOOiZx50Bt0gvhzA3J1/nC2aJi14ecdAplje6lAHVQ0D4NH8xgjRBpbAfLWyukIX9YDtSeGcdebM8cGJJiS53L/UYjnbhfIe0EUW61Ja5TMXLXeIKcfnrithEpd3cTxtshvTiUU02qtSNUTQQC1I97w89luEoERT9YCf21ty6N2DJDUytzn+yGsJDg5D7WVTVKJOFUhgkO4bsZMeQnWh2Gagj6kEm0OdiFzo4IjnKySfB072UKaAwepuNz55sw1AcGX6WedmtFfyU0TOiuKsmCnj9qcOzpIegkeEkt+lRJ0KvWDB5UEkqwdAMLBuRw8esDr0/NJ7ZFmcLPcDnDbYIR0oPPVCjt1Rs9HK6YrL702dmMKcCTNAZsU1BRklq7w05AGjQGv8JtZU5xWHKkS0zwoQG045jdtvKRDZWcPbqTJTp8UwMqqVLLIWJsoovmejmY6Jr/AfUVpnIYKYxUZbQmViISlY5m9hqChO1mjBRR+QoFwGMVPz5hOKsg8LxRtr2k2bKPmqXFji+2NwtYKgdrwjLMig/yh5xB1MGH2sTZnYrdC0PaisVdfLQGwkvwOgZEkYfDTtMQOcAinbMWCrG4YgiZCoVxvuxNnHgRXFNTB4eDLYJ+UsYrJQDbzFxfCIEpo5CsaHYYpAxnJRhtqlB8zHmIUeDak1WxnDcmYcvMHG1WMad9aKzeayL72/Fy6VubRCpK1VqdXHeI2b4ksuHD9dOaoz3YL7GLFka8XOKx7uY+GNEsIi2QLLm6Y+n2bkl41V5G9KrmbvnnpVGEBKtcYXfESVWotyCjp+myi52mBjoZJtvC6n7oLvCGX4wIhQl837Ns9sZ0j9VD2yn8j6E7G5f7oSi+UPyx5DRxBQp1dJCHEg6f6lczUZ66ZUMrulz9gcxlS0P89mO8+b1xbKoAQjWyngIwR5zd1TE36PNLqZHOw+2R0otNBfmEVL2g03V9041gpGMAbF33xnCvYibO4h80IVpiTjFOUMxM2jMO9EFU7WWOqWpBCSOZCOx2f2ztmN6YzycOWTkikwKE1KsFV+xVtBEods0CgkmByl5Qp51aoawtG3DZRT7txTEZrJdWn0udwoT9RmraJuLGfMKMKl7jMimVKeUCjFZyGjTGeNhAGRgutzANMIoyPzvToNGHdT5YC7Nst6mkf4YnaKjyZpLyfPbhP8JeegvLwWlNlEgD7/kwu62Cfsz5nAetJU2ocXwshxoC7kEHOkvSRIg1dfgx7nCFqDPXhEAul9C94SJsiVNPPI7Y/CoK89/FNcasT1m94iDRPXgWAD6juoU4IBbnFitYyk/Ptxj9Oflh0YO32Yhac22Z20MPmS+YqlrsA0WVQF0aSXo1qoxn+6jXejcTWqJOK3JWoNfHQEAdsbMhX948+SmudjcVJ3DoFOxU4MiE9lUWto7aFdwH5BLQdW7x9sB5BEzZDxhKIxd7q300xaQoyi2UvtRS7h1Xy/CRCyT+oGf0en1rOV1m3mv2R0dePdIVyHQBGkH2axUHg6ONCt7jnRVJvkUi0WbbUwuUvuMdn+k8nhvAE5Vx0VT+fZ3M3CKWq3CS6z0qFIsy2iOA6MM7vHOfFaK+iIunRR8gJ89FZt5bIEs4bkIejMRf2YdqZ2oVLuO9ewg7SnQosuGbEEMWeV40UgvFeXCgqvMYqH2qXULctpgj4I9yXZ3M08uFspFv+8XRDKWIDC+M9wKtAcCaDhB+WzjJSiiDx1m6nx1uzUViExS1TteQq49DJOUJQIFnUvphGkJdfCHDT/I8JcuJQa+OCvZx0SxIufsJvtJ7KY6kus0ApgoZgxoVtcWtVId90ExZWYs4GgKJMKoBBvGCzF+kw3jhdl7kQXSLZJDlsXDdrJ95odpBrNd5qwASz2xOxmhpi0RpLNBsNToY6F6RAS/LOuhOM9cOGQpcNoq/YHKLwXmB+uqq4uvNAM3ACkqv2bQVCXrQIXLZjAz4V0ZhazpBkLT7zHudUOqgMjSKQuwiiaE+p43JrQD2K+oMqOpqkx/mioT2mcYTVNlRmnWzW+HxqGpFzK4pBAEzkIrHhYWZE2ukFW6U4QigFwXo3noZFPKjh4OZyVOxQ7fGKGM6tCwBs2EYV8UUlDu6fjXDvwrA03vkabuoogOpR4DQc++sosYFF4i9uKW8g4JuVNybhGq4X0iHHKhWZULtGR3EjfUFVoD0KG2OUUFyMDxLFoAj+P9hTyzwccG8zs+JPd7w1u4bqAFjqoOoFF9/xDYFz/qIxvAUQWuX/h5RltYxJXoLpvF0fgJ55TTpJY4XKrBC21TRFWPJZ09wf4wcxGuZKvpwswtwfd9Y5qhhMqG29IZpZVwlm8vTh7gyw+cpkSx9qr3snUzZfTZOaSVSbcYeqPGyUhw9jV/GwZudx8zKa+EEDN1PLizrYu7z5QQ2kj47Pwxx6zaAzybzX4DT5XS+dL68LDrJW0LoJapHDFmVkXOKRs55/wwtTHOSAtiZKkQmoZ9VJj+wCextVNnZtzkF5Ob7dYsuv3TBCNZn3UqY1yMABEv3AH5sKjz9uP8fbZ2qoP8UfSeZsbHZiK/Z+WlUKzhH2zlZpXRUe4Ix4dy1ddQ616F3iJlT4GKd8D4AxTz5PtXGDDfz0QCvlZG5zoCXzpdoQ7pzE8+IJzuovwKusaFrYl9xiv0xR2/fUaVfdLmWp/xPH2xx1D6jMd4xQr8tirsihO+o9dnjFECTvo+Y5AVRMdGn1GkDxOvkBnilehdkT5juCIa2WeYFXaHKmq9NfrNtyH6DO9BgobUOfQzfCMiGF+UjteY9hkjD7KbwOmayj6jkz7ZTW59Rp0V5B7hPsN/MHTbU+lBdjH/cJ/hMkAYhVqhn2RYgulAH0zfBXX9QdYmAjfOwlWVIO4+YwFDgRHDfWDZsg7C6Ow+0LgFQ9nBg1H2HQSo98HiTTxikq4P1lVxtTr7fvZBeVNmHywClGmjCjDOEbGw9T6QNDzOEYPl+2DOB7c4I5pFRDhG3/fBPJH39OBAfhqnzUDFrNVwFKHOEj77GvOu/IC/InJEVr6/zoOBE6qxzKBwyOOcUCtUKqguD57F1xPZQQXbTShqu0Ehfr49oSTPZcVrSnFPnCHRC6LwCWCjdEkAjFmsIG3SevUB3/Uiz9mnX6GU8Q6ljJcoybYokdB6NC1zTqgXI9F1fsrtSWDmvSYHVZ9yP4axUike3nmgWtE7liLlVBjyTiWqrFy3FOSoxW2XFaQLl/BrpWK78FbK6PvAlbI0eCtAXml0z0qtR1m0OmcdxpszmFbV9oP4c9YFyolvm98i0K0MNX6tgCHygtPt/Hi4UlicFzdEnlI4OE0viysB5UG+iOnGMHtJP62D3JGhHO/CyHOD4vpXsqjwcA1xVxkzupi8cUMXlL3EFs4/yFh9LFRZwA1x/CgsUO6AZEkvezz/aa/rZY9MVfmCOCv5GPrL6r5dEQkDPajMWhbo2I+za+RY+iN4aEK9eUhkPGlTxnzXZ73MUv+Ix1fxMcNFqLw/UhtzBkSWa6tZInWZw1Npn1uv8UxByxPJj4vk+VakynI9TyQvxuTgRABvss0SrRV4CRhLexFfoeBjW6Q9SkmW74d5swx5MxxpaMkWySLFxxSc1pIeXOPpydIA9UvICbv2Et5c+PuXF4vken+Q/Ci9Tvvi80+LzM5IpsiZj/5LOh1Ab1o8hnvmK3yRuwKbWaq4BbPyfIgrgzIH6gzaJZFuInksTkFjwguUXkHnj6NlLKIM2yH8IrGKiIEQzxzw59MJDJb+CqVHh8ATBZkukp7DJHF1rBwTFUqM1P9dQabL7sSkAXr6arEyrp+x/CG3KMdRRRZ6gc/npKJ/xHYOaLx6HlMqriNxeti8/hUBc8bmU4Itobi4UvQzBZKl/AFJ6EfdeOTfRZrdKdMeFGlOkCZ735NpIqU2QimenPAv2JQgXueR5YhSqaTygc5IcRQBYs1nSVIDZsm/ZyUrAeISI4UeJpRDoyLS/u3XIm2U0riiyJKGCAc+sSInksNSgts02aWRo5QcPJMiucrKB5eQSqpYBlelnxaaNMtcQOCE3s/ZReSBVW55z1OT5QjjGQOqyHAZBHnPqJRolSD9CR7zw3KecoIc2du8NN52/rIdCFmHla3U0SMiEissUZlri6krSYPnfUv8jJfOLztanhT/MjlGPj83omfKtsj0GAn9aChTTiNK58YbSxr2WJLK7icqLI2ZELzcfJYmtH2W2k99ELxhxNtp81RmCXBhyYq67PJaOZZZUTIoWEqBlYsw72lHpsvKlCTfJBLJC6lJgeXBxTjBFTYp788KSxPWEV8NRkWqxM3KiceLZEGHJTvhVj1i84wwEaVRnuFYJVOtsYzVYPYs5yJDGir4NCtIaY+TLcvHK4NsOop06+zkaXLQEEj1fA1fFoljVdt5QWndCyLxaf4UG6dUJEvMPKEqa3YSX6rmChXcUx5P1ME9I5Jln/CEYbkozmcJciR0UisqQYezKhU5B8fZt3h4TQJiybocsolvFY2m5awgmapcZaFS5Zoi7CvE3GWjEmuZEuyau1hhRanCE+fzJ72l9JIZuEYqNepqhpTJLHE4WJH6O3mKFPgFlqJ5BlhOcT7PCVaH8QpPChaH+SJJouAJ4nE3vuxR55YUTiyiIVkKqGUjoiQBLaJv/vC0ZGlFJgY0LGCJdjAZn6MUSxnp3ihLKWryesRhqRFZ+ORIkCGnFkurustsWb+TJZmBum6yUg8ENC+kQspbVFbxxf+PvTdbbmNJFgSfpa9IoWFdiSoQElVsXTPp1lVTJCVBEhdx03Y0JJYkmCKYCeUCkNLlWD/NB4zNN8yH3S+Z8CXWjARAidV9bfocMx0iIzw8dg93Dw9388A+w4FWqhSeuxklVlp2PNUZiiGhNGPqX+EASO0S03dOi4dq5IqmTFILiVIwmuzwwDr0pmMja8ciiX0zy1yCx2bGC6nM46Vv5bFmjwUAyoqHmV67R02Vpoa2S2nk+5q71MUk0jzxpsOx7Nu172AVfavaXUpDdt1gyGZNnazWPaax5o3PSSyNCkUm6JAAu3RPnIqxKeTtQumdreOtfbXaYQo1I/sqSgS9KTRb+u6Jla+6Br3dTLWUBWia0vUIM2uYxo5L+FzGJENXowYU0vuCi1ML4gBBP6fDjxrq00imdTfV6pBpxyanLBOPHLZVpxujchSrVJd5VfBHusZhIRO12DVRae80YCxLk00y72aZZvK1Z7Kzhy4je6Vw+DnTnsw/sJlTlWyzodcq3cNxXll5ahozmaz5xi2JxpUVk7HKMPmxlxq+N7ZkwjTTORazNpFF9mzOLEtVuk25NlQBgz87l4m7Dm8zlCNOpltvukq8fjuyclRDTziZKJA9cj9GVqYSiaYq2WbaLkdGhtpqKjFN3OZGKs9iulSq4KT0ussLlbyj+itBtzWv8lqlrW+oY1SmOetsW6UbjEYi5+LtgbE/mhLU0FnsyrSu58R/oTIPdi12xEivcCRmnsuUOHmKRKv0PU3e+0aiorJjmfjK6NeZHNaX6rqEx1tCvzRYge8yceuqNyjc+fyscs3NUEpCZPLKhwoUuQKrwLXcPVsmd9ArVKrBIJyrVINHuJDIN2vZhPcKxN5v+yrdPP9lIj1y8FGZgQ2iiKVcSxgtRR13ckQ24KytSPOHhZntq+1jYkO4kvN3G4MQAbYwwrM6cqz8fVMYfW1l7Tli5Wcrd9cV+/atbB9N+WhBbFfEoA92vi0OvXUzbTnmhZN9GZlH1KWda0tGx26mjfm9lV3RsmzZ2eJkOjzXvNHW1M4FxPZ0vLTKdx1uf9vKfUXB7M0KDq0Kthyu/MoqTu8i7Tl5ZUGo8ZKJJvN3ppa5ICGK8U3MVHWkqcTu5r7BeFrJmveUyQ5b25VbxdfwvZGVqeiHHI+KHnlnZOUollQmo2xg3wtIWrRuMqfHqoDBn47UvUU/sy83yBzgNcVgycb4hy880i7+6dOfiwT/9Er8MynNaC0cwgVuVHUkl1HXc23yrqmvRd418VrEr/+sqBzrVIt1msUanZ1PZVfR2FVVYF4dlldb5dc3+ZVDdbqhOg2QV9tTo6/xqWv82pqKbsanmvFrZlwti6Nk8etYPNoUrxbBVRk4GoOqiK9W2XrTXuT9zFyDn4yruU90NVcYQ/ABBLChw/18xERlDcNzJRPVcEJCla16H6tkl6t6YWQ5TNVbO0udj7HtL2z9iR1PvRyHoiT7hlJQ3xyoYhx+qkJ9cKBG4/B9FeqjA5WOwxex6Y0K6gfsUPZFTIP+MvZNyb0tohVvIbcbNuyRaLQDvovm91YleOWRF9TtYMv4ksEWP1UwVbAUi7G8t7B42zJajOWF3Rae+gqmdCEmuXKPutKATBYuY6ts1kkgFMY/gobkdBttr69UcKhGS189fP4ZsKdU9t93kJDfMTJI4OvFKtieBSZjiFbhyCOcFTBuii2+FDxJbxQFFefOElj0IGkHj8mZm11NvlQ94Hfwl6uK863LCRD+n4E2GZJLX9Vjulrgwuqt2Wa3HZQZzeCrJ95twH/oMN7sqmUjp/CoCyEjM0862FxTiJ+nZNQamAvmmzhiP4h/H8W/V+Lfsfj3Vvz7JP69F/9eiH8vxb/X4t+WtBv0N/HVE2oikNY3SViCG7oP8PGOPz7Cxzp/vIKPD/xxjB9T+ngLHx/54xN8vOKP9/BxzB8v4OMtf7yEj0/88Ro+3vPHFlq+44fu9etYE/jX9EzAuPcdrIlpNa5994Fom7e+MwCwL31fP5FJill8qZIUr/ZCJinCjwlav7iF37aG7BCTjCNhGxMcvdgRtsm4071EVJbS/mSNk7TCPIUk60J30kTmylF6NdHExK/wOgYc9k1sF5OqF7FvSky3FGAfELZyDbulk23GOdEZSiblFCHVWxqiXQTVSrI3a8i7mS06KDHJUo1hiq372imJxzP1Xm/XyOjEMUXZTyjZEs3HWN5Qg71MyAAFbD9szdwPQOte/H2GNKtv74Dds3RbnzAF4htq9m2N00xV14uEE9VQM6vpVtovkaU01FtTWAbVy8N3WI11d/j9CSUp/mjNMa95iQla9XVA30rPcP0E+cM0j+zh2VuT6fZcDyHZXpjrkGQoxwbQH+sSbgMquYiuU8UaXwB641Yuw+9c775DSDBv0s6eUILaZhEmeLRqOwll6B3ZX6MUvSFHnKKqi/FbxtRU9AbAwGn+kSYeh1DveZoXcMQrJQ4CamFlit89vUSakGBex30CNGeOQu3TGloQWTdwhUrTzS8xzVC6ra+hMZFH4dYskUfXqwgev1l3attPMMXQmL3FFENbliOHXaspu0o8dkgv1hyufAcS7Buy8RSTjAuyz084ReHGBFM19grxWFdV509kkmryEJNMbcV7LGeqKo5gbJxrqg1Isi6pxlCuckfVe6JSFU2HJOuG6hgStPy/uYb3pr5Jd68jR9C0k8qdxDYLI8bd0HWTk7ReJWIo82Zog8Gci6FLlWy06HsiE53j77uENpQ4bzJO0zqU17K8cSe0wW0y9W0fOcmydOIqKhdCcckZ/uPxgrPt66BtxubcBp1IYM/Bt2VmqQLcI+MqKGfUNt38nMhU+2SbcY3Opc4PlWxPfyrTjbNsNKU0d4eXMt0i1NvcEJ+GdT8z89RuUCUcQ5tSp6tdyZ333NgU3BzrwmZfwlv3NZ9jmaquaza5MuO2psdljcuaQ05y1kDGhc2rml1Os25qrrli46Im4Wb77mmGXJ15fTKMVZoiAGecZl6evOdRde9O9rhd5tVJwmnem5OpzDRoeZ+RW7cgV7FMNEj6d5loUPWMu1x/BXLGHXduQApuiqkPOeSm1N9/dC0IRYBkqkniU26ZpVjucq2WXnk/1okKpUyztMoTM1WNwTmnOjrlTW6VT6UcT808tcFlCVehnMdmhlrUnGrpjCMebkNl/Llpq5k2bZ1PPA4FaXI1Q28dzVBvGn6v6o8+OVB7cfi+iuu9A5VPQ0HxXagXDlTW9EG9dKASL9RrByr2Qm05UAdx+Lra+kM/FIf6LtIBHBUP/w8M6PS8+RAUDZq1O8o6w1RsooQDsA86Sn28B/ERIewKaxd0jdtOjdNmeFUd++8OVLMZfq5CXbk6xWZ4WYX67OoUm+F+FerS1Sk2w40q1L6rU2yGURVqw4EaN8PrKlTkQPWb4VkV6todiXEoqLsLdeZAnTTDYRXX0IHqNsPzKtS5A3XUDCdVqIkDtdkMe1WongM1a4Z5FSp3oHaaYTatQGVrNtRFM0yqUMmaE3ZDF3ibhF3YApRtF4sd5AzbkKsdlFhZR35Vap3WFY+uuGR0VSnUXLNDHEGsjNPmz+zmpPkzft6JkgE+TAMcN6cUQ+Mo+1J+ld4qEyeu6VECGoRRIvbfMADhSrmjb/4sKa633byyDRFHYofsOWt3HB556EbsrLdxuF+lQaUzKl0vVOFAHXmhRg7UphcqdaBmXqgNZ7mNw81qF8cOqt1xOKtC9R2oH+Mwri7KEwdqzwvVdaAOxuF2dasfOVBvxuH3KtRWYkO9G4dXVajNtdAEWh+Hn5vORFsA38bhpQMwndoVfRAjXq1o5jT64zjcqELtOFCvBK2rDvmx3jUM92kcRk3a2DbohUSonnxqnwXSpR/jOLd868UtG9G7ajAywz2rdCIIrxkB69Mg0wF6vAdj3KLwjPKghwjpSejQpN21Sk8ZcH7d6qVzDwQSMUGt+e1wDom7qPTwdnVue+v8btVJkar0a+ycK122joF/NK+by1SivOrJKHjL1nqVyOPIrfhsUcX4BNhQKoFKo77H02rdO4m3x0OrYiuQGr5tj84Krgjd46nYV+bh37R6JQ+vONAqwuADnVhl8FxsF4HEXu3nViMoTBgONHjaktVHuagdEMTsH62290m19y/rBv6ztaw4ABxgZrdQXLty4cQzseyo/1jzjk2WkRMMyaqajpwC6TuzvWQDy99p4N7aHY/MJVr+s0Oy27TkoLYlPWt5QFyxu5ugN35CkFtVQkRGp5JbUZvz6oFBLk/jHP/CufRcXnmDG9QtDh+WkWtDcrryRXBoJUQNfop+D4zW9q2pAv11g/yCIBZB9W/T2Hf+EVlUx60GZN1bRVaaVUiH6mrgRSWeUcvAdWL29Va1O5zF+3GYlFWG3OFloIWx1ULt3shxh5JVqvxcxVVOrUNUBtekA4ad6fhQNcsqrmn5a7heJJ52WbhUbM+FuL45o/o5ROwO4156Z76w6pTRQd0q4RscdJievBzbBvZgxx44ZLXgfioOYeMMgNMbj9PZBjyzDmMZbM/py9jfzNRqpgpW+r+unf2a4bSWlhm61dfUZbfNh7XqYjmxRoRv0RevlSN/u7sWNmQgvS3W/tKezhvJQCkhKw346IroQuqrkoCXfp5pZA0vxnWlVnLz0tLLJ72qPeDSpRA6RAY8ucFVHzFHGLT90Myl54FVPtBDPmalfdRz1N6F03jsPyoubCrp+J1qkPvcrLg1z+5p+a59ZEAcZN59BdynVJv81rOIfzjbmkIjL+z9Jw+qPQuVWo2EC5+4OUfY8w6mw0kGfLT6fBrI3yA8HodChmrxUkA04JYHf0K2yKy07r0r7ULzLiEMBl3zNuhyuRGYUTtjYkQRhGK1xjZdnFYm/MU4vASxHNrB9lcPHrUqwvdBaQbc1cVfcvFKgTfObL8eh67qe81BKXkr7KnFrnBfkXbY3UczQbjJIB6rNIUWXJ29fpqJ8X7waOFSVUps5bK3s4EOwHAgp/bQ6uC/pL6TAaSt+gWvV1pGgXiE0NtaIatNwX3LCXSRXPFGVxPBQMIFNJlUYiIvO0bPSSBKTR3/s1OtDYFEmM8vX7Xb2TbEYjV1Qe76EttTmd/JodgO0Zcw3MNgbZ9IE3Bvg/9uJfzjWIwi/ngZboa4esAZtjJKlcvTVr9XqbMg2Mi2ydWE4YvbYAgAl9LBVhuu9uCWES7i4eYd7v/gZhWuweB6DG7ewSIALj3hhhIuT+H2Eq6+4FIMrqICVFpP23ALCvd0cBcMt8RwDwY3W3DnBXfpYAAAd7/BZ4gLKv6BKgwUXaDGAhXVtvj7Xfy7Fr/PoB7x7xzgRVpP/M3Fvz5sDlDNin8xCAAC9xT+in+F+JfCX5F2Iv52xT84xUbiOxX/ZuL3hfi3K/79EP/2xD/YbC/W2mDqFGytwUIEp13sZATWF18t4qWiuufDGz55yX+SgIEoFiPTxa14nn2o5Ynq3noT/7ykr2Oyz/yobEQPcTE7tsZSmWhZDdvherIvhgOtr2BfCvZR8jXyT2MjfMJVI/3HUowNpqmcJYNBkEUs2uCajqxEW7+P0Q6Z8oboxjJGMPLqHeMPvw3zNVYBBiyNtjYDjrkeRR3Q+ldbBkvDXXQOFA46l1E2isAPUgiWokTSc9rGMSHJaf/iPE1NKZL5pbLKL/0MgADwaEk6SV6qwZXi08BzdlCuYC2Lojdgn204bOIAK1tfsVs/gwnb+j54RKcNUJOsM4sxNBT3ui27B91XjkZBUcJ+RwEA334rt2Wgy++Qdy70Bhia/knvaXLcGw5lhPZM1Y5eV6xC35qyLhxTwX5H6OSOIGmClKMbq+THptVK7ehFQPXwr4b9MK8WKuRUVYfplV1rPunhaNrlIIc4QavFx3ZZ4iQtiLc2hOGFwtOUTz50dcDvnXYrP0BW/S+qUJvVVr6sw1VX+es6tHUFtuwCZKlrr1FawKFgcTCNEGtDYwS2IAVvMyUw9fCqCrSTKSCW3tTC/4xHnJgU0/RF5aKCAJ9VaN/n0srWyInzcwMhyeqYp8VF3SgSNOlBAhrtqaLHjDLNNLQ49gH6K4HTjp2yuanGo2QZnU0ClzjCuefaVzUDjIx2GH6/OXds5JJ3bJV1ZBaMJ5Fb55Rnl0Qf1bsdOZbSYFOXQxqjFLpMmpG6ScpzhpQH9zkVn5BN0xR3eMmkqP6UMAo1OG6A8ZpD10I+lZ93DHhsBlFncjFXZmSl9uARcnRt9hdaPWsohKYVhTNwanq2fM+nXx59NfoKT08q69UgWdL9eYc1CVRix13HZgmMQmnC9yYTmkDqFLGD6Ci2BZwgsRraWLTCbZxnFp/x0XmrRJEiybzeYgpCukBvklkZcggyRbr5nLphSe5lHbqVB6W//CkObrzegCkywixOVWxzE8CIsThVcbsNAHwVyrNsk6tJRmnYK/mW0QuZJQakfqLqhU1MWPna0Qs5NSEvIbCYB+ib2UjRPS/QtgkkV64H7rsJJ8hcBG+k6qpdRdZJIpVvQrXkJkF7WchA6k1wFShXQPBE2FjHlF0koWwUi5beVpXmiBlPiCv4RhKf8f64ApRKIH5dXAEYAwDvm5NEsejLbZtt940f2sxXTzm2tmN6X2YeEDZDY5Bvs6IKwtY/DIIG+1UgNi5jIHheUIVhsz7ZHC8MG/UZMNO1KhQb9ZlQT6pQSRXqX6pQsQVFj1qqUGwWd4v+DbwwbDhnwDyuArHdnGz42IeIreYYhsz/q1BsK2ZBedcBm4sx4JWvRrbAYxB8VlMFYgM8BoJXMlUYNr/TMJ65Y7M6OU74oKIKxWZ1JpQHF5vVyQW86hsoNoSTrBG/667CrT8JDZhq/jeV7y//QZfn179VmI8II9UzWw5xIBPgCom4qqcQTGa+68fHaJBdpTLF4mfQmlSBaXkFxdliFG/0o9UjH4rhYhSvdSuO9t9VMEwWY7gyx0IQtAqO3mIcnzUOekxQQZIvRnKpkWz4RiNb/Kh830bxuIIjWYxjQ+OApzIVFPFiFJFG8dGDYboYw7XGgG8kKjiai3GcGS/s96ZrFRTjxSgOmwrF9vpGBcPJYgxDqxFPKij6i1GcG5OKzwQqSLqLkUwcJNWWHC1G0tNI6OFABclsMZLcReLbtzuL8WRTvW9XPU25WIwi0SjefDisYNhdjKGryU/1jUkF4WAxwiONkB5CVZBE0zuQmz5bchPyMYWEsRmZjNIYJvICJSYQixjzgcb+2n7YMP7axlZtJKvMB4qToiorHMZSVgAhd16+dMNThRGj80ipogzHPFXIxIKsBfvhIKyvemxCajc/3sE4NgcjL6LJYqgzWFWGMDNPtBYzjkYCImu7V5zDnISWFE0rubOz9Wr9sHu8ddLdednd6R5+gtu66IqNPLcXw7cckdyuNU5CSzRnLHu7B905tS6Gt2qN8y4tJlON0Gi0lGQfgnFnowVhHhlhnB+IBQZP+EZRBk3UkyXKdv4bLdH8pRzxB49wAur0FeqszxxKUUN0fthE5yjxUJ1NTXX4Lqp6xsS/SHZIBU9L7XKslfKJqn1m1I7P1yqVX98Fzdv30Ly7omf/f6aec8kfTVgIlgjLEDcP+AIi5y0xl85WSyxLHpckcnUaQiO/qiBcuJ/jqbMLavbznr2fZ779vK35Vnp6XsFyMLqD7bzh3c7fdeXqrX6l/jd3Uf+1t/6rpkEYx9Wuv7uLqiNv1Z911evJdaXm9buo+dxb86U56OhMoVL7t7uofeKtfd8YcjCRq9T94S7qHnrr3tB1g2uJStUf76LqM2/Vh/rg8jqTezW6g2OrVzm2ljojFpL2BcRuk3RaBq1Dey9I1sxdSz4RoQp1GUn/rDKUKMsoZYGxZ+DGszKKx3cxgf0n5gSi8m8cXUYJRcHl38bgVm6dv2XGrbN5qTH13GhMrTmo4No2cemLlArcdxOuTGZZb2LcHnCjFc3Xi3GXX4o5A/n2LgbyxBnIAd+ognsLvE3Ft2lQd8VCJFbP//hmFL0vqR7ZjwHdR4MtfdvcG9taa7wXleHejTDKeCurn+Spaie9PIc3vOXo3LyMWQ4N2oAyInx883soyHXZ7XHAA0MLyeQXcOAlsMICMRRNGwJz6gacS1f46pYZ7UDmFtMQTlE0rdKl2NbKNB4gCy2ZDd86N72MCzM35dCUU2kYBL7VrEv3gUwNwbQvU1fmcHX7vYwzjDxpgsvksDDgFY9jqJ0Tn9/Oo7sQl340XaLF7xSRaPFvpTLVdPRjWj2BvydWEz6N/vktOtMtqnpzWdTA9yOfNlVj7FpPKB1cL+6C0nWf+M78c92EQ3pA6d57TO+g7qPKcQXvMpemevSIc6qMipE31Ut2nx8ZOi1/eRejtllpOT8gxBXCvyFZPfLDDPWldM2G6h1eIjptfX0XbU1/s622RYVzm55R2gJ+4cDDL1RwvTVx5dpswYY6lFBK025IgVGVHdi6iyEcV4bwP/GA9PUW2MKHlc6IHN4Fv55X+HV4xElsJj3itMkmMz309DJUQBZ/Dly0mELbW4KCpGDFHQ7banH9ynhOuta/hz6dgxDSigAMlFsBmaKd9/KwaLWC5pfiq9la8YnForHgdSzXJ2+j66D5s7gJkrQIztJScAFi7qG3p5aVmvGsFYkVhdm+p6ypv7Zlju+NOWWq969NtLjTfARqcofRnF4HaK3NCIKlxmAoeGshyjT/E/ddXYOVak2/kw+/nGW9fRcbPatsdPWMXq5Opox5a56xJYKgTb5hY0lBvqm0FIj+LVi1Xe00Ds/jPKDI5cEgTYpeLDaQ1O0F+Eon4MdvAeMKjsTEnTLmU3z0E/WGnYY1R7JmweCxe259Q6hHF3ziVZmWuxjaaR0NrZo+mwJhnXT51gKSpicuVDMJ7Sdy0xY7jfgy/YrFlWJSj8Ghfont2pzcxUCMKgNB5SoPAdSiKTtDwaPTOyV8aNHvDS5m8DhFrh10N5IJcdKIc4evtmgJ4KsFdBcPFTQ1dkTPjziahJscTwvMU+ux2IC+wiZcLNHrryAmosyUgxDeI3rzj6BJWVRD56xXYFx7aGARJ9iQB6sMAqmCuMGbP/75D/UqRAxQoZ8uhvL3P2QrOD/X2chBgpmuvwOFbPQN0hllfpzQWxs596HqJYlz2I/EfFDH/kSVQXPSKc6jJLSGV70ambLWvIkv3xSBqwIkDKBIX6k15rvq4bVrQnQX3PheReqpKGOUskI9ZZWtnOrtY7nQrDT18+h/dVPL0tKckz8Cp5WXd9HK2ZPfaGWhW7kpPQM4jdy/i0Ze/EojSVS7TKfRpno1IQvKDqS6A3tZTQ827qIHu781zHp3GQ51q+Yod9HQnd9p6IkezgPpGMG9P76LRjZ/p5Fd3cgN8rfgqg3uoomD31uyG/yWx12wmwZd6O1UaexdND3xaltGehHuxZ4nq+d3UfWPyqjFdOTFCYpqJV0SiL9qB+tm4TveagAg+9jZjD3KrFlpKGSkKwjXfvVOzoTfWbgXBvdnOzmvGsreRWNj7zrYNaQcdD7h2rWmd1D1m1uME3m+CFUDf5g0nR1buPa7d9HGg9+Zyz1DmlEuM1xD5buYxMI7iZeusWTVtvguhqj0Vv4CLP23xvAiX/w4HD+zXVjvdNnNwj647sDWcTxFDppk+LfsWu4598NWxygg3SHMuu3gXUkP93e73of7sy57tDRdTPg8ThRpCjHKwckL/VKp+SXe5DTUb3aBQgikJ9qGnYAwSVqcSNn5BIKLNJwUhCoN77YnoHZCr4lOmlUnhytqWN8WhChi5IsvK5dvNQ0ITrGg+D1/w/omvyPsyF1KSeTyM5Szh7N80wrflWChB3/QrUnLWQ6DWdWVXNZpkvKBXQFPSSpEN91PHz4ERwgrBNBJs9HDYdY7Kx4+fvT40crq44eU0VAiUQOzVzi7UUVZh3Hl0b8wsv9SwfYvt8KzVotnrWH4AkEDR7e1putFY6jQxYr5djFsCOyOGgeuAzN4mNoUzG8Q56hCy8vJJM2KaNgO8GHwOB0ICAGQB+F/edjpdFpBL4sC9NUVDVF/o+clH8eDKFxtdfLJWNCLxsNGq3MWj4FAszljSw6M/8Vt3MnStDiQs6tca3bA3ZPSLdiDEIhhaQoymzcC8qUGtAp0umoSvjz6igVL0nJJUXr6ZRX1TOROGpbhgxjodf4l+eo4l96PzqIsEkK1VjOCX+mM/Epr/RWXBlonGPelMZhOn7r2LIKKvgge/EM6yWwpFzqsEyEAHBd2TBv81/8a2Df4AqLlH/A3HYwtRa+ZrdUBWN01AapVcWgEYXQ1iCZF8BOyn4p9G9yAt+0AcbVwUdzI5pciURAFuG/sgntTqy9WjRVIt/qGXD0WVh0scSnUBvgc/OL/GhksjQxVJ5VEVCvVVruRJsOYX3/THhe7KD57CKgeQlHaTHWNGEaTKBkKknvAZd3aFcC+vDqvbUoFF7gpqJaf1xwgE/YOojS5h2JwFpGj7j5pmVsaUkHJjLufQZl7iJORW+BNB9yaGb6VaIM62K3NtRFng3IsDvjM2mVZlKfjaYQbLbk5dXTNTrNI22U1DN70J+r+B4WPLBRkNiE+KLb3PuLLAV87KCDbREUXGXCTEBTm7sArKXd36+GFbD1mFhEUqzjpTeKVv3ce4Y433V/8A4zXITExd/0qpkhiiH4EDDIgPkM1P8kS1KICt2pOIYkmYcLGGgwNpcXUFqZnnsJW6hpIoPfGBMAlUkKufQiBroY1plBJUzbpX4PHCleTrxQUauSGyFkX5LXpxyr9EKdck8+yx9ovl5wy1F9bc1bpNQPZh2NBAgAgcRX+zrlEfWxWrXi6+v4qoysp4lTpPkquyJu7msbEP2qJMRwPpgbWXnIdyj6DB4x8FoMeIyT/j/fQVYCc56f6chD6/IbjvIZaky1NtaX7D7kEZPozdTvA1wL44l7wUAmuE/Y8Q+/wQ+sawChTZnEDaKn+XFE0pIKqzOYgKuOhiQmeo1cRiMRaDPB4eQVeKFeKxXmqnj/PLV5bsrZUbX3z65JRkr31cWZtaXiBXi0pEueVeOIr8aS2xGVvUCkg0mrh4Z16pQA9cZ9bZsXTMHrzXluMXvxXSlHyglJiBdYUnLc24Xl9dV+szqnt26yoFBBp9TXgg+3q1hOptWXIrUOlECXXD7pvXw3KBSUee4s8rt/NY9/uHc+p5cpT4GoOPPpqqJTAVFnm5v49h9M33OPgCNLDVIkEHt4ZMJqS6vLae46vfO8qNGBMSjyBuOJZIiHRyU4IN7r70WjramJAUCkQ2sgnUz+LehfszJlPAK72qUqI6Umeeyaw8CxzhZwHbAqUDeGZX4GOo3XSM99wofX73MFCU3jvUNWXlQNVU7bylNKHZFSEVciWtnJh/tCHini7pbqJtdT3svL80odjbLfU7La3pQYqt6WLaqkfU/PpmG9KVHZowi6xGjnCKy5HhBQUlZJ4SakycvWOxyawZpxdSBbHnVX9U/BpxMZMtCQq+AZILwwjHsMeWuR+IS5a20t96beD9CsYTbGoz8ZJgm00GaYTElpSyRveu5d86YNZWYGSVB9sTU7ENjpRTvdCh/iodl7v9C6j3ETeJ+QOBAlHKXZRSFAs/xpit7kivACWMuM5VeEDRD3JU4Px5Cabyo/EVXywDQhNHkX5CGEsubyeRB4DNYxvOtQkQGm8XADvvTPMNgJlmxipJjPSTHgiinCuru6mQm51T91R94DQkJtd74MfK5H2hWzn9NrZgWWTtkxzmD2ayv6Xna80f28tprxK8XfkwXbvhIxX7P6/bQd7lhUL1jIzWlM/Uf8G0zSTWGsG3ZhuBhVC0QnMhTPfpTl1P28cNM7hPHMEISg8YymoakO0U51ZABZtEJKkHB416jDEj5+JP/+q2ii+/va3lg/PDiDZU0igHTvmaDrLBvCP7DVqnN61m+wBd3HEz19Ch6ovv00lHvmoJZy7aY1qqFh1TjyUGv1wmuT0ZwAuMeOrLoVNArUM/WoGN7BJDKMoQXNsoTuxyGShVQt9uv3MYAvHYLsH49o0iFazomx9YKNutohoNZk+kZpAr9eRXJQY+CksWvimQCQbq8wGeOYawuGZTkpS+zSEcqWWeCQvGOoCLS+23tUS2CRnGOoCklUN1Jpxx8Id5uacYTZXDLXHf1YYba09JhDmf8sZaFYUipYCs0kEnsgF7qlb9W+EG1axz9SG5TvExSUHbRZHUmB2pjSayIe8hzgMZfhpJxyovrVgJdnPKemAb7R+XfmPlSMt3d46uYZKnozJzBGWJTFHw5jxzuxoq5l9xSOHjAV95keVXi1DwQd7T/KO0t1J3aK8M2qzg1p0y07XE7Z6mRJdBabqnKhDsNcVUskZhpaS9yqk0m5O5W5OeTf3cepIX0jbasrh8JyDbipgoV99c2LSJPI3BDMqDcHUuQ25Ekvi9s0Q51jNeEBGqxUYYA7XMTWC/2n+VDEQsu0qPmAm8SD/EHPDIfuRyEYrZMVIAJ/Xfyb+/GtgVy7SgJ0YVdmJUdusIv2qDuEp7znVZ88lwpyLB+roG1UKg3gyJvAtvYu+pRGTBJUupwlUS0XwTlH2cAQMKijnG82YYvA15N/mIL1UVgiNZi8ZnMMjS/yYpoNeH+6BrjlheJ30LuPBfnRmJ6xTqfv3vtIcjuBiMxOy5JcRyEaZ+ONvDpiGi8rR5To6hTUSt6Nh3DuUpiIyla7bbleVdXnLy+zB7gwltlHLLbuAkY45YkB2jfdZU7bFkVbddiTStTuiTPJSDCkduhqkFfRUGnQ8l1TysJeNogIf2MDlOUQGwMt8ecGpbvNZRsbrCIB7+Nfgv5+c7B3tb52cBH99SHFaBQlAVPrmzQ8IAjYCanMDFXaTBusptFF+gOnFmYw4BIZEb9rB7oxsin44NkU/fHE/tujPG7YzEqLKaxm18V0T7Wny9GnwKcO4M7szQw1Au4CtVmgRZ3o5g9UD/jTNHtr/9I2DacoqSgdHbODRQj/xdKCfSJ85FQgQ/UxShtR6EPqWeg8u4ZEhuJwrAlvoSBlBSYJPcaEE7+EmofAgcSi5wm2FkSjZH4XR+BJH3/cyMhLkQylVdMNO6F1tuBDE66l885P7Th+GNVmDdX2qkP5wVYluqgmrNWhyeRme+Xm9qVCAv04VeaRss49KamWeuFFgRcGdGHP8soqZg5O6by0tK9ZjQ55XSKvJDA3293rJx9O7LFwvkZrlHBiI9vzerA3v04C/lUkHkAT3bjJhHRL6MroQpb3BtHgUg5EdJb2b2dZvezM7Jth0HJ5UQ4IfOFB9b9D6Nw7UhTfu+zsHauCN+77uQB2bocph0Pb8hpZEBuXIjvXAjnFcr1I1goO1NngEHsrv/SfiG9hVmTADAPIfLpNeP5FJT2TSS5W0JpNeyCSZ8B4Tinis6obvTH1uYb70dS9TczgtSvLwJNMOMQmPOUrYxgR2vSUTj7DlY92AS6xALIMnqpEna5z098cyKYUkFEdlygRODvCvLxOKBBOiKD67ZqNZXn5TzFDBEjluCqZqj/mqJkSj3e3L9BGla4NfmfEKzrLCNkqXecfQ7Bx9n8mkLibpeDFqQ5SYji9HZNoHhMX9BttHzYlOfsm2tZSR6AyZtMkpg4K4JJm+S6DSQlJhgF5yiACZNsa0+Idq6iHMfh4VOMqC65DpO11KV13COqwARWrQmmaWTP0oU42oPmp923lqQctkI66PGic7T20LSNacDi/7mBKjq0gN/1Em09SioxRz3g5KTAK9r6oAU4jRV2MDYPJwVvMbUyIY6su0t2uYFhWFu46/TSmH2TtFHBJKZj0AzxlWxx4N1XxgdfFEjwQWJYc0+1YXfqxRjjNtH5oyWTUKUlRcI9VbGCd5pyITP0OatQjfVc6OT5iC4YpUDWucRiFtZGwjTlRzjQny6bpMLSk1cUdiPaZ0t4n9ktKlZ0fVBoQHBl+mTKeYYoO9QzBMAs8OKhl7gDf7Mun7E0pSKx9ANN+h6AeMo2BqDow+HXAaMUSqhZR6qcCa0HUI+qMmG6voXZm43maUZuPaplS1rw+opFoF19D4sZ7tcYbfau3gp4yhIlN7lKouSRQDsSbTbQI1xORBb6z34zF0aWxT2HVIslr/HSvq/dBbGyYVbQtkygZ0AN1+yZQLaMW3mao9w+9cH2aHkIByCi9UqAWMcGTC2RNKUKdYhAmGHkJtjoQy9IHXX6MUfd6NOEVVH+O3fNusiCKBkc9f1XbAD0b3R/oYP4TGnKd5AW/OVaBkKH2uqdsUv3t66zUhYWQM3CdAM9Izn2T4reYSP8dpH8iJTV/BI9CoemRcwJEhOStVC1R7Jshi9bDdxiw89CzaeAB9PhunPWNUizWZpse1pDSQHxILwR4iiDWFWQdIK+SaGhVYT5Gxv8/xe2gdpNMEEytn0qemSlfLGpPAWkj18gmm9DRX9hZTVnXnckgYVvxZyewrqF6qcxXRXMPESpveN1W6qq/p8PA7UFa+auFtP8WkeKhG9/MTTlHNwIQ0U2g+ZJgAMcrU+YUpgljooS8xCSOTMruAtYOvKbXIERHamqlpeCKT1CgNMYnCWHJfERPc06lTvqxKJxsV4WQM5ZSxmSJqT1SqYhEgCbX2an4hIVGfm4DpxLvmT4b2KT6CpungZWp5ADEgB2uKJDc5qbup6A9DHRvHzgaDHdms+6VKNlr0PZGJDgv/XUIf6creZJy2/07RJln+nQbb4DZRkG0++jjJZO7PuYpDl2uPS87w89sXnH1gsdzbjO3A5q5PJLCHk94ys1QB7tGBZnFzRm2faZ8TmdobW1SmzFSGxR3OuCl7Ni/4QyXb6yKV6QYvN5pSmkutSpluna7b3ELTZ7vakJmZp7aJKmFzYmWp09W251ExXuwroszN2TGZqX0Jj1Fr1CjGMnVH7R2uzDhBelx2e31DHXac5CyOjAu/M3iDXU7D8C5qM3HFbz4cqoOOm40+WjYMQjnrcobnoB9yO7oHu4cGZ/Y+VsmbbC2sKLSRJVa3WeqtnaXohkze04RwqNMUWTrjtFdGP9/zlL50aNEeD8rLOMsLIaAV6IHubZyoBf5OQeiFkHDalu/MnMpMczds8tiZvHKf27RlnodXsUw0jsTvMtE4FTOeps3ag/GM52TT3lEFt88c2kNuSjWkipLlLQhFTWWqeYKl3LIN8+jpcq0YJUlthlgnKpQyDYPmqKPGTFVjcM6pL+wjbZNbZXp1V1R1auappS1LyPg4iujFZobaiJy6bp59EQ/3uj7/PkMSrGxB1QZxbszNLuTsbB1v7auVCA1ryojd3D9M4/jePBCYhOGiFUM4VZq272vzApZ3U0/48h9dusgZm9cY/Geva15q8E3HqydmhPNdAtkPZ2QALlty0KW/V06Lvq8hPP85APXhWJf6vKZVhZ/XUFf4Q7El7T8Vh38qDv9UHP6pOPxTcfin4vBPxeGfisM/FYd/Kg7/SYpD66D4NvtTmfinMvFPZeKfysQ/lYl/KhP/VCb+qUz830KZ+I0Nn/tTW4V3Rbo7/vMNjJhB2wfFSLWoDRabtvnggwfkUtK2V2zatuhYSJmef0uUD1dpjN5ROpEwts0VT6RPSXKLJ5Hw84P4mVVBrMLwoY36cxUrjd5XkZ87TiLHVAY+w2g+JqN52Qjwg59dk/9RNo4np6UBck9GBr3GkEBWR7pmR7i9P++brnFUu5U7JCOyQ5NduHt8F3GWejHpvOKTASXgOUizEh+igkZFcMCuGMENqWHolE87spvWN2uqm+Xxm2S2ajqnVVOrVTfar2Cm0GMMA18L8OlWfxry4t+AYM+NOO1cCmo0Rmvmq2KSpUU6SMcPswiYqOFK0csvGu3gA9TReNx5JH6D4/zxNIT5A8KKVYESBtruvLOQTxr//d8DO0OvpVY7eAM68u3wyyY8QP8kPWJAJLV3kLMJLR6Cv6iiEDQeAPQLf1Rwjcd4yE57lWy11T/AJn4X/gw8OAIIVvdRAYgedwW12OSMS9DYY/VicEaZYBMP04tIkKY3XbsdXzayr08FHrv6drCZMeYTUsRcrrl1n8Ca2pTh8rgNT0WbXcB1GA8e+JOpQSayVoe1xu1gQtWh3uc8pZ6Qwq4HL4o3OcigO07t4GJhQ3ez+bgvanH3aBDrUUdJ3SL41pWVgiomm4iTcz38UEK9IL9EcBh1Op0J0TJRm3oC3g721/SAfevWDNiH+goE3t3Mh/ijMRMf6hBvrC1uuVhQyG30bPzdqca/sebFTwv79bP7tidagfJL1tkA9SUmbozFhEAkwpW/P47+Dq+tGm6m2NRQBp4URHkBJzA5KBclHj1axSJ2HhfAFiHjRsD/8ugRAut0BuyST11GQsBPGNjO4wLbuLx2UsGOlMlQFqCm2Hl2DRTmSMI/NiugLAVODmONxj959HcGN7IY/Cgbb43jQVwgT76vozvCCK1RNX4YcGzbCl+jZ+DXyjEwTFy0aHHY1CUi/hgL3COFjNol8OjjUqyI3iiSOxJUND2MC2ruxHs3LXOZUUNeGUs5WqtZysdMo0VrgTf/AP/bQOXY2n14YyKyxKco/5WxfoQTo2es6FdAYC80kYOlj1Pe9fQ2i3qodt2sUpJXgHjXxCNp0Tq6eo3P4gFOQf5wAHrS8RhcfxpE6tUM8RxNmdrF8NTraXAc4tjm2UCOISjSSR+x6Z45cBGGZUSOk1WcRyCbnYdfGuN4dA4rujHsZReNr05XKkfQRiJXBOlnqRVFXIwj70hsQg82EmMkBLkS30iuiHgdTdWXerdIaGdRPxdSMGqHK90zHkl5a/4Ec7mfSB5AOvg1fRwsci0hGT73zWdW4973Z0DaHvDQa/GV4B69HewkNH0ERF9BbzIZX7M8J2Zro3rqvwzxvBHbxJ1G1OVFbscNeCMLmJj3M8kmjGOIlBnZ6GgpVpJ5C4jGYdF7ee9yMkbFEhyWA7ENimhb7uzIbr2NSTD1iviYpecVA3rg9vDF7/cjKISAyT8H6Ey+vglBtQUvZ3IbRFeTSAjrUVIAZ8djH7lbUQ2ZpI3IyVaaeI9bFdUPAJAzcxQ/zZxepil0EZYWDM7GeS8ZgfazsrAciiC4OVHsvYMNty1sydzfNxyM1zObNUQuFFn0Y3s/D3qTXj8exwWG9Xs5Q7V4LIaum5ylAmiK6LYA3cRPO/HVLV6cWuTy9Uwx0Ye3m5lxOhrhxLhrKBUzFhXUcycP7sEnvzLI4CG5zAZRLtdBXvblxceGS9/uzcFcXRO8cH5x2l/8yrRvz/D0vOWsH87Q9CITpF7OOl8YQpA6qtJDzb/Plj5R9Rqxz9SLzEV6ndSvtAm6R7ZlEqf4lVpqUupCfgdnA9fc51B84iktyeNnPIyx9GdVWpyBF5l5Il7N1FdFnsOyx8vzFxKB1ZfPxF5cOhs3GJRZjhxc1z2EZlNrqAKJ6bIi/+1MrWURJOLXRi3efWjCOfBsjVmaXfB75gYqyE7Mp+kN3pH8ddaLxzJDcVD3QRS7mMpxNaRkMiEqStHifViAKGELfhCmK9hKUIgTKxVPo+F6IcuMe3lxNBnaibYsr+aY8G97ZvptaY8ISc6inchQMctJceAvIJL77nTp2cX9+5CqtmZ4g2b4U1m/wqnsKCrcZW41VKsZEOF76ovR2hcLqyDxcflant3vSRGdaPpLdAw31UUqVQDNa9CKeo3e26Y2fuSDRXMRYmthi2lJ3WZc3nbdcfmkJPYyi6vsOq0PuCElbYOfa6MxB0yfukb9yDtI5c/1GimgOmTuZTC8jgq0V6T9EOJgPnjkVYVS/M0bYFVhiHkhS9k0IIV5wB7FseoXbsv647T/FFoEuYMp7e1GKeg9iBi9PI/BMKRowEY9U6JErxzG4AIcJmkwdVVnWZxmMZiigSwJnrAetdCj1ap7lIvNup0Oxe6Ag+9Tpp14/wzSszM093vwyMfMdQ1KXCubGLNoiR5qIj1zS7aIikT0ErF7e3zInTniJK+Dz+EQ/CZSy6LZki2TN8q3buGtm3Q9q9+Lir8x9+PZmrMfDS7oOHzZZail0ErjVauC4aymAjkoWFE0Y+g158yT84q55yhZrLWDyRwmVLcHbCctKnFOZPd8zab47IcDGyJOnfdd2DpfW1Rnb3nWxh7gkwFxeQu4nFz2KVtdpk+KJ7XQ5tSxZJVxxUvhKhM/tmQVsU1XbXWLPRfN1V8Yl5JOa6u2KdVWrvpUF/6dIjkPSNqQ53ixusxu9CPsZaPykpbA5/A4LFdbdRtstFq/E1jyMFd/uuqsfiWdHIfFKsGMV10BzRwBo2l8CG16DqH+nAmXzXJ5iTEN/ERpEck1IByzAg+CqqNsSVo055jsVauJL8URhvWQqvF6zVWZ/X61ebVaOM/Sf3K1J6vVQRUy4Ik4a7Fmc4ZjxQTzxbOBd6nKsn6lMrnlGqZgizy1pG6/38cEqn1pMj5u7SfjOLmQ50Ai1b8TGO8e2vSjQXMfmthH6aCrhi1LQWU5mJJJX4HB94YJqV1XHcl2jq5RS3e45bqrrPdcvY2Epjb1kiR9piffp3llq35wr/QarQA3PMpTkrenUQ1EPBSnZwrDUgMAjgk/pNl46MvHVu6sGteVB+Qf9CkLe5Zol6ooy/fRvVY/Hg6jBMQ5V1w8LG9NgitDg3tAGuuSHkatK+lfmb3asSOsWu2RcU5U1Oyoo1EOmnmrkTHI/4rK7bLWxpytutqfaFDS4O2s3nbPxv05shooqczD68I9vFiNdRwelsyQJc5WVHv1OBwmLeUH9stXksFhUZdiTDYkmL+paCLP5lwb1dv4c0GqsrBnt2ufb0EPcS2yaLoLPTiZLn20+oZs2p8jj+KQgYbaIgm7dLIOlqcyeuyXojHP7rNkVqT70Zkgt+c0UHK8HzwiMtJPSyG4befGTV/HeJZilPj7o0fa1OF1V6l9BI5SKn3OUvwBLR+gn7jGrJclUikU0W0nqHyEQCh6RgSjNxYbhQBQ/E4G16QJ+uGwPcE4msK9gKgcBm9vDkfDauGHQmh8B4WssfpBg3+w6l4WKvysWAYb0eq9lbz0RCRvlp9BPmasphxQU9bVeXAes5gBdFctRZuMtlxVd17sLStk55MIXAQuCQ2PSsbjGOYkWq4QduebPDgu8Q6Z1gksRZxl6+gQay3yHRQfatgjQxcF30d5xIoc7Yu6cXgeBeRQMojBjSFoFDKMuFmInEGaCSQTiDWZjJAw4AVSp9Fq3546vcMYCVV7lHr6tBQR/gi9v0pC9hoaODwR7o5v5VwYqGcVbtFXEfrVXL5JcF3fyrbo9LeSNKmLm3hcIZwmG/VqFRkrsPraUxHiRM66cx7l13kRXe4hB+U96vHB0Ya8bjsnB6+4aNipZZwf4GWEcphKnzn6pXQuSwRHFGU9mEGPiZdYyqikz22jh7xIJwdw85gM6m7jYayIKkTeSx08Das5G+dpDCz3t1V3bN/OoWzyKvKhdXFrEZVjIirNvnP04nRodXo62WcLiM8hD6wAPCyln1Sz58pBKg0RD26Gg0G3ltVF9ZHF9n9iM2gNEB1Yqk32QicVe5XSKNfN7XoGfT6Pqp7SeNjq99UKZTTPX67PeKXpW9i1eTLUuhx4jAlJHmWzmL3BQqRE9YtCLlb3luqw5+7vhdNhUClzhIO2Dlj29bd6D854/X2vyVEN/lRt8Mt/wgzRM75jqU+aN16v/wnVo1NpZYB0Dz1PKzsujRLkjkXNu1yVcjr+7x5h9Lb1Xm1r7y1o7z13wCgBvVT7qfC92kZDjFcAEM19uQrD+1V+uk2nmDm/23Lp6Nqz6u7dk36vvZkckIebVTOk1shg/A+n+x5pEmCC/7y9DdD9uV6e7gK1lyj0BrnEJXotiPw9nO5PwAy9h/+9II4o4mVs8zDIsIqxAcLY8OuJZKvYHika/po64HJ1GSUA6Qq9TcToptVmGdZF+vK8lCZ4GPj0/g0aPV/PYTMMLMxpWCxGRGf7mXsBUK2cVHvLS0lWxWwyYFV9RlWfuzq+Hr8i46OsNxhEk0I6NB8Ilkn6VKe7YV4WijPQloaZCvgN4UYoEgyEO5ATR1SPVybZi9zboD9yT361TfdIi77q0cGePbT0sPL+RMnZPX8hUjnaemJSZlduCgQ4sj29VWjCV1OrIVesqfG4h0881FkgR+iq4H266K7Bcz2b7dQvM22m5Z/snCY72XHv42QxNmqkFytqD6FY+uiRa8pDTH2LfA9s48NwdT2E6zTece/8O9pzjSAK8ODw4UN73G8r203nDAca/7GCba7pUtMZELYaPA7jHepJubP85ZuqdEnNEm6Aa3hYsAW2ONmO+N8eUNY+/G8E/7uGjDP43wRBIC2G/01Blx/D/z7B88MX8L+X8L+tUtp437/3ChKO4X/foXQJFexOdf7HEl/cQRb87xwQNwHoPWS8hv+9NdBhU99is7AJO8vW/gYRw/96aOsIvzZXsTHAI606LdrG0AaA/QiAUvjfGXpCgIxz9HkBTblY9TUVtvt7CE817uW5+EUzl1OEroA0p6SBSCkillhKZN6MPp7C0+2NvQBVbEHzZ3YDEbxiiuB1D6TkDtjzwyOudoCfILBC1GD+hOWMDx8GE3qUQPbPYJkUDwJwkUGRwqyKySAbqOXruucL8NaspWMQgs5iqp7NlR2D2ufKDBuCuWx1nVwZkvVGm7sA3HvVIjJ9oXHc6qqBlMP43jeEAur0aP+deWyJsdOP4lbFCdDIG4L8Nxo3gTyqT81hr+s5tso6ECFaT3DDIdVGYjDMvLBlGvKoGXpujQEE1vnyVXZUvXMUrbAeiLJdvDa4w+hA/CiO7O6MFG1+Zz2eLPqMVL5HxGehzztERuRrTjG3Zhy5BrFCQZwHlzEGFAp6fIEQsDudhoxjBUuh29UhsNion54VKo7Xi97GGFyWeRH0I1GVLFUXS27U169kZb/ky1nVJfkgRdYtmC+o3YgChVMjg3d0gdI+idZo5aV9tfLc9ab3DE7vCVHV3NiTJ8xSslKnC69/Hjl5r3vJcBxlUKw+ppO3yHo/zQpQpWXpeFkM5klxu5pBw5pHtykjLV1vU6agt2JgibxUFRFqfTf52gMeeOmDsAYBBb+S9fXyiz3TpPc287AvWL3xdLme5VGxUx37UJxNQRhL+0Cm7CdpQps4ZBK5AMlxDRI5AR40+9ZKCsWJKjGg4Ys5PAdiseNB87yjPs18Xtzvy6g0wMzUCrZWXTvEKR70wJmQaA2eSrJLksD0Zr24cNF1BPE9FB9h3CEmp0MGoQKDqCPPUXjREYEflJIS0IHz2n4I2A4aL5GoBkUKJ1gWR9OIbYShlkDsoeAMnxESTvXYqNPplPjQaN5Qv1jYRcrUGSpKoNM9I+qodzKsCNcFh1KenYuuiRKesTTLdobQ6jIKE3sQFU6ql2Jt4rEjKQQ93DYy6JZQF1TRH4sOS7oU7XHUiYcUxdu/yWCWw74MCU0NSCk6YE2BYQTHJQYC97a0FaQq6K0T39oIfD5SAPfSkFbMCZ0gyIIJaZ6/VG9kApwqLY3/ZkFF/tF8Du9o5YfgWxQ3x//JzY5Vhhz+tfnz5CY4pxWnTm64MOMxErxkX/CSnraBdBgn7ArByLAWC/hRAuOR52JlCGHEmEgw9kQnAPv6nea0ox5tEruk8erY6M2FmzvxbGfaz80F+/nU3rbASSfMSTMGsS+bHbLa1042zObAz5dpBojo/QE3Jx4JMQptRgG2DA2kNk5zKy7qKhk2+DtscZQng3HUw0a9p83aaqt1JYhR0ebfLMSqJQd5HUyUEOyIQJvSJ8GNvQJuzPjr1gjJXt+Y1NDKmkMOX9aRQ2krL2Od86sBAW4+YEkw1Ll/NEEChrHJ1bnwvEMPanwDK6k49VbVRp9WlZwmB5VXMkduJhv+sJQzPn9h6oMGGiv72PxZBtpjIwuMz4NSbjNBBsh/kKhGruOb+cO8tdww38URu9wZ69mTDmprh4oulHozzce8gcZUAbFQOKYQdFaIFuDBaRzId0dilBmjqsnfdTJhPsRvAK+OgSFziY8NfEIZqDRqgjLi6jTsQWv797JTCbdQsgJ3M0nJ7SYp6J2JUZQd4/eu9fNm7yi1XXj7CBqUzN055JPG2AfvZWvLZ/Vbi9e9tbeMlfA7m4sEfdpFmkU3xMAHmRwJdeAZzpHuLSPDIZPjQfO80wNIMwvu2LlNJ2LDlxP2syEVKG2klWKtPFilJlYkLKATMkLxPZkutqJIlrhKjMYN+ag3JRdy4Nask6SzUF41IezTIJZXRIegoz2UydM2nQsa7W6ypx5oEjlNEwVeGoN9YpaSQ41eqxSbaPYHB68l3SVp11T0kEppCHT7gxXBVKueyYJxx+kDar2CfwMO3MmRS7LaFmY+4XnX+47Wt712PKYIesGhZwPUbAeMQ9CLQRQNkaL89A5rpS00GZCwNe5NcriCmsqx1AFNgcjweMYdNemgK9dfYoyMYY87anYMKPVsTcwSYE2MBXi7eRLTNKdZc0bW3JADck9T1cooZtVRPa2PwST7WhYkStELFHgn2OiNx6JlaAoW9KMzEIMZmizNBDhQH1VESC9ZUOaouIpgnxZRsMdvsBXNCSZAQpUznU5Da3R1W0GOfuYOocF2C+oD7fKUlFngnkWf8vHzjtZeMASzZubGqFYjNWWeaiJ2gyO4TlXPVNSTGBWRUJLYNZV6TZhVMf31VyaJM1TXDppGz+CUgFohFVwgQXOEBPqqK360VEOkPMVtYW92ouz+mgOHm5PQ2ZAfbYymJs3C6hXI2EWufMXJDyeaP8FPdYe0nPHZtcAjpbKbduAVuYhahZI+qrlUOy4zBGhbZ2eeQb+vzevgnp2nSXMh5irrJDA0EgTVkNakOKGrBIAuB8OWQzemBt145j3vFtSx4GjW1U75NG4tfawbNcs9bZ8Krnuttulxi6jQsI5U0J02DzOvCNjw1X5mZidILUiriBZrZjAKnCbwZBrMWvReMu9TMDNTQ1r+Flx6IOyZoK/93uDCo9JURwNqQ2yfhJDHno07Gel6wlYHoqGzM84Y/ATRY4swJFGnZk8OeuXovOBLNiEjmI2X6hPYpVPYlXoUJJFQVw82AXU1+wu6b0tqbR95JD4uU1Is8tLPOyCzPzeUgehN0hoxpVgEtRqx1cpxGHlmJFZbsAoZqN/IHInMnJlvJ7dhrx3vaszE60fplG3oRrW24EZpXhN09lSjrKT0KEG946HOBlpsmk/pZwdmA1JpViRSit7lxORSsRFiYJ8bUpBcHyf160MLENwoXiiSesLKOJErg4l/ySqx9HYVQJmgl8yvQC//G8PpKRBqh9zcgi6hBICK16YiTkBS1rtK1BArVi09XGxCQpIUZ6TXqrop4OXNFR/KdNgt1gwYaPpydZI+TfSbNWskecgiTy0EmMdSpbM1uFhiHTFPpQIkNVTr5BeWKiOiHlkym9bO+jSbMHLBjRaoBetx0mEwFJ3/YWvUKloGi56m7eCE5U3VAa5Mt13AtIPZwh7YsrHncJFuFWe9PDBc3uk+7yiKAaL6rC21j74xMOR4vc8f7JhD0Qp2Fo8M1LsHL9BMUEnhgHAmuo49qGPUYi5pVNXS7KEtsuW2xlZMGPMgKToN7445C72yOCfHSIKJlh8s0VqDYKXJAsY3iWeyy+oLN8fTYKQS9qUZ4iH4xBGscVHQPsBz/eBg66AQkssloreTDCghagx7EOHCB1/NlOz53HNV8dsFE+tenkcZ7nAmLxvSs9S1Pu9I/2vimQIp6Lc4rbopF+5JtSV5NukZIJ8A8863W5xGJvO9xJmk7mucY+nklseSPklYgc7nyYkSCn53uH7t/KfoDZ04P+idRV2y8A9TvACD84F+BcAnWK5QXTYhVVo2sZkbH+Ihms0Y5znQmpRsmbQ3apinnwG9yOHMm1uzF7ecUC+TccezqXiEdGkewWQNUmQNOmeg1R5fVzbp8trGWKwX4BqbreUEIamAgYLPTG2dYXJgCAfoAsN0mha34ap8ilc38uQmvpeWmRBMiOOtkTyh1aVW8yUt2+zBGr/9aBDFUzGCPdUKm80HAUlwXhySUgw0NrEimWdKMq/hw/ySNbe1jUyVXxGme4J6xiYu4o5fXaq96GtJ3lDW0mxITfqo5UDaLeNZLOsl/AqEo+eDjKa8QbdHJQmnprzEyhdHYuTZxnXUrspP9s1/rIZp2qpZ5ZWb/5g0QhlKu5mxHad8kZ9ZF/mZe5GfVS/yq5Nfzp983e6yKpotWrISo7tKJRWFmB7LL9X5CyGum+ZYSQZ0myD7gyMLqzXr0BlsuvKVSaZHX0MepXANBPNMn2IoXmgsnODBoQQVAjFMYahkIZnGf1jGh9iBR3NUViAPFeraLDauf+6bd2bztouecF56JS89EhBDU+9z2/XHhqZKMeCxMtVaA0s9bmgJa0w5tFJYlforbyxiEm1bYX0rL8QyJOVTONR/3ii6LG/y9BXzdRyNh+qKXXFO8ABcyRfs2b7Km8vafZeGMsyHtwaLv2DmpmldLAoOpimYF32VaLIw8jqw2bKq9hMDuuiTHfYYthi9eVuymbO5+mmN0m+1Ds1ecacga4PcY3LX+HoTC2m+iFXS9k2p1b0GymP4JgjOw2FMWhwpJOEUs+TExmMPHrm70OigvIO2xDvVUeypb5IKJbjJmaL+FDTmbTCKLirmNGSERqmukXTLGraa9eZbcLYVjtMPvQSlwKtvCOyWsGV2TTOsRSlXZc0M0TU8GC0FhPR0uRYYcv2dNsLSGbhtMXUnN/f9k+SK5bJd/5MmrNpEbZJoOpNVqlpp2v1cOpCA6vYcyNXo76Z+B4aRxekw7FOkK82q9dvBCMxsp89ZchNiPmzS7tk6SXChcfS4VM5DDsyZ/G0Sd6ODF7nE1yD9VVWQoGcwO5c4WMzrC7kKGUBOlex1m0+Ooh0Yqp6nYhKck8R4GqJHU+C07WZBvA2vDa1YKr6e2dLhg8p9MIGeMN/V2AGjIXkb3GhZyiJN8yv2/s87kWDls0F0gGErNgzv2rjagVrqI1AVJ+WJ1pi8TDPSoWvViRikiqalTsWiV8m17th1TR8WrToY0pnLjqtHDH/7W9vQDgpJLpuveZnZ8zCFy96JFmj8zBQwYjMwXDJgRbUsLVq1SwnyftXU0chm1TDcDSsbJEcunfmUCkXLW+uOVatxVumSo2VKVtq7M6e9ZKQ5cpsp1abWDpjP7M8WSn2zOnFAZ7h2wNwJ3zpQrxVro63IKeFXij8VuVfkZaaPAPIvwlTrumWZFCsTWB+B0lymQ6Uow0eqCKu+UMyWVta4hnLTTF/mAIVyCfS1QaBdKyHV15ZJ12ommTePtR5468l9773rQOasYh2nrKgUUTVYeiK/Gb27EkfENQt10lwZKe9Jpt9gpeEJizUtk3vpQzLKOXwuS3J2kin2AIq2jGP7xj5Ae8Ph1jRKindxXkRiYsIGdrLRth5T7JmjoK3opJ0U9uctnEHPpTkBHEbdnXbwXdnU7IULTLrk7QpgGAYU/OpnoAzl3pK/OjWBlvmemLi32C/XvCv4jsl+zRA08sGq7sJHmJKReTuMU/ExM1n3a+jRSWaMDt1kZ/NVGjNzcmE/TDM9M3OMYOQ7Bu/LhDweRivwXjmJYA/phwoz/VDh5plF2hylD6/6a0WfPGrej1m7In9yDDFHVb0jxZA6Za9BE+whrKWa5vqVh7YxWoaND1DTnba64KihY3VkrI6K6QZXDwpvc6/NnWEbwEoxT9tAmEoIyTaS5aIk/TrAAbZTkvqMbjbel6xwcetgvt5iQZetzBQenPr0I2OuTr8XuH2n8Im/r5bXlV7RqfCLg8fOLnw1ve26NdnWOqouPzNsGUc6rLAyJyA+1LxVr+NHpUUM3oFW7nJdVa7UIyzDUjocQoUDdF+7LOABmamKzWZWnigR4VlwhTR1rpDM4XdvkopFN0k+/S2MWFgRSD3WdCRI8PNy8JKBXvdywwgJ7uflqOCHnidFZ5x0usV3NO/zbfrOe0adFquxTGlxnpuy0OK76aXbpS6wFPLqfnBtQArLHkLebNoL1LWDcAfPxlHUsLz4Osta0f4HXHPWdP2iVs+5qgdq5VkfUAx5Yow8XO+oZXFO/nsxbLMascp4KXWxO1Zm2aZF7xSRaM6XpJoLJanq+NwopcuzOk05DU5TE9vqayuPYWCfLwEMMmoXMgjptObBP7E4gszAs4zE1oDAcJE3hNJ6lezumhhanrSsEz2LLtNp5PbBfDTitqNigk+6jOSgOhSuPb6LCmlEyz5/TtcV/ydZxTN2gxL02G4/uhJHbt4OZuexEBNmaTkegs8I0ZEsQ3flp3p6fI/mF82R35JVTYA9+snSo+8dem/7zPH3tsWZBMXCGbdaDjLnRUb1+st5mGFFBF906zXnVs37YsN3hFa80TxwrQvFJn5Qb0FqsDD8ClCabuJFoj5+FT7RjaDixaAnMhJigc7iUalMx+zrXnUGg6Am3+0dxD+iCulwH9dzo4yXWvbIOE8BbdZt7mN/uZaXedu/Ph4rTymGoXgJhuJTelhfWk/SSVACtmB/LVQP5lotx02ByiG70vnX+olxL9QUVD+cf0ul3lSmWWBc9dR65pBLT1YzVzjFIjXP5ZObYFhm+O6H7kEEKeINJ8XTG6U152msPhqvsxi/xf2CdblY+173ubqHy/QNY/ncut5oYQNKK800IPl5U6N/t08fejGn9Em0FpKw7l2m0o0Yt1M1amp5rtnvEeWV6ZL6HuuJCDwR+pWmYQS2FCOiYcQym4RXDZ29k6wmR92QTytiF62uvOC3Zhb1Md/TkgtHujNhk8DSYYQfZFXscj0LKj6NhzUWsdOOxg4DTvbhP209ruSdHR5WyhRSFRubprS8JL0tth82T9XyLRWO5Z4s39J3i1rfpt01jL5WPOjmtgP77dk92VinhITUTbfvyWt7V1h+IuBCcncKAyo2nWT9a/ToVtxJKQXISSmY5TfVdnVG6KOWe9Vu64c8zEVp6WTNmZYDaM20crplq3dKZ7G49txzpmHJ0X1QLPXcvdH8Wd40jFfvK4HAetm7FryoWEz9KKJntIlYZUD8LScFxS2dFFAvaddzxaBYdr0VQJ5ELUDE4m7AkdToBIcmYJQHoToV22ws0NbkrAU/oVoUZmLyC5cGqRCkM0bQsX0hTKt29d7FPVo8/CNrcffvanGPbrG4+7S4R7+3uJWesG5ha0ViaduQVTz9HXZdT38PpKtaw5bOMoBDrcw6eHruxDn+xSPecIq33a06xUPnVBm+szAYvTgxTyrk3Yhhi78kX+Up1bTMFk3/QHLfTQ1o0aMC1UviRxPsLkWeIcmTzgOEcyljkz0mQTXliUoDKT3yjfsV749pH1AKdm8Eh784IqXKa47PN4H8JAKG4WCcFnPfvD4D/+rIW2zG+QQVIDyiIGcJiUYcylsSk1+40hV5vBJYrDrCEzh6Q8y/ZF+10YLDIqiHxFgALhX5YjBTzipinO4xM0VikX75CnYwlWax84dY3xmhM08K2sWnOz0Xta4anQFHLV2pcaCfMvxCd5p1LutAhvaRcKmzMfRSnpkA5wNJR2F73gnlfaNa3COQYr6Ak5GOGoqvrWAkIW9c56Ox0hZgdXPUBKz/dReB0mnrhsE+iHHPCNki6kDspPD0C/h1XZ9M8q8BeCfV86d0HVk0GfcG0bATHMHTgQqbaxYS3O5//I//t4XeCIbD4FIcpbGg/3r+g1ksJqosGKsoxiTebOc/ZPdHbvc9BqaVpf28Yz7cBfnHbbIexbrR07PUmZT5uXqbTAqSOmzOnNTuuampz9VLXdcZJ8PoavfMstYGUryy2rLg8sk4HkRA11frtH9wp0ot5NVNjSMdmXpFu682EgA3Klga4Pxb7iCfehFqf1an3frVNnhQWQ2pVaRxa2CFd8/s5u7zaq4qd2L2Jly7P1DKru6KvSyaxqngimTWLBYHZpIG4zQZic9+hBGgBOipWkVYdsFEydWU1dDAeSRwuuR8zZ8DZ1nXNcTUprqNAX3q1NWnvja0qA14z95QelSNQZCuuIdepls1lIfITFH0BuceSoPuTyAiV5r8FWT2givkwRdJlz3BPAwkjzl/OGXk8z4c/o3Hjx4/WXm0uvL4SYP88m6Xpl9e3PmHvUxs+2f8dYBe+J+pcAqyE88cL74QHVHs/nQGJ48oZ1MqA7H0yWKc11iFPPAw3akMltjUUZXgou9QkACkMS5CySfLbYEB+cJGd5SkqICSjz9QUJAvQjjogDa09ilTYMyOu528dxbtIeM9NUxuSN+n7HSc2hF+KOsW9ZRUVHmd4BxxFpcGUmWZPGX313wBRE6h4Q6oUpPqZ5ImK/C0ZWV/b8Oq2HklMTWqkrhItWcoA1DQmI9FdUU6vmC9CYtugdsWsbDpoY4QWIK/uRhbyumNUgiy6xacXl5z1cNdN9G3oCw9MV5E2cphqZah8S3jh9Wgkytofb0iBqSIwX23O/4HdHVqjJVxK2Lsh84kVUaheH78tdGqe3LCvfUd5os67HVrcgMMunSBpL0UmU6E1CtMOiSlH6ZjQahE8jMQw/rTkP30fwf/8uNOmYCU+GXcYU/kYWMcj84hdkNgpA172UWj9dWMULiRjon2XUbBRMXHUyEKz8VIBVEyjTNoYVJAbMJgkph1ClFN1xAnGN4E3KsZqWdCLBQVRlHi5kziSaN132oRsMvj3jVGbMR2HHWhZWLJkO95bMLHndomrKwMoE8r4KFklIEeZGWSxZe97Nqt3AOZR2JNDZeDLSCMznKgcQIq/WgJyBGM+FIYz9IlwIYQRSNbpuNEPZeAlHFUl6g8znv9MQfx8IBCMJdFc4MwC2cFoRbNBwItmAmEmTsHjKV+9BFg/rhTp+aPOMIsGGuqasEo99NsGGUL9wBBLV7/BLdw7RPYonVPUPPXvMQ0Z70TyIK1zh1csM4JatEa5woXjDzwAovGHWEWjjpCLRhMhqkfJgSYP0jUmvlDhDC1A3SWJmJp95K8JusyTdKarFkER9ZKAoEgx/NhLqNhXF7OhxFMe9xPx8P5UHMgcHdd5St5/COaB5JfLgS5HC4EGY/mgZwLiQfGfX5zJNT8Fkmo+Y2SUEu2a7wM1OPlwP4+H0zOC3AaogzzOfOmZ0lIMSBLQopBWQxpzNnywEu11pjB5YFv1+bxLYAf3wr674ugmbpmArzMxegtgsgvF0FcDhdBjEcL2zFeBAFcbi3MLB4W5ytZNCrHPQ/5zc97QsxYOe/FmY+BVgC+vnKWr5OcBb2zmeyNg4NAiA8xnF/BRXSdB72pkDjxU8icoMjqTSa5lAMuQfkKfPcr4ruF/Ci6FX7c0ShPD4prUVgitYoqfJ3797tCRpvGwxIv/q5V7VkUyNhuwQqKHTneXPJ9P4TlDPKyn4NHQPB6GcHdC3qE7Nw/xr+Ig/wl5HjZ04vhiAqoqwgR4gmWtwOgLuIPrEuQbQcX4iMqBp2WaOBOWkRPg0MhuuE1FrjFzYM/wLeF6PK/vm1zHcG/B4LHjc7EbA3/7Y/TIOvhXWRx3ksE9B7JqP/qlvo3AXr/jE2AhDhDdpOo6Q4+C+E3p7BSIxAgSdoJXqQiD/omr8nAZ04AL5BFr8BdO6BDOZ8KnKLMpwRCqjfkRG5waAmBf07df5qp+3NW/jPOyrP74w5dYpvhKw1KW8YP00mUwLl2YYWs1OUCDHyr96NBjCHuHxgzCAwY74H1Ln/Jg36WznJg14MbHYn1qI80WCGO8y16Nz/ucLh4KxSoUdFhVqJOU2l2zpSWESuHdGhMGHVGnXYwLCN6ETooMxjUSTqOB9etDjanM+nlOajqy9E5Rl+9k1aJ0yoZp72hbBm3RCyZzDahxOYPoySOhjUNmt3dMGXRN3K0LirWQzaMxvE0Ij2ZtBv1tGSZxWPrOoX4NOynV0J0TK+uV/Cyo2ZRGavie2l1F+8K4BXUZnop9i2WwijboV6C/u6f7mbxSJTAbZBExSzNLqT5ndjxZ2Jbnz/8+Hr/4Yeof5AOLqICNvkK2B/kMCwbB3tiv3LtK3k2EHt5GGfgxXMaCbity4lYSwJ3ehkXMKj/8X/933AFJqvSHuZFbbj2xLIg49LWKUW7piuDW/esYfaM44lKZKIuIaePgIhRkHH4AeQ0P4+igkmb+AOSZ6/V+SP5I7G7fBpfjqC3p+3glDCoL0AjP0g+pt+ICz/0AOWA90M8Hg562RAI9JB6CVd0EzRUfRqcnhfFJH/68OFfO9FVD4ymOoL6nULJRYNrdNcZWlxhZ2KB/ea4JugBVGwfQJX7BgpznG7Pb7uJMRDUOp0BadCIgr8kaRL95ZQ60e/l0VEW374b64wZEAg62FVHriBLAww27esOQK8IArlMb9JEnDQ5BLhNcciMzkg0wV/yaHyGfcH4yleVfS2KZz2bCvj7o42CoUTQQ7UOdkF2YA9s8PC2IdhD4h6cEvBpcBb1CrE+OhTYPR5k6eRcDPJt6tWllqxbF3DqH0WCz5DOkJdvgFFsyRYYJZwmDMbxpJ+KTflBnIW3GgZVMphB0WXnQZZawVJma5Dm3/pQERzaii+qtonoHoqpkJaQh8Oaru1EswBBgV+ZxFfROKdRukdC/XIYCNZCAZHe1ZG203eW/jL91BeFdYyYETleSbKunIRX06F9UUbWqQFUgFYaGlHQy4HrXkGuOJgIIR76UtPz2yMi5q8dXPzeeOiL07scF2bMvb0J40Qa9bZR+KD116ofnF/EJkdo95dHyHg44R8d6brF4OD9Z6EUuqiAOkJMlyqSrf2jgYxtD7mdPxqQwJ42/miY3brq2qws8iI1TQmgwOeuMw5KinwavNqpWZeGSsgWNqH5QthkOgihr6669Tj6goBe5CC3FaRDEj0Xexxa0uEzbbDcNElmZaWIBB0UC28+F7xo0TJV6bqVg0Z3GXr+GkQBZsNE/0QplIpBYqQ+0wAhG3l7fFjMi7BXDuP09gixmBehHNfb45Ql56J9J4Tg30ANMrQXP1k+wauiDcq9fSUahazhV49TFogWL02jWepgu3S3Z3Q1ibIYyFxvvEyvtgx4yRgASy+7Fxy+2GzRqIFgDzOS3360oCjsfzBdhweNoBvguZBy+kshDN8e8Rk445Qo8gCM98QAy4QVyOYpjzIhYR8KAm0TY7Dc2yBeZgl53tm4gA3dnpwwO2S5G+EDdw4moGcomutTCpk4IJOgl6NGmx3Yl1LXnXVCyXF30xG1+UDZUNuZcToSMsto6fkGRlcI0oEoJxUkciuThsNhPieKC4YjpqzhEDRQMMp6CYhW/WutqwlJgGkb0kfbFAR4W4hzbPI0+F7WnWN7gRS8xRmWpVO7FuZU/YUPqHPqFSTLH5PJOK5gES2hZ0rb6TAaI12Dw6jbX2JYBwTNL7HE9hd7iH6vgO2U4GkIAG1f+1HAzlSGUseIQH/ReODIPyuRfIABJk+Venk+t03GwgTLQqAakoTLmcf2SRNDfismWjGNo5lcFIK5c9YX7dZfOJ1hZ0JAQ6Atp4jkFF/M4++N8zQeCJEK6bVgJ7PFe0VhfvduO5AtFRSXfz2kh6fb/u4JQS8G3WOuttUphdo1XW52JK7TAG2ngzxltT3uU7AXxqdhqFaUnNT+/8STpPhNMrw+mdwNFQZEop8p3COIwVyhaw/ESGynIgYwckCfmZGSV5xsYgh7ztAPTZKa+kyLxFxyw6orv8pBxEkMoo4Qy4dLMw+WHmguAePNdyB196xWsGkSW1eqw4SGaSkyLImcASyVxJrIGbgl2wA0daEQdboZDWGcUOOOejK8khFbKeCTo3P//lEenZXjYHYeJZiVB0kkCuQFzHA7GDooSKTZXS+Lc1wSfbqk2tjdP6CLjTiidwDre12QwUk1BwsULtn++legAivDCNwzAev5178Gh+don3oJco6gLPrGKMhKLULFwO6A1+MeKlvpeSwNUQSvEfAgEPMEx6xYzkcHh2jWDBEIIGsln0QDWDVKB6mnDoYY9jteTdDQcoM6wUZ6eZkC2YDHDKLzTPyf3l8JXvfy8xVQN1r6ZSmSnv48F/k3ncFYCBDR5WAiGWUhe/9x2hLlBRcoBiLDQA8+BLPZbIX10uIIuuykvRjEXAfP/e6ZVI22abPivSCro+U8y/mH6AjxAC/qxIqDkw2Giw90vBUg4+X8BZpbLKZGp8dxDlem4GULLO9M4+eVoOAbIFxyjDnoic88BgsJsukQy+IF1SFXPhwJYj3ivaULjWtEG8cGYl1IXwYWTxAc4IRfAy6Iq3cF77bgdTKqhNSaivGoFZMM6xH8Bw16cNzhgfEXOYp04yskjmu8ofnjFPr1x+lT5U3BaeLfjAYi/FlvnFsFknRBGZ7Sp/KCbhADzSTY02UJpRSuhkR4kZ2p08tcYpSnSWLe34HG3rQi70cwmoo8dez71B/OReEyGPGYAW/yPXFK4rLMIzF32yJ/GJ+dwfsh4AA0RRSLCZ6vq1sc/53lRp01P/JqjjW/OIQcY34QlGh+6BY8HwgRTizn2TmGkmfNM641OC0xgrM5G5JYH2VxDY0Wc6Dx66Nzo1uzyz64Ncd09HeCTVqiT4Mv3Ll2gD36KhYRpzwNdIfILqmHvonHZKfEu0bwW0kBhaA0F6nAiPOaWTLRAJJp8DLmlFRaE1RYR1NHX+0ehj4YZ0kLseMyOryeRLe6eAIwMMNQyyPY7m5voblEXj2lBTtZ5oUk6cHpHw3g3x+eF5fjZ4KmgPj8D0G4V0Sv/2icYnn5MlHWQMtvmb1oSeV1m5Bpu9VnXsRZvx0k/a8t+2bEViOhxKmu/v/jf/w/QXTZj4ZA2EJ6UYIx1FpwPIN6CNLJfgEuo6OcXv2BegfiA2ZD7K6SaXEUjT2/uM/yYU/tNUmWktpDF0IrDaOL2/KlVQrMiGDC4IWeIBN4A0h63z8aQMVNioByqdZt0UgOEx9eW0EmzkexAtqkRJSWN8Ztyq9aICgN7DwjBLj1EUuvxrjl9eH2O9XaAl669kACZRVDnMBbQ7ra5QEwdANLatrZc55+tWRiFQKu5CUELyRKlkW0rArA4ZaJovOqAseMsCgZ1y8zzbVYrauwvV++2GA/wubUnZtH24G5Z1Bsjq6E0EB8LJaVsuZBRXEuoCleqLUYIHJClPhHQL1EjIdsdsaSIN718HHJQwoZgp6X7gGHNmLkAoZIID65FFMt1j/cc5EzCtoF8/Q02zzWZkPozglPd3ImL0Q0IMd4bqzLOxB8LieWTt0tCO32YOB9WyeV2dd4G/O5BgcZ/1XFNe+dzFDLs8i41KgxZlIoE33RFIkLSwJ0d0Lyuak34csjsiKMsk1QOahTVfE76kDxXCPrql7GV6zAR1y+e2S8BTNP5d7Va4VSVSdxV21j/QumdxVflpcLaxblxZ4Kff2q3q/Xd6t6we7p1QeJ8O465a33a5212IYqN1Rz2gn2I36ySUtBZ8nNRsQZ7WNAXLpvjGk6HporXApEQRSjYEXNEwVl79vIFnIuzwhl05STfAj6Xzq0558qgpHIxJkx7iWjEg7aHsYuHcH+M0REMTovBL1c+xcpcBNdEN38zEYyS9UCBX6AfYzA113fWbewAY+wPmJeYD460guDhpmtXWJQUoijQKruxdYsAPdT89Gs+VZgFvWJ6prPhaP8okgnlfTLtI+84P17dYtqj6sjg98zHRk+noISBIJYoGyYs6oPaFg0jQeRqRB1jpUiLQfnS6gZP7AAjusOkZpaR4GDTglprwLMw29iRRzA04jhGUjtZf25s0nFsRXivDE0wKwGP4vWBb/VTYRIWR2ESQ31OEwnARQFPVMPDq+oqBjnZHOI6j7um0UY+mlRpJc1KF5g5kIc4+isrhHvRNbc8nNOc1yURmHW7EAwKwtDVep+8yvMFSrn+OrEa1d10PfYtdh3NqZVPQoGUhWOGsNbaJZ91z713LpXxKhXI1fsHGqkDWTefXf0S9sSHVSu55F2XPYG5/BgDIQQlOrlKDKnXHcP92vYbiE0aRV+/WALqkw88ubUucKQNJpJdggsLGmSydODHFOBwSaL+12Ta5B3NqQbMUK4Ic8Kmg/WM8r9N7EdStTIbdLthGyNRmdefJgyyjt3Gy1X0U40SpnLnrh18ssQZS71+NHj/7ayurryeJUMpER3YB96Brj2RJTmoHqcNR57mC+XG2avFlejlFe6FjnYB8/vct31+uDkrMbbxv3qcwIY630MYxcU6M/kadAYZr2zYkUMz6OV1ccN8JZIrkyUa8X1ftW1Yval8X9KlUkDPPeRuxtwdEMylIqfZSZ+ib+G+zstBT6NkmHKnh5/pDImJkVa/CEj7JJfy7mBMEJwatIOPq+1TJ/BZadIQWikuqEPPwOwhooD6cXYchdFYKJZ2CgMXcYNvGkFwzTCEOqCcE3G0SXej0npF+oIZGk1KuZYsGepYBz3M7gwQCERUYai28F0rR2sZxeg+msHx4IoiKMSFVazrDcJroV4L98i9QZADsW6Hl/j26Ibd66+9aXHMXAg1VBzpn1m2zPX4asnjuBM7pDiPBeytOMjk1M7l71JaHoxNJ0YFufPKZ8DZ5Arz5HtyvN5MOrAJdnTYNTqfEvjJGx0GlYAgULAnDZ/FjcwCU3pHOfmNIBIv9rZDZznVP5ZIMP16PmcBn8LkpblW7OUzsRu2AuOGrZoh53+SX+Wympf/tiiWYcxAmdn61IfBJ5xit4KSuroNUiMfgcl9A7K8LgCInKjZbgrvd7BeZpTEzZHeXb8Av7yv4IEtIvD2BEQwJ9gFAntuU86LJVNIB9AkyiDABvkcd9oxJnsNSBQ7QBviNSEF9fdYdg4OQGFMPIsaDzaMIPaSHqgSpMxAyMIG9gMnJxpRzRNLIMKOoh3CcRM3juLoWprfPD0uyOIsdiIG+fxmJykkYfUqKs9pHaVk9QxOUnlsxOcz5kkF/wlkdt5yHEJt0yTJQ0q/MxyvIpmZUBG0budcR1/QD3AePHs9a6rc6uBQZyirsdGWCCnX4BN7KFvQFAjgX/AsMXO/oJ+dMZu0+HlE6RLV8h4LFicBroOzM97F1EnWEdiAEKyLipxCcwkRYtTmx2eo1ScTkmZjopYhIBrlQDiraVjUvhJ74SdU8tln3L1n2O4WNtRH7mftP0hgkO+Dj38RD+HBA+vXqSdURYJmUscCNLboelMln3vQrNQVHoa7PTbZgKbsz8NLlS6DLG8p1KUXfdTselI4MsLPnkH0nTlTZ9dUtKbsN2drZOD17uHJ1vHWzuHB6Ih0hMvCM0N1QD06Nxw26NTjRBuDastDZCc2alv9ro3NJzuzfUBTGuRXSIexpfkSVWHHIm6HaftHKQGgo8oT6NmjSqIjV6yel27sBjWSoecuuWyt52/Gs4xlRdHwcLAM8/b7oJDyTkpT+fSQ2SOdw6w/kzZDbRTVKdsRO7uGmtL/eY2MIIt1fjnReeK0qO3f44Bq3LSWUWzpPNaP27p/tNfXPqs8zqVlr77iDmobq2G7JVBhE2f2m6e9LEdaweElstLOm1/3rTZNXivLNL9CJ42cXgLRI3dIUecsuN0kFiOMK0jxXKFyTMMjna1H94O2gMdJaBg2MIYJOMOqefDn8G3uBhH8DBCxdhARFWHqteJHYKJmRq5YMbpCG5HyTljMAFjSPT4SJKlmCkKLX4jfQu6i8E3BW3JUEg2ita+eWRWjrS66Irs+1/isAUh2r6qKNwyRGiNFefnVixGd+S3u2E1nZ03Gi2G87rqitf1xYqn+pfsq+NHFm+O0CsvSLEVD7KnlShJbeSYQ9sFalmheuATtbRb3sEbrecdwzqyJWDA5yW04p1OpqDlN+y5vmPcXhmMYFsH3ytgmUHwN/yBm4Vkygm9jIw7xidmGyif2jVgthA8rXzzGwFwvknjBfnGJ2Zz5LqY4tRhUpSgTzTYCsZ32DJjtjIM8FiPIKyNGT2DnarVFlh1C5DuSwdpYN5a0Lp4lIQ0laOWW4oceqpaeHKry4j83NpBFSkyU+CFd+vhE0bFABm1g74b7qaQ/Zu3ZqWzuVMjdkVqRaAyw1rDIjZnm0NMpDrYRXgiBDT6LeTNEzFGEKoJSOupdBtLGmq8dZQteRqctkDok7B9FfQandia+PtmeBhrZWEgiLTDDhmEkNypqA0VHtE2u3DbB240mmCrrbaC5qRWZIu5dARGUrE81W2OfanSTAyG6yG0yqDdMZ6Gc4NPDtNjPXmbh6bIqHWmIEKLWtAHDqNX+OGkKLMAmRumzQ9n7oS5gLy5pQtccXridPxDbgTLf7njDr8yCx3Qjc6JLSF3CsLd6Ag4p5ZKYto5l0xTRz0rlXEkblRbYWIKlg29jZXhrORc2sot0dPQke+9vWp1zuKx+AzDLxCY5iviTxQpQB0MV28A6KATTR2SFXqtYmvLUyIxTgnnnEgq54RzUiTWWSTpxHo/tDLaQYMfWgc6wiuHEGZFkYp5QwoM1Kv+9AVyhWlNrI3eCpo21fiH2QybJDTos9EykRmHFeAyPkGiM7/NUniSATyFXgVI/GXRjyZH4mgFN1Vf2Z5zPkOutcqKmxuw4SkIFp91IYHsINtVyx/7dYUn8HZLBc547ahQQk/sDI+aRRdmDX9dOVbFGJURi1pbj1LXUBHWTuBi85RxQ38YErqWvSwcZojSvL605kGdRrCof6u2qNf33iZJjMu1TKkaPA0krcOyTZPmYW6jGMvC5kgdh6chSt2xbFuMN/huczSuhS3SWNxGVYWjZVrmk2rN5nmwzm2jV0RToXAhg14UP1NjyQm+3WKB6xGTJcyGzI1w0tCFpDt7AzepxN1UUvIyr+OJUhIO+kLYJWnNFKIeuIiq0R6NSqSeSPTMuWBwsISxDKrNcRVu9JhK9kONqUzwj6kBrsZUlbjNmMpCxpjKJHtMDf5o/phOF4ypROQbU1XJwjFVnZ0zpopNUoOqUvyjahZQw6rL3GZcVSljYFWaPbImNzd/aOMFQ6sw+cZWV7NwcHWX60ZXRiWXcdVfphmFmJGDlM9iiHMrP+8NwGNVw/8YtfHUkDS9R/lz9fTUDb6Db3jkrSnf+xuvX8FeNwbNrYxx3pJyVl+04OKZEVZ4btj4ub3S9sHUEzMVhrLav6qU5vSLHt9We8YWwLJd8zpo6AOoQaCsMxvo9V/y1NQjDOULGKttYjWZkeM5KApqAVx1lTm2ZnwrZ2QVDER2rI66tZYx+KOKIq6fo8SJekLSsBYpFKid0ApqrXBfsgoMxyKm/kA5gvDGFPdw03YpYKTtoJRkWtJwVoZTV4vfV+Z4RYYiDbhoitGHRQYWJixGtoM+BqpjzW1PWa40YOYarU6wGQ/BCkBMaE9IYE49PwMS4Qi6bbpiwviXqBx4fmrqBwyxgM3mHYnA2DdV3r8dnCccGTlNJhw59KlUd0oVmQ5fvZvsKSBW7Ek1vTVXoFc9sHxeGPNl0UHPlFULO/KPv6PaFwYg8Hd2zbglwYYC5bDrym/RUk/pWzYVKZevqWeVphI1P2Cqa8QtihcIlN6CDaXppjhtdGg9//+oe7M1t3GkUfC6/BQqted0ZikzRWpXuqt9qH3fd9vtokiKpMRNJLVW+vZ8czszDzP351H+JznYuElUpl1V/X9f291lEUsgEAACEYFAILJbQs+M5Y/MsBv7TViv9l6noJp73Ym3KO6rFb1/eA0r3/2tMEwOyyAqLV0Meyvyu1Xt222RRq7je/xAt68rv937UGfQMFLMdr4DPhIm6Adwc6q8jZEXGzkMjfHFiHQJXMcc6LTzIRh26Acw9Vd7G9vgjccwjEs+jAmYkaty+Uw93zmh3g4udXNykZK+uzo/xG8vK79NnNCr2WE0Ol+M6hC0WQzqx3+UPIFQljdJAzavneFr1S8hORoKZIMmOVWCJ8sUPlgG/+yuD//MwCmQSQzchCCMJqsIdAXea7m787+u7RjgfYbbWw5jD8QND3t+4cssuGYwDTphqezxkTg2R4PvmbdZW3oCoohy9x7aMwrQFA74NBZ7B9ipAcO5vwHc9gAuAwDJe3johiW6feMzuS7RYZkMnU1EfIB5j6i6xFQV3UPr4GwglmNyNWpJTMbODTD8Svq3wNvH8D+7O3cj04iHDvYE6C7x/W7ytjOZZNqTjtPvbvrqPYQVWur8CeTcEes8mPauX0hgjyaJJkGlvbu7eJsz8Hl/LcPeOnJHnuLekTVpCD4mW4RXFZ23CoMuZMiHRWRl7cl/7B7m4uYILdgFxO0GrmWegqcztySCwFr1OcXjoXQWJxnnK3f2kLN/tyT27A44cZDMK+fy5dL/3H2ktXRfCEd+yoG34IM+wzgcAHIWkt2jT9fTyPEpg4/P/uYBDFel0ePMl4kPF6ZwtxDqTqiHDAFCvGJunwFEbnNJf3yhiOsxEjYJ3GM6x7PJc6u5d/1MXuOlaFECnQZp78Q3gyh/qDqZpA/YnwaprWANx+N/A5wA362DH+SWnWVycce79GlteZ6uxg65kkCjRFXRl6xi3Yn4X9QiWWBQWmBlcjYGdJRgMmBNWLsBOaQyef8ZF+OJgxMy2d7BlVzcWbau4u8ohvIMyPGV1I5Ch3ResFlZgboaSX2OBJuF6lvQbdd3ZRfsv/A2uMA3hdMdvscMd1XUKcK9foMemxYnw7uH8Ckh6PT//nd02PnMO9UfydA+vv8d1v/2W6BBibXchogU4JcaLtp1lj5pn/SGvEw6tHUTPZgL0Kjbgnr3HZ25x1o3DXcqZ6r4GkCBZvDDyR7KJuDL8IrYH8D7BsLW9yP8AJG992MbwI1TgGz21yGG3VG+m5g30YI3Xk7+EzY3BiDxEP/ZTXBlGZhMUp+QC/w9dKUPplwUw7fXPz658YLvoTN8MNeXeaM2Z4H/IP/xe+jGflEd5JJMUD1k7f9OnHndGzac50MYuBDvwPUlomtV4RfdndKh2e+QBOKjN36sEDBGXyli5rwjW6RLesf+REIpBa1Qrk8Bjj7kFPpGSOd8/4//EQkDQ07bXTAk88O7ny7gOg/0QsBvdOPukgmhwEeoOQcI4KZeou+R32AGfOM38hEnAAyiITdeCL4hywhM6IfIxVpCLqEeztDV5HYHCAF/9opfTP3vYoy+2r7qzqLGCBHneUdMgj7EIeqZMxI+9LFUGzbFI4GJHNpJ3OrHJ0ReQHfn21cTuX9fthfOP1DQC0XBHMT3oNJQklVvLHyjIBxlHPfs10jITvvhelt2KnwILAonNYRi+EFwB8Po/atlPY8dZ/ERNew758zl2v3wg6PrmyeXVQl+f3QQg2P45Mxtwh28Bi6W4v337lSBSteA7yNvz08fGgFXNpz+AQla/i0UKxttzpgiiQZCJpbwB+du5kMklA8DOn5yuFAQ9S8Pjuzunxbf3HthCns5YSBDIgMFb7di/U2oo/0F4QL+Jdi8g4rF7zdQwlk+h3QURPCbw4AC6+D+excaqAxn2pVrfrgXgbfbB2c9SAr2OXQSkspB1uHMhCu6XbJ8sDNdtePbK+5/QJoLnYoXwO9DRvKiyAd8dzpcHvqAt/Ib1A14tpBVcEFUgz2hUFa/uov12onVXck3+TtsqIt8155diHjtfJ9Ccq0XPPqjPvmVEw98oPPYrPDrxZUX371cZKX47tl6TXEI+NLtK8gdvmPvv17TuBNPaBNzMEWdck4vDANymxtKpgYgfAU8FAZqjSs7GKUSBgTg7DgguB0XLDVuSSwYyjhUR3e2rFhQF43/8su7XyL/U5E5QbOEiL9iZE89pTK5JyryGKkPi+9gQXT1zQ11B+Yo+CR1efTYIY4nAoo7yU+g1lAQUHKrXix3hmUcLZ/E7DZ13SbvTOnmCQfT8dqwwZIBEOLoZqWtN4UluyzC0+hfUURndMAP6IR/Ol4Qd/G7T+zjmXrMf7m/+8Q8Lr7cx0WwCN/Tj+8TUTAV9JZ+gKe7UIUn7xcIpigU4aVNxIrvgDLN4S8En/x2/VvRd4eMIy8c4fmmyZ6c2UUGjMRS9UrfQ57ifj0BpFUwzkgaQOwGAXlCELsrfzXId1Dyh3fu7eII7AmC8OFPzAlyZO9eHP6PmxRXHcCGkqOqwAseUfiw3HM8fjgcng7JJ90U4wmKouLWXoS2PGKhTaRgbANinkUfMExuAYbsi1IRCiSB/8HyAEcFnZdpyBQIpp2+gToIiT1WhFervQwSySrhJrTAvsGxBjw2hQbsaCADjqqb84o16TsGtQ7Y3n/aOEKc4cqjYBQWaDX+ivAECrN5YE1+IKzu71BcThiB7tdLkqPnWkEyGLyIj/gwBazNpaUrYGoMvYwHbxkigQl8w2vcACJoHmDSgSo19FMwkc4KD0VWF6v7CtPAHfP7uyiaY8QktCIWuqvZ+uA9UQa74H9wzPl2ZhnqupdAZldI54Bq2sGRiHwl7yO/wHkcd7OQLfTZTy0UQdIhy3OAJ95F8eSJ+gh3T/oESYTkyE/QgYIQDwdc+GSzgPHCKJUWdvZ/i2he+ft7RH8Gs0QL/XvnDNI96KLzG/Thk/P7C7yE/O1PMUSMUAvl/Seuo0v84ZpCg4J3KzI8zlzGkh+UgABLQ7bzNxegt/WB4YG3ICz/+nhrgCFKgUXhrrZXJt9vhOLvf/eJAG6v7r/9FjIrEWZQoIISlNtBRxMgCx9e6CdQoHHZWdpu8Q9/jg9D2FYc7Or64RGeev+nzSUGYl7CjtGX8wrFeMa5gCl8+hSFcVYwv4OHN206EUnvabRrboQT9K3J5POKAHY2oNBellbpfIROPGYj2Ufw16tDy/x2jep8uf+rBmJn/EcOw9i4PQhjI2wI1DSgKaRoBPz1aCqxewrsTKHjAEeNzk/SXukjpW7z5l81Apwk7E1de4QB8/7TRqGIcUcB/cJGwpcfOho0GI7cY+YxEwH/903xg7YSk381gVFcxP9QCg+Ix8ZNEqMCoTQGXCQHyfsYILFqS9Jh+yMkxrTl1uTX49o6Ppo7DQY3hRsLv0P2vhD6DmBxkPBWNVipqBsnNE6RO+4+AmM3R5woo9ZDpK5xTyg0G7sC5ITR5KynHxmidn3kJOObo2RAAIQ/OUzEhRzv719BH7+SPn71+hgS9+XrVw694NIYtoH6encH3zsybcuRRFAMuDJTHH0tt8rtcmf0dTTvlQGc4Uld6goUQ5A/LRApkGOJjOMjPglYskBmNS/emHXsAVyQLf6BxJxGL6wuhaZwwsf5sEEY8OvXiLZT4MWYn7AvBdLHnaJQK7/DpYCKHvNAXJTHTaAAYhc1vBzX8BcFH1FIeJxHvAvcJkn8pp9wmC2IJpRlkMTiq4OBwLbdXNiyA+WTkwqve+M6XhJxb0AX3H3N4mLEyg9+RTw6ACF/5bNwIYTfv8dHHc8h44ZcWhD14Q+AKvG/BoWvqAZlefzjGQ0F9n+D4tuz2753JZVMG7CIjgA/MtAfAumWPwMJgK84ZrBgEp1smbNgUJ84emrJipdYmx3BnxAcmvREpvXNdigrf+ccRyd9gLE4cx1/PZFcZHB9e0ndIdXm/t4xWZXKFWbcGn3tMdXy12F9AdcKncZ5I6bQKn8tdlvjduerU3BaL41qsFCGCinVLM8LXWZQ+joclXtIkw4p1GZmLphsIgxMu95xS+RyIQUGTKcZKJVNhJQaMu1ey+1WgnSr2G334EybMK1x+euoNigPa91WCfZJSPvi5kGT7PvEHQqr5z/vxS8O/YpXPJh2YIE+R0gIJ1w4eIa8A1qIzPng+M73HFhgmeOwv8iCh5VroK9WoH1XIFW9qJM40N8NOO7Rud8mb1roaI9o7ai0Z+f89PB/fUGmzWjAAH2JBYYClWYC79nheV4UQpzg9+cBWw4M8mu91v+fw478CWzcAxWdyf6KyxOD5RNKvIv/6+4z/3vq2/0j/Dfh/Xv38fn9y6fRZ+vLfdw97kZ1wtv49BA5CawJl5kGbTs8e/qCmBeo4fMYgG4ULBAICC3Bb6QsokpuKvpC6RCkmww/7i/P+2SrBCUUeSNMYO8uCIUphB9d4CDJYGG3Az+7g9RhO2RcRXw7BZ8muBMAcYjL0XCogvJdoI7TEa6LAjLj0z/Y9B0q+4QphX+jzgLxj3YSIE08ikEAEKkKIPYcVLvDlmgPDpzyTqE2hBUo4UL3F0OY+EuBJoPhH1HAeMR9UdnvICoKWfIqPb15A4hR12zlCdaDmRXU3J0TmcidJFGy+qO+KRK1JMCwo97kcAuhk5sVAeUM0B8ZwQDr8kbivwHtH50mQV6JInNAswx07NoQGSvYJ5jqsC5GUe6iX+FJNzzGcPnZ5+XnA+RnMLioLZhov8Q/n2x9bBjOoc39xTIcW/wYcLq7HfiPv2X4jXnre+TCQzBBxYIQcanxsBQNgIYlh/CZheEOiOFHr4EQ74sL4IEzJnzg6ysNW9tZfESVFQW6nEYvPFouC3jcNdqOvgJv+Ra85RW8wmvwbEnfwdeGbgN0SvggNh2I8X99fv9poy7tL+/j8pMN/VD8VLq/nPaYdhbUW+7oixH6cHurgmEVgeBUkkX5artCjv7s0k3+56+3xAgXGdprKhHCmvxbMTyOJq2DGYDYL4oOTe7LECcMYqJ3rh142IQscAw9ZHmr+FmTCnmRAXf2ObTvcKU7oWDcEuiA0cHkNhWgWERqQOko6gZlDmNxV7TBDwVxJ98g/JU0QI6U7nkbd0I8zfl4xuv34c/Q6t9IIBi8TeBb7FJQnLmjwI8HkuPMH3fmkmQwbHjT+yf083fcp2Pvf0e1/WZrlBAyKIiB8cGhAa16bM1BIeCg/f533Ow/gLIGuvwIOxuCEsyLIUJEv71//3tgiVyM9P2397/7Wr1w55a1lWCibb8IhkDVrDtTP1i+KWQJgvZGPFlckMP1oUsVjLCF1Gjit6YfoGkjeKiEm/kIMy14mPTFF38WVoD7BvFQBV8/B7xTofONrCGZ3t8O1P+9oNjgC3Xm/t67egU7g0LVwl0xCOcnlAdD0zpGg59Ij56MnSWRCyqOTv0TGvXn6w3Yd7vIL+cTUOHOsgHyk5IPkYtxCGx6iMLfPT4+EmHo16NBWkUnfGQcL8aE1PSGhSTcHhlvUNxdGtdBBhsAiHyhR0pcM83PJPOVoboaKZ8/ozdW0JkENYAGDDXyHGgStuKMp/MvGVZSDH35MEUva0AKhIz6t2B4eR9KiqCJ6HjYN4TPtxbexQ4Lc+BTAIRyvlmBHeFh4scnOCIukyK4wrQPwWLkkWkkQ7i83JMf3KRXqgG5gEN2P7eWkxJayStGtHV32m6I7QuXDkyIj3h43KKIjSH/oLc4CD7+hmseirCQbwCeCf75BOB/8eLkkV0SFHHUaSTcuE9RoPacYXO4rvUEw1if7i60T0+Ygj50QvS7YPgwuGnXuA+j3Q+BvjCkOL0Mg+ukoCvet/RCn7aNg2A8IEH9IeLFOQ4IYO4G63hkkXJPZNvz98cxD3nIRX0GG0wYRF9P6Q5VWN1JQzo/wcAvaHEF2pnEyPvUX/PnC7T84oS/4MMPiFFOY6+ITO4W7m/jHggFTzSY8DSY7VRAifSXcsUKb4vwdn/4eEeYckUo4i/5FilCpZwgTcKEnSvau2znTxD/Qgy+SY9gyz4LYni7blD1oKr7FyLkjJB/cOCDJ5H3v8Of6JkTtBR+erPJ3y7EwGCuqzC5/uRQLCQSoR+jb5cGhBChWrba+h4dAOG9i3B9vyDrsHKVFEQURy37+LwlqPBlVA4Ph1M06i8C3z2+yA6g4qSW0CEaNNg7O2VQIf35Fs6Xts03jK0+b/g3OAxW7v2F/KpE1NDhYdoe7RjXRf/hL6oJIusWDWLrBLaPQBOvirit3+TrWLtIblhf4//6FPuv//X/fIkTQwEpGY4oqfD4X//3//tf/+v/u13Hj/Gr1HKqOraQ7yLX91PrlunCmTVFx38pZNYQa64zr6Bl+4255lDbqwMmsg8A4jnCzjZh9M7ARudof+gg7BGGnXx0F05I2uP7312wF/ocxqil6xsLyigDVtv4VyfpFInai3w6A6J5uCjmT7iQiT8iodjziXa6Ev/X3d9eTNA6+g9YOy94BHXt3jVKeWiEib1tWUP+kX70/RKmSvKD7Dr8AIfIxE4Vd3neOLLxZGmFVQ2S+vDaidrDjRO7IEd9bXA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
diff --git a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part003 b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part003
index 6c4bbd3..fc2df4f 100644
--- a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part003
+++ b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part003
@@ -1 +1 @@
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diff --git a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part004 b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part004
index d9944a0..437247d 100644
--- a/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part004
+++ b/plugins/data-analytics/assets/datascience-table-widget.html.gz.b64.part004
@@ -1 +1 @@
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s1eAqwDXPxcNiOmrnPUTpp7L75idfzoRcDLud+VhdF+uXPqWRiGrXsCCCxfWCqnlLyRzrysgIUt2eQsLY23xOArd1bzWi3o9iIe1gJ15fVkdAOvfvNEOgUnobbUXE4+X1Ft7kcJ3z10kJ1W6TWABBWhPGPMRJFRHnN2iQFQhDkzJhQqTNvMy8gNA1zjM8eHepAEwPbnV+uFQaf+3krktP0Ot8aCYZwJmE6oIBnONexukS5zULhnlzbO/flM1FqOlQOMcUcDPKOdQO3JJyzWMdV3dxYpPHwS1STGCo7qwka0nqVFfQXaLf6zd4/yAdlTaR9ylN9Yp5skqkhhCoTj3NN811dwSOw6kMofwjfLskaY8BfskWOL14OWFbZouQDJHYhXBiosjjKhFgj4fEgJrEHOhe7vamf94Pp8/vNmYWCW4bUe7AmusGo7sp4JzhohMPxS10jXoeGhpsFLrpNYDN0hi+ia8iEGBJuwT5+hJunBrzNWIkC1dIznn0cDOUWvxoNYZ/HyeZHEyWlpChDtRQotJppVuLRKbPJdfPR9m2YBqVrpcnt7OJhnzYwM+XJnb1s+neIgL4ekWTub31XbPpaYqm3WxoDP0kdfx4BX4CI4n15X+NpKN1EYt540gOgkXb4F/ZNNd490fDY7257tdvjRezRQRqtquI17+6INhf3mbL7KieLywgp+1HiTiKLtSxLsUP8BTDniv3zVGiwsjzKQDQHi/sZ25lcLzpgJRQdVFmX3YdantYxxnYIYFqfYaRYS4IteD18G2qWGELr6+YcFPhfEvGQxx02rZxhrQWVy9B8d26aB9OgolGXjxfTxDKbS1g4r6nYkHFxy8G4sFMkSjXcTquM8RSl5IkiBAIsZo7W7hDIqo3sMEBFIgB2j9Q0K/PyE7evGiiIg1sAA3CZ2URxmevSEnrF3EwPhHdh/hRGBubn1QpWUVpnd6BNwc5lEFA49YYx2/S61kqHu2ycgsWPruPK0h5txfkGjL49dQp441lUAL0loDge52E50f7flqiKf8srMdDjxSFtL+yuY8eproQ4Arkn+BVqU1qEaHq8goU3OgDUuj5v0JaIuP6l020si5l4QmEYboA3feRAQkps2dzGq1Kx1httDuk3a9h1i4cdGMq/P76s5V4fvS7PELta21ppvTYwBzXei2BHHNIgZLUjRSjUG/+yZXSmr0pJV3StVOgADWtbQtU3w6gwTEYoSBZB2Ucpj5bPXh+Lqsjo7ABHDZCksjt6Q9LRMUzsA1lqtDwExNFleOjYUXuEfuPyHOiWc4WUPy8kqdnSz/3e1wL9BuzyG0MEI1+4NXtzkZ8SHMJoGS6fw3IWSobZAwx3qjWM4AcVEFnQob4Cc36RvvLppfFZorjcyimgV9GqmJM8b8gtTTmXzg9eKQGwhQbd/MBh7gjh92ei0lrpQszm3U9vnwbwV5W/WxuXH5Mt7ffw3MESiFraSW6CW8CgFW8ru+9ukyvJ4zNGKmqef+6ttq63oy5O/8/Iw1jFn5vOj+K5fethIzvc79F/TwCkhKX1WqQ2UmiRsOlFkcefFRk7lwPUwb5wGUjUYTq3REAePYlRkukG1nX2o9pTPXgMCfEW08FTl6ge0ZWWgIvkLh06fQVJSjdAKiTaY1ZU8tBht6Q3q/KiyKz6I6mkXWu40vMYGxQxNaCLZOOWId0guSE06VP9Wh1GTPCtnoZycMwTea1Q81RkyZZJy3xwvuEL03UJwAON9KfWEBAhc51nQI2vpLgbks/7QjZaTTrGIIcX6P2ncaOvhb+v6No6CZ9t25Ls1J0qeKzjvTw8aGS7wFnwGaPOI+gLWjPgtk8EMCRet3TKYobwgQQJz2o9xtD11b3oA9y7nt2hUMGHnJ1acFE3QkdqSnEjsE1h2n5KyCoxyw2pWX/R7iJF6+O914UG7d6ASnaT+ZEu+zuWrzOnHxq0cfexX3hXl4UbqhpWGkrCGdG9BC6hU4lMBNWQJ5qWaxLapJG8YEq25VgTZb/2sXnZ3+skjZ3LvjlJLiFRvRAYTVSswehggcEkHj7YzU/zm+44iujB3ikXVB6nrIHoZUWyQn6WhmKMatFiwargKpoHmFS+qohHRGFWhVe2TVtB2UfUtVvfF/WeJdp3UFUfXKhsW3MSQ5alpmRIyzUZDA0z32o+SVd6rOvNAFNGC1Dpr5N9T4yTS25+miqxiFlNFroKY2lHzg7JYVFD79AwubDY8SybfQNxI8SF+hJ6KTxXOrzqirWolHbAXOgvaNfF0b8ixpV5L0IbgX8m1QY9L9dDp4+PwVgTI60xt+v4c6fv8TPZJToQrE/L3JvE7GDWAYXDMQrg41Nm5fZkSyx8KzPivKhMFL3Z2yZeIz9j8VCmmaRxxZmQ1atBB6YCIiaY6KXPi3YQP+bPqzQ8go6TZ3xxl0UySuWavBJMxx/sCQj4dsdWtRcXvzYuMr/e73ojH13jvG3eGrJKDXnzWxzbxkkJ/eAWdThp2+pHgq7/0dFovEcMhpgmOVpGcYigBMOOR0P8BbMEQBbZbYYNIKCyaXfSHjQaVSZNswb1MMRXWEA2Rvvpc0sMJeW5rwOqO229XxShHvt5LNx+QT16VUwWu1OuKYJs5c1JsvhyXv0fOdTE92YddOZdc2ZeVS3fR8n0IHagLHywuc8L2NsCQvQ950AHBwki7kICwzNqtqXOZ5RWBoQXQwwXVfKBMeFposjvAT6UEukoXJf2MDrLAsPo4oAvTB/d4o1JJ/BlJNQKGv05k3xpHqqpCc09q+UGB/Hhfvv/KmHZVmfGx+5GCWGc3x+B+U6Typl56hQXS2FU0KMQkqFGZWxRZjZPspHlca2v9stctYe9DyK0U03sG8G84WZD9LqZH2v/vr8fzogfcV93vq/01uEnFgEB/J2xAaP1+u+kYCyS7igK4JItU3YriJAfV/ETb86VO9rvMx9yW8lHjHL65LOpLRg9haOYyaLszmoNNU7/h8ZLtXrwvEybMHCXAdIc0QG9tavn0HVdMk2KrENE8Y5CpGzno1Ln0813DmCTjXuwMDwsmQm5CMQUD7Qj9QMCmx7Yu2yF4bRmR61+MXXdZb2zfQiT/KFO+itkstKqo6P9D83k6FrgjokGx3rz3bC1o2eddCmKcyHEvtEo0M/Zpl9EdRTWRNRmmLJ7PyPCsleBjcVgt9DoiC2Vp3fAZbsgzaRYr5Ip/zflSTy91fLSYp3VJK9PsV2uzGGAjmy2zIjzqwhNtyVKMqmcla0f8moiVGpU3oIwjVeoK+pYWLo1Xw376aatwY8UnBjaVWGxMwSf3X94yz9sBYLIHCniS+YgjdRtHJqFReggYfPvOKjKorhr+crdMF955Zg8VvEuDw0W9SLpsMV5TcdXzMhF/IU4BX9OVake9F11z+Ra2J5qtcXHlGkP4YD+b31lVxisDRop0WXrXrM7UN94B00MEtqiEKatQZNhzKo53mQlsvgsIWHFx01hc8gZonzVP86NoOB+7SHLyeJCLOb3+cJQnYngBrZkswl2JGQA+tL8JbljJ+R5YObHQ6ZtUaD5Pf9+NiE11Nm6vJUtietUwfng0rOR1wXpwjNHR1MJTUDQyW+6hHUmkg7cvX9RHYw7Zh3x+0bBZbyQuxfkBaMX99P8GWerFPIKmQb4yB7roQAtXN55JqL0k85qSu60r5SQ/iflPmOebqUM/3I0I9l7K6DOmlLuDeI1NM8rY6AdBVMsBvFm8/hrg2GIp+79j5OEXxWIarGyFSlbyTp7JqxM+zsPjHQjqrIuT2k7cK8vi5AajkMxyaGaLMU7kifmMhpLUoQNF7BaN+AYkfuvjdHmjmZA4iC73k6mPtN3xFrqreI3+JIWoHvGvi4E3c0xkNXfMX7355242+85YXVtZ/pYhKw+kHuj0b9ccjKYBqi0cxIX2Qi5J8x57COyPjaPGbwDjX7JIfU+ERa9s4khXqkWXgsMrw9We2025mpeQFDtwiciqfr1G3d2OcMfkkGTQPTDu62t2YcafnAy+Nsd7POOwCDw7FUCRmMw36U0cYoJe9Wm4HK0MLIqW/uLei0ssZ97I1hsce7CqfOolglkOubv2xac0aPTNaKjClkFAULMlZxg+1WvDSNwUJ4zM6E4XKme305WzqwxQzujLLr7zraQSGdblu2T0xnZrlk+9bQWLCEXac6Foay7OP0MUGWl3a1wFNPiAHPHvfT8Z+S/mdaF0IUbIpUu4iBkJsF0EXSRPtAHyK/CmqBCfBlFSw6TQOQD/wxQXChNuiWo2phf+zV3ehKdUYOd/4AAp1V3cWFCL+HZGuO+nxJs8aD3dVmB7PlpedPk3ePiOwbrtNeFs7yyTLn1MApXG+gesoLvyYtf/m/EQRgH5yCX1LwCuceazPXV7jqWi6JkkrWcXqWpRGXvrWlh/WR8UbZWQF+qImbDN+LsK23YO2eQzuu8ezDMu864pogFeFe979yUJsNfe4vjeUbbMxQ5siM1jfXr8NrKJwXouAkSxi6n7DZUHCGI/XVZpvDV4t8Lkwc6xkQ0uQOphVvFWauxvywP3m0ytD+v3ZpCJ+2mG2YiZfKrTKs64Jynfu99fn2kN9SU2IPbsrAT1J8N7kcQzdWAi21N5dp8LQGDKTREcHc3an6KM0jL4SmycUUekotjBtbZwscUyO0xkArkC0OmR8VSx+fRQfNF4i5CyRMaXmu5cpohvePbCFfkqq3vtPqRbPtewN5rZCA9Ipa5s3EtwWxyZsylIeYhHsuO+YZAm5lrNRC91oNpJyCh4L1WCcUyMOpZYfw8ywXoulOWFHby89w/ujhXjjoaLFHGB19EWW9nxCcP6FPzX8AMs4w6ZhRNSrMNAEi2v7yz5Do1Wf93sykabpjGbWibrCJszWePEKvfj51q6Tc9bkfybOh+zj6IQHjbUexOcaAgY9yvS+Q4YPA4xhozYqiUmnfInDjjGY+CgUeQ5YyGJHtjjrvzdQaNKwP1Ox3mv8GKqnaWA3/8cDerPaDWB8OimIe8jlmEcRNOm/Br3o0aCd/llAalfFyt3Hj/3HLz8AOWe9+HUYRDIftf3k/230bntk9arfbEj4PssuX+j4ec8C9CNp+IZG8zslc/MQKE1pCFWKRDcUnhIqYnm/c/iDOi+uj8pBvapGU/R3Z3BOoIc0zPahihZielXYUoEOfz+waRdYamAQLncg58PbpjQEfIXEM9znNxogf41nLnPVmuS/NTNNr5m+JAG86yQorQQrruW3E8lUcfgE5AKJZzKrSReHuBoIPQLtY4PVmmE7K8GzHYWft1P9vHUu18u/3Wtt7tXk3sZx4Mauv80ionY/AIX8S7Ht0myhF/LAMv+91BQhiZ4WqB0LNl1ZTHB6FDkChuB3r9fFOsS1aJ4NoO3z5yYtrO6KnH6BjiADh92O3L+vptoLvPf0L6Sp6ScpV7JT6rBTaWd6Qd7D3MXNrq8zscB8hnJkXFVdKaE5wYA76gs2wLamfcgs4tHhJcvRpauvwperNrlex2AxsqPmaOhVPM2ChcCU7ejLQr1OMXGyakduvFtmUeMys6lobsZerpALvWA65ERCnZLAXXlI+s/XhOWjZJ0Mr4k0nmfTq2HlOVvIldKCcG3qnjafav1OiGzVzscnX5vlhg3CjWxZDTi9M4oT4DNpfUMlxYJWX9CtWsDQdWUo4UfMjf1vwt9HDtB++CtlEM8dvbjXx6j0/Mh4NssqEssP930VqocQCCAd9zSRn7/gTAdNibymtd2zq25KTbq2V7JsBkLjfmqVMCg/7WRgO9vdX4UWHLS6JZZaPHN0cE5jf87QxMbLZYpF4jvCbGtvbFTWu3RXBEmKM1WE93DidmeleNsKSTXSm9/qw/NGPul8wbYaRYTYqr8Bw7e66HQKmQ6EQwGCvvmDNPPrgOLO/qrWpIiQ+urgL6IfBF8a8jOhL3yoczfj2q++A4EObiAktGt0UHhetPN3SY800I7t+dFrDyvVhZ+RIdDhk/6ah1m3+t3a/X4tMuohZ5q0K8MBVPfPyd97RVwfY5QiuInU8zuJe3I+U5Qp4PHdQd5L2/Zkk9xX3bS+W1xt1++j6GtYvM99hNYjDSLKVKfltLRNeoVUDUfrLmh/AXeOenABJOBkZAgtfcI5iC9irQuSekOOG+iI7DEIsY6JYyT6EyZ0ArcGuOeMIph7woTglzbea9zbGm3nTZ+wy5hvKGiXQ0t0DvXxpoNdnltIsbnZmfhN6R2w6ba8OhtUWyipjAHSKGTcvMX0R/lP6RrSIEirC+bbtn9uFix++bD+fE21iuDOnzTIXCC/8SNhnNlf7TQydN5SKpOrLvfqwMSZbnQpjvZ0d232qtUuv/Af//kctpYuwgO9ABSf3uI1pjVUBo4bIROs2YK2s8n5XM1r4aB32HrUem+3d5ipLvix5wnvfzUJqBsMQQWAZ5de6UkmehwKPtWJp+tnEZx9gu/4mI6Qefs0S6rblhj2iFjtc4Ot70sPvkH6EIDlRDUC8WQHE/IBxNy6cZzB3v8ZO9vbyvYJu3N2Z1T/yF7kxKm7awN9Ik2bourEOrGXRjKlTvS240S5IRtPFOnDOhgPWsk0O604/pLgYJXx0ZASWLgTFveYfijLgcr+dtQOnKLqV8fNIpkFJWCViXyr8+sExJphqpI7jc25lJxHEdnTPSuGtduQXgmlBFT6KVdMmWWhH7iR7aJY303WRmlZUFtlWxUEfH/aT19NpNmkvSnPQ+YA0WvDkWsBQ/T1d/CIzzSmDUgudlgyUGDU8ulG183MRZ5XwFH4SRBtca5bIj5JNvL2uhOazG6qw54tb8AuRHwjlAMpzs/JHpTZAD2m97TwpR67tJG+eM2lJK82MWpaYwRJaPgXBWKIekh1GJzgeFljO8bQaqpfDVfzIRGbqwybzXtoAQNcYokfkdURguyCY+RXYofKxgWPRyOu/lHFt4+Sfzas6q6W1iJ3874jqc5b3apfCi/Jtl0BTMKJV6wPN1aFqG8yp8fMFJzieHslQWr0HoQafMn7D4KTSEpGtPf7wLCKUN5/fbkPVPjDmVHAcdvbidxN2KNMvDHrEGEmmc6nIn3L+oJgYUiFxP2hGqd9+mIwJ/aLZ6KIUmtBN6mpp+/tRmlI6Gwz0UBXn+rQ7ts5MYy1qq+LJAK+yxUGovJ/L6c+Lmsi5I2D70rR+E78v15u+sFRsxAKRwnahr2sXplcR/6LEIrKbyNVWPWzwcjUsJp0/Cex/1VyyhK4N+2JjtkRlhUOaaTgg3XwpGkFmJRKMHYhm74GBuKB0P0SszMogqyFtvuJYvulZhA3TyoSN+1VwKdvA+Q25p8fJKJyLxK+D75VvXq/o+7BjvvdBT88vSMauuZBtHRTi1GC9FIoX7LBFX6VX8CV4VWFOzbkDj6bZ8KTq6cqPkRt4hDzdiHWK/t1yAo88/38B7pG1qugwVZc1oCK4bNSvcS5B7H2BvpIMOWPyq1l4qT9H9OmVEPd1hNXmCLYKoR0yFcDhr0GveKid3Xd5ArpiWEbLrDuxzYpmThthumni+4tZEl5jhdTZoVFvK63BdDM/2nRoNPg5dsnbzU1YvKHRtbqU9LrRec22xMhF14vZBgjhF2EH9Bt9VeT4IM7vP+FaCX+detXsiy+AizGRVa8tnhWhu7b0G4JcVRYqwCoCW3K3grbD6+SO1I7yPDMSqz3HgcRZPPV76zWlhS8Q0rErAaNjAl9Xr32DsZ8okhaqKLkluiPb724jX7IyB3H0PsylevxuPSfXvx2Uz2tXLRIzvW7+mm9OmBrua4eFDAVH2bwa4SP2L2OvL4JuY4V2CVOTgboQTH7r4ZsHWVj0REQp27tgTIbuN9lsAf2HmW+CL3ZMx1GG6BQMRcPJDV18GBX2fr5paR3il5fy6WDlptMs4dvLLureqIojYHJ+uVRKB4qvnv4CTxVMjVTMGVHNt288IlL6pE4NAILTZYeOmWFixuuc6hdAc9iHZI2OFwyxe5ElEDfObT6NY1cKW+YI+r1+qCdsxE6B222ilmqClJM3/gUlqPpmBHzK5bOGcpX0XSt6qOxljFoy7tR+1l7nOhGP6P8hHTsiCKjRKVEVfL5ZVRWReCNweD7MnqRpk4GN1cczyeF2nzxyZ59cGEEWTreSrJdJfWH9dcrAO9jZnTwqEqOh1DzbKsVqx8yFRU0GH8MqafSbm49JiaYg04GKCdjlXEaAK4HHdSRvUzmA+JvHPvE8uj2mB8t5/GgTC78siP5sxhHMRFyisSJ9VRld9DK8d/MOjmcMSvrE8l+xo64YRuIj8T0LEdOVQsRnp6IfixXf06EU9utC6DwBxE3EuTQWZcKvnbXxJjgHx8/rJkxPB4cO1gKRzBI58nBnQZXkgYF8aC9FpmZJdx1xlo8Dm/EoNrCGwMJztJd61Ks1dBh2neBL05IY2FUPvZFGvnT/VyYIj3+AwkJPMH1+vNWNRuZ6yln8KDdbdBpyLfWjdGesoQANQG6LRaDRVIPfnX7HJuyZGNV8THXDW6JH4QWAunWZRNX3wIzbZYyB+txi/crbY4sJn6KhXRAq0cPUwqMFAIhHuJkuoXulDxJ1yf7ultGicgt/9vI06em+6WY9ncO11b0gTJKOiyLR0U9P5SgBo62W7VjlrnglEoBAfC9f+MXNvAdNkiY5u6rm8bJpWMT18lmWdlkzXFxbqfRuzTOkZm9x1BuRz33WmpA50JKGpuDD3EGndHbEsjkSME0tJVpgfJmwGpD+FBXLwZ2GUA9BD+Hl0JjvVfH5Uf1u2UW9V1fOTzsKzhXS6x8WcAnTtQ/CSyS3nuZcbcWoiFdnSaqSQffHpD9vDqfRsLZ/vlv6B845Nz54iVKETbIDayZkDViToyZNWpskCpFQhh/u/Kip8nDoMw1jxWOubhIVW13SmgLeAHmYIKytdcHi5by1RGX2zSjI7LBtv4ptiYBITjoDQgw0xz24GaN49bwcXg8gbWlHF58EraFP3Pgyfl8Rx/YLOojULg4eDXHEkcyg9hmJ/KROy1HmxHelkhxEHNHdtV7gh9qkN9FDIStcjXaTvX64cqkxEuYJMLqwP+S7/Oa3ilfqKzgVb0bc8hIOluQkODxo1IMyCqb4jT4mgprXXFUJK/hf7n/mOjIwW2IyxJ3XXVGhNkbJ5io9tXWJeJeT3UttSmB6x2MCJaE++u+1iFDoNEfBbLXlW8RqFseqQrz5MQIrg+7WREVhY4n/yChldzMyS5LT7Z7eZ00w2Dh+NDMVlwAq1Sm1raiOhyAkTRRBmS6lGy5OZn18Kbqee/e3BFU8QQW/jVRxmOOf0rl0RcYUsgM78/DaV4w1uJUWJjSNGRrfRK0QpfBFczlGYpt1OeCS8rmahWV2gf6ju4ky+AcBFgJTnhY41ukA1rdi4zwnNkiQ+KPKf4hcVUPLI79tb4bQ1i9dfWXPd5JZeXdUMhOoHeQlDXO8oYKCEL1+t/wkObf41T7nHr/4qOuAAwK24HXbUNlp9WCRJrjuortZBbqsjPh9nkUzQNjnSwQrVsn9SRFrtyA42touYU06AACMw2dIsewJU7Z1uoBaU/1kRhG8+REHQdQp46B5JTXs04nup/H+OniuGkgy4p0oHbpZ32TiT/A5RNCzFh2myXJ6K3soRSiblZFbZ6/lE+AcmWIYd837mx9g558yC43O0WSI/Yg8YnBth8dPztUcQ5LjLxuOOwRivHWndKR/1hBva0arhCaDxFzdD+Lt5RSl0AguNEXsWPRpHREzHqVH/Z8fMSBeksBtJwPoNYVKXrt5KJaMXeJiOf/fWMsvfXDyfszAv5jJo3tGZ3lq93fcCL0fM8DXJUssh2n7PRkkxxsoE6ft8tjJFRuNuKxgF1dw9XMKLuoN9s3Kw7FddzNAFiucvxQ5QgicYeDaSMEn01GDeoqrMidsK7DXfTv11dki0oW8Ya89WkWOBfkQATdZaFiwNPi9Q30QAhoLN55jNNdQoWBO83TFsRl8xVlZrubu3p0Kt+qmYkMfBIuR3lEIiFEQKLK1WVwQxLezAPCxPMHORFHzREEQYGGAA6nNzmWGsU5QlXM4kkIspbLufGm+KKGSbCISooCZVCkVTdAgr8+3HIGwIOHBlPZJKmIl6ZH6AUFUDj7LGiZQWHWNJ9gvW9uaKrTTkYrMp6B4He6N33z0cg/uz7wdehRdxUy0/T7frfhCET4fS5KstkFW/vZTY4EJf1U1a59pubeet+T4o194M2nX3kLaeqPTAhpi4A1QaLN4gIojlon/IP8kwGr2+S8XpNSNFBT6lWRbgbZd1QB2nqdZPdKLeXS159egk5ThMXn+sgN4DgYKwPSyU0vXs7aeclGjc8Ma4m1YwyhyEzSNQJrrI9nkrc3PyNXneBnejmH6jm2TOcurVnOR5H4JgHgcSsxLLxSLoZ4rTB3y3ehE1phwbRKyKWclY+2f8VQwOXZ/+sdQhioN1T1thuO5ZqE2P7WM2eRn+sn6Idv+8/noc9vcKBQbNs9EjEwwG/hVn4faOKN4KDCMhCiVz1zmvvheDoI5WdL2hhRFxanQdxQjA1DLy8NJAvboZ8nIpitzDKYeBkHPyMvR5tNz+xCbdg1AoX74fYKXu2ihZlVsX9Ta0/7yIEyodxNvtkoUOKUXW1CH0W/3hezxYDTcbWVwRIGGqE62OLZGtUtRP5C8lhYHi6uAgeF1UmU4ocONbGSGDgCaixAxbDSazzhZIPiYO/iAHD43RqaQKVTYG5aOThnMEUYkPWBMDTqyk1CGzNle5BBMONxxuEI6TPaTrKLAnUU+NcBlAH1lmh4aTG3w+n2eBDqovi3uxOj2ceql1zArOQSCZrj4ALNeysRdG8WPHg5ib2rshsU17fwvOd6F2LWArG8o3BqQLes/sDjB61n1tBeBxxx3Ojn0ZXmTsYrrddK8fAiTIyjO/Ntvrw1sjI1G9RJc33aFoxpLj833TCOdZACJhzz8GbMk/r7yeX+FUISaPnA7va45S3s8lvcall4l4PeUy83x3ecT9L1X9Y4uqypUwchHlLa6o7nPoVxWWJ3einmXmCyCw0PtxXBg+MW9Z0TMT1ZIODs6PvO/ZlSmpDXs8lKbtcEGfE1H3/Ljd8qqtu4/rxdg36WKTw17ygEGY/0xl2zbk7afjZT34dJrBbAZ1C5yiDW0CqgYM+s3LhSu+eDKfXehV/p+Eyvfs2qW5RLdlRKPb3AN1VQRkjEujkVd+Q7VpSOW5SlW/ZKollXm0G5adjkgDMl49v0WYWZEmDxP33LELVfUqHVGL7fsGN+L6BXkyPpFgPnDD6maSQyHONxWSxQpIVB5gdqHniTKQD82myDM0j5JsYMzGaZYi90ezeH5kWICyp1PJzy7rZah+nhTE6fNPtnlb8fJQbLoYolQsdU8FnRoI0ujx/6KQzveM76BNZ63gjCCOmGdS/cinek7V6prRcJuw7ZgiRrhfGnLfh9AlbGh0tvGhmOzg6S0qnr8Kh5vGqbGqoSnDUp3lTAqpB1sbq2RhKJvB0Wwl/FrryA6u6ptLfOZNB1pbFQVeEpTxV3qfdiLS7eEKxDgqXyQF0MCGagJDqaLrN4h8d9i5Ck6mJVVUzBm7QX+MhaiVb/I7kY6CTu4fgUr7b9dCLKoLVtCzySy72jaPHXJDiejg7TfX4Vpka040MfDNysaG86Vrg+Uu9XXPM4+Vcx75JGlSXtI5SRTj5a5VMu6UG/RMrYtsSbFD0LxjXIcrlWakna0kegmr0jRWNluv1Sy3i1JHDC/XZtV+xQoLGKB8xZhy+OJK4y4TFnYpptmde6LhOFeQ8G/7ArcThwWSvwBEyjdvrryWcvGtDnKyxCNoQXtVxGbd0hjxvRxO6Rt+pyda9yk8ZU8b/xGLvZIi89ReW3cUy5FVBt8lH69n56aaN9fKl6dg+hXr/z16dgm0Xp8u3v/i637ov4+QL9gMOgpTu6IrIj6+nstgPFNN590uYjhm+dPcu8BDtRcCH2NxLc70L6V/FvpGDeR8ZpOHj9+qMhBkqGQ5nBd5Tcaly/GIw9Ws3MibDe1XpP1RIruov4yXG8lWs3sG8U6D4OyE7N6IvVKYWTy5W/0t+dM+tiHFoefjB20HIw4eGsG4UEncffIXffp9oZezPzlLfX//Bd/3nxDBufDfbF6ro7u49jfa2Re4CvruC2LAjlpKkE3i5+2c+41lzXTt2X6vEMXacqdFc9Bz2nB5wCMwwwc8z0CB/fUbTdmrG5mjgAani7gOgUAWCCb3KPt2TJewRtuwybOnZ5vbVB0PjJyG/++m80VdyAHulOaCKhhQ7h587a4KSFP6q7+AFOyAg1limGl6DdF3yRG8lqnB+PbbT0XGHwLN4aqWIt6gG1wsz9eJEmL9lWJNZrxnModN78kLNeLE+FrF8QYqTXN074CqVjKNjJMfQa2bshf8fyfcIFoBTW7NQFYiPppWASPwRJD+yNQk5XUo8mJC5tpCb1iMIM8/M2l1Z5Qu87hFVHlfFK/FC4coNk/vCLjnPFYCZP6iW3rcfISBk/Q0zdEX40UvtkkBD54bkQd1oTiS3jYoyhK3iHqFkVimJTulehqjh8sxsNpWSYTA/rtFDcB3VFALiexGjvj11p7v9qey9cOGiJDw6QVTkpyXTpqQIIA3Y9iJijoY3+DELDYg+59m7qiRWsqeGDiB63xV+jJiBc5MmTdga/ovPL/SDDp604HTFDahibL+9NoIY4sGo0GzWbWPZav7mKaDArwrT7XamxBugYzan1e79yYANjpVJabRGDXwJoGO1mjz2Bww7CBx+i1sT8rbnUNGLzRuuDNfDmFsJMh6MePZZVJ1UTueu7KppWKz21fGazQIO2+X4djf8O62J/adfLqd7edqmbqD0lMK1tZpS5Hf02G59van2LSk/g0UYiMZM7cUOHOHLUzu7kaaxO5y7Whz7QmiyOaer/ADR3O7udNMhXT2U4pvcZkYMYwyh+IbyTP0dwpIIfZxefvhReBfJY1lKHyPXQqEfIP2scJ91JB1dSum+ysDXqJS3TbaA64HTgZ+PDLr8962PReV5kkO0Vm1l4uCYcrvxaY9WQ/K5Le6TewRmgdffU+NR2mk2vxFR/+xBpi8CRHf6LsCYSfP36qMVsGbQO5szWkOwX41XUoMxhyt+9sPVDiLj7JyNnRnyzicDObbguPIgV9qhfYJilFAhagD9s5a7heRQyBdJUrJxClyUCE5kbvkefujJJOcYP+LjI0ro8CoNsPCh+EkH8ABzvQZQElaEB9pojl4xJ9/CjfCIx16o0freulPAs5Bo/C1rhb7niDXQrr6eJsGv/b04TtDc53IcdqVAEWskgil68vAdxuocMiovrMoXsC2t8VV9aGnX7Mgk9HL07TOrlquo+XPN2ivwMoxV5Kp8/VGn7SwQD5dWMLumLnO3kr5KJVn7X0Pvn6ooC1FubuhCvgZgEhQOccdSMs4CRQfBF138P2AeNOJd/Ktihh3560AlWmD2obFJD3MQHz6aHFJKiwXpiTQOz7ta2eGWJdKIhihvUflnjWoowU26sFNEi/73q3QxN1pR7GiCtyShAKfoz4CB8S0SwkNfNgEfHVqPvRVKL11phwP1t3UYzDWJ9HQ9MsHsWF7y+r84KO7L/wbrupC6QVW1rqmiZgSJqlmvA/jDcilrB3EqNEK/3fd52EFv8XK/bRqVpKCL/juZ1731JDIHp4WnTHM34Vvi2r7RHRlvgc6ZxeTiKvtBmfQU555oUIL5Z4XMpHLFdrNZusIDTapwL1D19/08uxFmxp+Bli64mrGz46j+ChRWXogecjX7Arg77ItQ9N39Rn6V0TbN3/EA59rhZjYpFrssNo1XSgaiELHDyFZ2ek1PFlcFoAl1/CbFwoVBay2yTWRPsjhfGHGI2uQRmj3mc9PNYJ09YZKPA9u7guJMFafWBf0slywaoLZLZpBQJd9YPsPJPU1c/qLDUiLCswg7/ucC2wX4qlb33c33acBX7+MJY79KWS0KzWicBg1ab+jGSML77F96H32nk7oegm0UTmXQnWfbsCy2UDBLzBOJtPfkpP23HRZ6uVFfAPsXCnZtMAEFZyfG+N+gTD4fqmsNydQMdOLB8d6Qe+PgkbAfNzZvjS7FYcWeRmSI2b10PixQHEf4ROE2Px+V69AvAxuvOy49hXl9xsNy/MrEVQDrFXXMmC8Sr8j3Z6yQv30yCAOhAm86fh0lixJNJqeuvsq/GmjN0U2iyH1FfMLHawvypl7qK2yPERp6UUoAAdRClK7f/TCn4+fWM1/VVZue7TN/9yRGxVYW30gvkjdQkCmfzqixMSPeYJ0YhnKhyhuVEhPVusmML49nXiP0th0+Itvl95w/N16ztpTvxxxIldFEVNPk4MwACCr2vFf8biTWfAbFcLlP/YMOHH6WIA9zmrdkXx2z7fRtsN4UpFLkxD4gijoKLU7cmdr48lDpydPlc1HCNZtu71vrHQkqPuOeyIw1xE36aAv1bCkKxVMcAyok4XBXR+2m60bu4o+yaAtpgufxYGbj4DyzrcIvAoEREfkB1eg9oawKXa9v7xCKxD4AlnJYVUBDWludDmIXQ1423eARsxTTdKFrW6x9y4bw6TILzIQVSj06S9SbMnKKQ92f+NfQ0qBVYPwX8q4ajNlwhtot/VZaQOOYG8M2OM7TqDKJv9PAKAo97wBZYie5EqaIIYGOe+XQbYve3li9pBzAdgtt60XDK7xDtGNIO0Lx/fAo4IfInX9Op/bToakAkEGM4BJzOZS2fidJTdX1pctaVy5eDz1tNwW/vtgUeVLxoJOAVPu7YZh6z4KGNO33o5zSbNmwh/JtEv18hZCDW6PzSPKl6deqbzLdieoXgInUxXm7ADbUzeBDZlJT1eYPco76wCJpuUmK7Ji1aZMQE+xRGA1tjcrGdtS9vWOsI7L4A/6+1RVU3HJj67eLWgIYO1KQcPxoVixhqrURCI9Cfz3zoVG8Sxd+D2cVQlffhmZmMktzM+WWlUlV8G+w182G1vw+Ntq1+NLEpDFIzR7rByDU6l0digBxRoZy7hmCKrQPkwxLDdBaFJV5g3/uCqBD/j7me8TxMbt3bG1yOaQ6mbEgsewesLnwcEg1Bxmo1Prwo3Da/XTsjj4QYbW9vY8GMLM3V9hQhO6DbGZk1uQFnLfuFiRzsZmsZInZHBIp37KAXwW7N8A/vF3s7OphKbQbX4KtQoOQPQ10S6YK++0+wWOtTGu3WSJMImY7Zbsu5v8orWMhDsHAbl1jWeY7Y+Ats5vba4Y69n6X+iN+ece04oydK6yydl47LM6UhpZFiqRT38QgG6xSxyySsJ0Q6QtFxoMbbrK8+FHBI24P77+zlV3qOkV7qWBida/+YPsgMB35b5Ah6UEt8kDCQv7hSqs+lFXYD0ONCyFWtQCJTRGDOr4I6rIn2kDZzaHpi4mF9sJl5r3935HiD7NhLG+HMUn4h3yWLzHnYkk0/lKruWJ743CxDsjHPKqwPIlIoRrsTvu+x6RwZCIXhb/OgiSsMWqEF3jeYXSFYiJsYhVp/zY0+1c26LogZeG8xlLJ7hAeeEDvJh0rMAm762IaYTjAYZ4780S1a0NnXVso0+25wfOZNbERStsAek/TXnoOmR2M+I8j0sABTpj7sw9mQkPC0zNOV2KhkzoPpe6UrG+ojWbn3nD4n72fpMat9n13Xyj/QpugKyfCTeBOk2S8aHyxTFjtt3MNZlqpKrfFVNyMvlSvdbbCq/1d/qq0eKWhvf5PdGrV6r2DMRaTwdhkQULFyQFVghsC/5JUHnyI+LZs4GOLw2mbMNMhNNpoggo4jiXkJJALdg7eied90dFGb/1l80zHMsJY5hvswMRrVaWHctFLEaagPsVqgDparQONqsJMkYSmkoQJztSsw+FIj1rvf8EfOJROAeHA1y2Z5UV2hv8sS9yLrSmc6jhk/H30G7OAoGBFXiCn2BJnT95e2q3C7+nGx2AJc5q5/YgmWCzeMa6KIdWn8DcR0i+52QqGDTsr4H+zj51R4QgpMFTZEFwje+HJB6Cw25aSKKZFDodKVLrd8TXJ7Qv+c0efznOUp190/ZemWoeqwEsk9s5OtN+dyTtqydlPPuo12/oy36tCHQA5YlOJ4KZ1XOaqY5TdxU/KRfjPUZdpP1QqKRobuzmIbfLkzzFDDZVzycMQ8iehWf12QJmdZITmwO1qCF7gdnWttBTj2+QaluEa2BmakcW0ooK7fDMIDwSQG8iIENoLjguBev9n3SFvKBC/fs2H2QXEBsheP0nsZmO8U89+yn2Oo84qz5myLzPgvAZkD9RwxY+mW1GQpD17isu0oxxEMO51GQZ16fpAS/txB62Ng6hiRH+61kwsiC2RoPRdlXz7DKw4Jx1pepvNliK92ABqiH7dFj4E+diAw7LzG9EgKEX1entbLyljC/OMLU+l4TXCyXZh3Dos1967Geh3k6UZ2DUkZgX9KScC26uNI30DwBKXb1f/FfC5XrvRiVkra6tjYkmFlGzUr9CF6+yvnFYwjyy/wZ/ArZRXQ+hfp1f/ttyS/w4hsVPfkeD97qjTvlMYTMSglIKGJoNVozdzWSQdAEf6vYspLcPqUppNEcAV9DDXwBjA/n8qkPBSe+EmNdRv+O1LxhPqjwCECtt5iaMFTZ5Oq+w55fL900HtYyToW9anZmA1/RAB/g17emjUU7ffH1oGbt9l/pCVpE3+bJjfVoukknyNkU64FPXh/fsIJPghdeyxOTrAhENds9bcTr4fzAgHSX77z88Gr8smFE+dd4dqSvUs90Ixqj9jf8LSamtgnyt0XeqfzUG03JSH1UAi9JYn3c4PJnYvVXJ8Azo0NawftF6US3oGtv2mR2GFdJpyFCpXsC7YAoNFQso7Mq3JeCiWNBqG0sh7b4sHL17KRh1kY4B3oUtj4QfqPGlj29rOsDs2CAjYEa84brTOTpj6Lz2G0QiqLgB7GgtyUd08H0Hab3Xr8+RJGyiJRgw33nzNjOY8wp5322Hvqr3zUlY4mYuRMe2DFO/8R7rGMp0cnSXp77YwOpjeCu95VE74LCPvhUi7khDPVrM+r+Kq1oOrkusXxFTp/EnA0Z5qZg4zP/c5+1/Cz9SFO5fHfDV/+MtcQgxy+Ecn7MJjm8oPVUhHW3PhDRuxrwApnstMvEBhPsRk2uZXX/gaZ8WGk93KMcBK5h/t2+ju2dk1mTtyim77NvpuJDmnxxLNmmMScQHTRNREtVdAE59apVC8vE3csfzgmBUysbJ8mx6Ec488+RtTggJxNuozbKY+7uNsixblOCJ2v3C0sulH4JgqFG+MEdDwTYHFi/AhuvJ79Gj5+5+3Otcadwr3fW6+a6Vlz0EaibwH4cmngNVIjc12XcsyHFIHza8eNJ7cYivNziI6AVBWHR6DsGR5PEAyuGCsSu+pZMyOP1S7KN+BBcTUQd4QaFFADv4Pp+lZTFgiGY8DH59nrhAti50Aht3iVoDvnxohqlkWkJJm0Bwj+Z+ULvaYsPdU7MFbAbniv8FC81NjqveY/2EPI3hDpg5BK6dBPH7U3q7V4khJKvzpKY1fl+Q+/TUnGj8IcRy7EgUPaa/kZLmdo8ZL5pVc6/iLi8lwa0n2Nw9p3knG2cQuyc1dgFTk40394PMe+jhvsE6ucEWCnxu/XfAD8TfhUbOFmNgI6cYvuoM1vGtE0QUXMAmA+ZMnrAwSpmdDdFKkLgGuNywde/nn2fII9as4liLNI5fuHHaEcMvzvO2yQyzNvexYvjqfUu56DNca3bhULE98S77eBxEzB4PdNuzn1VeVF3Sj5Afq5TdSLzgXAPw4+FWYZsrxtTHPBx1zvda81Hj8GnOoeyiUCDm3CSrxFoQOmWSTEcMAtD+oD5pBMJK03sGbKSeK7JAG6Cv5R6zjsCe0ee/z4lqEV/Zj1WlaMh3+pNBY5XBIXC7E8UzhVc2e4aKEcee2l9dsaX9vrRpX2O0PPg4dIOlJgOXNV6pPlBrNJ874QmuLtDFZ4KEMJ0/6m8keywuleJjIro24aSSN5n5yrsaEQGXrLMRvr+nTWXcJWw7ujxoPRKb33CZZLttM70LihQ/05YQiijBM8mOprd9DcjQfPZvLy8p4mZ3aI3ED+awrnJs0zr/veUF6gyhwylfNj0MCjlJD9fbf4gg5VV/H2/ZX0PoOvelvPdb0vBTzHITXbOrAT7OLJKy2VO+zY6Uic02OX+Lr0BYU8xbhExwTJkLvg2z9MoqoUWB3bEbWJpZlthS2REFM4Sli88cp0c9QWQfGFc8Rnrc+2QSU8dIXxE0bfH9THvqV4fA6JERH4QK426L4BVhpEqaeYMzdYtv2aEmcFmPjrefOOI5L0HOuReV1wfIAFuPC8QPGF5odhS16a1k+Rvn+P3zLfmVa6/yuFItFwlCAWog9mfMtt+oMUzOuLho5U2+FqI/RGQrwRnhBUflxVrJnR2u+/CJkw7CfaQYYpSUWIIx7n+79ssKuMxKlFf+JL7/nF1n0dyQeYN/M1M6416TIVx8etbWO02A7oENenES+L1IrWetdEeCZWTH4Pp7U5N2TX7Ib/WNzSw+DSLT6fC+FCPEuKCitrHyczfRGeuP7AU2HaumlepBEMUplxBCxWXknI0g2qtn8iD9rPDV1F7cJxtss1Ase+nS7QPOSz9Zw7MtGtrSSs4ph0bA/jf7uHC0Bu80p0pWE2cKVBvN+gezo5oPPmeo0evDWZG0oItFVbLt9hNtltHFU8PnQxVx0tdqEiWHNuDSZgTjSyZqA/qgno1Oe5X58A7HkSzXFtFCkSTO5/vikqLZeA3RnWcY0a/jBYyF+a10tOpgrVZBTpHGB4dhe57kpwTx1LjTRgt87AHZHetq21O1odXcwZysc7tjeeHQzephtNex4Nq+PR8ToGAuHWFtq1XYftdXv/ZsQl02CJHFknrWqx0thCJNzFwXV/fcnP2VHvXqcmDGWyrPU76cIyNsYwbGYHNdl4kR7Ra+aYet9Mu3YZGj2UQ62/TYcxsdbHeSsYcYvjA7xXW6HMi7ORopmAEpOdBMNE7R9NOzBk8ZRmYmKNyqcI7HebZivOqYc5XOPMweNVqi0cg218JXrQ8a/eSQvRe4Mgh/iUvbfW9capL72mqwZFkBmkr1zsdV/HBL0KBVDnMglrzQpYrR2VF0iwvzs0uBeiyT8E3chviv+aurtbmUkZRLHp1sCn9DeUjvl0q79RgQSvElj9PLkoQxAi8SlEseMH/i9wYLLi2N4U/hyhi/PNypkfOSDJ7IW++cJ0LD96J2BdzzAfLXk5wivSopUJfW+HzhdQx9t/W0HHltfRmEXL08xTRZ1nM3DDkNzkug52iD7OqxVNhRgg1xVo4qrotDk2TYPUJfK1b708flcwJ0HC5bzL0ibUCgGjOR3nem4eC9ey3OOr1N/ll+ZFY711jXOX43cQJJaO0lo4/O3APqdhxjP/OGpM8Yo1En4KMYHK4ujAnhZqUl5AyfC8R4fuwKCoiB0Q5g714wfXOl1kjMsQFhg2sLgLtiue+w/15iDdSigYCQ2Lb0KEHQBPOKI6swselUXeTZmBJLakPVz+Bd/WxH1vXmd5jnMwzgHpJzwVl/z9ghQvRSq3jl+9q6rGzRyyiKW71tI3qj54AA4t1fbrvqOQgRnKS7Ffb4DMRx5xcKI8yygka9/PmFl9KppixhZ46zljxhNfoUptTcRxl1ZqBcU9stJp+mIfs+W3cgrBbMqvuIV3JuDSyVx2bvzO2T+R23TLLVHbwcXsizmCGcE3YrENtq6H/rdmqvZ9Fu1Td2j5EAu89PJy7m7QTOpmDkWoupb5+8xxAX+MGyc01z96R46v/fU4ofwhGdbO8Fo7cLt5DT1lDaCnvS6YyZsJtIV6YXNTAdmu0J/yMf9A4Yu3Tnz+zM99w9qp4iqrIp2y/8rzO2Opdog/RCTvMeFLJqStjXLi+7IvUz3TskGgqsGsgOUz8kL6XuDLqONvNfrMJ5Z0E4gPrP/MjT+F1/fhCursdESMpM8Mg+0gdjCYaEPgE8YCd+HvpW9xzbscikCq+m8b7gUxsWFAtosN9Wzxt7oyBtTud6cfFWAOe1pyMnzxFrb7lItk4aqxw3hIXmFrGRnaNRmCrHrIUPuQRqX0+1O/aseBd4ITf20QkbONm5H7RAXFkXnUBMik07p+/UUiCeewciFEhIpegxfo3SSJPkaZmATbmcs0ACDt1jkcTD07v9KKEmFbNHqUpiZ86Qyq02+lyul2IhkXap7oJNsdK1b9x+W26BDr6XZbSnJYu7S2u0hScMvmpsK25vmd8zb4zBlIADkrDQdKvqtW/j8+KchA4FstC5+p9meF9AGJAVoRzWPMX/BhOUFDgT/FIjrRCFA+t3H8XB2C1NRDKS1J3WH8VwgPGRSWeSrK4v3voCY+EFHfmR8Uz7UOy2zaobdPcGvfE7C+cWFMByY1GoTX3rnJ5cHFtx7CMTOyY+AUuQYky8+FkQSGcaunUJfcOrD4XPqbyj1PKS4k7Avj/qQUADythijaeobt3+OOraTh4+81DlbEcMFX2iNQfsVwJtITVRq1JpfpSkYQgNjwvaE8Ey/FqRV4KgbKK1QbHM94/A1f64qifEG1HGohqq5yz3uydDqUjGtlg7tLDOEOkX5dWvyg5cnt0ZpUXEvlVZwz9Ul21WIoDcFVlqKt/bkps6ZCKoDdt2NeYNyN6zfmVjSdsw7rfbeLSfGxRW85C8mMgoKHDSzCfR01PgGccnZUe37OFJh5ttsRb5y5jbiSY7C8WNGR3hFSRf+4xgGZQa1Y/VmMOOMDDIZXViPiHvGQQsiyv/C1ETjOJBdoQCZnGhzZueba3NzVGKDGYNFiwXH+avjjSn/O2S8bun1ymrKzWPhgLsLCOTZ7OqKTJAD81ZuKyxBlPkySUwe+AhxJCZHoSfdixHzWLWWUxLL8uO/SG8Lp0m3yH19i7cVvqT/NKI32SjJSRyv/7A2nq8Pr4dU52IVqZFASWscVuJCQlKz/Rj+Mcfh4k/7HzTQsTVkDTqz6+9VovJccL+DD387eKVBgYJqbCm4ctd0WJc+rqJGXhCiccD4WheMmc9PZbkWzCRWVpY0nibZ+69Imi/ZXkyUzuC4o1432/osuNfoVcIZNAye1t7Df6UapCMniDfeIX95qbQs12WyJfHhhfGMGHE8+vQO2F/tpFVh+ENpqYPy2yHhyrBoacgTCdKQDIHuRIS5UH7BO3XxdulHdo9z3CnUW+KwzNpie26jH9Dm/J4d2/f3bASzoJQEy4oh7bb3hxhETN/2A6H470IyiLYA9mM0I1rxhzXDO2NBVG8bONrm8yevTCyKyn/xtkQUMf2lnUKncbRZ72EEoSHB2i+2IYf9PvkwM/MQquekBwNI49TnNYfdwqMWS14wNspd2qtKO93Fo5BD2skdspZZcT81KpFyxHvrgAd7XMsQIcRnZKvg5nQb+Ea7IBL+2WgKyOr3eKCmuvruHx3gir94H92u4945QOYRub5Ap77w84Y/odiUZDLCGOZ7GFCR8qlOvjQR2821Ma4WjQNMLAWq6W++oy0/Hf0+ZPHLWhda7VI5M5KTq+YudBh7RQ7exOkyTbei4WwNsm4gr8uF4BIJnTKu+LpaLn+FwwZfpGzuq0/37pnO6UJcEEVjWKOSaT4ndyo0u/D9NOgfZVwXjibnNMXzNTNYU1J3yK9PiLTLFKwMsPgU3aepXidMnFkxkOle1v8JR2QpDe0D3XRmcPaK0A0VNkQfXrzhlJTPsmbu+ToOCbSEJUcXVeVvaujlovZ0NAoX8zjlhybDfzIElqETdyIT8npdneb+xkLnwuww0rV8aSXPVXx75n6p7ZcQrA7q5JFnfz06UT/UThGBXcd/AJ+01qAEHOtOi+O06H6vTym6wyPDhopR4q56rZjmbIB+bOoaR/S0MIBYdLJBAWesf5EhnI9IQOeQ1IYLfVlDWk1v2K7V0zvvp1EaFj5ddvBUPSwDg2MKR4YvNWr0V2e/HTyijIg7ZWoYtLqItc+6G44++L+SbZDwkW8iqbNEJx4DPhqQ2VeC2eEbTeybcyPR5hZ65deK8OfcHGaNRSt1Unr0yqBGlU3XcC214UP7TycTXPYViOxxHwOi/PE6nPJJbxV/74mLZzsh0aNR4sFDkSKd/Yie1F5/kFJC3+GsxIvVcZBy4qCcqF13/gx6KbipFkHBgphWHj/v2dnlIlsku2B8+9rhR0DN4ZYQ4S5ik124Mg4fg4kH5eDaus0Wu56PhzjK5hE7CVASBdVHnq04G6Np95209Mj1hGg5c89f872H9+vj705vMqPPoKOVfk0JGqNmB++J10O9knrDCBg/U1amgsq3qlGFeg0axNmpD4feOOg6uK+poAE1L3ZmFFQK/AW73IG003sN+aCWgqiX1TD4vjKTCjUkZpSo8TfUpOf12yESm4Mk1SmET6zptDVjGyloHqDYZ9K0uaH9vio1KIkDKNQnCcjYqk9hf6caxzmh8fcmoVzT1A/PFXTFje6txRi9msrRiv1drMRNzN994zf21+rbAt5I0ihyMRtDEiWCZbihp+XcmTCOJ6Kn9GSKsHFc3Uzg9WYuvD1OMLM4ULZ1oCZ2hoQ5diOozBisjjJaHTI6YMn8a2KRN8RPWBpMuQ95u6Uws6A3fLld0axurHxSAVDnj89qXuIexQCLbhVPNYxMRlYfGgN73Z6RK5SDr+Xqj5NGFq6dyfB40daa/HtGijDanNV+tN49LZD95dtyPGYf/IJBw7XwaOwTpbVIoVCagS2MlqyFi465J5JcqAbURVuis484v1zM5n+FSGIlgeKneEUYJjOYKgOi/8Mvx72Sh6AOakB3bJj7rSYBIz4t1o0tL4vL6/pUS6bqfY+kcYe4xTPSFbyOp8XxnuxdVPoVYbgtUlAV2K6a/jvATGNAemY0+MFTCqkusZpu7hj04pOqCETyKiurbuHda9XzC5xTbp2foltnuN8lwRjVjivhMv7wGNXpjAfeqfAssB/Ns5rx8ru+vVjRcd37M7/3G1LI1EnJNurXDjE+AetlOfOehgdZ6ftVLPAUSSoqYuCWS/y4/8kqzP0JymW8wDj8PHA1zzd3sVFXlqNjD5TR0VU0CQLm0nggTYdbRL14WqdCqFHkxBb0kbQjg19yXQLIXKJUBThPYSiv2ABft8d2az9r7WpJlAq7GEJIu/WMH+fioBEL/Zp/80vQf8BGS1hwqSl3pQEAghuK89FZe3nBJlxL5xlIJ8RFeARmjRXskJsjCk2CVpMCJJ1UEPrPHHTvEmqkbO/wFtXsiaIaZReBS/DZJAhTTJ5MzIvDYElu/S9u5mgTWkoT6OOgLpqi+CG/9+U/RTNDrCm9wgaYHpvdQk3l1spyPeE2DXr75uWERA7qcMXb8hUL2rl5gp2Y/2G3mhlIvjoBidAvk0R0fJDAG4mjykcXt9zhmX6pdKiiyfDwQ3hIogd2DH671anXFQVA03C3W2/pMYC184+pD6SkqJRiC6DevyrtCrpIE+E690vO3X2ov/nOAJaGC2n6y0BurZ5Ie/WzLKPVptNlGJHElDrJpbv/VBl4+Ap0a9SFExnDSyEOnulgCCDXN3vT5N7Js6y/sJgC3oHswIiqB4PfkGnr5XLCq5jbWlhqgWTPD/68Reg301KP8EPNYkMrJ7xZjD0KelNufKIHD1DtR6W3LXFfMXpMABmLSIEgVhFcy8Kzar2qC4FPHwpdjO2ZZAOucJtR/555MpF5iafCreN6LWZK517yVSMMfMy3Uoa9+LQTBLNf2qkBdk8SdRSecUuc2DyF4suGTFcO+WorwCoB+NfaG6CbIKajEjWrGlt8kBecj75gHWtvnw67yc2r2k+jMn7SBaAZN5fe6BaLDujnIdTYz1ydPCu8fxnbCnaKgYSnU/cwgZO1onpwbTZi9T1N05szHovWI0P8m1nO7/xjgbNaJqLzNQYiOLirFDtTjR5p/6kaEZhF4QIgLqQ3r3L9BpyR1+EM0rVxB4vreZMlW9CypVjR8QEyk6CER/0qRIpMOzY9aV8eOP/00i47/ETZO/rRlW+0JrBKcjlD2PB1HnB/hib6MS0waZ+NJwdS9Q1QRxGTimgRO0RjzrSuDb9QYO/Pdhen8uoA/tmHWqh+kv+QxM6uXtvTnu/T6IoInpnupQ/WPgMZHbpu1XvhAH+9tm1bnXYxC3S+y6o8uoMQBVQApogo5kRRLP73WEQdAUZBn5jBRm37PvCvjDRChj2Wm1Uj7nN4+lZiycXI4XY7RbsR1VaLNjmjW2Pf4Lvs3nUjPV+L/9+dVIAi300cvR1CgIdS2aAPdzM4zd0rz8VIAmmYZWLpoX7AtuXegP2xbTfnAEL/dwg9Sv5m5hhV7OOykWZX7739tK2BnGII5uNylWC/KYCgUuOtqxlMIVjei4RsJ8tXC+UUpYkPVNgcUfi5D+Nh9N+SoYBM5clwnhgb9LlaBmDvA0mYbn/cAMxDL8BemgNqAZgl1PpO9Tn6B6Fh7QyTk01ukX3ohOTgjZtyvaGeKvQ/nxdwFO/DcnphaWwMLUYPQ8FHSHGFk5eB7xAL5o1uEWjkzc3+NiM5YSTzNnnjUG2pIZXDkC1yvntI6vvjZVTPsxj8QLs6RTrVvoAUhwPKdCvwO7yKkmhmNDIbgFOUv9Jvi07jye9B82lM4Fya+29xi2teJPY/UZ1LQWvZR5kX/uFAHnaEgcC+7ukfIgl/24lzbPrDSJuYMF1V10LOkqjHoR1k/qdY41+1d79NI2mgRhbdd4dasfElt3eVvlLKcJIh0NgPWU2GUN60wH573lrlcqv3dhpsgPXpTs4OEsjO3f+IHRXIiKjQCzvjcmnDkse3des4QyRyQ9YUSF9IKwFn6SABYizpETJjd1B+Jwl6b84v/f47aERKsZzJ9oDp6CkMnbJ/GXCVxtPZaGniMHWdzOQvwqVxhMyRcJbSHNz+Rtx4b0RK4mRNxaGzL/28dc4jSxsXpcJf8GXv/WVGMWKRYH5YQtiKquqq9OVCSSzvfUP25t/ozz4zzcirGUyyijR9Ay23HO79voDZfAKRh7kQElEzBEzGdPCkkUCu3cg0p4gzVgEyGCPqSjDDllL+rHe5nzWn9+0FaA3R/qvRmNwd6v8RTnzjS0an0Lw0y2i64z+tsWFwC9lsisiq+++A+VB9Uqnjkcuncnn3o/6yIj13L88LF4xvFHZS39SDVt7LofO3gxykIQ28Y7FMO/9ttrOTtxpVPbLnRAam6CHWFiYz8lBoQNv3LbIpr2f3fToQEur83T5tmbmOyXtUySm/fhCqBiivIsTgbVF3p/Rb5QJxB7S64mxIYzQbP7rc8PFOd+AARz/ZXxxS1NjqVj3U7S8g6EhKqrBKsJdEN0+CS+HYISl0AgbtUDEoHXPhXygNesbrDAz2lOiTI0L5zqRc8hjLlUWJsNMIXMSyN9b61ZF4CECYAFjommMgtEPlGYFKiSWWuVeV6IbxQKjRbwEbSqDrG6+CVctlmnX15spWgx5qj1a6kDVaxMk+mds7PUx7JP3UbQSvlogtZYLB7+yk/zQDrGJ0zpl5VKvXEgwBrDBmJRDs87tw/Rs7RgfhcEeTK23jjBRrL2+t19+z64dAZcZPREUE4N/u0OxKASOcYoJDV1w6s/W0pZWgIoM9CktIWFefSVOes60z4xCYqSDmisEispSTFiv6EzQAOx4VpgbKknQNUUVoiKOOZM/9C7MLSmOOArOc5vkMBow0hAZbXNwo7EQ1d7Tla1nXOyJwA/6DtZQ/TVmSuluIvJzkiGQjIfHumb0B1Og0HxHADupba8/28Iv9Qm2GTXDDHni5/4zpjCqXc4uQhOK8UyARDWUpHxb6uNUx9x9YfEGY58UDa64AjdDjt2Nk4+eseePxyWLplJIwa2DMigq8YeRlpbqdnY+8fbuBRxrDkE6/J5zy6LUMxdPAYPC4jsOZfsMtPo9BSHNg/XBBu6BegZ0DUo2/JxKqCgATx6qaD0+2YohFJ6TrtODbg9TYTHQZRX4D4XClB5T6fHMZQmrS24FiJD9L9d2GSQIgkN5OPXw+PLx6EN5gXqczpV1oFvJLlYIJqHWfosC3iOYiJdFuIC25Zj36kB9Z2ct7eZBy0TH5B6AIWkYuTyju8hOTWYijD9BHrzdmN8LfD+0GSnzViRA34CYSgWrWmewY95hHBWMdESx68777G9hNtTJpyjzuOjUbxroPxmTpf5RiFtWBWSJo/tK2va0qKj41LXBBQ6j2M6fwE4PEDsS95JGWvecgV03LiKHSiHzJRAEsyH1aPgXefm2zzpe6gqZroeKIBB16s9qorSqKiCQB6dkkbYD/04DMipBOd8JpYYVYiWJBRLRHMkDwY2bLIt+b9jtm1vDHDcnHRtB59rJpsD4l4a0sRAZOUuH4FR+OuzOKWr0MqjGAkBsd7osCF21OEz0lSEfDFfQdVOLeu1CPMepnlm/4gRUI8qzwxRsiHlZq+Vs1C04w3HksCzYuwcJnd5p9HTYFwXynWMXtHviiddhGtK+pEYHM9z/AvcQ7BRFGbGwYOwHNK6nC6XBgCfEon5qTVYTYTQGJeJg//RXIDgycIKjl0PZ3ziYvtx2qMHD1skC6FiwJfxOPoo+26VZKYqh+XC65mKPyfnkE7peUsyyNnpxBW17p3/A0nGLQntc9gdC0J7u2PPagS8/jaRZ/xY5jt/aaCrwAj+KOaQ0gZ7VCfm0pmOW4v6bZEqjq+bwfTECpzKU62wYpeay/dlDw81dOSxg7WvSNN2Y7XggbbwSIYhiGcFGO39fm6Ie5vmaRdFyRBIZl8aY6VMbabMdyH5eaVK2qXE2nvHN9np6JXUBD5js6nVDBTIbvihAmSMVAYqC0m7dEnwE11Ttrs8vACmLcqzSuLcNMT0ZMvA370C3jBmW+axBAc6KZieqq1am4G6XlVXrwMq0VmOPlLSq5nWdrjlH+DOzmcz17vdWsA7qbMtj8TlP3Sxf+2x21uJqtyOyT5UUPq4jZIuUVKscUsW1iurdlTxmnaUFg493CASK8qeV7BjxH9RoeJ45EjYlPv6As8z4wHQEXgcxbJwnLeAoFu4RpGCXxlRM+ykCFEEeT4pkU4mXKkVoPTBrtln7y8N7/bvhfOH4XebYnf2sPNA1Mct9h8gu33KXr18FTOgLr5Z8YFZ5uNKCVxwMHARE7SvE0Vt5g75wi28NYvbVKH2Ba48MrSl52fiZNWa4KGWH07ktV3H2EfC+YmtoV/ecZXvmf57LsXnjCCgCGdX576IpS90IOm8GkpUWB2aMA4zO5ohZ0ofWSw1CtM8c0NAvSXs7cFdoRBUIsrC9kkY7cGpXTiOy8kyjozBzD9ieJWj0pzn05YmVLb9CX08eNQDL4HdPpADC41J0tOvAZB5KlIhrtrdeugHyZPtNppcKnZssjW0qLhrXjB/5aWdWp4PfftbJ+YX3eVv0vOpPFmLQTqYsL2mRhT1vp2h55+fYrmjucOkuQ5zdmVyPW910DC4hORTwM+fGvXcwTdf9D/7VQwobFZhTzGiKlv4D9hPVYUq52K3sJhmJooDVBoInRiMNA9/rDE9KhCZ8M4GbDW0fvvvTSL9n6jupwrAlLmM4cJiRlOTJM1+H4Ta4DhGY6VLiPrCippYFQVu3P2eXkNFHFRKxRO/X/aD2iGa8acsFZpTMLIchM9EKuw09fstxKK8LHOelXnTfvrdl2VlC0tF6K/qq30zoWvfAl75dVi+fRYbX5PP38uv7pYhsxXXTTAFNAIJrA/jSpaQDzVhegD82LtnjPkdVe1nPDUKnNFfZxcZiv7oleH4gD8LZiyW8RGPoa2IbEvZTYBqr9iGBlqHwVPoeQEqBHVm0miAjMYfUii+8fkr1/uh2yi0Qt9zFw+KdMmtKbaIG4ZrE3lwUKMYK5TSjqRUyGL8mRBo6xJLo/iCAiSLsLKJWrQV7AO14kDpoBuqNjQr65TOOpUukidmJYGKAoUPEGxcAVxxkbbuKatynY/exKn/zBc7s8WnSWLiCHgbl83LeVIBD9xjzXiBMo5d62WiDYsvW3Q0kiGG7RV9ni9nO2dQcVs66qXBQSQslSYgC2UYYMBrib0KHYsr7R3m+fEgTc7D9kh+zUY8L/GShyFFQBr/joe0hq/RVOCXSk2kAoVwiLCTdn9gphDGbxd/TUrskNH7Xi9OARDj36ks2S++Y5Dv9gMPllWeflJts8eF9A9e+o2CJt54un+MJJwecAM3GzMBDxqeIWjWKfnm/dPs32wmihasO905sdTkmUeq2cfN07tZKJm40QeH3bYVE/VzWxo2E8AKNWRIo4zMITSwi1tuWCse/3bmkEUcrJIdZrH8UReCSnZn4SdQ6adfVfyGel3hz0ZIbXss5aCbelv4a4/hYW4/IiNWsEQ2W5e854C9Ls1n7IH4dBsHaomq993HnEyibhvlw7lJ9akiXZUDwujltcIeyrSHwj3j0/11kDcu1mOvxqzlayyShwMA7pLyeoNFJHmZNjxk0Qj0x88cbOYbA0ZqvhExPzmebg7roja5f7q4kRh9vaqf8+2N+fPxk5rszd8yaWwhpeN9eLYrB2Tzoazoo+1yDsyDOswLi39D3u+LLZFnIYaV+xtvmJ8F7YwlzOrOW4pJ5eL2PKnobQWvANvAvgUg/YyIS5BQ6A226wPXMJryJ4WTc0Tz08CL3w+2ucuRi8Xga/K9eMHupzUPIsGO8qrQf282e4flS5aPp8be5PQPS846hUdIDI23NTTc9AOB2z3AQ2QfgAPnOFiqEzIsqQCtsxnAxuCJxV+ZZLCLca5FrtsGr8aw0JVR8bRPyxlHV7+JIGPCloU2SWQFeulgZjPAqrJ0HsWS4lMiKkzNiRJ816w/LtitiRlO7tODgepjKCY1EmEO2cQVekRKybUVqNtqnjg5FFoeQ7g+2ZRubt00EooEMYyLjuRT20R9j+ucVgjM1pmrtQCKis/aQlRCROaQ8wNR2q4fwMFUUYgt2GFrQliU+O1KIHEQwiey4qhSHLG53FR2i7e/A5vIssmY0LOtEw79df3kBQiXAsO2jdP+/kw11sZjUn8JMzwP48R8EC0jKjHn87ccF0BMO34SIqtmGwxoNIXvBr+UX9rJQZq20p1y0aJfaBKBiFsFs0yZYNfyw0haEuKOHhwH6Q61QE1jrt0aUAyk8BLOsoSS0DBl4N+yZSjYS8MqGzsZkuChEteUAPC+MDQBVWjtXnCtzuGYYNucpi/fJ2soMt4lVsKkp3ijy+h499j69cV7LZIaM6EGjG8YtMhRySfv+r3NdzbbtlwOARtC25LVWOIpsGJ1B1lzb/yyXNTraP1dcMXieoDtNYIOdCwNeNH7WlMroYY8Oz7GURVNC2t5ex6vT9qdV6bgq163r+5Ev/RsewLBdv6Lb0PMdkr5AUYHF4jGhZt6QMWVHGBr+qobtq54LlJWOO19mkqZA9HxPhNCu3fxIh+z0YAMgq6mnH+oDaIcg5NocD1er86HlaXQMPA1webEuv3fftULSmZaVeBu36mKhEKrzseK1y8cxqFAV80c4iK1TV9xZc316gL9KmjZ8eqUSyxCp11I3Yx3Gg99ujW9tmPkTkG5Qh/bKb9SMBGT9eiKRGQFUvRBXq1DL0mMluEmvhLVkjiB7bINWACwk2lKWhIoVhNLJJdm8TM7i7jNE3tsNQtnMKQcxFAC3XjiIuOjWyEjfUyr/Wiu8pv2/dJF08q9j3wVjsVM+oMBs84WXMfdkuqd5qnydJO+7QbbiOSE+GnsBjmNrl8MgLeqJh4t1nD8rrI0ItS1vrIvhk1TQstCZurMoUXmZ59Qv3Z3HDXiunFowMS/mO7BT8RPU8ML+zFbH7x5E4w0ze5XsEm4xS9feJwtpZVoum/iILdU0Yfet3ZZUivkfIeo/X62jF5xIjTvvhQXIAuPkgWlJXQE5jNA86h5nP6x801im1UAKGwTKTe5gFR71GuQX1MWfWRkFd80Yl8bQrcJ1zqDgrAa5suThKTJPtCm9uSay0jUj6Q7uOb4A6PckMTgpbLTX5E4S7tea+W2Ow+pBnGzFVCqifiqDMoeP2RwhvnPnYg8dlscfroi/GM/H5JxQO2lfhj0T8lZYSvlbgQdqaqiTKWbf0ZwE18PxYQdrDjaPmUfUt4HUbTL5vLy5fALmBX6fvJD1lnA9Bor6k/ESkbdUsDgCvza5/Ym+rdDdYbfE4/9bg/HG1/8JBss876gBTrdKeLiiVrWmSD6SyO09/gwSqBg9K6Q9II3ALOC9rqPV+dgS326dpmyMeGooz1lb/FS8ZmFNHg90dEkfEidblOyeDaZRmUQBa76iSp6gYvA9B7nXLssNbigz00IaISavQO9DXClmyUigDjW2jc25nM6x+CnFx9lPMjZEWPav968fn/ZCdzCmS6bn5V1JyaFk39T68wH+K1eow7LqKsaCiFXHl/voKxqArWqFNKQ9v/1a8c2WBFE6kfPvTfCAisfgnv9v8cyR+We8TAXrwzHzKkdFbK/w86yq4l6yG2ISve2DiFjX4wsYZPcEIlAYtgneWjToDXvN4nVDZeUH7EDMJBTjZDR9mEpahUWxD2JJaitQYql9TjwCPn7OMinEAGzK3PqJ/JhTQSjkHzpHqwNxGEPI8vU6qeu9yuYSo0QxLXZhvQuvAtvsF0Ss60B16VT0MpGLwM92yfku1KDxFrkUWJJtSvA1+g9DNNpU9XjIvoBdKJXNuNL/qbIkjXRNolisuVJJHHkKwaiSGc1AYBF5Zkpzcv2mtFiduD9k5d1N0lFPtYkJyUT0yCGGBu54e8C9P++/dDWfJPdLav1VuUItSzxuEPdDTF9sSthuDj+3+p64BOETJ+G9V9CxFXNrDVJOjrO9V2uWHAfrbTr162dBMI3ZIEfdoRij/bh2jG6IAjmchvzuL7TdG2BzodBXsgR6u0QJygmJ318dBmtQl0gf7sJxaR1HE+fvOUs7feMeOaUdSmJSOobqWwB+Mnp6tkFd04daJx+d/9S09AUbkMrwM/eI3pm0Ha9mnTnL8UBm7TKw6HYeL+BjJXeup/T2ipkoi4riNRfmyHSofgg3pVjVO0Yx7coRrTIqXtFxKVBKt5pjGw6/5k/fpmED+65bpeB1wE4Iw9JqDoRl3SffQj3K64FcHFmgjkCITujBIBsKf8lYt5yM4qTAE6DIuhYUFko61Bsxe13fiImiRiZYo79Gypo90tolTSp6QqaZSdOEc1e3pcLJJXb32I0rAPAwbu869//dsSWDxulEJqKZJgFyBsCH/mvL6ZUpAnR18T2AzVki0kdW5HqXq4+XEELP1t9r8AXCZ40oVIWChzog1FaXoLAZ0Grh3qNkZbooICddnAKvK8AOe4LHwOmUnibAHO/yZGPwSgtzJMt/SofoRJkS+O883PS1AFC8UiAALEoXVf873dEr3C/qbKMmJHTYaYIxX2bhHKmVbHG6tuujunTeSHB//ypTbKaHhVu1lfeaxyD2SOSCT8OVH5SpbjAQ7+4yWKKSh4gdmsi+ve83VVqtwwole0ETLe8F/Jr/5Zg3fyeeSJrERFJm9JXNz+YKi4y+UA1vZ2daqe1XTG+ZORJW4a4IogCLXZR8VutpifiMEJ3CRJhTVqm/Jhw1ImtCZJLq0LEOQ3k2952TutsFAHuhA+Zhk0R6bsn9SwYH8MYjzBbtFhVH1NOiqQLXwAk9E9IbvsIDW4c3AHhAk+gVdIB1nWNAtkh4M538fXT1YvLZIfPBT2TKNUR9vtemxXr38Mg6Jahn+oMS7nBnsvQ/AQK9YcubklCi+GX0sYv38d4twl8+CXn8rVIzsUniycy9UtZ7OeUSqgDP4qOf2FIJ0+buovqUCIqgDo0sYaPRZwbbcCwjHdtXc/4xfyAu6EifyXD7lWLa49aWE3y8eHjD/pA1SdPnN8jn4xPshRYqstlHziYXM8aOXqlB8NWYUzc/3/qkRsXqWzP7F4RvWTvr8+zsKnGndtQ8nMYmI/hJ2Y4vhkRWp9O77lUDNpYcbYia33NWmuqQp9gOBsDtCF4EAXrW9AKny5HxZbAN2R17eyFEC0WT5F8uG6v2UNXxUhn3Xvfztwi4R1WP6jrRDTnUow3ftLv5fjc2SXnrA5PQN41fHPey0kTgzno7mGc0n1Quw5Yf7QurhDrkzd9G/ju3Rw/8v2RlRY7csuViOVR+edy9D7x70Ak+Z8uIXtV1Jj/2YHUa5XTG6TKgwOq7Y82tr9ORbnU5orUGx8r5K+bZo0xULJNr8jgATcDBLLvD/hIPF3u5Q+jq9Tjd8+BxOVN4F0l8VNhlQrc9rvOuBYvB6p4JJTyzLgmYcmjs4u2HQ8edOZSbQ68g90e2+USGwpGrfh8RCnKZOOUu1Bb5tRswBZjlOFlzsdAqNFguuJrD17LlOITslUc2Txr/0B8A38OwNhCAq0oqb8rv4NSpyJBcgxBTEhS3mM2cNXKjESMz+mEDNVyPjglvZ8f9RpJpPOuUV13yCPHNjLho480YV33EpyV6lhNyaSfd+JhRb3Rrl2MUbUHfvAQGgsSuIoC2OIfSkqyZi12vIdq7vRkt0AVHJ3ukpWLj9H4WtNaY9ZwOgahbLxmV1S7xjw/k1cyqslS4ifdnQu1Nt1CjOpsrqU8fVPRAp6zpwaGu8S99WxfgmSegBexssuEdoqYTCWJ7XRXF9Yqpf0Jm73Ygf+ttlsOnJyv/hLr8SAFLkpc5BJ6em1gnjxYIlVTh5XmLSwv5yF2AdnrM6N1P/RhPzFVRApP1wJ9CvcYig9WQ9TuawE9dkVHV+xk/4k+/eYd3pJZKh9oEHBoVLFnq+NyIXmSP43Mmy6+9FN+Nm5lxVrruAibhkYM5aYpmLp9PC3H6FIydkzdvPJjn1tccRNppXKObpt6icsVTnC75p86eJ1p2Tqoa2fiF5I+ZVgHSND49fpU3NTCxahurQHYUQkBN+SAxz6ENAHZN/6IWAJn69tM2SAxxFNGcsFZM8r0PkylaTBHhZRk02Nxn+bCIHLXsR/T8l6OjYI3SSoaL/8b+cR2iLBe3/jj41iQOIp1FFhKK4wwvLrcpdX2uGC4xUxY4FesKtoMkZ+EQglRH/1MBl7BPTuswbSsB1q/KjdC+ZnHd/CFG3PpUijCqqwE3nb/eUnZF7fHTroTvBHnXk319tmxeWTvn5GN3Uqu9fYhN/UcOCt5WS/eM5Jdaywskp/y1mpl4XKnDrH/fzsoZQjaPz8NxB0SomidmybzhEtXyRHRnUCeJ2ScU38OlXOgRcET2HvUUMtz5JXVXiW2fn1jx1djCGemjYJ6oO5ex+qJxIH3OMsFnFSbQcFuw6/bmrBckHGOlUMB/mz097i02KJbhmWHkl6G/ytxSJnemO/r3Pa41d2v5TuiIVAIhw5paRDTzTf8+licF2DptNBzP8zprGfw1UR0sywYUPw3g8uzVbozTAF/2D8fHPdBfksyczPfAg/e9Ep5OZW753bGBihdgTSX2MP1/BRfQ5u8wnPNE39u5pftEyavPRN58a1/vCCNB7JDvbPThykxQyCCcyHY+zrOREaNf3doeG8u8IABFRYYvV1p4eB6aSNMAEcyqSnenAG89dKxhQCx4gYxgYKZWhzThGMIGUVGxKkk0cjjuzQQ+Bj6Cd6WC4RHo6MdRn5Nvra0hoTyGPKV9NRX1AmiKhDvuYnHgO2rxKRWJtgjtlrieATuBf6Zeeg9YQ7yXC/wzMDFp+jx2/WC8JvZ533+3+yvwehABGUQjJYyKnPZRlJEHCywKKPgsfqh/KBa2Vk0MG97bs4pwdpUQ/PkzOzcw8stYqUfWiMsCufJHCkPUhYJoejGvU5KOFyG7SBroKJs1t/gt9Kzw2CPpZv1TQR1nzZi9B4ujE+OH/4UofFP20RVhB+tJhJ1JKMwQmrAG+XvBBEiV2XgQgd7vsnXpbh6fRGE07CHK+lxJgeyvN621jrVcauqYEwFmAbG1DzI7xintoHFF4P4qbQJqEUyVrJjcWRWg/fLiGHt5qHlZCPUFgzbBixgDfhclUqcioJnaawIINzyQOBitJsuW1x6oa8S4G497uHKFmnshBtHJVGGn+sz2ZzyFqX0qyq71Klxb2ThmGjYdwQnvKgSNjzxXGn8hbX0Qmfsj6Lz2G0QCqLoB7Ggt6Uxvfe2ozfTO18fIkVKFo4Cjzdzz4nNQMdtjF5PL/5E+ik0wPjmEliwd6srPiN0fOM4TqIqrWsV0I9GgEbXu99q7nM+bJSGEaSbFrakeDbUSCxzmjNbht/iBG+uJ6gQ3pccaJbAiE1wUpYxKxLrf05m+xCNxgH3VvvoD/z+DlVO/Ucd0It62OfbqMeae5YSPn7Cgu8L+UEmqMzTpY1FCMznqFZD+04niEhQ3lbJNUSToQBE1WAueGcydKiMpnttjaduTBstmEBFPlgBaZ+Pt2e4BIBAXbbTz9QSaT2bgqRh9rdwsSLSKjaXgUZ9U9jJnkgH4uX8xr6jGXz72FQYlvzA6ob1bdujJfrBpr+BbMopy4yKEAPiCiLwt0Q33Sw/+gdrQioQ3K82MJVXCzCk+8+D7rHSflOzjY8vjtT7Kfkpca1cxTpflNMEcnjW4Xpuc8dDNGo/ty0kY7CXPX+LxQG64BoDkM8Riy8b8CNksh23wSc4OvgY1RQ3n2khWo90kM/1zYHP21zPHIPGFvRtdXhMOfGmz5apebVi5KY+ZZQEyU9xancMzzRYypUEwfzLgFOxD9P9JgJ9BC0ul/xv/H7n0UzN31yCXwFnq55aLuQblzmg6JFYcx6TfUrGKmoFMLSYSejRsDe5Kkc5yl9iTT+Z1btXwI6M9hWVcMJgNg03hNIx4FqrPAjCYbaRGdFb5/oQA5eB8HkfEJj4Gly40/j13M0Gsay7bujgeDfnbMgT6TnpJzQOLbGJPwVvpiPCwt+zq29xrZ/YUfgNawAaDKE6NqlNeHVoidAY163sICcKAL++tBw6mhVWQT7Es1z2ERofGvPlCLH6ru4q+hNgVVFOE8g44zOgX+1zXevLtc3/xFs4OuQBLeQ0+JVcP9l+QyYDekaw+PQYkJt1vPKz/NMuFtcia2zc0/ieRDoz2MQX5TH+bOgutea0LIAry4LQwc5GM4F4prQIuXYRaNfQoKaUKlo2blduaIA57eDSkVyHZK9rbZ7M9sqpDC8aTKgJhJ9sAfzXjgGaEOdqI9VSzdFmMlYQ77Lx2NQG9UUdx5Hevusij6J6ek+Opzkgl6UeF0/cXxivM4jUUy3SnOMaNK8NMWx1+l02jj8JqThKoWZlcCpb/lkc9pPwX1Y/uyA0WeNrb8ad5F7WcR7qcal3vXJK/j5exUBF00qf41V5mzdf8gxCS//o1R0GFsqmBEveiPlrr9DmtDbpFkqNJL/QHQ5CXRArVBOg9qltJK2DvthpahYht4KReyeUhrSO70z7XYkYRT1op+TCjcx7dEc5W5I398ba679i5jiuBfJXNvs17PDkwMojxI66rNwKVzUKtEYaFP62i70RRpzsSd4o/0WC4FwMxRXymSJY6TNPl33OvNc5EuvrHpddFAHMvSF4ChqtBQihXSNJ8G5jA7KA0Ze1oCtClYS1eC41uO+77aWAz75iY/D9Fj8xs4J2uinaUfuVFnzns5kUWqOQRevSKaZ5YRjlovTqtT5FtdFYklbaFEawB6cHS3Fh93j5Sd7mj6NRs1jI5lYIe6m+/tPoysscawRCRZHxFF6cAXDYKxB/1Eo/QG/SJKLsTRNtPFJR6G5STNDrlv6VqI86ngA0v/TCtOy+/Vi7U4k6UiQ/Z+LV8kU1yoJarFXBi6tGcnQuXH4fhwRfW0k5NNLQLLWjN+se3MpI8mGLbBR3VHyJm8U/1tdHKBuwlo+ls4pc2gEd049GRkoFskZ/4IJ1bI/7vBcDf8XN8gdgchwtOWMpIA0ScXpDibzewxIlEYWAdpbHmSYVUUOuEZdN+rLahDyV0LyZvY1oqa7r8uSG6qoYXJ5f696dRxuymM9NY5+FDC3Ez8pQ+cE0YqTKodOIOOTjVcJUpfpxf3KsPIOWJB8EogkC5mbw64TwsNn8J7bNs65vYApqaRk0i5misCywwmnc5EQEqyJpPC5VtyYGa/3esWhyvVQMik9aQQndK6shImI39IkjlFhyIbfXNAXyyged4jpCuehTzT+iV9mYtTq4NPS+5JDF0N3kqq0jcn4WxXy3aFHxGhj68k1aeHAWI/bAj9k4U2WDd9UGFdwhQI0GL95QQUcBSNiKOaYYJb4a0Fkq7uZMX7zkBan0v2j+wfALUXE/dyJf7tcNrsRoV8OZ1AOfyRTYuCZwfziDat9cGmd/UA5+FGnc+82tfml6WyIQtJW5ahC+DSUJSoz98RsstFBDvp2p1xuR1xo3+NMgJII8vNgkjvxESRqOxM9vBrygFm5XbkBKEJqoWAFQ9/wnNEVxU0CaqzFsDuM0lsv0ZG23ElQwAEH+drsAH1coEIzO2Y+k5iU0+XmDYqSi/nsPiBWqMtwEh6DGpbCyNWGZZsiEx/Zh3+QK/ixM80hOHZJYCHmm3GiLExhdt1SDsj7dEGmGr0zEvZ4wDMbKJQIeyIabAWxOuAG66Cm9AuIEKs4SReqUsAGE2EdMinDPJ/ughOQ3zZfPQ+3VNicfap4lGi2zeATZuogBzSi69sTY7ZaXajGN282pDXA4s17Jahgas2I0UqX2y7clhGnSRFDKJVnG93lf9Q8CrUgmhGeISTUhmVwRPheTbLYdtoOZ1X5rgzTG+TFjVYXgZIy368ckPte0b19x3PdUSiV5jzipjtc/RchQbIP0LHCXQeKBKIBlSbMc6dATd6oF8OqIhxHp3D3r7PjZcKYERpZPk4bXEeuOrhWzpfc0uOJ6Nb0+GhDN71XrB51tPhUWIucbI0Tl3C/fhg9Dkj34nMUXZ1ZmBYqYxA9INHWc/jJ6gkpqgPDcqDUrax4lXsE7VgPlpRrY7+olHqdD18u+yHzTIyHDKq0oVvNzCiuWvCfCsBBa+ApvPZPmETcpGY1iwyD4SuhtzfYZnPgxLamfCP4ZipvjqzBFSmN3XX7BN3xRg/93g9W/y+KyJlg+tVP8dgo03q0K4Zwmt5izcQsiOGcu9DC597AMx/u8rqNCTzXTCNxJ8BYi08t9Rza7r6rkqS8/dzzEklCYqA8gxrMgsBLco7oT0L/049BKs/npcNFXNXIo2C6nh5l+En2PDBngW57hoPF83cdYKbihq7eJ/uNFM1XcB/hQPzTiPwaf0dJwcylKm+csUP/3Nz8pFH5/X8QM4ZhWXHM+jlt1pLqFgG7zMUogDAdBtdRmSPGmjDwY8s1Ie8/6norQSXPx+DbtNLiXKPab49eEvB70OdfcFWCHyROAp0GtMvofu6uJuibE7SJ8ceHyyxSbkGPB0ha07SQI/uKEc8G18hg2U8uoe/0+bertdl+9dGMetz7f9RdtwMzWTnPIzaVrl2M6Ppts3yoO6iTRNx0T+oRTRn4kl0mIITTXHj3+WZv7dCKGk+zu+YEEudCHZgsYEDa0l6jvl5w9o+/Z66TaUFkzEURpAXPMKuh5Qzz/Dv5NfVt1DYjuep/SIKeam0GWOkPQTWeXlWsTv9HqLbQeXFtEEaUy4tvrYm4iG8KEIKSdbEDz2YuiTPTJx9v9lY/jV6tRkRSwA6VKF2rFjnWjBp9IvBBj6zs9wMPgJzDXLLWIIAbZatZ7Gzu6pFK5e0di0+8dlHvqrkthPO5LCMLWDxyLTY/l2GelztdYm2gms9T/itqVDB6ed+YO36q9rIslRLsSqEbteqzUyb+WKRqQI2o8ahSYUAeHb0egRW335M0wYO80BNHjc7mTmuAkR5Be9tvGTeCcUcvrn+A6m81NsKnxczCruHayy4eor3oLxqLgr6+e3wNTKx5FtKjCD5x6n1V/GNg2LAuhi+2qUNIiKXNSXCfHBTqhOxBi6vKp4o33/BQ16Xqn4QAt90/2Aogre/4/OUf12DFH7gWtfIPUnM89y/7VAg3W4I/y8Ui4Og+TOs2Cu68BDR0Bw8mO7CqdEkkpczIpzQqCj2QdOe1ncbEBveGDyMuKB78LIKgg8XbN8sPMwLeWIrhQfwFQmZEmV9q5zljQ0TMsEB4mXfLhTtrm7Bg6uERfqFFuxI6fVOLL4mxJHA4KWYxozLYoT7hdrY4FU6Mh1Kk2Tz+OEz84R/JWnDdw5UsUgE4nO+4VUhPmdxQMhBbRbxiBdUPJCNIgRpKWXmX32eDlGkCrGtYmPwYntgxG8ovj3GzjtGnl/MCzCiBMf8q+CKAEZ7oHF4IBXGzDStLzbBW7hHpLUGjZKpRy06g1z5laPb5mGFYo/dDOiO0zBrn1RdPfh55850B9a48IQjyZBOgULpueEdbsgVYWwMV9Dpsosl20tR/P4Oi5h9hM3S2JbpVOmfaO/l3wUoGr6mYyAvVRNRWwD8nYNucMxj1Y7CaZpyeuofsb13qrjM8Ihz7Si19qXQKGyR0c7G8+lF8fMDJZZjCFb9OARDP6GHoLK76ZPLCOVfrnpwzaHuN/OLzs1zLJzvi1vvLXUpIlTqzoZiBzOakh2rlT0m3QMPDdGhPSHGojoljGzMGKO0gkYNjD0SoRq57fKaFzcZoobk2SlEn+51MPulPAtBdhCVzPJ25+XGGU8y56UwBj2EX5IGWysA5Dz32SeL5ZWCk8ljxk7KGK0XO56AHNDSg3zMPxgHnhd0TwC/MBd/ehGdYwV7mNtpb08ZsiHdCsLRcgtECDj2jIoJPZfwGHNPpEDmKc1CEo1a/0KzDtYso+dOkbO8ndHXnVlzmPqUJ/YnUDS+FT8qBlDf/gY++4gEPLbKPVx5otS1nu3nL6XWAKJbqftiHxP9QzwJ787oi2xA5kfziMvVj7Noo5JQs6+6GxTT737E/rWMkfs3xCGOhxOCSn0TjS31U081unqX3dlIZRya4w2icZRx0rKrsq86/Tnp/aqizpR7zBOgDkvc6ImuZbZAvbOTqVrH7mcXZpnQ6wkCSHlZbPlcAO1/PyM6IAuofRC+5pJBuKUCRX0ydJZUvuAAUdJEh33voAd53YCiRD/Enjjounk1e6u6E39E+wrNFD+ISFqfRJhvAUnP/o0C/esMEvfwFZVsYURZp4BYBZ7J8oKXocgoVoUHwDu44BS0MU7IiL/j4WcmYjkpOpmp2X+aA4wiPkQjVeLfytdP0ru2Wmpg3ZXgA4hqOG34NGyQfHdkzG3r/WkQ1YhD79Q9z2szu3X6us3mhMuktECjAtSQfxl2njy9gVb+yv6JEDqhuFm2137bmAu8V3nUEoAyq/rnlrdBSCZgw/qrQBSLTiuJQza6uuWntHmcbs426KKXbNCh18d4ccv/cLn56JO62hvhvsHNNXG3iwnTJWyd0uZT9p6h4lPMkQsGuEdErk+BOqo2aMfrZoFJszB2CsEOo+ywkZ8+9LpvxgQ12Ny2xrAkVUIXYozbMEM6C/ZHhGFcP8HBx0Mfis3jC1s+ymG+lQOQxnba+C9kUyzXYM8pUOzBpvYuDyeRFrQBjBPWQC8TjlRUSTLeYtao2CuuMfHmIe2zsAQH1ljeli49zAz7v2AsiIVNW8HK9m0ix8SDWibA4491JYFZu2ZD5k0ObaYedNSYGY8pmlB9LRcGDdXpjacdinLiZaG1rxk+63sLsbfWq3kiWq+3ZNqHwKl+C42E0A9oythbJW2cxWas7yfih4OZ0QNxgpRaxYXGCRe0OXPrBwjV1o+zrcr3M0uzH5Bj6i/LDRL3CKn8PeoisO0S9YLEzMdJVt3pL5GYL2B6XUzHz9qhxogUWtLaG153i0VTSJTgNEymVAAmRbnVMp8p5P0iwQPUgN8Mml8cbF391B2yeHn7UiwmsBR+LOUajFIxt0PyvY6pIUDltwhKit2hyRDitOQxI5gEkJziSP53ffAt9heGyu0mtJ7Ctycz1si/z94N3yBZDocMzBkeV8t1tN9vGxzsEaQQhgYFvGAGFjofTmeEoUx4UHAh61DYkIoAExpPQDkzYMM0Hi5h9dzjPV/YBLlS+30JoCCkmP8ZHj9uVFP0t6VxEwDsQT/moSRgYO4FoDIWhiiZGPI82qrWzQX+gBtJOcAw2zVltmkCagKJ20kVlPoZb8z/TcFKPjyVOycnL7QM7wK9K7RwabJFAZEIK39VRXj/BxpNwgEFwzdMx9V++VZ5m/SKyHYNDveZJIfoT2tGZhmFkDMf257UO04RIRoiNAwA9LZDV35ZQOC39zZ1rqw++uUnZATnxYY4omWmrjRuHGuEn+8HU/gCUxvl2HvoPXBSbuF4bAZMcQq7eTYvTEdavKEZCwA+RRewO95a+f+jsmRGi9IEKqoghpVMnG0ZfGDe5kyYcVcLlV/f68hSAppSOSENjerBXX2iI7D1t0QuGG82wOSxHut1npZ2fFv+VOGN5L0eKI8VI16LrKj7DJfefX0l6/Kq3PVkKXOdZ7vG9gym0k2gsj5amP4AdAUbkXIQlK5t1TxWmuov7al6SD58efVww9P+SYt1ui+Gq0Ob7Wey6UmqcXWWdu4QKNVVtEjVBXf94Yeg4CTDgHwEYrUqarjIuD8F7yJuhmGtqd2vTVWhZfSLTIRmhkyG4rB3RUk9Fkkcr8M+BYB4DMIqOToWKHsSTFbejvwpNvIvXgjprVR0NpOm/xDoJL59DpZvF4Edh8OsgoHIdbmqKQSh5ru6VK2KId0Fcdi2KddI8oxDiTxkSvmGSC7Mna72M9vMWVur5i/4978Qbp/jqj2nzHAbVOFREnnWxxz9LTDld16PkylyJEhogzxydZd1omnDOxOVJ/aGF3YqmlcgHjE/3rUlBZWGx3exUve/MHM19SqwzeQrXuXa/8R9c0oKwvntXXxw+KoeFqhM7q7YV1eOZ6/SMUtoQd3MVWjVexuss69WE+VFz1BmD/CLDsezwyI3e7FvcOGm6zLAvHffmL0r5mb1qZJoQrEC4B+nlli5oQdB5E4BveHLRBnhZXQSYCBjuOusFTCHTu7a47F7CVd9L/HEQMbUaqLI9p/c0llJbbBRqIIf7McO2HcbhxnKsmMMMp3UWsFRRfsFCoemz2XCa7wo1t6Ss8KCpJBnR4UCebM/f9Skw6sCTCOlvk9UfVZO7D9gHlGQWi3bnT1xiGtfLp/79r85mKpllmp4O9aJxkfDn8/vXApK/cHugI6/f5mDNTTSS+cuT50RGoO2G85zs3IBaUBSYQ1IYmWdV4JFsZB+kEJMSO1cZruy426+T9F21pHEmgG70FoCBTtgbwmX7PZU6C+zE8GToywclLcYiBGfmgKhttaEiWKHabVGgMRQpSU6CycBKLISoWVy+EwWRgRsuFxYjrRY89iiH/TyLiMWdXMX1wQYuKWMr9re1T2eHqiUHMfNmLiSlBBbp9uaJK4iyFbSTM9a5toQv8W5hnm4Km7OmENBzkUj4X0bYLyafK9B1qglTlAV0YMuhb+NtWjomWTgz/8hwDZkVaTZA7W08FQBzC+VwrkhLizuPUcwsVWylMmVaoF2gKjOBCy4GQQXxw4MuI5DGBJ5XcbhSxXlhVLV8NYBZQKXUXfiGfoLjeYSvovRgUbaAos6CiL8DQe8iz6aT5ZnQBpC56Pu2BgCloyAgk7Z5KPhMaa51mmHR/girL/p78wxZkWvA72mBJCehiA1L0GppIiYWI+FrcR8+YA4BwopeNtC9JgiSqSttfo1f5+tcRC2KDEA5RiLPxxBktz/J8LynxBDWrj/EWsPntKqL1yT/1rw5K5LTMb6LfKNGeh3fNXzr2hx9lHEGG0/Qwk2FrkI6l9pd/Xtwx+yn8CPFIFebh75HP6lZr7hd3fh7Xzj4tFwB30i0t+66uYbJOFRGRRSl6cIaYOpsGQDfOwbWsfRqnwzXtLU6qBpgE+Ty/6J6M2ZkHaaFjvo1/7guXZfj4R9xLT4JC+tuft21IWesCam0kT8gGrfKW15vGsZY4yNH+xZwAIwgHylac2RRIzRQ4e8+DD9URe3Iy2KlhRwnvXXij557Pid97A5KM98+TKG++8qYh2Pje65pjU0VD3Q27wdvbAnGeUsWYsuUdG3qq2vPtel5UTb1yoqyU0W95al1+hu2bhGzDSRRPDmzYmznL1mhzo+6gAuMyjKjsq/rEgl1VYkt+b7efaBRmqcwu3z8RJyZ6LCOBE9/ch9desc2v3bKvffLN+yQKs60wY3vyxdXJAtxIA1iWyatxOhd716Tp29MPCzZ+FRiV6GFd9Vz8Pu4E+Jwym869jSsJ15uEkt4D5a94ufv5UzMUvQExv2i8Ffh0GU1E1O8CoUk+YW7e5wy5D9xCJsQn8RryY+DA3PU/LDp72p+BhHy8l7q3iML9S1ebj/C0877iAG5/QS5D2GJCyNDokeoDahqZ5jdA6j0lbdkK5o/Wav8M4Il78G2224qLGcVhT/JF+9mGN70Am3FBKRskY8omteLn/uPFM5x6o+O7UTlGWuoJja82YKYm0CMAIE0YZ5yWuRm2yFGAOX7JpyksyS31QU4lXUgH6InqxFLpQ8xuICUhJmgmn4+/QtFaoiWI6UvFWoFdaGP6FAxLZx+Be+GZobF3B3h5d4up3BGt+wM79/jyegeubkkz2imtytjq2z6V4QIcywEHmULweBq460msMwHxcIluhOIvBIgZ8bfuwyHDD0Wv9YcANHKNZ0yz/ZlYl73uJzTHk8p2k0kdh6y6sIafgWgQd8hTA64dWEgasA8kK0BNYqZIm6hc5A5p1bvGw81DmD5DiyA1fGTj563FuxliqFb2fdCKjhvboeFVrQ9pqaJS6xiNevTyne56EhJv4jTc/B5WMb99DLEBg0loHSMGWNlOhND9Vb1cPkrfa9youJMNLRq4rLWkW4tqd9mXrA1Pe5RXuoV1uzV/Y11AlByVYqnQg/tSrVCrP3OMTeOeMy50dIhEU7dQgK8K3FadG02ooRJPlaqQtQmfva39TeOtsvtJz9zJfLqxJBc2aKLlPkthICSZ/neNNaZtXlCK6MHk5Mj8Qxvo8cASmvzpEzKNGT4YjkCulPX4I1qjC6YzX+WAdiSbey7QRn0izNyFEBbHKN4p6QY34FRBrv07QiQp98Kj9LGbHa2NUuoG+y/pEvQylFWzrLIX2VlpWETqAmbP7KhzuY6AFOOWAI73DHv5gF0wpl/QRr/ykevG8tt9KUSaN6DoRRiHuL1kqjmvuvQbybPINPuflF6SjR6gkmHNa3dF4qD4PyvnMhaH40BxWzceNHpn5oKSiBBtpo5ugNcyCDGKTrop+GQg4ej3cHa7VlqiwEauXo442IaJXHYKAj4oZLGw5A5UIBP/Ilmn4wicLzSjV9Pm295L6puxr7LfwIl9YsNawyqfR1R+NsFZQFSqE642SQuF5Xb8Gdz4aBBgzO+3Uzu5GVDlBm4Iy9xv3GulO0QU+mqrHF0j3IDfOlckaiAwlu8e5Mv+WEyD2ow4zLxkC3RAcSMCASVZXohrKRrwTzpsqwkR8ZZkkJDr8wUFx70jJYMmsnMOTiPLKQKlcfy59c0ZTGADcCkORf4rxjUldyFTQ6f7lb9Bz0GjM0f1S7lY5bkizByl79niBwspLuQldr/R6+d4/9Pvjc1f915sE3xNrmg8xHolirkWm27eL8fzlOE73ip3Q3e8fYh4P1ETGeC0TBUaPd9dxoOqJasFUBbjq5UqjuXCE0y5PpXFkQaCT+M6GDaiQj654CSN66qa6FLg7hbPStae3Rq7XWqrBSaknd+dSmoUkJ8Y+X0As8kStHxing2kVnCsDIuIPez6z1GWRHVSFo5+ZLhnu6c+Tct04YSWW0GwYsi9Z+x+AxCAvEJLVGz6ds+yiSsNz+AT/L/vqzKRL4PlgmcaysZFQbfHaluyHvdq/xuzzCsXg41Edd3qrQaSxwaNow3pCFA32gWgw1WBpPj6H+sSL926HQ5ctlZ3YVQlxjIcqc0QQSMSiiDUhQSuGn6hHzUZ0Gv78Uv3pco6S457bY8zBxGJFdbp/p/L/I0HkFQ0+rirx4V/RvRlLOGn/niQh4Hwiy+/TelkTklGKSj1Jy6UttUHfL9mrsyxBf8lJvGajIwszqHNNwNFHmEYMq8QhNy1gXFMVFfOwEAdFoQyAHiwipFM1veb8200qauDzBr4MWrQuDBVCY/ctZyp5uyu05zDnNMZMPJChh09soIqg647w4qelG7fdJTkVJRJFLDuQBdurm0xJcWpslTSIUcKqfluBAPi6xtVxtkfu1Ya1N8+ezwPu77fNgfhm4n3HyXrHSPhd8MgAheL3CPW11+NsglaOWxiDx/voVFBfT1Vn6Y+4aKb/ZxScVKCdgMHwkES+5RkDCMd7Tu/Ym7mVxJW3gPucZfniYkEPAbxDehIGCsdNM5zRl28jQRKnFofEDiE3zZO43YUSFdzJRNUYc7uKZOqEEzWKHme2t6CINRbYx4a6gTeEB1ePSIHH/IKdykgi89gIhIm6kNBykKcSmndpolwYYCEX9raaXj2ixCelpk3FL5uXAeJtHmmsndw9P2mCekszY/9kKgTosLcBOhoSHwsLlU5MirHxluKAAaX4WpfmhRkcAVPZAYBEKwTfDEZatMbwex4pEWEjmVCHoGdntSUPAtioqsgC3+XYRNAWIMC1S0ppxmdYp3PuXJ2IOV1orakd+Lh/AKc4XvLHqkYsINmlhh+2RAnZNjLLFHYprZXbO6G9xXDNzIbUljUn/32471EiZF+8EX6juwW4j4ll+zRWVMVHdD3dw/1pcArntJjVfeDqb5xMC/tfdlO6nMCHXXld0aWkyLwj6EJmQyUAL7Av5tsqHuV/28JdMdWLl/Un6FEj0T8W81OwOaB16G67eBcDxzc42V1X8HxZXnx5wNI2sgAIaZwijhv/T3qDHT4JYEIIiVbaNqmw0M+UrpPBC6XBeojgboEMvIlCLIGQMJkl7m54xBEexgEQtEq5hi86QNvf4F8JHzIGajJVmV5a/zRPeqOvBUmKCkTF/f94y9KT7PsKiSCXA0AJUj9y2jaOmr1tbA4Gi5hSUatjyiWCpaJgFpDgDAgtz/ZenqrqyJ6CTAL36DGTqb6EubIWqcod+1FsQyHpE0vtcRzX1Rj2/+i/nO5FgMBA2lHM1TECMNOdpPvopKdgshEOaBpywi344dTUW73rX6g/DiwAzxw0W+8X+s+RwtaFwRSbBYs1VMo8M+RAHlUIMkL9Q6haXJRZ7nYArhWPCL6tI9Xgga4DPBgbgoUgyi5LALoBxdhTi8kFqFN0juMkhgp45vYFntPQ9q4nseI84iaIzN3fZ4lxX0J8n/vj8FD1jnsaSDmzb2pBJl23OMw303IH9KT0v21GgMNRMmgjZDRV7Mo16GErgAYodOyoAwh6SgwrsaD9wK2gah9YAhJ0C8E9KEcFyWl4ggtsFd8CE8Zhs/01lZL3o9aN8Mr1zBef2yiThUHIdrYFnyVx/8fBBIVvy9Ho5WY5YD60wv7IeVY2cqrivx0Oxnf+DyqQHt1lWV9ObCAm9fhAETNyJoYKuFmSoSLDi6ZO5fcmUyLmRZVn/AfbTqTO1iLB4E7DMLgH91nDShGpcvKWUG7MB5/J5Ebo8s3q/CTbu60h9fwppssdMxaCyOxeNBc4JzKB4bAq5+lMub0/2MaBnPKwALtOf2FsEnXMTnelHRFn3gsa4y2sLen9+iVILsRDfQ1jAkAywerx3HqyfwOVyl192MUla1KdOAvWle4B8orQgnreWqarP2ir1HOH9ucoWUETI9FMabXSN/dNDwIWIf4feHYjoRa6tfHB+UfJmc9wCj5A3Qra2GolzL1DE7FgvrWFJ+cJKCyOaDbLTIAE9wAG0KnskOFqYAlZrE9G18RwpGH72Ie1MLVxTUNBUFHcb70OjFhaw2ML+IEFomSiUEF5fFEJ0FGuDRBKFfKxjZ9iXZ3Axa1rJNlluOHds+Izo0Gb109R1HjF+ZlweaxT6OJT6Z1oMcc46a4DeBunxYORpTHaxeyfoRltqxnJyAVu9IImVNDeTZ5wh8VivCMgH5feTMkTDX4SHLe0yYovY45fmoRFVNT3DMnSrI5RdZr8a1aRpnaMQKvThYtwAOKfGiKI6M82ix67Kf68oK6AW832dtq6U39HDLcOfwXGaRNTxixEqPPdBWcYYsKRLyZ95Fb3pnHq1rwukN0HH7436HttNCybELS5ehqc7unq7nnl2FctLGoSqNJcsz8sKoPZ9vN9eU5pS1Ny+CG9cJ59d048avojo/n+f0P0T7NbxGYOObwxvm9fc9PhmKbQ0Cm2joLtd31Ki6k26b/OhEpNdjHOYuPZxHjSdyJQQ4Wx/ymAgg2Pdfj2sODpNOiKHdEXO95n/H2RS9Cb/bKoI5v1KCpJLJCu9LNXz5s9sX8kP16BBbtaK8M98nSxnr1YF+JcfbMUoTYLZYnOxv8WWwcR9e5U28LdGm13ZcoaHdE5mGBlvpfphn4zNKnLslfyoH80w1L8fuBvcs0Zf/W1eN1bWluJr4/kQj8iLKEnbktNayoPFKnIdJcPafGJYcscuapLwr6yyAwvFv4EpdSQzUxdsTq3Qjlw255WpiuxQof7LupwAxDcQgI6ppRa9O+AYzOObRTN72hWUJwqjkHo5dK+bbP9XjU9PKxWf8ouVcyecN0NtncjhCKWTMOv3NsySxmTM23W4tgcM5DRCfiOzQ55Oa5HEGqrlPSMY/Aem0+cujTyZyi54zWt1k4xtOeSD0FjdGCUnHy40dhxDdDc4VEKOB5/G4SzcSG8KiCqO3DnMBkHmAKp2i7BDR511tB41NwkOx2EgzC1KlMowBtmE09lXWc6wFdkOQzFWst3mJU7K3iPzDYcVJyz2cUOxAaG2HwKR4tWBDESeOHL9Icqngp8QI/0POgtulaLUH7B4uDP8MgjR4FgI2hWYDEA0++IJhVbxdIjLDcD/wqzS3KbNVmKXX64dX1CXCF1UXq50zQQ/w4IhJ8Yb4wYI/zxVWIygm/A8/IQOcbLnuoV1tYDb7VkS3SSq4jzro1PxL/Cp0V1kicqIr6yUAqQA/Dz6lhQYuHhJbSLEFQzR3yuVoHnA1SFOBXJeZOl4AK7l5czYYog11N3wRxsdrwKg6HcAp7bzkwwGfnR17xGX9jTY6zZ9+bCFWuBMtzgWUHp2o50i0DR59+Uajhx1kJ5mrPAXTS2aj5Bj4Vgv10rrs1v9W74NiBl1/CVYJnBGzJp2Br0RKt8KaJThBIlPFGonAE0BRx77X7GXwsU9qps5q87oYoBnrPv3w4r2Bnvl4TkqMjut/fZ39Na3DuhftZ/8NfAoDeokqJBIqOAja/sQ+nlfh88R+7xrlK9pYhhQEIXTlMWD2d+XoZL4GvmomTourb+hpzGKDQM6gB5q2zU5Te2pd2oVItDI52W5FKFL4dE5q5cT++VSARGbP4WXyZfXlt6/7FUX2RdISapvukXjHbOwX6SL+rrYXMyjg6VuleBxGXILkUxQAT2/94M95NlgIMwxmym3HRYI63nf5TeSyZer0vtPi+Pg+WF7kcpvGycHQCGN1I9x+fNbCdyhFi/Pw/paM5GMJn6RAHqMJ0ADC36SKcR19fuZCChh85k/CczJy8kjFN/ICRRrt5mU2sDgoDRODkCwTlPkvy5DAZwsGvrumuXn/6JqkLhZoVRRlnLVnqroCeRYRXW2Gkpxe9roE1xRQGS8WdsMBrzOisSly6s68wX0rKpi5d/qg/sFT6lFRBXAHbUgwVJ1kzGfNpI+i7QnvB+BGGmm3mAXX2TUsqMnATJGuu6+YRIwz3V65xJDYO7Ht2o9Vj5eEjjI0Rj+HTOs/W1mU43fldBF3M50wHh02HrR8/TVUn6DUWPDKAOgAqaMK25J4/QRJ3OtoMmY2odG+MDhU4GoFDGWK6VAe7yoJE8tzw5w5ETBKWPO76Px+/QjiA6Q7CKGCFdDRSL48WBNIu4HLXF6MGjmQoSZaqyqMKnIJs5O1FavnHKa9gZ6smRc0p8gpYDDVWa41p0OgrSYRVtqmb6OBb+smo9l0YDEyWvN36MkjfeVU0q/NgeIMmkfWK0H/LhgLTDlY8+MHQnuTAS6ZiROiRoMIAsCQ9qgW1pOD1loMmuM0wqUiGE9jWio6EY+m/GG1TW16/9DLXUx7ivaPUqGIhQYJ1OQWCXmVS4nEhRol5YFWDGG43aJHGOggf9B4iMbnMnMqcHlrRQZKKnxM274ITxaypq4rwvG52oT1CvIK/47iEdKPWwK7p+BhlPrGARqJyDV6919XJTONnA4wtPGf2K1LpAecUpZGmQrfS/4iGGzQNr5Dei9CLc3XMoA1XOHbkCgvnWRbs+SBUbuTDJPsgOyNIjOkNEY4xxffj8p3kXajrzcwDvjXcmuN3Mx3vemB+DBvZ8oiizdkPMe1K/v2tQekrXEYsTqqrNah3Vo2b1X00z446G3+23+dY5y04C+b6gbjuBQNh25sQW+oifBgKbpmzOmz55la1ITV6eHzchA8hUTeHuLVvPF98eSh3dl10Gredu7yGYupMQdO7EHvyLmVcLtgW56rLb19ej6KmqLe8Dmd2ygc07mOG3fazuoQHmcEEI19zFqvaMKNqP04gWlQjH5k41CWnmx8yU1DBSGit2q6A9NKQS7mGpzvl8SWJpK4KSfcbD7El4lopECTHgtK+b1Ih2bhdzEP1qk3P4AK5ymEnCHXlw6SkcSRkRPL4HlidoB6Gevzy+8zF2wPkBb58t4uAnXXla6joOeceIFUK3aVfvPgEmCrJnXfvSN3s5EpWlO9TfEqcVX2fCU30mfX/NCUACh2TpV5wSnPQE4RRUPu5BKzZG1M5QsahPIWl7abGOUGGJcaJSzE2VCCZaRANLDlY/p1kKndbJTz8LQzzca/K/kpaqdq6Gmgu+tltI2/v7XdLGoPIHABX3a7bvvJkpquq4MdSzHAuylojr1eqr2cFyvzgK/oA+Dbt5AlZHoRLHHesZQ6tQOVtW8RJHKw/nRUh22ISqUhKsvW7wlmTh/WSasSdaaaO4uFwfNO9ZAQUHbyIHbGfNaFx9D/5QvJ+NWAMZrsZB0mGZlWUmASG8OCi7g1vNR5Anna4YLZ4Ezf0x8XLw56oVrTVlZ5YAsNUaPI0gKnWwrw5x3XJHZjqnb69auFMBdgcRasGCySGL9k37/cm3eNnWEjjDDFPu9OWcIie2bNzb5LO/UJTj7vvdtT5wZywI8Asl6EZ/C1xX4HnMOkS2Nr2yyhNfY9OdzClqcIXe9Xgd1kiT4UGBTc0/5q7SKbhRJXZ5efI3K0Llm5bdA4HyWl52fx642L2GfFuwRGxVHYOjvLoHBbdBLmOff35NU1QAMibmS0HJ2fQ4v2k0KyCtDpuSYWBqD4IYmxQnghUbCO2CmNKjLOh4fpLMam+Mb9Q7GrZjyCRR5sKXcysZcLL/qJIldo1eWjuXGmC3gttcl8iWn5Xl44yP0ESJhIPW+R6mBsG4IadKUkpbl0jmjkOSKtWZ/UB7+bG4Xt9XxtG2FSXp4i78MQ7/LLxt+qTybWg3YjLu87OlqDrQEOEyiTifyUdSNMVfNlgSRg7U0b46F4xZ3R24464UHYlWsd8unYbegMwU38D2/Z6gK5swRKGOiyMcNShd7bVRttEF+cmZml8Pk/n/C1Tayd+zqANqDib86dIUlLyVjDqsdMuwXChugREXXA3ilLcR5Z4CMvU52UaTNIpbd8MCIepHoafirTbvJORzk8jrQLJ3pgeHKFxaCMgzff+23dZi739MBFpP1cSRRodIej82BTduUkv9nr4y8A3dGK+Sqqf5y6d6OrAIM29yenaYi9jgJGEltLZ/V6A2URFBNF3FBj7DsaM5xLujy+UE5ou4xwOsIjotJY3gQG/BA1wYxMqv5JFeaSF6sn38V/YdLlFsxMacqZ4E+te2dZQ6lg+015AjmJaf1PCOM4CMqOvJBg8/sPYuxVBt+OCHc0Uq101dgT9BkBHHvXa2Btm+bO1jyno5J199dcoz+P3cpFRB5X486jvN9rPhzvleS8UBiIsXTFoy/9xv1zOQ7Lmn6LoW5xKd0Oor5jlZ9firRtmWnHoBOpCfj71TJr6tl/itxMB8pgNohst+6vvJ717N7lML03mDKuhiNf3SUJXOecj75de8G5+LEQyw97NpQuQtf12eVY0i3V5yYwDHH0nvJwBYzK8ssK/P9R9hNtKqfT/E7gENPAJG6R+YrxBvQDizesAXHetb/WZncZ2bhAg7CIYm8u5FN5jkzIRy0lZRBt+Y2kHj3Xf7m+p5S9KxldsWL23wcrd0vV2mtxgjSCQC6vxWbVCe4TtZH5mgkAMo0GTPNbklzsXNjUZ9AXpp1lYARYM18WZWxlNHC03/KCczmseoe0C04CMrjHEjF8/jVV+UWnhQAFkgDwoCCdFNDYHaZDpZvC2J+uxxBuofAx1bMCumYeIG5/+/585iUNtTQeKjYSPPP9sFZ9toxFL/7VdEgwtCau/V3OXSfUdLIwQLNvCH3qffRNFUZK/Fuotkso5uQyMRe1jHhhgosUXeZS8UjKB07QPZzJkdX2qxBqYEtlkBXPdkyELjjy3qsRLL13V+3dgCpCr4At5zZEX04s+co6nB2G+MBepaNeACA66sEP8EByFdWgiJ2OuEepjGTm9u7L+2240GqNBD77abQfZGF8sd6WRSV4IPLYoab6cJ2Llw5QElK4sKf0ALq9OCGnzTgO9szbgsSdR4LaKMYkPhLL5b9ZVPKGBWZymIGkTIs4VNrhsjUZUCA7D+lQ+ZlJKHrqbzj6qJsjoKTdkXN/REqQzWtFONhUBWzhLwbiGzP5xSxJ/Qp+O89AsaVY2LwqvTIYLd/1EKX/ycWSAutCzpJMg14R69R0KmZ+IbI/4jC5FfyJi97fmmdnX8r1dbxarXpJScxGRdgkCt1q7fRB/knGD+q69SuEzkBFuS5OW4gGN1IG2OatIHXx21ISQ0kOn8TUku0QEydWV3yjdhbD3nXr5ssnxuTpV/VKRrrOpP1NN710nE2j3ig2ekaD4qI+aFJvYC8W2VWlU//TMnr7fYoqtffp4H8OCI0VjDH3A5yGG0Vi0E9uIVaoVG6QXbFJHGhOA45luZzWHQUeV7h20EuP8IDqfcjBzE4+3xIiDC0IZITb/vuITUas4ovRQMqFLu3bUjoT2IrFaOYMcWCxbM9Z65Z9PM6P50jLKhUteZfNr9LnwnuPMfUzwLxBj6Dbrpq5zOg8zgXPB3AbAb74ssiIA8V9739msOLIs+H1+BdvzJqL7ck4jD5wxsXjvPS/eB3kJ5JBDYmP++5YcyMGBnn53N2KXM90NpaysrMysdJWcQeCjNKqIznlBqhIhyIfuegH3+s0aspCwBY+r+1GXVq1N2xIQRqaUhnkeXqTytguPll3j0nKbylpYY8fBojqGSrsRe2bEw4iR7FWZV047uOX9ssaxg4z1bktowpfqmW6bukRB427RKe7JWvdELrvE/LS2pxVcUZ15tdQhhSWCzlh3AdeayIWWedtVdq35Bm7Lk4FETC16v9YXPevQqoEUediADzurZ05F3u1vJ+bOtmYHYrxltzrcny+kE8/v2a3UVYByjyaDxubQQTsXZV1tinIf2YnN2rzObHZ2uXhaY4w7xMfrkVLSDIZprzQMdSs1vGm4lMlQh4s+mXYGsnukR+cTtKvvWhfp7OLl/oggN1twPlrNnTaDJjLX3K+Q47aPWDRiC7Q5ASe/Op+grnnYzvBJa4wcqANCMDihYXplo4DgSaO0hVkH/DrSze5lzRZNTV53BZBOQdacms0YEmkf1wNq05bMMjXQu/iBn9QcU26hrWEHQRVbQ5uyKcECi2DVJjpdj6qLitSHzErRKW8RwlQHDkgXalzrbPBTC2vvFXUpz4wL03RJkNqDyMIxUWx1hmS86ij7+uFiAe2lgPUFFnRtGuJSbxZdY8wBhtUI5Fw/TtQl7JD1484uQQd3bILA/0QpB6SlH6BTWewgcJfblyi+qlnoblRuUDRMVw5bG90AK2c120ME2aBN3Pt2pWjzO7K6HFeJE6NVifGhtBoOKKi5Eky92nYxsr/p9zSs1WsbcqPevMx3NQ7v9Fu1dvGAaW0SuFlzO4S5Y7XudKVWh5ir/Ykwa0xWZm3VgY46jjF7FtitSxWhy8gRcB8e64hbvnDq5eLuofFow65H88qhiNWtbRlfTg/rIbmxVtIJI7jRrDHd9RQL7+ycVfksHwXKmZacsWyttyrb7GmWXhv0BFfgt+cz36nsMHdJA3/aRxBgTw/d1vqwPw3GRx05qQrTOqELA0SDJ1U8MIddrbFctFqr0ugi4avWRtbxY6c0XMw1krjwjKTjgjoE4dGpYfTnFiljRH9gUu3DiUCO/LTPaL3ldMvOGxfcxlp7VCp3zlyLb4/VkjltLCvMShqVFqpdMkS7J+2OxfaoP0H7Ttsdt9ode7KfoGuW6q2X6xHVNNA10lZ3JwJq9Gu0bSBj+DwmRnVVne1Uc8s64Ci7O2JegQhyMBkjTKczpCzLPZ/lYmk+qiGCorgSwmKKy5Jz1ZCdy7RdbZiz42KljrAWjx77I1dAhMOh155ALAineZBL6IdZ37XpAzXHmxxJz9iy0Z5USktxyHZnF8FkhdPUZvsIs11XMLtpHI97FCeslja5MAt0ic+mjTPH1i9jddK8HLlKz7bX3f6gh5PjOj7d7KQDU140EQO9OK50QFWM6LKbvjLqjneEblYr7plyxRmyKh0ZY3UmVuOFdDCsHmFehviqJjjnVqd93haZ3tTuMceVo+vVfXFSWjjUojslLqUG1uCHKjh6rT4vQgdxXVsuRqQpKv0dK29cVbj0S5VJrdwdK/NZZ3t02syqQcoNtCssVuXFabxgFsPldqc31htuBiENqXJg5h2oP1ivZbjaJ7HKvL9Qx2OmBcJTCr0s5ktz3BLssgzyVvKwgJjTxIL76hA4y7UzOJ9tvDoVWt3ztCRfms2RXO5M+O5kNBhXILPnaCK9tOnhgqiA3IagRpu9wA9kC8HqdVkeTmYKNTToKn/UqjJN65PLuHIutraK2zgVz4MB03ZPUnlvlOrouUY1HE7YDGftYv+0lDvDtuBW7BUzLK7sFn7B5aGCKuuVuteRbQ8rCsAuYOIBGFOZnsAw3D5uzbKun0lky6Ig5qk2eHKOgYh/fu5hGEyq9uHAKV2Rptpjs3OcOLCzUjvm1DRlCxqPkR6xqICYUcOPyqK8WI4aVGeoItZsWYWnG0TZVRyY6XWZ6Vg6dIDBQZCeZg860wY7Xqr25dLa9BlioRfbG1I2ZGprGWWYUppHpCct1dqqpzftZWO27/babbVZJ7tNgprRQnPS7eyKWGWynSzE0XGi1CvC+GzAysnmaZa1Fg4I9U1TQ9T+pQfXOIRUZrzLYu6I1ATMhTWork3buFLHuZ4g7Bd2+2SuT2V7jEuMXjkrlePOAbJpac5gTFVmTcOUq6jADI/VLb9u9zerNTiscHMxGxzozgJtzvublgDD2rTqjOgTB7MwdF6q0/6+Mb4UIeOgzBvCxty3pwMMsTuO22lPz5a+kCFmu4SrTdySYIq3vG9LL7BKpXoSp6XZUp5qI2rlfZNnIcz2zaO9YQak1F0rxb504VqjNTSC7BWvELM1u7BXwgTXTrquHFSbb26Ghk12+yPtfB6hJl4D1BuKRuxQCpf3yt7mLBktHzSkzJymfHe8hPoKireXZ/Msq73xeKgX0dJZpHFCLM84gcVpzTL33T5e6/db2rp+Ys4gkJmsz+zWra3ExXohr11h3Owho+XqYpYbkKGTjrmwx2INXhxIC2yqr1/K8Iy92OMz0+0j9HZ9YdAmVDGpQb0/1JoDfHaSFiVi0WC6xUanWNP32NH7nSNmE3IXK36w7RnDUYmfEL3GChnA9YE4qsnDJbptFPkWN9lIbKVIr0QbM/Xjco7VLgtjNZeccWcn+1nxhm9u2QVESiNsqh7bQh8hJ7PRATclfDaUu4v5fKMxKl3qrTB+UgXaWR5MHM1l4dXy4sCyK2jKiTOOxqnPXAzyqHWHRaItzAhVX1NLqm9PyMGcJfbH0g6iykTzIPKKrtVQgxMscUHXOZhZrHTWLOrrKdOoCKd2ddqblWVrcuLQCzM5GONZcaybW1MbrxAMc9qELgxtdbvjtZZ2VEt9WxJHfWXTOewAHUwDqqOVosselvpJPtpWh1Kqo7U0wPTjpdHCJa3EbfTiGsfKzQ7fMrfYjF6VIE3b6WbPRccwK19I3WogG/gwrcBcZVfC9xVW1dkl22z0T+t6m3YPwP6sSlWWJnbl2gEYmIbRE+q9IUE5Z25b1RbozHKh41oxessyuYQok6IaI3nZ6JaZ4m7qTuxitYPqvbo9E6dQFSvN8BmFIvPeaLU+rIUZb6L9sdOwD3J3Wqy4zLatqu6QbrVBeDEbXs7qqt1dzuZ7GmLpo6TsIHoxmIpDipMuq+JlJe0u5a64IYqiAnQTH/RHyKm4FxnosmDNbQV4iJLR1mxTN4xh5XSizJPVhXo0dJnpg9mlqUwxbqKuOi2xPwKutEHw1IFmTgezuD4vVAmVSWrsmgh2bOnLJotivc60TdZ4QajXFwumDFds/VJ1KsPm3l4KZH/fl4so43LdhdiHx2VZKbfJCo3sprNyfdgdTQ4L3GEuOq/ZHF6eDe3Z2RmuWry5FubHxVzq9mqbCeYY/HjCNE82NcOZFeKAFP9YFhvywuwfh/xAbW864lFd7WbSalYXqaMM1zSheV6ibXh06tPDWuPI15u1eq3OLxc1vlZbuWjTGLf72LG/X196SlUdNsnTVDFJvdHDFXNJ1zes2Zk0zUPZ7K/KqiO0dc7un73b7oZw6BmWQVhky6wtVrWmYk93LUwbKLYKIa0FpHTaFQRmjeFxtHTXTbM1W86qVXMFD2Y2IbfIHU9LB8Jckk5tsO076t5iG9hkOT0VtUW5PdNYssUprZmFHyT87FQujFvhu/Bgsm06BLQiBHIyIqu7VgdfHxEZBan4zK05S3tygpCTQDaHZq/DOE1Op0dt9kLNYGltMiAigyyxbqyaG9IaG6PdcTs2yq0jMzROAGa5hI+jGkhiKGMlVrgmWjQuzWp7pszBoSq1GwMBGiKNRr0sca4kI/slC8/05QlZrUuzM0SI5yUp84c1wStDU+jMTY5n2sjCkSYyCOGM46qGsKRTplVt2UHpOYYSOoT0xQrsNHobi14IPOJgaBFjdwt6Bw/w5mxCNgfLA2NqK1IZuL1yU+4Ro6oIic0dMkBby5G1Hll1SRdOpiivZ1UV3mIu0+26h9oIXxZrgz2i8/wWRHrVYRuCmyDKUTtNeN3FgUmubDeTgdXSe5CI80MMPsyF6rIE99FBsetQQn8KzaUSdTKcPobuDU1uVY15vTLr4uS6tTIpnJtNekvdlWh6M0awdRca7Ka80CXdwY6aisa0v2gQCk846420OIjHLWY4c77Xq61dYq4TEFs6SQpfH7nioagMF1YJGk6KOEm3ZhVxWUZLgxK597hXOjhWxR1jkLidIycbRZ1y3+2DyKFfYWi6LVruUJ5R2+O2SrgjtMI5xdKQKM/7K2N4qk4PiyJUtlinO0VdVyttqg2Fs6oU8ETVTWct94pTFAWebXesLKiV2BsNOqNGdQOz54X/vlscj0rd6bSLTWzndAF0oCRXnLDCma6B159fvhU4VZdJ8+uXs8pxyJdvv//y9y+/fOiqar4F//wnQ5rkuymwMvvnF0nkBfPLfxX+1y+FAq1Kqv5u0N6Tj4L/5Hcw/P7OGO8U/1GwSf3r+zutMqwDPn+7PjMsHYThbBJAIxVWysC8GywNUhpSd9Po3i2FYfXsBJPVTTEDD5CYuhrDT6k6mA5mUaaUoiR85O8nPSEPMosULKXwuUgFlrTd2wSTdcx3TRflDMHekyTcZ8/vsSr5LDVHtkyWeQE+n7uJRzF2SBb7juARbPjxrfArh3Mkx6UAcSgBiEMAkMU5NANYTmIsexhpmiUygDCURAk+A9BqlWYzoEgKFPFBCQIYiTQomgJFfVAUrdIZUCwFivmgEFLBc3af3r4PChNVmk6BEilQIgCFMK5aTbMqBVoOQVGuTKZAKynQSkgAyISJFGg1BVoNsSIVjEmDpqRaxQNQiIVTBNw0yjMqvkaJCqe+hcOxHX1LrUHp3klNaiQJFEKJHRDN0jUpIebbCCCIYqtAJTPgaAY8EDbJlCmumgHHM+CBFCsQRjBZcCIDHkiSoFCSwjLg5Qx4IE0cRZgKkwbPx8zgFAPdQHmvdyiH8YYF2GdcbWokAx/8qkNEFSG/pXAlzL6HjiLpI6+rwFY/RuqbBZYBZgGJ4dTjtulKHUMqvG+6E3gAbLhLikQIBE2ieUTYfXyBseI4iIsrnctKknrOIYwmLVNUlTSmAD7kWxUmOCiL7RF9j9FGNFIsE8Oq6t6mcmg8k7oiKnwaWQAf0YigFMTFsGmicrwqFHh/ZTSGxu2IwlqmTko5q0a+4SZ00g2tYYXzfmKLsYxovguqDUTiMUXnqa8IihSiP6UCAv2WgiZpU7TZjIv2+Rj43dDHxqxJ3PXGVpdZxXqnLNMEDI/wARLIrzD6Voj+QN8hPMZrQKoE9uNR60kCvFLorrJNQKcWTfp4n3XqERwNRldB6n5W3kGcxgYq8ZblcPZhuMdvmc3lcCm1UgDylsPIuDmOR1CZVZIChNFC8F+pgP2WgjRYiaXNnCMaECWJxlUdrmfiLS+4C6BihJgkBUyfDg5JwNtfccb7uT5ndJIHKsDaIvvwAIbqcI2JgoXeclQTr/wWWz+J/65yvuUpGBzDYwgkEzc43p6DsXcppkUcYEMSyBt5N0jFiNlCkhEt490xkpDX4QykIedCykwG0o/gc4ERR8pCi1I+sPcg5lYA79Nht6F5gX4lZp+8lcHfDCOm4+4ANhbBACn61pQntU+wMqQhUCqpM8k5KKI5MdeiqbrpayCIN95l0nk/i4wpfBTKROU+nEBKXASIVrAY4G3RHJyKqrBXSFogAUZKZYA+skEEhGBQDFUAQHu4rgCVOICvHiAjiXMtHI4rcWgUAvF8qgxX+AfigPPQA21+sIQh35kTbS2RbAUHNngUOyAiKzGB0QkZiiBXftjiJZ2Y5qWh327QCX2/wcYgYm4zssa3h4nUK51fxeB4XYyDJc3uDYx0ROMJsNBHvztSnG2f7DSadG9ODtXRFIo02Byy/Aw5B1xO7DUczYEDBKZZm0u5nyBk9hpmWGm4XDCQNKbh0nsCY2mQ5D6ijC4NldrFNcGJ6VgQvqdWDEazYCl8tzQhrk4qOAMvSd+fkcubMp4Gy2FNauM+WA57iCxUHnsqCbAwbH1pO+GcJ4QdQqb2FIxmwT7dUwj3xK4kL5p9bVP+lM+FFMClduSnYBmgz/fjg32+HT9jeGk3/oz0hFvCnoZMnw4wlgZJbuaWnafhPjtBWQ9xjeM9R3vXG9zC83yHkOsM/MHrwvFC5R13kYwxr+Y2fBp3JJFjCR/FnUfkTKKFVe4ZsWUMY8JaxoHkJBXRWAiSFX1SIUKwnKOZOrERYEbhk8cg2qUJwv10JTZvCwFgptSaMIAJ0HTFNHd5heVJL4W9v5e/f/nlf8og2SULX0FGwbG68Z4sv3ua+80vy/tF+w9FNb/mVe4DmHTx3pv9uz9+rd7/Cle8n9hopm7vY4gKCb8isPfzLTshrzIdm3p7/BYtmockW3qO47hVGH6FUe8njuJOsT9WHwgehZkYguNvhdtffrafRfcglcsggL/lkZO6JoiT4z+6iy1JTv4lwrUycC19hLWJ9NTsFADKcN5PZpU7gkw+88rxjPeTmZ66bXhlar70U8WlX0nO+0nw+vnSMhrV5R4Vlz0MuujJ5vHkZBU2Xr0UDdorH7jpElyi7JzA9UNl2vCuAaMqGJ3F9lKh1tdC7K0AV8BfZSJHBV8t1AbEcSSGoGwa0Sul2vB8AGQEIItAfNKIBMYfrNaGFHJ0Whg/UK+9nWK46h3gHAb+YL02pJIiSARJql+6YhvcRLAQRlcTJvqZmm3OsUoVae9wIjQ977ygRkW8sGqG44XoT6kQFc0elGD/qcmO12gzt85vn9B/821ZMh+Udv8Nxd1bATtL2T8r8971Y5mFUqX6tGTT4Ily72cz8u4CHoD/lIJuAj8MJXXztZJuln9YAtvnhd2/r80YiVjOC9dymzCiOC4vivv/vRf/j/VevBr3/MiV+j+McH5mfPPzo5ufFNv87Mjm58Y1/x1Rzc+NaZ6MaH4onvnvi2Z+fiyTvW0OwpL/i26bUwHJvykceSkYeTUUyb9Kpqvez8+9Ss6PPH5m3PFU1PH3L9/BR89eBbs2BGAc/FgDWHYNaNNHwSsdevj8C1HIeyeLSnSp5n8O38MQ9JtfvPoe4DrrpOajolSwqHjxLwjDQwCGMjMB4tv1ZzTksYzzzJXzUQBWSU2MAepoSdS8sesNJAR+4PCmz6BBLCJRpH69WjZ80gIqgyDL++xTmabltsloKKQdCEEiNa/IGL37PYrN8gq+wcW8eAHwcHj7G/AHcFe1zGhbSYqACwFTrrQlJ3CiwzL3Z3wX/XqhJSvBGGBLED1auuGRCJ4FD7J8MIW35GcmFGAoNKDQMtiG5hQMVRKZR1eQMVlCcaF9FASRYVjF54NnRUgQAIKIQGI58zp2AwbqKGqGaHiPbM+q06QUTTFVLWcHPsWaaoieCwNnnZX8cquHILo/RkP9uC1ji4boK0Zck3yZX88zWA84LUMDp0Ux70k8GZ3FRR92EvhD55AOAvLXkESFvd5tw2ErgcR6GutfokfUZLb6XbFkVhfptNDCA0gD5r0DEIrVAy2KsVsPOrizKL3ADxiXNMZgOIMmGI6fesPrfQhMX9KOcBLr3DnkPrJ30WRl44ayUDhYhilybtQdEaDwSu26eTVIqJZnSK7MjB3mtPAjywqlzAdMAPWGQukHah31KnymEfcNXagroiIAeQXaE55GTRWj/QZNPTGYvPNxFgCfgt4Kr0lEl0kpR4wfEgniFloQJaZwRzDhlt/D8P+Rft1DkS8gVmF+f1bbvF29hv0q/ixzXqIfcFB59VQ8TelNh58/LFeC0u4110qlzGTgBnMRfwRhR94TTqUt4z3EGrf0dxX+auwSEdi3+NJx1wPMGqkwIe6bTSYp4D5ARuxvRI3CilAR36vR+Qy8jf/M668BsctHAYmf9tBUpix2/Fy/eIDzXKRHokULfuOpR3zQmPV4vwHPH3rdsEssuDj0TF/hAb63f7jYg8kfJBcZ9s8ElJCId+Q+CnhgX302et8tCjkKXC67/QrStiCsjuQVN7xXmVSrkcDz9CwbWFwP2Zcv90UWWtV31gagxq2bTvXcqemGe/BpDbd8Wx34akg2CixpgCwtnJAY87cTG3kQvIFtB9J5UgQ3+r4T+HNaFk5/rCKJk36b8Wz4+CT1DyV4awO7bhKOKYcfH32y36y9AvGwFz350zMx6a26FSwbwL6rHGewnqFBk0v6llGkQ5se6WwYt10jiiiOkzxr5CUbUVbibfDoJQWW7pmThufxb+PXLsTYkEcN7QUxPstSDw5Ag69P7ngMm7+VhR91yN2mk7qX16bmR1vFUlvF/k9tNZuBXE08MPlBhOa9gX5/Zve5KVgiDsdy4nAEyY/D370MOww6nwnFmA9O1K+x2F1xRbWUdAIdvgUJEzBUrv8vW8hhkHDdStonFkIvmc6eZJCGBbFEah/esq+TISTICILsF3E8DlSvW3mZsoQEPomGA7cG/cgqPxpyvyTsjDb9Y8KZOOHPJQZeIzpwnqFri4oEz4aJeZhiJOTXHHKoSKS5MlgDLGb6c/3DDWgQSfBvmAN8eEUUSyJ1b8C4mgCOJU1L9+6/TBNsGoQKX0wA8KUAvxW+SMG7J5d+D+Inm8095VHj5XOooiaxXFS63ynoIUrU7jhVjccBGsmz0X1ccjDQSCM/Pc/Nxe+slZ/8BH3/FGue2aDM4yfqkeNMpOIPAvh3P+58qtSUa1BvDZU5VZicisvt2ICYw5K8729YoTpdeSQq/sSHrMovNnxWK7qt/pzYfJZe6c4TzV+FHIQZ6wF8Dg/IDYzHrRCZEyz8ACMepMVRiFBJhhzR5yiHuyv+bzmJxO18pL5c9GIN71tueSafLWEI7jVgfgDueM+Zb2+5kMkQPDnjNQOagzzCdD/ASBQmo90xLEcCXU9mRt9xPLgYUC2dZv1LgyDWvlYbojgw0J5U3v3Ukf1MdNcmhVymeCT5WkUCLdSjC6/HFXiOlEUJbM8SgZVVVN9IvRUW7RH48D5nec81vBVGrCKpb4UGOGcq8ElvhStsJlDS9MDD+g/AdkCqwZJHkEh6/7x7I4G0fGL966+4cK5VvivIkXW9SbkSTKcy2SryFQ2Iq68F/ziWyDl9i8HGasMJ2FTjc3wKrcpXN/uU3TVdr3FANIGNoD08/wp7kr3X/Xqp9wJrBm/+M7gx+K/YzKsl8hL7wv8QZa+YQgZnOjFXMGUpNi9um6Hfr8PpQloKiR+lxJDE1f4RgoemJ3jdVdrgFZSEvYSjBH/H8Jwv7gWvnNuCh1u+bu07sMQ8a8Z2d3cr8bpafChZXXty377ow8vJqDiTR1istz2UOTi+DPvnF86SJIP2gqpbn7v3usYSXpZ4D+tnCO9oDAxBtvB7dqk7G8/9fkhSO1mSCQ9gGmV4E5FhdqII99OYbYpmcOedd8YiNx+87jj74PV5MBi8gpAwT0ICnGJ+vBSe1tDbVeLdk/lJth0/aFGQmGB7wtTiUAxzslyAxSflFA0gOA7w4JYzeOXm5GmpeQ7VE1yObcJul1gP+HAz1lkuYHe5gN3nAvIJF5JYY1eB1ejbyskdniyvtywovRt5upmKRJ9Q0Vvo/EjL0iQAl2haRq6ZzCqhk/nO8Q/KAPn5OvOSWb23+YT7zbEmyf6PZ5x9fqdH6omb9km5XR/B637vx31qv992nzhSzlXBgap8JSq28FYoe6L9lmeU4sSn1Shs68kc2DDbrdx3Wa9JLLvO0xFTEkP0OwOyFEeOqPKphj+2tuhdO0Pgd+1MrrFIJyz54kMRT3wokRRfbtjw4DAmKwjf8Qw1t8ai8HAmOxQylif54GGr0dORY64pz2s+SnPybiNSHoqwHSm9f+EtesckrMDrrUQZs/pULJDXPPGkIc1tNMrsLx625bYbZQNINGGici/2nwstn84lknn/J0qd15yUp+6VR14eymNVrIj0MLjMaQ76kagz2SkS8/sJY5O7ULp16DMh5qRFd5qJfk7O9MCDZpuMwvFsq1E8u8zA3z85uTde96Sdc10b6n320jZobJVF56uoFAydp97iv0nQ+8p4AcN/e4vzJe57Uze7SJ53eNBx81yykTmkuT04ny4b9ePcfX6PbY9Sy9SF56c7t+MOPXvBmzwfCRuRvgh/YHoe0BTvXYy8xJ2uxaxO6tFvn72D8t712yNjETat/ZzFUor1FLqo7/LKjVjHZRZN0gJm8bzIgrRBfWKpz7q90o4x3lIULnJtLApeqf6vyOZlyh3JXrCkqiY8U64T/alVrPzmo2h72a6x53kYa+r6bKmn0MW6dF6XTI4QEu1f4azHTWBJQeVUtjINYU+HPYmGsM8lc6857DXZXLuoPofP7756EGv/gx6sB74i24n19I5fcOV3Ly3yffU7muOsgR0DHPCMFxtPHl72MkxeaTXbQvQS9gep1YNa2HORU6wP4uYJ0v0Zie3EujQeYXvsDz7Hle07eSHfT2tYshklIaR0G1MmoE4Y+PstTS8UxHJpEnJr8s9NfobVqa18Rs0zHUxJUV7bgZ5c4Kepyh0Z39GfHyP3R49EPraoB0kxhQDlV88xfLtjTXNcUMaC5kawzzUlxWCfa0164gbw8/jzbifVv4H0h0Qke6rybc7tV9p9ji7RV5WPTo9V428GMFSSeDie2zwefEUz0Rx+B1Umwvvh/C7KRJ7IOF6R5W2BH0lFvrOyZrp5lg+JHPA/uBjJCxqCFf3LCvbfuu61z0pUSE8fPi9zBdefCR/3U65V85rrPsuFXmi0+wdx0oPi+QM/mGnF+8e3f/cLec9VT3Okfu2ue5rCV8Wf06X3gJp4n+DT1BBPEpNeMdMj+MyKqe6/ZOiXHH3tWKQBnusjzM1C0y1p4S+lfqHL44WTcqdC+4jZuZ2HP+5NfjCUv9+EmNxYrNEwTANv7YYY/pRyJWuWqe+spGqTWI5JibUs5ySE0D3DmBNFAp6zxudG3q8rejnHRyGeedw6EArpbohM80qe1fb/JwlhW11+5cKj71peSNvOf9aDkLr2xDKLp39xKYimLJplQAQW3Y95n+OK+q+329uPD4r1ft9IYihd2wBGQQFa6f+GGsbSyQCx12EDQq7sxXZyArAkwYzAqYBN3JkS9A8AFyuQtuhxzLO7d2BvoeBzBP195VrpX/9hA5L+4wP+V+mPki/Vv8DDP0retbv/zosW//Lh/yC9bFdiCzTIfow/vwR9AV8KXiz3LpEUK/35pUmaZKGmkJILQI3wEjoE/Ctc9Y+g6+yvKzl/MKIdIb02gn35K7bBPwS4IDLh0y9/5a0CSIYTU2JIE108CcQAzi+WJwCDdpMv/orJkWCrIH7/84sGzLkJaAFcAwgSK5fA0rHdpT5q0WJR91Sw0O1TcJEMEGtXjpWSLEsw7No9EuCJff4rsXR20i1iDKbGP1+JiGMI/98USW6FjRpxdl2H4qqx8LsjCj7EFf8v98WVagCJ44+G/orjzPBZv2pqrDEjjsb/7PFZZ2+cDvd4HQjNtjcreJtDe1yF/GMdrBK8zWjIH5YUAfuWPAAO3gJgS7oRE6wYHr9SeP6CAxqcS6AZpgwm/G/bEvG4UWIRAA==
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
diff --git a/plugins/data-analytics/assets/demo-product-growth.csv b/plugins/data-analytics/assets/demo-product-growth.csv
new file mode 100644
index 0000000..5a877a1
--- /dev/null
+++ b/plugins/data-analytics/assets/demo-product-growth.csv
@@ -0,0 +1,33 @@
+week_start,acquisition_channel,signups,activated_users,paid_conversions,revenue_usd,support_tickets
+2026-04-06,Organic,420,248,78,15600,28
+2026-04-06,Referral,180,126,48,9600,12
+2026-04-06,Paid Search,360,205,60,12000,30
+2026-04-06,Partners,120,82,35,8400,10
+2026-04-13,Organic,432,257,80,16000,27
+2026-04-13,Referral,187,132,51,10200,11
+2026-04-13,Paid Search,375,214,63,12600,31
+2026-04-13,Partners,128,89,37,8880,10
+2026-04-20,Organic,445,269,84,16800,29
+2026-04-20,Referral,195,139,54,10800,12
+2026-04-20,Paid Search,392,225,66,13200,33
+2026-04-20,Partners,137,96,40,9600,11
+2026-04-27,Organic,458,279,88,17600,30
+2026-04-27,Referral,203,146,57,11400,12
+2026-04-27,Paid Search,410,233,67,13400,35
+2026-04-27,Partners,148,106,44,10560,11
+2026-05-04,Organic,472,288,91,18200,29
+2026-05-04,Referral,214,157,62,12400,13
+2026-05-04,Paid Search,455,236,64,12800,44
+2026-05-04,Partners,161,118,48,11520,12
+2026-05-11,Organic,486,299,95,19000,30
+2026-05-11,Referral,225,168,66,13200,13
+2026-05-11,Paid Search,485,230,59,11800,55
+2026-05-11,Partners,176,131,53,12720,12
+2026-05-18,Organic,498,306,98,19600,30
+2026-05-18,Referral,236,177,71,14200,14
+2026-05-18,Paid Search,510,221,54,10800,66
+2026-05-18,Partners,191,142,58,13920,13
+2026-05-25,Organic,510,312,102,20400,31
+2026-05-25,Referral,245,184,75,15000,14
+2026-05-25,Paid Search,530,210,49,9800,78
+2026-05-25,Partners,205,151,62,14880,14
diff --git a/plugins/data-analytics/assets/html-report-runtime.html b/plugins/data-analytics/assets/html-report-runtime.html
new file mode 100644
index 0000000..131259b
--- /dev/null
+++ b/plugins/data-analytics/assets/html-report-runtime.html
@@ -0,0 +1,16 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <title>Data Analytics HTML report runtime</title>
+    <script type="module">
+      const target = new URL("../src/html-report-runtime.html", import.meta.url);
+      target.search = window.location.search;
+      window.location.replace(target);
+    </script>
+  </head>
+  <body>
+    <p>Redirecting to the local widget source for development.</p>
+    <p><a href="../src/html-report-runtime.html">Open Data Analytics HTML report runtime</a></p>
+  </body>
+</html>
diff --git a/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part001 b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part001
new file mode 100644
index 0000000..a99759c
--- /dev/null
+++ b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part001
@@ -0,0 +1 @@
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diff --git a/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part002 b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part002
new file mode 100644
index 0000000..f23bae6
--- /dev/null
+++ b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part002
@@ -0,0 +1 @@
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diff --git a/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part003 b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part003
new file mode 100644
index 0000000..bb14e84
--- /dev/null
+++ b/plugins/data-analytics/assets/html-report-runtime.html.gz.b64.part003
@@ -0,0 +1 @@
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7PQu1qyQEfe7qcMIbWeXX+KrEPYv1jGn8s8vVzgd1bZFhCVjSUgSdfSNjvZ0BrXpqvq202vw4QEJhX5O88SGG6ULARUIi4UGw4ZP+hR+OMVikxFbXW/GcMIOrOq9KKNTdMGVCz9Z2S1Mqv5MtrpKCKOgjcrC2BtPIBaq6IHPhHexmIiwkZzYWd2BU0aeXwiTkPdJlotQG+zBMTVlrYub04S/dpx0Yw9gNmFUYvnnWed019RMgCXlddrCqfVWbznqsg+JK+VH5WHe2HjwYPuu8YdL15MA+M4IqSEo1NlW1ct6VrP/StVCL7jCgC6VThBPFYSlL4HMyiDUZxJ4JM4MGML4S4WI5BjRQoehrzWUt7AAGPbHBmxms/9R1chmHtuO/ASZyp+XIR2M+B4cC+WjUSQvIR1OBfHTcSQ/H+ihpdDiykI+OHzwYCeOfYyiFIR/ZLODDl0MFPCU8+oOH9Q9fdEkW4EBGeANNl79nzOXogB2WvzPlyh56+O6LXoib7jtehudB8iYr6sIqSsnfryMYatoheJq9UaScV+FVMKpvctOMNa+5en/dNgO5LuF+p9fnMXLXq0ZAEd2u4fea5RnkSF+WUQqngFJeQsAPEQOQCuQBu4aTaLnRujHhoSgUq6oGzsNQxJzAJ6IuD0NmZHN1YAPAFrJHhewRy75/wM5CJZ1uKlPdvkYmoBUu4qc2wWE9abRWNT/6h7qCsYsQLgrer7bsxzZjgIZnLvReBBMMA2vglY9IF3MUfAIOfTDAabjoixh8Oenh4Z84RC/Lm6sNz+KTnpaG0LcbH3cO+29r92+A/nzozbm6BIro94yUq4Iw24eEIUaR5ufFTyTCU/9nUU5giiQiE5eReRrRh5R5Q3KYrbZ6T3yNFNV/U1E17EOV5jcUvAy+M6mSXhx0jM+LNil3lbW4a2y8XWGwzX1XLg/ylj4+85pTDua2y7jtQEeeSAw3ghf44kAiBljOW16wltCvNX9f5ogffcagMFQNrChrCG0GrpDx3ci9KfhcLXeRd4Q1cs4uxJUtar9aYuTAhbpdM+mwLykbajMM8P2KdnCQAd/1mwUBKPKplE8E9YhK/AgjUpiqvD+oa7eSlnl8JjvWTQjlWAe+jDgnMLJu+sm9oUBh3QM6ez/MFMCCI4TbtXBE8q1UWJtT24jjBSR0npugENxewZrNZmoURaSPJV83Vi7J136XhSjmyqPQYCkBV/FH4RRVhSKOTyJ3FUYRF64iopO/GmjTgKeI+vEWnXrc+zf+fO/+TTw/uH+T4lU4/w1o0zvU79M5GmdDutUIr37W2RD9Fp7RNbzAUhoqSayTiCuR90CW9lu/JzlA32kfsprIYlRau7iUimtEulJ7ogKsUqmA5HV1Xv4lFoHX2sOrqNMR4WEB7wIyo6trC3hpw6WH1MuOwV8yb8TwkNnJygrsNEsP0GAkii8ROkuAu9lvBKiNO+biWCGRQnpxBcxeLlq2Jyrs5R6Lp+tjIJVRJ8ZAs6s+CGzR6tQ97wzl/lAvjNtuCEuij7BPE0j9fc8AT+xttm5XY+AnDYqEXaOpI0Pren1x5ElgG/w0Jy2cA8qIH6WFD+MoCoZZnPRdOyKIrnYSHAdoBBpQuBEdHGRR8I+We0Flvjw+ht92P4qjRjr0x+iJL9JRaJBoNVzdxNAgeljFk+PGpoNxQkYYJSR3jnbuI77xzajNGIHPGUGsGIGKoheisgTVC1AQ8aPJ8trlDpjfqnnC+lBGXecxYXmQrpCHGfNV0HsZDjzNRUYbas3OWMQ8pyBWUzUiI6tzj/Ph284L4dsmPPbZKQV9O5Gx6o5kr1xAr1xDrwzQDP0KYyTu47yDHfiV58GEfyHvrj1v4LiX8s4U63mwPfgkH5umep7vuC/lY9EzyMhxHbwkifsT/X3J5G592L6L2cY4pxlklOO+zz1m89w96+w+a3rvuusb7SZG0Nt9+r7RUvfPO2frL90s6AQB/H7uXDY+uVHQ2V+9bHyGaZwEIGzBo7cYYW4fuuLF3H1H16uX4i6kkHxRgHerL+fuB7xN9K0PZR3UD0HUf+t+cJ87Lly9g0zyEi7ewdfp+oP7Fi977uduSYQaSdM4EPyQVYIkpAGFdIHJTy4L6L8m1JCjth8oIiSq+QgSU5BQSOCTIJ4EkG74AQTGUHhgq/kN9B5YRufvrGVAhhESy0BG9IBbrnf+O7kgZEQVsCyIZ6F8Jvirp6LJ0jtfvlOUoBYHeksyuIII7Uk449xezSkgn8cYdCaFP72ONuMk0jZHQZ58spaej8Os3v+9ictrz8TFDIyRp4VHhgBmsMpUpzIAX56XamXvEOTanPVFuepUjqg0vLjVUQvTkQJzpEiHsETBb+nxfVY89ebfbIgqVVkNLsutlLtX7bdddnKTY5YfbGoSQ5OH75ZWgQw/n0QORmWKvohKshL8w6gMFDEpEEwt1ECczzZ20Iw506cBG4+cOcw6kPNuc+YjbZEb5yBHZdWDKKJrVwwhDl9jx4pMZnrulbWDE5YlqdTUp7NZnW9h0tP4kqgxBSFlBcRYBFJoV6doeWLey/px8BJb2xLkDlxQG2mBEEYFnDajrWRQJYtLvclhQRsIZtj+FyGY69nTJjIPP30nvYmhzW3xII7YMxV+rhbM3RuWHEOV24nhCe/7j9+5vs9kfZVcgvWtbsRvQRIvr72WccQtZsq14suBsoQTm2ims0UfZQISxNVC7o4TcwggPBhoHGGX28Itnu17nVX4XuOJQcRbikAUrO4M1uKgzPEsENiLSwyF2sGz5rfI2wwMI6coFNFpygx99DaRjCO8/iptRqHTDMxFdXeT5tvuadIF3K5TrTcavGU2S/I+5q9QiaCDxdAMn83oGmQBYlRp3zqBoHfIK+xTCL/z+kAYV6iIH3gm6z/t/MGOF/7Fz5VKjWRIq6tGfk+/Eer+5xlS7D5GE6fIcMigFNHsA3W20WVeiqm+FFNjYJopMM2h3MWPccr5EtE7FcvFCK+H4voYr2NxfU7Ch15ExoriVg6n7sg9ds9vK3PUJh2gmrGL3k/uaWf8lE6WdPgWvT2cutPVY8fNzDZVPok6pw8eJLdZYwTl4o6P+uFOokKkrQhOlQWBuMeFx8tWf+zCi6ABP3gNnTpaPeeCxGQuvIVOTItGLqRx3CPTIvHkezYFKqsOhxcvntCVJP+ceker/+62T6AlduUNLIutJyOXKTmmUhUm5Q8hXQidluV9JgJh2pFSC/5DUdF/yNPPxCm03wk9Bl4rj6alV5JwJKtG6TSee77tBG00avFTVFDEHCTGWshKtG39nCfu88Zvzcbjo0YP/WYbXDL46UAHVJPjjcui1Abg/j/1vMOew7GdNcQwmgQgGo/Xv4JhFdAJBXldmSnIJ20NZSxdeMoOb0KnxEr75m66CxPYNKTVR4aK7G/Vtvpz4duSYJeF+CfSris1Copx1WcGCAfPad/mo7IRfTbaJa+uxSsrpKYhUOjAG85NDvX5RW5VDcTBwQ2RJqp0Yf8bjtr9oyONuCKZp4FgmWOEG7YYl5TS0MVooBZVjH6gikH+XVJEU5VAWC4mtwQJddxgXsApQagwjyBzE0eZTKgi6fyAw/cjH18zCNsOfU/7aRoAcTTD1tjb+kbqEUS17FxWulcKyRrd6Nu5U4WnKBMZv0Q6e+CANIsBavgb0sbxBwYxmT1jJzIlz/Nl2hBo5fXYL89ahfrC09BgHfbaNpq7VPjzQcNHODYSPIyBFkQs6LFnna98QrsoT52LKMAuOVyRsq9XJUoI6nbGDIS0/7OEs57zA4MfD0pxcr4CX9mKPx0cp9zsyO6ZjPNCfRoGHFAy5xLtL37BT/6Jukt0rXVvoBdDip/cFNGTmyJ0clOETm5V2e9n8Tlkjo+PUwTqbf4DWV58LpxNE7p+f+4PMdDaWmsjZzFv5yU09MrcTfLFCclJLQc3928mJkoglfKYHHo9AMECjX0XmiirRBg4VF62t/m5RMuE4NqxLFscOqVAI62sp71dM25LWIG/3+AJgpIEBiAtzMed8JVp0NIy3FaJOFXw4OB4j8rRgoKM68cN6OvGEDWj99q+5Frzgk9aZlkLlLYbOgsWQN2YLVc3IXTW44dAFxhx0k0pyjnKdzLEueQAMHflcxLhoTLz23g85VqC51cYxsmsZXw9Ul2QLvRiqSg97/dUIX0tcyfhRZY5k1T5xPfoDLT4UX3aObdEhV+rZ1OmUTMR3J5zoAKifpkJonSat03uFtEgBSNAoOzM8sJ5F2t0fAkUqoHg505JJJcoH8clchqJiRuxqAYKxPgkCUcNGBFrHBeSF8/JJ5B6jpMH6tcQ+9p7aAh91p2fX/Vdlkj4JVBalu65SIclf4IuGqM1XDyeTqIUdy8RNnJn5/zKrcEVnrdlwOCdGgjhUGqd7K25oDGHdCLTVhMztY6BgdmlE+/r6wKyQk4hBZc3zZFFlqcttBCS454M4enNVMsKy5yxLM7NouLU3y+NUm+tQ0kcndhGvThlormcuo5rEXss65B2YiUtxFJaGAoWF5v4L4k77kQUZy1x1kO3iadIU1QCDcvna5EoLRc5+h5RAR3yp/M2/J3OLWc51bfDYDxWrpzh5KTMX+7ooD525m7mD0S8lGY5dS/xbVPT8Lb+o3fwmsvzTv9uTnd599A7NOov9FoUo/BNronptzkjTsWZCv2RpH//JlYbeL2IiK09n+e0XV26dKkcZLc/jJMI8YtysyqwrOYHtrBrR2OauzqQTM9evH5ji5dxn1XKw0i7fCUlLk1/fCzpsOMEbhqN4RRk0UnNeqYCmcDkk+Xf79YTlLv1V1gdeC1/LtVrZpPxx+AcxMV2hHrJODoQWw+h6n1xSlFGEhfm1HkcpaiWJ+hOWqN9Um5Kbz7U7Os6pGoHIzXGQ6noHM+lgQHVYyqKGc07hFrpkxGx503XZDGevvCEC6F73pmWhPwZzmYrQxnyZ2iH/MG4kCwQFIoCk6+XAV5gtWWoHB0UJo5UqCCSBOATU2PNx+TZwmd/O3B1R+gBUT14XGQfTv3fLIfsOif/SYVifpvPCR9F7YXYMtXQDovKjNtUbO4sruSv5ZUUcdsi1DBfdh33pHP6pT5yp3IZdTG+FA1VMb7shfWcIbG61503X2TT3QvHHXSMT8iUq+yvuQX+CTTgqlPiynHqlmYVbciXsN85vUMJVb4DA1V6le/AVf67LzqTgzpq9k/RXuWVbv4R2qm85rcvOx8PoIfPHXe3UzgxO9c1NSdmL/U5zuUcDVL2qjPJWvP0Z5333hvxwfbyM0k0Y4m9P7Ap5+6u91KojEdkwPC8M0V7lqMv1E6o/+dOznlZneE9a5ae0Czt8QWkHQUYtj3zgWrRdubGjxCpGohtd5rQL0gr+tlLPyU7FKHW6Lth+ly9EuZr2HipaWht0CCfB4lQFd3QuVWQvLwAck6lvYr7RUhEW835HE12yO6lEOMkvgDhdXDiGGOXLPGjVFhb2TZqTbT2UdbW58aRR7HHMOiskPfJrvsBJ+dnYMFlPsM+2hcp7yl0SHdfmc3Np677oeEHzEo7Dow6SqlaGluuiqUAkiGsJaOgG2ewwGnhJNdaPB9pnCfQocm1g4GbomwfeDz2JF7/Ikb+UbNZdpIyl6q1gNkbv2aV8tEGu9143FTj0yzU7k1wnP3ZdSOJPgAGx+CNofP4vQKHd4eYjqEcv67zW51sfMvipkHn2kPtQuCO8FIeDwwD9xiI4vrBg4E3DbzWdnunDb872EFBZxR4W3A1gZkxoMFrY8ozrn03CAXHgftz12nDzxi6sl2dEBjWr5AQfhLqN0hZP8uZPcxmUS6/4/2KhJjF56wSJoMp/qLr/gbFX3TnwC46QP+/CBYauLuwEEFTEPrXu4GxPAmj9iSYt5XN5StXP5QSDOw/yQ9y7h4FnZeCRQ1pCT4KnDLhTpb02aXZLvip5GMhdgxeIVJeCmzNiF9a8DJLrZSoXo/S9ruydfe2UsxRQOux8W4KjCXTJwb5OeUbRZTApI9+Ya948Mq9QRkbz8qQ4UmcNBAzZfjVT94h7EB7ivCoI9Basynhi4DkMvJ658J4eCDjEHOoABBoKam4hWLaKip7Jl6oOuD1WzF0R126o+l50pVRYDOQovFCjmrkXXflsawV2Jp1zTRndPSBzA4kGjYaG1z616ntlSkDAVwEeez+iEV7R3DY2ojCxJdEh0Qw3NVOy42etZiPpQH96zC/iUGg3Slq59wvWKrop0V4T/Tg1QcLOupsiNr8KYLhPW16ICU6Qlc6lUesaj/UtwXPAQHtTysx+qclgSv5UyviVdljgdI/XQb1P10K9a+MYi7duHPtph1gHpfusJOIgFDn2hWwqNU5++DeaPKW2hdaGcLytX8SRkQnRHy4zr73NtQ0IGPp/CZTmXUn3tBLcdpsw79mr31Iv3jVbMdlz4Us9noEOUWX9fWyoXUJ0pPlTAzuCLnfe3bcc/8lcA+huMNphXYwNAFLZNr41jYwUdEAZhcmca4T5ekK7Pd9Z25RlTzBmlacYE3LIoTKGbtL7Szqts7eFQdxFMOqg7NdDGd/mozrf79/809RxByXeHiujoVa5aNdPqJqYKYVAUjt0ZISXVg4w88suW6DIk2BRBFHcUaWzthzwgOoqt2/vF3U7ju1qKKO8S3qKGp5RaI1imAD71VXNb5sFTE7RLGQDrw08L502x+77Su83Ou2D7qFpWUwm13NZvteU0tFtNKkcqU58WzNj68WGwPBcoKndxqF5eTwxPgz48H5Va6EsSrBRrN4gWllFSrWp4HXxab8q1u+Uu1zCXIgTZ5kuCB3H2U9zYBlzDPvgsQS2ku9KE1w/aW+H0gEiUuewkQr8wamkE8VSV58qV8aefIlTyWjYXifhIR0ckAF7fIkyjYCv8K0OdMSi4R3pNTRVtU5OwaKyuW+52UrYwOvi+WjYY6q5dmCZFLvrpI+X5CUwNx1ys9B5yzwRvz06fAs6KklXIdVgGfKGFyZE4MQ9Bb3YPWz4MGDac4aekVZO1IUBvudsnCczT4HTzsfurCZe1c62Gz5evmGiPQkoNnV3keYoet4msHGJkiA3DD6rRR4MR0IvShcN+el+ugfP4Nwa5YENNFl+tiXeaFQiIIvAm9NXMoZOQ4qBDpL/LybcIcfEdKdYEAKu57Xr0po1eymIKUunsX5itCHFYhHX4qZSsJk9Yg/0ZAksMzKpde8O+8qTdpzyDOcJimwgFw/o0QCkhd9d2NDN6NPiLQfRXTlUi2CHZy6mvv3f55C8nAc1GSsZimhkOQB/wRGYLuUQVST3mXAdlZV1KWIsoy20I0oWDyicvNhKO4bNh5Vm6HK/cg3kO8nTb4lmxPXNr27bjfv6NEofRdvTYSTMiJ8W0KD+mBJ6kq4TmrOCdP4sgqCqqa9iZI8JkryQAew9ucDQXPXGI+eWOPt9iBOOUH+ZTzwz+N4ejv7n8rwfkKZdKV5W6I7NkRnRxI4DWwixD2vJIXbUsJdN4t3JBogGJLo3wu14icPlam+Edq+31qaU6ssoa5K1Y5zpxH+/4+0ikItI4juR2tZe6mXNaUebQrd58aWVG3CxQJ+8l0qzqr38aPY8mHF1KhMuN7iAyIdVXNgaxQF8317YLvYsna8DMrR6DRGBpPgRV/CVDsJqBXCAkComJRLML5UwHRuuZ7i3QFfWUTGD36YvBEWHEalj4RPdFuQV2gfwvrsJ1tS2a0a0qYc0eZfNqBOfXgmx3M34O2GJoh4ijDToiD5KCS0/tbGP/qLx7av7cP7LrAzk/WHnX+wkd0NisYSelBJVzgRoBjcep0NKDwkvaF5TXAnuYEosUGG4Yj1DMsNw4Ychx05Djs0Dq0dPhA/Qt3ynLSoWEA9FYLA51ha2fPvPJ5cFTVhKhkYoqIeanInPVRRdhKzVg4X8Mu89mZXfGSJDic3aFyXi6cwC5bgWyl4K5ZbKEud5v7TPy/9vtcyOrTbrb956rjtnuMCVgZadv6CNUfrHVi3hHpKfM3OaeFKrdVkREPWOaKnE32dvAC99j7Q5Oi4zwOme/6FTczngUtbzfbhDxvu1gZ6xthFCcXVnzIAxXOT6PbnJklnxPB+y45zikpZXza8nejBeB9ghHjt+IEG9e8DXYL3PrCPHuRZTURnNdGdFLmafN7CJ8+CirOAD2aNPQuUH4RbdiBUYOkLtfflXP5/wvRkFb988y1z8S+r8pP/hqPpkNHTeKg/J11u0OEl9Me74SSIELlFn7m3pIDfmitJX2TQ5b5DIsQ/LOo1d396jqsZIkjhL8JzAidhaJ7BXtFBn4N2sGKzPXP+a+IB5IFAdUwAnH84NxDkMNrrbOyAiLhHMTDCPYqB4YuHsfhJ9zBahjmIH/KKoc3O6ygbr4nPiK2a8odFyOxwMp28okGLo93wJMzS9sbckfE9rVaMWSuoo5Qwh82DFTtYU3EhgzUE8Lx2Sh0pBESJ7Udx2HvCwdiF50NmYYeLB+UeTK0NE8LiEYOepav0jwShUBXksPBvqieNaDZroVF73BEDfZzEk/qN1KaHc9c+9I5Ws4c+fD5arWerLeehr4MVkw1Y071Cs3boeoHDBxchBqtFA4nS8Mq67mGj5TLEauaKVQ8akbPuOxg++TDrrdGXVjs6vH3MELb3TLT2IBcDsSm7VmOjYuSFTHWt+JRxMqF+E2FK0X9XB3kKBM6oigrR0P758oXzsN5q+M4q3Ifi3mcA3Yxy8lGnI+wWIifl0wvkFCEshoiFK2eWHRs86QQy0ZXzJKNTnYSAFjEodeIe9hw3o+CKiSPcbwMFFkKObWvABHElrkugfYw9lvXMSl3i/YaQ+QzcFk/k2xE6v3tN949WGwYgctc2HjnuhIK8yQdw/8emvP4BX/pXkCsyR4CQfc4Z0DHrKVTfok0oi8BJ98x/Xj5pQ9IkSKfj7Cifgz+2MvIXVuBYm+1lUDL5EWb028aR8iiEIQ+1t8eR/6kNOtBMTrgGJhI5hCVvTpZG9CxiyNx7wgm0czNXNhYY7iJMp/44/EIiyVF6Hgw9bUsD5ItrXGae6D4waOis/eZhO9EpE5Ygad8ICy2DX8BxC+Ct8tMjqy9Xmxm3Q2Ny7Hny5miCBsjM5BhScQNkYwgEL8wN5se7I4FNwkwQTzmd7FEgch5T2+p9E6Wa3/onHJH+ZM/GeRHRuDPqZUZN5qFFS+Yx9tqNjNIequDtPnZkPO8goGhEjkKzWSJ/+7Agd6A1KUKJDXH9TN1phwggcSM9SohbksC0HcYja3DZM1OjBw9WbCJmqRyP3WBtjztZx3IrRwoXBK5lZ7HABeQaoORn95woboRtO4Yvjuitd4N/2zIsvYzVLTZD/X26EXuQP6YIC5xJB805kpM76eRrbcNHOB7ypfzTHh5anxLTXFMgyHr8T9XR8cqk0BiBGgFtP3UePJhIlALc1InZmTe7yhZov7NFim/5Wrj8TznZ27n6CvxCWrWegtSJPRzTrgH34MmavEKP83AExBNGAq0zHMFLfGI9FwdYfdfusPbE1XN/qoVPkpCVn8hsBoRRFGZXVqK1UscWE4zJlGCO9lwKuwEVpF/ylhcXkbro70K7as8jf3wNIn5ak/UmUslcnCjtsXWMmHDF+2xWHzKlPAbV7t+H3tTh6Nu6Lo5raD/PmWFZU2R7pWg2YRt3qGZ3CuOqKtaPEygIISvca52TZBRN8S/EXTnJ22vALb7+Qmvuq76ftg/Rh7Tv9v9owZ+JDL/a/2MT7/yrfk+XfM1L3g0RC3EwFa5+5dU9Z9z3iLjvf064MBtR4ELVDsN0atYN0lAXj1XhYy/GOAU/AqtcocMIyqZMS9X2qSl3Tc35E4VOWFqEXmxUxkKlFcSxp6502cWkAifZkxfwcWZES0uUFgnQoVtONtvTwFwaugdpl3oFxIQLyf7EifLLcYC+tbglKH1ReJDPx3AM/D0uvQONqObgakdXs1moLnz9ZI9ieOdCrCV77WgPVsbc43SvHe+hT24utexYLF9d+bqzc+Oj6zssVFcO1Gw2ns1SqJZEFWODMNgzzn+KLWfuhPZAkQTsxVAmNIdCMVSHvhv3Ovtr0zQgB6F6HeTp6z1CRNErFb1+Q4ehAmmYUskdQRPGnW4vw2gUX64NfWDmY62eeoUujTi6QUcmSII/pkGa5VJQETGugXKyyUqYEAjUYAwqJq5w9EWf0DOJyxqQP6Qzf4K0bwWT+hjggv9+AALQRZB02OTycNtjv4bZbMndIWyhQSoUb3FHFuVJkb3Nk2EhbbSIbBcUlSsYaYB61B+NyMPoDbDKIILa95NASDfw9VE8nFJm9Fkxwpn9GAbFH13DnuY0iHjXPHhQlo6SQRdhACOMSYFDh2qvW1BBCPuTMJVQ3XXdgiSYxBdBZSNAAEN0XQW5q2UBV87dHlep/ax9E7UPrJpv3BUWStM3VS6xFRIFc5RdaQk/OdhlYRwf7izLXGWTO3BD26k2WbPu5ayXDIpvTq9oYRGqnqMjmv7DEOWqjzLI2cc4BtrqLHoNYj9xC4SxJTE6UABrL6Rers7xiPb3NPQXMn6qgZxzmmqGQDBZIClWuK/A6rmmHVg60hsa6OXCTxvoPVczYdnoFUXCckPcbjzPhCgQAHHECKVH6ANO4aUBNOi7ATJPKcsli4U6B1uM7nvRiBpcD1VgFKCuf32UjcGecuoEk3OCo05cakGvdnzXX0sI0LpuqHSwp6g0LjDrMM+sI8SBorAqe52+r2rdkPh8ffcSHqte+4gztO9+gkfpxYlbW2P+yXBXhjvycq+z1txxd/c6W+57yCeaonsHSQ/39sFo/+LkF5zyQLFGpXlm6VrNhnTBno/0tTds6Xq+d3vd7ue7KWxJKvMyZU5tkPD5J2WdX+zvP+1P+7oNcL8WwO4TzzXYemEei3BQC1AIf//9/gMORPju2+S/pfKeG6k+0RIDTE/9SMg8S4TCCPosgj6L1GlquUCYQLIEkiWqaxlwdlUr9Rxd1DhtQXJiTWqJ5t53yCBcmliTwK6VkxImff3w99Ttra4LJbiot9BXmsA6WyWbgcONHu4H4AchY4uvN8XrzZ4W83UAF3gouwmzz+f2ayTZXGvEm74W5EqS0Au0oWC42nscEhN7dyVD2GIreugzLUXu7hn1NeZ7+HKPw2Xn9EpAm6TFJxrFkFUKCF8KxsDwPqA2UIHF+/D1WInKUDMQJdUeYaXZhgd6wY4VLTJUbbFeHb7f63GRwFvDcAABVCio0UuGay3bTjl1GqV2OLzc67lZ2TogUFQw9hSiqHCIaasza9kayKXJ9T4UjDE16p/2HK9uld+RQCyZlFcqvuSgpVqbRzvq6pVSVl8ukyA9+EJ4DqEv9XjMZnUC4BAtTXDRZRvTakFWbb5K5Fkflijszk4E4qgSLwOH3cDg+HXWOweMzeoZGY7yk28l49iykVn57c7sH2I/NuylvXOvv/oWiW61f6/Xp2C53pJ8YxC6en3x5TrI5VGaoaQJ3bL36e0bJRlDZ9aTqpc2Fi6uXmqMkejhPmQ6U1zldetxADzPl7+kNcPtoTXL1X7a29xocnGTSYNG1mNTX0ycOWwr0YhYEsVxXUd0HCJBfJR7oiy5vgHpC8NNxG7qzIUcDsMVqaasqR57mSRx0gl4Z9AjxB4M0tQ/oTi4KJzUjv1wHIyU7y+5cAVyc/dFfNij8wUuvwtKfyJJ8+1UKA/LiBMlU3HAlEuETCfQZJhIdB7cBohOG2RJIG205G4B2vcpnASwHaW9R75Oj4JNYKuySos2ndaivnx3miG2crEKGUYLcNoVL9wp8NEcTYNcJkh4Sovka+pcd4qHbFpjMw/GKYzacV12vXPTlclcbWEyLyRSpM0n8t5eXS2QenLCQiOnQ/f69aj+Yk/MqIDPjYzILOv8uP/+He48U4SuRD+1F8JSAaToGzQHIeKTTHRuHaiCVHe2R/tEIkXHOaAHAfAzXRPaUJLqgbB0QJyluCf6fXFvu/v+razBG0gNBOvugSwdA20TkbSxvU+erqfDJDzPnv2tBv89JYAH2FiMO/foMj0NguxebZjEaRon4UkYPWsnIKvXbiA986/ATW/j2J+E4+t2Tfte4FNX3g7jUXAl0qV+lLq1BmwgxkEjvYb6TtxadxxGZ2/94T7dv6Kc9/aDkzio/ev1PbeGmchP59hxntgfx75mX8Vbt/b3jR38V5a2MYGlaJTLAX+SLPSTazf3nBJDeTvH+K/wcdwfsKIGcYJoejgbx4FbS04Gfr216dbU/5trzUeFMujA4hvLsD1c7lbaTrFLJUrZ4ISVpJQJtb8f03+VmeiL39ogAdhQWRYhBpaW1SqWpVBi2jUCLm1Mwqt6GNVSyJ0fboljAo1sjvCfU9v8h1tj/juFjicr4cYVjODW+VX5y3TCWqCfubWd86uK4iZAVDuF4pifGu8SuMX6NjePf/Cd8izn0wRmG8skHkC27cGmP9iqyEaBNlguuqdvbT/eqPqWhAFn2eQTM4vKM8bC1NHkEw8g2+ONzUGzKtt1MB7HlyybeIDZWtvHzWZVlwDD4R0Ct5Bl4G9tVvZiYrGNhPjCwN/Y3tgsZMDjw0YrN0qNH5rN6pGiLBv5LI9Eltb24+GwPMtmYVhlpp3m1vbocXmmrfx3NinL5ubjYXFmiyyPit8RmfzRD4Pjiu9s57+zI9uz+Wh7uzzLD8XviH57tLkx2hmVZ9rJf2eDsmxvDx6VtAeFlwhN7opD9AizBY+ONxdmKwzTD48g23AYbC/MtpnP1qJaPn48DBbm2/rK1j260yibfIVR2xKjtrGz+Hs/3Il6Tb7C6G3LibJ1/PjxgnyP7zLBRrAnQuPsk4aClbaGcdF0Lsnauj2f4rnvyhpNXgWLbX15MUMuybxx24VDKUDvMkFUnrvMDpXnTlNDZbrTvFCZ7jQpVKY7zQiV6U7TQWW601xQme40EaRPWesutZN5Nu5SOZln8yvqtnWXZUPmKYzrY/mdjZ2tUUUeYUqhJNonf5v/7W//i0wuavVzEqlTgXXfSIensA9s10Z+cubQ9oc2Qu0ozupC95Jhgs69MaGE9xxXJbHeYvZ7PcpfIaH+fWOA/7C6c6zOGoO8pXywhz8f+7DPOh4HJCDiL0z1JKB9LMm400mk3rRrLfjXxFsf6hYJROZ2DSToADak+FyoV2poWo63Uk2j7xFJXCZpqnuVJudnP4hH1/KdW9vYaUr5Fht3LGQ0cUr3JN+y9jFsZFO35FlDZtHvzsfQ7XZy86g0de1ZTet5GhJ4sqSE6mSs1Mpybp9T+wrkc1ovynIO4W+Qhn4ktoyFAsreLy+nQbgPSwpjicpKFPOoUAZ7rHIRDcfTTGxT0W1WEEO+/2qHmT8I0cOoc6/Ruter7OjyhAuGzGSQaWkS29mcqmrehT7vSIxLqWwpMZX38cb5VS2Nx+Eoxx4H5CsqJqhI25D4n5il0NxGQx3DU/E2lzBpqY2VfIpxoxzXWcRlmkuZSFXPVne9MU6gCA0+NF80LFfNXD3yHDm9OBG9vZTFNaB52WlDuBJRnhP/XCoNCklFqgaNkK4VZ/JJfFnN0hcXlx8ou+cLeSXy+m0bqQKC3Nx5DHMFPCtU9Da5ZW3tzM9qD6mAhQtdGRMqmWliNEr1nQVdqMMreG34KBTL7QuWcN0aaTQXpxHxBHiiUXDsT8eZHHqLKQtiQKKpeNyQXxQVZxRLTpb5Bhs1KfESERxsSaLbd6BMSybptdaG0InRo0s5fCCJ47NxQLGaU/QHj04kwRFX0+O8bc816j1h86obm4teqZ9bcRvgaSEWkKGLCzzkhF8ZIqddg+VmOoZy4UFqGg8EME2Chrbjrt3LIMG9Wsut3RuLq4pmmRbkart4jJaMzncfl20xLhLbuxEIcO+yJd8QNE4HGjRqinTTz1cEEzm5ItT4qDUes39p0CqPFFJdEeQK4zGWTLpH0vSi4yDvZhVl5YZKkTAaeExCqoAnxU8xltpCKZg+749GNIIteKB7TCm4W8VFukyr7pg8UlVc6B2tXxZpDRZVZcknstSrRnrqjyxVqnjg1pq1Daz1xiP408A/pHiH7ZX831rLWU5euLk6C66JU0tbMl2JBEbFlwScyOUGOL4EdxCTGfLXalmcf9cye6VKcigZSI1c3y6vyk5zktYCPw2gZ7LTEnJgc07AcTQkGofS3N+eEy6fRTYj27EZmRVsxxa+cK48UbLGdnW2ilxo/ZbJIFSNoYxxhe1TwaSaIrgUhYzS35Htt4QTATpT0eJbtU6E7+EL+Q8it8r4A59Seno8suVTHr4nJ1UIu5SykaMxSwMQFEf2EWFhSGlM6d6UG4zH4XkaEu+/PIX+IG5O2wqkz7LamSVlCcOmT1FHA0PCbuClySWdrEUXiORN+KfGj+RMzNCuiboZ0qm1thaO62cQvcLjaxXwzLwqrVCDCbcYJk2xy21JPNVaDevzlIyCXpdWgb8uVIOMXJd08neQWVoLZvWdZRYmjFmjWVsJJ2hAA4LHXzewivHJiusVTjYjxcMZjBhR2D8zWdNuinR6K2lRRd3K+JosfZTfiMrCB+N4ePbka7hIWb8WF1j5eRFQvKReWrxZVrGNHbtmbGaIOqgECzdG8quDKaxNUWVv36qLjbjSXCSHSAnkHOQqt/YY/pNqQE0fm0BzzXznsTN2xoLD6BSEaPFEIHkoOU7NKSsNFSLlM1O4GAtYbdSS7qq6l71QYpD1WEob7FllHx+SkSOG3kuDUefesT9OA6nzNfLM2tZGZQFt0gZThuUdVClqlZa8VBVny4FN+ofDtchwQhSKxn9kNrEdHI8ckBcLdhOmUrfes/xJ7PfRAvZbvV+R/tUNSnHrHde5j6nlvBE7mifVe5ucUC5SEcv4HIek9wFaYG8kq9hkjfwLlqjqPuLnKtWdw1PZ9kTfcwP66G6L7G2bRUOr29Zgc/rxRklqph2wkrestGlAAVLFthfBJRvC2pRqfBLEkyBLwuEHEIdCRAgSeZkS4jbyO3lgqTC0aq0qiSbtuDnp3jGrg5YB2PkRl/mLOjX+CdKADn3y2rl9jRdtNbbLPluqtc2Hm67ezN92G/T1W6AFilGzCdHL5VZBX3DbNbek3e32IDiOkyDXfn+QQrMyaj9iR4pqCTRJI+vI/a2cVseq9pIJicroOOBO2cDcVWYo1V9IVSyz2fz78Sb+kzoISaL37pX2wJqy4bC64s4fypes9IXlmsElaqNW2RHIbXamZfo7yRKlUJ9nf43mmn7FmeCG1FN9zeZ1gTY0x4z/Og0pr1Mpy9HiNt9+lvAP1VsL52iT1HrfovWxV6Strx4MIelVNl829DjMFC8vyPWoMvMT9IJgUuECQbRcLterOBfPmbYC+ceSCjNB+DZqkFEwjGVgytoUF0/s0mWfKErEt1KvXqX84HTZIattKVR52lp0Oi1X2yxUcyxdDAXB5xOV7CHFw3SYxCC3DIJT/yLEMZDHpcUql6/oVVYn5vSzeBZcPiGrjoeNrGHVZpA7ZqsJo/HyLq1YqO/GgvMTPMf7voaz2i2iui9SxxRMc1R+EdqYZDxtn7Ml11g+K7nOrqrzyskvEPuS6irgt81bl7QXi3SYKt8QOuo20tqSofyqbqusc+l0KR7mLNo2i9TlPgyOU9tpljge5HiTWpmKdiq88wQbdXMPKxlfOX3zGDnlagJs9cJMvF7lR2pcClXC5tAfD+s4xWur2mFCyJ5SJ6eFqEfNJ+XrOz+NU11mnSxVHQ3q00C56KH8X288wpHRk8donPixFFce0XNdVOENDUE4zmuX+AvDfsr6sPJYKt8V5YRRyxe4mFJKh9A+jFvQbcIbg7fMUlmyMK3lq8ndjIgIb2DZjG9tN4tnsSM/PQ0Kh7HcKen2R7GTOxpGQA02z6/WW2tbyzQgVZzbtnnKd2rp2pk3e02C0XQYjBqTWLFcvBd2cZYF1EO38ERtrMre+MeZFOnYEWxjpCKKozNYC+aArfznSUOEtKMrAVsLpJBPXBBZcANbSGVm7rLP4+Hy+sP7F/Dp++3Ww/Wn63SugU6TT9dxZaUrNH599nSdfv4Gz7PJ+Nnf/h92eBoO3FkKAA==
diff --git a/plugins/data-analytics/assets/html-report-shell.html b/plugins/data-analytics/assets/html-report-shell.html
new file mode 100644
index 0000000..382bec8
--- /dev/null
+++ b/plugins/data-analytics/assets/html-report-shell.html
@@ -0,0 +1,225 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <meta name="color-scheme" content="light dark">
+  <title>{{TITLE}}</title>
+  <style>
+    :root {
+      --surface: #fff;
+      --bg: #f7f7f7;
+      --surface-tertiary: #f3f3f3;
+      --text: #0d0d0d;
+      --secondary: #5d5d5d;
+      --muted: #8f8f8f;
+      --border: rgba(13, 13, 13, 0.08);
+      --border-strong: rgba(13, 13, 13, 0.14);
+      --grid: #e9e9e9;
+      --blue: #0169cc;
+      --blue-dark: #003f7a;
+      --green: #00692a;
+      --purple: #8046d9;
+      --warning: #e25507;
+      --warning-bg: #fff5f0;
+      --positive: #00692a;
+      --positive-bg: #edfaf2;
+      --ds-bg: var(--bg);
+      --ds-surface: var(--surface);
+      --ds-surface-secondary: var(--bg);
+      --ds-surface-tertiary: var(--surface-tertiary);
+      --ds-border-subtle: var(--border);
+      --ds-border: var(--border-strong);
+      --ds-border-strong: var(--border-strong);
+      --ds-text-primary: var(--text);
+      --ds-text: var(--text);
+      --ds-text-secondary: var(--secondary);
+      --ds-text-muted: var(--secondary);
+      --ds-text-tertiary: var(--muted);
+      --ds-blue: var(--blue-dark);
+      --ds-blue-500: var(--blue);
+      --ds-purple: var(--purple);
+      --ds-green: var(--green);
+      --ds-orange: var(--warning);
+      --ds-neutral: var(--muted);
+      --ds-font: "System Sans Variable", ui-sans-serif, system-ui, -apple-system, "Segoe UI", sans-serif;
+      --ds-radius-sm: 8px;
+      --ds-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1);
+      --ds-chart-body-height: 320px;
+      --report-reading-width: 800px;
+    }
+
+    * { box-sizing: border-box; }
+    html, body { max-width: 100%; overflow-x: clip; }
+    body { margin: 0; background: var(--bg); color: var(--text); font: 14px/1.55 var(--ds-font); }
+    .shell { width: min(1140px, calc(100% - 32px)); margin: 18px auto 56px; overflow: hidden; border: 1px solid var(--border); border-radius: 24px; background: var(--surface); box-shadow: 0 4px 16px rgba(0, 0, 0, 0.05); }
+    .topbar { display: flex; min-height: 48px; align-items: center; justify-content: space-between; padding: 8px 16px; border-bottom: 1px solid var(--border); font-weight: 500; }
+    .brand { display: flex; align-items: center; gap: 9px; }
+    .mark { width: 18px; height: 18px; border-radius: 6px; background: linear-gradient(145deg, var(--blue), var(--purple)); }
+    .meta { overflow: hidden; max-width: 45%; color: var(--muted); font-size: 12px; text-overflow: ellipsis; white-space: nowrap; }
+    main { padding: 48px; }
+    .reading { width: min(var(--report-reading-width), 100%); margin: 0 auto; }
+    .wide { width: 100%; margin: 28px auto 46px; }
+    .kicker { margin-bottom: 12px; color: var(--blue); font-size: 12px; font-weight: 600; letter-spacing: 0.08em; text-transform: uppercase; }
+    h1 { max-width: 760px; margin: 0; font-size: 36px; font-weight: 500; letter-spacing: -2px; line-height: 42px; }
+    h2 { margin: 0 0 10px; font-size: 24px; font-weight: 500; letter-spacing: -0.7px; line-height: 31px; }
+    h3 { margin: 0; font-size: 14px; font-weight: 600; line-height: 22px; }
+    p, li { line-height: 22px; }
+    .deck { max-width: 750px; margin: 16px 0 30px; color: var(--secondary); font-size: 16px; line-height: 25px; }
+    .summary { display: grid; grid-template-columns: 145px 1fr; gap: 24px; margin-bottom: 26px; padding: 24px 0; border-top: 1px solid var(--border-strong); border-bottom: 1px solid var(--border-strong); }
+    .summary-label { color: var(--muted); font-size: 12px; font-weight: 600; letter-spacing: 0.06em; text-transform: uppercase; }
+    .summary-body { display: grid; gap: 12px; }
+    .summary-body p { margin: 0; color: var(--secondary); }
+    .summary-body strong { color: var(--text); }
+    .metrics { display: grid; grid-template-columns: repeat(4, 1fr); gap: 12px; margin-bottom: 46px; }
+    .metric { min-height: 126px; padding: 16px 20px; border: 1px solid var(--border); border-radius: 24px; }
+    .metric-label { color: var(--secondary); font-size: 12px; }
+    .metric-value { margin: 8px 0 5px; font-size: 26px; font-weight: 500; font-variant-numeric: tabular-nums; letter-spacing: -1px; line-height: 34px; }
+    .metric-note { margin-top: 7px; color: var(--muted); font-size: 11px; }
+    .pill { display: inline-flex; padding: 2px 7px; border-radius: 999px; background: var(--positive-bg); color: var(--positive); font-size: 12px; font-weight: 600; }
+    .pill.warn { background: var(--warning-bg); color: var(--warning); }
+    .narrative { margin: 44px 0 20px; }
+    .narrative p { max-width: 730px; margin: 0; color: var(--secondary); }
+    .card { overflow: hidden; border: 1px solid var(--border); border-radius: 24px; background: var(--surface); }
+    figure.card { margin: 0; }
+    .card-head { padding: 16px 20px 14px; border-bottom: 1px solid var(--border); }
+    .card-head p { margin: 2px 0 0; color: var(--muted); font-size: 12px; }
+    .chart-wrap { padding: 16px 24px 16px 96px; }
+    [data-recharts-chart] { min-height: 320px; }
+    [data-recharts-live] { display: none; width: 100%; min-height: 320px; }
+    [data-recharts-ready="true"] [data-recharts-fallback] { display: none; }
+    [data-recharts-ready="true"] [data-recharts-live] { display: block; }
+    .chart-fallback { width: 100%; min-height: 320px; }
+    .chart-fallback svg { display: block; width: 100%; height: auto; min-height: 320px; overflow: visible; }
+    .chart-note { padding: 12px 20px 15px; border-top: 1px solid var(--border); color: var(--muted); font-size: 11px; line-height: 17px; }
+    .table-card { width: min(var(--report-reading-width), 100%); max-width: 100%; margin: 12px auto 46px; }
+    .table-scroll { width: 100%; max-width: 100%; overflow-x: auto; }
+    .table-scroll > table { width: max-content; min-width: 100%; }
+    table { width: 100%; border-collapse: collapse; font-size: 12px; }
+    th { padding: 11px 14px; border-bottom: 1px solid var(--border); color: var(--muted); font-weight: 600; text-align: right; white-space: nowrap; }
+    td { padding: 13px 14px; border-bottom: 1px solid var(--border); font-variant-numeric: tabular-nums; text-align: right; white-space: nowrap; }
+    th:first-child, td:first-child { text-align: left; }
+    tbody tr:last-child td { border-bottom: 0; }
+    td.positive { color: var(--positive); }
+    td.negative { color: var(--warning); font-weight: 600; }
+    .caveat { margin: 46px 0 0; padding: 16px 18px; border-radius: 14px; background: var(--surface-tertiary); color: var(--secondary); font-size: 12px; line-height: 19px; }
+    .caveat strong { color: var(--text); }
+    .source-tooltip { position: relative; display: inline-block; padding: 0; border: 0; appearance: none; background: none; color: inherit; font: inherit; line-height: inherit; cursor: help; text-decoration: underline dotted; text-underline-offset: 0.18em; }
+    .source-tooltip:focus-visible { outline: 2px solid var(--purple); outline-offset: 3px; border-radius: 3px; }
+    .source-tooltip-content { position: fixed; z-index: 1000; top: var(--source-tooltip-top, 8px); left: var(--source-tooltip-left, 8px); display: none; width: max-content; max-width: min(360px, calc(100vw - 16px)); padding: 8px 10px; border-radius: 8px; background: #171411; color: #fff; font-family: ui-sans-serif, system-ui, sans-serif; font-size: 12px; font-style: normal; font-weight: 400; line-height: 1.4; overflow-wrap: anywhere; white-space: normal; text-align: left; text-decoration: none; box-shadow: 0 8px 24px rgba(23, 20, 17, 0.2); opacity: 0; visibility: hidden; pointer-events: none; }
+    .source-tooltip-content, .source-tooltip-content * { color: #fff !important; font-style: normal !important; text-decoration: none !important; }
+    .source-tooltip-heading { display: none; }
+    html:not([data-source-tooltips-ready]) .source-tooltip-content { position: absolute; top: auto; bottom: calc(100% + 8px); left: 50%; max-width: min(360px, 80vw); transform: translateX(-50%); }
+    html:not([data-source-tooltips-ready]) .source-figure > .source-tooltip .source-tooltip-content { top: calc(100% + 8px); bottom: auto; }
+    .source-tooltip:hover .source-tooltip-content, .source-tooltip:focus .source-tooltip-content, .source-tooltip:focus-within .source-tooltip-content { display: block; opacity: 1; visibility: visible; }
+    .source-figure { position: relative; }
+    .source-figure > .source-tooltip { position: absolute; top: 10px; right: 10px; z-index: 2; padding: 4px 7px; border: 1px solid var(--border-strong); border-radius: 999px; background: var(--surface); color: var(--secondary); font-size: 11px; font-weight: 600; line-height: 1; letter-spacing: 0.01em; text-decoration: none; touch-action: manipulation; }
+
+    @media (prefers-color-scheme: dark) {
+      :root {
+        --surface: #212121;
+        --bg: #171717;
+        --surface-tertiary: #303030;
+        --text: #fff;
+        --secondary: #cdcdcd;
+        --muted: #afafaf;
+        --border: rgba(255, 255, 255, 0.07);
+        --border-strong: rgba(255, 255, 255, 0.16);
+        --grid: #3a3a3a;
+        --blue: #48aaff;
+        --blue-dark: #48aaff;
+        --green: #62c887;
+        --purple: #b58cff;
+        --warning: #ff9e6c;
+        --warning-bg: #4a2206;
+        --positive: #7bd99b;
+        --positive-bg: #143b22;
+      }
+    }
+
+    @media (max-width: 800px) {
+      .shell { width: 100%; margin: 0; border: 0; border-radius: 0; }
+      main { padding: 32px 18px; }
+      .metrics { grid-template-columns: 1fr 1fr; }
+      .summary { grid-template-columns: 1fr; gap: 10px; }
+      .chart-fallback svg { min-width: 720px; }
+      .chart-wrap { overflow-x: auto; padding: 16px 18px; }
+    }
+
+    @media (max-width: 600px) {
+      .source-tooltip:hover .source-tooltip-content, .source-tooltip:focus .source-tooltip-content, .source-tooltip:focus-within .source-tooltip-content { display: none; opacity: 0; visibility: hidden; }
+      .source-tooltip[data-source-tooltip-open] .source-tooltip-content, html:not([data-source-tooltips-ready]) .source-tooltip:focus .source-tooltip-content { position: fixed; top: auto; right: 16px; bottom: calc(16px + env(safe-area-inset-bottom, 0px)); left: 16px; display: block; width: auto; max-width: none; max-height: min(40vh, 280px); padding: 14px 16px; overflow-y: auto; border-radius: 14px; font-size: 14px; line-height: 1.45; transform: none; opacity: 1; visibility: visible; pointer-events: auto; }
+      .source-tooltip-heading { display: block; margin-bottom: 6px; font-weight: 600; }
+    }
+
+    @media (max-width: 520px) {
+      h1 { font-size: 32px; line-height: 38px; }
+      .metrics { grid-template-columns: 1fr; }
+    }
+  </style>
+</head>
+<body>
+  <!-- Deliver this HTML file itself. A screenshot/image preview is non-interactive and cannot replace report.html.
+       Wrap every source-backed visible number in this source-tooltip pattern:
+       <span class="source-tooltip" tabindex="0" aria-describedby="number-source-tooltip-1">VALUE<span class="source-tooltip-content" id="number-source-tooltip-1" role="tooltip">Source: {{source system or connector}}<br>Table: {{fully qualified table or view; list all material tables}}</span></span>
+  -->
+  <div class="shell">
+    <header class="topbar">
+      <div class="brand"><span class="mark" aria-hidden="true"></span>Data Analytics</div>
+      <div class="meta">{{SOURCE_AND_DATE}}</div>
+    </header>
+    <main data-report-audience="{{REPORT_AUDIENCE}}">
+      <article class="reading">
+        <div class="kicker">{{KICKER}}</div>
+        <header data-contract-section="title"><h1>{{TITLE}}</h1></header>
+        <p class="deck">{{ANSWER_FIRST_DECK}}</p>
+        <section class="summary" data-contract-section="executive-summary">
+          <div class="summary-label">Executive Summary</div>
+          <div class="summary-body">{{SUMMARY_PARAGRAPHS}}</div>
+        </section>
+        <section class="metrics">{{OPTIONAL_METRIC_CARDS}}</section>
+        <section class="narrative" data-contract-section="key-findings">
+          <h2>{{INSIGHT_LED_FINDING}}</h2>
+          <p>{{CLAIM_EVIDENCE_INTERPRETATION}}</p>
+        </section>
+      </article>
+
+      <div class="wide">
+        <figure class="card source-figure">
+          <div class="card-head"><h3>{{CHART_TITLE}}</h3><p>{{CHART_SUBTITLE}}</p></div>
+          <div class="chart-wrap">
+            <div data-recharts-chart="chart-1">
+              <div class="chart-fallback" data-recharts-fallback>
+                <!-- Replace with a readable inline SVG or compact fallback table generated from the same reviewed rows. -->
+                <svg viewBox="0 0 960 420" role="img" aria-label="{{CHART_ALT_TEXT}}"></svg>
+              </div>
+              <div data-recharts-live aria-hidden="true"></div>
+            </div>
+          </div>
+          <figcaption class="chart-note">{{CHART_NOTE}}</figcaption>
+          <button type="button" class="source-tooltip" aria-describedby="chart-source-tooltip-1">Source<span class="source-tooltip-content" id="chart-source-tooltip-1" role="tooltip">Source: {{source system or connector}}<br>Table: {{fully qualified table or view; list all material tables}}</span></button>
+        </figure>
+      </div>
+
+      <article class="reading">
+        <section class="card table-card">
+          <div class="card-head"><h3>{{TABLE_TITLE}}</h3><p>{{TABLE_SUBTITLE}}</p></div>
+          <div class="table-scroll">{{SEMANTIC_TABLE}}</div>
+        </section>
+        <section class="narrative" data-contract-section="recommended-next-steps">
+          <h2>Recommended Next Steps</h2>
+          {{NEXT_STEPS}}
+        </section>
+        <section class="narrative" data-contract-section="further-questions">
+          <h2>Further Questions</h2>
+          {{FURTHER_QUESTIONS}}
+        </section>
+        <section class="caveat" data-contract-section="caveats-and-assumptions">
+          <strong>Caveats and Assumptions.</strong> {{MATERIAL_CAVEATS}}
+        </section>
+      </article>
+    </main>
+  </div>
+  <!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->
+</body>
+</html>
diff --git a/plugins/data-analytics/mcp/server.cjs b/plugins/data-analytics/mcp/server.cjs
index 8cfab9d..1f13713 100644
--- a/plugins/data-analytics/mcp/server.cjs
+++ b/plugins/data-analytics/mcp/server.cjs
@@ -8,7 +8,7 @@ const readline = require("node:readline");
 const childProcess = require("node:child_process");
 const zlib = require("node:zlib");
 
-const SERVER_NAME = "datascience-widgets";
+const SERVER_NAME = "data-analytics-widgets";
 const PLUGIN_ROOT = path.resolve(__dirname, "..");
 const PLUGIN_MANIFEST = JSON.parse(
   fs.readFileSync(path.join(PLUGIN_ROOT, ".codex-plugin", "plugin.json"), "utf8"),
@@ -27,7 +27,11 @@ const DATA_ANALYTICS_LOGO = {
 const DATA_ANALYTICS_ICONS = [DATA_ANALYTICS_ICON, DATA_ANALYTICS_LOGO];
 const TABLE_WIDGET_URI = "ui://widget/datascience-table.html";
 const CHART_WIDGET_URI = "ui://widget/datascience-chart.html";
-const ARTIFACT_WIDGET_URI = "ui://widget/datascience-artifact.html";
+const ARTIFACT_WIDGET_BASE_URI = "ui://widget/datascience-artifact.html";
+const ARTIFACT_WIDGET_URI =
+  `ui://widget/datascience-artifact-${encodeURIComponent(SERVER_VERSION)}.html`;
+const ARTIFACT_WIDGET_LEGACY_VERSIONED_URI =
+  `${ARTIFACT_WIDGET_BASE_URI}?v=${encodeURIComponent(SERVER_VERSION)}`;
 const WIDGET_MIME_TYPE = "text/html;profile=mcp-app";
 const MEASURE_NAMES_FIELD = "__measure_names__";
 const WIDGET_RESOURCE_DOMAINS = ["https://cdn.openai.com"];
@@ -38,6 +42,8 @@ const TOOL_NAMES = {
   renderChart: "render_chart",
   renderTable: "render_table",
 };
+const VALUE_FORMAT_DESCRIPTION =
+  'Display format for numeric values. Use "percent" only for fractional-rate values: pass 0.98 for 98%, 1 for 100%, and 2 for 200%. If reviewed data is already in percentage points such as 98, use "number" with unit "%".';
 const MAX_WIDGET_ROWS = 500;
 const MAX_WIDGET_COLUMNS = 80;
 const MAX_WIDGET_DATA_POINTS = 2000;
@@ -143,6 +149,26 @@ const WIDGET_DESCRIPTIONS = {
       "app with field wells for re-encoding exploration-ready rows.",
     ].join(" "),
 };
+const WEB_WORK_MODE_WIDGET_DELIVERY_GUARD = [
+  "When surface = chatgpt_web and mode = work_mode are both positively identified,",
+  "or when mode = work_mode is positive and surface is unknown or otherwise not positively codex_desktop,",
+  "do not call render_artifact, render_chart, or render_table to deliver visuals or reports;",
+  "the trusted Work Mode rendering path may drop standalone plugin widgets that lack appContext.",
+  "Preserve the delivery mode already selected by the owning workflow. For already-selected inline",
+  "visuals with exactly one supported bar, line, pie, or scatter chart, treat charts_widget_v2 as",
+  "directly surfaced and emit its live genui content reference before fallback; use the outer shape",
+  "genui{\"charts_widget_v2\":{\"content\":{...}}} without Markdown backticks, not",
+  "standalone assistant text; do not self-declare it unavailable, search for it, or print its payload",
+  "as bare JSON. Keep app_block conditional on",
+  "the host surfacing it for a richer composition.",
+  "Use image-based/static charting only after an",
+  "emitted native reference is rejected or fails to render, or when no suitable native renderer exists;",
+  "then use a compact table or other non-MCP fallback only when no visual renderer can be delivered.",
+  "For selected reports or dashboards, use HTML or another non-MCP",
+  "surface. Use image-based/static charting for that native-render failure fallback or when the user",
+  "explicitly requests Python, a static image/file, notebook-oriented output, or export. Do not",
+  "say a visual rendered above unless the selected non-MCP or native Work Mode surface actually rendered.",
+].join(" ");
 const SERVER_INSTRUCTIONS = [
   "Before rendering a report or dashboard artifact, call validate_artifact with the",
   "complete manifest and bounded snapshot. Fix validation errors there first; do not use",
@@ -151,6 +177,7 @@ const SERVER_INSTRUCTIONS = [
   "complete Data Analytics dashboard or report manifest with a bounded snapshot inside the MCP app;",
   "this is the default reader handoff for report and dashboard work and should be attempted before",
   "static HTML, localhost, or file:// delivery.",
+  WEB_WORK_MODE_WIDGET_DELIVERY_GUARD,
   "Artifact snapshots must be bounded: at most 50 datasets, 2,000 rows per dataset, 3MB total",
   "payload, and 200k total inline source characters. Use the canonical artifact snapshot shape:",
   "snapshot.datasets is an object keyed by dataset id, and each value is a plain array of row objects",
@@ -164,9 +191,16 @@ const SERVER_INSTRUCTIONS = [
   "Cards, charts, and tables define reusable renderable assets; blocks establish the artifact",
   "reading order. Report artifacts must include at least one chart data visualization block and",
   "a first markdown block whose body is a # heading matching manifest.title.",
+  "Give each independently editable major report section its own markdown block. Do not put",
+  "multiple peer ## headings in one markdown body; reserve ### headings for subordinate content",
+  "that should remain in the same card. One headline metric does not mean one metrics[] entry:",
+  "keep short, directly relevant directional comparisons as later labeled badge metrics, especially",
+  "when the same comparison appears in the executive summary or findings.",
   "Native artifact charts must use encodings.x.field plus encodings.y.field or encodings.y.fields,",
   "with optional encodings.color.field for grouped tidy data. Legacy manifest chart fields",
   "xField and series are rejected; use validate_artifact to check chart shape before rendering.",
+  "Give each native artifact table a defaultSort with a declared column field and asc or desc",
+  "direction chosen to make the initial row order describe the data clearly.",
   "When a validated MCP artifact report or dashboard needs a hosted Site Creator link, call",
   "export_artifact_package and deploy that package instead of hand-rolling standalone HTML.",
   "The exporter preserves the real artifact runtime and serves /api/manifest, /api/snapshot,",
@@ -284,6 +318,7 @@ function toolDefinitions() {
       format: {
         type: ["string", "null"],
         enum: ["compact", "number", "percent", "currency", null],
+        description: VALUE_FORMAT_DESCRIPTION,
       },
       unit: { type: ["string", "null"] },
       align: { type: ["string", "null"], enum: ["left", "right", "center", null] },
@@ -480,6 +515,7 @@ function toolDefinitions() {
   const artifactMetricFormat = {
     type: ["string", "null"],
     enum: ["compact", "number", "percent", "currency", null],
+    description: VALUE_FORMAT_DESCRIPTION,
   };
   const artifactCardMetric = objectSchema(
     {
@@ -505,13 +541,19 @@ function toolDefinitions() {
       id: { type: "string" },
       description: { type: ["string", "null"] },
       dataset: { type: "string" },
+      sourceId: {
+        type: ["string", "null"],
+        description:
+          "Reference to manifest.sources[] for this card's data-source modal. Required for cards used by dashboard or report metric-strip blocks unless source is provided inline.",
+      },
+      source: sourceSchema,
       filter: { type: "object", additionalProperties: scalar },
       metrics: {
         type: "array",
         minItems: 1,
         items: artifactCardMetric,
         description:
-          "Metric card values. Use metrics[].field; do not use legacy card fields such as valueField, deltaField, label, title, format, or indicators.",
+          "One headline metric followed by concise comparison context for that same metric, such as a prior-period value, target, or delta. One headline does not mean one metrics[] entry: include a short directional comparison when it is decision-relevant, especially when the same comparison appears in the executive summary or findings. Put independent secondary metrics in their own cards, charts, tables, or narrative. Use metrics[].field and signed: true for signed changes; do not use legacy card fields such as valueField, deltaField, label, title, format, or indicators.",
       },
     },
     ["id", "dataset", "metrics"],
@@ -620,6 +662,20 @@ function toolDefinitions() {
     },
     ["field", "label"],
   );
+  const artifactTableDefaultSort = objectSchema(
+    {
+      field: {
+        type: "string",
+        description: "Declared table column field used for the initial row order.",
+      },
+      direction: {
+        type: "string",
+        enum: ["asc", "desc"],
+        description: "Initial sort direction.",
+      },
+    },
+    ["field", "direction"],
+  );
   const artifactTable = objectSchema(
     {
       id: { type: "string" },
@@ -632,6 +688,11 @@ function toolDefinitions() {
       sourceId: { type: ["string", "null"] },
       source: sourceSchema,
       layout: { type: ["string", "null"] },
+      defaultSort: {
+        ...artifactTableDefaultSort,
+        description:
+          "Initial table sort chosen to make the first view answer the table's question. The field must appear in columns.",
+      },
       columns: {
         type: "array",
         minItems: 1,
@@ -652,12 +713,14 @@ function toolDefinitions() {
       body: {
         type: "string",
         description:
-          'Block body for markdown or html blocks. Markdown blocks render body; do not use "markdown", "content", "text", "title", "html", or "height" block fields.',
+          'Block body for markdown or html blocks. Give each independently editable major report section its own markdown block with one peer ## heading; use ### only for subordinate content that belongs in the same card. Markdown blocks render body; do not use "markdown", "content", "text", "title", "html", or "height" block fields.',
       },
       cardIds: {
         type: "array",
+        minItems: 1,
         items: { type: "string" },
-        description: 'Required for type "metric-strip"; references manifest.cards[].id.',
+        description:
+          'Required for type "metric-strip"; references one or more manifest.cards[].id values in display order. There is no preferred or maximum card count: include all and only decision-relevant metrics.',
       },
       chartId: {
         type: "string",
@@ -700,7 +763,7 @@ function toolDefinitions() {
         items: artifactBlock,
         default: [],
         description:
-          "Top-level artifact blocks. Required for dashboards and reports; array order is the artifact reading path. Markdown blocks use type \"markdown\" with body: string. Metric strips use type \"metric-strip\" with cardIds. Custom HTML blocks use type \"html\" with raw HTML in body and auto-size to content. The content, text, markdown, title, html, and height fields are not part of the artifact block contract.",
+          "Top-level artifact blocks. Required for dashboards and reports; array order is the artifact reading path. Markdown blocks use type \"markdown\" with body: string. Give each independently editable major report section its own markdown block rather than putting multiple peer ## headings in one body. Metric strips use type \"metric-strip\" with cardIds. Custom HTML blocks use type \"html\" with raw HTML in body and auto-size to content. The content, text, markdown, title, html, and height fields are not part of the artifact block contract.",
       },
     },
     required: ["version", "title", "blocks"],
@@ -799,6 +862,11 @@ function toolDefinitions() {
         "artifact cards. snapshot.accessIssues is reserved for missing required data in partial",
         "or blocked artifacts; use markdown body blocks or source notes for optional source limitations in",
         "ready artifacts. All artifacts require manifest.title and manifest.blocks.",
+        "Do not use this tool as the report delivery surface in positively identified",
+        "ChatGPT web Work Mode, or when mode = work_mode is positive and surface is",
+        "unknown or otherwise not positively codex_desktop; use HTML or another non-MCP",
+        "surface instead because that trusted rendering path can drop standalone plugin widgets without appContext.",
+        "A successful tool result is not delivery confirmation in that runtime.",
         "Refresh and export controls are v1 agent-mediated prompts; do not",
         "include live connector refresh actions.",
       ].join(" "),
@@ -830,6 +898,19 @@ function toolDefinitions() {
       [
         "Render a compact Data Analytics chart from already-reviewed provenance and table data.",
         "Pass source.query.sql with the actual SQL used to produce the chart table, plus source.query.description for the human-readable query summary, an exploration-ready table, chart, and display.",
+        "Do not call this tool for inline visual delivery in positively identified ChatGPT web Work Mode",
+        "or when mode = work_mode is positive and surface is unknown or otherwise not positively codex_desktop;",
+        "for an already-selected inline visual with exactly one supported bar, line, pie, or scatter chart,",
+        "treat charts_widget_v2 as directly surfaced and emit its live genui content reference before fallback;",
+        "use the outer shape genui{\"charts_widget_v2\":{\"content\":{...}}} without Markdown",
+        "backticks, not standalone assistant text; do not self-declare it unavailable, search for it,",
+        "or print its payload as bare JSON.",
+        "Keep app_block conditional on the host surfacing it for a richer composition.",
+        "Use image-based/static charting only after an emitted native reference is rejected or fails to render,",
+        "or when no suitable native renderer exists, with a compact table or other non-MCP fallback only",
+        "when no visual renderer can be delivered.",
+        "Use image-based/static charting for that native-render failure fallback or when the user explicitly requests Python, a static image/file, notebook-oriented output, or export.",
+        "A successful tool result is not delivery confirmation in that runtime.",
         "Use the subtitle for a reader-facing insight or takeaway not covered by the title, not for source names, query ids, table names, SQL intent, metric definitions, or provenance.",
         "The table should retain useful",
         "dimensions, measures, time columns, and grouping columns so users can change",
@@ -855,6 +936,10 @@ function toolDefinitions() {
         "or exact lookup rows. Use after running a durable query when the user should see the",
         "sampled rows that support the analysis. Pass source.query.sql with the same actual SQL",
         "source payload shape used by chart widgets so the expanded table detail view can show the query.",
+        "Do not call this tool for inline table delivery in positively identified ChatGPT web Work Mode",
+        "or when mode = work_mode is positive and surface is unknown or otherwise not positively codex_desktop;",
+        "use native Work Mode table rendering when available, or a compact conversational/static table fallback.",
+        "A successful tool result is not delivery confirmation in that runtime.",
       ].join(" "),
       tableInputSchema,
       TABLE_WIDGET_URI,
@@ -863,7 +948,14 @@ function toolDefinitions() {
 }
 
 function canonicalWidgetUri(uri) {
-  if (uri === ARTIFACT_WIDGET_URI) return ARTIFACT_WIDGET_URI;
+  if (
+    uri === ARTIFACT_WIDGET_URI ||
+    uri === ARTIFACT_WIDGET_BASE_URI ||
+    uri === ARTIFACT_WIDGET_LEGACY_VERSIONED_URI ||
+    uri.startsWith(`${ARTIFACT_WIDGET_BASE_URI}?v=`)
+  ) {
+    return ARTIFACT_WIDGET_URI;
+  }
   if (uri === TABLE_WIDGET_URI) return TABLE_WIDGET_URI;
   if (uri === CHART_WIDGET_URI) return CHART_WIDGET_URI;
   return null;
@@ -1293,6 +1385,25 @@ function validateReportRenderableTable(table, fieldPath) {
   });
 }
 
+function validateArtifactTableDefaultSort(table, fieldPath) {
+  if (table.defaultSort == null) return;
+  if (!isPlainObject(table.defaultSort)) {
+    throw new Error(`${fieldPath}.defaultSort must be an object`);
+  }
+  requireIdentifier(table.defaultSort.field, `${fieldPath}.defaultSort.field`);
+  if (!["asc", "desc"].includes(table.defaultSort.direction)) {
+    throw new Error(`${fieldPath}.defaultSort.direction must be asc or desc`);
+  }
+  const columnFields = new Set(
+    asList(table.columns)
+      .filter(isPlainObject)
+      .map((column) => column.field),
+  );
+  if (!columnFields.has(table.defaultSort.field)) {
+    throw new Error(`${fieldPath}.defaultSort.field must reference a declared table column`);
+  }
+}
+
 function validateArtifactBlockManifestShape(manifest, surface) {
   if (manifest.blocks != null && !Array.isArray(manifest.blocks)) {
     throw new Error("$.manifest.blocks must be an array of top-level artifact blocks");
@@ -1419,7 +1530,7 @@ function validateArtifactManifest(manifest, surface) {
     for (const key of ["valueField", "format", "label", "title", "indicators", "deltaField", "deltaLabel"]) {
       if (card[key] != null) throw new Error(`$.manifest.cards[${index}].${key} is not supported; use metrics[]`);
     }
-    for (const key of ["id", "dataset"]) {
+    for (const key of ["id", "dataset", "sourceId"]) {
       validateIdentifier(card[key], `$.manifest.cards[${index}].${key}`);
     }
     if (!Array.isArray(card.metrics) || !card.metrics.length) {
@@ -1455,6 +1566,7 @@ function validateArtifactManifest(manifest, surface) {
       }
       validateIdentifier(column.field, `$.manifest.tables[${index}].columns[${columnIndex}].field`);
     });
+    validateArtifactTableDefaultSort(table, `$.manifest.tables[${index}]`);
   });
 
   validateArtifactBlockManifestShape(manifest, surface);
@@ -1597,6 +1709,19 @@ function validateArtifactChartDataCompatibility(manifest, snapshot) {
 }
 
 function validateArtifactSourceQueries(manifest, sources) {
+  const cardIds = new Set(
+    asList(manifest.blocks)
+      .filter((block) => isPlainObject(block) && block.type === "metric-strip")
+      .flatMap((block) => asList(block.cardIds))
+      .filter((cardId) => typeof cardId === "string"),
+  );
+  asList(manifest.cards).forEach((card, index) => {
+    if (!isPlainObject(card) || !cardIds.has(card.id)) return;
+    const cardSource = sourceForArtifactItem(card, sources);
+    validateActualSqlSource(cardSource, `$.manifest.cards[${index}].source`, {
+      allowSqlFile: true,
+    });
+  });
   const chartBlocks = new Set(
     asList(manifest.blocks)
       .filter((block) => isPlainObject(block) && block.type === "chart" && typeof block.chartId === "string")
@@ -2693,7 +2818,6 @@ function chartSpecFromVisualizationSpec(spec, args, resultTable, id = "chart") {
   const lineStyleField = encodingField(spec, "lineStyle");
   const xAxisTitle = axisTitleForField(spec, args, resultTable, "x", xField);
   const yAxisTitle = axisTitleForField(spec, args, resultTable, "y", yField);
-  const horizontal = isHorizontalBarType(spec.visualization_type, spec.settings);
   const encodings = {};
   encodings.x = chartEncodingForField(spec, resultTable, "x", xField);
   if (usesMultiMeasureSeries(spec)) {
@@ -2728,8 +2852,8 @@ function chartSpecFromVisualizationSpec(spec, args, resultTable, id = "chart") {
     type: spec.visualization_type,
     dataset: String(firstValue(args.id, args.label, "default")),
     ...(Object.keys(encodings).length ? { encodings } : {}),
-    xAxisTitle: horizontal ? yAxisTitle : xAxisTitle,
-    yAxisTitle: horizontal ? xAxisTitle : yAxisTitle,
+    xAxisTitle,
+    yAxisTitle,
     unit: presentationValue(spec, args, "unit"),
     valueFormat: columnValueFormat(resultTable, yField) || undefined,
     settings: spec.settings,
@@ -2860,7 +2984,7 @@ async function handleRpc(message) {
         },
         serverInfo: {
           name: SERVER_NAME,
-          title: "Data Analytics Widgets",
+          title: "Data Analytics",
           version: SERVER_VERSION,
           description: "Render Data Analytics charts, tables, dashboards, and report artifacts.",
           icons: DATA_ANALYTICS_ICONS,
diff --git a/plugins/data-analytics/package-lock.json b/plugins/data-analytics/package-lock.json
index 6296871..e35a71e 100644
--- a/plugins/data-analytics/package-lock.json
+++ b/plugins/data-analytics/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "datascience-mcp-widgets",
-  "version": "0.1.34",
+  "version": "0.2.6",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "datascience-mcp-widgets",
-      "version": "0.1.34",
+      "version": "0.2.6",
       "dependencies": {
         "@lexical/history": "^0.44.0",
         "@lexical/link": "^0.44.0",
diff --git a/plugins/data-analytics/package.json b/plugins/data-analytics/package.json
index 046148f..0a2a6fb 100644
--- a/plugins/data-analytics/package.json
+++ b/plugins/data-analytics/package.json
@@ -1,13 +1,14 @@
 {
   "name": "datascience-mcp-widgets",
-  "version": "0.1.34",
+  "version": "0.2.6",
   "type": "module",
   "private": true,
   "scripts": {
-    "build": "npm run build:chart && npm run build:table && npm run build:artifact && npm run normalize:assets",
+    "build": "npm run build:chart && npm run build:table && npm run build:artifact && npm run build:html-runtime && npm run normalize:assets",
     "build:artifact": "cross-env INPUT=datascience-artifact-widget.html vite build",
     "build:chart": "cross-env INPUT=datascience-chart-widget.html vite build",
     "build:table": "cross-env INPUT=datascience-table-widget.html vite build",
+    "build:html-runtime": "cross-env INPUT=html-report-runtime.html vite build",
     "dev": "concurrently \"npm:dev:chart\" \"npm:dev:table\" \"npm:dev:artifact\"",
     "dev:artifact": "cross-env INPUT=datascience-artifact-widget.html vite build --watch",
     "dev:chart": "cross-env INPUT=datascience-chart-widget.html vite build --watch",
diff --git a/plugins/data-analytics/scripts/normalize-widget-assets.mjs b/plugins/data-analytics/scripts/normalize-widget-assets.mjs
index aa15d2f..945a092 100644
--- a/plugins/data-analytics/scripts/normalize-widget-assets.mjs
+++ b/plugins/data-analytics/scripts/normalize-widget-assets.mjs
@@ -10,6 +10,7 @@ const widgetAssets = [
   "datascience-artifact-widget.html",
   "datascience-chart-widget.html",
   "datascience-table-widget.html",
+  "html-report-runtime.html",
 ];
 
 for (const asset of widgetAssets) {
@@ -50,7 +51,9 @@ function sanitizePublicCopy(html) {
 }
 
 function localDevRedirect(asset) {
-  const title = asset.includes("artifact")
+  const title = asset.includes("html-report")
+    ? "Data Analytics HTML report runtime"
+    : asset.includes("artifact")
     ? "Data Analytics artifact app"
     : asset.includes("table")
       ? "Data Analytics table widget"
diff --git a/plugins/data-analytics/skills/analyze-data-quality/SKILL.md b/plugins/data-analytics/skills/analyze-data-quality/SKILL.md
index b98585c..f562326 100644
--- a/plugins/data-analytics/skills/analyze-data-quality/SKILL.md
+++ b/plugins/data-analytics/skills/analyze-data-quality/SKILL.md
@@ -1,18 +1,18 @@
 ---
 name: analyze-data-quality
-description: "Assess whether tables, query results, files, or dataframes are trustworthy enough for analysis, modeling, dashboards, experiments, or pipelines. Use for grain, freshness, nulls, duplicates, schema drift, broken joins, referential integrity, distribution shifts, leakage, backfills, source mismatches, automated quality checks, and data-quality regressions."
+description: "Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite."
 ---
 
-# Analyze Data Quality
+## Related Skills
 
-Assess whether a dataset is trustworthy enough for analysis, modeling,
-dashboards, experiments, or downstream pipelines. Start with the intended use and grain, run the highest-value checks for the data shape, and report concrete evidence, analytical risk, likely causes, and the smallest useful remediation or automated test.
+Use $design-kpis when the work is to define or redesign a KPI framework, metric definition, guardrail, or target rather than checking whether existing data is trustworthy.
 
-## Skill Configuration
+Use $validate-data when the work is to QA an analysis, chart, report, or recommendation rather than investigate the underlying data.
 
-### User Context
+# Analyze Data Quality
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Assess whether a dataset is trustworthy enough for analysis, modeling,
+dashboards, experiments, or downstream pipelines. Start with the intended use and grain, run the highest-value checks for the data shape, and report concrete evidence, analytical risk, likely causes, and the smallest useful remediation or automated test.
 
 ## Workflow
 
@@ -126,6 +126,8 @@ Do not dump raw profiling output without interpretation. Tie each finding to an
 
 ### Output Standards
 
+For stakeholder-facing data-quality work, pass the completed findings to $build-report by default so the runtime produces an MCP app or HTML report. Do not stop at profiling output, a notebook, or chat-only findings unless the user explicitly requested a quick inline answer or another primary artifact. The structure below defines the report content.
+
 Structure the response with:
 
 1. dataset and grain summary
diff --git a/plugins/data-analytics/skills/analyze-data-quality/agents/openai.yaml b/plugins/data-analytics/skills/analyze-data-quality/agents/openai.yaml
index eb761a9..aa8ec29 100644
--- a/plugins/data-analytics/skills/analyze-data-quality/agents/openai.yaml
+++ b/plugins/data-analytics/skills/analyze-data-quality/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Assess tables and datasets before analysis or publication"
   default_prompt: "Assess a dataset or table for quality risks before I rely on it for analysis or reporting."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/data-analytics/skills/build-dashboard/SKILL.md b/plugins/data-analytics/skills/build-dashboard/SKILL.md
index 08bf779..ac1824e 100644
--- a/plugins/data-analytics/skills/build-dashboard/SKILL.md
+++ b/plugins/data-analytics/skills/build-dashboard/SKILL.md
@@ -1,8 +1,14 @@
 ---
 name: build-dashboard
-description: "Build source-backed analytical dashboards that help teams monitor performance, explore drivers, and act on product or business metrics. Use when the user needs a dashboard, scorecard, monitoring view, BI dashboard, MCP artifact dashboard, or Streamlit dashboard with clear metrics, filters, validation, and handoff."
+description: "Build source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monitoring view with clear metrics, filters, source definitions, and QA."
 ---
 
+## Related Skills
+
+Use $create-data-context only when the dashboard work explicitly asks to save data context or create, update, inspect, or repair a semantic layer.
+
+Use $analyze-data-quality when dashboard metrics disagree or source freshness, grain, joins, or definitions could affect trust.
+
 # Dashboard Building
 
 Use this skill when the user needs a dashboard rather than a report, notebook-only analysis, spreadsheet, or transient chat summary. A good dashboard is summary-first, chart-led, scannable, and organized around what the audience needs to monitor, understand, or act on.
@@ -13,19 +19,16 @@ This skill owns the dashboard brief, delivery-mode selection, metric definitions
 
 ## Skill Configuration
 
-### User Context
+### Runtime Delivery Routing
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+If `surface = chatgpt_web` and `mode = work_mode` are both positively identified, do not select the Data Analytics MCP artifact app and do not load its dashboard specification. If `mode = work_mode` is positive and `surface` is unknown or otherwise not positively `codex_desktop`, apply the same non-MCP delivery rule for safety. Use a connected BI destination when the user selected one or it clearly owns the requested dashboard; otherwise build portable HTML. Use Streamlit only when the user explicitly asks for it. MCP servers and other callable tools remain valid data sources.
 
 ### Source Discovery And Verification
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
-
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
-
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -49,7 +52,14 @@ Use $gather-business-context when dashboard purpose, metric definitions, operati
 
 Pick the first delivery surface that fits the user's need and available access. If the user specifies the destination or surface, use that instead of the default order.
 
-1. Use a connected BI tool by default. Use the BI surface identified during onboarding or user context; if none is specified, look for an available connected BI solution before choosing another surface.
+For positively identified web Work Mode, or any positive Work Mode signal without a positive Codex desktop surface, use this order:
+
+1. Use a connected BI tool when the user selected it or it clearly owns the destination.
+2. Otherwise use HTML for the dashboard handoff.
+
+Do not choose the MCP artifact app in Work Mode without a positive Codex desktop surface, even if its tools are visible. For all other environments, use the default order below:
+
+1. Use a connected BI tool by default. Use the BI surface identified from the current request, current-run context, or explicit user preference; if none is specified, look for an available connected BI solution before choosing another surface.
 2. Use the MCP artifact app when a connected BI build is unavailable, too heavy for the request, or the user needs a compact in-Codex analytical dashboard.
 3. Use HTML when BI and MCP are not suitable and the user needs a portable static dashboard.
 
@@ -96,6 +106,8 @@ Build enough metric breadth to cover every family that is relevant to the dashbo
 
 Map the selected families into dashboard roles before building: hero metrics for the default view, diagnostic metrics for movement and breakdowns, guardrails for interpretation, and detail metrics for lookup or follow-up.
 
+Let the decision determine the number of hero metric cards. Do not pad or truncate the set to four—or any preferred count. Give each card one distinct, decision-relevant headline metric. Use chips only for short comparison context tied directly to that headline, such as its prior-period value, target, or delta; move independent secondary measures into their own cards, charts, tables, narrative, or detail views. Keep coherent cards in the same strip; odd counts are valid because the artifact renderer balances rows responsively.
+
 **Escalate when metric design is the hard part.**
 
 Invoke $design-kpis when this baseline metric-family pass is not enough, such as when the dashboard needs a deeper metric framework, target-setting, formal KPI tradeoff analysis, or clearer definitions than this workflow can safely infer. Pass the dashboard brief, business context, source context, existing metric definitions, and constraints so the recommended metrics fit the audience and use case.
@@ -130,7 +142,7 @@ Record the source or query path when it would be hard to rediscover later.
 
 ### 8. Hand Off The Dashboard
 
-Include the dashboard link or local artifact path, what validation was performed, source or access caveats, and any remaining sharing or operational steps. For MCP artifact dashboards, follow the validation and render handoff rules in `specifications/mcp-artifact-dashboard.md`.
+Include the dashboard link or local artifact path, source or access caveats, and any remaining sharing or operational steps. Keep routine check details in support artifacts. Do not list internal checks in the user-facing handoff unless a check failed, was unavailable, or produced a user-relevant caveat. For MCP artifact dashboards, follow the validation and render handoff rules in `specifications/mcp-artifact-dashboard.md`.
 
 ## Dashboard Quality Bar
 
diff --git a/plugins/data-analytics/skills/build-dashboard/specifications/html-dashboard.md b/plugins/data-analytics/skills/build-dashboard/specifications/html-dashboard.md
index 452f553..41b5558 100644
--- a/plugins/data-analytics/skills/build-dashboard/specifications/html-dashboard.md
+++ b/plugins/data-analytics/skills/build-dashboard/specifications/html-dashboard.md
@@ -11,6 +11,7 @@ Use this when the dashboard should be delivered as a portable static HTML file r
 ## Build Shape
 
 - Build a single portable HTML file when practical, with compact embedded data and no external runtime dependency unless the user asked for one.
+- For every portable HTML dashboard, read `../../visualize-data/references/recharts-html.md`, start from `../../../assets/html-report-shell.html`, and use its packaged Recharts runtime plus same-data static fallbacks. Keep cards, tables, filters, source notes, and fallback content as semantic HTML that remains readable without scripts.
 - Lead with the dashboard's primary metric context, then trends, diagnostic breakdowns, and detail tables.
 - Keep filters and interactions limited to controls that materially help the reader explore the dashboard.
 - Preserve source provenance in a visible sources or methodology section, including query links, source tables, freshness, definitions, and important filters.
@@ -20,5 +21,6 @@ Use this when the dashboard should be delivered as a portable static HTML file r
 ## Validation
 
 - Validate that the HTML opens locally, renders charts and tables, and has no obvious JavaScript errors.
+- Check both HTML states: live Recharts mounts replace their fallbacks when scripts run, and the same-data fallbacks remain readable when scripts do not run.
 - Inspect the rendered page at desktop and narrow widths for clipping, overlap, unreadable labels, and broken controls.
 - Confirm that cards, charts, and tables reconcile against the reviewed source extracts before handoff.
diff --git a/plugins/data-analytics/skills/build-dashboard/specifications/mcp-artifact-dashboard.md b/plugins/data-analytics/skills/build-dashboard/specifications/mcp-artifact-dashboard.md
index 9311c8f..826411a 100644
--- a/plugins/data-analytics/skills/build-dashboard/specifications/mcp-artifact-dashboard.md
+++ b/plugins/data-analytics/skills/build-dashboard/specifications/mcp-artifact-dashboard.md
@@ -2,6 +2,8 @@
 
 Use this when the dashboard should be rendered by the Data Analytics MCP app in Codex using `render_artifact`. This is the first-party dashboard surface for bounded, reviewed dashboard payloads that should stay inside the Codex/MCP Apps handoff instead of being created in an external BI platform.
 
+Do not select this specification when `surface = chatgpt_web` and `mode = work_mode` are both positively identified. Also do not select it when `mode = work_mode` is positive and `surface` is unknown or otherwise not positively `codex_desktop`. Use the HTML dashboard specification or a user-selected connected BI destination instead.
+
 ## When To Use
 
 - The user wants an in-Codex dashboard, quick analytical artifact, prototype, or source-backed dashboard readout without creating a Tableau, Databricks, Looker, Power BI, or other BI-platform asset.
@@ -17,12 +19,14 @@ Prefer a BI platform dashboard for broadly shared production dashboards, third-p
 - Follow the current `render_artifact` and `validate_artifact` MCP chart schema instead of duplicating chart-field rules here. Validate the manifest before rendering.
 - Validate the complete manifest and snapshot first with `validate_artifact`, fix validator errors there, and make only one visible `render_artifact` call after validation succeeds. If visible render fails after validation, record the blocker instead of repeated visible retries.
 - Default to built-in artifact blocks for dashboards: `metric-strip`, `chart`, `table`, and `markdown`. Use these native blocks for ordinary KPI strips, trends, bars, rankings, tables, caveats, source notes, and dashboard structure even when custom HTML would be faster to hand-author.
+- Every card referenced by a `metric-strip` must attach canonical provenance through inline `source` or `sourceId`, just like native charts and tables, so its data-source action always opens a complete query and evidence view.
 - Pair it with a compact bounded snapshot containing reviewed aggregate datasets. Avoid row-level payloads unless a small detail table is essential. Use the canonical snapshot shape: `snapshot.datasets` is an object keyed by dataset id, and each value is a plain array of reviewed row objects. Do not put `{columns, rows}` table objects inside `snapshot.datasets`; keep column metadata in `manifest.tables[].columns`.
 - If a required source is blocked by permissions, set the snapshot status to `partial` or `blocked`, populate `snapshot.accessIssues`, put a clean access notice above the dashboard header, and state the exact missing role/table. Do not bury the blocker inside a chart card.
 - Keep the shared hierarchy: hero metrics first, then trend, then diagnosis, then detail.
 - Render the editable dashboard title from `manifest.title`, the last-refresh date, and a Refresh action in the top bar. Treat the top-bar title as canonical.
 - Keep snapshot freshness quiet in the top bar. Render a compact status pill only when the snapshot is fixture, partial, or blocked.
-- KPI cards render as individual cards from one `metrics[]` list. The first metric renders as the large value; later metrics render as labeled secondary badges. Each metric declares `label`, `field`, optional `format`, and `signed: true` for signed changes.
+- KPI cards render as individual cards from one `metrics[]` list. The first metric is the card's only headline and renders as the large value. Later metrics may render as concise badges only when they compare that same headline with its prior period, target, or delta. Each metric declares `label`, `field`, optional `format`, and `signed: true` for signed changes.
+- Choose all and only decision-relevant hero metrics. Do not pad or truncate a strip to reach four cards—or any preferred count. Each card must answer one distinct monitoring or decision question; use supporting badges only for direct comparison context, and split independent secondary metrics into their own cards, charts, narrative, semantic strips, or a detail table. Keep card labels and badge labels concise enough to remain on one line. Odd card counts are valid; the renderer balances rows responsively.
 - When a metric label is not self-defining, include a card description or nearby markdown that explains the metric in reader terms, and include the exact calculation in `source.query.metric_definitions`.
 - Back every chart with a dataset that remains useful beyond the visible chart encoding when safe reviewed context is available. Retain useful dimensions, time/cohort fields, candidate grouping columns, numerators, denominators, benchmarks, ranks, comparison-period values, and adjacent measures so the expanded table supports inspection, filtering, and realistic chart switching.
 - Percent values rendered with `format: "percent"` or `valueFormat: "percent"` must use the same numeric scale consistently in the provided data. Use decimal rates for computed numeric fields, such as `0.149` for `14.9%`, or pass preformatted reader-facing strings where exact display text matters.
@@ -61,7 +65,7 @@ Prefer a BI platform dashboard for broadly shared production dashboards, third-p
 - Use full-space blue-shade cell heatmaps when a dense row-by-column comparison is needed: right-aligned row labels on the left, adaptive column labels below the grid, and hover tooltips for exact values.
 - For complex custom chart types such as heatmaps, leaderboards, funnels, waterfalls, and box plots, let marks stretch when there are only a few rows; when marks would fall below their readable minimum, grow the chart block height instead of introducing vertical scrolling inside the chart body.
 - Let Recharts v3 auto-size Y-axis gutters from formatted visible tick labels with `width="auto"`; use explicit nice ticks when the data domain needs stable labels.
-- When a non-stacked multi-series chart omits explicit colors, use the fallback order blue, purple, green, neutral, orange, yellow, pink, red. Stacked bars use the generated app's blue/purple token-shade fallback set.
+- When a non-stacked multi-series chart omits explicit colors, use the runtime fallback order blue, orange, green, purple, red, pink, yellow, neutral. Stacked bars use the generated app's blue/purple token-shade fallback set.
 - Disable chart intro animation when deterministic screenshots or hosted previews matter.
 
 ## Handoff
diff --git a/plugins/data-analytics/skills/build-report/SKILL.md b/plugins/data-analytics/skills/build-report/SKILL.md
index b30dad5..932d3f3 100644
--- a/plugins/data-analytics/skills/build-report/SKILL.md
+++ b/plugins/data-analytics/skills/build-report/SKILL.md
@@ -1,26 +1,35 @@
 ---
 name: build-report
-description: "Build polished analytical reports for executive, product, business, and technical audiences, and act as the completion contract for Data Analytics report runs. Use when the final artifact needs an answer-first narrative, evidence-backed findings, charts/tables, caveats, source metadata, and either an MCP app report or an HTML report with Seaborn-generated charts."
+description: "Build polished analytical reports for executive, product, business, or technical audiences. Use when the task needs a durable answer-first narrative with evidence-backed findings, visuals or tables, caveats, and source context."
 ---
 
+## Related Skills
+
+Use the focused analysis skill before building the report when the report depends on market sizing, metric diagnostics, KPI reporting, product/business analysis, data-quality checks, or validation.
+
 # Report Building
 
 Use this skill when the user needs a durable analytical report rather than a dashboard, notebook-only dump, or transient chat summary. The report owns the reader-facing narrative, audience shape, evidence placement, visual/table placement, caveats, source metadata, and handoff. The underlying analysis should still come from the appropriate analysis, notebook, data quality, diagnostics, KPI, or product-analysis workflow.
 
-If this skill is selected directly or included by a report-mode workflow, the run is incomplete until the selected report surface exists or a concrete blocker is recorded. Do not finalize with chat-only prose, an inline widget, a local URL, or an ad hoc artifact that skips the report shape. Treat inline summaries and notebook outputs as progress evidence, not as substitutes for the report. Once this skill is selected, reserve charts, tables, and previews for the selected report surface. The absence of the word "report" is not a waiver when an upstream workflow has already classified the task as report mode or when the requested deliverable is a durable analytical answer.
+If this skill is selected directly or included by a report-mode workflow, the run is incomplete until the selected report surface exists or a concrete blocker is recorded. Do not finalize with chat-only prose, an inline widget, a local URL, or an ad hoc artifact that skips the report shape. Treat inline summaries and notebook outputs as progress evidence, not as substitutes for the report. Once this skill is selected, reserve charts, tables, and previews for the selected report surface. A user can explicitly waive report creation by requesting an inline, chat-only, brief/no-artifact answer, asking for no report/file/artifact, or selecting another primary artifact. Do not infer a waiver from the absence of the word "report" or from a direct diagnostic, recommendation, sizing, or readout question.
 
 ## Skill Configuration
 
-### User Context
+Choose exactly one report delivery mode for each run:
+
+- Codex desktop: use `mcp-app` by default.
+- Positively identified ChatGPT web Work Mode: use `html`; MCP app report rendering does not work on that surface. MCP servers and other callable tools remain valid evidence sources.
+- Work Mode signal with unknown or non-desktop surface: use `html`; do not treat an unknown surface classification as permission to render an MCP app report. Only a positive `surface = codex_desktop` signal may take the Codex desktop MCP default.
+- Any other runtime where MCP app report rendering is unavailable: use `html`.
+- Unknown or unclassified runtime: default to portable `html` rather than omitting the report.
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+On Codex desktop, use `html` only when the user explicitly asks for HTML, offline portability, a file-based artifact, or no MCP rendering; a requested downstream conversion requires HTML; or an MCP app report was actually attempted and failed because the capability was unavailable or a documented renderer limit was exceeded.
 
-Choose exactly one report delivery mode for each run:
+If the selected MCP app report cannot be rendered after one targeted correction, fall back to `html` in the same run unless the user explicitly declined HTML. A renderer failure changes the delivery mode; it does not waive the report. If neither surface can be created, record the concrete blocker and the attempted surfaces before finalizing.
 
-- `mcp-app`: a Data Analytics MCP artifact app report. This is the default for safe, bounded in-Codex reports.
-- `html`: a portable static HTML report. Use this when the user asks for HTML, file/blob sharing, Docs/Slides conversion, offline portability, no MCP app/widget rendering, or when the MCP artifact renderer is unavailable, unsafe, or over bounds.
+If an MCP app report was already rendered and the current context later identifies Work Mode without a positive Codex desktop surface, treat the MCP artifact as the wrong delivery mode. Rebuild the report as self-contained HTML before final handoff instead of explaining that the MCP artifact is acceptable.
 
-Select exactly one delivery mode per run. Do not build an MCP app report and a static `report.html` as parallel outputs unless the user explicitly asks for a second delivery mode as a separate follow-up. For HTML reports, charts must be static PNG images generated from the Seaborn template workflow and embedded in the HTML. Do not replace those charts with interactive, app-specific, inline SVG, or hand-authored HTML/CSS chart primitives.
+Select exactly one delivery mode per run. Do not build an MCP app report and a static `report.html` as parallel outputs unless the user explicitly asks for a second delivery mode as a separate follow-up. For every selected HTML report path, including Codex desktop explicit HTML, ChatGPT web Work Mode, and HTML created for PDF, Google Docs, or Google Slides conversion, use the precompiled Recharts-in-HTML path in [recharts-html.md](../visualize-data/references/recharts-html.md), with a readable same-data static fallback in the delivered HTML.
 
 ## Workflow
 
@@ -46,6 +55,8 @@ Select exactly one delivery mode per run. Do not build an MCP app report and a s
 
    Inventory the source data, metric definitions, denominators, assumptions, requested cuts, caveats, notebooks, SQL, scripts, query permalinks, source documents, and reviewed datasets needed to support the report. Resolve ambiguities before drafting claims. If a requested metric or cut cannot be supported, record why and state what evidence would be needed to add it. Preserve process notes, source inventory, and reproducibility notes in source metadata, source notes, or supporting artifacts, not in the visible report body.
 
+   When this skill is selected directly and the user explicitly asks to "use sample data" or create a report "using the sample data" without naming or attaching a different sample, treat that wording as selection of the bundled synthetic demo. Resolve [demo-product-growth.csv](../../assets/demo-product-growth.csv) relative to this skill, analyze those rows, label them synthetic, and do not search the workspace for a substitute or invent replacement data.
+
 4. Distill the report spine.
 
    Before choosing a delivery surface, write or mentally verify a compact answer-first report spine with these entries:
@@ -110,7 +121,7 @@ Select exactly one delivery mode per run. Do not build an MCP app report and a s
 
 10. Hand off the selected report.
 
-   Lead with the selected report artifact result or blocker. Then list only the relevant MCP app artifact or HTML report path, plus supporting source, SQL, code, notebook, and chart artifacts. Self-audit the report against the quality bar before handoff. If HTML sharing or conversion is needed and safe, resolve the presentation surface; otherwise record why sharing was unsafe, unavailable, or explicitly waived. Do not substitute a chat summary for the report.
+   Lead with the selected report artifact result or blocker. Then list only the relevant MCP app artifact or HTML report path, plus supporting source, SQL, code, notebook, and chart artifacts. In HTML mode, the actual interactive `report.html` file is the primary deliverable. A rendered PNG or screenshot may be included only as a labeled non-interactive QA preview; never use `image.png`, a screenshot, or another flattened image as the only report handoff. If the host cannot render HTML inline, attach or link the `.html` file instead of flattening it. Self-audit the report against the quality bar before handoff. If HTML sharing or conversion is needed and safe, resolve the presentation surface; otherwise record why sharing was unsafe, unavailable, or explicitly waived. Do not substitute a chat summary for the report.
 
 ## Report Standards
 
@@ -124,11 +135,16 @@ Read [mcp-app-report.md](specifications/mcp-app-report.md) when the selected rep
 
 ### HTML Report Specifications
 
-- Use HTML mode when the user asks for HTML, file/blob sharing, Docs/Slides conversion, offline portability, or when the MCP artifact renderer is unavailable, unsafe, or over bounds.
-- Preserve the shell templates' `data-contract-section` attributes from `assets/executive-report-shell.html` or `assets/technical-report-shell.html`.
-- Generate `report.html` plus PNG chart assets from the Seaborn template workflow. HTML report charts use the Seaborn template workflow and embed generated PNG files; SVG may remain as a supporting source artifact, but the delivered HTML should embed PNG charts. Inspect those image assets before delivery.
+- Use HTML mode only under the delivery rule in `Skill Configuration`; on Codex desktop, do not choose it before an MCP attempt unless the user or downstream conversion explicitly requires HTML.
+- For every HTML report path, read [recharts-html.md](../visualize-data/references/recharts-html.md), start from `../../assets/html-report-shell.html`, and preserve or adapt its report `data-contract-section` attributes to the selected audience specification. Use its packaged Recharts runtime for live charts and keep same-data static SVG/table fallbacks directly in the HTML.
+- Preserve the shell's automatic system appearance support in every HTML report: keep `<meta name="color-scheme" content="light dark">`, semantic color tokens, and the `@media (prefers-color-scheme: dark)` token overrides. Verify report content, charts, fallbacks, source tooltips, and focus states in both light and dark system modes; do not replace this behavior with a manual-only theme toggle.
 - Keep the HTML reading path report-like: answer first, visible segment titles, evidence and interpretation together, caveats where they matter, and source metadata preserved without appending a visible sources, notes, or reproducibility appendix unless the user explicitly asks for it.
-- Before handoff, inspect `report.html` and the generated PNG chart assets. Use `report-to-google-doc` or `report-to-google-slides` only with HTML reports, not live MCP app reports.
+- Give every source-backed quantitative value in an HTML report a directly associated source tooltip. This includes headline and KPI values, inline numeric claims, table values, and other displayed numbers derived from retrieved data. The tooltip must identify `Source: <source system, connector, or artifact>` and, for structured data, `Table: <fully qualified table or view>`. For a value derived from joins or multiple inputs, list every material contributing source and table. For non-tabular evidence, replace `Table` with the precise `Dataset`, `File`, or `Document` name. Never invent provenance; mark an unavailable table or source explicitly and preserve the gap in source notes.
+- Implement HTML source tooltips as real hover and keyboard-focus affordances using the shell's `source-tooltip` pattern, not only a native `title` attribute. Keep the number readable without interaction and the tooltip text concise. Associate each tooltip with the number it supports; a shared tooltip is acceptable only when it unambiguously covers every number in the containing group and all share identical provenance.
+- Use the same shared `source-tooltip-content` style for narrative values, KPI and metric-card values, table values, and chart figures. Do not create card-, narrative-, table-, or chart-specific tooltip variants. Foreground, background, typography, padding, radius, and shadow must match, and tooltip text must explicitly use the shared foreground color instead of inheriting a muted or emphasized color from its surrounding value.
+- Live Recharts charts and their same-data static fallbacks cannot expose source provenance for every mark through HTML alone. Give each chart figure an accessible source tooltip that lists every material source and fully qualified table used for the plotted values, but put the hover and focus trigger on the visible `Source` affordance only, not on the whole figure or chart image. Keep query text, credentials, request ids, and local paths out of reader-facing tooltips.
+- Add a visible source affordance to chart figures so the tooltip is discoverable; do not rely on hovering an unmarked image. Keep the dotted underline or equivalent visible affordance on sourced inline numbers.
+- Before handoff, inspect the generated `report.html` itself, not only the shell or a screenshot. Verify live Recharts mounts render, same-data fallbacks stay readable when scripts do not run, and the delivered file has no sibling chart-image, local-runtime, or remote-script dependency. Verify that every source-backed visible number is wrapped by `source-tooltip`, each tooltip contains real non-placeholder provenance, and every chart figure has a visible source affordance plus tooltip content whose hover/focus target is the `Source` affordance rather than the whole chart image. Compare representative inline, card, table, and chart tooltips and confirm they use the same computed foreground, background, typography, padding, radius, and shadow. Do not flatten the HTML into an image as the report deliverable. Use `report-to-google-doc` or `report-to-google-slides` only with HTML reports, not live MCP app reports.
 
 ### Narrative
 
@@ -140,7 +156,7 @@ Read [mcp-app-report.md](specifications/mcp-app-report.md) when the selected rep
 - Start major report segments with the takeaway, not setup prose. Use headings that carry the insight rather than copying generic labels.
 - Include visible titles for every major report segment in every delivery mode. Titles are part of the reader-facing report contract, not just private metadata. In HTML reports, render titles as heading elements inside sections. For MCP app reports, follow [mcp-app-report.md](specifications/mcp-app-report.md). Use story-specific titles such as "API ARR is pulling ahead of plan" rather than generic labels when the evidence supports it.
 - Keep an obvious top-to-bottom report reading path. Do not turn a report into a dashboard grid unless the user asked for a dashboard. Use a single-column document flow by default; half-width visual blocks are acceptable only when labels, series, and interpretation remain readable in the selected surface.
-- Use report markdown blocks for narrative insights and interpretation. Keep chart/table headers neutral, and put takeaways in adjacent narrative blocks rather than hiding the argument inside visual metadata. Markdown blocks are the primary place for the story.
+- Use report markdown blocks for narrative insights and interpretation. Give each independently laid out or editable major report segment its own markdown block; do not place multiple peer `##` section headings in one markdown body. Use `###` headings only for subordinate content that should remain in the same card. Keep chart/table headers neutral, and put takeaways in adjacent narrative blocks rather than hiding the argument inside visual metadata. Markdown blocks are the primary place for the story.
 - Reports should be substantial enough to stand on their own. Do not deliver a report that is mostly a chart/table bundle with a short summary. The reader should be able to understand the answer, evidence, interpretation, caveats, and recommended next step from the narrative itself.
 - Use flexible section names and story-specific ordering, but make sure the report still does the core jobs: answer the user's question directly; explain the metric, cohort, denominator, time window, and comparison basis when they affect interpretation; pair each important number or visual with plain-English interpretation; explain why the finding matters for the decision; state what remains uncertain or could change the conclusion; and include a practical next step when the evidence supports one.
 - Use rich markdown beyond the executive summary when it improves scanning:
@@ -149,7 +165,8 @@ Read [mcp-app-report.md](specifications/mcp-app-report.md) when the selected rep
 - For executive, strategic, and diagnostic reports, the first narrative content after the title should be the executive summary. Do not insert an unlabeled lede, duplicate summary, methodology block, caveat section, KPI strip, or metric card row before the summary.
 - For diagnostic and strategy reports, do not insert a default KPI-card row before the executive summary. KPI, portfolio, MBR/QBR, and one-section-per area reports may use a compact KPI-card strip after the summary when headline actuals, deltas, and status signals are available.
 - Do not use a broad comparison table directly beneath an executive summary as the primary summary artifact for KPI, portfolio, or multi-area reports. Use metric cards for skimmable top-line status when headline actuals, deltas, and status signals are available; use tables for supporting exact values or audit detail.
-- Metric cards should put the headline value first and render supporting measures as labeled secondary context.
+- Metric cards should contain one headline value, but one headline does not mean one `metrics[]` entry. Use labeled chips for short comparison context tied directly to that headline, such as its prior-period value, target, or delta. When a decision-relevant directional comparison is already used in the executive summary or findings, preserve it as a later comparison metric and set `signed: true`; move independent secondary measures to another card, chart, table, or narrative block.
+- Let the report decision determine the number of headline metric cards. Do not pad or truncate the set to four—or any preferred count. Include only distinct, meaningful headline metrics, group coherent cards in the same strip, and split unrelated measures into another strip or supporting table; odd counts are valid because the artifact renderer balances rows responsively.
 - Percent values must use a consistent numeric scale in the selected surface.
 - Every major report segment should pair a claim with evidence and interpretation, then answer "so what" with an implication, risk, opportunity, recommendation, monitoring point, or next action.
 - Include recommended next steps when the evidence supports action. If the evidence only supports monitoring, say what should be monitored and why.
@@ -160,7 +177,8 @@ Read [mcp-app-report.md](specifications/mcp-app-report.md) when the selected rep
 
 - Base every major claim on saved data, code, query results, notebook outputs, source documents, or rendered charts created or inspected during the task.
 - Tie important numbers, charts, and recommendations to readable source metadata near the relevant paragraph or visual when the selected surface supports it.
-- Keep raw paths, notebook locations, temp directories, request IDs, table names, and implementation-facing audit details in source metadata, source notes, or supporting artifacts unless needed for trust.
+- Keep raw paths, notebook locations, temp directories, request IDs, and implementation-facing audit details in source metadata, source notes, or supporting artifacts unless needed for trust. In HTML reports, expose source-system and table names in the source tooltips required by `HTML Report Specifications`; keep them out of the always-visible narrative unless needed for trust.
+- Keep source SQL in source metadata, source modals, linked query artifacts, or supporting notes. Do not paste source SQL into the visible report body or final chat handoff unless the user explicitly asks to see, write, review, debug, or receive SQL or query methodology.
 - Do not add a visible bottom sources, notes, or reproducibility section unless the user explicitly asks for that methods layer.
 - Preserve source datasets, query or transformation artifacts, rendering code, output folders, assumptions, omitted metrics, and chart QA notes in source metadata, source notes, or supporting artifacts even when they are not visible in the report body.
 - Surface enough evidence affordance for audit without creating a heavy visible claim graph. Prefer block-level source references when the surface supports them.
@@ -171,9 +189,9 @@ Read [mcp-app-report.md](specifications/mcp-app-report.md) when the selected rep
 - Every report visualization must have an adjacent explanatory paragraph immediately before or after it in the reading flow. The paragraph should state the takeaway, how to read the visual, and the implication or caveat needed to interpret it; chart titles, subtitles, captions, metric cards, or table headers do not satisfy this requirement by themselves.
 - Chart and table subtitles should add a reader-facing insight or interpretive context that the title does not already cover. Prefer direction, magnitude, inflection, rank, comparison basis, scope, time period, unit of measurement, filters/exclusions, denominator, cohort, sample size, or basis of comparison, such as `Growth accelerated after mid-April and ended at a period high` or `FY2021-FY2024, values in millions of USD`. Keep descriptions and subtitles hidden by default unless `showDescription` or an equivalent flag is needed to clarify the reading path.
 - Prefer human-readable dates in visible markdown, chart titles/subtitles, captions, table labels, and generated-date chrome. Keep ISO timestamps in query bindings, source metadata, and other machine-readable fields.
-- Do not use source table names, raw query ids, dashboard slugs, notebook paths, SQL intent, metric definitions, or implementation labels as chart/table subtitles. Put those details in source metadata, source notes, or supporting artifacts unless the source identity is itself essential for reader trust.
+- Do not use source table names, raw query ids, dashboard slugs, notebook paths, SQL intent, metric definitions, or implementation labels as chart/table subtitles. Put those details in source metadata, source notes, or supporting artifacts unless the source identity is itself essential for reader trust. For HTML reports, source-system and table names must also appear in the related number or chart source tooltips, where they remain available without cluttering the subtitle.
 - Before rendering report tables, check every table subtitle against the table title and visible columns. If it does not clarify what rows are in scope, what period is covered, how values are measured, which filters apply, or what comparison is being made, rewrite it or omit it rather than falling back to the source name.
-- Do not shrink visuals into unreadable sidecards. Use full-width blocks for dense charts, long labels, or HTML PNG images that need more room.
+- Do not shrink visuals into unreadable sidecards. Use full-width blocks for dense charts, long labels, or HTML chart fallbacks that need more room.
 - Put report charts on their own readable row or block in the reading flow unless a half-width layout has been checked in final context and remains legible. Do not use narrow right-rail chart cards for report evidence.
 - Report trend charts need enough observed data to make the shape worth reading. When a report chart would show only a few anchor periods, route back through `$visualize-data` to request a finer grain, longer lookback, or meaningful segment breakdown before rendering. If the source cannot support that without misleading the reader, replace the trend with a KPI strip, grouped period bars, table, or concise narrative comparison and record the omitted richer trend in source notes or supporting artifacts.
 - Report tables default to spacious density for low-row, narrative, presentation-ready tables. Use dense tables only for audit/detail tables or compact lookup data.
@@ -203,5 +221,6 @@ Before final handoff, confirm:
 - definitions, denominators, baselines, requested metrics, and caveats are clarified before final claims are treated as decision-ready; omitted metrics have a reason and needed follow-up
 - methodology depth fits the selected audience, and omitted requested metrics are explained with the reason and needed follow-up
 - visuals and tables were routed through `$visualize-data`, inspected in the selected surface, and remain readable with useful subtitles, adjacent explanatory paragraphs for every visualization, source metadata, and supporting notes
+- every source-backed quantitative value in an HTML report has a working hover and keyboard-focus source tooltip, and every static chart figure has an equivalent tooltip, with exact source-system and table, view, dataset, file, or document provenance
 - sources and reproducibility notes, chart maps, and omission reasons are preserved in metadata, source notes, or supporting artifacts without cluttering the visible report
 - the final handoff lists the selected report artifact plus relevant supporting evidence, not a chat-summary substitute or an unrequested parallel app plus HTML report
diff --git a/plugins/data-analytics/skills/build-report/assets/executive-report-shell.html b/plugins/data-analytics/skills/build-report/assets/executive-report-shell.html
deleted file mode 100644
index 081a760..0000000
--- a/plugins/data-analytics/skills/build-report/assets/executive-report-shell.html
+++ /dev/null
@@ -1,70 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-  <meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>{{TITLE}}</title>
-  <style>
-    body { font-family: ui-sans-serif, system-ui, sans-serif; margin: 0; background: #faf8f5; color: #171411; }
-    main { max-width: 880px; margin: 0 auto; padding: 40px 20px 64px; }
-    header, section { margin-bottom: 32px; }
-    h1, h2 { line-height: 1.15; margin: 0 0 12px; }
-    p, li { line-height: 1.6; }
-    figure { margin: 20px 0; }
-    figcaption { color: #5f5750; font-size: 14px; }
-    .callout { padding: 16px 18px; background: #f2ede6; border-radius: 12px; }
-    .executive-summary-box {
-      padding: 20px 22px;
-      background: linear-gradient(180deg, #f6f0e8 0%, #efe7dc 100%);
-      border: 1px solid #ddd1c2;
-      border-radius: 18px;
-      box-shadow: 0 12px 28px rgba(23, 20, 17, 0.06);
-    }
-    .executive-summary-box ul { margin: 0; padding-left: 22px; }
-    .executive-summary-box li + li { margin-top: 10px; }
-  </style>
-</head>
-<body>
-  <main data-report-audience="product stakeholders">
-    <header data-contract-section="title">
-      <h1>{{TITLE}}</h1>
-    </header>
-
-    <section class="executive-summary-box" data-contract-section="executive-summary">
-      <h2>Executive Summary</h2>
-      <ul>
-        <li><strong>{{Summary bullet 1}}</strong> [1]</li>
-        <li><strong>{{Summary bullet 2}}</strong> [2]</li>
-        <li><strong>{{Summary bullet 3}}</strong> [3]</li>
-      </ul>
-    </section>
-
-    <section data-contract-section="key-findings">
-      <h2>{{Insight-led findings heading}}</h2>
-      <p>{{Claim first, then evidence and interpretation}} [1]</p>
-      <figure>
-        {{Full-width chart or table}}
-        <figcaption>{{Short evidence note or caption}} [1]</figcaption>
-      </figure>
-    </section>
-
-    <section data-contract-section="recommended-next-steps">
-      <h2>Recommended Next Steps</h2>
-      <p>{{Actionable next step grounded in the findings}} [2]</p>
-    </section>
-
-    <section data-contract-section="further-questions">
-      <h2>Further Questions</h2>
-      <p>{{Important unresolved follow-up or narrowing question}} [3]</p>
-    </section>
-
-    <section data-contract-section="caveats-and-assumptions">
-      <h2>Caveats and Assumptions</h2>
-      <div class="callout">
-        <p>{{Only caveats that materially change interpretation}} [1]</p>
-      </div>
-    </section>
-
-  </main>
-</body>
-</html>
diff --git a/plugins/data-analytics/skills/build-report/assets/technical-report-shell.html b/plugins/data-analytics/skills/build-report/assets/technical-report-shell.html
deleted file mode 100644
index 112afb8..0000000
--- a/plugins/data-analytics/skills/build-report/assets/technical-report-shell.html
+++ /dev/null
@@ -1,66 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-  <meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>{{TITLE}}</title>
-  <style>
-    body { font-family: ui-sans-serif, system-ui, sans-serif; margin: 0; background: #f8fafc; color: #0f172a; }
-    main { max-width: 920px; margin: 0 auto; padding: 40px 20px 64px; }
-    header, section { margin-bottom: 32px; }
-    h1, h2 { line-height: 1.15; margin: 0 0 12px; }
-    p, li { line-height: 1.65; }
-    pre { overflow-x: auto; background: #e2e8f0; padding: 14px; border-radius: 12px; }
-    figure { margin: 20px 0; }
-    figcaption { color: #475569; font-size: 14px; }
-  </style>
-</head>
-<body>
-  <main data-report-audience="technical">
-    <header data-contract-section="title">
-      <h1>{{TITLE}}</h1>
-    </header>
-
-    <section data-contract-section="technical-summary">
-      <h2>Technical Summary</h2>
-      <p>{{Main result directly, with the most important conditions}} [1]</p>
-    </section>
-
-    <section data-contract-section="key-findings">
-      <h2>{{Insight-led findings heading}}</h2>
-      <p>{{Claim first, then evidence and interpretation}} [1]</p>
-      <figure>
-        {{Full-width chart or table}}
-        <figcaption>{{Short evidence note or caption}} [1]</figcaption>
-      </figure>
-    </section>
-
-    <section data-contract-section="scope-data-and-metric-definitions">
-      <h2>Scope, Data, and Metric Definitions</h2>
-      <p>{{Scope, tables, definitions, filters, and grain}} [1]</p>
-    </section>
-
-    <section data-contract-section="methodology">
-      <h2>Methodology</h2>
-      <p>{{Method summary, validation approach, or modeling logic}} [2]</p>
-      <pre>{{Optional formula, pseudo-code, or query excerpt}}</pre>
-    </section>
-
-    <section data-contract-section="limitations-uncertainty-and-robustness-checks">
-      <h2>Limitations, Uncertainty, and Robustness Checks</h2>
-      <p>{{Known limitations and the checks that matter for trust}} [2]</p>
-    </section>
-
-    <section data-contract-section="recommended-next-steps">
-      <h2>Recommended Next Steps</h2>
-      <p>{{Actionable next step grounded in the results}} [3]</p>
-    </section>
-
-    <section data-contract-section="further-questions">
-      <h2>Further Questions</h2>
-      <p>{{Important unresolved follow-up question}} [3]</p>
-    </section>
-
-  </main>
-</body>
-</html>
diff --git a/plugins/data-analytics/skills/build-report/report-to-google-doc/SKILL.md b/plugins/data-analytics/skills/build-report/report-to-google-doc/SKILL.md
index 017a1d4..2070bda 100644
--- a/plugins/data-analytics/skills/build-report/report-to-google-doc/SKILL.md
+++ b/plugins/data-analytics/skills/build-report/report-to-google-doc/SKILL.md
@@ -10,12 +10,6 @@ HTML mode. This skill does not convert a live MCP app report directly.
 
 The expected path is HTML -> DOCX -> Drive upload. It is acceptable for Drive to host the upload as a DOCX-backed viewer file rather than a native Google Docs MIME type. Do not use the old Google Docs batch-update request path.
 
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ## Workflow
 
 1. Resolve the HTML report.
@@ -67,7 +61,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 
 6. Hand off the link.
 
-   Return the Drive/Docs URL, local DOCX path, source HTML path, and validation performed. Connector success alone is not enough; the handoff is complete only after the uploaded file and local DOCX structure have been checked against the source report.
+   Return the Drive/Docs URL and, when useful, the local DOCX path or source HTML path. Connector success alone is not enough; the handoff is complete only after the uploaded file and local DOCX structure have been checked against the source report. Keep routine check and preflight details in support artifacts. Do not list internal checks in the user-facing handoff unless a check failed, was unavailable, or produced a user-relevant caveat.
 
 ## Standards
 
@@ -79,7 +73,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
   bullets/numbered lists, links, inline images, paragraph shading, table cell shading, and text styles.
 - Keep the report text column readable. Tables, charts, rendered table grids,
   screenshots, and visual blocks must not exceed the DOCX page text width.
-- Preserve charts as inline PNG images from the source visual, aligned to the same left edge as text and tables. A chart with missing bars, missing legend swatches, or all-black/all-white marks fails validation even if the DOCX contains an image object.
+- Preserve charts as inline images rendered from the source visual or its same-data SVG fallback, aligned to the same left edge as text and tables. A chart with missing bars, missing legend swatches, or all-black/all-white marks fails validation even if the DOCX contains an image object.
 - Preserve multi-column report blocks. A two-column grid made only of titled mini-tables can remain a two-column rendered image capped to the text width;
   mixed two-column blocks with narrative text, pills, callouts, or non-table panels should preserve that content natively instead of dropping it.
 - Do not expose customer-level details or sensitive links that were intentionally omitted from a sanitized report.
diff --git a/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/quality.py b/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/quality.py
index 42bfb8e..3fe5134 100644
--- a/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/quality.py
+++ b/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/quality.py
@@ -259,7 +259,7 @@ def build_preflight_checks(
                 stats = chart.get("render_preflight_stats") or inspect_png(
                     image_path,
                     expected_chart_colors(chart),
-                    exact_color_scan=chart.get("kind") == "two_col_table_grid",
+                    exact_color_scan=chart.get("kind") in {"two_col_table_grid", "svg"},
                 )
                 color_hits = stats.get("expected_color_hits", {})
                 expected_colors_present = not color_hits or any(
diff --git a/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/rendering.py b/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/rendering.py
index 5ce2c2c..a89879e 100644
--- a/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/rendering.py
+++ b/plugins/data-analytics/skills/build-report/report-to-google-doc/scripts/report_to_google_doc/rendering.py
@@ -93,8 +93,8 @@ def render_simple_svg_with_pillow(
     """Render the SVG subset produced by the report builders.
 
     This intentionally covers the report-chart primitives we commonly emit
-    (`rect`, `line`, and `text`) so conversions do not depend on visible browser
-    automation or system Cairo libraries.
+    (`rect`, `path`, `polyline`, `polygon`, `circle`, `line`, and `text`) so
+    conversions do not depend on visible browser automation or system Cairo libraries.
     """
 
     try:
@@ -109,13 +109,15 @@ def render_simple_svg_with_pillow(
     image = Image.new("RGB", (width_px * scale, height_px * scale), (255, 255, 255))
     draw = ImageDraw.Draw(image)
 
-    def scaled_points_from_path(path_data: str) -> list[tuple[float, float]]:
-        points: list[tuple[float, float]] = []
-        for match in re.finditer(
-            r"[ML]\s*(-?[0-9]+(?:\.[0-9]+)?)\s+(-?[0-9]+(?:\.[0-9]+)?)", path_data
-        ):
-            points.append((float(match.group(1)) * scale, float(match.group(2)) * scale))
-        return points
+    def scaled_points_from_attribute(points_value: str) -> list[tuple[float, float]]:
+        values = [
+            float(value)
+            for value in re.findall(r"-?[0-9]+(?:\.[0-9]+)?", points_value)
+        ]
+        return [
+            (values[index] * scale, values[index + 1] * scale)
+            for index in range(0, len(values) - 1, 2)
+        ]
 
     for el in svg.find_all(True):
         if el.name == "rect":
@@ -170,33 +172,18 @@ def scaled_points_from_path(path_data: str) -> list[tuple[float, float]]:
                 radius = width / 2
                 for x, y in [(x1, y1), (x2, y2)]:
                     draw.ellipse([x - radius, y - radius, x + radius, y + radius], fill=stroke)
-        elif el.name == "path":
-            points = scaled_points_from_path(str(el.get("d") or ""))
+        elif el.name in {"polyline", "polygon"}:
+            points = scaled_points_from_attribute(str(el.get("points") or ""))
             if len(points) < 2:
                 continue
             fill = parse_hex_color(el.get("fill"), None)
-            if fill is not None and str(el.get("d") or "").strip().endswith("Z"):
-                draw.polygon(points, fill=fill)
-            stroke = parse_hex_color(el.get("stroke"), None)
-            if stroke is not None:
-                width = max(1, round(svg_float(el.get("stroke-width"), 1) * scale))
-                draw.line(points, fill=stroke, width=width, joint="curve")
-                if el.get("stroke-linecap") == "round":
-                    radius = width / 2
-                    for x, y in (points[0], points[-1]):
-                        draw.ellipse([x - radius, y - radius, x + radius, y + radius], fill=stroke)
-        elif el.name == "circle":
-            fill = parse_hex_color(el.get("fill"), None)
+            if el.name == "polygon" and fill is not None:
+                draw.polygon(points, fill=color_with_opacity(fill, el.get("opacity")))
             stroke = parse_hex_color(el.get("stroke"), None)
-            cx = svg_float(el.get("cx")) * scale
-            cy = svg_float(el.get("cy")) * scale
-            radius = svg_float(el.get("r")) * scale
-            box = [cx - radius, cy - radius, cx + radius, cy + radius]
-            if fill is not None:
-                draw.ellipse(box, fill=fill)
             if stroke is not None:
                 width = max(1, round(svg_float(el.get("stroke-width"), 1) * scale))
-                draw.ellipse(box, outline=stroke, width=width)
+                line_points = points + [points[0]] if el.name == "polygon" else points
+                draw.line(line_points, fill=stroke, width=width, joint="curve")
         elif el.name == "text":
             text = collapse_ws(el.get_text("", strip=True)).strip()
             if not text:
diff --git a/plugins/data-analytics/skills/build-report/report-to-google-slides/SKILL.md b/plugins/data-analytics/skills/build-report/report-to-google-slides/SKILL.md
index cfa26b8..48eea54 100644
--- a/plugins/data-analytics/skills/build-report/report-to-google-slides/SKILL.md
+++ b/plugins/data-analytics/skills/build-report/report-to-google-slides/SKILL.md
@@ -10,12 +10,6 @@ caveats, and recommended follow-up without opening the source report.
 
 This skill consumes an HTML report. It does not convert a live MCP app report directly; if only an MCP app report exists, build an HTML report from the same source evidence as a separate report delivery mode first.
 
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ## Workflow
 
 1. Resolve the local HTML report.
@@ -56,7 +50,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 
 6. Hand off the deck.
 
-   Return the Google Slides URL, local PPTX path, source HTML path, and verification performed.
+   Return the Google Slides URL and, when useful, the local PPTX path or source HTML path. Keep routine check, preflight, thumbnail-inspection, and import-check details in support artifacts. Do not list internal checks in the user-facing handoff unless a check failed, was unavailable, or produced a user-relevant caveat.
 
 ## Standards
 
@@ -69,6 +63,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 - Keep gross metric reads, net/incremental reads, causal claims, and caveats visibly separated when the source report distinguishes those lenses.
 - Use editable text and native PPTX tables wherever practical. Use native PPTX bullets; do not fake bullets with typed dashes.
 - Preserve source charts as evidence images. Do not screenshot the whole HTML report or paste whole report sections.
+- Support self-contained Recharts HTML reports whose chart evidence remains available as same-data inline SVG/table fallbacks or embedded images; the helper renders those into local chart evidence images before building the deck.
 - Use one chart per evidence slide unless the report has a stronger grouped comparison.
 - Keep table slides legible. If a full table is too dense, keep the main comparison in the deck and move extra detail to speaker notes or source slides.
 - Source notes should be visible but quiet.
diff --git a/plugins/data-analytics/skills/build-report/report-to-google-slides/scripts/report_to_google_slides.py b/plugins/data-analytics/skills/build-report/report-to-google-slides/scripts/report_to_google_slides.py
index 662137a..0b5b63d 100644
--- a/plugins/data-analytics/skills/build-report/report-to-google-slides/scripts/report_to_google_slides.py
+++ b/plugins/data-analytics/skills/build-report/report-to-google-slides/scripts/report_to_google_slides.py
@@ -9,12 +9,14 @@
 from __future__ import annotations
 
 import argparse
+import base64
 import json
 import math
 import re
 import sys
 import zipfile
 from dataclasses import asdict, dataclass, field
+from io import BytesIO
 from pathlib import Path
 from typing import Any
 
@@ -115,6 +117,7 @@ class ChartItem:
     takeaway: str
     caveat: str
     svg_markup: str
+    image_src: str = ""
     css_text: str = ""
     image_path: str = ""
     width_px: int = 0
@@ -511,6 +514,52 @@ def chart_semantic_label(svg: Tag) -> str:
     return ""
 
 
+def image_semantic_label(img: Tag) -> str:
+    for attr in ("alt", "aria-label", "data-title", "title"):
+        value = collapse_ws(str(img.get(attr) or ""))
+        if value and looks_like_chart_title_candidate(value):
+            return value
+    return ""
+
+
+def image_caption_text(img: Tag) -> str:
+    figure = img if img.name == "figure" else img.find_parent("figure")
+    if not isinstance(figure, Tag):
+        return ""
+    for selector in ["figcaption", ".caption"]:
+        caption = figure.select_one(selector)
+        if isinstance(caption, Tag):
+            text = text_of(caption)
+            if text:
+                return text
+    return ""
+
+
+def data_uri_png_bytes(src: str) -> bytes | None:
+    match = re.match(
+        r"^data:image/png;base64,(.+)$",
+        src.strip(),
+        flags=re.IGNORECASE | re.DOTALL,
+    )
+    if not match:
+        return None
+    try:
+        return base64.b64decode(re.sub(r"\s+", "", match.group(1)), validate=False)
+    except Exception:
+        return None
+
+
+def embedded_png_dimensions(src: str) -> tuple[int, int]:
+    raw = data_uri_png_bytes(src)
+    if raw is None:
+        return 0, 0
+    try:
+        with Image.open(BytesIO(raw)) as image:
+            return image.size
+    except Exception:
+        return 0, 0
+
+
 def chart_title_score(text: str) -> float:
     words = re.findall(r"[A-Za-z][A-Za-z0-9_-]*", text)
     score = min(len(words), 14) * 0.8
@@ -550,6 +599,14 @@ def is_material_chart_svg(svg: Tag, width_px: int, height_px: int) -> bool:
     return width_px >= 420 and height_px >= 240 and bool(visual_marks)
 
 
+def is_material_chart_image(img: Tag, width_px: int, height_px: int) -> bool:
+    if width_px < 180 or height_px < 110:
+        return False
+    if image_semantic_label(img) or image_caption_text(img):
+        return True
+    return width_px >= 420 and height_px >= 240
+
+
 def is_chart_note_text(text: str) -> bool:
     normalized = normalized_label(text)
     note_prefixes = [
@@ -604,14 +661,19 @@ def iter_content_blocks(node: Tag) -> list[Tag]:
     if node.name in {"h2", "h3", "h4"}:
         return []
     classes = set(node.get("class") or [])
-    if node.name in {"p", "ul", "ol", "table", "svg"}:
+    if node.name in {"p", "ul", "ol", "table", "svg", "img"}:
         return [node]
     if "figure" in classes and node.find("svg"):
         return [node]
+    if (node.name == "figure" or "figure" in classes) and node.find("img"):
+        return [node]
     direct_svg = node.find("svg", recursive=False)
+    direct_img = node.find("img", recursive=False)
     direct_text_blocks = node.find(["p", "ul", "ol", "table"], recursive=False)
     if direct_svg and not direct_text_blocks:
         return [node]
+    if direct_img and not direct_text_blocks:
+        return [node]
 
     blocks: list[Tag] = []
     for child in node.children:
@@ -714,6 +776,44 @@ def add_chart_item(
     )
 
 
+def add_embedded_png_chart_item(
+    charts: list[ChartItem],
+    img_container: Tag,
+    section_title: str,
+    next_para: str,
+    prior_items: list[str],
+    parsed_img_ids: set[int],
+) -> None:
+    img = img_container if img_container.name == "img" else img_container.find("img")
+    if not isinstance(img, Tag) or id(img) in parsed_img_ids:
+        return
+    parsed_img_ids.add(id(img))
+
+    src = str(img.get("src") or "").strip()
+    if not src or data_uri_png_bytes(src) is None:
+        return
+    width_px, height_px = embedded_png_dimensions(src)
+    if not is_material_chart_image(img, width_px, height_px):
+        return
+
+    chart_title = (
+        image_semantic_label(img) or first_sentence(image_caption_text(img), 120) or section_title
+    )
+    context_text = chart_context_text(section_title, chart_title, next_para, prior_items)
+    charts.append(
+        ChartItem(
+            index=len(charts) + 1,
+            title=chart_title,
+            takeaway=chart_takeaway(chart_title, context_text),
+            caveat=context_text,
+            svg_markup="",
+            image_src=src,
+            width_px=width_px,
+            height_px=height_px,
+        )
+    )
+
+
 def parse_sections_and_charts(
     soup: BeautifulSoup,
     style_text: str,
@@ -722,6 +822,7 @@ def parse_sections_and_charts(
     charts: list[ChartItem] = []
     tables: list[TableItem] = []
     parsed_svg_ids: set[int] = set()
+    parsed_img_ids: set[int] = set()
     parsed_table_ids: set[int] = set()
 
     for heading in soup.find_all(["h2", "h3"]):
@@ -759,6 +860,15 @@ def parse_sections_and_charts(
                     style_text,
                     parsed_svg_ids,
                 )
+            elif block.name == "img" or block.find("img"):
+                add_embedded_png_chart_item(
+                    charts,
+                    block,
+                    title,
+                    following_context(blocks, block_idx),
+                    section.paragraphs + section.bullets,
+                    parsed_img_ids,
+                )
         sections[title] = section
 
     fallback_title = text_of(soup.find("h1")) or text_of(soup.title) or "Chart evidence"
@@ -775,6 +885,18 @@ def parse_sections_and_charts(
             parsed_svg_ids,
         )
 
+    for img in soup.find_all("img"):
+        if id(img) in parsed_img_ids:
+            continue
+        add_embedded_png_chart_item(
+            charts,
+            img,
+            nearest_heading_title(img, fallback_title),
+            nearby_next_context(img),
+            nearby_prior_items(img),
+            parsed_img_ids,
+        )
+
     for table in soup.find_all("table"):
         if id(table) in parsed_table_ids:
             continue
@@ -1245,6 +1367,25 @@ def render_svg_chart(chart: ChartItem, out_dir: Path) -> tuple[Path | None, dict
     }
 
 
+def render_embedded_png_chart(chart: ChartItem, out_dir: Path) -> tuple[Path | None, dict[str, Any]]:
+    asset_dir = out_dir / "assets" / "charts"
+    asset_dir.mkdir(parents=True, exist_ok=True)
+    image_path = asset_dir / f"chart_{chart.index:02d}.png"
+    raw = data_uri_png_bytes(chart.image_src)
+    if raw is None:
+        return None, {"error": "chart image source was not a base64 PNG data URI"}
+    try:
+        with Image.open(BytesIO(raw)) as image:
+            image = image.convert("RGBA")
+            background = Image.new("RGB", image.size, (255, 255, 255))
+            background.paste(image, mask=image.getchannel("A"))
+            chart.width_px, chart.height_px = background.size
+            background.save(image_path, format="PNG", optimize=True)
+    except Exception as exc:
+        return None, {"error": str(exc)[:500]}
+    return image_path, {"renderer": "embedded_png_data_uri"}
+
+
 def image_is_nonblank(path: Path) -> tuple[bool, dict[str, Any]]:
     with Image.open(path).convert("RGB") as image:
         extrema = image.getextrema()
@@ -1265,7 +1406,10 @@ def image_is_nonblank(path: Path) -> tuple[bool, dict[str, Any]]:
 
 def render_charts(report: Report, out_dir: Path, preflight: Preflight) -> None:
     for chart in report.charts:
-        path, details = render_svg_chart(chart, out_dir)
+        if chart.image_src:
+            path, details = render_embedded_png_chart(chart, out_dir)
+        else:
+            path, details = render_svg_chart(chart, out_dir)
         if path is None:
             preflight.add(
                 f"chart_render:{chart.index}",
@@ -2259,6 +2403,7 @@ def build_manifest(report: Report) -> dict[str, Any]:
                 "title": chart.title,
                 "takeaway": chart.takeaway,
                 "caveat": chart.caveat,
+                "source_kind": "embedded_png" if chart.image_src else "svg",
                 "image_path": chart.image_path,
                 "width_px": chart.width_px,
                 "height_px": chart.height_px,
diff --git a/plugins/data-analytics/skills/build-report/report-to-pdf/SKILL.md b/plugins/data-analytics/skills/build-report/report-to-pdf/SKILL.md
index 9dbd052..5b60d31 100644
--- a/plugins/data-analytics/skills/build-report/report-to-pdf/SKILL.md
+++ b/plugins/data-analytics/skills/build-report/report-to-pdf/SKILL.md
@@ -9,12 +9,6 @@ Use this skill only when the user explicitly needs a PDF from an existing Data A
 
 The expected path is static HTML -> Chrome headless print-to-PDF -> PDF verification. If only a live MCP app report exists, create or retrieve the matching static HTML/export source first.
 
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ## Workflow
 
 1. Resolve the static source.
@@ -23,7 +17,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 
    When the user provides a hosted artifact URL, first determine whether it exposes a usable static export or package source. If the static export is unavailable but the validated artifact payload is still available in the current run, create the matching static HTML/export from that payload and preserve source provenance in the handoff. If neither a static export nor the artifact payload is available, stop with a blocker instead of printing the live app shell or rebuilding the report from memory.
 
-   When creating or repairing the static source, keep visible metadata reader-facing. Do not copy internal artifact runtime fields, package plumbing, or validator/debug state into the report body, header, footer, or source section. For example, omit raw labels such as snapshot status, package path, widget type, manifest path, renderer IDs, validation status, and local temp paths from the visible PDF. If a runtime detail matters for audit or troubleshooting, preserve it in support notes or the final handoff instead of the PDF. Translate data-state caveats into reader language only when they affect interpretation, such as "synthetic demo data" or "partial source coverage."
+   When creating or repairing the static source, keep visible metadata reader-facing. Do not copy internal artifact runtime fields, package plumbing, or validator/debug state into the report body, header, footer, or source section. For example, omit raw labels such as snapshot status, package path, widget type, manifest path, renderer IDs, validation status, and local temp paths from the visible PDF. If a runtime detail matters for audit or troubleshooting, preserve it in support notes rather than the visible PDF. Translate data-state caveats into reader language only when they affect interpretation, such as "synthetic demo data" or "partial source coverage."
 
 2. Generate the PDF with Chrome CLI headless print-to-PDF.
 
@@ -63,13 +57,13 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 
 6. Hand off the PDF.
 
-   Return the PDF path, source HTML path or URL, and verification performed. If the static HTML/export had to be created from an artifact payload because the hosted URL did not expose one, say that briefly. If verification could not be completed, state the gap clearly.
+   Return the PDF path and, when useful, the source HTML path or URL. Keep routine check details in support artifacts. Do not list internal checks in the user-facing handoff unless a check failed, was unavailable, or produced a user-relevant caveat. If the static HTML/export had to be created from an artifact payload because the hosted URL did not expose one, say that briefly. If required checks could not be completed, state the gap clearly.
 
 ## Standards
 
 - Preserve the reader-facing artifact content: title, narrative, charts, tables, caveats, source details, and generated-at or source freshness details when present.
 - Omit app-only controls: top bars, share menus, edit controls, refresh controls, drag handles, hover-only menus, and interactive-only affordances.
-- Omit internal artifact and conversion metadata from the visible PDF. Do not show raw runtime fields, implementation state, package labels, local paths, validator/debug labels, or status strings that exist only to operate the app or export pipeline. Keep audit-useful internals in support files or the handoff, and use plain reader-facing caveats in the PDF when a data state affects interpretation.
+- Omit internal artifact and conversion metadata from the visible PDF. Do not show raw runtime fields, implementation state, package labels, local paths, validator/debug labels, or status strings that exist only to operate the app or export pipeline. Keep audit-useful internals in support files, and use plain reader-facing caveats in the PDF when a data state affects interpretation.
 - Prefer a text PDF with selectable/searchable text. Use a screenshot/image PDF only when a faithful text PDF is not viable, and state that text may not be selectable or searchable.
 - Keep the static source as the PDF source of truth. Do not reconstruct the report from memory when a source HTML/export exists.
 - Do not create a model-authored PDF from scratch as a normal fallback. Direct PDF construction is acceptable only as an explicitly labeled last-resort workaround after the user accepts the quality limitation.
diff --git a/plugins/data-analytics/skills/build-report/scripts/embed_html_report_runtime.py b/plugins/data-analytics/skills/build-report/scripts/embed_html_report_runtime.py
new file mode 100644
index 0000000..982ae53
--- /dev/null
+++ b/plugins/data-analytics/skills/build-report/scripts/embed_html_report_runtime.py
@@ -0,0 +1,298 @@
+#!/usr/bin/env python3
+"""Embed the precompiled Recharts report runtime into a self-contained HTML artifact."""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import gzip
+import json
+import re
+import textwrap
+from pathlib import Path
+
+RUNTIME_MARKER = "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->"
+RUNTIME_ASSET = "html-report-runtime.html"
+RUNTIME_SOURCE_ID = "data-analytics-html-report-runtime-source"
+CHART_RUNTIME_STYLES = """
+[data-recharts-chart] [data-recharts-live] { display: none !important; }
+[data-recharts-chart][data-recharts-ready="true"] [data-recharts-fallback] { display: none !important; }
+[data-recharts-chart][data-recharts-ready="true"] [data-recharts-live] { display: block !important; }
+[data-recharts-chart] [data-recharts-live] svg {
+  display: block !important;
+  height: 100% !important;
+  max-width: 100% !important;
+  min-width: 0 !important;
+  width: 100% !important;
+}
+""".strip()
+SOURCE_TOOLTIP_SCRIPT = "html-report-source-tooltips.js"
+
+
+def plugin_root() -> Path:
+    return Path(__file__).resolve().parents[3]
+
+
+def read_runtime_html(runtime_html: Path | None) -> str:
+    if runtime_html is not None:
+        return runtime_html.read_text(encoding="utf-8")
+
+    asset_dir = plugin_root() / "assets"
+    parts = sorted(asset_dir.glob(f"{RUNTIME_ASSET}.gz.b64.part*"))
+    if not parts:
+        raise ValueError(
+            "HTML report runtime bundle parts are missing; use the packaged plugin or run npm run build."
+        )
+    encoded = "".join(part.read_text(encoding="utf-8").strip() for part in parts)
+    return gzip.decompress(base64.b64decode(encoded)).decode("utf-8")
+
+
+def read_source_tooltip_script() -> str:
+    return (plugin_root() / "src" / SOURCE_TOOLTIP_SCRIPT).read_text(encoding="utf-8")
+
+
+def runtime_fragments(runtime_html: str) -> tuple[str, str]:
+    if "Redirecting to the local widget source" in runtime_html:
+        raise ValueError("HTML report runtime asset is still a development redirect; run npm run build.")
+
+    styles = "\n".join(
+        match.group(1)
+        for match in re.finditer(r"<style[^>]*>(.*?)</style>", runtime_html, flags=re.DOTALL | re.IGNORECASE)
+    )
+    scripts = "\n".join(
+        match.group(1)
+        for match in re.finditer(
+            r"<script[^>]*type=[\"']module[\"'][^>]*>(.*?)</script>",
+            runtime_html,
+            flags=re.DOTALL | re.IGNORECASE,
+        )
+    )
+    if not scripts:
+        raise ValueError("HTML report runtime bundle does not contain an inline module script.")
+    return styles, scripts
+
+
+def safe_json_script_text(payload: object) -> str:
+    text = json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
+    return (
+        text.replace("&", "\\u0026")
+        .replace("<", "\\u003c")
+        .replace(">", "\\u003e")
+        .replace("\u2028", "\\u2028")
+        .replace("\u2029", "\\u2029")
+    )
+
+
+def compressed_runtime_source(scripts: str) -> str:
+    """Keep the large runtime inert until the readable fallback has painted.
+
+    ChatGPT's HTML source view pays a high startup cost when every report
+    contains the raw minified React/Recharts bundle. Gzip keeps the exact
+    precompiled runtime self-contained while avoiding giant JavaScript lines
+    in the authored HTML.
+    """
+
+    encoded = base64.b64encode(gzip.compress(scripts.encode("utf-8"), mtime=0)).decode("ascii")
+    return textwrap.fill(encoded, width=76)
+
+
+def runtime_loader_script() -> str:
+    """Return a tiny replayable loader for the compressed runtime.
+
+    ChatGPT can serialize a rendered desktop preview and later reopen that
+    DOM at mobile width. Keep the compact source and loader in the document
+    so the serialized preview can rerun the runtime and repair stale SVG
+    geometry; the large decompressed module is still removed after it runs.
+    """
+
+    return f"""(() => {{
+  const source = document.getElementById("{RUNTIME_SOURCE_ID}");
+  if (!(source instanceof HTMLTemplateElement) || typeof DecompressionStream !== "function") {{
+    return;
+  }}
+
+  function sizeIsStale(current, serialized) {{
+    if (!Number.isFinite(current) || current <= 0 || !Number.isFinite(serialized) || serialized <= 0) {{
+      return true;
+    }}
+    return Math.abs(current - serialized) > Math.max(4, Math.max(current, serialized) * 0.08);
+  }}
+
+  function shouldBoot() {{
+    for (const host of document.querySelectorAll("[data-recharts-chart]")) {{
+      if (host.getAttribute("data-recharts-ready") !== "true") return true;
+      const mount = host.querySelector("[data-recharts-live]");
+      if (!(mount instanceof HTMLElement)) return true;
+      const svg = mount.querySelector("svg");
+      if (!svg) {{
+        if (mount.querySelector(".leaderboard, .heatmap-grid-panel")) continue;
+        return true;
+      }}
+      const viewBox = String(svg.getAttribute("viewBox") ?? "").trim().split(/[\\s,]+/).map(Number);
+      if (viewBox.length !== 4) return true;
+      const rect = mount.getBoundingClientRect();
+      if (sizeIsStale(rect.width, viewBox[2]) || sizeIsStale(rect.height, viewBox[3])) return true;
+    }}
+    return false;
+  }}
+
+  async function boot() {{
+    try {{
+      const encoded = source.content.textContent.replace(/\\s/g, "");
+      const binary = atob(encoded);
+      const bytes = new Uint8Array(binary.length);
+      for (let index = 0; index < binary.length; index += 1) {{
+        bytes[index] = binary.charCodeAt(index);
+      }}
+      const stream = new Blob([bytes]).stream().pipeThrough(new DecompressionStream("gzip"));
+      const runtimeSource = await new Response(stream).text();
+      const runtime = document.createElement("script");
+      runtime.dataset.dataAnalyticsHtmlReportRuntime = "true";
+      runtime.textContent = runtimeSource;
+      document.head.append(runtime);
+      runtime.remove();
+    }} catch {{}}
+  }}
+
+  let started = false;
+  function start() {{
+    if (started) return;
+    started = true;
+    if (!shouldBoot()) return;
+    void boot();
+  }}
+
+  function schedule() {{
+    if (typeof window.requestIdleCallback === "function") {{
+      window.requestIdleCallback(start, {{ timeout: 1500 }});
+    }}
+    window.setTimeout(start, 1500);
+  }}
+
+  if (document.readyState === "complete") {{
+    schedule();
+  }} else {{
+    window.addEventListener("load", schedule, {{ once: true }});
+  }}
+}})();"""
+
+
+def chart_ids(payload: object) -> list[str]:
+    if not isinstance(payload, dict):
+        raise ValueError("Payload must be a JSON object.")
+    charts = payload.get("charts")
+    if not isinstance(charts, list):
+        raise ValueError("Payload must contain a charts array.")
+    ids: list[str] = []
+    for index, chart in enumerate(charts):
+        if not isinstance(chart, dict) or not str(chart.get("id", "")).strip():
+            raise ValueError(f"charts[{index}] must have a non-empty id.")
+        chart_id = str(chart["id"]).strip()
+        if chart_id in ids:
+            raise ValueError(f"Duplicate chart id: {chart_id}")
+        ids.append(chart_id)
+    return ids
+
+
+def validate_shell(shell: str, ids: list[str]) -> None:
+    if shell.count(RUNTIME_MARKER) != 1:
+        raise ValueError(f"HTML shell must contain exactly one {RUNTIME_MARKER} marker.")
+    marker_start = shell.index(RUNTIME_MARKER)
+    host_pattern = re.compile(
+        r"\bdata-recharts-chart\s*=\s*([\"'])(.*?)\1",
+        flags=re.IGNORECASE,
+    )
+    hosts = [
+        (match.start(), match.group(2))
+        for match in host_pattern.finditer(shell[:marker_start])
+    ]
+    if host_pattern.search(shell[marker_start:]):
+        raise ValueError("HTML shell chart hosts must appear before the runtime marker.")
+
+    starts: list[tuple[int, str]] = []
+    for chart_id in ids:
+        matching_hosts = [start for start, host_id in hosts if host_id == chart_id]
+        if not matching_hosts:
+            raise ValueError(f'HTML shell is missing data-recharts-chart="{chart_id}".')
+        if len(matching_hosts) != 1:
+            raise ValueError(f"HTML shell must contain exactly one chart host for {chart_id}.")
+        starts.append((matching_hosts[0], chart_id))
+
+    unexpected_ids = sorted({host_id for _, host_id in hosts if host_id not in ids})
+    if unexpected_ids:
+        raise ValueError(
+            "HTML shell has chart hosts without payload entries: " + ", ".join(unexpected_ids)
+        )
+
+    starts.sort()
+    for index, (start, chart_id) in enumerate(starts):
+        end = starts[index + 1][0] if index + 1 < len(starts) else marker_start
+        scope = shell[start:end]
+        if len(re.findall(r"\bdata-recharts-live\b", scope, flags=re.IGNORECASE)) != 1:
+            raise ValueError(f"Chart {chart_id} needs its own data-recharts-live mount.")
+        if len(re.findall(r"\bdata-recharts-fallback\b", scope, flags=re.IGNORECASE)) != 1:
+            raise ValueError(f"Chart {chart_id} needs its own data-recharts-fallback content.")
+        fallback = re.search(
+            r"<[^>]*\bdata-recharts-fallback\b[^>]*>(.*?)(?=<[^>]*\bdata-recharts-live\b)",
+            scope,
+            flags=re.DOTALL | re.IGNORECASE,
+        )
+        if not fallback:
+            raise ValueError(f"Chart {chart_id} needs its own data-recharts-fallback content.")
+        fallback_body = re.sub(r"<!--.*?-->", "", fallback.group(1), flags=re.DOTALL)
+        if not re.search(
+            r"<(?:path|line|polyline|polygon|rect|circle|ellipse|text|table|tr|td|th)\b",
+            fallback_body,
+            flags=re.IGNORECASE,
+        ):
+            raise ValueError(f"Chart {chart_id} needs a readable SVG or table fallback.")
+
+
+def embed(shell: str, payload: object, runtime_html: str) -> str:
+    ids = chart_ids(payload)
+    validate_shell(shell, ids)
+    styles, scripts = runtime_fragments(runtime_html)
+    embedded_styles = "\n".join(part for part in (styles, CHART_RUNTIME_STYLES) if part)
+    fragments = [
+        '<script data-data-analytics-html-report-source-tooltips="true">'
+        + read_source_tooltip_script().replace("</script", "<\\/script")
+        + "</script>",
+        f'<script id="analytics-payload" type="application/json">{safe_json_script_text(payload)}</script>',
+        f'<style data-data-analytics-html-report-runtime="true">{embedded_styles}</style>',
+    ]
+    fragments.append(
+        f'<template id="{RUNTIME_SOURCE_ID}" '
+        f'data-data-analytics-html-report-runtime-source="true">\n'
+        f"{compressed_runtime_source(scripts)}\n"
+        "</template>"
+    )
+    fragments.append(
+        '<script data-data-analytics-html-report-runtime-loader="true">'
+        f"{runtime_loader_script()}"
+        "</script>"
+    )
+    return shell.replace(RUNTIME_MARKER, "\n".join(fragments))
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input", required=True, type=Path, help="Authored HTML shell with runtime marker.")
+    parser.add_argument("--payload", required=True, type=Path, help="JSON payload with a charts array.")
+    parser.add_argument("--output", required=True, type=Path, help="Self-contained HTML output path.")
+    parser.add_argument(
+        "--runtime-html",
+        type=Path,
+        help="Optional uncompressed runtime HTML for local tests; packaged runs use bundled parts.",
+    )
+    args = parser.parse_args()
+
+    shell = args.input.read_text(encoding="utf-8")
+    payload = json.loads(args.payload.read_text(encoding="utf-8"))
+    output = embed(shell, payload, read_runtime_html(args.runtime_html))
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(output, encoding="utf-8")
+    print(f"Wrote {args.output} with {len(chart_ids(payload))} Recharts chart(s).")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/plugins/data-analytics/skills/build-report/specifications/mcp-app-report.md b/plugins/data-analytics/skills/build-report/specifications/mcp-app-report.md
index b5e7928..b96b3cf 100644
--- a/plugins/data-analytics/skills/build-report/specifications/mcp-app-report.md
+++ b/plugins/data-analytics/skills/build-report/specifications/mcp-app-report.md
@@ -2,6 +2,8 @@
 
 Use this when the selected report surface is a bounded manifest/snapshot rendered by the Data Analytics MCP artifact app.
 
+Do not select this specification when `surface = chatgpt_web` and `mode = work_mode` are both positively identified. Also do not select it when `mode = work_mode` is positive and `surface` is unknown or otherwise not positively `codex_desktop`. Use HTML report mode instead.
+
 Before creating or revising an MCP app report artifact, read `../../../src/analytics-app-core.md`. It defines the shared dashboard/report artifact contract, bounded snapshot, source safety, runtime behavior, validation helpers, and MCP-specific payload/encoding rules.
 
 ## Build Shape
@@ -10,21 +12,22 @@ Before creating or revising an MCP app report artifact, read `../../../src/analy
 - Set `manifest.title` to the reader-facing title.
 - For MCP app reports, make the first reader-facing block a normal `type: "markdown"` block whose body is a single `#` heading matching `manifest.title`. Do not rely on the app chrome as the report's only visible title.
 - The title block is only the report title. For stakeholder reports, the next narrative markdown block must start with a visible `## Executive Summary` heading before the summary content. Do not collapse the executive summary into the title block or rely on bold opening paragraphs, KPI cards, subtitles, or metric strips to satisfy the executive-summary section role.
-- Use reader-facing headings inside markdown blocks to separate major report segments. Do not rely on chart titles, table titles, or non-rendered metadata as the only visible title for a major segment.
-- Use `type: "metric-strip"` with `cardIds` when a KPI strip is needed; do not create standalone metric blocks.
+- Give each independently laid out or editable major report segment its own `type: "markdown"` block with one peer `##` section heading. Do not place multiple peer `##` headings in one markdown body; use `###` headings only for subordinate content that should remain in the same card. Do not rely on chart titles, table titles, or non-rendered metadata as the only visible title for a major segment.
+- Use `type: "metric-strip"` with `cardIds` when a KPI strip is needed; do not create standalone metric blocks. Every referenced `manifest.cards[]` item must attach canonical provenance through inline `source` or `sourceId` so the card's top-right source icon opens its data-source modal.
+- Choose all and only decision-relevant headline metrics. Do not pad or truncate a strip to reach four cards—or any preferred count. Each card must add one distinct, interpretable headline; one headline does not mean one `metrics[]` entry. Use supporting badges for short prior-period, target, or delta context tied directly to that headline, especially when the same directional comparison appears in the executive summary or findings, and split independent secondary metrics into their own cards, charts, narrative, semantic strips, or a detail table. Keep card labels and badge labels concise enough to remain on one line. Odd card counts are valid; the renderer balances rows responsively.
 - Connect block evidence to source references when the surface supports it, and preserve the full audit trail in source metadata or supporting artifacts.
 - MCP app reports must include at least one native manifest chart block backed by reviewed snapshot data.
 - Default to built-in report artifact blocks for MCP app reports: `markdown`, `metric-strip`, `chart`, and `table`. Use native blocks for ordinary narrative sections, KPI strips, charts, tables, caveats, and source notes instead of hand-authored HTML.
 - Use report chart types supported by the current MCP artifact schema. Keep report interaction bounded; use dashboards, not reports, for filterable exploration.
 - Include report-native visual/table blocks when quantitative evidence is available and useful, not only markdown summaries.
 - Do not render filters or dashboard-style exploration controls in an MCP app report.
-- Metric cards use one `metrics[]` list. The first metric renders as the large value; later metrics render as labeled secondary badges. Each metric declares `label`, `field`, optional `format`, and `signed: true` for signed changes.
+- Metric cards use one `metrics[]` list. The first metric is the card's only headline and renders as the large value; later metrics render as labeled comparison badges when they describe that same headline's prior period, target, or delta. When a short, directly relevant directional comparison is available—especially one already used in the executive summary or findings—include it as a later metric rather than omitting it to preserve a one-headline card. Each metric declares `label`, `field`, optional `format`, and `signed: true` for signed changes.
 - When a metric label is not self-defining, include a card description or nearby markdown that explains the metric in reader terms, and include the exact calculation in `source.query.metric_definitions`.
 - Percent values rendered with `format: "percent"` or `valueFormat: "percent"` must use the same numeric scale consistently in the provided data. Use decimal rates for computed numeric fields, such as `0.149` for `14.9%`, or pass preformatted reader-facing strings where exact display text matters.
 
 ## Source And Data Requirements
 
-- Every native chart and table block must expose provenance through the canonical `source` structure, either directly on the block or by `sourceId` reference to `manifest.sources[]`. Put runnable SQL/code in `source.query.sql`, a human-readable query summary in `source.query.description`, table names in `source.query.tables_used`, material predicates in `source.query.filters`, and metric formulas in `source.query.metric_definitions`. Do not rely on dataset names, source labels, or source order to attach provenance.
+- Every native metric card, chart, and table block must expose provenance through the canonical `source` structure, either directly on the item or by `sourceId` reference to `manifest.sources[]`. Put runnable SQL/code in `source.query.sql`, a human-readable query summary in `source.query.description`, table names in `source.query.tables_used`, material predicates in `source.query.filters`, and metric formulas in `source.query.metric_definitions`. Do not rely on dataset names, source labels, or source order to attach provenance.
 - Do not create artifact `manifest.filters` just to document SQL predicates in a report. Report predicate provenance belongs in `source.query.filters`; `manifest.filters` is only for interactive controls.
 - Every native chart block must be backed by a source dataset that is richer than the chart's visible encodings. Do not create chart datasets or expanded source-data tables with only the plotted fields when the reviewed analysis can expose more context. Preserve useful dimensions, including relevant customer, account, company, segment, and product names, plus time/cohort fields, potential grouping columns, numerator/denominator components, ranks, baselines, comparison periods, and adjacent measures that help the reader audit or re-encode the chart.
 - Retaining a potential grouping column for auditability does not mean the shipped chart should bind it as `encodings.color`, series, or grouped/stacked behavior; only add visible grouping when it is a second categorical dimension beyond the axis category.
@@ -34,12 +37,12 @@ Before creating or revising an MCP app report artifact, read `../../../src/analy
 ## Chart And Custom Block Rules
 
 - For chart widgets and native manifest charts, provide the chart family and fields explicitly according to the MCP tool descriptions. Validate before rendering; the app should not choose the first dimension/measure, flip horizontal bar axes, infer grouping fields, or guess units.
-- Use `type: "html"` blocks when the user requests report customization that requires custom rendering, including requests to use Seaborn, Matplotlib, a bespoke visual/layout treatment, or a chart form that cannot be represented with built-in report blocks and native chart/table options.
-- Route custom chart work through `$visualize-data`, generate the chart from the Seaborn templates as a compact Matplotlib SVG, embed it in the HTML block, and document the exception. Keep ordinary narrative, KPI, native chart, table, caveat, and source sections in built-in blocks.
+- Use `type: "html"` blocks when the user requests report customization that requires custom rendering, including a bespoke visual/layout treatment or a chart form that cannot be represented with built-in report blocks and native chart/table options.
+- Route custom chart work through `$visualize-data`, generate a compact static SVG or image from reviewed rows, embed it in the HTML block, and document the exception. Keep ordinary narrative, KPI, native chart, table, caveat, and source sections in built-in blocks.
 - Keep custom chart HTML blocks as containers around generated chart images with title, caption, alt text, and source context. Do not ship hand-authored SVG, CSS bars, canvas, or JavaScript as the chart itself.
 - HTML blocks auto-size to their content; inspect the rendered artifact for clipping, missing images, and collisions with nearby blocks before handoff.
 - MCP report chart cards and detail pages should share the same native renderer and chart-type compatibility logic. Do not embed the standalone inline chart widget or rebuild a separate detail-page chart implementation.
-- Do not emit inline MCP chart or table widgets during report-mode work, and do not treat inline MCP chart/table widgets as report visuals or evidence previews. Report charts, tables, and previews must live in the selected report surface and be backed by reviewed snapshot data or static PNG assets.
+- Do not emit inline MCP chart or table widgets during report-mode work, and do not treat inline MCP chart/table widgets as report visuals or evidence previews. Report charts, tables, and previews must live in the selected report surface and be backed by reviewed snapshot data or static embedded evidence.
 
 ## Revision And Rendering
 
diff --git a/plugins/data-analytics/skills/create-data-context/SKILL.md b/plugins/data-analytics/skills/create-data-context/SKILL.md
new file mode 100644
index 0000000..67eba0e
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/SKILL.md
@@ -0,0 +1,71 @@
+---
+name: create-data-context
+description: "Create, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite."
+---
+
+# Create Data Context
+
+This skill creates and maintains semantic-layer skills for Data Analytics. A semantic-layer skill is an explicit artifact the user can inspect and cite later. It captures how future analyses should interpret the data, for example which metric definition is canonical, which dashboard or table is the best source of truth, and what caveats should be checked before answering.
+
+Data-task routing is owned by the Data Analytics `index` skill. If the user wants Data Analytics to do work with data now, route to `index`, whether the work starts from connected sources, uploaded files, pasted tables, sample data, or an existing semantic layer. Examples include answering a metric question, building a report, or checking a dataset.
+
+Use this skill only when the user asks to save data context or create, update, inspect, or repair a semantic layer.
+
+## Runtime Routing
+
+Use the current runtime's classified `surface` and `mode` values, plus the capabilities exposed in the run, to choose intake and persistence. Build and validate the same canonical semantic-layer skill package before writing it to any destination.
+
+For intake:
+
+- Use structured intake when a supported form action is available.
+- Use conversational intake for open-ended details and for runtimes without a supported form action.
+
+Choose one persistence destination by default:
+
+| Destination branch | Select this branch when | Action |
+| --- | --- | --- |
+| ChatGPT personal Skills | `surface = chatgpt_web` and the run exposes a product-backed personal Skills install surface, such as a native skill draft/upload/install action or an installable skill package preview. | Install the generated skill into the user's personal Skills library. Capture the installed skill link or Skills-library reference returned by the install surface and include it as a Markdown link in the chat response. Verify through the available Skills-library, list, or fresh-thread discovery surface when the runtime exposes one. |
+| Local Codex | The user asks for local persistence, or `surface = codex_desktop`. | Write a filesystem skill to `$CODEX_HOME/skills/<area>-semantic-layer`; if `$CODEX_HOME` is unavailable and the local runtime clearly uses the default home location, use `~/.codex/skills/<area>-semantic-layer`. Validate the written skill and verify it is discoverable in a new local Codex context when possible. |
+| Portable package | The current run has no supported persistent skill destination. | Return a portable skill package or source plan and clearly state that it has not been installed persistently, including the exact persistence blocker. |
+
+For dual ChatGPT web and local Codex availability, install the same canonical package into both the ChatGPT Skills library and the local Codex user-skill directory.
+
+For ordinary data work, return to the Data Analytics `index` workflow.
+
+## Skill Configuration
+
+### Audience And Language
+
+Write for Data Analytics users, not plugin maintainers. Explain what context would improve the current or future analysis in practical terms. Avoid implementation terms such as raw state paths, connector ids, cache, runtime, metadata, or preflight unless the user asks for debugging details.
+
+### Source Links
+
+When referencing sources inline, prefer clickable Markdown links over plain labels whenever a useful URL exists. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain labels only when no useful link is available.
+
+## Semantic Layer Setup
+
+Use this flow when the user wants to create or maintain a semantic layer. If they are not ready to create the layer yet, use the same flow to produce a short source plan for what the semantic layer needs.
+
+The output is a visible skill or plan. Build the semantic-layer skill when enough source-backed detail exists to make it useful. When the available detail is still thin, return a practical plan that explains what source to collect next and why.
+
+## Creating Or Updating The Layer
+
+Create one semantic layer per coherent product, business, metric, source, or reporting area unless the user explicitly asks for a broader shared layer. Infer the area from the provided context when it is clear; ask only when the answer changes the crawl, destination, or resulting skill.
+
+Choose the destination from `Runtime Routing` using the supported persistence mechanism exposed in the current run. For local Codex creations, default to `$CODEX_HOME/skills/<area>-semantic-layer` unless the user chooses another destination. Ask before placing a generated semantic-layer skill inside a plugin.
+
+Before crawling, build a data-source list that explains what was checked, what is missing, and which sources are lower-confidence. For direct creation or draft-file work, write it to `references/source-inventory.md`; for planning-only work, return it in chat.
+
+Use source-backed evidence. Favor durable sources such as transformation code, tests, maintained metric docs, and verified dashboards over looser signals such as query history or team discussion. If sources disagree, keep the conflict visible instead of choosing silently.
+
+Keep generated semantic-layer skills compact. Put detailed metric definitions, tables, query patterns, caveats, and evidence into linked references using `references/semantic-layer/skill-template.md`. Preserve provenance and avoid copying raw sensitive data, credentials, row-level examples, or long private messages into generated files.
+
+After creating or updating a semantic-layer skill, read `references/semantic-layer/weekly-polling-automation.md` and always offer weekly refresh when the layer has a stable target path and usable `references/source-inventory.md`. Weekly refresh is optional: do not create it without explicit user approval unless the user directly asked for recurring refresh. If the offer cannot be made, state the missing prerequisite.
+
+Return the created or updated path or installed skill link, source coverage, any user-relevant caveats or blockers, future-use guidance, iteration guidance, and the weekly refresh automation offer or prerequisite blocker so the user can cite the semantic layer directly in later prompts. Tell the user they can ask to refine definitions, add sources, update caveats, or adjust the semantic layer as their understanding changes. For ChatGPT personal Skills creations, include the installed skill link in the response.
+
+Keep routine validation details in support artifacts unless a check failed, was unavailable, or changes how the user should rely on the semantic layer.
+
+## Output Contract
+
+For semantic-layer work, report what was created, updated, inspected, or repaired; source coverage; any user-relevant caveats or blockers; and the exact path or installed skill link the user can cite later. For create or update results, include the weekly refresh automation offer or the missing prerequisite that prevented the offer, and let the user know they can iterate on the semantic layer. Keep routine validation details in support artifacts. If the request is ordinary data work, say that it should be routed through `index`.
diff --git a/plugins/data-analytics/skills/create-data-context/agents/openai.yaml b/plugins/data-analytics/skills/create-data-context/agents/openai.yaml
new file mode 100644
index 0000000..09a7adf
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/agents/openai.yaml
@@ -0,0 +1,6 @@
+interface:
+  display_name: "Create Data Context"
+  short_description: "Create reusable data context and semantic layers"
+  default_prompt: "Create reusable data context or a semantic layer from my metric docs, tables, dashboards, or source notes."
+policy:
+  allow_implicit_invocation: true
diff --git a/plugins/data-analytics/skills/create-data-context/plugin-author-config/automation-config.md b/plugins/data-analytics/skills/create-data-context/plugin-author-config/automation-config.md
new file mode 100644
index 0000000..460a7a9
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/plugin-author-config/automation-config.md
@@ -0,0 +1,26 @@
+# Data Analytics Automation Config
+
+Plugin authors edit this file when adding, removing, renaming, or clarifying default Data Analytics automations. Keep setup mechanics, thread creation, pinning, readback, duplicate cleanup, failure handling, and automation tool usage in `../references/automation.md`.
+
+Default automations are offered when their prerequisites are met. Later journey automations are offered only when a focused workflow reaches the right point. This config should stay friendly for plugin authors: name the automation, say how often it should run, and write the instructions. The dedicated thread title is derived from `Name`. Runtime mechanics such as heartbeat kind, RRULE conversion, tool calls, target thread ids, pinning, readback, cleanup, and state metadata belong in `../references/automation.md`.
+
+## Automation Entry Shape
+
+Every automation entry must use this shape:
+
+```md
+`automation_id`
+
+- Name: human-facing automation name.
+- Frequency: Data Analytics user-facing cadence and local time.
+- Instructions: thin launcher instructions for the scheduled run.
+```
+
+Do not add model, reasoning effort, heartbeat kind, RRULEs, tool names, user-specific target thread ids, canonical automation ids, install status, readback evidence, cleanup state, notification copy, or per-user preferred times here. Those belong in `../references/automation.md` or in the explicitly created automation/thread artifacts, not in hidden local setup or context state.
+
+## Default Automations
+
+`weekly_semantic_layer_refresh`
+- Name: Semantic Layer Weekly Source Polling.
+- Frequency: weekly on Mondays at 9:00 AM local time.
+- Instructions: Check the saved semantic-layer source inventory for source-backed changes, propose safe updates, validate any edited semantic-layer skill, and report conflicts, skipped sources, or permission gaps.
diff --git a/plugins/data-analytics/skills/user-context/plugin-author-config/source-category-config.json b/plugins/data-analytics/skills/create-data-context/plugin-author-config/source-category-config.json
similarity index 74%
rename from plugins/data-analytics/skills/user-context/plugin-author-config/source-category-config.json
rename to plugins/data-analytics/skills/create-data-context/plugin-author-config/source-category-config.json
index c0cb40f..f944ff0 100644
--- a/plugins/data-analytics/skills/user-context/plugin-author-config/source-category-config.json
+++ b/plugins/data-analytics/skills/create-data-context/plugin-author-config/source-category-config.json
@@ -1,6 +1,6 @@
 {
   "schema_version": "data_analytics_source_category_config.v1",
-  "description": "Author-owned Data Analytics source category catalog. Runtime source resolution, preferred plugin routing hints, relevant helper skills, fallback, and durable user-approved preference rules live in ../references/source-category-runtime.md. Skills decide which categories they attempt. User-specific source-routing preferences and semantic-layer registry pointers live in $CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md.",
+  "description": "Author-owned Data Analytics source category catalog. Runtime source resolution and fallback behavior are owned by the index skill for guided workflows and semantic-layer references for reusable data-context workflows. Skills decide which categories they attempt. preferred_plugins values are routing hints and examples, not an exhaustive source list. Data Analytics does not persist user-specific source-routing preferences or semantic-layer registry pointers in hidden local state.",
   "categories": {
     "structured_data": {
       "label": "Data warehouse",
@@ -24,7 +24,7 @@
     },
     "behavior_signals": {
       "label": "Behavior signals",
-      "preferred_plugins": ["Amplitude", "Mixpanel"],
+      "preferred_plugins": ["Amplitude", "Mixpanel", "PostHog"],
       "relevant_skills": []
     },
     "notebook_lab": {
diff --git a/plugins/data-analytics/skills/user-context/references/automation.md b/plugins/data-analytics/skills/create-data-context/references/automation.md
similarity index 65%
rename from plugins/data-analytics/skills/user-context/references/automation.md
rename to plugins/data-analytics/skills/create-data-context/references/automation.md
index a84414b..58c3686 100644
--- a/plugins/data-analytics/skills/user-context/references/automation.md
+++ b/plugins/data-analytics/skills/create-data-context/references/automation.md
@@ -1,6 +1,6 @@
-# Data Analytics Onboarding Automations
+# Data Analytics Automations
 
-Use this reference only when Data Analytics onboarding is setting up, checking, repairing, or later offering recurring Data Analytics automations. Keep this file as the runtime owner for automation setup. Keep ordinary onboarding flow, copy, and workflow choices in onboarding.md.
+Use this reference when Data Analytics is setting up, checking, repairing, or offering recurring Data Analytics automations. Keep this file as the runtime owner for automation setup.
 
 The automation catalog is ../plugin-author-config/automation-config.md. Always read it before setup and use its Name, Frequency, and Instructions; do not duplicate focused-skill workflow rules in automation prompts.
 
@@ -12,32 +12,32 @@ After the current semantic layer has a stable target skill path and a usable, po
 | --- | --- | --- | --- | --- | --- |
 | `weekly_semantic_layer_refresh` | Dedicated projectless thread titled `Semantic Layer Weekly Source Polling`, pinned | `model="gpt-5.5"`, `thinking="xhigh"` when supported | Weekly Monday 9:00 AM local time | Configured `Instructions` from `automation-config.md` | Send one setup kickoff to the pinned thread |
 
-The refresh automation is optional: do not create it during the first onboarding orientation, before those prerequisites exist, or without explicit user approval unless the user already directly instructed Data Analytics to set up weekly polling.
+The refresh automation is optional: do not create it before those prerequisites exist, or without explicit user approval unless the user already directly instructed Data Analytics to set up weekly polling.
 
-If a tool cannot set model or thinking for heartbeat automations, set them on the target thread or Semantic Layer Weekly Source Polling kickoff when supported, record the limitation in onboarding state, and do not fall back to `gpt-5`.
+If a tool cannot set model or thinking for heartbeat automations, set them on the target thread or Semantic Layer Weekly Source Polling kickoff when supported, report the limitation in the current setup result, and do not fall back to `gpt-5`.
 
-The onboarding thread is never an automation target. Do not rename, pin, unpin, attach a heartbeat to, or send kickoff messages into the current onboarding thread while setting up Data Analytics automations. Only the dedicated automation target thread may be renamed, pinned, and attached to heartbeat automations.
+The current chat thread is not the automation target by default. Do not rename, pin, unpin, attach a heartbeat to, or send kickoff messages into the current thread while setting up Data Analytics automations unless the user explicitly chooses it. Prefer the dedicated automation target thread.
 
 ## Fast Setup Checklist
 
 After the user approves Data Analytics automation setup:
 
-1. Discover `automation_update` and the thread tools: `create_thread`, `set_thread_title`, `set_thread_pinned`, and `send_message_to_thread`. Use `list_threads` only to reuse a clearly matching existing pinned automation thread with the exact configured title; never treat the current onboarding thread as reusable, even if it has been accidentally renamed or pinned.
+1. Discover `automation_update` and the thread tools: `create_thread`, `set_thread_title`, `set_thread_pinned`, and `send_message_to_thread`. Use `list_threads` only to reuse a clearly matching existing pinned automation thread with the exact configured title; do not treat the current thread as reusable unless the user explicitly selected it for this automation.
 2. Read `../plugin-author-config/automation-config.md` and identify the default automation id, name, frequency, and prompt.
 3. For the default automation, create or reuse its dedicated projectless thread, capture the returned or reused thread id, set the exact configured title on only that thread id, pin only that thread id, and keep its thread id.
 4. Create or update the automation with `kind="heartbeat"`, `destination="thread"`, the configured prompt, the configured cadence converted to the narrowest supported RRULE, `status="ACTIVE"`, and the exact `targetThreadId` of the dedicated automation thread.
 5. Read back `$CODEX_HOME/automations/<automation_id>/automation.toml` when available. Confirm `kind = "heartbeat"`, the stored `target_thread_id` matches the pinned thread, the prompt and cadence match the config, and no cron-only workspace fields are present. Use `automation_update mode=view` only when file readback is unavailable.
 6. Send one kickoff message only to the pinned Semantic Layer Weekly Source Polling thread after readback succeeds: run the configured semantic-layer source polling instructions now.
-7. Update `onboarding-state.json` with only operational metadata: canonical automation id, `kind`, readback status, target thread id/title, target thread pin status, and semantic-layer refresh kickoff status/thread info. Do not copy semantic-layer content, polling results, source URLs, or source data into onboarding state.
-8. Stop. Report the concise setup result and move to the first hero prompt step. Do not inspect hero workflow skill files or run another guided workflow until the user picks one.
+7. Do not update hidden local setup or context state. Keep operational metadata in the created automation/thread artifacts and summarize the result in the current response. Do not copy semantic-layer content, polling results, source URLs, or source data into setup metadata.
+8. Stop. Report the concise setup result and return to the active user request. Do not inspect focused workflow skill files or run another guided workflow unless the user picks one.
 
 ## Readiness Rules
 
 Only say the default automation setup is complete when the default automation is heartbeat-backed, targeted to its dedicated pinned thread, read back successfully, and the Semantic Layer Weekly Source Polling kickoff has started or was explicitly deferred by the user.
 
-Do not mark setup complete after a plain cron automation, an automation attached to the onboarding thread, missing readback, missing target thread metadata, or a missing first Semantic Layer Weekly Source Polling kickoff.
+Do not mark setup complete after a plain cron automation, an automation attached to the wrong thread, missing readback, missing target thread metadata, or a missing first Semantic Layer Weekly Source Polling kickoff.
 
-If `automation_update` cannot persist a dedicated heartbeat thread after one repair attempt, use the fallback path: leave semantic-layer refresh manual, record the limitation as `environment_api_limitations`, and report it as a fallback rather than full setup.
+If `automation_update` cannot persist a dedicated heartbeat thread after one repair attempt, use the fallback path: leave semantic-layer refresh manual and report the limitation as a fallback rather than full setup.
 
 Ask for explicit user approval before deleting automations, pausing automations, unpinning threads, or cleaning up duplicate/stale Data Analytics automation resources.
 
@@ -51,7 +51,7 @@ Do not paste source precedence, evidence standards, update boundaries, validatio
 
 ## User-Facing Result
 
-When setup succeeds, use this shape and continue to the next onboarding step:
+When setup succeeds, use this shape:
 
 ```md
 Semantic-layer refresh is set up and read back cleanly.
@@ -59,11 +59,11 @@ Semantic-layer refresh is set up and read back cleanly.
 - **Semantic Layer Weekly Source Polling**: checks the saved source inventory each week and posts its review summary in the pinned **Semantic Layer Weekly Source Polling** thread. I also kicked off the first source polling run in that thread.
 ```
 
-When setup cannot be completed, use this shape and continue to the next onboarding step:
+When setup cannot be completed, use this shape:
 
 ```md
 Weekly semantic-layer refresh is still manual because the automation could not be persisted and read back cleanly.
 
 - **Current setup**: the semantic layer and source inventory are saved.
-- **Refresh status**: manual for now. I recorded the setup blocker so refresh can be repaired later without repeating semantic-layer setup.
+- **Refresh status**: manual for now. The blocker can be revisited later without repeating semantic-layer setup.
 ```
diff --git a/plugins/data-analytics/skills/user-context/references/semantic-layer/connector-playbook.md b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/connector-playbook.md
similarity index 66%
rename from plugins/data-analytics/skills/user-context/references/semantic-layer/connector-playbook.md
rename to plugins/data-analytics/skills/create-data-context/references/semantic-layer/connector-playbook.md
index 9af38e0..823409a 100644
--- a/plugins/data-analytics/skills/user-context/references/semantic-layer/connector-playbook.md
+++ b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/connector-playbook.md
@@ -1,24 +1,30 @@
 # Connector Playbook
 
-Use this reference to choose source-specific tools, handle missing connectors, and keep evidence collection safe.
+Use this reference to choose connectors and callable tools, handle missing source access, and keep evidence collection safe.
 
 ## Connector Resolution
 
 For every user-supplied link, table, channel, repo, or artifact:
 
 1. Identify the source type and the most specific connector or tool that can read it.
-2. When `tool_search` is available, search for that connector or tool by product and action, such as `table metadata`, `query history`, `team communication search`, `document read`, `code repository`, or `wiki page`.
-3. Prefer installed first-party connectors and dedicated source tools over generic web or browser access for private workspace sources.
-4. If the relevant connector is not installed or not callable, ask whether to install or connect it when an install flow exists. If no install flow is available, ask for an export, pasted excerpt, local checkout, SQL text, or alternate source.
+2. When `tool_search` is available, search for that connector, custom MCP server, or other callable tool by product, source type, and action, such as `table metadata`, `query history`, `team communication search`, `document read`, `code repository`, or `wiki page`.
+3. Prefer installed first-party connectors, custom MCP servers, and other dedicated callable tools that can read the needed source over generic web or browser access for private workspace sources. Choose by source-of-truth fit, not by whether the route is native, MCP-backed, or CLI-assisted.
+4. If the relevant connector, custom MCP server, or callable tool is not installed, attached, or callable, ask whether to install, connect, attach, or enable it when a setup path exists. If a configured warehouse source such as Databricks, BigQuery, or Snowflake is not exposed in the current Data Analytics session, say that clearly and ask the user to ask their workspace admin to enable the app or connector for the workspace before asking for manual fallback. If no install flow is available for another source, ask for an export, pasted excerpt, local checkout, SQL text, or alternate source.
 5. Record skipped or manually substituted sources in the source inventory.
 
 Do not use `request_plugin_install` unless a single exact connector or plugin is known installable in the current environment. If the user explicitly asked for a connector that is not available and no install tool can install it, say that and give the closest manual fallback.
 
+### Custom MCP Servers And Callable Tools
+
+Use a visible or lazy-loadable custom MCP server or other callable tool as a first-class route when its name, description, action schema, or the user-named source indicates it can read the needed source. Do not require the source to appear in `.app.json` or configured `preferred_plugins`.
+
+If the custom MCP server or callable tool is expected but unavailable, say it is not attached or callable in the current session. Ask the user or workspace admin to enable it, then offer native connector installation, uploaded or exported data, SQL text, or another manual fallback when useful.
+
 ## Source-Specific Guidance
 
 ### Tables And Query History
 
-Use the available data warehouse connector, metadata API, or SQL/query-history tool for table metadata, schema comments, owners, lineage, and query history. Use the plugin manifest to identify declared warehouse connector choices when the user has not named a specific source. Crawl namespaces from broad to narrow: catalog and schema first, then specific table metadata and representative query patterns.
+Use the available data warehouse connector, metadata API, SQL/query-history tool, or already-authenticated read-only CLI when the user names it or it is already callable. Use CLI access only for metadata, query history, and safe aggregate results unless the user explicitly asks for raw rows and the data is safe to inspect. Treat pasted CLI output as manual evidence. Do not recommend installing or configuring a local CLI as the best path merely because the configured app or connector is not exposed. Use the plugin manifest to identify declared warehouse connector choices when the user has not named a specific source. Crawl namespaces from broad to narrow: catalog and schema first, then specific table metadata and representative query patterns.
 
 Capture:
 
diff --git a/plugins/data-analytics/skills/user-context/references/semantic-layer/skill-template.md b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/skill-template.md
similarity index 95%
rename from plugins/data-analytics/skills/user-context/references/semantic-layer/skill-template.md
rename to plugins/data-analytics/skills/create-data-context/references/semantic-layer/skill-template.md
index 2b2343e..d375ec3 100644
--- a/plugins/data-analytics/skills/user-context/references/semantic-layer/skill-template.md
+++ b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/skill-template.md
@@ -47,6 +47,7 @@ Use this skill to answer <area> data questions with the source-backed context in
 
 - Treat this skill as source-selection guidance, not as a substitute for live reads.
 - Preserve metric grain, time zone, date columns, filters, and join keys.
+- Do not let a saved delivery preference broaden the current request. In particular, a data question answered with SQL is not an SQL-delivery request; include source SQL in the visible response only when the user explicitly asks to see, write, review, debug, or receive SQL or query methodology.
 - Label stale, inferred, partial, or conflicted evidence.
 ```
 
diff --git a/plugins/data-analytics/skills/user-context/references/semantic-layer/source-intake.md b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/source-intake.md
similarity index 86%
rename from plugins/data-analytics/skills/user-context/references/semantic-layer/source-intake.md
rename to plugins/data-analytics/skills/create-data-context/references/semantic-layer/source-intake.md
index 2d45c13..ea4ca8c 100644
--- a/plugins/data-analytics/skills/user-context/references/semantic-layer/source-intake.md
+++ b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/source-intake.md
@@ -27,7 +27,7 @@ Use or adapt this intake prompt:
 ```text
 Set Up Data Context
 
-Codex gets better at data work when it understands the metric definitions, tables, filters, and source-of-truth context your team already uses.
+Data Analytics gets better at its work when it understands the metric definitions, tables, filters, and source-of-truth context your team already uses.
 
 Send anything you would point a new analyst to. I'll organize what you send into reusable guidance for future analysis.
 
@@ -37,7 +37,7 @@ Good starting points:
 3. Data tables or catalogs you frequently use.
 4. Places where data definitions, caveats, or changes are discussed, like team channels or threads.
 
-Reply with any starting points, or say `skip for now`.
+Use structured intake when available, but do not include an explicit `Skip for now` option because the native form already has a skip action. Include a `Starting points` freeform field for the user to paste context, links, table names, dashboard names, repo paths, channels, or notes. Do not include `Add starting points` as a selection option when starting points are collected through the freeform field; the user can skip through the structured intake skip action or the fallback `skip for now` path. If the structured response action is skipped or canceled, or if it returns an empty answer list or empty submission, record the semantic-layer setup as skipped or deferred and proceed to the hero-prompt step instead of asking for starting points again.
 ```
 
 If the user supplies only a broad area name and no starting points or useful context, ask for one or more starting points from this menu before crawling. If they already supplied starting points or useful context, organize those inputs, ask once for any missing high-value lane only when it would materially improve coverage, then proceed from the available inventory. If they cannot provide any starting points, offer a draft-only semantic-layer skill skeleton and label it as ungrounded.
diff --git a/plugins/data-analytics/skills/user-context/references/semantic-layer/weekly-polling-automation.md b/plugins/data-analytics/skills/create-data-context/references/semantic-layer/weekly-polling-automation.md
similarity index 100%
rename from plugins/data-analytics/skills/user-context/references/semantic-layer/weekly-polling-automation.md
rename to plugins/data-analytics/skills/create-data-context/references/semantic-layer/weekly-polling-automation.md
diff --git a/plugins/data-analytics/skills/create-data-context/scripts/data_analytics_preflight.py b/plugins/data-analytics/skills/create-data-context/scripts/data_analytics_preflight.py
new file mode 100644
index 0000000..aa78c55
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/scripts/data_analytics_preflight.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+"""Emit a stateless Data Analytics context envelope for compatibility.
+
+This helper does not read or write local setup or hidden context state.
+It intentionally does not read or write local setup or hidden context
+state. Older tests and maintainer workflows may still call the script name, so
+the payload makes the no-state contract explicit instead of failing or touching
+local state files.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+PLUGIN_ROOT = Path(__file__).resolve().parents[3]
+SOURCE_CATEGORY_CONFIG_REFERENCE = (
+    PLUGIN_ROOT / "skills/create-data-context/plugin-author-config/source-category-config.json"
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Return a stateless Data Analytics context envelope as JSON."
+    )
+    parser.add_argument("--workflow", default="ordinary")
+    parser.add_argument(
+        "--request-mode",
+        choices=("ordinary_workflow", "orientation", "direct_setup_status", "guided_setup_workflow"),
+        default="ordinary_workflow",
+    )
+    parser.add_argument("--codex-home", type=Path, default=None)
+    parser.add_argument("--state-dir", type=Path, default=None)
+    parser.add_argument("--max-context-bytes", type=int, default=200_000)
+    parser.add_argument("--section", action="append", default=[])
+    return parser.parse_args()
+
+
+def read_source_category_config() -> dict[str, Any]:
+    try:
+        return json.loads(SOURCE_CATEGORY_CONFIG_REFERENCE.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {
+            "schema_version": "data_analytics_source_category_config.v1",
+            "categories": {},
+        }
+
+
+def default_codex_home() -> Path:
+    env_home = os.environ.get("CODEX_HOME")
+    if env_home:
+        return Path(env_home).expanduser()
+    return Path.home() / ".codex"
+
+
+def state_dir_from_args(args: argparse.Namespace) -> Path:
+    if args.state_dir:
+        return args.state_dir.expanduser()
+    codex_home = args.codex_home.expanduser() if args.codex_home else default_codex_home()
+    return codex_home / "state/plugins/data-analytics"
+
+
+def read_plugin_install_suppressions(args: argparse.Namespace) -> dict[str, Any]:
+    path = state_dir_from_args(args) / "plugin-install-suppressions.json"
+    if not path.exists():
+        return {"ids": [], "entries": [], "path": str(path), "status": "missing"}
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {"ids": [], "entries": [], "path": str(path), "status": "unreadable"}
+    raw_entries = data.get("suppressed_plugin_installs") if isinstance(data, dict) else []
+    entries = raw_entries if isinstance(raw_entries, list) else []
+    ids: list[str] = []
+    normalized_entries: list[dict[str, Any]] = []
+    for entry in entries:
+        if isinstance(entry, str):
+            plugin_id = entry
+            normalized = {"plugin_id": plugin_id}
+        elif isinstance(entry, dict):
+            plugin_id = entry.get("plugin_id") or entry.get("tool_id") or entry.get("id")
+            normalized = dict(entry)
+            if plugin_id:
+                normalized["plugin_id"] = plugin_id
+        else:
+            continue
+        if isinstance(plugin_id, str) and plugin_id and plugin_id not in ids:
+            ids.append(plugin_id)
+            normalized_entries.append(normalized)
+    return {"ids": ids, "entries": normalized_entries, "path": str(path), "status": "present"}
+
+
+def build_payload(args: argparse.Namespace) -> dict[str, Any]:
+    source_config = read_source_category_config()
+    categories = source_config.get("categories", {})
+    suppressed_plugin_installs = read_plugin_install_suppressions(args)
+    return {
+        "schema": "data_analytics_context.v2",
+        "read_only": True,
+        "stateless": True,
+        "workflow": args.workflow,
+        "request_mode": args.request_mode,
+        "state_tracking": "disabled",
+        "ignored_compat_args": {
+            "codex_home": str(args.codex_home) if args.codex_home else None,
+            "state_dir": str(args.state_dir) if args.state_dir else None,
+            "max_context_bytes": args.max_context_bytes,
+            "section": args.section,
+        },
+        "context": {
+            "user_context": {
+                "scope": "current_run_only",
+                "state_tracking": "disabled",
+                "source_routing_preferences": {},
+                "semantic_layer_count": 0,
+                "normalization_complete": True,
+            },
+            "source_preferences": {},
+            "source_category_config": {
+                "schema_version": source_config.get("schema_version"),
+                "categories": categories,
+            },
+            "semantic_layers": [],
+            "connector_confirmation": {},
+            "connector_setup_summary": {
+                "status": "not_tracked",
+                "action_required": False,
+                "has_setup_gaps": False,
+                "ready": [],
+                "active": [],
+                "needs_attention": [],
+                "needs_choice": [],
+                "fallback_or_closed": [],
+                "not_set_up": [],
+                "plugin_setup_opportunities": [],
+                "unresolved_core_ids": [],
+                "core_complete": False,
+                "next_action": None,
+                "user_facing_guidance": (
+                    "Data Analytics does not track local connector setup state. "
+                    "Resolve sources in the active workflow."
+                ),
+            },
+            "suppressed_plugin_installs": suppressed_plugin_installs,
+            "hero_prompt_candidates": [],
+            "primary_hero_prompt": None,
+            "extra_hero_prompt_candidates": [],
+        },
+        "control": {
+            "response_mode": args.request_mode,
+            "final_obligations": [],
+            "conditional_guidance": [],
+            "setup_progress": {
+                "status": "not_tracked",
+                "core_setup": {
+                    "complete": False,
+                    "remaining_step_ids": [],
+                    "next_action": None,
+                },
+                "task_list": [],
+            },
+        },
+    }
+
+
+def main() -> int:
+    args = parse_args()
+    print(json.dumps(build_payload(args), indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/plugins/data-analytics/skills/create-data-context/scripts/record_plugin_install_suppression.py b/plugins/data-analytics/skills/create-data-context/scripts/record_plugin_install_suppression.py
new file mode 100644
index 0000000..3f7bae2
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/scripts/record_plugin_install_suppression.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""Record a dismissed Data Analytics plugin-install suggestion."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Remember that Data Analytics should not suggest a plugin install again."
+    )
+    parser.add_argument("--plugin-id", required=True)
+    parser.add_argument("--tool-type", default="plugin")
+    parser.add_argument("--reason", default="user_dismissed_request_plugin_install")
+    parser.add_argument("--codex-home", type=Path, default=None)
+    parser.add_argument("--state-dir", type=Path, default=None)
+    return parser.parse_args()
+
+
+def default_codex_home() -> Path:
+    env_home = os.environ.get("CODEX_HOME")
+    if env_home:
+        return Path(env_home).expanduser()
+    return Path.home() / ".codex"
+
+
+def state_dir_from_args(args: argparse.Namespace) -> Path:
+    if args.state_dir:
+        return args.state_dir.expanduser()
+    codex_home = args.codex_home.expanduser() if args.codex_home else default_codex_home()
+    return codex_home / "state/plugins/data-analytics"
+
+
+def read_state(path: Path) -> dict[str, Any]:
+    if not path.exists():
+        return {"suppressed_plugin_installs": []}
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except json.JSONDecodeError as exc:
+        raise SystemExit(f"invalid plugin-install-suppressions.json: {exc}") from exc
+    if not isinstance(data, dict):
+        raise SystemExit("invalid plugin-install-suppressions.json: root must be an object")
+    return data
+
+
+def main() -> int:
+    args = parse_args()
+    state_dir = state_dir_from_args(args)
+    state_dir.mkdir(parents=True, exist_ok=True)
+    state_path = state_dir / "plugin-install-suppressions.json"
+    state = read_state(state_path)
+    entries = state.get("suppressed_plugin_installs")
+    if not isinstance(entries, list):
+        entries = []
+    plugin_id = args.plugin_id.strip()
+    if not plugin_id:
+        raise SystemExit("--plugin-id must not be empty")
+    now = datetime.now(tz=timezone.utc).isoformat()
+    replacement = {
+        "plugin_id": plugin_id,
+        "tool_type": args.tool_type,
+        "reason": args.reason,
+        "source": "request_plugin_install",
+        "suppressed_at": now,
+    }
+    updated_entries: list[Any] = []
+    replaced = False
+    for entry in entries:
+        entry_id = None
+        if isinstance(entry, str):
+            entry_id = entry
+        elif isinstance(entry, dict):
+            entry_id = entry.get("plugin_id") or entry.get("tool_id") or entry.get("id")
+        if entry_id == plugin_id:
+            if not replaced:
+                updated_entries.append(replacement)
+                replaced = True
+            continue
+        updated_entries.append(entry)
+    if not replaced:
+        updated_entries.append(replacement)
+    state["suppressed_plugin_installs"] = updated_entries
+    state_path.write_text(json.dumps(state, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    print(json.dumps({"path": str(state_path), "plugin_id": plugin_id, "suppressed": True}))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/plugins/data-analytics/skills/create-data-context/scripts/validate_data_context_contract.py b/plugins/data-analytics/skills/create-data-context/scripts/validate_data_context_contract.py
new file mode 100644
index 0000000..c9765a0
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/scripts/validate_data_context_contract.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""Validate the Data Analytics semantic-layer setup contract."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("plugin_path", type=Path)
+    parser.add_argument("--plugin-id", default="data-analytics")
+    return parser.parse_args()
+
+
+def require_contains(failures: list[str], path: Path, phrase: str, root: Path) -> None:
+    try:
+        text = path.read_text(encoding="utf-8")
+    except OSError as exc:
+        failures.append(f"Could not read {path.relative_to(root)}: {exc}")
+        return
+    if phrase not in text:
+        failures.append(f"{path.relative_to(root)} missing required phrase: {phrase}")
+
+
+def require_absent(failures: list[str], path: Path, root: Path) -> None:
+    if path.exists():
+        failures.append(f"Removed path should not exist: {path.relative_to(root)}")
+
+
+def main() -> int:
+    args = parse_args()
+    plugin_root = args.plugin_path.resolve()
+    skills_root = plugin_root / "skills"
+    failures: list[str] = []
+
+    required_paths = (
+        skills_root / "index/SKILL.md",
+        skills_root / "create-data-context/SKILL.md",
+        skills_root / "create-data-context/scripts/data_analytics_preflight.py",
+        skills_root / "create-data-context/scripts/record_plugin_install_suppression.py",
+        skills_root / "create-data-context/tests/test_state_helpers.py",
+        skills_root / "create-data-context/plugin-author-config/source-category-config.json",
+        skills_root / "create-data-context/references/semantic-layer/source-intake.md",
+        skills_root / "create-data-context/references/semantic-layer/connector-playbook.md",
+        skills_root / "create-data-context/references/semantic-layer/skill-template.md",
+    )
+    for path in required_paths:
+        if not path.exists():
+            failures.append(f"Missing required path: {path.relative_to(plugin_root)}")
+
+    for path in (
+        skills_root / "onboarding",
+        skills_root / "user-context",
+        skills_root / "create-data-context/plugin-author-config/data-context-config.md",
+        skills_root / "create-data-context/references/onboarding.md",
+        skills_root / "create-data-context/references/onboarding-examples.md",
+        skills_root / "onboarding/references/onboarding-state-template.json",
+        skills_root / "create-data-context/references/onboarding-state-template.json",
+        skills_root / "create-data-context/scripts/init_user_context_state.py",
+        skills_root / "create-data-context/scripts/reset_user_context_state.py",
+    ):
+        require_absent(failures, path, plugin_root)
+
+    phrase_checks = (
+        (
+            skills_root / "index/SKILL.md",
+            "Ordinary analytics workflows do not require saved data-context setup.",
+        ),
+        (
+            skills_root / "index/SKILL.md",
+            "This index owns task selection, setup-adjacent routing, and guided workflow continuation.",
+        ),
+        (
+            skills_root / "index/SKILL.md",
+            "Any path that determines there is no usable data for the active task MUST offer `Upload data` and `Use sample data` before the turn ends.",
+        ),
+        (
+            skills_root / "create-data-context/SKILL.md",
+            "Use this skill only when the user asks to save data context or create, update, inspect, or repair a semantic layer.",
+        ),
+        (
+            skills_root / "create-data-context/scripts/data_analytics_preflight.py",
+            "does not read or write local setup or hidden context state",
+        ),
+        (
+            skills_root / "create-data-context/scripts/data_analytics_preflight.py",
+            '"stateless": True',
+        ),
+        (
+            skills_root / "create-data-context/scripts/data_analytics_preflight.py",
+            "suppressed_plugin_installs",
+        ),
+        (
+            skills_root / "create-data-context/scripts/record_plugin_install_suppression.py",
+            "plugin-install-suppressions.json",
+        ),
+    )
+    for path, phrase in phrase_checks:
+        if path.exists():
+            require_contains(failures, path, phrase, plugin_root)
+
+    for skill_file in skills_root.rglob("SKILL.md"):
+        text = skill_file.read_text(encoding="utf-8")
+        rel = skill_file.relative_to(plugin_root)
+        if "Mandatory pre-answer gate: Invoke `data-analytics:user-context`" in text:
+            failures.append(f"{rel} still requires user-context preflight")
+        if "Use the returned `data_analytics_preflight` envelope as the source of truth" in text:
+            failures.append(f"{rel} still treats preflight as source of truth")
+
+    for skill_file in (
+        skills_root / "index/SKILL.md",
+        skills_root / "create-data-context/SKILL.md",
+    ):
+        text = skill_file.read_text(encoding="utf-8")
+        rel = skill_file.relative_to(plugin_root)
+        if "../onboarding/" in text:
+            failures.append(f"{rel} still links to removed onboarding skill")
+        if "data-analytics:onboarding" in text:
+            failures.append(f"{rel} still routes to removed onboarding skill")
+
+    forbidden_refs = (
+        "state/plugins/data-analytics/user-context.md",
+        "state/plugins/data-analytics/onboarding-state.json",
+        "init_user_context_state.py",
+        "reset_user_context_state.py",
+        "onboarding-state-template.json",
+    )
+    for path in (
+        skills_root / "create-data-context/plugin-author-config/source-category-config.json",
+        skills_root / "create-data-context/references/automation.md",
+    ):
+        if not path.exists():
+            continue
+        text = path.read_text(encoding="utf-8")
+        for phrase in forbidden_refs:
+            if phrase in text:
+                failures.append(f"{path.relative_to(plugin_root)} still references {phrase}")
+
+    if failures:
+        for failure in failures:
+            print(f"ERROR: {failure}", file=sys.stderr)
+        return 1
+
+    print("Validated Data Analytics guided-flow/create-data-context semantic-layer contract.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/plugins/data-analytics/skills/create-data-context/tests/test_state_helpers.py b/plugins/data-analytics/skills/create-data-context/tests/test_state_helpers.py
new file mode 100644
index 0000000..2fc928f
--- /dev/null
+++ b/plugins/data-analytics/skills/create-data-context/tests/test_state_helpers.py
@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+"""Tests for the Data Analytics stateless compatibility helpers."""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+
+PLUGIN_ROOT = Path(__file__).resolve().parents[3]
+DATA_CONTEXT_ROOT = PLUGIN_ROOT / "skills/create-data-context"
+PREFLIGHT_SCRIPT = DATA_CONTEXT_ROOT / "scripts/data_analytics_preflight.py"
+SUPPRESSION_SCRIPT = DATA_CONTEXT_ROOT / "scripts/record_plugin_install_suppression.py"
+
+
+class DataAnalyticsStateHelperTests(unittest.TestCase):
+    def run_preflight(self, *args: str) -> dict:
+        proc = subprocess.run(
+            [sys.executable, str(PREFLIGHT_SCRIPT), *args],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        return json.loads(proc.stdout)
+
+    def run_suppression(self, *args: str) -> dict:
+        proc = subprocess.run(
+            [sys.executable, str(SUPPRESSION_SCRIPT), *args],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        return json.loads(proc.stdout)
+
+    def test_preflight_is_stateless_and_has_no_setup_obligation(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            payload = self.run_preflight("--codex-home", tmp)
+
+        self.assertEqual(payload["schema"], "data_analytics_context.v2")
+        self.assertTrue(payload["read_only"])
+        self.assertTrue(payload["stateless"])
+        self.assertEqual(payload["state_tracking"], "disabled")
+        self.assertNotIn("state", payload)
+        self.assertNotIn("files", payload)
+
+        context = payload["context"]
+        self.assertEqual(context["user_context"]["scope"], "current_run_only")
+        self.assertEqual(context["user_context"]["state_tracking"], "disabled")
+        self.assertEqual(context["source_preferences"], {})
+        self.assertEqual(context["connector_confirmation"], {})
+        self.assertEqual(context["semantic_layers"], [])
+
+        summary = context["connector_setup_summary"]
+        self.assertFalse(summary["action_required"])
+        self.assertFalse(summary["has_setup_gaps"])
+        self.assertEqual(summary["plugin_setup_opportunities"], [])
+        self.assertEqual(payload["control"]["final_obligations"], [])
+        self.assertEqual(payload["control"]["setup_progress"]["task_list"], [])
+
+        suppressions = context["suppressed_plugin_installs"]
+        self.assertEqual(suppressions["ids"], [])
+        self.assertEqual(suppressions["entries"], [])
+        self.assertEqual(suppressions["status"], "missing")
+        self.assertTrue(suppressions["path"].endswith("plugin-install-suppressions.json"))
+
+    def test_request_modes_do_not_create_setup_or_user_context_state(self) -> None:
+        modes = (
+            "ordinary_workflow",
+            "orientation",
+            "direct_setup_status",
+            "guided_setup_workflow",
+        )
+        for mode in modes:
+            with self.subTest(mode=mode), tempfile.TemporaryDirectory() as tmp:
+                payload = self.run_preflight("--codex-home", tmp, "--request-mode", mode)
+                state_dir = Path(tmp) / "state/plugins/data-analytics"
+
+                self.assertEqual(payload["request_mode"], mode)
+                self.assertEqual(payload["control"]["response_mode"], mode)
+                self.assertEqual(payload["control"]["final_obligations"], [])
+                self.assertFalse((state_dir / "user-context.md").exists())
+                self.assertFalse((state_dir / "onboarding-state.json").exists())
+
+    def test_suppression_helper_records_and_preflight_surfaces_exact_plugin_id(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            state_dir = Path(tmp) / "state"
+            result = self.run_suppression(
+                "--state-dir",
+                str(state_dir),
+                "--plugin-id",
+                "salesforce@openai-curated-remote",
+                "--tool-type",
+                "plugin",
+            )
+            payload = self.run_preflight("--state-dir", str(state_dir))
+
+        self.assertTrue(result["suppressed"])
+        self.assertEqual(result["plugin_id"], "salesforce@openai-curated-remote")
+        suppressions = payload["context"]["suppressed_plugin_installs"]
+        self.assertEqual(suppressions["status"], "present")
+        self.assertEqual(suppressions["ids"], ["salesforce@openai-curated-remote"])
+        self.assertEqual(
+            suppressions["entries"][0]["reason"],
+            "user_dismissed_request_plugin_install",
+        )
+        self.assertEqual(suppressions["entries"][0]["source"], "request_plugin_install")
+
+    def test_suppression_helper_replaces_duplicate_plugin_ids(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            state_dir = Path(tmp) / "state"
+            self.run_suppression(
+                "--state-dir",
+                str(state_dir),
+                "--plugin-id",
+                "bigquery@openai-curated-remote",
+                "--reason",
+                "first",
+            )
+            self.run_suppression(
+                "--state-dir",
+                str(state_dir),
+                "--plugin-id",
+                "bigquery@openai-curated-remote",
+                "--reason",
+                "second",
+            )
+            data = json.loads((state_dir / "plugin-install-suppressions.json").read_text())
+
+        entries = data["suppressed_plugin_installs"]
+        self.assertEqual(len(entries), 1)
+        self.assertEqual(entries[0]["plugin_id"], "bigquery@openai-curated-remote")
+        self.assertEqual(entries[0]["reason"], "second")
+
+    def test_preflight_tolerates_invalid_suppression_file(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            state_dir = Path(tmp) / "state"
+            state_dir.mkdir()
+            (state_dir / "plugin-install-suppressions.json").write_text("{not json", encoding="utf-8")
+            payload = self.run_preflight("--state-dir", str(state_dir))
+
+        suppressions = payload["context"]["suppressed_plugin_installs"]
+        self.assertEqual(suppressions["status"], "unreadable")
+        self.assertEqual(suppressions["ids"], [])
+        self.assertEqual(suppressions["entries"], [])
+
+    def test_source_category_config_is_exposed_without_readiness_claims(self) -> None:
+        payload = self.run_preflight()
+        config = payload["context"]["source_category_config"]
+
+        self.assertEqual(config["schema_version"], "data_analytics_source_category_config.v1")
+        self.assertIn("structured_data", config["categories"])
+        self.assertIn("company_docs", config["categories"])
+        self.assertEqual(payload["context"]["connector_confirmation"], {})
+        self.assertEqual(payload["context"]["connector_setup_summary"]["ready"], [])
+        self.assertEqual(payload["context"]["connector_setup_summary"]["active"], [])
+
+    def test_source_category_config_documents_preferred_plugins_as_hints(self) -> None:
+        config_text = (
+            DATA_CONTEXT_ROOT / "plugin-author-config/source-category-config.json"
+        ).read_text(encoding="utf-8")
+
+        self.assertIn("preferred_plugins values are routing hints and examples", config_text)
+        self.assertIn("not an exhaustive source list", config_text)
+
+    def test_deleted_stateful_helpers_are_absent(self) -> None:
+        self.assertFalse((DATA_CONTEXT_ROOT / "scripts/init_user_context_state.py").exists())
+        self.assertFalse((DATA_CONTEXT_ROOT / "scripts/reset_user_context_state.py").exists())
+        self.assertFalse((DATA_CONTEXT_ROOT / "references/onboarding-state-template.json").exists())
+        self.assertFalse((PLUGIN_ROOT / "skills/onboarding").exists())
+        self.assertFalse((PLUGIN_ROOT / "skills/user-context").exists())
+        self.assertFalse((DATA_CONTEXT_ROOT / "references/onboarding.md").exists())
+        self.assertFalse((DATA_CONTEXT_ROOT / "references/onboarding-examples.md").exists())
+
+    def test_contract_text_keeps_setup_optional(self) -> None:
+        index_text = (PLUGIN_ROOT / "skills/index/SKILL.md").read_text(encoding="utf-8")
+        data_context_text = (DATA_CONTEXT_ROOT / "SKILL.md").read_text(encoding="utf-8")
+
+        self.assertIn(
+            "Ordinary analytics workflows do not require saved data-context setup.",
+            index_text,
+        )
+        self.assertIn("route to `create-data-context`", index_text)
+        self.assertIn("This index owns task selection, setup-adjacent routing, and guided workflow continuation.", index_text)
+        self.assertIn("name: create-data-context", data_context_text)
+        self.assertIn(
+            "Use this skill only when the user asks to save data context or create, update, inspect, or repair a semantic layer.",
+            data_context_text,
+        )
+        self.assertNotIn("Route to data task", data_context_text)
+        self.assertNotIn("What should Data Analytics set up?", data_context_text)
+
+    def test_focused_skills_do_not_restore_preflight_or_repeated_context(self) -> None:
+        current_session_contract = (
+            "Work only from sources, files, and instructions available in the current "
+            "conversation or runtime. Do not read or write plugin-scoped persistent "
+            "state or claim future recall. If a required source or preference is "
+            "absent, ask for it or use a clearly labeled current-session fallback."
+        )
+        old_preflight_markers = (
+            "Mandatory pre-answer gate: Invoke `data-analytics:user-context`",
+            "Use the returned `data_analytics_preflight` envelope as the source of truth",
+        )
+        focused_skill_count = 0
+
+        for skill_path in (PLUGIN_ROOT / "skills").rglob("SKILL.md"):
+            text = skill_path.read_text(encoding="utf-8")
+            rel = skill_path.relative_to(PLUGIN_ROOT / "skills")
+            for marker in old_preflight_markers:
+                self.assertNotIn(marker, text, str(rel))
+
+            if rel.parts[0] in {"index", "create-data-context"}:
+                continue
+            focused_skill_count += 1
+            self.assertNotIn("### Current-Session Context", text, str(rel))
+            self.assertNotIn(current_session_contract, text, str(rel))
+
+        self.assertGreaterEqual(focused_skill_count, 10)
+
+    def test_connector_playbook_treats_authenticated_cli_as_valid_existing_access(self) -> None:
+        connector_playbook_text = (
+            DATA_CONTEXT_ROOT / "references/semantic-layer/connector-playbook.md"
+        ).read_text(encoding="utf-8")
+
+        self.assertIn("already-authenticated read-only CLI", connector_playbook_text)
+        self.assertIn("when the user names it or it is already callable", connector_playbook_text)
+        self.assertIn("Treat pasted CLI output as manual evidence.", connector_playbook_text)
+        self.assertIn(
+            "Do not recommend installing or configuring a local CLI as the best path",
+            connector_playbook_text,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/plugins/data-analytics/skills/design-kpis/SKILL.md b/plugins/data-analytics/skills/design-kpis/SKILL.md
index ad73f31..3bfad84 100644
--- a/plugins/data-analytics/skills/design-kpis/SKILL.md
+++ b/plugins/data-analytics/skills/design-kpis/SKILL.md
@@ -1,27 +1,26 @@
 ---
 name: design-kpis
-description: "Design KPI frameworks, set targets, and develop measurement plans that help teams make product or business decisions. Use when success metrics, drivers, guardrails, targets, or measurement approach need to be defined or improved. Use $metric-diagnostics when the task is to explain why an existing metric moved."
+description: "Design KPI frameworks, metric definitions, targets, guardrails, and measurement plans for product or business decisions. Use when success metrics, drivers, guardrails, targets, or the measurement approach need to be defined or improved."
 ---
 
 # Design KPIs
 
 Design KPI frameworks, set targets, and develop measurement plans that help teams make product or business decisions.
 
-## Skill Configuration
-
-### User Context
+## When To Use Data Quality First
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Use $analyze-data-quality first when the task is to reconcile existing metrics, dashboards, tables, owners, or sources of truth.
 
-### Source Discovery And Verification
+Return to this skill only when the user asks to define the metric going forward, redesign the KPI framework, choose guardrails, or set targets.
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
+## Skill Configuration
 
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
+### Source Discovery And Verification
 
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -80,7 +79,7 @@ Compare aspirational targets with what the team can realistically influence thro
 
 ### 6. Deliver The Recommendation
 
-Keep the final recommendation concise and decision-oriented. Use $build-report when the user asks for a polished artifact; otherwise return a compact metric-design brief with:
+Keep the final recommendation concise and decision-oriented, and deliver it inline by default. Do not load `$build-report` merely because the KPI framework uses evidence, compares candidates, or contains several metrics. Use `$visualize-data` and `$build-report` when a data visual would materially improve the answer, such as by showing how a proposed target compares with historical performance or benchmarks, or by clarifying tradeoffs or candidate scoring. Honor an explicitly requested report, dashboard, notebook, spreadsheet, native document, or slide deck as the primary artifact. The recommendation should include:
 
 1. initiative summary
 2. recommended metric candidates, with definition and rationale
diff --git a/plugins/data-analytics/skills/gather-business-context/SKILL.md b/plugins/data-analytics/skills/gather-business-context/SKILL.md
index c7b7166..b75e67c 100644
--- a/plugins/data-analytics/skills/gather-business-context/SKILL.md
+++ b/plugins/data-analytics/skills/gather-business-context/SKILL.md
@@ -1,17 +1,17 @@
 ---
 name: gather-business-context
-description: "Gather business context from connected or provided sources so downstream analysis starts with the right framing. Use before deeper analysis when an analytical question depends on context the prompt does not provide, for example to understand what a metric means, how the work is defined, what changed recently, or which sources should be checked."
+description: "Gather business context from connected or provided sources so downstream analysis starts with the right framing. Use when an analytical question depends on missing context, such as what a metric means, what changed recently, or which sources should be checked. If the same prompt asks for diagnosis, recommendation, or a deliverable, gather context first and continue to the focused skill."
 ---
 
-# Gather Business Context
+## Boundary
 
-Use this skill to collect the business context needed to understand an analytical question before doing deeper work. Focus on what the topic is, why it matters, what changed or is being decided, who or what source is closest to the work, and which definitions or artifacts should frame the analysis. This is a retrieval and extraction skill: gather enough context to set up the next step, not a final report, root-cause analysis, or broad background scan. Skip it when the prompt already provides the needed context or the task is fully self-contained.
+Use this skill to gather framing context, not to complete the downstream analysis.
 
-## Skill Configuration
+If the same request also asks for a diagnosis, recommendation, dashboard, report, or other analytical deliverable, return only the context needed for that next step and continue with the appropriate focused skill, such as $metric-diagnostics, $product-business-analysis, $build-dashboard, or $build-report.
 
-### User Context
+# Gather Business Context
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Use this skill to collect the business context needed to understand an analytical question before doing deeper work. Focus on what the topic is, why it matters, what changed or is being decided, who or what source is closest to the work, and which definitions or artifacts should frame the analysis. This is a retrieval and extraction skill: gather enough context to set up the next step, not a final report, root-cause analysis, or broad background scan. Skip it when the prompt already provides the needed context or the task is fully self-contained.
 
 ## Workflow
 
@@ -27,9 +27,8 @@ Start broad enough to avoid missing relevant context. If too much comes back and
 
 ### 3. Search From Discovery Points Toward Authoritative Artifacts
 
-Before searching deeply, identify the enabled or provided source families likely to contain useful context for the task. Start from saved user-context and semantic-layer anchors when they exist, then make a focused pass across the relevant connected or provided apps when they can establish current definitions, decisions, source-of-truth context, or useful verification. Do not search every connector by default, but do not stop after one good source when another likely source could add useful detail.
-
-Within those source families, start where the task is most likely to reveal useful context or links, such as a source named by the user, a report or dashboard, a planning document, a work tracker, or an owner discussion. Follow linked artifacts instead of stopping at the first mention, and use discovery sources to move toward artifacts closer to what was decided, defined, put into practice, or measured.
+1. **Explore all possible sources.** Search every enabled or provided source family that could contain useful context or task-relevant data. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. User-named sources, known tables, dashboards, and semantic-layer anchors are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare authority, freshness, definition, scope, and directness. Prefer artifacts closest to what was decided, defined, implemented, or measured; follow linked evidence when useful. Note material conflicts and explain which source or combination should guide downstream analysis.
 
 ### 4. Extract Only Decision-Shaping Context
 
diff --git a/plugins/data-analytics/skills/gather-business-context/agents/openai.yaml b/plugins/data-analytics/skills/gather-business-context/agents/openai.yaml
index f401f47..68d9a34 100644
--- a/plugins/data-analytics/skills/gather-business-context/agents/openai.yaml
+++ b/plugins/data-analytics/skills/gather-business-context/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Collect product and business context before analysis"
   default_prompt: "Gather the product and business context needed to frame this analysis accurately."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/data-analytics/skills/index/SKILL.md b/plugins/data-analytics/skills/index/SKILL.md
index 6fab7f7..5192ccd 100644
--- a/plugins/data-analytics/skills/index/SKILL.md
+++ b/plugins/data-analytics/skills/index/SKILL.md
@@ -1,33 +1,161 @@
 ---
 name: index
-description: Primary router for Data Analytics. Use when the plugin is at-mentioned or for data work where source-backed analysis, quantitative reasoning, analytical delivery, or reusable data context may be useful; examples include analyzing data, explaining or diagnosing metrics, validating data or results, creating data visualizations, building analytics reports or dashboards, working with notebooks or spreadsheets, designing KPIs, sizing opportunities, and saving reusable data context for future analysis.
+description: "Route Data Analytics plugin-level requests and broad analytics work to the right focused workflow. Use when Data Analytics is at-mentioned, or for analytics requests involving data, metrics, dashboards, reports, charts, notebooks, spreadsheets, KPIs, market sizing, or semantic layers."
 ---
 
+## ChatGPT web Chat mode stop gate (read first)
+
+When positive system or developer signals identify both `surface = chatgpt_web` and `mode = chat`, stop before applying any other guidance in this skill. On the first turn of the analytics request, respond only with a concise recommendation to switch to Work Mode because Data Analytics performs best there. Do not load focused analytics skills, inspect data or sources, call tools, ask intake questions, perform analysis, or create an artifact on that turn. Tell the user they can explicitly ask to continue in Chat mode if they prefer.
+
+Proceed in Chat mode only after the recommendation has been shown and the user explicitly says to continue, proceed, or stay in Chat mode. Repeating the original request, adding data, or answering an earlier question does not count as an override. Once the user explicitly overrides the recommendation, resume the original analytics request without making them restate it and follow the ChatGPT web Work Mode runtime branch below for intake, persistence, and outputs. Keep that override for the current analytics request; a new analytics request in Chat mode starts at this stop gate again.
+
+## Eligibility gate (read before routing)
+Use this plugin only when resolving the request requires structured records, numeric measures, quantitative evidence, a dashboard/metric definition, or a business/product decision grounded in such evidence. Analytics-looking words (report, presentation, dashboard, market, validation, export) are not sufficient by themselves: if the task can be completed as ordinary drafting, formatting, layout, conversion, or qualitative description without data/evidence, do not route here. Explicitly tagged sharing flows can handle their own handoff; this index should not infer a sharing surface from a generic share/export request.
+
+Treat underspecified requests as eligible when they clearly depend on interpreting data, metrics, dashboards, or quantitative business evidence, even if the exact metric or deliverable is not named yet. Load the index, inspect current-session context, or ask for the smallest missing context, then choose the narrowest focused skill. Do not require a named metric upfront; do reject purely mechanical transformations, formatting, code fixes, syntax snippets, or generic explanations that do not require interpreting evidence.
+
+When eligible, choose the most specific analytical skill; when uncertain, ask one clarification rather than opening a generic report/export skill.
+
+## Launch/segment decision cue
+Eligible product-analytics requests can ask for a product launch, rollout, prioritization, segmentation, experiment readout, A/B test interpretation, or ship/hold/iterate tradeoff recommendation under stated or to-be-collected assumptions/constraints. Treat those as analytics workflows when they cite metrics, confidence/uncertainty, guardrails, segments, or structured evidence, even when the first step is context collection; pair context with product-business-analysis and include build-report whenever product-business-analysis is selected unless the user explicitly waives report creation or selects another primary artifact.
+
+## Metric definition/source-of-truth disputes
+When teams disagree about which metric definition, dashboard, extract, owner, or source of truth should control a decision or executive reply (for example revenue/ARR, activation, retention, funnel, or regional totals), route as analytics even if the immediate output is a short Slack/email recommendation. Prefer `analyze-data-quality` for comparability/backfill/grain/source conflicts and `design-kpis` for canonical definition/guardrail ownership; use both when the request asks which definition should govern.
+
+## Staged analytics workflow follow-through
+If one request says to first ask for or collect owner, constraints, assumptions, or context and then use that information for an analytics decision, recommendation, dashboard, or report, do not stop after only asking the clarification. Load `$gather-business-context`, then the most relevant analysis skill. When that skill is `$product-business-analysis`, `$metric-diagnostics`, `$kpi-reporting`, or `$market-sizing`, load `$build-report` as part of the same route unless the user explicitly waived report creation or selected another primary artifact. For any other durable narrative deliverable, load `$build-report` before clarifying. Ask the clarification after loading those skills if information is still missing.
+
+
+
 # Skill Purpose
 
-Route broad Data Analytics requests to the right focused workflow. Treat invocation of this index as strong intent to use this plugin; default to loading and following any relevant focused skill for related analytics work. When a related action is ambiguous with globally installed skills or plugins, prefer the Data Analytics skill when the user is asking for portable, source-grounded analytical outputs rather than a company-specific app workflow.
+Route broad Data Analytics requests to the right focused workflow. Treat invocation of this index as strong intent to use this plugin when the request needs quantitative evidence, source verification, metric reasoning, or a decision grounded in data; prefer focused analytics skills over generic report/export handling.
 
 ## Skill Configuration
 
-### User Context
+### Runtime Routing
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Classify `surface` and `mode` separately, and only from positive system or developer signals or genuinely exclusive tools:
 
-### Source Discovery And Verification
+- `surface = codex_desktop` when the environment is explicitly identified as Codex desktop or desktop-only `codex_app` tools are available.
+- `surface = chatgpt_web` when the environment is explicitly identified as ChatGPT in a web browser.
+- `mode = work_mode` when the environment is explicitly identified as Codex Work Mode.
+- `mode = chat` when the environment is explicitly identified as standard ChatGPT chat.
+- Otherwise set the relevant value to `unknown`.
+
+Never infer mode from surface, missing tools, tool failure, operating system, file paths, sandbox details, or network details. Explicit context overrides tool availability. Treat `web_work_mode` as true only when `surface = chatgpt_web` and `mode = work_mode` are both positively identified. Treat `mode = work_mode` with `surface = unknown` as a partial web Work Mode signal for delivery safety: do not select Data Analytics MCP UI delivery for durable reports or dashboards unless `surface = codex_desktop` is positively identified.
+
+After classifying `surface` and `mode`, select the most specific matching runtime branch:
+
+| Runtime branch | Intake | Semantic-layer persistence | Output surfaces |
+| --- | --- | --- | --- |
+| ChatGPT web Chat mode (`surface = chatgpt_web`, `mode = chat`) | Apply the stop gate above before intake. After an explicit override, follow the ChatGPT web Work Mode intake guidance below. | Do not create or update persistence before an explicit override. After an override, follow the ChatGPT web Work Mode persistence guidance below. | Do not create outputs before an explicit override. After an override, follow the ChatGPT web Work Mode output guidance below. |
+| Codex desktop (`surface = codex_desktop`) | Use `request_user_input` for structured intake when available. Use concise conversational choices as the fallback intake path. | Route semantic-layer setup or maintenance to `create-data-context`; local creations use that skill's existing `$CODEX_HOME/skills/<area>-semantic-layer` default unless the user chooses another destination. | Data Analytics MCP UI surfaces are available. Report-mode work routes through `$build-report`, which uses `mcp-app` by default and reaches HTML only under its explicit fallback rules. |
+| ChatGPT web Work Mode (`web_work_mode = true`) | Use `$answers-ask-user-input` or an equivalent native structured intake action for structured intake when available. Use concise conversational choices as the fallback intake path. | Route semantic-layer setup or maintenance to `create-data-context`. Use ChatGPT personal Skills persistence when the Skills install surface is available in the run. | Only after inline delivery is already selected, use native Work Mode rendering. For exactly one supported `bar`, `line`, `pie`, or `scatter` chart, treat `charts_widget_v2` as directly surfaced and emit its live `genui` content reference before fallback; do not self-declare it unavailable, search for it, or print its payload as bare JSON. Keep `app_block` conditional on the host surfacing it. Default durable reports and dashboards to HTML; use connected BI or another non-MCP destination when the user selects it. MCP servers and other callable tools remain valid data sources. |
+| Work Mode with unknown surface (`mode = work_mode`, `surface = unknown`) | Use `$answers-ask-user-input` or an equivalent native structured intake action for structured intake when available. Use concise conversational choices as the fallback intake path. | Route semantic-layer setup or maintenance to `create-data-context`. Use portable persistence unless a ChatGPT personal Skills install surface is explicitly available in the run. | Treat report and dashboard delivery as web-safe by default: choose HTML, connected BI, or another non-MCP destination. Do not choose Data Analytics MCP UI delivery from an unknown surface when Work Mode is positively identified. |
+| Else: all other or unknown runtimes | Use `$answers-ask-user-input` or an equivalent native structured intake action when available. Use concise conversational choices only when structured intake is unavailable. | Route semantic-layer setup or maintenance to `create-data-context`. That skill chooses an exposed ChatGPT Skills, local Codex, or portable package destination. | Use output surfaces exposed by the runtime and focused-skill rules; default to portable outputs when no runtime-specific output surface is positively identified. |
+
+For ordinary analytics work using supplied context for the current answer, keep the context current-session only and continue through the relevant analytics workflow.
+
+### Saved Data Context And Semantic Layers
+
+Ordinary analytics workflows do not require saved data-context setup. Use current-session context from the request, conversation, connected source reads, uploaded files, pasted artifacts, local repo files, explicitly named semantic layers, or user-created semantic-layer skills discoverable in the current runtime. Saved-context and semantic-layer lifecycle requests route to `create-data-context`. Other analytics context stays current-session only and follows `Runtime Routing`.
+
+### Guided Flow And Source Setup
+
+This index owns task selection, setup-adjacent routing, and guided workflow continuation. Apply its stateless first-task flow after plugin intent is established for get-started requests, open-ended prompt discovery, uploaded or demo data, and walkthrough questions. Send pure capability summaries to `Broad Orientation And Help Requests`. If the user already supplied a concrete task, skip intake and treat it as a custom question.
+
+Use only the current conversation, visible installed plugins and skills, current-run tool results, uploaded or pasted context, and local files. Do not show a source, project, dashboard, table, SQL, file, connector, or provider picker. If the user asks to analyze data but provides no data, source, or discoverable callable source, ask for the smallest useful data artifact or offer the upload/sample fallback. Do not create saved data context from this flow unless the user explicitly asks for that.
+
+#### First-task intake
+
+Use the structured form contract below. On Codex desktop, call `request_user_input` whenever it is available. On every other surface or mode, invoke `$answers-ask-user-input` whenever it is available. If the surface-appropriate structured intake action is unavailable but the runtime exposes an equivalent native structured intake action, use that action. Do not fall back to conversational choices merely because the runtime is `chatgpt_web`, `work_mode`, or an unknown environment. If structured intake is unavailable, present the same task or fallback choices compactly in normal conversation.
+
+Before presenting connected-source options, identify likely data lanes from the runtime-provided skills inventory, installed Apps and Plugins, recommended-plugin list, callable tools, current conversation, uploaded or pasted artifacts, local files, and discoverable semantic-layer skills. For analytics tasks, actively look for source-of-truth lanes such as warehouses or SQL databases, BI dashboards, product analytics systems, spreadsheets, source-definition docs, and relevant communication context; use callable tools, uploaded or pasted artifacts, local files, and current-run source reads to determine which lanes are immediately usable.
+
+Use recommended plugins and installed plugin metadata to identify setup or install paths when a likely source lane is not immediately callable.
+
+If `tool_search` is unavailable and the runtime exposes `ALL_TOOLS`, inspect `ALL_TOOLS` names, descriptions, and plugin provenance. Treat callable tools as stronger evidence of immediate source access than installation recommendations.
+
+When any warehouse or source-system tool is callable, offer a warehouse-backed task first, following the source-of-truth ranking in Connected-source option copy.
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
+1. If the user supplied a concrete task, skip intake and continue at `Custom-question access`.
+2. Otherwise inspect only the runtime-provided installed Apps and Plugins inventory plus callable tools already visible in the session, including custom MCP servers and other callable tools that can read the needed source. Do not read connector records, call `functions.list_available_plugins_to_install`, or treat `.app.json` declarations as installed. If tools are deferred, use `tool_search` when available; otherwise use the runtime's callable-tool registry, such as `ALL_TOOLS`. Query by the name, source type, or task-relevant capability of an already-visible or user-named connector, custom MCP server, or other callable tool to verify callability; do not search only a fixed provider catalog.
+3. If at least one useful connected source exists, invoke `request_user_input` on Codex desktop or `$answers-ask-user-input` elsewhere once with exactly three options: two distinct tasks backed by existing installed connectors or callable tools, followed by `Upload your own`. Prefer distinct source families; if fewer than two are useful, derive multiple non-duplicative tasks from the same connected source's capabilities. Never include sample, installation, or unsupported options.
+4. If no useful connected source exists, offer exactly `Upload data` followed by `Use sample data`. Do not add `Connect a data source`.
 
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
+Use `Dashboard`, `Report`, or `Data task` as the header. Ask `What should the dashboard be about?` or `What should the report be about?` when the artifact is known; otherwise ask `The Data Analytics plugin turns data into insights, recommendations, and decision-ready artifacts. Try it out with one of these prompts:`. Keep labels under 80 characters, omit recommendation suffixes, and preserve the built-in free-form Other field for a custom question. If the surface-appropriate structured intake action is unavailable, render the same choices compactly in chat. Canceled or empty submissions defer the flow.
 
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
+Connected-source form: `{ id: "data_task", options: [{ label: "{connected task 1}", description: "{connected-source description 1}" }, { label: "{connected task 2}", description: "{connected-source description 2}" }, { label: "Upload your own", description: "Upload or paste your own data and produce an analytics report with findings and next steps." }] }`.
 
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+No-source form: `{ id: "data_task", options: [{ label: "Upload data", description: "Upload or paste your data and produce an analytics report with findings and next steps." }, { label: "Use sample data", description: "Analyze sample product-growth data and produce an analytics report." }] }`.
+
+#### Selection handling
+
+- Connected-source task: use its existing connector or callable tool and start the implied focused workflow. For pre-populated connected-source options, always invoke `$product-business-analysis`, `$visualize-data`, and `$build-report`. Choose the controlling source automatically; do not open plugin installation. If the source unexpectedly cannot provide usable evidence, use the fallback form below.
+- `Upload your own` or `Upload data`: ask for the smallest useful export, SQL result, data shape, screenshot, metric definition, or file, then wait.
+- `Use sample data`: start the demo contract immediately without another confirmation.
+- An explicit request to "use sample data" or "using the sample data" selects `Use sample data` when the user did not name or attach a different sample; resolve the bundled demo immediately instead of searching the workspace or asking for an upload.
+- Other text: treat it as a user-authored custom question and continue at `Custom-question access`.
+
+Do not ask the user to choose a provider, project, table, dashboard, query, or export. Do ask for the actual missing data artifact when no usable data or source is supplied or discoverable. Ask for source confirmation only when already-available sources conflict in a way that changes the answer.
+
+#### Custom-question access
+
+Only a concrete task supplied in the user's message or the intake form's Other field may trigger installation. Choose the focused workflow, then decide whether an existing plugin, app, connector, custom MCP server, other callable tool, uploaded or pasted artifact, local file, or current-run context provides enough fresh, authoritative evidence. If yes, choose the controlling source and start.
+
+For missing evidence, check visible or lazy-loadable custom MCP servers and other callable tools that can read the needed source before native connector installation. Treat a custom MCP server or other callable tool as related when its server or tool name, description, action schema, or the user-named source indicates it can read the needed source category. Configured `preferred_plugins` and `.app.json` entries are routing hints, not the source universe.
+
+If required evidence is still missing and a plausible connector lane exists, use the app categories in [.app.json](../../.app.json) to map source families and call `functions.list_available_plugins_to_install` once. If current-run context already lists exact suppressed plugin-install ids, remove those exact ids; do not run the legacy preflight just to obtain suppressions. Then call `functions.request_plugin_install` sequentially for every exact returned candidate useful to the task. Do not guess ids, narrow to one generic "best" candidate, stop after the first useful candidate, call installs in parallel, or use `request_connector_setup`. When an install is declined or unconfirmed, record that exact id with `record_plugin_install_suppression.py` and continue with other useful candidates.
+
+Warehouse exception: if the custom task needs structured data, no warehouse is usable, and the user named none, keep every returned warehouse candidate. Ask which warehouse to use, naming all of them; install only the selected exact candidate. If one is returned, offer it directly. Never choose by manifest order or configured preference.
+
+If setup is unavailable, declined, unconfirmed, canceled, or still yields insufficient evidence, you MUST ask this fallback question before ending the same turn: `{ id: "fallback_next_step", question: "I don't have enough data to complete this task. How do you want to proceed?", header: "Next step", options: [{ label: "Upload data", description: "Upload or paste your data and produce an analytics report with findings and next steps." }, { label: "Use sample data", description: "See a clearly labeled synthetic product-growth demo; it will demonstrate the workflow without answering the real-data question." }] }`. Do not add another custom option or recommendation suffix.
+
+After successful setup, start the focused workflow automatically. Do not show post-setup flow-control choices.
+
+#### No-source completion invariant
+
+Any path that determines there is no usable data for the active task MUST offer `Upload data` and `Use sample data` before the turn ends. This includes no useful connected source, a required source being unavailable, an install tool being unavailable, an install prompt being declined, dismissed, canceled, or left unconfirmed, a successful install that still cannot return usable evidence, and a connected source that unexpectedly lacks sufficient evidence.
+
+Do not end with only a missing-source explanation, an installation suggestion, or an instruction to connect a source and retry. An installation prompt may come before the fallback question, but it never replaces the fallback question unless setup succeeds and the source returns usable evidence in the current run. Use the exact `fallback_next_step` form above for concrete tasks. For `Use sample data`, say explicitly that the bundled demo is synthetic and will demonstrate the workflow rather than answer the user's real-data question.
+
+#### Connected-source option copy
+
+Treat a visible installed or callable surface as warehouse-like when its name, description, or actions indicate warehouse, SQL, query, table, schema, dataset, or database access. Rank useful options by source-of-truth fit: warehouse/source system first, then BI, product analytics, GitHub, tabular Drive/files, and finally the best-fit document or communication source. A task must remain executable through its connected source.
+
+Use source-specific labels for non-warehouse tasks and keep warehouse labels provider-neutral. If one connector fills multiple slots, vary the task by real connector capability instead of repeating copy. These are defaults, not hidden prompts:
+
+| Source | Label | Description |
+| --- | --- | --- |
+| Warehouse or source system | `Analyze business data` | Analyze warehouse or source-system data for trends, segments, outliers, and next steps. |
+| BI/dashboard | `Analyze dashboard trends` | Analyze dashboard or BI data for trends, gaps, and follow-up cuts. |
+| Product analytics | `Analyze product usage` | Analyze events, funnels, retention, experiments, and behavior changes. |
+| GitHub | `Analyze GitHub activity` | Analyze issues, pull requests, reviews, and blockers. |
+| Email | `Analyze email trends` | Analyze threads for themes, trend signals, follow-ups, and next steps. |
+| Drive | `Analyze Drive files` | Analyze relevant Drive data for findings and next steps. |
+| Calendar | `Analyze meeting patterns` | Analyze meeting topics, attendees, length, frequency, and next steps. |
+| Notion | `Analyze Notion content` | Analyze pages and databases for project status, decisions, and themes. |
+| Slack | `Analyze Slack activity` | Analyze messages for active topics, blockers, decisions, and follow-ups. |
+| Teams | `Analyze Teams messages` | Analyze chats and channels for topics, actions, blockers, and decisions. |
+| SharePoint | `Analyze SharePoint files` | Analyze relevant SharePoint data for findings and next steps. |
+
+#### Demo data
+
+Show `Use sample data` only when no useful connected source exists or a selected workflow still lacks usable evidence. Resolve [demo-product-growth.csv](../../assets/demo-product-growth.csv) relative to this skill, label it synthetic, analyze it with reproducible SQL without inventing rows or findings, and route it through `$product-business-analysis`, `$visualize-data`, and `$build-report`; let `$build-report` choose exactly one report delivery mode.
+
+### Source Discovery And Verification
+
+Use the relevant semantic layer as a starting map, not a boundary.
+
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
 Before querying sources, building artifacts, or drawing conclusions, determine whether the answer requires a specific source of truth.
 
-If a required source is unavailable, stop that path. Tell the user what source is needed, ask them to make it available or provide a reviewed fallback, and do not treat weaker substitutes as equivalent.
+If a required source is unavailable, stop that path. Tell the user what source is needed and do not treat weaker substitutes as equivalent. Then apply the mandatory `No-source completion invariant`: offer uploaded data or the clearly labeled synthetic demo before ending the same turn.
 
 If the missing source is only optional enrichment, continue with the strongest available evidence and label the gap when it materially affects the answer.
 
@@ -47,31 +175,35 @@ When referencing sources inline, prefer clickable Markdown links over plain brac
 
 Every Data Analytics plugin run follows this order:
 
-1. Run user-context preflight.
-2. If the user explicitly asks to onboard, set up, customize, remember, save, inspect saved context, reset setup, ask what Data Analytics remembers, asks what Data Analytics can do or how to use it while onboarding is missing or active, or create, add, refresh, inspect, or maintain a semantic layer, route primarily to `user-context`.
-3. Apply semantic-layer lookup from the preflight envelope when the request names or implies a product area, metric, table, dashboard, query, or recurring business question.
-4. Apply Source Discovery And Verification before source queries, document search, notebook work, report building, dashboard wiring, or conclusions.
-5. Apply the Source Access Guardrail before source queries, document search, notebook work, report building, dashboard wiring, or conclusions.
-6. Choose the response mode: `inline` for bounded lookups and `report` for explanations, decompositions, recommendations, or larger analytical answers.
-7. Select the minimal primary/supporting skills, then do one companion-skill pass across installed skills for clearer non-analytics surfaces, semantic layers, or methods.
-8. Read and follow the selected skill bodies before source queries, report building, supporting-skill execution, or final drafting.
-9. Before final response, apply the focused workflow's completion gates plus any preflight `final_obligations`.
+1. Handle pure capability-summary requests with `Broad Orientation And Help Requests`; apply `Guided Flow And Source Setup` to open-ended action or prompt discovery, first-run task selection, explicit guided-flow requests, and setup-adjacent prompts that should choose and run a data task before deeper setup.
+2. If the user asks to save data context or create, update, inspect, or repair a semantic layer, route to `create-data-context`; it uses the current runtime's classified `surface` and `mode` values to choose ChatGPT personal Skills, local Codex, or portable package persistence.
+3. If the user asks to answer a data question using an existing semantic layer, treat the layer as context and continue with the appropriate analytics workflow.
+4. Apply semantic-layer lookup only when the user names a semantic layer or path, or a relevant semantic layer is already discoverable from the current runtime.
+5. Apply Source Discovery And Verification before source queries, document search, notebook work, report building, dashboard wiring, or conclusions.
+6. Apply the Source Access Guardrail before source queries, document search, notebook work, report building, dashboard wiring, or conclusions.
+7. Choose the response mode: default to `report` whenever the run analyzes data and produces findings; use `inline` only for an explicitly requested quick answer or a truly bounded lookup with no interpretation.
+8. Select the minimal primary/supporting skills, then do one companion-skill pass across installed skills for clearer non-analytics surfaces, semantic layers, or methods.
+9. Read and follow the selected skill bodies before source queries, report building, supporting-skill execution, or final drafting.
+10. Before final response, apply the focused workflow's completion gates. Treat create-data-context output as optional unless the user explicitly asked to save data context or work on a semantic layer.
 
 #### Response Mode
 
-Use `inline` for bounded factual or computational answers that can be delivered in chat without a durable artifact: metric lookups, schema or table questions, one-cut rankings, and simple comparisons.
+Default to `report` whenever a Data Analytics run analyzes real or sample data and produces findings, explanations, comparisons, decompositions, KPI readouts, diagnostics, recommendations, or decision support. The user does not need to say "report". If the result would benefit from evidence-backed narrative, source context, caveats, a visual, or reuse outside the current chat turn, choose `report`. When uncertain between `inline` and `report`, choose `report`.
+
+Use `inline` only when the user explicitly asks for a quick chat answer or the request is a truly bounded factual or computational lookup, such as one metric value, a schema or table question, or a single calculation that needs no interpretation, visual, source narrative, or durable handoff. A short prompt is not automatically an inline task: comparisons, rankings, trends, segment cuts, concentration, and movement across multiple values or time points should normally produce a report.
 
-Treat short quantitative prompts as Data Analytics work when answering them requires explaining how numbers compare, break down, concentrate, or move across multiple values, groups, or time points. Keep these routes lightweight by default: use `inline` for bounded answers and escalate to `report` only when the user asks for explanation, diagnosis, recommendation, or a durable artifact.
+Do not classify a prompt as bounded merely because its evidence might fit in one table or chart. A question asking which dimension, category, cohort, entity, segment, or driver drove a metric's growth, decline, change, gap, or variance is a diagnostic route: use `$metric-diagnostics`, include `$build-report` unless the user explicitly waives report creation or selects another primary artifact, and include `$visualize-data` when a chart would clarify the contribution pattern. Current-value lookups, totals, simple time series, and direct current-value rankings remain inline when they do not ask for drivers, movement, variance, diagnosis, recommendation, sensitivity, or a readout.
 
-Use `report` for explanation, diagnosis, decomposition, synthesis, recommendation, or larger analytical answers whose value materially improves from a durable artifact. When the user asks to interpret analytical source material or reviewed results, choose report mode when the answer needs evidence-backed narrative, caveats, source metadata, or a reader-facing artifact. $product-business-analysis, $metric-diagnostics, and $kpi-reporting imply `report`; every `report` route includes $build-report unless the user explicitly waives file or report creation.
+Every `report` route includes $build-report unless the user explicitly requests an inline, chat-only, brief/no-artifact answer, asks not to create a report/file/artifact, or selects another primary artifact such as a dashboard, notebook, spreadsheet, native document, or slide deck. $product-business-analysis, $metric-diagnostics, $kpi-reporting, $market-sizing, and stakeholder-facing $analyze-data-quality runs imply `report` by default. A quick KPI status update or brief KPI readout is an inline output choice unless the user also asks for a report, document, deck, or durable artifact. Do not infer a waiver from the absence of the word "report", a direct diagnostic question, or a request for an estimate or readout.
 
-After choosing `inline` or `report`, use `$visualize-data` when a visual would make the result easier to understand, especially for category comparisons, part-to-whole breakdowns, rankings, movement over time, or more than a handful of comparable values, rows, categories, or time points. Prefer a visual pass over a scan-heavy table, and let `$visualize-data` choose the form, decide whether to render a chart, and align any table or prose to the visual takeaway.
+After choosing `inline` or `report`, use `$visualize-data` when a visual would make the result easier to understand, especially for category comparisons, part-to-whole breakdowns, rankings, movement over time, or more than a handful of comparable values, rows, categories, or time points. Prefer a visual pass over a scan-heavy table, and let `$visualize-data` choose the form, decide whether to render a chart, and align any table or prose to the visual takeaway. For report-mode runs, include `$visualize-data` whenever charts or figures are needed; primary analysis skills pass visual intent and evidence forward rather than rendering charts directly.
 
 #### Skill Selection
 
 - Pick the smallest useful set of primary/supporting skills.
 - For report-mode runs, state the selected route once in a progress update, such as `Route: product-business-analysis + product semantic layer + build-report`.
-- Use `user-context` for explicit onboarding/setup/context requests and for durable Data Analytics preferences, source pointers, semantic-layer registry, semantic-layer setup or maintenance, or saved workflow notes.
+- Use this index's guided gate for source/task setup across all Data Analytics skills, including explicit setup, get-started, first guided workflow, setup-status, offline/demo fallback, walkthroughs, and active guided-flow continuation requests. Keep that gate free of unsolicited saved data-context or semantic-layer creation.
+- Use `create-data-context` for explicit semantic-layer setup or maintenance outside ordinary data work.
 - Treat a plugin mention as a starting point, not a boundary. Add an installed external skill whenever it is the clearer owner of a complementary subtask, runtime surface, delivery surface, artifact type, or domain-specific method.
 - Do not reject an external skill merely because a bundled Data Analytics skill partially overlaps with it.
 - Do not maintain worked route recipes here. Once selected, the chosen skills own detailed step order, supporting triggers, and output contracts.
@@ -79,37 +211,35 @@ After choosing `inline` or `report`, use `$visualize-data` when a visual would m
 
 If several focused skills apply, sequence them in the order that creates the most useful analyst workflow. For example, metric diagnostics may precede KPI reporting, semantic-layer setup may precede dashboard or report work, and product-business analysis may feed a recommendation-ready report. Keep this index as a router; do not perform focused workflow logic here.
 
-Before finalizing future Data Analytics instruction edits, run `python3 plugins/data-analytics/skills/user-context/scripts/validate_user_context_preflight.py` from the repository root. Treat a missing mandatory pre-answer gate in any `SKILL.md`, including helper skills, as an audit finding to fix before release.
+Before finalizing future Data Analytics instruction edits that touch guided-flow or create-data-context behavior, run `python3 plugins/data-analytics/skills/create-data-context/scripts/validate_data_context_contract.py` from the repository root. This validator checks that guided flow and semantic-layer setup stay routed to their owning skills.
 
 Prefer examples that route to focused skills without extra setup, such as:
 
 ```text
-@Data Analytics diagnose why subscription ARR moved last week.
-@Data Analytics build a KPI framework for the customer onboarding funnel.
+@Data Analytics diagnose why a key business metric moved last week.
+@Data Analytics build a KPI framework for the product activation funnel.
 @Data Analytics analyze paid workspace retention and recommend what to investigate next.
 ```
 
-For follow-up messages such as "yes", "walk me through it", "what happened?", or "show the steps" immediately after a completed guided workflow offers a walkthrough, route to `user-context` and use `../user-context/references/onboarding.md#workflow-walkthrough-on-request`. Explain the observable steps, tool/app calls, retrievals, source gaps, and artifact assembly at a beginner-friendly level without revealing hidden reasoning.
+For follow-up messages such as "yes", "walk me through it", "what happened?", or "show the steps" immediately after a completed guided workflow offers a walkthrough, answer from this index. Explain the observable steps, selected workflow, connector setup attempt, offline or demo-data fallback, clarifying questions, source gaps, and artifact assembly at a beginner-friendly level without revealing hidden reasoning.
 
 ### Broad Orientation And Help Requests
 
 For broad orientation and help requests:
 
-- Handle open-ended Data Analytics asks from this index before choosing a focused workflow.
-- Route requests such as `what can you do?`, `help`, `orient me`, `what should I try first?`, `how do I use Data Analytics?`, setup-adjacent capability questions, and similar plugin-level requests here when the user needs orientation more than a specific artifact.
-- Before using this index-level help answer, check the preflight onboarding status. If onboarding is missing or active and the user asks `what can you do?`, `orient me`, `what should I try first?`, `how do I use Data Analytics?`, or another setup-adjacent capability question, route to `user-context` and render the current onboarding step from `../user-context/references/onboarding.md` instead of this default skill-map answer.
-- Use this index-level help answer when onboarding is complete or quiet, or when the user explicitly asks for a compact capability summary without resuming setup.
+- Handle broad capability-summary asks from this index before choosing a focused workflow.
+- Route pure capability-summary requests such as `what can you do?`, `show me the capabilities`, or `explain Data Analytics` here when the user wants orientation rather than a task choice.
+- Route `what should I try?`, `what should I do?`, `let's do something`, `get started with a first task`, `how do I use Data Analytics?`, or `choose a guided workflow` through `Guided Flow And Source Setup`; route requests to save data context or create, update, inspect, or repair a semantic layer to `create-data-context`.
+- Use this index-level help answer for capability summaries regardless of setup history; only explicit setup requests enter setup-specific handling.
 - Answer from the skill map in this file using the default shape below.
-- Include concrete low-friction next prompts that start with `@Data Analytics`, name the analytics job, and use a realistic anchor.
-- Include a short setup context section from the `data_analytics_preflight.context.connector_setup_summary` envelope when any source is not active, needs a user choice, was skipped, or is otherwise unresolved.
+- Keep the three generic examples below for capability and plugin-detail presentation. The two connected-source tasks plus `Upload your own`, or the no-source upload/sample fallback, belong only to structured first-task intake.
+- Include a short setup context section only when the user asks about setup, available sources, Data Analytics configuration, or the current session already reveals a material source gap.
 - Keep setup context analyst-facing: name the practical source, use model judgment to explain the likely user-experience impact from the source label, configured preferred routes, setup action, and suggested next prompts, then give the smallest next action or fallback.
 - Show at most three highest-impact gaps by default, and never more than five setup-context bullets total. Prioritize gaps in the order most relevant to the examples you are suggesting rather than following a hard-coded impact catalog.
 - If all sources are active, keep setup context to one sentence such as `Your core Data Analytics sources look ready; I'll still try each source only when a workflow needs it.`
-- When the current context or available sources identify a real metric, product area, dashboard, table, SQL query, semantic layer, experiment, funnel, cohort, or decision question, use the relevant focused skill's inline first-run and next-step guidance to make the suggested prompt contextual.
 - For setup context wording, be direct and practical, for example: `You won't be able to properly validate a metric from live tables until a warehouse or SQL source is available, but you can paste SQL, schema details, or exported query results for now.`
-- Use static examples only when no suitable context is available.
-- Do not expose raw status names, onboarding state fields, connector ids, or implementation terms.
-- Do not perform connector reads merely to answer a capability question; use the preflight setup summary and any current session app or tool availability already visible in context.
+- Do not expose raw status names, connector ids, or implementation terms.
+- Do not perform connector reads merely to answer a capability question; use current session app or tool availability already visible in context.
 
 Use this default answer shape for broad orientation and help requests:
 
@@ -125,8 +255,8 @@ Setup context:
 - {Only include when useful: source readiness or gap plus practical impact}
 
 Good first prompts:
-- `@Data Analytics diagnose why subscription ARR moved last week.`
-- `@Data Analytics build a KPI framework for the customer onboarding funnel.`
+- `@Data Analytics diagnose why a key business metric moved last week.`
+- `@Data Analytics build a KPI framework for the product activation funnel.`
 - `@Data Analytics analyze paid workspace retention and recommend what to investigate next.`
 ```
 
@@ -138,24 +268,30 @@ Data Analytics turns connected or provided business data, source-of-truth contex
 
 Semantic layers are source-backed local skills for product, business, metric, source, or reporting areas. They encode canonical metrics, tables, grains, joins, filters, query patterns, caveats, source precedence, and validation gaps.
 
-Before answering questions about a named product area, metric, table, dashboard, SQL query, source choice, join, caveat, or recurring business question, inspect the `semantic_layers` registry returned by user-context preflight and installed skills whose name or description matches `<area>-semantic-layer`, `<area>-data-semantics`, or equivalent semantic-layer wording. If a relevant semantic-layer skill exists, read it before selecting tables, writing SQL, reconciling dashboards, or giving metric definitions. Treat it as the domain-specific semantic map, then consult the connected or provided apps and verify high-stakes claims against the layer's cited sources.
+Before answering questions about a named product area, metric, table, dashboard, SQL query, source choice, join, caveat, or recurring business question, use a semantic layer only when the user names a semantic layer, semantic-layer skill, or path, or a relevant semantic-layer skill is already discoverable from the current runtime. If a relevant semantic layer exists, read it before selecting tables, writing SQL, reconciling dashboards, or giving metric definitions. Treat it as the domain-specific semantic map, then consult the connected or provided apps and verify high-stakes claims against the layer's cited sources.
 
-When no relevant semantic layer exists and the user is asking a repeatable domain question, offer semantic-layer setup through `user-context`. Users can create multiple semantic layers for multiple product or business areas. Do not merge unrelated product areas into one broad layer unless the user explicitly asks for a cross-product semantic layer.
+Semantic layers may guide source selection, analysis conventions, and explicitly requested SQL delivery, but they do not broaden the user's requested output. Apply a semantic-layer preference to include full SQL only when the current user request explicitly asks to see, write, review, debug, or receive SQL or query methodology. A data question answered by executing SQL, including a query-backed answer, is not by itself a request for SQL.
+
+When no relevant semantic layer exists and the user is asking a repeatable domain question, offer semantic-layer setup through `create-data-context`. Users can create multiple semantic layers for multiple product or business areas. Do not merge unrelated product areas into one broad layer unless the user explicitly asks for a cross-product semantic layer.
 
 ## Evidence And Handoff
 
-Data Analytics plugin files use lane placeholders such as `~~structured_data` for whatever tool, connector, MCP server, plugin skill, pasted result, uploaded file, or schema description is available in that category. See [DEPENDENCIES.MD](../../DEPENDENCIES.MD) for provider options. When a required connector, plugin, MCP server, or source-of-truth lane is unavailable, follow the Source Access Guardrail. When an optional lane is unavailable, continue from pasted query results, uploaded files, SQL snippets, screenshots, schema descriptions, or other reviewed evidence and label the gap when it materially affects the answer.
+Data Analytics plugin files use lane placeholders such as `~~structured_data` for whatever tool, connector, MCP server, plugin skill, pasted result, uploaded file, or schema description is available in that category. The configured apps and their categories live in this plugin's [.app.json](../../.app.json); treat it as the canonical mapping for which configured apps can satisfy each category. Other discoverable apps, custom MCP servers, and callable tools can also satisfy a category when they credibly expose equivalent information. When a required connector, plugin, MCP server, or source-of-truth lane is unavailable, follow the Source Access Guardrail. When an optional lane is unavailable, continue from pasted query results, uploaded files, SQL snippets, screenshots, schema descriptions, or other reviewed evidence and label the gap when it materially affects the answer.
 
 Source rules:
 
 - Gather source-of-truth context before writing SQL, notebook code, dashboards, reports, or conclusions.
 - Prefer reproducible notebooks for fresh SQL, Python, statistics, modeling, source reconciliation, or non-trivial metric computation when a notebook materially improves auditability.
 - Preserve relevant SQL, scripts, query permalinks, outputs, source links, and caveats in the final artifact or supporting notes.
+- Keep query provenance separate from the visible answer. Do not paste or reproduce source SQL in the final chat response or reader-facing report narrative unless the user explicitly asks to see, write, review, debug, or receive SQL or query methodology.
+- For ordinary data questions, lead with the answer, evidence, and material caveats. Keep full SQL in `source.query.sql`, a source modal, a query permalink, a notebook or query file, or supporting notes; a source permalink or source action is sufficient in the visible handoff.
 
 Delivery surface boundary:
 
 - Startup must work when the user does not want MCP widget or MCP artifact rendering. In that case, continue through the selected chat, notebook, SQL, HTML, BI, Streamlit, spreadsheet, slide, or other non-MCP surface and keep source notes in that surface's normal supporting artifacts.
-- Use MCP inline widgets or the unified MCP artifact app only when that delivery surface has been selected, is safe, and has not been waived by the user. Before shaping widget/app-specific artifacts, read `../../src/analytics-app-core.md`; keep MCP mechanics out of this startup router.
+- For report-mode work, `$build-report` owns MCP-versus-HTML selection. For other MCP widget or artifact surfaces, follow the selected surface's rules; before shaping widget/app-specific artifacts, read `../../src/analytics-app-core.md`.
+- In `web_work_mode`, treat MCP UI rendering as unavailable by routing policy even when MCP UI tools are visible. Do not call Data Analytics MCP UI tools for delivery in web Work Mode, and do not describe an MCP tool result as rendered above. This restriction does not prevent using MCP servers as data sources.
+- If `mode = work_mode` is positive and `surface` is not positively `codex_desktop`, apply the same MCP UI delivery restriction for report and dashboard surfaces. Do not use an unknown surface classification as permission to render an MCP app artifact; choose HTML, connected BI, or another non-MCP surface.
 - Do not expose hidden reasoning, credentials, secrets, direct personal contact/payment identifiers, or unvalidated calculations in any user-facing surface. Reviewed customer, account, or company names may be included when they are needed for the analysis.
 - Once the analysis commits to a source table in an inline or dashboard route, expose a small deterministic preview when safe through the selected surface's normal preview mechanism.
 - If a preview is unsafe, unavailable, or blocked by access limits, record that briefly and continue from schema, documentation, or other reviewed evidence.
@@ -164,11 +300,12 @@ Delivery surface boundary:
 
 Report completion:
 
+- Once a report-mode analysis has findings, the run is incomplete until a rendered MCP app report or HTML report exists, or a concrete rendering blocker is recorded. Do not silently downgrade to chat prose, a notebook, loose charts, or an inline widget.
 - The report run must choose exactly one report delivery mode through `$build-report`.
-- Choose a non-MCP delivery mode when the user asks for HTML, file/blob sharing, Docs/Slides conversion, offline portability, no MCP app/widget rendering, or when MCP artifact rendering is unavailable, unsafe, or over bounds.
+- Follow `$build-report`'s deterministic surface rule: Codex desktop defaults to `mcp-app`; positively identified web Work Mode, partial Work Mode signals without a positive Codex desktop surface, and runtimes without MCP app report rendering use `html`; unknown runtimes default to portable `html`; desktop reaches `html` only for explicit user or downstream-conversion requirements, or after a concrete failed MCP attempt.
 - Do not end with only an inline/chat summary or a localhost URL. Treat chat summaries as progress updates.
 - If a required deliverable is skipped, include the explicit omission reason in the final handoff.
-- HTML report charts must be Seaborn-generated PNG images.
+- Every selected HTML report path follows `$build-report`'s packaged Recharts runtime contract with a readable same-data static fallback. This includes Codex explicit HTML, web Work Mode, and HTML used for PDF, Google Docs, or Google Slides conversion; delivered HTML must not depend on sidecar chart files.
 - If the report includes charts, evidence tables, or custom visualizations, satisfy $visualize-data's chart contract and QA before embedding them.
 
 Final review:
@@ -188,11 +325,11 @@ Use $design-kpis for goals, primary KPIs, driver metrics, guardrails, scorecards
 
 ### kpi-reporting
 
-Use $kpi-reporting for KPI updates, scorecards, business reviews, executive metric summaries, target or pacing readouts, and leadership-ready performance narratives. Add $metric-diagnostics when the update must explain why a KPI moved.
+Use $kpi-reporting for KPI updates, scorecards, business reviews, executive metric summaries, target or pacing readouts, and leadership-ready performance narratives. Add $metric-diagnostics when the update must explain why a KPI moved, and route the final readout through $build-report unless the user explicitly waives it or selects another primary artifact.
 
 ### market-sizing
 
-Use $market-sizing for TAM/SAM/SOM, opportunity, spend or revenue pool, customer count, unit volume, commercial upside, and sensitivity models.
+Use $market-sizing for TAM/SAM/SOM, opportunity, spend or revenue pool, customer count, unit volume, commercial upside, and sensitivity models, then route the final sizing answer through $build-report unless the user explicitly waives it or selects another primary artifact.
 
 ### metric-diagnostics
 
@@ -212,7 +349,7 @@ Use $build-dashboard for analytical dashboards, scorecards, monitoring pages, BI
 
 ### build-report
 
-Use $build-report to build exactly one durable report surface selected for the user request, such as an MCP app report or an HTML report with Seaborn-generated PNG charts.
+Use $build-report to build exactly one durable report surface selected for the user request, with data visualizations when the analysis benefits from them.
 
 ### report-to-google-doc
 
@@ -234,10 +371,6 @@ Use $gather-business-context for docs, dashboards, chats, planning notes, launch
 
 Use $jupyter-notebooks to create, edit, and verify reproducible notebooks for SQL, Python, statistics, modeling, cohort or funnel analysis, data-quality checks, experiments, market sizing, diagnostics, and report support.
 
-### spreadsheets
-
-Use $spreadsheets to create, edit, inspect, render, verify, export, or Google Sheets-handoff spreadsheet artifacts.
-
 ### validate-data
 
 Use $validate-data to QA methodology, source selection, SQL or query logic, calculations, visualization integrity, caveats, and whether conclusions are supported by evidence.
@@ -246,6 +379,6 @@ Use $validate-data to QA methodology, source selection, SQL or query logic, calc
 
 Use $visualize-data to design, implement, and QA charts for reports, dashboards, decks, notebooks, scorecards, trends, decompositions, funnels, cohorts, distributions, uncertainty, and executive KPI readouts.
 
-### user-context
+### create-data-context
 
-Use `user-context` as the Data Analytics durable context, setup, onboarding, semantic-layer registry, and saved preference owner. Route explicit onboarding, setup, customization, remember or save requests, saved-context inspection, and reset requests here.
+Use `create-data-context` for semantic-layer setup and maintenance. Do not require saved data-context setup before ordinary Data Analytics work.
diff --git a/plugins/data-analytics/skills/jupyter-notebooks/SKILL.md b/plugins/data-analytics/skills/jupyter-notebooks/SKILL.md
index 7ef6e8e..bd7d3b7 100644
--- a/plugins/data-analytics/skills/jupyter-notebooks/SKILL.md
+++ b/plugins/data-analytics/skills/jupyter-notebooks/SKILL.md
@@ -1,17 +1,15 @@
 ---
 name: jupyter-notebooks
-description: "Create, scaffold, edit, refactor, and validate Jupyter notebooks (`.ipynb`) for reproducible SQL/Python analysis, experiments, modeling, tutorials, diagnostics, data-quality checks, market-sizing calculations, and report support. Use when the notebook itself is a deliverable, review artifact, runnable analysis companion, or handoff artifact that other people should be able to skim, rerun, or extend."
+description: "Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable."
 ---
 
-# Jupyter Notebooks
-
-Create clean, reproducible Jupyter notebooks that are easy to skim, rerun, and handoff. Treat the notebook as a reader-facing analysis artifact, not a scratchpad dump. Notebook work is not complete until the notebook executes successfully top-to-bottom, or the execution gap is called out with the exact validation steps needed to reproduce it.
+## Related Skills
 
-## Skill Configuration
+Use $validate-data when notebook results support a recommendation, shared claim, or decision.
 
-### User Context
+# Jupyter Notebooks
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Create clean, reproducible Jupyter notebooks that are easy to skim, rerun, and handoff. Treat the notebook as a reader-facing analysis artifact, not a scratchpad dump. Notebook work is not complete until the notebook executes successfully top-to-bottom, or the execution gap is called out with the exact validation steps needed to reproduce it.
 
 ## Workflow
 
@@ -99,8 +97,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
   dashboard links, extract versions, or input file locations.
 - Make computation deterministic where possible. Avoid hidden state, manually edited intermediate values, out-of-order dependencies, and unexplained cached outputs.
 - Prefer explicit environment setup cells or notes when the notebook depends on nonstandard packages, kernels, credentials, or local files.
-- Include validation status in the final response: executed successfully,
-  partially executed, or not executed, with the reason.
+- Execute the notebook when the task requires a runnable artifact. In the final response, do not add a separate routine validation section for a clean run; surface execution gaps, partial execution, or unrun notebooks with the reason because those affect whether the user can rely on the artifact.
 
 ### Code And Data Hygiene
 
diff --git a/plugins/data-analytics/skills/jupyter-notebooks/agents/openai.yaml b/plugins/data-analytics/skills/jupyter-notebooks/agents/openai.yaml
index ba6af76..403f5f5 100644
--- a/plugins/data-analytics/skills/jupyter-notebooks/agents/openai.yaml
+++ b/plugins/data-analytics/skills/jupyter-notebooks/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Create and validate reproducible analysis notebooks"
   default_prompt: "Create a reproducible analysis notebook with inspectable code, outputs, assumptions, and validation checks."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/data-analytics/skills/kpi-reporting/SKILL.md b/plugins/data-analytics/skills/kpi-reporting/SKILL.md
index a6acd08..63700eb 100644
--- a/plugins/data-analytics/skills/kpi-reporting/SKILL.md
+++ b/plugins/data-analytics/skills/kpi-reporting/SKILL.md
@@ -1,8 +1,7 @@
 ---
 name: kpi-reporting
-description: "Produce leadership-ready KPI updates, scorecards, WBR/MBR/QBR summaries, target and pacing readouts, operating status narratives, and performance updates for known KPIs. Use when the work is to define KPI reporting context, validate metric definitions, present actuals versus comparison or plan, summarize validated drivers, and state implications or next actions."
+description: "Prepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare against targets, explain validated drivers, and state operating implications."
 ---
-
 # KPI Reporting
 
 Use this skill to turn business or product metrics into decision-ready operating readouts for leaders and teams. The job is to define the KPI contract, report status against the right comparison and target, include validated driver context, and state the operating implication clearly.
@@ -15,19 +14,12 @@ Use $metric-diagnostics when the readout needs fresh driver investigation, then
 
 ## Skill Configuration
 
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ### Source Discovery And Verification
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
-
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
-
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -53,6 +45,8 @@ Start with the primary KPI, then add the smallest set of supporting metrics need
 
 Lead with the metric that matters most to the audience. Do not add every available cut or comparison; include the metrics and slices decision-makers actually use, plus any that materially explain this update.
 
+When the primary KPI is top-line, composite, or otherwise not directly actionable, define its driver decomposition before interpreting it. Use an existing metric tree when available. Otherwise identify the smallest useful set of component drivers, such as numerator and denominator, volume and rate, mix, funnel stages, segments, cohorts, or operational inputs. Do not invent a causal hierarchy when source definitions do not support one.
+
 ### 3. Lock Metric Definitions And Sources
 
 Confirm the KPI definition, source, time window, reporting cutoff, comparison period, and any target or pacing expectation before interpreting performance. If a target or pacing basis is missing, ask before treating one as authoritative. Use $analyze-data-quality when source quality issues could change the reported metrics.
@@ -95,15 +89,21 @@ Translate the evidence, driver analysis, and business context into the operating
 
 After the analysis is assembled and before shaping the final readout, use $validate-data to review whether the numbers, methodology, caveats, and evidence support the claimed status, drivers, and implications. Resolve material issues before sharing; carry remaining limitations into the readout.
 
-### 9. Shape The Readout
+### 9. Hand Off The Readout
+
+End by handing the validated KPI readout to $build-report unless the user explicitly requests an inline, chat-only, brief/no-artifact answer, asks not to create a report/file/artifact, or selects another primary artifact. A quick status update, brief readout, or findings-in-chat request is an inline output choice unless the user also asks for a report, document, deck, or durable artifact. When no explicit human waiver was given, the report handoff is mandatory; do not infer a waiver only because the user did not use the word "report".
 
-Use the output shape the user requested. If they did not specify one, ask what format they want before building the readout. Common shapes include an inline written update, a document or report, a slide, or a slide deck.
+Before handoff, make the readout explicit:
 
-After the format is selected, load `references/report-templates.md` and use the matching pattern as a starting point. Adapt it to the audience, evidence, and artifact.
+- headline status and operating implication
+- actuals, targets, pacing basis, and comparison periods
+- validated drivers and unresolved uncertainty
+- audience, cadence, and requested delivery surface when known
+- charts or figures that would clarify the readout
 
-Use $build-report for document or report outputs; it will handle report visuals and route chart work through $visualize-data. For inline updates, slides, or deck drafts that do not go through $build-report, use $visualize-data only when chart selection or visual QA materially affects the readout.
+Load `references/report-templates.md` before handing off and use the matching pattern as source notes for $build-report. Tell $build-report this is a KPI readout and pass the status, pacing, driver, uncertainty, audience, cadence, surface, and visual guidance above. Do not render charts directly from this skill; pass visual intent and supporting evidence to $build-report so $visualize-data owns chart selection and QA.
 
-For slide or deck requests, use the user's requested output surface. If they want a presentation-style artifact, use $build-report to create a report artifact or HTML. If they need a native slide or deck, finalize the KPI story here, then pass the content to the appropriate presentation surface and keep the layout simple. Ask which path they want when the requested output is unclear.
+For native slide or deck requests, use $build-report to create an HTML report first because `$report-to-google-slides` consumes an existing local HTML analytics report and does not convert live MCP app reports directly. Then invoke `$report-to-google-slides` on that HTML report. If only an MCP app report exists, build an HTML report from the same source evidence as a separate delivery mode before conversion.
 
 ## Standards
 
@@ -127,6 +127,7 @@ For slide or deck requests, use the user's requested output surface. If they wan
 
 - Quantify drivers whenever the evidence supports it; do not use descriptive prose as a substitute for sizing the effect.
 - Report the few drivers, contributors, or known non-drivers that matter for interpreting the KPI movement.
+- For top-line KPI movement, structure validated drivers as a compact decomposition: top-line actual, component drivers, largest contributors or non-drivers, and residual or unresolved movement. Use an additive bridge only when the components reconcile cleanly; otherwise explain the relationship and uncertainty.
 - Separate validated drivers from business context or hypotheses.
 - Do not elevate business events into causes unless the timing, affected population, and measured change support the link.
 - State whether the movement is broad-based or concentrated when that changes the operating implication.
diff --git a/plugins/data-analytics/skills/market-sizing/SKILL.md b/plugins/data-analytics/skills/market-sizing/SKILL.md
index 6469bb5..04cc585 100644
--- a/plugins/data-analytics/skills/market-sizing/SKILL.md
+++ b/plugins/data-analytics/skills/market-sizing/SKILL.md
@@ -1,27 +1,26 @@
 ---
 name: market-sizing
-description: "Estimate a market or opportunity size, such as TAM/SAM/SOM, by defining scope, choosing a sizing model, checking connected context and public sources, and presenting transparent assumptions, sensitivity, uncertainty, and validation priorities. Use for market or opportunity sizing; not for KPI reporting or metric diagnostics."
+description: "Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities."
 ---
 
+## Related Skills
+
+Use $visualize-data when the sizing result needs a chart or figure.
+
+Use $build-report to package the final estimate, assumptions, sensitivity, caveats, and source context whenever this skill is selected, unless the user explicitly requests an inline, chat-only, brief/no-artifact answer, asks not to create a report/file/artifact, or selects another primary artifact.
+
 # Market Sizing
 
 Use this skill to produce a defensible estimate of a market or opportunity from connected context, public sources, transparent assumptions, and auditable calculations. The job is to define the market, choose a sound sizing method, distinguish evidence from assumptions, test sensitivity, and state what would most improve confidence.
 
 ## Skill Configuration
 
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ### Source Discovery And Verification
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
-
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
-
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -85,7 +84,7 @@ Keep derived values traceable to formulas or code rather than hardcoded outputs.
 
 Use $jupyter-notebooks when code is needed for source harmonization, calculations, sensitivity analysis, or reusable modeling logic. Keep formulas, inputs, intermediate calculations, and sensitivity logic inspectable.
 
-Use $spreadsheets when the user requests a spreadsheet, workbook, or Google Sheets deliverable, or when a market-sizing model would materially benefit from editable assumptions, sensitivity tables, charts, or polished workbook formatting.
+Use the `$Spreadsheets` skill when the user requests a spreadsheet, workbook, or Google Sheets deliverable, or when a market-sizing model would materially benefit from editable assumptions, sensitivity tables, charts, or polished workbook formatting.
 
 ### 6. Test Sensitivity
 
@@ -97,10 +96,19 @@ Use ranges when uncertainty is material. Do not hide uncertainty behind a single
 
 ### 7. State The Estimate And Validation Priorities
 
-End with the estimate, method, key assumptions, uncertainty, and next validation priorities.
+End by handing the estimate, method, key assumptions, uncertainty, and next validation priorities to $build-report unless the user explicitly waives report creation or selects another primary artifact. This handoff is mandatory when no explicit human waiver was given; do not infer a waiver because the user asked for an estimate or did not use the word "report". This workflow owns the sizing model and conclusion; $build-report owns the reader-facing structure, visuals, evidence placement, and delivery surface.
 
-Close with the market definition, estimate or range, method, main uncertainty drivers, sensitivity takeaways, validation priorities, and practical interpretation for the user's decision.
+Before handoff, make the market-sizing conclusion explicit:
+
+- market definition and measurement unit
+- estimate or range
+- method and calculation chain
+- key assumptions and source support
+- main uncertainty drivers and sensitivity takeaways
+- validation priorities and practical interpretation for the user's decision
 
 If source coverage is thin, say which major inputs rely on proxy assumptions and what source would most improve them.
 
 Use $validate-data when methodology, calculations, assumptions, caveats, or source support need review before sharing.
+
+Do not render charts directly from this skill. If a sensitivity, scenario, funnel, or market-breakdown visual would clarify the estimate, pass that visual intent and supporting evidence to $build-report so $visualize-data owns chart selection and QA.
diff --git a/plugins/data-analytics/skills/metric-diagnostics/SKILL.md b/plugins/data-analytics/skills/metric-diagnostics/SKILL.md
index 82fe49b..191be91 100644
--- a/plugins/data-analytics/skills/metric-diagnostics/SKILL.md
+++ b/plugins/data-analytics/skills/metric-diagnostics/SKILL.md
@@ -1,8 +1,16 @@
 ---
 name: metric-diagnostics
-description: "Diagnose why a metric changed or differs from expectation by reproducing the metric, choosing the right comparison, validating likely drivers, and producing a calibrated explanation. Use when the user needs to understand what drove a metric movement, anomaly, gap, or discrepancy."
+description: "Diagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy."
 ---
 
+## Related Skills
+
+Use $gather-business-context when business context is needed to understand the metric, analysis period, ownership, or plausible explanations.
+
+Use $product-business-analysis when the task asks for a recommendation or tradeoff decision after diagnosing the movement.
+
+Use $analyze-data-quality when dashboard trust, grain, freshness, or source disagreement could affect the metric.
+
 # Metric Diagnostics
 
 Use this skill to diagnose why a metric changed or differs from expectation. Reproduce the metric, define the comparison, quantify the movement, validate likely drivers, and state what is verified, likely, unresolved, and useful to do next.
@@ -11,19 +19,12 @@ Clarify with the user when a missing input would materially change the analytica
 
 ## Skill Configuration
 
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ### Source Discovery And Verification
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
-
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
-
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -55,7 +56,7 @@ Before explaining the movement, confirm that the metric is defined correctly and
 
 Confirm the metric definition, grain, aggregation logic, filters, joins, exclusions, freshness, lineage, and any disagreement between trusted surfaces. Keep this source check focused on issues that could change the answer.
 
-Treat memory, saved context, semantic-layer notes, and familiar table names as source candidates, not source selection. For broad metric questions, run live source discovery against available tables, dashboards, metric docs, semantic registries, or other source-of-truth surfaces before choosing the controlling source. When both are available, inspect at least one business-facing or top-line surface and one lower-level source surface, then state why the selected source owns the answer.
+Treat current context, named semantic layers, and familiar table names as source candidates, not source selection. For broad metric questions, run live source discovery against available tables, dashboards, metric docs, semantic layers, or other source-of-truth surfaces before choosing the controlling source. When both are available, inspect at least one business-facing or top-line surface and one lower-level source surface, then state why the selected source owns the answer.
 
 Use $analyze-data-quality when freshness, grain, joins, missingness, schema drift, outliers, unexpected categories, or distribution shifts could affect trust.
 
@@ -71,7 +72,7 @@ Do not search for causes until the size, timing, and scope of the pattern are ve
 
 Choose the smallest set of cuts and checks likely to explain the pattern or strengthen confidence.
 
-Choose driver dimensions from the metric's operating logic, business context, and source shape. Prioritize drivers the business usually monitors or can act on, not every field available in the source. If the relevant drivers are unclear, use saved context or $gather-business-context to understand how the business explains the metric and what changed around the analysis period.
+Choose driver dimensions from the metric's operating logic, business context, and source shape. Prioritize drivers the business usually monitors or can act on, not every field available in the source. If the relevant drivers are unclear, use current context, a named semantic layer, or $gather-business-context to understand how the business explains the metric and what changed around the analysis period.
 
 When using a lower-level table, do not limit the driver analysis to fields surfaced by the first query. Recreate or join the business grouping needed to answer the question, such as model family, model superfamily, segment, region, cohort, product taxonomy, or customer hierarchy. If the grouping cannot be reconstructed, say so before simplifying the analysis.
 
@@ -119,6 +120,8 @@ The answer should make clear:
 - how much confidence to place in the explanation
 - the implication, next action, or follow-up that matters most
 
+Use $product-business-analysis when the user needs a recommendation or tradeoff decision, not just the diagnostic implication.
+
 Keep implications distinguishable from verified factual reporting so a reader can tell where evidence ends and interpretation begins. Do not claim causality from timing alone; state when an explanation is only a plausible hypothesis.
 
 Use $gather-business-context when the metric result is clear but business context is needed to interpret the `so what` or identify realistic next actions.
@@ -127,4 +130,4 @@ Use $validate-data when methodology, calculations, caveats, or the evidentiary s
 
 Do not treat artifact or report validation as analytical validation. Before handing off, confirm the analysis has the headline metric movement, driver contribution shares or effect sizes, source/window reconciliation, exact executed SQL or query references when queries were used, and caveats that would change interpretation.
 
-Pass the diagnostic substance and supporting evidence to $build-report. Let $build-report own the report surface, presentation polish, reproducibility treatment, and sharing handoff.
+Pass the diagnostic substance and supporting evidence to $build-report unless the user explicitly requests an inline, chat-only, brief/no-artifact answer, asks not to create a report/file/artifact, or selects another primary artifact. This handoff is mandatory when no explicit human waiver was given; do not infer a waiver because the user asked a direct diagnostic question or did not use the word "report". Let $build-report own the report surface, presentation polish, reproducibility treatment, and sharing handoff.
diff --git a/plugins/data-analytics/skills/product-business-analysis/SKILL.md b/plugins/data-analytics/skills/product-business-analysis/SKILL.md
index 84daa55..a67cb79 100644
--- a/plugins/data-analytics/skills/product-business-analysis/SKILL.md
+++ b/plugins/data-analytics/skills/product-business-analysis/SKILL.md
@@ -1,27 +1,24 @@
 ---
 name: product-business-analysis
-description: "Analyze product or business data to inform decisions with focused quantitative work, decision-relevant context, measurable opportunities, and a clear recommendation. Use when the user needs data-backed evidence to choose a direction, prioritize an opportunity, evaluate a change, understand implications, or decide what to do next; not for routine KPI reporting, metric diagnostics, or dashboard building."
+description: "Analyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next."
 ---
 
+## Related Skills
+
+Use $metric-diagnostics when the recommendation depends on explaining a metric movement, anomaly, gap, or discrepancy.
+
 # Product And Business Analysis
 
 Use this skill to answer product or business questions with data-backed evidence, context, and a recommendation. Give the audience enough trustworthy evidence, interpretation, and uncertainty framing to choose a practical next action.
 
 ## Skill Configuration
 
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ### Source Discovery And Verification
 
-Use the relevant semantic layer first when one exists. Treat it as the starting map for candidate metrics, tables, joins, filters, caveats, source precedence, and known conflicts.
-
-Do not stop at the semantic layer or the first plausible source. Search across the relevant available company source lanes, including structured data or data warehouses, dashboards, company docs, team communication, notebooks, code repositories, and other connected company knowledge or data that could change the answer.
-
-For source-backed analytical work, always verify through live source reads. When the answer depends on data, run fresh data queries against the available structured-data sources before drawing conclusions, even when the semantic layer already names likely tables or definitions.
+Use the relevant semantic layer as a starting map, not a boundary.
 
-Use the combined evidence to determine which source controls the answer, note meaningful disagreements, and state why the selected source is authoritative.
+1. **Explore all possible sources.** Search every connected or provided source that could contain task-relevant data or change the interpretation. Within each structured-data source, run fresh catalog or metadata discovery for relevant schemas, datasets, tables, views, models, and metrics. Known sources, tables, dashboards, and semantic mappings are starting points, not stopping points.
+2. **Compare duplicates and conflicts.** When sources overlap or disagree, compare ownership, freshness, definition, grain, coverage, and directness. Use the best authoritative source, or combine complementary sources when needed. Note material conflicts, explain why the selected source or sources control the answer, and verify selected data through live reads before concluding.
 
 ### Source Access Guardrail
 
@@ -119,7 +116,7 @@ If evidence conflicts, say so directly and explain which interpretation is bette
 
 ### 6. Hand Off The Recommendation
 
-End by handing the decision-ready recommendation to $build-report. This workflow owns the analytical conclusion; $build-report owns the reader-facing structure, visuals, evidence placement, and delivery surface.
+End by handing the decision-ready recommendation to $build-report unless the user explicitly requests an inline, chat-only, brief/no-artifact answer, asks not to create a report/file/artifact, or selects another primary artifact. This handoff is mandatory when no explicit human waiver was given; do not infer a waiver because the user asked a direct question or did not use the word "report". This workflow owns the analytical conclusion; $build-report owns the reader-facing structure, visuals, evidence placement, and delivery surface.
 
 Before handoff, make the recommendation explicit:
 
diff --git a/plugins/data-analytics/skills/spreadsheets/SKILL.md b/plugins/data-analytics/skills/spreadsheets/SKILL.md
deleted file mode 100644
index 3a6d895..0000000
--- a/plugins/data-analytics/skills/spreadsheets/SKILL.md
+++ /dev/null
@@ -1,178 +0,0 @@
----
-name: spreadsheets
-description: "Use this skill when a user requests to create, modify, analyze, visualize, or work with spreadsheet files (`.xlsx`, `.xls`, `.csv`, `.tsv`) or Google Sheets-targeted spreadsheet artifacts with formulas, formatting, charts, tables, and recalculation."
----
-
-# Spreadsheets skill
-
-This skill includes requirements and guidance for producing a correct, polished spreadsheet artifact quickly that completes the user's request. When producing spreadsheets, workbooks, or Google Sheets-targeted outputs, you will be judged on layout, readability, style, analytical workbook conventions, and correctness. Follow the requirements below for how to use the APIs effectively and how to verify your output before finalizing work for the user.
-
-For analytical tasks, you are especially judged on correctness and quality. This skill improves spreadsheet construction and formatting; it does not own the analysis logic, source selection, market assumptions, or business conclusion when another workflow skill is the primary route. For analysis prompts, aim for an output that can compete with a strong analyst-built workbook, not just a functional grid. A good default shape is an executive summary or dashboard first, then assumptions/sources, then model/detail sheets. For simpler tasks like creating a template or tracker, prioritize doing the spreadsheet build and edits quickly, while ensuring the user's request is fulfilled.
-
-For additional stylistic best practices, follow: `style_guidelines.md`
-
-Read `charts.md` when creating or editing substantive charts, dashboards, or chart-ready summaries.
-
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
-## Google Sheets-targeted output
-
-### New Creations
-
-For a net-new Google Sheets request, create and verify a local `.xlsx` with this skill first. The native Google Sheets deliverable must then be produced by the Google Drive plugin's spreadsheet import action, `mcp__codex_apps__google_drive_import_spreadsheet`, with `upload_mode: "native_google_sheets"`.
-
-Do not use Computer Use, Browser Use, blank-Google-Sheets creation plus Google Sheets write APIs, or another direct-to-Sheets construction path for net-new Google Sheets unless the user explicitly asks for that alternate workflow. If they do, mention first that output quality is expected to be best when a local `.xlsx` is imported through the Google Drive plugin.
-
-If the Google Drive plugin is unavailable, use the plugin-install/user-elicitation flow to ask the user to install `google-drive@openai-curated`. If the plugin is available but `_import_spreadsheet` is missing, ask the user to reinstall or refresh the Google Drive plugin before continuing with the native Google Sheets deliverable.
-
-After successful native import, the user-facing deliverable is the Google Sheets link. Treat the local `.xlsx` as a build artifact unless the user explicitly asks to keep or receive it.
-
-### Edits
-
-Use the Google Drive plugin's Google Sheets skill for edits to existing Google Sheets. The local `.xlsx` creation and native import workflow above applies only to net-new Google Sheets deliverables.
-
-
-
-# Tools + Contract
-- Use Codex workspace dependencies for spreadsheet artifact work: resolve them through the workspace dependency loader or runtime skill, then treat the returned Node/Python runtimes, package directory, and verification details as authoritative. Do not use system `node`, system `python`, global npm packages, or repo-local installs.
-- Use `@oai/artifact-tool` JS library, which exists in the default Codex workspace dependencies node_modules, for authoring, editing, inspecting, rendering, and exporting spreadsheet `.xlsx` workbooks.
-- Run builders from a writable conversation-specific temp or workspace directory, not from the managed dependency directory. Outputs and scratch files may live under the OS temp directory.
-- Start by setting up the work directory to work with normal Node module resolution: link or junction local `node_modules` to the workspace dependency node_modules so `import "@oai/artifact-tool"` resolves.
-- Prefer one executable `.mjs` builder; patch and rerun it when iterating. Do NOT use shell heredocs or keep extra builder copies.
-- If workspace dependencies or `@oai/artifact-tool` are unavailable, report a setup blocker; do not guess paths, install packages, use system deps, alter module resolution, copy/import bundled internals, or do a broad file system search.
-- Do not search package internals or dump prototypes to discover APIs. Use the API reference below; if blocked, run at most one exact `workbook.help("<api_or_feature>")` query before building.
-- Final response: include a short user-visible summary and standalone Markdown link(s) only to final `.xlsx` artifact(s), one per line: `[Revenue Model - MNST.xlsx](/absolute/path/to/revenue_model_mnst.xlsx)`.
-- Do not mention internal tooling or support artifacts such as builders, rendered previews, JSON/CSV/log files, or scratch files unless explicitly requested.
-- Do not use alternate workbook creation/editing libraries such as `openpyxl`, `xlsxwriter`, or `pandas.ExcelWriter` unless the user explicitly asks for a non-artifact-tool fallback.
-- For analysis outside workbook authoring, use JS or spreadsheet formulas when sufficient. If Python is needed, use bundled Python libraries, save JSON/CSV intermediates, and have the JS builder create the workbook. Keep auditable/user-editable calculations as formulas.
-- For nontrivial work, use `update_plan`: build quickly, verify/render, repair meaningful issues, then finalize without long polish loops. Incrementally rendering your work and assessing overall aesthetics, formatting and correctness along the way is very important (rigorously inspect the output and be confident in quality), but do not get stuck in a long render-verify loop. As part of your plan, think about the best practices and conventions to follow for the specific type of spreadsheet you're creating and the best way to structure the workbook for readability and usability.
-
-# Analysis Workbook Shape
-When a workflow such as `$market-sizing`, `$metric-diagnostics`, `$kpi-reporting`, or `$product-business-analysis` owns the analysis, use that workflow's structure first and focus this skill on workbook quality. Good analysis workbooks usually include:
-- `Summary` or `Dashboard` for top-line answer, key metrics, charts, and interpretation.
-- `Assumptions` or `Inputs` for editable drivers, scenarios, date ranges, and user-controlled variables.
-- `Model`, `Calculations`, or `Detail` for formula-backed derivations and intermediate tables.
-- `Sensitivity` or `Scenarios` when assumptions materially change the conclusion.
-- `Sources` for compact citations, source notes, and provenance.
-- `Checks` only when correctness depends on linked calculations, source reconciliation, or model integrity.
-
-# General Rules
-- Start meaningful edits quickly; avoid long upfront API exploration.
-- Use `references/artifact_tool_api.md` when concrete API syntax is needed.
-- If these skill instructions are already loaded in context, do not spend a shell turn re-reading this `SKILL.md` from disk. Move directly to the prompt, attachments, and workbook build.
-- For workbook with multiple tabs/sheets, create/populate non-formula inputs/tables and sheets prior to populating cross-sheet formulas.
-
-## Approach for quickly building a new spreadsheet
-1. Setup: import `@oai/artifact-tool`, create workbook/sheets for new files.
-2. Build quickly: bulk-write headers/data/formulas; then formatting/validation/conditional formatting; add charts/tables only when needed.
-3. Use additional focused calls if helpful for streamed progress.
-4. Near completion: inspect key ranges, scan formula errors, render all the sheets and verify, run a validation pass
-5. Export `.xlsx`
-
-## Making edits on a spreadsheet
-If a user asks to edit or add to an existing spreadsheet:
-- For visual fix requests, start with the smallest plausible local change rather than applying sheet-wide autofit, wrapping, or restyling.
-- When making edits, ensure existing formulas and patterns are consistent. For example, if asked to add another column or row to a table and there is conditional formatting applied to the whole table, it should extend to the new column or rows as well.
-- If specific cells/rows/columns are specified in prompt, limit edits to those ranges unless a broader change is clearly necessary. The exceptions are when other parts of the spreadsheet depend on them, e.g. if there's a dynamic chart that is based on the range of values in a table and a new row is added, the chart should include that new row. Another example is if conditional formatting was already set for a table from A1:C5, and you add a new column D, the conditional formatting should be updated (or deleted and re-created) to cover A1:D5.
-- For column resizing, avoid autofitting by default: instead, inspect only relevant data range, measure the longest text entry in that range, and set columnWidthPx to an estimated width based on text length (with a reasonable min/max cap). Use autofit only when the user explicitly asks for it.
-
-## Handling queries and questions
-- The user may ask questions about the sheet instead of requesting an edit or a change. Simply answer those questions about the spreadsheet based on the context available rather than making an edit the user didn't intend for. You can use inspect to learn more or directly read values/formulas/tables etc via accessor methods.
-
-# Error Recovery
-On first error:
-1. Read error text.
-2. Run one targeted `workbook.help("<exact_api>")` query only if needed.
-3. Retry with minimal patch (not full rewrite).
-4. Continue from existing workbook state.
-
-Do not loop indefinitely on similar failures.
-
-# Quality Guidelines
-- Keep layout readable and bounded, contents visible:
-  - avoid extreme width/height from unconstrained autofit
-  - cap oversized widths/heights after `autofit` + `wrap_text`
-- Prefer formula-driven logic over manual painted cells when logic is expected.
-- Derived values must be formulas (not hardcoded) and legible.
-- Use absolute/relative references correctly for fill/copy behavior.
-- Do not use magic numbers in formulas; reference cells (e.g. `=H6*(1+$B$3)`).
-- Blank editable templates must look blank/neutral before user data is entered. Count, ranking, best/worst, IRR/RATE/XIRR, variance, and status formulas should guard on required input cells and return `""`, `0`, or a clear "No entries yet" state as appropriate. Alternatively, prefill with a few rows of example data.
-- Include at least one visual summary for tracker/planning requests when appropriate (KPI block, chart, dashboard area).
-- For dashboard, visualization, chart-ready analysis, budget/reporting, trend, schedule/timeline, and KPI prompts with plottable data, include at least one native Excel chart unless a verified export failure remains after simplifying the chart. Do not silently replace all charts with styled tables.
-- For presentation-ready analytical workbooks, plain range formatting alone is usually not enough. Prefer real Excel structures where useful: tables, freeze panes, filters, data validation, conditional formats, and at least one chart/KPI/dashboard visual when the prompt implies summary analysis.
-- In rendered previews of dashboards and summary sheets, check financial values and row labels at normal zoom. Widen columns, adjust row heights, or move chart panels until important numbers and text are not clipped, awkwardly wrapped, or hidden.
-
-# Completion Criteria
-Complete only when:
-- Workbook content is populated and formulas compute.
-- No obvious formula errors in key scanned ranges (no bad refs/off-by-one/circular errors).
-- `.xlsx` saved to `outputs/<unique_thread_id>/`.
-- Layout is organized, legible, and aligned to request style (or default formatting baseline).
-
-# Verification Rules
-Before final response, verify values/formulas and visual quality.
-
-1. Inspect key ranges:
-```js
-const check = await workbook.inspect({
-  kind: "table",
-  range: "Dashboard!A1:H20",
-  include: "values,formulas",
-  tableMaxRows: 20,
-  tableMaxCols: 12,
-});
-console.log(check.ndjson);
-```
-
-Inspect targeting:
-- Prefer sheet-qualified ranges (`"Sheet!A1:H20"`) or `sheetId`.
-
-2. Scan formula errors:
-```js
-const errors = await workbook.inspect({
-  kind: "match",
-  searchTerm: "#REF!|#DIV/0!|#VALUE!|#NAME\\?|#N/A",
-  options: { useRegex: true, maxResults: 300 },
-  summary: "final formula error scan",
-});
-console.log(errors.ndjson);
-```
-
-3. Render sheets/ranges to verify visual output (skip if already verified and no style changes):
-```js
-const blob = await workbook.render({ sheetName: "Sheet1", range: "A1:H20", scale: 2 });
-```
-Make sure you do at least one visual pass of all the sheets in the workbook before the final export.
-
-Visual requirements:
-- Fix severe defects before finalizing: blank/broken charts, clipped key headers or numbers, unreadable colors, obvious formula errors, default blank sheets, or content outside the visible working area.
-- Ensure logical labels or titles appear once, texts are all clearly visible, and merged ranges exist where labels or content intentionally span multiple columns.
-- Do one focused visual repair pass after the initial render. Do not spend additional passes on minor polish once the workbook is correct, legible, and exported; note any minor limitation briefly and finalize.
-
-4. Keep verification compact:
-- Inspect key ranges.
-- Avoid huge NDJSON dumps.
-
-5. Export:
-```js
-await fs.mkdir(outputDir, { recursive: true });
-const output = await SpreadsheetFile.exportXlsx(workbook);
-await output.save(`${outputDir}/output.xlsx`);
-```
-
-6. Finalize immediately after successful export + compact verification.
-- Do not export extra `.xlsx` variants unless asked.
-- Do not keep iterating on alternate designs once requirements are met, unless asked.
-
-# Source, PDF, and Attachment Processing
-- Use bundled runtime libraries for source extraction. For PDF or 10-K/10-Q style inputs, read PDF via bundled Python `pypdf` when available, then use one small structured extraction script to collect all required facts into a dict/JSON object. Avoid many ad hoc `rg`/`sed` passes over the same text.
-- Keep source notes compact: record file name, section/table label, and enough context to audit the number. Do not paste large PDF excerpts into the workbook unless requested.
-- Bundled Python libraries available for extraction/analysis include `pandas`, `numpy`, `pypdf`, `python-docx`, and `reportlab`.
-- Bundled JS libraries available for document/PDF work include `docx`, `pdf-lib`, and `pdfjs-dist`.
-
-# Artifact Tool API Reference
-Read `references/artifact_tool_api.md` when building or editing workbooks and you need concrete JavaScript API syntax, API discovery, import/export patterns, existing-workbook inspection, feature-specific notes, or a runnable example. Use the workflow and verification rules above first; load the API reference only when needed.
diff --git a/plugins/data-analytics/skills/spreadsheets/agents/openai.yaml b/plugins/data-analytics/skills/spreadsheets/agents/openai.yaml
deleted file mode 100644
index f594051..0000000
--- a/plugins/data-analytics/skills/spreadsheets/agents/openai.yaml
+++ /dev/null
@@ -1,9 +0,0 @@
-interface:
-  display_name: "Spreadsheets"
-  short_description: "Create and edit spreadsheet or Google Sheets-ready files"
-  icon_small: "./assets/file-spreadsheet.png"
-  icon_large: "./assets/file-spreadsheet.png"
-  brand_color: "#107C41"
-  default_prompt: "Use $spreadsheets to build or update a spreadsheet artifact with formulas, formatting, and verification."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/data-analytics/skills/spreadsheets/assets/file-spreadsheet.png b/plugins/data-analytics/skills/spreadsheets/assets/file-spreadsheet.png
deleted file mode 100644
index 736d02f..0000000
Binary files a/plugins/data-analytics/skills/spreadsheets/assets/file-spreadsheet.png and /dev/null differ
diff --git a/plugins/data-analytics/skills/spreadsheets/charts.md b/plugins/data-analytics/skills/spreadsheets/charts.md
deleted file mode 100644
index d9b0db7..0000000
--- a/plugins/data-analytics/skills/spreadsheets/charts.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Chart Requirements and Guidance
-
-Use this when creating or editing charts, dashboards, or chart-ready summaries in spreadsheet workbooks. Keep the chart useful first: accurate data binding, readable units, and legible labels matter more than decorative styling.
-
-## Chart Requirements
-
-- Use native Excel charts for chart deliverables when plottable data supports the requested comparison or trend.
-- Chart from a bounded source/helper range whose first row is headers, first column is the displayed category or time label, and remaining columns are plotted series.
-- Link helper values to source cells with formulas when the source data should remain auditable.
-- Keep the full source table available when helper labels are abbreviated or grouped for chart readability.
-- Do not place charts over source data or important notes; reserve a bounded chart area with whitespace around it.
-- Percent, currency, date, and count axes must use an appropriate number format or visible data labels.
-- For narrow chart-edit requests, preserve the requested edit scope and do not silently rewrite unrelated formulas, formatting, data tables, or workbook structure.
-- For chart edits, inspect the visible chart area and source range for formula errors. Preserve unrelated pre-existing errors, mention them in the final response, and fix them only when they directly break the chart or the user asked for repair/audit.
-- Render every meaningful chart sheet before export. Check for blank charts, disconnected or stale ranges, unreadable axis units, clipped labels, overcrowded tick labels, unintended multi-color single-series styling, and visible formula errors near chart inputs or outputs.
-
-## Chart Guidance
-
-- Use charts when they improve the answer; avoid redundant charts that repeat the same point.
-- For dashboards, reporting, and analysis, place charts near the KPI blocks or source tables they explain when charts help the user make the decision.
-- Multiple charts are useful when they communicate distinct KPIs, comparisons, or trends.
-- When the workbook needs both a detailed data table and a chart, keep the detailed table for browsing/filtering and chart a smaller helper range with only the plotted fields.
-- For single-series line charts, explicitly set one consistent line color and marker style. Do not rely on automatic Excel chart styles that may vary point colors, segment colors, or marker symbols inside one series.
-- If markers are used, keep marker shape, fill, and outline consistent across the series unless each point intentionally encodes a category.
-- Prefer a clear chart title, but do not use the title as the only place where units are visible when the axis values need interpretation.
-- Axis titles are optional when the chart title and surrounding worksheet context make the dimension and measure unambiguous. Axis number formatting is not optional when the unit changes how the chart is read.
-- Add data labels when exact values matter or when the axis unit is easy to misread. If data labels make the chart crowded, use fewer labels, improve axis formatting, or enlarge the chart rather than leaving units ambiguous.
-- For long category labels, create readable shortened chart labels when the full labels would clip or wrap badly.
-- For summary tables adjacent to charts, widen the category column, wrap only at natural word breaks, or move long descriptors into a notes column rather than allowing cramped within-word wrapping.
-- For time-based charts, if raw dates would create crowded labels or unreliable date-axis grouping, add a grouped field such as Year, Quarter, Month, or Week to the chart source.
-- If a chart fails export after optional styling, simplify styling before removing the chart: use a helper-range chart, reduce custom axis/series mutations, then retry export.
diff --git a/plugins/data-analytics/skills/spreadsheets/references/artifact_tool_api.md b/plugins/data-analytics/skills/spreadsheets/references/artifact_tool_api.md
deleted file mode 100644
index 63a1c7f..0000000
--- a/plugins/data-analytics/skills/spreadsheets/references/artifact_tool_api.md
+++ /dev/null
@@ -1,466 +0,0 @@
-# Artifact Tool API Reference
-
-Use this reference when building or editing workbooks with `@oai/artifact-tool` and the core `SKILL.md` workflow is not enough. Keep API lookup targeted; do not load this file for lightweight routing or non-spreadsheet analysis.
-
-## Contents
-- Imports and startup
-- Build patterns and conventions
-- API discovery and formula lookup
-- Existing workbook inspection
-- Feature-specific notes and pitfalls
-- Quick API surface
-- Runnable JavaScript example
-
-# Using artifact_tool APIs (JavaScript)
-
-## Imports + Startup
-
-Import existing workbook only when needed:
-```js
-import { FileBlob, SpreadsheetFile } from "@oai/artifact-tool";
-
-const input = await FileBlob.load("path/to/input.xlsx");
-const workbook = await SpreadsheetFile.importXlsx(input);
-```
-
-Import CSV text directly when the source or intermediate data is CSV:
-```js
-import fs from "node:fs/promises";
-import { Workbook } from "@oai/artifact-tool";
-
-const csvText = await fs.readFile("path/to/input.csv", "utf8");
-const workbook = await Workbook.fromCSV(csvText, { sheetName: "Sheet1" });
-```
-Prefer `Workbook.fromCSV(...)` over hand-parsing CSV rows; clean or analyze CSV with Python/Node first only when needed.
-
-Create new workbook:
-```js
-import fs from "node:fs/promises";
-import { SpreadsheetFile, Workbook } from "@oai/artifact-tool";
-
-const workbook = Workbook.create();
-const sheet = workbook.worksheets.add("Inputs");
-```
-
-Final export:
-```js
-await fs.mkdir(outputDir, { recursive: true });
-const output = await SpreadsheetFile.exportXlsx(workbook);
-await output.save(`${outputDir}/output.xlsx`);
-```
-
-## Build Patterns
-- Prefer block writes (`range.values`, `range.formulas`) over per-cell loops. Matrix shape must match the target range (for example `"D4:M4"` should be a 1x10 matrix, row x col).
-- Seed scalar formulas once, then `fillDown()` / `fillRight()`. For dynamic-array formulas (`SEQUENCE`, `UNIQUE`, `FILTER`, `SORT`, `VSTACK`, `HSTACK`), write only the anchor cell and let the result spill after.
-- Use `range.displayFormulas` plus `range.formulaInfos` when you need to understand a spill child or a data-table output cell.
-- You do not need to call recalculate; calculation automatically happens.
-- Date handling:
-  - Prefer real `Date` objects for sortable/charted/formula date columns.
-  - Apply date formats explicitly (for example `yyyy-mm-dd`).
-- Use JSON-serializable values for non-Date cells: `string | number | boolean | null`.
-- If a cell is intended to display literal text that begins with `=`, write it as a value prefixed with a single quote (for example `'=B2*C2`). This includes formula descriptions, validation examples, and labels; do not write these cells through `range.formulas`.
-- Create every worksheet referenced by formulas before writing any cross-sheet formulas.
-- When rebuilding dashboards, delete drawings first with `sheet.deleteAllDrawings()`. `range.clear()` does not remove charts, shapes, or images.
-- Verify with `await workbook.inspect(...)`; use `workbook.help(...)` only when the quick surface below is insufficient.
-- For formula audits or tracing how a formula is calculated, use `workbook.trace("Sheet!A1")` on the key output/check cells. It takes only a cell reference and returns the full tree, so print a capped summary. Do not dump raw traces.
-
-## Conventions
-- Use camelCase API names and option keys.
-- Cell/range addressing: A1 notation (`sheet.getRange("A1:C10")`).
-- Drawing anchors (`sheet.charts`, `sheet.shapes`, `sheet.images`): 0-based `{ row, col }`.
-- Drawing offsets/extents use pixels (`rowOffsetPx`, `colOffsetPx`, `widthPx`, `heightPx`).
-
-## API Discovery
-- Use this quick API surface first.
-- Use `workbook.help(...)` only when blocked by uncertainty.
-- For help queries, start with exact feature/path lookups (`chart`, `worksheet.getRange`, `worksheet.freezePanes`, `range.dataValidation`, `chart.series.add`). If an exact path fails, one broader wildcard search is allowed.
-- Do not repeat semantically similar help queries.
-- If one help query returns 0 matches, reformulate once, then proceed best-effort.
-- `render` can be used to examine an existing workbook visually and for visual verifications.
-
-## Efficient Formula Lookup
-- If you know the function name, use exact lookup first: `workbook.help("fx.PMT", { include: "index,examples,notes", maxChars: 3000 })`.
-- To browse a family, use `fx.*` with a category regex. Useful categories: `financial`, `math-trig`, `statistical`, `lookup-reference`, `logical`, `text`, `date-time`, `information`, `engineering`, `database`.
-- For intent-based lookup, use a short natural query plus a narrow `search` regex of likely functions.
-- Keep `maxChars` bounded; if results are noisy, narrow `search` rather than issuing many similar queries.
-
-Useful help calls:
-```js
-console.log(workbook.help("shape.add", { include: "examples,notes" }).ndjson);
-console.log(
-  workbook.help("*", {
-    search: "fill|borders|autofit",
-    include: "index,examples,notes",
-    maxChars: 6000,
-  }).ndjson,
-);
-console.log(workbook.help("fx.PMT", { include: "index,examples,notes" }).ndjson);
-console.log(workbook.help("fx.*", { search: "financial", include: "index,examples", maxChars: 4000 }).ndjson);
-console.log(workbook.help("fx.*", { search: "math-trig", include: "index,examples", maxChars: 4000 }).ndjson);
-console.log(workbook.help("lookup with fallback", { search: "XLOOKUP|INDEX|MATCH|IFERROR", include: "index,examples,notes", maxChars: 4000 }).ndjson);
-```
-
-## Reading existing/imported workbooks
-- On existing/imported workbooks, get a compact summary via `inspect` to understand what already exists and where.
-- Prefer `inspect(...)` for workbook understanding and discovery across broad areas.
-- Prefer direct getters like `range.formulas` when you already know the target range and need the exact rectangular formula matrix.
-- If formula locations are unknown, prefer `inspect({ kind: "formula", ... })` over reading `range.formulas` across a very large area.
-- Prefer to set `maxChars`, `tableMaxRows`, `tableMaxCols`, and/or `maxResults` to prevent large dumps of data.
-- For suspicious or high-impact outputs, use `workbook.trace("Sheet!A1")` to audit the dependency tree from final output/check cell back to source cells. Trace output can be large, so summarize by depth/node count before logging.
-
-### Inspect for workbook understanding
-- Compact summary:
-```js
-await wb.inspect({
-  kind: "workbook,sheet,table",
-  maxChars: 6000,
-  tableMaxRows: 6,
-  tableMaxCols: 6,
-  tableMaxCellChars: 80,
-});
-```
-- Quick overview of sheet ids and names: `await wb.inspect({ kind: "sheet", include: "id,name" })`
-- Formula discovery in a targeted area: `await wb.inspect({ kind: "formula", sheetId: firstSheetName, range: "A1:Z30", maxChars: 2500, options: {maxResults:50} })`
-- Checking existing styles in a targeted area: `await wb.inspect({ kind: "computedStyle", sheetId: firstSheetName, range: "A1:E10", maxChars: 2500 })`
-- Common `kind` tokens: `workbook`, `sheet`, `table`, `region`, `match`, `formula`, `thread`, `computedStyle`, `definedName`, `drawing`
-- Inspects can also be used to zoom in on specific areas, especially for target edits:
-```js
-await wb.inspect({
-  kind: "region",
-  sheetId: firstSheetName,
-  range: "A1:Z30",
-  maxChars: 2500,
-});
-```
-- Inspect output may include JSON records with `"id"` values (for example `"ws/r5qsk5"`), which you can resolve back to workbook objects with `wb.resolve(...)`:
-- `wb.resolve("ws/...")` -> worksheet
-- `wb.resolve("th/...")` -> comment thread
-
-## Additional feature-specific notes
-
-### Merging cells
-- Merging cells is useful for visual headers, title bands, note/source blocks, and labels that span columns.
-- `range.merge()` merges the target range into one cell; `range.merge(true)` merges across each row in the target range.
-- `range.unmerge()` reverses a merge.
-For example:
-```js
-const range = sheet.getRange("I23:N24");
-range.merge();
-range.values = [["Source note spanning the recommendation panel"]];
-```
-
-## Common API Pitfalls
-- Do not set undocumented attributes on remote objects.
-- `Workbook.create()` starts with no sheets; add one before calling `getActiveWorksheet()`.
-- Use matrix sizes that match target ranges unless you intentionally spill.
-- Create every worksheet referenced by formulas before writing cross-sheet formulas.
-- Avoid full-column formula references such as `A:A`, `$A:$A`, or `Sheet!B:B`. Prefer bounded ranges sized to the editable table, e.g. `$A$6:$A$205`, especially inside `COUNTIFS`, `SUMIFS`, `INDEX`, and lookup formulas.
-- If export fails, checkpoint-export after major blocks to isolate the cause: base sheets, values/formulas, formatting, conditional formatting, tables, charts/rendering.
-- If export fails after formatting or charts, simplify optional styling first: nested border configs, custom chart axis/series mutations, broad autofit/formatting, then nonessential drawings.
-
-## Quick API Surface (High-Value + Common)
-
-### Core workbook/file APIs
-- `import { FileBlob, SpreadsheetFile, Workbook } from "@oai/artifact-tool"`
-- `const workbook = Workbook.create(); const sheet = workbook.worksheets.add("Sheet1")`
-- `const workbook = await SpreadsheetFile.importXlsx(arrayBufferOrFileBlob)`
-- `const xlsx = await SpreadsheetFile.exportXlsx(workbook); await xlsx.save("output.xlsx")`
-- `const inspect = await workbook.inspect({ kind: "sheet", include: "id,name", sheetId, range: "A1:C10" })`
-- `const help = workbook.help("worksheet.getRange", { include: "index,examples" })`
-- `const trace = workbook.trace("Checks!F2")` // summarize before logging
-- Preferred: `const blob = await workbook.render({ sheetName: "Sheet1", autoCrop: "all", scale: 1, format: "png" })`
-- To get the bytes and/or save the blob to file:
-```js
-const previewBytes = new Uint8Array(await preview.arrayBuffer());
-await fs.writeFile(`${outputDir}/preview.png`, previewBytes);
-```
-- `const workbook = await Workbook.fromCSV(csvText, { sheetName: "Sheet1" })`
-- `await workbook.fromCSV(csvText, { sheetName: "ImportedData" })`
-
-### Worksheet selection/creation
-- `workbook.worksheets.add(name)`
-- `workbook.worksheets.getItem(name)`
-- `workbook.worksheets.getOrAdd(name, { renameFirstIfOnlyNewSpreadsheet: true })`
-- `workbook.worksheets.getItemAt(index)`
-- `workbook.worksheets.getActiveWorksheet()` (only after at least one sheet exists)
-
-### Worksheet operations
-- `sheet.getRange("A1:C10")`, `sheet.getRangeByIndexes(startRow, startCol, rowCount, colCount)`, `sheet.getCell(row, col)`
-- `sheet.getUsedRange(valuesOnly?)`
-- `sheet.mergeCells("A1:C1")`, `sheet.unmergeCells("A1:C1")`
-- `sheet.freezePanes.freezeRows(1)`, `sheet.freezePanes.freezeColumns(2)`, `sheet.freezePanes.unfreeze()`
-- `sheet.tables`, `sheet.charts`, `sheet.sparklineGroups` (`sheet.sparklines` alias), `sheet.shapes`, `sheet.images`
-- `sheet.showGridLines = false`
-- `sheet.dataTables`, `sheet.conditionalFormattings`, `sheet.dataValidations`
-- `sheet.deleteAllDrawings()` removes charts, shapes, and images before a dashboard rebuild.
-
-### Range values/formulas
-- `const range = sheet.getRange("A1:C10")`
-- `range.values = [[...], ...]` (2D matrix of values)
-- `range.formulas = [["=..."], ...]`
-- `range.formulasR1C1 = [["=RC[-1]*2"]]`
-- To read: `range.values` / `range.formulas` / `range.displayFormulas` / `range.formulaInfos` (for spill/array formulas)
-- `range.write(matrixOrPayload)` (auto-sizes/spills from anchor as needed)
-- `range.writeValues(matrixOrRows)`
-- `range.fillDown()`, `range.fillRight()`
-  - `sheet.getRange("D2").formulas = [["=..."]]`
-  - `sheet.getRange("D2:D200").fillDown()`
-- `range.clear({ applyTo: "contents" | "formats" | "all" })`
-- `range.copyFrom(sourceRange, "values" | "formulas" | "all")` source and destination must have the same shape
-- `range.copyTo(destRange, "values" | "formulas" | "all")`
-- `range.offset(...)`, `range.resize(...)`, `range.getCurrentRegion()`, `range.getRow(i)`, `range.getColumn(j)`
-- `range.getRangeByIndexes(...)`, `range.getCell(...)`
-- `range.merge()`, `range.merge(true)` to merge across, `range.unmerge()`
-
-### Formatting
-- `range.format` supports `fill`, `font`, `numberFormat`, `borders`, alignments, `wrapText`
-- `range.format.autofitColumns()`, `range.format.autofitRows()`
-- Excel unit sizing:  `range.format.columnWidth = 18`, `range.format.rowHeight = 24`
-- Pixel sizing: `range.format.columnWidthPx = 120`, `range.format.rowHeightPx = 24`
-- `range.setNumberFormat("yyyy-mm-dd")`
-- `range.format.numberFormat = [["0"], ["0.00"], ["@"]]`
-
-### Data Validation
-- `range.dataValidation = { rule: { type: "list", formula1: "Categories!$A$2:$A$4" } }`
-- `range.dataValidation = { rule: { type: "list", values: ["Not Started", "In Progress"] } }`
-- `sheet.dataValidations.add({ range: "B2:B100", rule: { type: "whole", operator: "between", formula1: 1, formula2: 10 } })`
-
-### Conditional formatting
-- Use `range.conditionalFormats.add(ruleType, ConditionalFormatConfig);`.
-- Use `range.conditionalFormats.add(ruleType, {operator, formula, format});`. Choose ruleType, operator, color, and style strings from the inline types below.
-```
-type ConditionalFormatRuleType =
-  | "cellIs" | "CellValue" | "Custom" | "expression"
-  | "colorScale" | "dataBar" | "iconSet"
-  | "containsText" | "notContainsText" | "beginsWith" | "endsWith"
-  | "containsBlanks" | "notContainsBlanks" | "containsErrors" | "notContainsErrors"
-  | "duplicateValues" | "uniqueValues" | "timePeriod" | "top10" | "aboveAverage";
-
-type CellIsOperator =
-  | "greaterThan"
-  | "greaterThanOrEqual"
-  | "lessThan"
-  | "lessThanOrEqual"
-  | "equal"
-  | "notEqual"
-  | "between"
-  | "notBetween";
-
-type ConditionalFormatConfig =
-  | { operator: CellIsOperator; formula: string | number | Array<string | number>; format?: DifferentialFormatConfig }
-  | { formula: string | number; format?: DifferentialFormatConfig }
-  | { colors?: ColorConfig[]; thresholds?: CfvoInput[] }
-  | { color?: ColorConfig; thresholds?: CfvoInput[]; gradient?: boolean }
-  | { iconSet: string; showValue?: boolean; reverse?: boolean; thresholds?: CfvoInput[] }
-  | { text: string; format?: DifferentialFormatConfig }
-  | { timePeriod: "yesterday" | "today" | "tomorrow" | "last7Days" | "lastWeek" | "thisWeek" | "nextWeek" | "lastMonth" | "thisMonth" | "nextMonth"; format?: DifferentialFormatConfig }
-  | { rank?: number; percent?: boolean; bottom?: boolean; format?: DifferentialFormatConfig }
-  | { aboveAverage?: boolean; equalAverage?: boolean; stdDev?: number; format?: DifferentialFormatConfig };
-
-type DifferentialFormatConfig = {
-  fill?: FillConfig;
-  font?: { bold?: boolean; italic?: boolean; color?: ColorConfig };
-  border?: RangeBordersConfig;
-  numberFormat?: string;
-};
-
-type CfvoInput =
-  | "min"
-  | "max"
-  | number
-  | `${number}%`
-  | { type: "min" | "max" | "num" | "percent" | "percentile"; value?: string | number };
-```
-- Rule types (`ConditionalFormatRuleType`): "cellIs" | "CellValue" | "Custom" | "expression"
-  | "colorScale" | "dataBar" | "iconSet"
-  | "containsText" | "notContainsText" | "beginsWith" | "endsWith"
-  | "containsBlanks" | "notContainsBlanks" | "containsErrors" | "notContainsErrors"
-  | "duplicateValues" | "uniqueValues" | "timePeriod" | "top10" | "aboveAverage";
-- Built-in `iconSet` names: `3Arrows`, `3Triangles`, `4Arrows`, `5Arrows`, `3ArrowsGray`, `4ArrowsGray`, `5ArrowsGray`, `3TrafficLights1`, `3Signs`, `4RedToBlack`, `3TrafficLights2`, `4TrafficLights`, `3Symbols`, `3Flags`, `3Symbols2`, `3Stars`, `5Quarters`, `5Boxes`, `4Rating`, `5Rating`.
-- Custom conditional formatting: `range.conditionalFormats.addCustom(expression, {fill, font, border});`
-- `range.conditionalFormats.deleteAll()` / `range.conditionalFormats.clear()`
-
-```js
-const grid = sheet.getRange("B2:J10");
-grid.conditionalFormats.add("colorScale", {
-  criteria: [
-    { type: "lowestValue", color: "#2563EB" },
-    { type: "percentile", value: 50, color: "#FDE047" },
-    { type: "highestValue", color: "#DC2626" },
-  ],
-});
-```
-
-### Tables
-- When adding new tables, set explicit unique names (`TasksTable`, `SummaryTable`).
-- You cannot have multiple tables over the same range. Before adding a table on an existing/imported workbook, confirm the target range does not already overlap an existing table. Prefer the initial compact `inspect` summary over a separate tables-only scan when available.
-- `const table = sheet.tables.add("A1:H200", true, "TasksTable")`
-- `table.rows.add(null, [[...], ...])`, `table.getDataRows()`, `table.getHeaderRowRange()`
-- Read tables: `sheet.tables.items` -> `Table[]`
-- Set + Getters: `table.name`, `table.style`, `table.style`, `table.showHeaders`
-- Toggles for table utilities (set/get): `table.showTotals`, `table.showBandedColumns = true`, `table.showFilterButton`
-- `table.delete()`
-
-### Images
-- `sheet.images.add({dataUrl: "data:image/png;base64,...", anchor: {from: { row: 1, col: 2 }, extent: { widthPx: 160, heightPx: 120 }}})`
-```
-
-### Threaded Comments
-Follow this exact API when adding a note or comment:
-- Required: First, always first call set_self to create an author `workbook.comments.setSelf({"displayName": "ChatGPT"})`
-- Create a new thread with a single comment: `const thread = workbook.comments.addThread({"cell": sheet.getRange("E2")}, "Source: <website>")`
-- To reply to a threaded comment: `thread.addReply("This is a reply to the comment")`
-- To resolve/re-open a thread: `thread.resolve()`, `thread.reopen()`
-
-### Charts
-- When adding or moving charts, do not cover existing data. Put charts in a reserved rectangle with blank gutter columns/rows around the chart area.
-- Fast chart path, no help lookup needed for common line/bar/scatter charts: write a compact helper range with text categories and one column per series, then chart that range.
-- If chart data comes from editable/source data, make the helper range formula-backed instead of copying literal values.
-```js
-sheet.getRange("F4:H7").values = [
-  ["Month", "Revenue", "EBITDA"],
-  ["Jan", 100, 10],
-  ["Feb", 120, 18],
-  ["Mar", 130, 22],
-];
-const chart = sheet.charts.add("line", sheet.getRange("F4:H7"));
-chart.setPosition("J4", "Q20");
-chart.title = "Revenue and EBITDA Trend";
-chart.hasLegend = true;
-chart.xAxis = { axisType: "textAxis" };
-chart.yAxis = { numberFormatCode: "$#,##0" };
-```
-- Fast chart path from range: `const chart = sheet.charts.add("line", sourceRange)` when the source range already has headers and text x-axis labels.
-- Advanced fallback only: avoid manual `chart.series.add(...)` and `chart.legend = {...}` on the first pass unless source-range chart creation does not work (for example, non-continuous data). Use a helper range chart first, then add optional chart styling only if the basic chart renders and exports cleanly.
-- If you want to set specific chart props after the helper-range path is not enough: `const chart = sheet.charts.add("bar", chartProps)`, then checkpoint export before adding optional styling.
-- If using compat positioning, always set position: `chart.setPosition("F2", "M20")`.
-- `sheet.charts.getItemOrNullObject("Chart 1")`, `sheet.charts.deleteAll()`
-- To update x/y-axis, prefer compact config assignments such as `chart.xAxis = { axisType: "textAxis", tickLabelInterval: 2 }` and `chart.yAxis = { numberFormatCode: "$#,##0" }`. These help legibility and visibility.
-- For month/date x-axes, prefer a chart helper range with text labels such as `Jan 2025` or `2025-01`. Do not rely on date axis number formats alone; rendered previews can show Excel serial numbers.
-- Chart types: `"bar" | "line" | "area" | "pie" | "doughnut" | "scatter" | "bubble" | "radar" | "stock" | "treemap" | "sunburst" | "histogram" | "boxWhisker" | "waterfall" | "funnel" | "map"`.
-
-### Sparklines
-```
-const group = sheet.sparklineGroups.add({
-  type,
-  targetRange,
-  sourceData,
-  dateAxisRange,
-  seriesColor,
-  negativeColor,
-  markers,
-  axis,
-  lineWeight,
-  displayEmptyCellsAs,
-  displayHidden,
-});
-```
-- Sparkline type is a string. Empty-cell display mode and axis min/max modes are proto enum numbers on the current facade; inspect nearby tests before setting them directly.
-- Sparkline Inline Type:
-```
-type SparklineConfig = {
-  type: "line" | "column" | "stacked";
-  targetRange: Range | string;
-  sourceData: Range | string;
-  dateAxisRange?: Range | string;
-  lineWeight?: number;
-  displayHidden?: boolean;
-  seriesColor?: ColorConfig;
-  negativeColor?: ColorConfig;
-  axisColor?: ColorConfig;
-  markersColor?: ColorConfig;
-  firstMarkerColor?: ColorConfig;
-  lastMarkerColor?: ColorConfig;
-  highMarkerColor?: ColorConfig;
-  lowMarkerColor?: ColorConfig;
-  markers?: SparklineMarkersOptions;
-  axis?: SparklineAxisOptions;
-};
-
-type SparklineMarkersOptions = {
-  show?: boolean;
-  high?: boolean;
-  low?: boolean;
-  first?: boolean;
-  last?: boolean;
-  negative?: boolean;
-};
-
-type SparklineAxisOptions = {
-  showAxis?: boolean;
-  manualMin?: number;
-  manualMax?: number;
-  rightToLeft?: boolean;
-};
-```
-- Range Alias: `const group = targetRange.sparklines.add(type, sourceRange, sparklineConfig);`
-- Edit And Delete
-```
-group.seriesColor = colorConfig;
-group.markers = markerConfig;
-group.axis = axisConfig;
-group.delete();
-sheet.sparklineGroups.deleteAll();
-```
-
-### Help / Grep
-Use `workbook.help(...)` primarily for obscure/advanced surfaces (for example deep chart axis settings, unusual drawing configs, pivot APIs, or uncommon option schemas).
-- `workbook.help("enum.ShapeGeometry", { include: "index,notes" }).ndjson`
-- `workbook.help("enum.*", { search: "ShapeGeometry|LineStyle", include: "index" }).ndjson`
-- `workbook.help("shape.add", { include: "examples,notes" }).ndjson`
-- `workbook.help("fx.RATE", { include: "index,examples,notes" }).ndjson`
-- `workbook.help("cash flow return rate", { search: "IRR|XIRR|NPV|XNPV", include: "index,examples,notes", maxChars: 4000 }).ndjson`
-- `workbook.help("*", { search: "fill|borders|autofit", include: "index,examples,notes", maxChars: 6000 }).ndjson`
-
-
-### JavaScript example snippet (runnable)
-
-```js
-import fs from "node:fs/promises";
-import { SpreadsheetFile, Workbook } from "@oai/artifact-tool";
-
-const outputDir = "output";
-await fs.mkdir(outputDir, { recursive: true });
-
-const workbook = Workbook.create();
-const sheet = workbook.worksheets.add("Summary");
-
-sheet.getRange("A1:C4").values = [
-  ["Month", "Revenue", "EBITDA"],
-  ["Jan", 100, 10],
-  ["Feb", 120, 18],
-  ["Mar", 130, 22],
-];
-sheet.getRange("D1").values = [["Margin"]];
-sheet.getRange("D2").formulas = [["=C2/B2"]];
-sheet.getRange("D2:D4").fillDown();
-
-sheet.getRange("A1:D1").format = {
-  fill: "#0F766E",
-  font: { bold: true, color: "#FFFFFF" },
-};
-sheet.getRange("B2:C4").format.numberFormat = "$#,##0";
-sheet.getRange("D2:D4").format.numberFormat = "0.0%";
-
-// Helper range links to source cells so edits update the chart.
-sheet.getRange("F1:G1").values = [["Month", "Revenue"]];
-sheet.getRange("F2:G2").formulas = [["=A2", "=B2"]];
-sheet.getRange("F2:G4").fillDown();
-const chart = sheet.charts.add("line", sheet.getRange("F1:G4"));
-chart.title = "Revenue Trend";
-chart.hasLegend = false;
-chart.xAxis = { axisType: "textAxis" };
-chart.yAxis = { numberFormatCode: "$#,##0" };
-chart.setPosition("I1", "P15");
-
-const preview = await workbook.render({
-  sheetName: "Summary",
-  autoCrop: "all",
-  scale: 1,
-  format: "png",
-});
-await fs.writeFile(`${outputDir}/summary.png`, new Uint8Array(await preview.arrayBuffer()));
-
-const xlsx = await SpreadsheetFile.exportXlsx(workbook);
-await xlsx.save(`${outputDir}/summary.xlsx`);
-```
diff --git a/plugins/data-analytics/skills/spreadsheets/style_guidelines.md b/plugins/data-analytics/skills/spreadsheets/style_guidelines.md
deleted file mode 100644
index 72fc4b7..0000000
--- a/plugins/data-analytics/skills/spreadsheets/style_guidelines.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Default Style and Formatting Instructions
-Apply these defaults to all spreadsheet outputs but ensure user provided style reference(s), template, or explicit formatting instructions take precadence. If the user specifies a style, match that style.
-
-## Formatting Baseline
-- If editing an uploaded/template workbook: render first, preserve and match existing style unless user asks to restyle.
-- Typical defaults when unspecified:
-  - content columns: ~10-24
-  - text-heavy columns: cap ~32-40
-  - row heights: ~15-20 (titles may be larger)
-  - avoid oversized body fonts (>12pt) except intentional titles
-- Use fill colors, borders, and merged cells judiciously to give the spreadsheet a professional visual style with a clear layout without overdoing it
-- Add data validation for editable categorical columns (`Status`, `Priority`, `Owner`) where feasible.
-- Unless conflicting style guidelines are provided: style headers, correct number/date formats, sensible column widths, and row heights, light borders.
-- Use larger text only for titles or major section labels.
-- Use blank space or slightly taller section/header rows to separate sections
-- Keep row heights consistent within each section unless wrapped content requires expansion.
-- When text wraps, prefer widening the column before allowing deep multi-line rows; if wrapping is necessary, increase row height just enough to fully show the content.
-- Before editing, inspect all relevant current styling attributes (fills, fonts, borders, merged cells, number formats). If changing values only, never overwrite or clear cell formats.
-- Maintain structural elements (filters, tables, totals rows), and never introduce merged cells in calculation areas.
-- If users are likely to edit the workbook after export, make worksheet cells the live source of truth: any downstream values or visual states that depend on editable inputs should be driven by formulas referencing worksheet cells, then styled with conditional formatting or presentation-only formatting instead of Python-precomputed values or one-time manual fills.
-- Use simple helper values when they make behavior easier to inspect and formatting easier to apply;
-
-## Document structure
-When creating a new spreadsheet, compose a clear visual layout with distinct zones: a title/header area, the primary table or working region, and—when space and task type allow—a secondary adjacent to the main table to cover summary section or instructions for how to use the sheet. When appropriate, design the sheet so it reads like a structured document, not just a matrix of cells. Use layout, scale, and selective merging to create clear sections and give headers enough room to breathe.
-
-- Vary font size and weight intentionally so the sheet has a readable hierarchy: larger for title, medium for section headers, standard for body text.
-- Let major headers occupy more space than body cells so the sheet can feel like a real document with sections, not a uniformly sized table.
-- Size status and other validated categorical columns to the longest expected label plus dropdown space; do not leave values clipped.
-- If the same label would otherwise repeat across many adjacent rows or columns, prefer a grouped header band, merged label, or legend rather than repeating the text cell by cell.
-
-### Create strong visual hierarchy
-Establish at least three hierarchy levels:
-1. page title band (larger type, stronger fill, centered, often merged)
-2. section/header bands (distinct fill, bold text, clear alignment)
-3. body area (light or neutral surface, restrained styling)
-
-### Use a theme palette
-Choose a small coordinated palette and assign colors by role:
-- primary accent for titles or major headers
-- secondary accent for subheaders or section bars
-- soft surface/background fill for the working canvas
-- neutral/light fill for body cells
-- darker text colors for readability
-- avoid harsh contrast, excessive saturation, or too many unrelated fills
-
-### Avoiding gridlines
-Define structure with explicit fills and borders rather than relying on default gridlines. Use subtle internal borders for separation and slightly stronger outside borders to frame sections or cards. Hide gridlines when explicit section styling already defines the sheet.
-
-### Use adjacent whitespace intentionally
-When appropriate, place summary cards, assumptions, instructions, charts, legends, and small supporting lists in unused columns to the right or below the primary table as compact bounded panels sized to their content. Keep these panels visually separate from the main data region with whitespace and aligned edges, but avoid oversized sparse blocks that consume more space than the information warrants. Keep short legends and short validation vocabularies on the main sheet when they fit cleanly; use a helper sheet only when the supporting data is large, heavily reused, or would clutter the main layout.
-
-### Align and format by data type
-Apply semantic formatting to entire columns or blocks:
-- text/descriptive fields left-aligned
-- labels centered or left-aligned depending on context
-- numeric and currency fields right-aligned
-- dates with explicit date formats
-- financial values with explicit currency/accounting formats
-- do not leave important numeric fields in raw General format
-
-### Use typography intentionally but conservatively
-Use one display-style font choice for titles/section headers and one neutral readable font for body content when supported by the workbook viewer. Keep body text modest in size, reserve larger fonts for titles, and avoid mixing many fonts or excessive emphasis.
-
-### Style the body lightly for scanability
-Body regions should remain readable and calm. Use light fills, subtle borders, and minimal emphasis. If one column is the primary descriptive field, it may receive slightly stronger text emphasis to aid scanning, but avoid over-styling entire data regions.
-For dense operational tables, use subtle alternating row banding together with thin light borders so users can track across rows without making the grid feel heavy.
-
-### Prefer visible summaries over buried totals
-Important totals should usually appear in a visible summary area near the top or in a side panel, even if table-footer totals also exist. Use formulas, not hardcoded values, and style summary cards as distinct panels with their own fill and border treatment.
-
-## Colors and borders
-- Use a restrained and professional color palette that matches the nature of the task: neutral text/grid styling, one primary accent family, and at most one secondary accent for exceptions such as warnings or special states.
-- Use thin, light borders for structure; use stronger borders only for important section breaks.
-- If the sheet includes progress indicators, status indicators, timelines, heatmaps, or other cell-based visuals, make them read as consistent visual bands or blocks with restrained fills, uniform repeated-column styling, and clear accents for milestones or special states rather than noisy repeated symbols or unrelated colors.
-- Ensure conditional formatting is applied properly (i.e. such as red for negative, green for positive values)
-
-## Typography and whitespace
-- Use bold sparingly and only to establish reading order.
-- Give titles, summaries, and section breaks visible breathing room so the sheet does not feel cramped.
-
-## Charting and plotting data
-When a spreadsheet includes charts, they should feel like part of the document rather than generic spreadsheet defaults dropped onto the page.
-
-- Create charts from a bounded source range whose first row is headers, whose first column contains the exact x-axis labels to display, and whose remaining columns are the plotted series. If the available data is not already in that shape, write a helper range in that shape first and chart the helper range instead of the raw block. Use clear titles and explicitly set axis titles and unit/number formats whenever the chart communicates a measured value or comparison.
-- When the workbook needs both a detailed data table and a chart, keep the detailed table for browsing/filtering, but place the chart next to a smaller chart-driving range that contains only the fields actually plotted.
-- Choose chart types intentionally (e.g., clustered column for group comparisons, line for trends over time, pie for share of a whole) and place them close to the relevant data.
-- Use tables for structured data that benefits from filtering/sorting; name tables clearly.
-- Do not place a chart so it is overlapping or hides data
-
-### Axes, scales, and labels
-- Use readable axis labels with sensible tick density; do not overcrowd the x-axis.
-- If needed, angle the axis labels if they are at risk of overlapping and this would boost readability.
-- For time-based charts, if raw dates would create crowded labels or unreliable date-axis grouping, add an explicit grouped field such as Year, Quarter, Month, or Week to the chart source and chart that field instead of charting every raw date.
-
-## Citation Requirements
-### Cite sources inside the spreadsheet
-- Use plain-text URLs in spreadsheet cells.
-- For financial models, cite model-input sources in cell comments.
-- For researched row-wise data tables, include source URLs in a dedicated source column.
diff --git a/plugins/data-analytics/skills/user-context/SKILL.md b/plugins/data-analytics/skills/user-context/SKILL.md
deleted file mode 100644
index 7cc92f8..0000000
--- a/plugins/data-analytics/skills/user-context/SKILL.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-name: user-context
-description: Load or manage the Data Analytics plugin's durable source-routing preferences, onboarding logic, setup progress, and semantic-layer registry.
----
-
-# User Context
-
-This skill is the Data Analytics plugin's source-routing, semantic-layer registry, setup, and onboarding layer. It loads explicit future source-routing choices, semantic-layer pointers, onboarding progress, and reusable setup obligations for Data Analytics workflows. It does not act as general Data Analytics memory.
-
-## Mandatory Pre-Answer Gate
-
-- For ordinary Data Analytics preflight, use `scripts/data_analytics_preflight.py` as the default state-read path when local shell access is available.
-- Apply the same gate on behalf of other Data Analytics skills that call `data-analytics:user-context` as part of their own mandatory preflight before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output.
-- When running the preflight script through a command tool, set the tool's `max_output_tokens` to at least `25000` so the complete payload remains visible as the registry grows. If the tool output is truncated, warn the user that Data Analytics could not load all source-routing preferences and semantic-layer registry entries in one pass, then rerun with a higher cap and do not use the payload until the complete output is visible.
-- Treat the script payload as satisfying the read requirement only when it reports read status for `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` and renders the compact context it will use in `context.user_context`, `context.source_preferences`, `context.source_category_config`, `context.connector_confirmation`, `context.connector_setup_summary`, `context.semantic_layers`, `context.hero_prompt_candidates`, `context.primary_hero_prompt`, `context.extra_hero_prompt_candidates`, and `control`.
-- Do not treat listing files, checking that files exist, or saying they should be read as sufficient; source-routing preferences, semantic-layer pointers, source category mapping, and onboarding obligations must be loaded and applied.
-- If the script fails, cannot render the compact Data Analytics context the workflow will use, local shell access is unavailable, or the request is direct context maintenance, actually read the relevant state files manually before answering or writing.
-- If the user-context file is absent or unavailable, follow `references/onboarding.md` for first-run behavior.
-- Read `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json` only when onboarding state, semantic-layer refresh setup, hero prompt progress, or progressive nudges are relevant.
-
-## Skill Configuration
-
-### Audience And Language
-
-Write for Data Analytics users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Data Analytics impact: what Data Analytics is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
-
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is Meeting notes: May 19 or Slack thread: May 15-21. Use plain text labels only when no useful URL or stable connector-visible link is available, and say (no useful link available) when that absence matters.
-
-## Purpose
-
-User Context keeps two durable Data Analytics-scoped things in a local state file: source-routing choices explicitly selected for future use, and semantic-layer registry pointers. Operational onboarding bookkeeping belongs in a separate JSON state file so `user-context.md` stays small, readable, and cheap to load. Do not use `user-context.md` as a general memory file for arbitrary facts, preferences, output style, caveats, accepted drafts, or "remember this" requests.
-
-Use this skill as the fast preflight for every substantive Data Analytics workflow before ordinary work begins. Use it as the primary workflow when the user asks to onboard, set up, inspect source-routing preferences, inspect semantic layers, create or maintain a semantic layer, or reset Data Analytics setup. If the user asks Data Analytics to remember arbitrary context, explain that general memory is not supported here; persist only an explicit future source-routing choice or a semantic-layer registry pointer produced by semantic-layer setup.
-
-## Reference Loading
-
-`SKILL.md` owns the normal preflight, direct read, narrow write, onboarding handoff, and first-run setup path. Load references selectively:
-
-- Use `scripts/data_analytics_preflight.py` as the normal read-only preflight implementation when local shell access is available. Its JSON payload owns Data Analytics state-file read status, compact user-context normalization, source-routing preferences, source category configuration, connector confirmation and setup summary, semantic-layer registry, hero prompt candidates, file provenance, onboarding progress, and final response obligations for ordinary workflows.
-- Keep first-run state creation automated through `scripts/init_user_context_state.py`; it copies the bundled templates into the user's Data Analytics state directory. Do not run it before the first direct onboarding orientation when no state exists. Run it after the user approves the first setup step, or when an explicit future source-routing choice or semantic-layer registry pointer needs persistence. When running through a sandboxed local shell, request elevated execution by default because the initializer writes under `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`, outside the project workspace. After initialization, write only the narrow supported `user-context.md` fields and related JSON state files.
-- Use `scripts/reset_user_context_state.py` for explicit debugging reset requests that clear local Data Analytics user-context and onboarding state. The helper backs up known state files before removing them from the active state directory.
-- Use `references/onboarding.md` when first-run orientation, core source setup confirmation, semantic-layer setup, semantic-layer refresh setup, semantic-layer weekly source polling setup, hero prompt sequencing, onboarding completion, onboarding status, Data Analytics TODOs, walkthroughs, environment conflict audit, or onboarding-state updates are active.
-- Use `references/semantic-layer/setup.md` when onboarding, direct setup, or maintenance work needs to create, refresh, inspect, or repair a semantic layer. Use `references/semantic-layer/source-intake.md` for seed-source intake and source inventory shaping, `references/semantic-layer/connector-playbook.md` for semantic-layer source-lane connector behavior, and `references/semantic-layer/skill-template.md` for the generated semantic-layer skill shape.
-- Use `plugin-author-config/user-context-config.md` for the copy-ready minimal user-context scaffold.
-- Use `plugin-author-config/source-category-config.json` for source category ids, labels, preferred plugin routes, and relevant helper skills.
-- Use `references/source-category-runtime.md` for workflow-time source attempts, onboarding connector confirmation, plugin-first source setup, ON_USE auth behavior, failure classes, fallback rules, and explicit future source-routing choice storage.
-- Use `references/onboarding-state-template.json` only as the default operational onboarding-state shape; `references/onboarding.md` owns onboarding behavior, transitions, and completion rules.
-- Use `plugin-author-config/automation-config.md`, `references/automation.md`, and `references/semantic-layer/weekly-polling-automation.md` for semantic-layer weekly source polling setup.
-- Use `references/onboarding-examples.md` only to audit or explain onboarding traces. Examples do not override the rules above.
-- Use `scripts/validate_user_context_preflight.py` and `tests/test_state_helpers.py` as the validator and executable regression suite for the Data Analytics user-context preflight contract.
-
-## Skill Experience Guidance
-
-Every user-facing Data Analytics hero workflow used during onboarding should keep its execution guidance in the focused skill that owns the work.
-
-Use the focused skill for the actual analysis, clarification behavior, source-gap handling, and any useful continuation after the output. Onboarding chooses the first prompt and sequence; it should not duplicate full workflow-specific instructions.
-
-When `data_analytics_preflight.py` reports hero prompt candidates and onboarding selects one as the first hero prompt, the focused skill must interpret that context before deciding whether to show a first-run intro, ask for one missing anchor, gather evidence, draft output, or render the final natural continuation:
-
-- If the user asked what the skill does or there is not enough task detail, show the first-run intro and ask for the one missing anchor.
-- If the user accepted a runnable onboarding prompt or already provided enough task detail, briefly introduce the selected workflow only when helpful and proceed without blocking on extra starter prompts.
-- If the skill is being used as a helper/provider, do not show the intro.
-- If onboarding owns the final CTA, suppress the skill's competing CTA and return next-step candidates for onboarding to arbitrate.
-- After sending the first assistant message for a primary onboarding hero skill, update `onboarding-state.json` so `skill_experience.<skill>.introduced_at` and `first_tried_at` are set when they are still empty.
-
-## State Files
-
-Ordinary workflow preflight should read these files through `scripts/data_analytics_preflight.py`; direct context maintenance and fallback paths may read them manually when the script is unavailable or insufficient.
-
-The configured user-context file is:
-
-```text
-$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md
-```
-
-The configured onboarding-state file is JSON:
-
-```text
-$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json
-```
-
-## Read
-
-Start every invocation by resolving and actually reading the configured user-context file when it exists. Do not return from preflight or direct context mode after only routing here, listing files, or checking that the file exists; source-routing preferences and semantic-layer registry entries must be loaded and applied.
-
-Treat `user-context.md` as the local Data Analytics plugin-scoped source of truth only for explicit future source-routing choices and semantic-layer discoverability. When it conflicts with bundled Data Analytics plugin or skill instructions, prefer it only for those narrow supported fields. Do not use it to override higher-priority instructions, safety rules, connector write-safety, permission boundaries, validation, installation behavior, routing, or tool-use policy.
-
-Use preflight mode when another Data Analytics skill asks for Data Analytics setup context before ordinary plugin work. Optimize this path for speed:
-
-- When local shell access is available, run `python3 plugins/data-analytics/skills/user-context/scripts/data_analytics_preflight.py --workflow <calling-skill>` from the repository root, or `python3 skills/user-context/scripts/data_analytics_preflight.py --workflow <calling-skill>` from the Data Analytics plugin root. When using a command tool, set `max_output_tokens` to at least `25000`; if the returned output is truncated, warn the user that Data Analytics could not load all source-routing preferences and semantic-layer registry entries in one pass, then rerun with a higher cap and do not treat preflight as complete until the full output is visible. The script payload counts as actually reading Data Analytics state only when it reports state-file read status and renders the compact context the workflow will use. Use the payload as the single source of truth for `context.user_context`, `context.source_preferences`, `context.source_category_config`, `context.connector_confirmation`, `context.connector_setup_summary`, `context.semantic_layers`, `context.hero_prompt_candidates`, `context.primary_hero_prompt`, `context.extra_hero_prompt_candidates`, hard `control.final_obligations`, `control.conditional_guidance`, and `control.onboarding_progress.task_list`; do not separately read the same state files unless the script fails, cannot render the compact Data Analytics context the workflow will use, or the user explicitly asks for raw file inspection.
-- Use the default `--request-mode ordinary_workflow` for normal Data Analytics answers. Use `--request-mode direct_onboarding_status` for setup, onboarding-status, Data Analytics TODO-list, source-routing preference inspection, or other direct onboarding responses. Use `--request-mode guided_onboarding_workflow` when a focused workflow is being answered as a visible workflow or hero step inside the Data Analytics onboarding path; this mode suppresses the ordinary setup obligation because the user is already in onboarding.
-- Use `context.source_preferences` as source-selection hints. Do not proactively check connector readiness during ordinary preflight. When onboarding or a focused workflow needs a source, it should try the relevant connector then and handle auth, connection, skip, defer, or manual fallback in that context.
-- Use `context.semantic_layers` as the semantic-layer registry. If the request names or implies a product area, metric, table, dashboard, SQL query, source choice, join, caveat, or recurring business question and a matching semantic layer exists, load that semantic-layer skill before choosing tables, writing SQL, reconciling dashboards, or giving metric definitions.
-- After reading user context, use the script-driven preflight payload for the calling skill, then immediately let the calling skill continue. The returned envelope is the authoritative, auditable preflight context; do not hide a larger raw-state payload behind it. The payload is not user-facing and must not be rendered unless the user explicitly asks for implementation details.
-- Do not browse linked sources, run broad searches, summarize unrelated sections, or ask setup questions during ordinary preflight.
-- If `onboarding-state.json` exists, parse only the relevant fields for whether onboarding is active, quiet, complete, whether core onboarding is complete, and any concrete next actions. Do not echo raw onboarding-state content into ordinary preflight output; use file provenance plus the normalized onboarding, source, semantic-layer, and hero-prompt fields the script returns. During ordinary preflight, return at most one concrete guided next step that the calling skill may append after answering the immediate request. If core onboarding is still incomplete, the next step should direct the user back to the next unresolved core setup item: source setup confirmation or semantic-layer setup. During direct onboarding, setup-status answers, or user requests for the Data Analytics TODO list, use `references/onboarding.md` to render the single visible compact `**Next Step**` block and mirror the compact high-level roadmap into the built-in thread task list when available.
-- Treat `context.connector_setup_summary.unresolved_core_ids` and `context.connector_setup_summary.next_action` as authoritative during onboarding. Do not advance into semantic-layer setup because a core source entry merely says `deferred`, `skipped`, `declined`, `unavailable`, or `not_applicable`; preflight reopens that source as `needs_confirmation` unless onboarding state records the user's explicit known-gap resolution.
-- Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source category configuration, connector setup summary, semantic-layer registry, primary hero prompt, extra hero prompt candidates, onboarding progress, final obligations, and conditional guidance. Treat saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources.
-- If `user-context.md` is missing, read `references/onboarding.md`, return that no durable Data Analytics source-routing preferences or semantic-layer registry are available, use the script-driven payload or documented manual fallback to provide conditional setup guidance, and let the caller continue. Do not run the initializer or scaffold state merely because the file is missing. Do not interrupt ordinary plugin work with setup or stop at a setup request. For a high-intent first-run workflow request, tell the caller to answer the user's concrete query first using bundled instructions, user-provided material, and available connectors or sources; if the requested artifact cannot be completed or is meaningfully weaker because a source or semantic layer is missing, give the best truthful answer from the attempted sources and name the practical gap using `references/onboarding.md#context-gap-note`. Append the ordinary-workflow setup obligation only when preflight returns it because onboarding has not started; do not offer to start onboarding when `onboarding-state.json` already says onboarding is active.
-- If a referenced source is inaccessible, stale, ambiguous, or missing, say that directly only when the missing source affects the current task, then continue with the available context.
-
-### Script-Driven Preflight Payload
-
-Preflight mode must use `scripts/data_analytics_preflight.py` as the authoritative payload before ordinary work continues. The script output owns `request_mode`, `current_skill_experience`, `context.user_context`, `context.source_preferences`, `context.source_category_config`, `context.connector_confirmation`, `context.connector_setup_summary`, `context.semantic_layers`, `context.hero_prompt_candidates`, `context.primary_hero_prompt`, `context.extra_hero_prompt_candidates`, `control.response_mode`, hard `control.final_obligations`, `control.conditional_guidance`, and `control.onboarding_progress.task_list`. Do not hand-author, recreate, or mentally reconstruct the payload when the script is available; use the JSON fields the script returned.
-
-For ordinary Data Analytics workflow requests, honor `control.final_obligations` exactly as returned by the script. The calling skill must answer the immediate request first, then satisfy any returned setup CTA or core-onboarding reminder. When the focused skill does not otherwise own a final continuation, append the returned template intact. When the focused skill has its own final continuation, fold the setup reminder or move-on path into that same final natural continuation so the response has one final visible CTA, not a standalone setup reminder plus a second skill CTA. Do not downgrade a hard obligation into a passive setup aside or a context-gap explanation. Do not use that hard obligation for direct onboarding/setup/status responses, focused workflows launched from the visible onboarding path, or responses where the immediate workflow must ask a required clarification; those callers should use `direct_onboarding_status` or `guided_onboarding_workflow` request mode when appropriate, and clarification-only responses should end with the clarification as the sole final natural continuation.
-
-When the script fails, cannot render the compact Data Analytics context the workflow will use, or local shell access is unavailable, fall back to the manual file-read path described above: actually read the relevant state files, use only fields known from those files or the missing-file condition, apply `references/onboarding.md` for direct onboarding/status behavior, and state any missing source or semantic-layer gap only when it materially affects the current answer. Restore script-driven validation as soon as local shell access is available.
-
-Use direct context mode when the request is primarily about setup, source-routing preferences, semantic layers, or context maintenance:
-
-- If `user-context.md` exists, read it before answering or writing.
-- If the user asks what Data Analytics remembers, say Data Analytics does not keep general memory here; summarize only explicit future source-routing choices and registered semantic layers.
-- If the user asks Data Analytics to remember arbitrary context, do not save it. Explain that this file only persists explicit future source-routing choices and semantic-layer registry entries.
-- If the user explicitly selects a future source-routing default or a future source to avoid, update only the matching source category in `user-context.md`.
-- When the request starts, resumes, updates, checks, or completes Data Analytics setup or onboarding, perform the requested sub-step and then apply the step cards in `references/onboarding.md` before answering. The first direct onboarding orientation with no existing Data Analytics state is a response-first fast path: render the orientation immediately, then initialize state only after the user approves the next setup step or a supported `user-context.md` write is needed. Other setup interactions include onboarding-status checks, source setup confirmation, semantic-layer setup or refresh, hero-prompt selection, and completed or deferred guided workflow updates.
-- Treat onboarding-status questions such as "have we onboarded yet?", "am I set up?", "is Data Analytics configured?", or "what setup is missing?" as direct onboarding interactions, not as ordinary context lookup. Answer the status through the common onboarding finalizer: if onboarding is missing or active, strongly guide the user into the current compact `**Next Step**` block with built-in thread task-list updates when available; if onboarding is complete, say so and offer one useful Data Analytics workflow. Do not end an incomplete onboarding-status answer with only a passive or optional setup suggestion.
-
-Outside explicit linked-source refresh, semantic-layer setup, or semantic-layer refresh work, retrieve linked Google Docs, Drive files, Notion pages, or other resources only when they are accessible through available connectors or explicit local APIs and the user asked to inspect or refresh that source. Do not use browser automation, in-app browser, or Computer Use as fallback retrieval paths for ordinary context-source retrieval.
-
-## Onboarding
-
-Use `references/onboarding.md` as the source of truth when the user asks what Data Analytics can do, asks to set up Data Analytics, invokes onboarding, asks whether onboarding is complete, asks for Data Analytics TODOs, asks which sources Data Analytics can try, asks to create or inspect a semantic layer during onboarding, or when `onboarding-state.json` says onboarding is active and a nudge is due. During ordinary preflight, do not show full onboarding; parse only relevant JSON fields and return the hard setup obligation or one source/semantic-layer gap note when applicable, then let the calling skill continue. When onboarding is already active during an ordinary workflow, do not ask whether to start onboarding. If core onboarding is incomplete, answer only directly requested Data Analytics work best effort and direct the user back to the next unresolved core setup item. If core onboarding is complete, preserve the workflow's own final natural continuation and mention resuming guided onboarding only as a short non-competing note when directly useful. During the first direct onboarding message with no state, render Step 1 directly from `references/onboarding.md` without creating scaffold files first.
-
-When direct context work happens while onboarding is missing or active, apply the current step-card continuation in `references/onboarding.md` before finalizing. This includes source setup confirmation, onboarding-status checks, source-routing preference changes, semantic-layer setup or refresh, hero prompt selection, and skill-experience updates. A recap of the just-completed sub-step is not sufficient while onboarding is active unless onboarding is complete, quiet, or stopped by the user. During source setup confirmation, surface unresolved `structured_data`, `team_communication`, and `company_docs` before optional source categories and do not downgrade installable core routes into quiet optional deferrals.
-
-When a focused workflow is run from the visible onboarding path, call preflight with `--request-mode guided_onboarding_workflow` so the workflow can answer normally without adding a redundant onboarding reminder. Focused hero skills own their own first-run banner, anchor rules, prompt execution behavior, and next-step guidance. Onboarding owns the first prompt choice, extra prompt sequencing, and state updates.
-
-## Action-Oriented Continuation
-
-Default Data Analytics outputs should be useful launchpads, not dead ends. In almost all cases, after answering the user's immediate request, add one concise next step that helps them act on the result. Good next steps continue the same workflow by iterating on the analysis, validating one gap, creating the next artifact, checking one missing source, setting up or refreshing a semantic layer, or showing one remaining hero prompt when onboarding owns the final CTA. Do not recommend moving to another Data Analytics skill as a generic continuation; route elsewhere only when the user's request has clearly changed or the current workflow's own routing rules say another skill owns the job.
-
-Avoid asking for setup context merely to fill onboarding state. Ask only high-uncertainty, high-impact questions whose answers would significantly improve future outputs, semantic-layer quality, connector setup, or the active workflow. If onboarding is active, the current step card in `references/onboarding.md` governs the next step; otherwise use the ordinary action-oriented continuation rules here.
-
-Show at most one direct CTA during ordinary workflow output unless the user explicitly asks for options, onboarding, setup, or the Data Analytics TODO list. When onboarding has not started in an ordinary workflow, that CTA must be the `## Data Analytics Setup Required` template from preflight, even if the immediate artifact is otherwise complete, unless the immediate workflow is blocked on a required clarification. Clarifications always become the final natural continuation and suppress onboarding CTAs for that response. When onboarding is already active and core onboarding is incomplete, direct the user to finish the next core setup item before continuing non-urgent Data Analytics workflows. When onboarding is active and core onboarding is complete, preserve the workflow-owned final natural continuation and mention resuming guided onboarding only as a short non-competing note when useful. For direct onboarding and setup-status flows, follow `references/onboarding.md` instead: render the single visible compact `**Next Step**` block, mirror the compact high-level roadmap into the built-in thread task list when available, and check tasks off as the user completes, approves, declines, or makes them unnecessary. Skip the CTA only when the user asks for quiet behavior, the request is sensitive or high-pressure enough that a follow-up would be distracting, onboarding is complete and the workflow has no reasonable next action, or the focused workflow already owns a better clarification.
-
-## Write
-
-Write only two kinds of durable Data Analytics user context to `user-context.md`:
-
-- explicit future source-routing choices, such as `Prefer: Databricks` or `Avoid: Snowflake`, under the matching source category;
-- semantic-layer registry pointers under `# Semantic Layers`.
-
-Do not store general memory, arbitrary "remember this" content, analytical priorities, copied source-of-truth links, saved dashboards, saved tables, saved docs, source inventories, output preferences, accepted-output preferences, quiet-ending preferences, automatic connector readiness, operational onboarding status, connector-audit bookkeeping, hero prompt progress, or semantic-layer refresh metadata in `user-context.md`. Area-specific metric, dashboard, table, source-of-truth, caveat, and definition anchors belong inside the semantic layer itself.
-
-First-run state creation remains automated when persistence is needed: run `python3 plugins/data-analytics/skills/user-context/scripts/init_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/init_user_context_state.py` from the Data Analytics plugin root, to copy `plugin-author-config/user-context-config.md` and seed `onboarding-state.json` from `references/onboarding-state-template.json`. Do not run this command just to show the first direct onboarding orientation. In sandboxed local-shell environments, request elevated execution on the first attempt because the target state directory is under `$CODEX_HOME`, not the project workspace. After initialization, modify `user-context.md` directly. For multi-entry approvals, read the current state once, update every touched source category in one coherent edit, update `onboarding-state.json` once when onboarding bookkeeping changes, then run `scripts/data_analytics_preflight.py --workflow user-context` or the relevant workflow preflight to confirm the supported context reads back cleanly. Keep `onboarding-state.json` compact: do not persist raw `list_available_plugins_to_install` results, full connector inventories, connector descriptions, copied source inventories, source URLs, prompts beyond the compact current onboarding choice, artifact text, source-gap notes, or analysis output. Store resolved route metadata, counts, ids, statuses, timestamps, and durable pointers instead. Never run parallel writes against the same Data Analytics state files.
-
-For semantic-layer setup, refresh, inspection, or repair, read `references/semantic-layer/setup.md`. Write durable layer pointers into the `# Semantic Layers` section of `user-context.md` and update operational setup or refresh metadata in `onboarding-state.json`. Do not keep semantic-layer discoverability only in onboarding state.
-
-For explicit debugging reset requests only, run `python3 plugins/data-analytics/skills/user-context/scripts/reset_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/reset_user_context_state.py` from the Data Analytics plugin root. The helper backs up active Data Analytics state files, then leaves the plugin ready for fresh onboarding. Never perform this reset for ordinary onboarding, source-routing preference edits, setup retries, source refresh work, or vague troubleshooting.
-
-Use this source-routing structure:
-
-```md
-# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer: Databricks
-- Avoid: Snowflake
-```
-
-Use this semantic-layer registry shape:
-
-```md
-# Semantic Layers
-
-- Area: Customer onboarding
-  - Skill Name: api-semantic-layer
-  - Skill Path: /absolute/path/to/api-semantic-layer
-  - Source Inventory Path: /absolute/path/to/api-semantic-layer/references/source-inventory.md
-  - Last Updated: 2026-06-01
-```
-
-## First Run Setup
-
-Use first-run setup in direct context mode when `user-context.md` is missing, unreadable, or lacks the bundled minimal scaffold. For a pure onboarding or "what can Data Analytics do?" request, show the orientation first and defer scaffold creation. If a supported future source-routing choice or semantic-layer pointer needs persistence, create the scaffold first, then update only the relevant supported section.
-
-For first-run setup after the first orientation, use `scripts/init_user_context_state.py` to create missing local state files when possible, then read `plugin-author-config/user-context-config.md` and treat the bundled source category sections as a minimal routing scaffold, not a questionnaire. Read `references/onboarding.md` before presenting first-run orientation, source setup confirmation guidance, semantic-layer setup guidance, or hero prompt sequencing. Read the relevant hero skill's experience guidance before presenting that skill's intro, prompt, or anchor rule.
-
-After the user approves the next setup step, supplies an explicit future source-routing choice, or semantic-layer setup needs a durable registry pointer:
-
-1. Run `python3 plugins/data-analytics/skills/user-context/scripts/init_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/init_user_context_state.py` from the Data Analytics plugin root, when local shell access is available. In sandboxed local-shell environments, request elevated execution on the first attempt because it writes to `$CODEX_HOME/state/plugins/data-analytics`. If the script is unavailable, create `$CODEX_HOME/state/plugins/data-analytics`, copy the content below `## Default User Context` from `plugin-author-config/user-context-config.md` into `user-context.md` with the required top note, and create `onboarding-state.json` from `references/onboarding-state-template.json`.
-2. Read the resulting `user-context.md`, preserving every source category's `Prefer` and `Avoid` rows plus the `# Semantic Layers` registry section.
-3. Update only the approved source-routing choice or semantic-layer pointer. Do not append disconnected general memory.
-4. Read the resulting `onboarding-state.json`, then update onboarding state with operational onboarding progress, connector confirmation labels, semantic-layer setup or refresh metadata, hero prompt progress, and skill experience progress.
-5. Continue the user's original request using the updated supported context.
-
-If the user declines setup, continue the original request without durable source-routing preferences or semantic-layer registry. Keep the explanation user-facing; do not describe internal files unless the user asks.
diff --git a/plugins/data-analytics/skills/user-context/agents/openai.yaml b/plugins/data-analytics/skills/user-context/agents/openai.yaml
deleted file mode 100644
index 1a55f83..0000000
--- a/plugins/data-analytics/skills/user-context/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Data Analytics User Context"
-  short_description: "Manage source routing, onboarding, and semantic layers"
-  default_prompt: "Read Data Analytics source-routing preferences, apply semantic-layer registry entries, and help me finish analytics setup."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/data-analytics/skills/user-context/plugin-author-config/automation-config.md b/plugins/data-analytics/skills/user-context/plugin-author-config/automation-config.md
deleted file mode 100644
index 25568c0..0000000
--- a/plugins/data-analytics/skills/user-context/plugin-author-config/automation-config.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Data Analytics Automation Config
-
-Plugin authors edit this file when adding, removing, renaming, or clarifying default Data Analytics onboarding automations. Keep setup mechanics, thread creation, pinning, readback, duplicate cleanup, failure handling, and automation tool usage in `../references/automation.md`.
-
-The default automations are offered during Data Analytics onboarding when their prerequisites are met. Later journey automations are offered only when a focused workflow reaches the right point. This config should stay friendly for plugin authors: name the automation, say how often it should run, and write the instructions. The dedicated thread title is derived from `Name`. Runtime mechanics such as heartbeat kind, RRULE conversion, tool calls, target thread ids, pinning, readback, cleanup, and state metadata belong in `../references/automation.md`.
-
-## Automation Entry Shape
-
-Every automation entry must use this shape:
-
-```md
-`automation_id`
-
-- Name: human-facing automation name.
-- Frequency: Data Analytics user-facing cadence and local time.
-- Instructions: thin launcher instructions for the scheduled run.
-```
-
-Do not add model, reasoning effort, heartbeat kind, RRULEs, tool names, user-specific target thread ids, canonical automation ids, install status, readback evidence, cleanup state, onboarding notification copy, or per-user preferred times here. Those belong in `../references/automation.md` or `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json`.
-
-## Default Automations
-
-`weekly_semantic_layer_refresh`
-- Name: Semantic Layer Weekly Source Polling.
-- Frequency: weekly on Mondays at 9:00 AM local time.
-- Instructions: Check the saved semantic-layer source inventory for source-backed changes, propose safe updates, validate any edited semantic-layer skill, and report conflicts, skipped sources, or permission gaps.
diff --git a/plugins/data-analytics/skills/user-context/plugin-author-config/user-context-config.md b/plugins/data-analytics/skills/user-context/plugin-author-config/user-context-config.md
deleted file mode 100644
index a37d045..0000000
--- a/plugins/data-analytics/skills/user-context/plugin-author-config/user-context-config.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Data Analytics User Context Config
-
-Plugin authors edit this file when clarifying the minimal Data Analytics `user-context.md` scaffold. Keep onboarding mechanics in `../references/onboarding.md`, source-routing behavior in `../references/source-category-runtime.md`, and placeholder insertion in `../scripts/init_user_context_state.py`.
-
-This file is intentionally narrow. `user-context.md` stores only durable Data Analytics source-routing choices explicitly selected for future use plus semantic-layer registry entries. Do not add general memory, output preferences, action-orientation preferences, connector readiness, onboarding state, automation setup details, copied source inventories, or arbitrary saved links here.
-
-## User Context Shape
-
-The initializer copies the content below `## Default User Context` into the user-facing file. Keep source category ids aligned with `source-category-config.json`. Each source category keeps blank `Prefer` and `Avoid` rows until the user explicitly selects a future routing default or a future source to avoid.
-
-## Default User Context
-
-# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer:
-- Avoid:
-
-## team_communication
-
-- Prefer:
-- Avoid:
-
-## company_docs
-
-- Prefer:
-- Avoid:
-
-## dashboards_bi
-
-- Prefer:
-- Avoid:
-
-## behavior_signals
-
-- Prefer:
-- Avoid:
-
-## notebook_lab
-
-- Prefer:
-- Avoid:
-
-## email_context
-
-- Prefer:
-- Avoid:
-
-## calendar_context
-
-- Prefer:
-- Avoid:
-
-## code_repository
-
-- Prefer:
-- Avoid:
-
-# Semantic Layers
-
-Store durable semantic-layer registry entries that future Data Analytics runs should load before choosing metrics, tables, joins, or definitions.
-
-status: not provided
diff --git a/plugins/data-analytics/skills/user-context/references/onboarding-examples.md b/plugins/data-analytics/skills/user-context/references/onboarding-examples.md
deleted file mode 100644
index 3101c2c..0000000
--- a/plugins/data-analytics/skills/user-context/references/onboarding-examples.md
+++ /dev/null
@@ -1,204 +0,0 @@
-# Data Analytics Onboarding Examples
-
-Use these compact cases to audit the core onboarding experience. They are regression anchors, not scripts. Governing rules remain in `onboarding.md`, the relevant skill's experience guidance, `../plugin-author-config/source-category-config.json`, `../plugin-author-config/automation-config.md`, `source-category-runtime.md`, `automation.md`, and the semantic-layer references.
-
-## Case 1: Direct Onboarding Orientation
-
-User says:
-
-```text
-@data-analytics let's onboard
-```
-
-Expected behavior:
-
-- Open with a compact Data Analytics onboarding orientation that explains what the plugin does, how onboarding works, and that the user can answer `continue`, `skip`, or provide a real analytics anchor at each step.
-- Do not initialize state files, create the user-context scaffold, inspect connectors, audit conflicts, create semantic layers, create background threads, install automations, or run hero prompts before sending the first orientation message.
-- Make `Orientation` the first visible item, then `Check main analytics sources`; do not inspect connectors before the first orientation just to populate setup state.
-- Explain that active or obvious one-app sources are informational only and will be tried only when a workflow needs them. Ask the user for help only where multiple apps are plausible, no app was found, setup/auth is needed, or manual fallback would materially change the result.
-- Include environment conflicts only when overlapping broad analytics plugins or analytics-specific skills create real or likely routing conflicts; omit that note after a clean audit.
-- Offer one concrete guided next action with real context, never placeholders such as `<metric>` or `<dashboard>`, while mirroring the canonical high-level onboarding checklist into the built-in thread task list when available: `Orientation`, `Check main analytics sources`, `Set Up Data Context`, and `Hero prompt`.
-- Use `# Data Analytics Onboarding`, then a high-level prose paragraph that does not expose saved state paths, cache paths, or internal onboarding status.
-- Explain hero workflows only when the flow reaches the first analysis prompt or the user asks for the full plan.
-- End with one clear action close and show only the current action the user can approve now. Use a compact `**Next Step**` label only when the step needs the label; a self-contained numbered choice set can stand on its own. If the current step has open questions, make resolving them their own step and include those questions in the action close; if they are non-blocking, say what `okay` or `continue` will do by default.
-
-## Case 2: High-Intent First Run
-
-User says:
-
-```text
-@data-analytics diagnose why API ARR moved last week
-```
-
-Expected behavior:
-
-- Begin the requested analytics workflow first and return a best-effort answer from available sources, even when no durable Data Analytics source-routing preferences or semantic-layer registry exist.
-- Use visible source evidence if available rather than stopping at onboarding.
-- Keep the artifact useful even while onboarding is missing or active.
-- Add the `## Data Analytics Setup Required` CTA after the answer only when onboarding has not started, unless the request is already being answered inside a guided onboarding flow. If onboarding is already active, preserve the focused skill's own final natural continuation and do not ask whether to start onboarding.
-- Add a `Context Gap` note only when missing source-routing preferences, semantic-layer context, source access, or setup materially weakens the analysis. The note should name the exact missing source, semantic layer, or decision anchor and the specific improvement it would unlock.
-- Do not add a generic setup note; the onboarding CTA should say setup is required before Data Analytics can reliably reuse source choices and semantic-layer context, and end with the exact line: Reply `start` to continue.
-
-## Case 3: Confirm A Selected Warehouse Route
-
-User says:
-
-```text
-connect Databricks
-```
-
-Context: the previous onboarding response showed active/missing source confirmation and Databricks was the selected structured-data warehouse.
-
-Expected behavior:
-
-- Treat the reply as permission to record or repair the selected Databricks route and run the matching install, connect, or authorize path when that path is exposed.
-- Do not run a proof query during onboarding merely to decide whether Databricks is active.
-- Mark `structured_data` as `active` when the Databricks route is installed, available, surfaced, or otherwise evidenced strongly enough for its route kind; let the first real workflow-time read handle auth, query, or schema failures.
-- If setup or auth is required, let that flow complete before moving on when the setup surface is exposed.
-- If no native route exists, keep the category as `needs_confirmation`, `missing`, `unavailable`, `deferred`, or `skipped`, and ask whether to connect or authorize, use manual SQL or schema context, skip, or defer.
-
-## Case 4: Missing Core Sources Stay In Source Setup
-
-User says:
-
-```text
-continue
-```
-
-Context: Data Analytics found Google Drive and Notion for company docs, but no active data warehouse or team communication route. `functions.list_available_plugins_to_install` returned Databricks, BigQuery, Snowflake, Slack, and Teams.
-
-Expected behavior:
-
-- Keep the user in Step 2 or Step 2A and make the missing core choices prominent before semantic-layer setup.
-- Say that core setup still needs the user's choice, list Data warehouse with Databricks, BigQuery, and Snowflake, list Team communication with Slack and Teams, and say company docs look covered through Google Drive or Notion when that is true.
-- Do not quietly write `deferred`, `skipped`, `declined`, `unavailable`, or `not_applicable` for a core category just to advance onboarding.
-- Do not introduce semantic-layer setup until each unresolved core source has an active or manual route, or the user explicitly chooses a known-gap fallback such as `defer core sources`.
-- When the user explicitly defers a core source, record the matching `resolution` value such as `user_deferred` or `user_continued_with_known_gap`; a core fallback without that explicit resolution should surface again as `needs_confirmation`.
-
-## Case 5: Semantic Layer Intake
-
-User says:
-
-```text
-API ARR
-```
-
-Context: source setup confirmation is resolved and Data Analytics has enough source access or manual fallback to begin semantic-layer setup.
-
-Expected behavior:
-
-- Introduce semantic-layer setup briefly if it has not been introduced.
-- Ask only for the smallest missing starting point if no trusted dashboard, table, SQL, doc, repo path, notebook, team thread, or recurring question is available.
-- If enough useful context exists, run the semantic-layer setup flow directly instead of asking a broad questionnaire.
-- Keep the ask grounded in one coherent product or business area; do not merge unrelated analytical areas into one layer unless the user explicitly asks.
-
-## Case 6: Data Analytics Automations Are Automatic And Approval-Gated
-
-User says:
-
-```text
-okay
-```
-
-Context: the previous onboarding response explained that recurring Data Analytics source polling can run in the background, listed Semantic Layer Weekly Source Polling with its configured cadence, and ended with a setup approval question. The visible step was semantic-layer refresh setup.
-
-Expected behavior:
-
-- Read `automation.md` and `../plugin-author-config/automation-config.md`.
-- Install or repair `weekly_semantic_layer_refresh` unless the user has already declined, skipped, or deferred refresh for the current semantic layer.
-- Mention the configured automation once with its cadence: Semantic Layer Weekly Source Polling runs weekly on Mondays at 9:00 AM local time.
-- Use the configured dedicated pinned thread named `Semantic Layer Weekly Source Polling`.
-- Confirm the automation prompt is a thin launcher for the semantic-layer polling workflow; the semantic-layer polling owner owns source inventory handling, source precedence, evidence standards, update boundaries, validation, and run-summary copy.
-- Record automation metadata in `onboarding-state.json`.
-- Immediately kick off one source polling run in the pinned `Semantic Layer Weekly Source Polling` thread, record its kickoff status under `semantic_layer_refresh`, and tell the user it is working in the background and visible in the `Pinned` section of the left sidebar.
-- Continue to the next onboarding step after readback succeeds. Mention the refresh-thread status as a separate `FYI:` line when useful, then offer the first hero prompt. Do not run the hero workflow until the user accepts the prompt or provides a specific analysis question.
-
-## Case 7: First Hero Prompt
-
-User says:
-
-```text
-continue
-```
-
-Context: source setup confirmation, semantic-layer setup, and semantic-layer refresh resolution are complete, and Data Analytics has context for `API ARR`.
-
-Expected behavior:
-
-- Show exactly one strong context-derived hero prompt first.
-- The prompt should be runnable as written, grounded in the available area, metric, dashboard, semantic layer, or goal, and ask for a decision-ready report through the product-business-analysis path.
-- Load the selected hero skill's experience guidance before introducing or running it.
-- End with explicit replies: `run it`, a written replacement prompt, or `skip`.
-- Treat `skip` as finishing onboarding, not as a reason to show more prompt choices.
-- Do not show three prompt choices initially.
-
-## Case 8: Extra Hero Prompts
-
-User says:
-
-```text
-show more prompts
-```
-
-Context: the user explicitly asks for more prompt ideas after the first hero prompt has run, been skipped, or been deferred.
-
-Expected behavior:
-
-- Offer at most two remaining context-derived prompt candidates.
-- Each prompt should be runnable as written and grounded in real available context, not placeholders.
-- End with explicit replies: `1`, `2`, a different analysis question, or `done`.
-- Do not turn ordinary Data Analytics work into a recurring onboarding screen.
-
-## Case 9: Ordinary Workflow While Onboarding Is Active
-
-User says:
-
-```text
-@data-analytics which dashboard should I trust for weekly ARR reporting?
-```
-
-Expected behavior:
-
-- Complete the requested analytics workflow.
-- Do not interrupt with full onboarding.
-- Do not include the `## Data Analytics Setup Required` start CTA when onboarding is already active. Preserve the focused skill's own final natural continuation, folding `continue onboarding` into that same final CTA when useful. If no focused-skill CTA exists, use the explicit onboarding `**Next Step**` resume CTA as the sole final close.
-- If onboarding is quiet or complete, produce the requested Data Analytics artifact directly and end with one useful analytical next action, not an onboarding recap.
-- If core onboarding is incomplete, fold the setup-resume path into the same final CTA instead of adding a second final onboarding CTA.
-- If this completed one of the initial guided workflows, offer a beginner-friendly walkthrough only when the user asks for it or the active workflow owns that continuation.
-
-## Case 10: Onboarding Status Check
-
-User says:
-
-```text
-@data-analytics have we onboarded yet
-```
-
-Expected behavior:
-
-- Answer the status directly from `user-context.md` and `onboarding-state.json`, or say onboarding has not started if the state is missing.
-- Treat missing or active onboarding as a direct onboarding interaction, not a passive status lookup.
-- Explain briefly that setup is required for the best Data Analytics experience because future source-routing choices and semantic-layer context help Data Analytics tailor future analysis work.
-- End with one concrete next setup action that the user can approve now, using a compact `**Next Step**` label only when it improves clarity.
-
-## Case 11: Scheduled Semantic-Layer Refresh Run Summary
-
-Context: the semantic-layer refresh automation runs on its weekly schedule.
-
-Expected behavior:
-
-- Produce a concise user-facing run summary tied to the saved source inventory, checked sources, source-backed changes, validation, and review needs.
-- For a no-change run, explicitly say no source-backed changes were found and list the sources checked.
-- For an updated, blocked, or conflicted run, name the changed files or the concrete permission, connector, validation, or source conflict that prevented a clean update.
-- Do not send a silent or empty update.
-
-No-change shape:
-
-```text
-Status: no change
-Sources checked: <source names>
-Changes found: no source-backed changes found
-Files changed: none
-Validation: not run
-Needs review: none
-```
diff --git a/plugins/data-analytics/skills/user-context/references/onboarding-state-template.json b/plugins/data-analytics/skills/user-context/references/onboarding-state-template.json
deleted file mode 100644
index fb728d6..0000000
--- a/plugins/data-analytics/skills/user-context/references/onboarding-state-template.json
+++ /dev/null
@@ -1,87 +0,0 @@
-{
-  "status": "active",
-  "orientation": {
-    "status": null,
-    "shown_at": null,
-    "last_shown": null
-  },
-  "connector_confirmation": {},
-  "core_onboarding": {
-    "status": null,
-    "completed_at": null,
-    "remaining_steps": []
-  },
-  "semantic_layer_setup": {
-    "status": "pending",
-    "area": null,
-    "mode": null,
-    "seed_source_count": 0,
-    "skill_name": null,
-    "skill_path": null,
-    "source_inventory_path": null,
-    "resolution": null,
-    "last_prompted": null,
-    "completed_at": null
-  },
-  "semantic_layer_refresh": {
-    "status": "pending",
-    "automation_id": "weekly_semantic_layer_refresh",
-    "automation_name": "Semantic Layer Weekly Source Polling",
-    "frequency": "Weekly, after a semantic layer has a stable target path and pollable source inventory.",
-    "last_shown": null,
-    "last_offered_at": null,
-    "canonical_automation_id": null,
-    "target_thread_id": null,
-    "target_thread_title": null,
-    "readback_status": null,
-    "resolution": null
-  },
-  "hero_prompt_choice": {
-    "status": null,
-    "selected": null,
-    "selected_skill": null,
-    "selected_prompt": null,
-    "selected_anchor": null,
-    "suggested": [],
-    "last_offered_prompts": [],
-    "history": [],
-    "last_shown": null
-  },
-  "hero_workflow": {
-    "current_skill": null,
-    "current_status": null,
-    "history": []
-  },
-  "completed_hero_prompts": [],
-  "deferred_hero_prompts": [],
-  "skill_experience": {
-    "metric-diagnostics": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "product-business-analysis": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "kpi-reporting": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    }
-  },
-  "environment_conflict_audit": {
-    "last_audited": null,
-    "summary": null,
-    "conflicts": [],
-    "recommended_cleanup": null
-  },
-  "quiet_mode": {
-    "status": null,
-    "reason": null
-  }
-}
diff --git a/plugins/data-analytics/skills/user-context/references/onboarding.md b/plugins/data-analytics/skills/user-context/references/onboarding.md
deleted file mode 100644
index 9b96cc7..0000000
--- a/plugins/data-analytics/skills/user-context/references/onboarding.md
+++ /dev/null
@@ -1,497 +0,0 @@
-# Data Analytics Onboarding Flow
-
-Use this reference when `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` is missing or unreadable, when the user asks what Data Analytics can do, when the user asks to set up or customize Data Analytics, or when `onboarding-state.json` says onboarding is active and a nudge is due.
-
-Onboarding is a mostly linear, nested step-card playbook. It is not a formal graph. Each step owns setup copy, output contract, action close, completion behavior, skip or defer behavior, and any loop or optional branch. Every onboarding message should handle one major user-visible step at most, mirror the overall progress list when the UI is available, and end with one clear action or question.
-
-Store durable source-routing preferences explicitly selected for future use plus semantic-layer pointers in `user-context.md`. Store only operational onboarding progress, including connector-confirmation labels, skill experience progress, semantic-layer setup and refresh state, automation setup, and quiet or complete state, in `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json`. Keep onboarding state compact: do not put raw `list_available_plugins_to_install` results, full connector inventories, connector descriptions, copied source inventories, source URLs, source data, polling results, research result bodies, artifact text, source-gap notes, or analysis output in onboarding state; keep that content in `user-context.md`, the generated semantic-layer skill, the pinned polling thread, the current workflow artifact, or the source system. Record compact route metadata, counts, ids, statuses, timestamps, and durable pointers instead. Do not create, read, or update durable connector-readiness proof as part of onboarding. Data Analytics workflows use plugins, apps, or connectors when the workflow needs the source.
-
-The first direct onboarding response is a response-first fast path. When the user asks to onboard, set up, or learn what Data Analytics can do and no Data Analytics state files exist yet, render Step 1 immediately. Do not run `../scripts/init_user_context_state.py`, write scaffold files, inspect connectors, audit conflicts, create semantic layers, create threads, install automations, start source discovery, or run workflows before that first message. After the user approves the next visible setup step, or whenever the user supplies context that should be saved, use `../scripts/init_user_context_state.py` when local shell access is available. In sandboxed local-shell environments, request elevated execution by default because it writes to `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`.
-
-Related sources of truth:
-
-- Use `semantic-layer/setup.md`, `semantic-layer/source-intake.md`, and `semantic-layer/connector-playbook.md` for semantic-layer intake, source inventory, evidence crawl, create/refresh/inspect/repair behavior, connector-specific recovery, and durable pointer writes.
-- Use `../scripts/init_user_context_state.py` to create missing first-run state files only after the first orientation message has been shown and the user approves the next setup step, or immediately when the user provides context that should be saved.
-- Use `../plugin-author-config/user-context-config.md` as the source for the minimal source-routing and semantic-layer registry scaffold.
-- Use `../plugin-author-config/source-category-config.json` for static source category ids, labels, preferred plugin routes, and helper skills. Use `source-category-runtime.md` for plugin-first source setup, onboarding connector-confirmation labels, workflow-time source attempts, ON_USE auth behavior, fallback rules, and durable source-preference storage.
-- Use each user-facing skill's inline experience guidance for first-run intro copy, starter prompts, anchor rules, normal next-step candidates, and onboarding-yield behavior.
-- Use `../plugin-author-config/automation-config.md` for the author-owned default automation catalog. Use `automation.md` for runtime setup mechanics: installing the configured default during onboarding, deriving the target thread title from the automation name, creating the target thread, pinning and renaming it, kicking off Semantic Layer Weekly Source Polling in the pinned thread, readback checks, duplicate cleanup, and failure reporting. Use `semantic-layer/weekly-polling-automation.md` for the scheduled polling workflow contract.
-- Use `onboarding-examples.md` for illustrative audit traces only.
-
-## Skill Experience Delegation
-
-Onboarding chooses which user-facing skill to try next, but the focused skill owns how it introduces itself and what a good first try looks like.
-
-Before introducing or running a user-facing skill during onboarding, load that skill's inline experience guidance from `SKILL.md`. Use that guidance for:
-
-- first-run intro copy;
-- starter prompts and realistic `@data-analytics` examples;
-- anchor rules and sensible defaults;
-- normal next-step candidates;
-- onboarding-yield behavior.
-
-Do not duplicate skill-specific intro copy, starter prompt variants, anchor rules, or normal next-step behavior in this file. If the focused skill has enough user intent to run and has not been introduced yet, it should render the compact first-run intro section from its skill-owned guidance before normal workflow output. If onboarding or another parent workflow owns the final CTA, the skill should suppress its own final CTA and return status plus next-step candidates for that parent workflow to arbitrate.
-
-## Common Onboarding Message Frame
-
-Every onboarding message that is not complete must include primary content for the current step, then leave the user with one clear way to move forward. A natural final sentence, a compact action frame, or a self-contained numbered choice set can satisfy this requirement. Use the reference copy as the preferred onboarding voice and structure: preserve its core claims, ordering, and plain-language tone wherever the current setup state allows. Adapt only to reflect real source availability, skipped/deferred setup, installed plugins/connectors, or the user's current onboarding step. Do not revert to the older rigid `Next Step` cadence when a natural action question or selectable choice set is clearer, and do not introduce extra setup concepts not present in the reference copy.
-
-Use this compact action frame when the step has multiple questions, a durable approval gate, or a complex choice that needs an explicit label:
-
-```md
-**Next Step**
-{One clear action or question the user can answer now.}
-```
-
-`Next Step` is the action prompt, not the explainer. Put step results, workflow introductions, setup explanations, and teaching content in the primary content immediately before the frame, then keep `Next Step` short and decision-shaped. For simple one-question steps, a natural final sentence such as `Ready to set that up?` or `Should I set those up?` is preferred. For chooser steps where each numbered option is concrete and selectable, the list itself is the action prompt; do not add a redundant `Next Step` heading or final sentence such as `Pick 1 or 2`. Do not let the whole onboarding message be only a next-step frame.
-
-When the current step has open questions, make resolving those questions its own visible onboarding step before introducing the next workflow. Include the questions in `Next Step` instead of leaving them only in the preceding step summary. Use a compact numbered list when there is more than one question. If the questions can be handled by sensible defaults, include the default escape hatch inside the relevant numbered item, including which defaults will be used and which optional gaps will be skipped, deferred, or revisited later. Do not add a separate summary prompt after the numbered list when the list already contains the questions and default path. Do not combine meaningful source, preference, admin, approval, or skip/defer decisions with a first-time workflow introduction.
-
-When introducing a workflow, skill, or new onboarding concept for the first time in the current onboarding flow, introduce it in the primary content before the action close. For skills, use that skill's experience guidance. For onboarding concepts such as semantic layers or automations, briefly explain what it does, what inputs or state it can use, what output or result the user should expect, the recommended default action, and the option for the user to skip or provide a specific anchor instead. Keep the action close to the small action the user can take. Use the New Concept Introduction Pattern below whenever the current response enters a new concept.
-
-When showing user-facing sample prompts during onboarding, teach the preferred invocation pattern: start with `@data-analytics`, then state the analytics job and the real anchor in natural language. Sample prompts should be realistic and analyst-shaped, such as `@data-analytics diagnose why subscription ARR moved last week.`, not placeholder-only prompts. If the prompt is meant to run against the user's actual context, fill it with a real metric, product area, dashboard, table, semantic layer, or decision question before showing it; otherwise label it as an example for later use.
-
-Do not create tiny transition turns whose only purpose is asking whether to introduce the next onboarding item. If the current step has completed and the next item is an intro-only step, add a short transition note such as `Okay, I've saved that. Now let's move on to the next step.` or `Great, moving on.`, then render the next concept introduction or next hero prompt card in the same response with one final action close. Preserve real approval gates: data semantic-layer setup should introduce the concept before asking for anything the user would point a new analyst to, with explicit skip or defer language; Data Analytics automations should use a short transition sentence and ask whether to set up weekly polling without a separate `## Data Analytics Automations` heading; generated skill writes, connector installs, recurring automation creation, external system writes, and other approval-gated actions still need their own approval when the step requires it.
-
-After the first hero prompt has run, been skipped, or been deferred, onboarding should continue to completion. Only when the user explicitly asks `show more prompts`, `next prompt`, `next demo`, `next guided workflow`, or similar should Data Analytics offer more prompt ideas instead of auto-running another workflow. Preserve any workflow-owned artifact or source-gap notes, clear the active selected skill before another choice, and enter `offer_extra_hero_prompts` only for that explicit request. Do not run another hero workflow until the user picks an offered prompt, names a workflow, or gives a specific metric, product area, dashboard, table, semantic layer, or decision anchor.
-
-Exactly one final visible CTA or self-contained choice set should appear in the response. FYIs, readbacks, and automation breadcrumbs should be separate from the final action close and should not compete with it.
-
-When onboarding is done, use this terminal frame:
-
-```md
-**Onboarding Complete**
-You're set up to use Data Analytics with configured source routes or explicit fallbacks, a resolved data semantic layer choice, optional weekly polling if you enabled it, and a first analysis workflow ready to reuse.
-
-You can start a new thread with any real analytics question, or keep going with another suggested prompt when useful.
-
-**Next Step**
-Say `okay` and I'll show another Data Analytics prompt, or start a new thread and include `@data-analytics` with any real analytics request.
-```
-
-Do not render `Next Step` as an H1/H2/H3 heading. Keep implementation details out of user-facing onboarding. Do not narrate file structures, cache paths, state paths, context-file mechanics, internal probe names, provider taxonomy, or raw state names unless the user explicitly asks for implementation details.
-
-## New Concept Introduction Pattern
-
-Use this pattern when the response enters a new skill or concept. It may appear after a short recap of the just-completed step when the only alternative would be a low-value `Want me to introduce...` transition. Keep exactly one final visible action close for the current actionable decision. Use a `**Next Step**` label only when the decision needs the label.
-
-```md
-## {Concept Name}
-
-{Concept explanation.}
-
-Example prompt: `@data-analytics {realistic prompt with a real or clearly illustrative anchor}.`
-
-**Next Step**
-{One compact action, approval question, or anchor request.}
-```
-
-For semantic layers, use `## Semantic Layer` and start the body with `A semantic layer is how Data Analytics keeps approved metric definitions, source-of-truth pointers, joins, caveats, and validation steps reusable for future analysis workflows.`
-
-For Data Analytics automations, do not render a separate `## Data Analytics Automations` heading. Start with a simple explanation that these are recurring Codex tasks for semantic-layer source polling.
-
-For skill introductions, use the skill display name as the heading, such as `## Metric Diagnostics`, not the machine skill id. Because the heading already names the skill, the body should start with `This skill...`, not `{Skill Name} is a skill in the Data Analytics plugin...`. Do not render user-facing CTAs with code-styled skill names such as `metric-diagnostics`; say `Metric Diagnostics`.
-
-User-facing bullets should be polished sentence fragments or full sentences. Do not start bullets with lowercase words unless they are grammatically continuing an introductory sentence. Prefer capitalized fragments such as `A validated metric definition`, `Key driver context`, and `A decision-ready recommendation when relevant`. Bullets may omit periods when they are fragments; full-sentence bullets should use periods consistently.
-
-## Progress Checklist Contract
-
-Mirror this user-visible roadmap into the built-in thread task list when available:
-
-1. Orientation
-2. Check main analytics sources
-3. Set Up Data Context
-4. Hero prompt
-
-`Semantic layer refresh setup` is a substep of data semantic layer setup, not a separate top-level onboarding milestone. Keep it in onboarding state because it matters operationally when a stable layer exists, but do not make the top-level experience feel like a fourth major setup track before the user sees value.
-
-Before every onboarding response, refresh the checklist from current onboarding state after applying actions from that turn. If the response completed, skipped, or deferred a step, mark that step accordingly and mark the next unresolved visible step as `in_progress`.
-
-Treat core onboarding as complete after source setup confirmation is resolved and the data semantic layer step is either set up from user-provided inputs, explicitly skipped, deferred, unavailable, or blocked with one concrete path. Source setup confirmation is not complete while `structured_data`, `team_communication`, or `company_docs` has an installable or otherwise actionable route question that the user has not explicitly resolved. If no stable semantic-layer target exists because the user skipped, deferred, or is blocked, do not require the refresh substep. Ordinary Data Analytics work should still answer best effort while onboarding is missing or active, then append the single preflight-provided setup obligation unless the response is blocked on a required clarification or the user asked for quiet behavior.
-
-Treat a plain `yes`, `okay`, `continue`, `ready`, or similar reply after a next-step CTA as approval to perform the current visible next step unless the user names a different action. Treat `skip`, `skip for now`, `not now`, or similar as approval to mark the current visible step skipped or deferred when that step allows it, then continue.
-
-## Step 1: Orientation
-
-- ID: `start_data_analytics_onboarding`
-- Type: `linear`
-- Parent: none
-- State field: `orientation`
-- User-facing goal: Explain what Data Analytics helps with and move quickly to source setup confirmation.
-- Entry condition: Direct onboarding trigger, first-run setup, "what can Data Analytics do?", or resumed onboarding with orientation missing.
-- Stay here when: No state files exist and the first orientation has just been rendered.
-- Exit when: The user approves the next setup step or state already records that orientation was shown.
-- Completion: Record orientation as shown only after state exists.
-- Next: Step 2, `confirm_analytics_sources`.
-- Output contract: Preserve the meaning of this copy; light grammar cleanup is allowed. Do not add connector results, scaffolding, semantic-layer setup, automation setup, or hero workflow output to this first message.
-
-```md
-The Data Analytics plugin is set up to help Codex do higher-quality analytics work. Use it when you want to turn product or business data into insights, clear recommendations, decision-ready artifacts, and dashboards.
-
-It can help you:
-
-- Analyze product and business performance and create executive-ready insight reports.
-- Diagnose metric movements and identify likely root causes.
-- Create KPI reports such as WBRs, MBRs, scorecards, and operating reviews.
-- Design KPIs, success measures, guardrails, and targets.
-- Build dashboards with strong metrics, clear layout, and source notes.
-- Analyze experiment or launch results and summarize the decision implications.
-- Size market, product, customer, or segment opportunities.
-
-It works best once it knows which analytical sources to use and which product areas, metrics, dashboards, tables, and recurring questions matter to you, so we'll do a short setup pass and then run a real analysis prompt.
-
-Here's what we'll do:
-1. Confirm the main analytics sources Codex can try when a workflow needs them
-2. Set up data context from useful sources you provide, or skip it for now
-3. Try one high-value analysis prompt using your real context
-
-Data Analytics will automatically help when your request clearly matches an analytics workflow, and you can always mention it explicitly with a real question such as `@Data Analytics diagnose why subscription ARR moved last week.`
-
----
-
-**Next Step**
-Reply `continue` and I'll check which analytics sources Codex can use, only asking where you need to choose, connect, or use a fallback.
-```
-
-- Do not: initialize state, inspect connectors, create semantic layers, create automations, or run hero prompts before this first message.
-
-## Step 2: Check Main Analytics Sources
-
-- ID: `confirm_analytics_sources`
-- Type: `linear`
-- Parent: none
-- State field: `connector_confirmation`
-- User-facing goal: Give the user confidence about which analytics sources look usable and ask only for help where a source is ambiguous, missing, or needs IT or admin enablement.
-- Entry condition: Orientation is complete or the user explicitly asks to continue setup.
-- Stay here when: Source classification has not run or the active/missing source result has not been shown.
-- Exit when: Every core source category is recorded as `active` or has a user-explicit `declined`, `deferred`, `skipped`, `unavailable`, or `not_applicable` fallback, any offered optional category is recorded as `active`, `missing`, `declined`, `deferred`, `skipped`, `unavailable`, or `not_applicable`, or the user explicitly chooses to continue with recorded known gaps.
-- Completion: Store onboarding confirmation labels and resolved source routes in `onboarding-state.json`; do not write connector readiness to `user-context.md`.
-- Next: Step 2A, `resolve_source_questions`, when any source category needs user input; otherwise Step 3A, `introduce_semantic_layer_setup`.
-- Output contract: Return a concise standalone active/missing source result with one setup-ready heading line, bold bullets for available sources, one sentence naming remaining source gaps, and one clear setup question. Active connector or app sources are informational fallback routes, not winners, when a related plugin is installed or installable. This setup step, not preflight, inspects the environment, calls `functions.list_available_plugins_to_install` once for the setup pass, and writes resolved routes for each source category. Ask only when there are multiple mutually exclusive installed source tools, an installable related plugin needs approval, no source was found, or the category needs IT or admin help. Do not ask for a docs preference merely because both Google Drive and Notion are available; record both routes and let workflows use both. For missing sources and connector-covered sources, prefer plugin setup, then direct app or connector setup, then manual exports or pasted context. Core categories are not optional setup extras: if `structured_data`, `team_communication`, or `company_docs` lacks an active or manual route, make it part of the Step 2 question, keep installable core candidates as `needs_confirmation`, and do not introduce semantic-layer setup until the user installs a route or explicitly says to defer, skip, decline, mark unavailable, mark not applicable, or continue with a recorded known gap. Do not introduce semantic-layer setup in the same response when source questions remain. When core source questions remain and company docs are already covered, use this style:
-
-```md
-After some searching, it looks like you're already set up with Google Drive, Notion, and Spreadsheets.
-
-Before Data Analytics sets up data context, I need your choice for these key sources:
-
-1. **Data warehouse** lets Data Analytics query structured data directly for current, source-backed answers.
-   Options: Databricks, BigQuery, Snowflake.
-2. **Team communication** lets Data Analytics search recent discussions for context, decisions, and owners.
-   Options: Slack, Teams.
-
-Company docs look covered through Google Drive and Notion.
-
-Optional sources can wait until a workflow or semantic-layer plan needs them:
-
-3. **Dashboards and BI, if you use them** lets Data Analytics inspect dashboards and BI reports for metric definitions, filters, and saved views.
-   Options: ThoughtSpot, Omni Analytics.
-4. **Behavior signals, if you use them** adds product-usage evidence for funnel, retention, adoption, and experiment analysis.
-   Options: Amplitude, Mixpanel.
-5. **Notebook lab, if you use it** lets Data Analytics inspect prior analyses, reuse notebook logic, and continue exploratory work.
-   Options: Hex, Deepnote.
-
-You'll be prompted to install and authenticate plugins or connectors for whichever you select.
-
-**Next Step**
-Reply with the sources you want to connect now, or say `defer for now` to continue with manual or pasted context.
-```
-
-Do not render separate `Active sources`, `Sources I need your help with`, or `Missing sources and practical impact` headings unless the source result is too unusual for the concise shape.
-- Reference copy: Active and appropriate sources are FYI only. Do not preflight-read installed or active sources just to prove they work. For each category, compare `saved_source_preferences` and `preferred` from preflight, configured `preferred_plugins` from `source-category-config.json`, legacy `preferred_apps` only when present, `.app.json` ids for configured preferred providers, the session `Available plugins` and `Available skills` blocks, and the single `functions.list_available_plugins_to_install` result for the setup pass. If one or more related plugins are installed with visible skill or tool surfaces, write them to `connector_confirmation` as active routes under `routes[]` with `source_kind: plugin`, `skill_surface`, plugin details, and evidence fields. Related means the plugin or provider itself matches the configured preferred plugin route, or its declared connector id matches a configured preferred provider id from `.app.json`; cross-source search surfaces are not active source routes for another category. Do not classify Notion, Google Drive, or a broad internal search tool as active team communication solely because it indexes Slack or Teams. If no related plugin is active but a related plugin is installable, highlight it as the recommended setup action even when an app or connector route is already active; explain that Data Analytics should prefer the plugin because it can add dedicated Data Analytics workflow support, and keep the existing app or connector route as fallback if the user defers or install visibility is pending. Rank plugin candidates by saved preferences first, then configured `preferred_plugins`, then legacy `preferred_apps` when present, then `.app.json` connector-id intersections. If no related plugin is installed or installable and exactly one plausible app or connector is installed or available, write it as active with `source_kind`, `skill_surface`, and route details when available, then let the workflow's first real read handle auth, query, or schema issues. If multiple plausible source tools are complementary rather than mutually exclusive, such as Drive and Notion for docs, write multiple active routes instead of asking the user to pick a winner. If multiple plausible source tools are mutually exclusive for the same category, ask the user which one to prefer and write the selected route only after resolving its plugin, app, connector, or manual route. Missing sources should explain practical workflow impact and list specific IT or admin options. Be prescriptive about pilot setup: warehouse access unlocks live metric definitions, table shape, query logic, and current-source validation; dashboards and BI let Data Analytics inspect dashboards and BI reports for metric definitions, filters, and saved views; behavior signals unlock product usage, funnels, retention, adoption, and experiment evidence; notebook labs let Data Analytics inspect prior analyses, reuse notebook logic, and continue exploratory work; Slack or Teams unlock recent discussion, owners, caveats, and decision context; Drive, SharePoint, or Notion unlock source-of-truth docs, specs, dashboard notes, and governance context; GitHub unlocks model, query, schema, and semantic-layer ownership.
-- Next-step copy: If one or more source categories need user input, make the `Next Step` only about resolving those questions. When the Step 2 source result includes explanatory optional sources before a separate core-source reply line, render `**Next Step**` immediately before that final reply line so the action close stays visually distinct. If no source questions need input, move directly into Step 3A with a short transition note and the semantic-layer introduction; do not ask whether to introduce it.
-
-### Step 2A: Resolve Source Questions
-
-- ID: `resolve_source_questions`
-- Type: `optional`
-- Parent: `confirm_analytics_sources`
-- State field: `connector_confirmation`; optional user preference saves go to `user-context.md`.
-- User-facing goal: Let the user answer or skip meaningful source choices before Data Analytics introduces semantic-layer setup.
-- Entry condition: Active/missing source confirmation found multiple plausible apps, no app, or IT or admin source gaps that need a user choice, preference, or acknowledgement.
-- Stay here when: The user has not answered, skipped, deferred, or accepted defaults for the source questions.
-- Exit when: The user provides the preference or source info, says to continue with defaults, skips, or defers the source questions.
-- Completion: Save any clear low-risk source preference to `user-context.md`; record skipped, deferred, default, active, or unavailable handling in `onboarding-state.json`. A user-selected source may be recorded as `active` when its plugin, app, or connector is installed, available, surfaced, or active in the current environment with enough evidence for the route kind. The state entry must include resolved route fields such as `source_kind`, `skill_surface`, and plugin, app, connector, or manual details when available. For a core source fallback, record the user's explicit choice with `resolution: user_deferred`, `resolution: user_skipped`, `resolution: user_declined`, `resolution: user_marked_unavailable`, `resolution: user_confirmed_not_applicable`, or `resolution: user_continued_with_known_gap`; do not let an automatic or stale core fallback make source setup look complete. Do not create durable connector-readiness proof.
-- Next: Step 3A, `introduce_semantic_layer_setup`.
-- Output contract: Keep primary content focused on the source result and practical impact. Put only the unresolved source questions and skip or defer escape hatch in the action close. Do not ask for a docs preference when multiple docs routes can be used together:
-
-```md
-Please select the sources you want to set up now:
-
-1. **Data warehouse** lets Data Analytics query structured data directly for current, source-backed answers.
-   Options: Databricks, BigQuery, Snowflake.
-2. **Team communication** lets Data Analytics search recent discussions for context, decisions, and owners.
-   Options: Slack, Teams.
-
-Company docs look covered through Google Drive and Notion.
-
-Optional sources you can add now if useful:
-
-3. **Dashboards and BI, if you use them** lets Data Analytics inspect dashboards and BI reports for metric definitions, filters, and saved views.
-   Options: ThoughtSpot, Omni Analytics.
-4. **Behavior signals, if you use them** adds product-usage evidence for funnel, retention, adoption, and experiment analysis.
-   Options: Amplitude, Mixpanel.
-5. **Notebook lab, if you use it** lets Data Analytics inspect prior analyses, reuse notebook logic, and continue exploratory work.
-   Options: Hex, Deepnote.
-
-You can defer warehouse or team communication access, but say it explicitly so Data Analytics records the known gap before moving on. Optional sources can wait; Data Analytics will ask again later only when a workflow would materially benefit from one of them.
-```
-
-After the user answers, install confirmed plugin or connector candidates one at a time, save any clear source preferences or accepted skips, then move directly into the semantic-layer introduction. Do not ask whether to introduce it. For routine source setup preferences in onboarding, do not render a saved-context recap or `Saved today` list; use only the short transition into Step 3A below, then render Step 3A in the same response:
-
-If the user confirms an installable plugin or connector candidate returned by `functions.list_available_plugins_to_install`, call `functions.request_plugin_install` with `action_type: "install"`, the returned candidate `tool_type`, the returned candidate `id`, and a concise reason such as `Use Databricks as the preferred Data Analytics source for data warehouse.` Pass the returned `tool_type` directly; it may be `plugin` or `connector`. Do not call `request_plugin_install` in parallel with any other tool. If multiple related plugin candidates tie after saved preference order, configured `preferred_plugins` order, legacy `preferred_apps` order when present, and connector-id matching, ask the user to choose; if no related plugin candidate exists, say so and offer admin, app, connector, or manual fallback. If plugin install fails, setup fails, or install succeeds but the plugin-owned skills or tools are not visible yet, record the category as `needs_confirmation`, `deferred`, `deferred_environment_api_limitations`, or `skipped_for_now`; do not mark it active and do not treat the failed setup as a durable decline. Keep plugin-first setup eligible for retry in future workflows when that source category matters. If the user explicitly declines a plugin, suppress that plugin in the current workflow and fall back to the next related plugin candidate, existing connector or app route, or manual setup for that category; save a future `do not use` rule only when the user asks for durable avoidance.
-
-If the user selects or confirms an installed or available plugin, app, or connector, treat it as usable for setup purposes only when the route evidence matches `source-category-runtime.md`, write the resolved route to `connector_confirmation`, and transition without a proof read. If the selected source is missing, use the matching source category and native plugin, app, or connector path: load any relevant helper skill from `source-category-config.json`, use tool discovery or exposed app tools if needed, and run the smallest safe read-only action only when a workflow needs the source. If the workflow-time read triggers auth or setup, let that flow complete before continuing and then write the resolved app or connector route. If no read action or setup route is exposed, keep the category unresolved and ask the user whether to install, connect, or authorize it, ask IT or admin to enable it, defer or skip the category, or proceed with manual or exported context. Adapt the source list in the transition to the resolved active or default sources, but when the standard onboarding source set applies, use this copy spine:
-
-```md
-Okay, that finishes source setup for now. Data Analytics can start with the active routes above, and I recorded any core source gaps you explicitly deferred. It can revisit warehouse, team communication, company docs, dashboard, behavior-signal, or notebook gaps when a workflow needs them.
-
-Before we run the first analysis, let's set up data context from whatever useful context or sources you have.
-```
-
-- Do not: call installed or active connectors during onboarding merely to prove they work, call `request_plugin_install` without a returned install candidate, call `request_plugin_install` in parallel, persist `available`, `verified`, or automatic `blocked` as durable state, expose raw connector ids, or list speculative providers beyond the configured preferred plugins or apps.
-
-## Step 3: Reusable Data Context Setup
-
-Reusable data context setup is the second major onboarding milestone. Encourage the user to provide useful context or sources, but do not create a semantic layer unless the user provides or approves the inputs. The refresh automation offer exists, but it should feel like a small follow-up after the layer is real, not a separate onboarding detour before the user sees value.
-
-### Step 3A: Introduce Reusable Data Context Setup
-
-- ID: `introduce_semantic_layer_setup`
-- Type: `linear`
-- Parent: `semantic_layer_setup`
-- State field: `semantic_layer_setup`
-- User-facing goal: Explain what a data semantic layer is and ask for any useful context or source the user already has.
-- Entry condition: Core source setup confirmation is resolved, or the user explicitly chose a recorded skipped, deferred, declined, unavailable, not-applicable, or continue-with-known-gap path for every unresolved core source.
-- Stay here when: The data semantic layer concept has not been introduced and the user has not provided enough context or sources to create, plan, skip, defer, or block the layer.
-- Exit when: The user provides useful context or sources that can be organized into a layer, asks for intake-only planning, skips, defers, or is blocked on one concrete missing input.
-- Completion: Record the inferred or selected area, supplied starting context, or skip/defer/block resolution in `onboarding-state.json`.
-- Next: Step 3B, `create_or_plan_semantic_layer`.
-- Output contract: Explain that a data semantic layer helps Codex reuse metric definitions, tables, filters, and source-of-truth context for future analysis. Ask for anything the user would point a new analyst to, make clear Codex will organize the inputs, and include the explicit skip path:
-
-```md
-## Set Up A Data Semantic Layer
-
-Codex gets better at data work when it understands the metric definitions, tables, filters, and source-of-truth context your team already uses.
-
-Send anything you would point a new analyst to. I'll organize what you send into reusable guidance for future analysis.
-
-Good starting points:
-1. What this should help with, like a product or business area.
-2. The source of truth for definitions or logic, like transformation code or repos, metric docs, a trusted data skill, reviewed SQL, dashboards, or recurring reports.
-3. Data tables or catalogs you frequently use.
-4. Places where data definitions, caveats, or changes are discussed, like team channels or threads.
-
-Reply with any starting points, or say `skip for now`.
-```
-
-- Do not: require the user to name a product or business area up front, merge unrelated areas into one layer unless the user explicitly asks, ask for a broad source inventory before accepting useful inputs, create a layer from ambient connector availability alone, or pretend an ungrounded skeleton is source-backed.
-
-### Step 3B: Create Or Plan Semantic Layer
-
-- ID: `create_or_plan_semantic_layer`
-- Type: `linear`
-- Parent: `semantic_layer_setup`
-- State field: `semantic_layer_setup`
-- User-facing goal: Create, refresh, inspect, repair, or plan one source-backed semantic layer.
-- Entry condition: The user supplied useful context or sources that can be organized into one or more coherent layers, or explicitly requested intake-only planning.
-- Stay here when: The source inventory, crawl, create/refresh work, validation, durable pointer write, or one concrete blocked input is still unresolved.
-- Exit when: The semantic layer is created, refreshed, inspected, repaired, planned with a stable target path and source inventory, skipped, deferred, or blocked on one concrete missing input.
-- Completion: Write durable semantic-layer pointers to `user-context.md`; write operational setup status, source inventory path, starting-source count when useful, and compact connector-gap labels to `onboarding-state.json`. Do not copy source URLs, source inventory rows, or crawl output into onboarding state.
-- Next: Step 3C, `offer_semantic_layer_refresh`, when a stable target path and pollable source inventory exist; otherwise Step 4A, `offer_first_hero_prompt`.
-- Output contract: Read `semantic-layer/setup.md`, `semantic-layer/source-intake.md`, and `semantic-layer/connector-playbook.md`. Infer the likely organizing area from the user's inputs, split unrelated areas when needed, and ask one clarifying question only when organization would materially change the crawl or destination. Resolve connectors lazily as the selected evidence lanes need them. Build the source inventory before crawling. If a needed source route is selected but has not yet been read successfully in the current workflow, attempt the actual source read only when that semantic-layer lane needs it, then handle auth, query, schema, skip, or manual fallback in context. When enough user-provided or user-approved source-backed context exists, create or refresh the target standalone semantic-layer skill, validate it, write the durable pointer into `user-context.md`, and report the exact created or updated paths plus connector gaps. If blocked, state the exact missing connector, permission, source, or destination decision and ask for the smallest useful fallback.
-- Transition after successful create or refresh:
-
-```md
-The semantic layer is set up and saved for future Data Analytics runs.
-```
-
-### Step 3C: Offer Semantic Layer Refresh
-
-- ID: `offer_semantic_layer_refresh`
-- Type: `optional`
-- Parent: `semantic_layer_setup`
-- State field: `semantic_layer_refresh`
-- User-facing goal: Offer weekly source polling only after the semantic layer has a stable target path and pollable source inventory.
-- Entry condition: Semantic-layer setup is created or planned with a stable target path plus source inventory.
-- Stay here when: Weekly polling is applicable and the user has not accepted, declined, deferred, or marked it unavailable.
-- Exit when: Weekly polling is accepted and read back, declined, deferred, unavailable, skipped because no semantic layer exists, or blocked on one concrete prerequisite.
-- Completion: Record the operational resolution in `onboarding-state.json`; read `automation.md` and `semantic-layer/weekly-polling-automation.md` for approved automation setup.
-- Next: Step 4A, `offer_first_hero_prompt`.
-- Output contract: Ask the dedicated weekly polling question only when a stable target path and pollable source inventory exist. Keep the reply choices explicit:
-
-```md
----
-
-**Next Step**
-Reply `set up weekly refresh` if you want Data Analytics to poll this semantic layer's sources each week, `skip refresh` to leave it manual, or `continue` to move on to the first analysis prompt.
-```
-
-- Do not: create an automation silently, claim refresh is active before readback succeeds, or keep asking after onboarding is complete or quiet unless the user explicitly reopens setup.
-
-## Step 4: Hero Prompt
-
-The hero prompt is the first value demonstration. Show one strong prompt first, make it report-first, and build it from real context gathered during source setup confirmation, any data semantic layer setup the user chose, and workflow-time source reads. Do not require a semantic layer before offering the prompt, and do not start with a three-option chooser. After the first prompt is run, skipped, or deferred, finish onboarding. Offer more prompt ideas only when the user explicitly asks for them later.
-
-### Step 4A: Offer First Hero Prompt
-
-- ID: `offer_first_hero_prompt`
-- Type: `linear`
-- Parent: `hero_prompt`
-- State field: `hero_prompt_choice`
-- User-facing goal: Show one runnable, context-derived report prompt that demonstrates the setup is useful.
-- Entry condition: Core onboarding is complete, or the user explicitly chooses to try a workflow with known setup gaps.
-- Stay here when: No first hero prompt has been accepted, run, skipped, or deferred.
-- Exit when: The user says `run it`, `okay`, writes a different prompt, names a specific analysis question, skips, or defers the first hero prompt.
-- Completion: Record the suggested prompt, selected skill, any selected anchor, and the disposition in `hero_prompt_choice`.
-- Next: Step 4B, `run_first_hero_prompt`, when the user accepts or gives enough intent to run; Step 5, `complete_or_quiet`, when the user skips or defers.
-- Output contract: Read the selected hero skill's inline experience guidance. Use `data_analytics_preflight.context.primary_hero_prompt` when available. The first hero prompt should select `product-business-analysis` and explicitly ask for a decision-ready report so the workflow hands the result to `$build-report`. The prompt must be runnable as written and use a real metric, product area, dashboard, table, semantic layer, or decision anchor from context. If no specific prompt can be formed, ask for the one missing product area, metric, dashboard, table, or decision anchor instead of showing placeholders. Use this shape:
-
-```md
-Now that the semantic layer is ready, here's the first Data Analytics prompt I'd try:
-
-`@Data Analytics {context-derived runnable prompt}`
-
----
-
-**Next Step**
-Reply `run it` to try out this prompt, write the prompt you would rather try instead, or `skip` to finish onboarding.
-```
-
-- Do not: show three hero prompts initially, ask the user to say `change prompt`, include placeholders as if they are real context, or run the hero prompt in the same response as the first offer unless the user already gave an unambiguous prompt or said to run it.
-
-### Step 4B: Run First Hero Prompt
-
-- ID: `run_first_hero_prompt`
-- Type: `linear`
-- Parent: `hero_prompt`
-- State field: `hero_prompt_choice`, `hero_workflow`, and `skill_experience.<selected_skill>.first_tried_at`
-- User-facing goal: Run the selected first hero workflow and show one useful Data Analytics output without an extra confirmation turn.
-- Entry condition: The user accepted the suggested prompt, provided a revised prompt, named a specific analysis question, or directly asked to run the selected hero skill with enough anchor context.
-- Stay here when: The workflow is running, blocked on source auth, waiting for the smallest manual fallback needed by the selected skill, or no viable anchor exists.
-- Exit when: The output has been delivered or the user skips this workflow.
-- Completion: Mark the selected hero skill tried and record only compact prompt disposition metadata in onboarding state. Preserve workflow-owned artifact or source-gap notes in the workflow artifact or current response, not onboarding state.
-- Next: Step 5, `complete_or_quiet`.
-- Output contract: Use the selected skill's workflow guidance and run the workflow directly. If the selected skill has not been introduced yet, briefly name what is being run before the normal workflow output. Do not render a `What Happened`, `Recap`, or walkthrough heading by default.
-- Ending:
-
-```md
----
-
-**Next Step**
-Reply with a follow-up if you want to keep digging into this analysis, or say `show more prompts` if you want another Data Analytics prompt after onboarding.
-```
-
-- Do not: show extra prompt choices before the first result has been delivered, add a second onboarding CTA, or claim the prompt succeeded if required source access is still blocked.
-
-### Step 4C: Offer Extra Hero Prompts
-
-- ID: `offer_extra_hero_prompts`
-- Type: `optional`
-- Parent: `hero_prompt`
-- State field: `hero_prompt_choice`, `completed_hero_prompts`, and `deferred_hero_prompts`
-- User-facing goal: Offer up to two additional context-derived prompts only when the user explicitly asks for more prompt ideas after the first prompt is run, skipped, or deferred.
-- Entry condition: The user explicitly asks for more prompt ideas after the first hero prompt has been run, skipped, or deferred.
-- Stay here when: Additional context-derived prompt candidates are still worth offering and the user has not completed or quieted onboarding.
-- Exit when: The user picks one, names another analysis question, skips, defers, or says to continue normal work.
-- Completion: Record tried, skipped, deferred, or dismissed prompt candidates in onboarding state as compact skill/status/timestamp metadata only. Keep prompt history bounded; do not copy generated artifacts or source-gap notes into onboarding state.
-- Next: Step 4B again when the user picks another prompt, or Step 5, `complete_or_quiet`.
-- Output contract: Use `data_analytics_preflight.context.extra_hero_prompt_candidates` when available. Offer at most two prompts, each runnable as written. Keep the CTA explicit:
-
-```md
----
-
-**Next Step**
-Reply `1` or `2` to try one of these, name a different analysis question, or say `done` to finish onboarding.
-```
-
-- Do not: turn normal Data Analytics work into a recurring welcome screen, offer only placeholder prompts, or hide the user's `done` path.
-
-## Step 5: Completion Or Quiet
-
-- ID: `complete_or_quiet`
-- Type: `terminal`
-- Parent: none
-- State field: `status`
-- User-facing goal: Stop automatic onboarding nudges once setup is meaningfully ready or the user asks for quiet.
-- Entry condition: Core onboarding is resolved and the first hero prompt is run, skipped, or deferred, or the user asks to stop setup nudges.
-- Stay here when: The user explicitly reopens onboarding or asks for setup status.
-- Exit when: `status` is `complete` or `quiet`.
-- Completion: Use `complete` when source setup confirmation, data semantic layer setup or explicit skip/defer/block resolution, any applicable refresh substep, and first hero prompt resolution are done. Use `quiet` when the user does not want more onboarding nudges.
-- Terminal frame:
-
-```md
----
-
-**Onboarding Complete**
-You're set up to use Data Analytics with configured source routes or explicit fallbacks, a resolved data semantic layer choice, and a first analysis workflow ready to reuse. Start a new thread with any real analytics question, or keep going with another suggested prompt when useful.
-```
-
-## Completion Criteria
-
-Core onboarding is complete when criteria 1 and 2 are resolved. Full guided onboarding remains `active` until all criteria are satisfied or the user asks for quiet:
-
-1. Source setup has covered each required source type as `active`, or as a user-explicit `skipped`, `deferred`, `declined`, `unavailable`, or `not_applicable` fallback recorded with `resolution: user_skipped`, `resolution: user_deferred`, `resolution: user_declined`, `resolution: user_marked_unavailable`, `resolution: user_confirmed_not_applicable`, or `resolution: user_continued_with_known_gap`. A manual source route counts as `active` only when its state entry records `source_kind: manual`; `needs_confirmation`, `missing`, or a required source fallback without one of those explicit resolution values still require the user to choose, install, skip, defer, decline, mark unavailable, mark not applicable, or continue with a recorded known gap before core setup is complete.
-2. The data semantic layer step is resolved. The user either provides useful context or sources and a layer is created, refreshed, inspected, repaired, or planned with a stable target path and source inventory; explicitly skips or defers; or is blocked on one concrete missing input. The semantic-layer refresh substep is required only when a stable target path and pollable source inventory exist; otherwise mark refresh skipped, unavailable, deferred, or not applicable.
-3. The first hero prompt is run, skipped, or deferred.
-
-Connector behavior is workflow-time behavior: Data Analytics tries the relevant configured source route when a workflow needs it, continues when it works, and asks for connect, auth, skip, or manual fallback when it does not.
-
-## CTA Arbitration
-
-Invariant: exactly one final visible CTA per response.
-
-- Ordinary skill run: the skill renders its normal next step from its experience guidance and its own workflow policy.
-- Guided onboarding run: onboarding renders the final CTA; the skill suppresses its final CTA and returns next-step candidates.
-- Onboarding review loop: final CTA is review, accept, continue, save, skip, or defer, not a skill continuation.
-- Helper/provider skill: no intro or CTA unless explicitly requested.
-- Ordinary skill run with an onboarding reminder obligation: merge the reminder into the single final action close or use the onboarding setup CTA as the sole final CTA. Do not render a standalone onboarding reminder that asks the user to continue onboarding and then render a separate skill-owned CTA.
-- Automation setup/readback: keep automation cards and breadcrumbs visually separate from the action close.
-
-## Workflow Walkthrough On Request
-
-Do not render `What Happened`, `Recap`, or another walkthrough heading by default in onboarding demo outputs, ordinary direct skill runs, post-completion guided exploration, automation readbacks, follow-up turns, or source-gap replies. If the user explicitly asks what happened, asks how Data Analytics produced the result, or says yes to a walkthrough offer, then explain the observable steps.
-
-When a walkthrough is requested, explain the observable tool/app calls, retrievals, source gaps, and artifact assembly at a beginner-friendly level without revealing hidden reasoning. Keep it grounded in visible activity: sources attempted, what worked, what did not, useful facts found, and how those facts shaped the artifact.
-
-End the walkthrough by offering one valuable next action: continue to the next uncompleted guided workflow, fix the highest-value missing source, save a useful preference discovered during the run, or take a practical seller action from the artifact. Preserve the one-final-CTA rule.
-
-## Ordinary Workflow Onboarding CTA
-
-Use this template only after answering an ordinary Data Analytics workflow request while onboarding is missing or has not started. Do not use it for direct onboarding/status/setup responses, and do not use it when `onboarding-state.json` says onboarding is already active.
-
-Do not use this template when the immediate workflow is blocked on a required clarification. Clarification questions always become the single final action close and suppress onboarding CTAs for that response.
-
-```md
----
-
-## Data Analytics Setup Required
-
-This is required before Data Analytics can reliably use your configured sources and any authoritative semantic-layer context you choose to set up. It also gives you a useful overview of the plugin's key functionality.
-
-Reply `start` to continue.
-```
-
-## Active Core Onboarding Reminder
-
-Use this only after an ordinary Data Analytics workflow when onboarding is already active and a reminder would be useful. Do not use it for direct onboarding, setup, status, TODO-list, or capability-orientation responses; those should render the current onboarding step and its compact `**Next Step**` block instead. Do not use the `## Data Analytics Setup Required` start CTA, and do not ask whether to start onboarding. When the focused skill already owns a better final action close, fold `or say \`continue onboarding\` to resume setup` into that same final close instead of rendering a separate onboarding block. When this reminder is the sole final close, make the move-forward action explicit:
-
-```md
----
-
-**Next Step**
-Reply `continue onboarding` and I'll resume with the next unfinished setup step.
-```
-
-## Context Gap Note
-
-Use this only after an ordinary Data Analytics workflow when missing saved context or a failed workflow-time source materially reduced confidence, completeness, or ability to act.
-
-```md
-Data Analytics would get sharper here with a little more saved context, such as trusted dashboard or table pointers, metric definitions, semantic-layer context, source-of-truth docs, or preferred output format.
-```
-
-## Environment Conflict Audit
-
-After the first orientation message, direct onboarding may include a lightweight environment conflict audit so the user can tell whether Data Analytics will be the analytics workflow that actually triggers. Do not run this audit before sending the first orientation message when no Data Analytics state exists yet. The audit is about routing and user experience, not connector availability.
-
-Flag a plugin or skill as a conflict candidate when it broadly overlaps with one or more Data Analytics workflows and could plausibly capture the same natural-language request. Do not flag connector providers or source helpers as conflicts merely because Data Analytics uses those sources. Do not write clean audit results or conflict lists into onboarding state. If conflicts materially affect the user's setup, explain them in the current onboarding response.
diff --git a/plugins/data-analytics/skills/user-context/references/semantic-layer/setup.md b/plugins/data-analytics/skills/user-context/references/semantic-layer/setup.md
deleted file mode 100644
index d2756da..0000000
--- a/plugins/data-analytics/skills/user-context/references/semantic-layer/setup.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# Semantic Layer Setup
-
-Use this reference when onboarding, direct setup, or maintenance work needs to create, refresh, inspect, or repair a Data Analytics semantic layer for a product or business area.
-
-A semantic layer is a source-backed local skill that helps future Data Analytics runs answer data questions with the right metrics, tables, joins, grains, dimensions, caveats, and query patterns. The output is an agent-facing Codex skill, not a warehouse migration, semantic modeling project, or BI rebuild.
-
-## Required References
-
-- Read `source-intake.md` before asking the user for inputs or deciding which sources are enough.
-- Read `connector-playbook.md` before crawling links, dashboards, team communication channels, data warehouse sources, code repositories, docs, or local skills.
-- Read `skill-template.md` before drafting or creating the target semantic-layer skill.
-- Read `weekly-polling-automation.md` before offering, creating, or updating recurring source polling.
-
-## Setup Contract
-
-- Identify or infer one coherent product, business, metric, source, or reporting area from the user's provided context or sources. Also identify intended users, destination, and whether the user wants intake-only planning, evidence crawling, a draft skill, direct local skill creation, refresh, inspection, or repair.
-- Default direct local creations to `$CODEX_HOME/skills/<area>-semantic-layer` unless the user chose another destination. Ask before placing a generated semantic-layer skill inside a plugin.
-- Create one target semantic-layer skill per coherent area by default. Keep unrelated areas separate unless the user explicitly wants one cross-product layer.
-- Do not require the user to name the area up front. Require user-provided or user-approved useful context or at least one starting source before a source-backed crawl. If the user has only broad intent and no source or context, ask for one item from `source-intake.md`; if they want a skeleton only, label it as ungrounded.
-- Build a source inventory before crawling. For direct creation or draft-file work, write it to `references/source-inventory.md`; for intake-only work, return it in chat. Before saving, summarize sources checked, missing high-value source lanes, and rejected or lower-confidence candidates.
-- Resolve connectors lazily when the active source lane needs them. Prefer the most specific installed first-party connector or source tool; when unavailable, ask for the smallest useful export, pasted excerpt, local checkout, SQL text, schema description, screenshot, or alternate source. If the user selected a source during onboarding but the current semantic-layer lane has not read it yet, use the configured route from `../source-category-runtime.md` and attempt the actual read only when that lane needs the source.
-- Do not create or refresh a semantic-layer skill from ambient connector availability, saved user context, or guessed app results alone. The user must provide or approve the inputs, or explicitly choose to skip or defer setup.
-- Keep raw sensitive data, credentials, secrets, row-level customer examples, and long private messages out of generated files.
-
-## Evidence And Synthesis
-
-Cover the available evidence lanes: tables and namespaces, verified dashboards, raw SQL, team communication, data documentation, code repositories, and existing local skills.
-
-Infer expected source lanes from the user's target metric, grain, domain, and supplied starting points. For example, a daily revenue semantic layer should look for daily-grain warehouse evidence, revenue metric docs or dashboards, and revenue-specific team communication before accepting weekly subscription or generic analytics sources as sufficient. If the best reachable sources are nearby but not exact, label the layer `Limited` or `Directional`, record the mismatch, and ask for the smallest source that would close the gap when the mismatch changes future answers.
-
-Use this source precedence unless the user provides a stronger local rule:
-
-1. Transformation code, tests, and authoritative data documentation, including maintained docs, metric dictionaries, source-backed semantic-layer skills, and user-trusted canonical data skills.
-2. Verified dashboards and reviewed SQL.
-3. Table metadata, lineage, schema comments, and owners.
-4. Query history as observed usage evidence.
-5. Team communication announcements or discussion, weighted by recency, authoritativeness, and durable links.
-6. Other existing local skills, treated as helpful context until corroborated.
-
-A user can promote a local data skill by naming it as canonical or trusted for the current domain; treat that as authoritative data documentation when it has clear scope and explicit metric, table, grain, or exclusion rules. Still verify high-stakes, stale, time-sensitive, or conflicting claims against the underlying cited source or connected app when possible.
-
-Flag unresolved conflicts instead of smoothing them over. The generated skill should say which metric or table definition is canonical, which is deprecated or dashboard-specific, and what future agents should verify before high-stakes answers.
-
-## Create Or Refresh The Target Skill
-
-- Name the target skill after the area and job, usually `<area>-semantic-layer` or `<area>-data-semantics`.
-- Keep the generated `SKILL.md` focused on how future agents should answer data questions, where to find canonical context, and when to verify. Put detailed metrics, tables, query patterns, gotchas, and evidence into directly linked references.
-- Make the layer discoverable by recording the durable pointer in `$CODEX_HOME/state/plugins/data-analytics/user-context.md` under `# Semantic Layers`: area, skill name, absolute path, source inventory path, last updated date, and future-use guidance.
-- Record operational setup or refresh status in `$CODEX_HOME/state/plugins/data-analytics/onboarding-state.json`.
-- Validate generated standalone skills with the relevant authoring validator when files are created locally. Do not claim the skill is invokable until the runtime can see the top-level skill; Codex reload may be required.
-
-## Output Contract
-
-- Intake-only: return source inventory, missing high-value inputs, connector readiness, and crawl plan.
-- Crawl-and-synthesis: return semantic-layer summary, source coverage, unresolved conflicts, confidence notes, and target skill structure.
-- Direct create or refresh: return created or updated file paths, durable pointer written, validation results, source-use checkpoint, connector gaps, activation note when relevant, and the weekly polling resolution.
-- Blocked: state the exact missing connector, permission, source, or destination decision and provide the best manual fallback.
-
-## Quality Bar
-
-- Preserve provenance for every important metric, table, query pattern, and caveat.
-- Keep generated instructions operational: future agents should know which source to check first and what to do when sources disagree.
-- Prefer compact semantic-layer references over copied artifacts.
-- Continue with a partial skill when enough source-backed context exists, but label coverage honestly.
-- Ask before external writes, dashboard changes, team communication posts, repo changes, connector installs, or recurring automation creation.
diff --git a/plugins/data-analytics/skills/user-context/references/source-category-runtime.md b/plugins/data-analytics/skills/user-context/references/source-category-runtime.md
deleted file mode 100644
index 0da62f6..0000000
--- a/plugins/data-analytics/skills/user-context/references/source-category-runtime.md
+++ /dev/null
@@ -1,263 +0,0 @@
-# Data Analytics Source Category Runtime
-
-This reference owns how Data Analytics resolves semantic source categories to real plugins, apps, connectors, exports, local files, or user-provided context. Plugin-author editable category ids, labels, preferred plugin routes, and helper skills live in `../plugin-author-config/source-category-config.json`.
-
-A source category is the kind of analytical evidence a workflow may need, such as structured data, team communication, company docs, BI dashboards, notebooks, email, calendar, or code repositories. The catalog is a routing aid, not proof that a connector is installed, authorized, preferred, or readable for the current user.
-
-## Ownership Map
-
-- `.app.json` and `DEPENDENCIES.MD` declare possible app-backed routes. They are not readiness proof for the current user.
-- `../plugin-author-config/source-category-config.json` owns static source category ids, labels, preferred plugin routes, and category-level `relevant_skills`.
-- `$CODEX_HOME/state/plugins/data-analytics/user-context.md` owns durable user-approved source-routing preferences and semantic-layer registry pointers.
-- `$CODEX_HOME/state/plugins/data-analytics/onboarding-state.json` owns compact onboarding progress, connector-confirmation labels, semantic-layer setup and refresh state, hero prompt progress, and environment conflict notes. Do not persist raw discovery or inventory payloads there.
-
-## Setup-Owned Source Routes
-
-Source route selection happens during onboarding, an explicit source-setup turn, or workflow-time repair for a missing or broken source, not during ordinary preflight. The setup or search step is responsible for inspecting the current environment, preferring related plugins before raw app or connector routes, asking for installation or setup when needed, and writing compact resolved routes to `onboarding-state.json` under `connector_confirmation`. Do not persist the raw `functions.list_available_plugins_to_install` response, full session plugin inventory, connector descriptions, or discovery transcript into onboarding state.
-
-Preflight is a reader. It should return the configured routes that setup wrote, plus unresolved setup gaps, but it must not choose a plugin, app, connector, or manual route on its own. Runtime workflows consume the configured routes.
-
-During setup, prefer source routes in this order:
-
-1. Installed or enabled related plugin from the session `Available plugins` block when it has visible plugin-owned skill or tool surface.
-2. Installable related plugin returned by `functions.list_available_plugins_to_install`, including a plugin that contains or declares a connector or app id from the category's configured preferred plugin route.
-3. Installed or installable app or connector returned by the same install listing or exposed in the session or tool surface.
-4. Manual, uploaded, pasted, or exported context.
-5. Missing or unavailable with practical IT or admin options.
-
-At the start of onboarding source resolution or explicit source setup, call `functions.list_available_plugins_to_install` once and reuse those results for all source categories in that pass. Also read the session `Available plugins` block to identify installed plugins, and read the session `Available skills` block when deciding whether an installed plugin has plugin-owned skills exposed. Load `../plugin-author-config/source-category-config.json` for category `preferred_plugins` and legacy `preferred_apps` only when present, and load `.app.json` to map configured preferred provider names to app or connector ids.
-
-Installed source tools are treated as usable for setup purposes only when their surface is visible enough to use later. If setup finds one or more installed related plugins with visible plugin-owned skill or tool surfaces for the same category, write all useful routes as active under `routes` and let workflows choose among them by evidence need. Do not force a preference question just because several non-conflicting sources are available, such as Google Drive and Notion for company docs; the agent can use both. If setup finds an installed app or connector and also finds a related plugin candidate, prefer the plugin anyway because Data Analytics workflows should use plugin-owned skills and tools when available; keep the app or connector route as fallback if the user defers or install visibility is pending. If setup finds one installed app or connector and no related plugin candidate, write the app or connector route as active and let the workflow try the actual read only when it needs that source. Do not perform a read merely to prove an installed source works during onboarding.
-
-Do not mark a plugin route as `active` unless there is enough evidence: the plugin is listed in `Available plugins` or was just installed successfully with `request_plugin_install`, the relevant app, connector, or tool surface is callable or exposed, and plugin-owned skills are visible in `Available skills` when that plugin is expected to provide skills. If plugin install succeeds but skills or tools are not visible until the next turn or session refresh, record the route as `needs_confirmation`, deferred, or pending and ask the user to continue after refresh. Do not silently mark it `plugin_owned`.
-
-Use only these setup evidence strings unless this contract is intentionally updated:
-
-- `plugin_install_evidence`: `plugin_in_available_plugins`, `request_plugin_install_completed`, `installed_plugin_skill_visible`, `plugin_owned_skill_visible`, or `user_confirmed_installed_plugin`.
-- `skill_surface_evidence`: `plugin_skills_visible`, `plugin_owned_skill_visible`, or `plugin_owned_skills_visible`.
-- `app_surface_evidence` or `tool_surface_evidence`: `callable_app_tools_visible`, `callable_plugin_tools_visible`, or `plugin_tools_visible`.
-- `connector_surface_evidence`: `request_plugin_install_completed_or_callable_surface_visible` or another explicit connector-surface proof returned by setup.
-
-Recommended active plugin route state:
-
-```json
-{
-  "connector_confirmation": {
-    "team_communication": {
-      "status": "active",
-      "preferred": "Slack",
-      "source_kind": "plugin",
-      "skill_surface": "plugin_owned",
-      "plugin": {
-        "id": "slack@openai-curated",
-        "name": "Slack"
-      },
-      "plugin_install_evidence": "request_plugin_install_completed",
-      "app_surface_evidence": "callable_app_tools_visible",
-      "skill_surface_evidence": "plugin_skills_visible",
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Recommended active multi-route state:
-
-```json
-{
-  "connector_confirmation": {
-    "company_docs": {
-      "status": "active",
-      "routes": [
-        {
-          "preferred": "Google Drive",
-          "source_kind": "plugin",
-          "skill_surface": "plugin_owned",
-          "plugin": {
-            "id": "google-drive@openai-curated",
-            "name": "Google Drive"
-          },
-          "plugin_install_evidence": "plugin_in_available_plugins",
-          "skill_surface_evidence": "plugin_skills_visible",
-          "setup_action": "none_try_on_use"
-        },
-        {
-          "preferred": "Notion",
-          "source_kind": "plugin",
-          "skill_surface": "plugin_owned",
-          "plugin": {
-            "id": "notion@openai-curated",
-            "name": "Notion"
-          },
-          "plugin_install_evidence": "plugin_in_available_plugins",
-          "skill_surface_evidence": "plugin_skills_visible",
-          "setup_action": "none_try_on_use"
-        }
-      ],
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Recommended active app or connector route state:
-
-```json
-{
-  "connector_confirmation": {
-    "structured_data": {
-      "status": "active",
-      "preferred": "Databricks",
-      "source_kind": "connector",
-      "skill_surface": "data_analytics_vendored_helper",
-      "connector": {
-        "id": "templated_apps_Databricks",
-        "name": "Databricks"
-      },
-      "connector_surface_evidence": "request_plugin_install_completed_or_callable_surface_visible",
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Use `source_kind: manual` and `skill_surface: manual` only when the user chooses manual, uploaded, pasted, or exported context as the configured route for that source category.
-
-## Runtime Source Resolution
-
-Use sources only when the active workflow needs them. Do not proactively read every category during onboarding, and do not surface installed connectors as durable readiness state.
-
-Choose sources by the fact they own, not by connector availability or convenience. When a workflow asks for stable ownership, approval, routing, source-of-truth, or "who/where should I ask" information, prefer the most canonical ownership or routing source available for that fact, such as a people directory, team directory, company knowledge base, maintained wiki page, verified routing doc, go-link index, source-of-truth hub, or saved source pointer. Use broad document search, chat/message search, public research, or mirrored sources as supporting evidence only when the canonical source is missing, stale, thin, contradicted, or the workflow explicitly needs recent discussion.
-
-For quick ordinary workflow runs, prefer a bounded first pass over exhaustive triangulation. A useful default is one canonical source attempt, one narrow fallback when the first pass is empty, thin, or misleading, and then a user-facing answer with source limitations and an offer to continue. If a connector times out, rate-limits, or consumes roughly the time of two slow source calls for a non-required enrichment lane, return the best available partial result instead of continuing low-yield retrieval.
-
-Do not spend more than roughly three to five minutes of active retrieval on a user-facing workflow without yielding back to the user. If that budget is reached before the answer is strong, stop searching and give a concise checkpoint: what was checked, the best current candidates or partial answer, the limiting gap, and one recommended next step. Choose that next step by usefulness: ask the user to clarify or steer when the search space is ambiguous, suggest continuing deeper when results are promising but incomplete, or suggest acting on the current best answer when it is good enough. Do not continue silent low-yield retrieval merely because more sources or query variants remain.
-
-For each source category a workflow actually attempts:
-
-1. Read saved source-routing preferences and "do not use" rules from `user-context.md`.
-2. Read the configured `connector_confirmation` route or `routes` from preflight. If the category is `active` and one route has `source_kind: plugin`, use the plugin-owned skill or callable tool surface for the active task. If several routes are `active`, choose by fact ownership and likely authority; use multiple routes when doing so would materially improve confidence, such as searching both Drive-native assets and Notion source-of-truth pages.
-3. If the route is missing, incomplete, broken, manual-only, or connector or app based, first run the plugin-first setup branch below before using connector fallback. Ask before installing unless the user explicitly requested installation. When a connector route is already `active`, still prefer an available related plugin because the plugin can add dedicated Data Analytics workflow support.
-4. For `source_kind: app` or `source_kind: connector`, attempt the smallest useful app or connector read for the active task. For apps covered by marketplace `policy.authentication: ON_USE`, this attempted use should trigger setup or authorization when needed. Load Data Analytics' vendored helper skill only when the configured route has `skill_surface: data_analytics_vendored_helper`.
-5. For `source_kind: manual`, use pasted, uploaded, exported, or otherwise user-provided context.
-6. If the read works, continue the workflow with citations or source notes as appropriate. A successful read is evidence for this run only; do not write connector readiness state.
-7. If the configured route fails, explain the practical blocker and offer the next best path: install or reconnect a related plugin when available, connect or authorize the app, install the connector when exposed, ask IT/admin, skip that source for this run, or use a manual or exported fallback. Preferred and `active` routes are always retryable in future workflows unless the user explicitly saves a durable `do not use` rule; when a future workflow needs a source and the route is missing, unauthorized, stale, or broken, try it again and prompt the user to install, connect, reauthorize, or choose a fallback.
-8. Save a future source-routing default or "do not use this source" rule only when the user explicitly selects it for future use or declines that source for future use. Do not use `user-context.md` for general memory, arbitrary saved links, source-of-truth pointers, or area-specific caveats; those belong in the relevant semantic layer when one exists.
-
-## Plugin-First Setup For Missing Sources
-
-When setup finds that a needed source category is not already covered by a proven active plugin route, prefer related plugin setup before raw app or connector setup or manual fallback. Preflight surfaces this as `setup_action: run_plugin_first_source_setup`, `plugin_preference_order`, and `setup_recovery.type: plugin_first_source_setup`.
-
-1. Build the plugin preference order from `saved_source_preferences` when preflight provides them first, then the source category's configured `preferred_plugins`, then legacy `preferred_apps` only when present. Use that order to rank plugins, but do not require the user to have saved a preference before plugin setup is attempted.
-2. Check the session `Available plugins` block for an installed plugin whose name, display name, id, or declared app or connector ids match a configured preferred plugin route for the category. If it has visible plugin-owned skill or tool surface, write it as the active route.
-3. If no installed related plugin exists, call `functions.list_available_plugins_to_install` if it has not already been called in this setup pass, then look for related plugin candidates.
-4. A candidate is related only when the plugin or provider itself matches the configured preferred plugin route, or its declared `app_connector_ids` intersects the `.app.json` ids for a configured preferred plugin route. Legacy `preferred_apps` may contribute only when an older config still declares them. Do not treat broad search, mirrored content, imported content, or a description-level category similarity as enough to make a plugin related.
-5. Prefer plugin candidates over connector candidates even when the app or connector route is already callable or active. If one related plugin candidate is clearly best by saved preference order, configured `preferred_plugins` order, legacy `preferred_apps` order when present, or connector-id intersection, ask one clear install or defer question unless the user explicitly requested installation. If the user confirms, call `functions.request_plugin_install` with the exact returned candidate `tool_type` and `id`. Do not call `request_plugin_install` in parallel with any other tool.
-6. If multiple related plugin candidates tie after saved preference order, configured `preferred_plugins` order, legacy `preferred_apps` order when present, and connector-id matching, ask the user to choose among only those tied relevant candidates.
-7. If installation or setup fails, or install succeeds but plugin skills or tools are not visible yet, keep the plugin setup path retryable. Record `needs_confirmation`, `deferred`, `deferred_environment_api_limitations`, or `skipped_for_now` for operational state, not `declined`, and surface plugin-first setup again in a future workflow when that source category is material. Use connector or app or manual fallback for the current run when useful.
-8. If the user declines a plugin, suppress that plugin for the current workflow and continue with the next related plugin candidate, the existing app or connector route, another user-selected source, or manual or exported context. Save a durable `do not use` or `do not offer` rule only when the user explicitly asks for that future behavior.
-9. If no related plugin candidate exists, use the same install-list results and exposed session tools to find an installed or installable app or connector candidate.
-10. If one plausible app or connector is available and no plugin route is chosen, ask the user to confirm it if needed, then record the app or connector route in `connector_confirmation` without a proof read.
-11. If multiple plausible apps or connectors are available and no plugin route is chosen, ask the user which one to prefer and record that route after selection.
-12. If no matching plugin, app, or connector is available, explain the practical workflow impact and ask the user to install, connect, or authorize a preferred source, ask IT/admin to enable one, defer or skip the category, or use manual or exported context.
-13. If no setup route is available or the user skips setup, write the manual, skipped, or deferred route and use pasted, uploaded, exported, or manual context for the current run when that can satisfy the evidence need.
-
-Example install-list candidate shape:
-
-```json
-{
-  "tool_type": "plugin",
-  "id": "slack@openai-curated",
-  "name": "Slack",
-  "display_name": "Slack",
-  "app_connector_ids": ["REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"],
-  "description": "Use Slack messages and Data Analytics workflows from Codex."
-}
-```
-
-Do not proactively suggest every installable plugin; show only plugins related to the current Data Analytics source category. Do not use `tool_search` as proof that a plugin is installed. Installation evidence must come from the session `Available plugins` block or a successful `request_plugin_install` call. Do not guess plugin ids; call `request_plugin_install` only with an exact candidate returned by `list_available_plugins_to_install`.
-
-When an attempted source fails, distinguish failure class before giving the user a setup CTA:
-
-- `needs re-auth`: the connector explicitly says reauthentication or reauthorization is required. Ask the user to reconnect or authorize that app.
-- `not ready / startup issue`: the app or MCP server times out during startup, handshake, client creation, or transport setup. Say the connector did not become ready, suggest retrying shortly or checking app or runtime status, and do not tell the user to reconnect unless the app later returns a re-auth error.
-- `missing or unsupported`: the connector or app is not installed, not exposed, or lacks the needed action. Offer install or admin enablement, skip, or manual or exported fallback.
-- `query/schema issue`: the source is reachable but the requested object, field, filter, or query shape failed. Use the relevant helper skill's recovery rules before asking the user.
-
-Keep user-facing wording practical. Say what source is unavailable and why it matters for the analytical job, such as `I cannot reach the warehouse path for this workflow yet, so I cannot confirm metric definitions from live tables until Databricks is available to this workflow or you provide SQL/schema context.`
-
-## Missing Sources And Fallbacks
-
-When a plugin, connector, or app cannot be used, keep the message Data Analytics user-facing. Say what source is missing and why it matters for the current job, such as warehouse access or reviewed SQL or schema context is needed to validate metric definitions from live data. Avoid implementation-facing labels like `category-state`, `preflight`, or raw category ids unless the user asks.
-
-Manual input, pasted notes, and exports are valid fallbacks after a real connector attempt fails, the configured route is unsupported in the environment, the user declines connector use, or the current need is urgent enough to bypass setup. Treat manual fallbacks as one-run inputs unless the user explicitly selects a future source-routing preference.
-
-## Core Onboarding Categories
-
-Show these categories first during onboarding:
-
-- `structured_data`: Data warehouse.
-- `team_communication`: Team communication.
-- `company_docs`: Company docs.
-
-Add optional categories only when the user's request, available sources, or semantic-layer plan makes them useful.
-
-These are core onboarding categories, not optional setup extras. During direct onboarding, if one of them does not have an active plugin, app, connector, or manual route, keep it in the visible Step 2 or Step 2A source questions before introducing semantic-layer setup. If setup finds an installable related plugin for a core category, keep that category as `needs_confirmation` until the user installs it or explicitly chooses a fallback. Do not quietly write `deferred`, `skipped`, `declined`, `unavailable`, or `not_applicable` for a core category just to advance onboarding. A core fallback counts as resolved only when onboarding state records the user's explicit choice with `resolution: user_deferred`, `resolution: user_skipped`, `resolution: user_declined`, `resolution: user_marked_unavailable`, `resolution: user_confirmed_not_applicable`, or `resolution: user_continued_with_known_gap`. If a core fallback status appears without one of those explicit resolutions, preflight should surface it again as `needs_confirmation`.
-
-## Onboarding Connector Confirmation
-
-Onboarding may classify source categories so the user understands what Data Analytics can use, what needs setup, and what may require IT or admin help. These labels and route metadata are operational onboarding state, not durable connector-readiness proof, and they belong under `connector_confirmation` in `onboarding-state.json`.
-
-When showing a category, explain the analytical value instead of only naming the connector family:
-
-- `structured_data`: direct warehouse or SQL access lets Data Analytics query structured data for current, source-backed answers.
-- `team_communication`: search recent discussions for context, decisions, and owners.
-- `company_docs`: docs, specs, metric definitions, and source-of-truth pages can answer governance and business-context questions without querying live data.
-- Optional categories should be explained only when offered: dashboards and BI for inspecting dashboards and BI reports for metric definitions, filters, and saved views, behavior signals for product-usage evidence, notebook lab for inspecting prior analyses, reusing notebook logic, and continuing exploratory work, email or calendar for recent operating context, and code repositories for model, query, schema, or semantic-layer ownership.
-
-Use exactly these user-facing statuses:
-
-- `active`: setup found or installed one or more source routes and wrote either route-level `routes[]` entries or backward-compatible `source_kind`, `skill_surface`, and route details when available. This is FYI only. Do not perform an eager read just to prove an installed source works; attempt active sources only when a workflow needs them.
-- `needs_confirmation`: setup found multiple plausible source tools, an installable related plugin that needs approval, or a selected source preference that still needs a route recorded. Ask the user which source to prefer or whether to install the plugin when needed. If the selected source is an installed plugin, app, or connector, write the chosen route and let the workflow's first real read handle auth, query, or schema failures.
-- `missing`: setup found no matching installed plugin, connector, or app. Explain what Data Analytics workflows will be weaker without it and list concrete IT or admin options, such as installing the plugin or connector, authorizing the app for Codex, or using a manual or exported fallback for now.
-- `deferred`, `skipped`, `declined`, or `unavailable`: setup did not choose an active route. Include practical impact and the next best plugin, app, connector, or manual path when useful. Treat failed setup, pending plugin visibility, temporary unavailability, and `skipped_for_now` as retryable while related plugins may become available later. Treat `declined` as a current-workflow suppression unless the user explicitly saves a future `do not use` rule. For a core onboarding category, write one of these only after the user explicitly chooses that fallback and record the matching `resolution` value above; an automatic or stale core fallback must not make source setup look complete.
-
-Use `source_kind: manual` and `skill_surface: manual` when the user chooses pasted SQL, exports, screenshots, local files, schemas, API results, CLI output, or another manual route. Manual is route metadata, not a separate onboarding status.
-
-If the user says `connect Databricks`, `use Slack`, or `Google Drive should work`, treat that as permission to record or repair the selected source route and run the matching install, connect, or authorize path when that path is exposed. Do not run a proof query during onboarding. When a later workflow needs that source, attempt the actual source read then and handle auth, query, schema, skip, or manual fallback in context.
-
-Recommended state shape:
-
-```json
-{
-  "connector_confirmation": {
-    "structured_data": {
-      "status": "needs_confirmation",
-      "preferred": "Databricks",
-      "candidates": ["Databricks", "BigQuery", "Snowflake"]
-    },
-    "team_communication": {
-      "status": "active",
-      "preferred": "Slack",
-      "confirmed_at": "2026-05-29T00:00:00Z"
-    },
-    "company_docs": {
-      "status": "active",
-      "preferred": "Google Drive",
-      "source_kind": "manual",
-      "skill_surface": "manual",
-      "manual_source": "uploaded docs"
-    }
-  }
-}
-```
-
-Do not write `available`, `verified`, `checked`, `installed`, or automatic `blocked` as durable source status. If the user chooses a future default or a source to avoid, save only that approved routing preference in `user-context.md`.
-
-## Onboarding Behavior
-
-The first direct onboarding response should not inspect connectors. After the user approves setup, run source setup or search, call `functions.list_available_plugins_to_install` once, prefer related plugins over raw app or connector routes, and write resolved routes for each category to `connector_confirmation`. Do not preflight-read installed or active connectors merely to prove they work. Active sources are informational only; do not ask the user to confirm every source when setup finds clear active installed plugins, apps, or connectors that can coexist in the same category.
-
-Ask for help only when there are multiple mutually exclusive plausible source tools, an installable related plugin needs approval, no source was found, or IT or admin action may be needed. For missing or connector-covered sources, use plugin setup before connector or app setup and manual fallback. When a later focused workflow needs a source, that workflow attempts the configured source route at that moment and handles plugin install, auth, connection, skip, or manual fallback in context.
-
-Do not advance from core source setup into semantic-layer setup while `structured_data`, `team_communication`, or `company_docs` is still `needs_confirmation` or `missing`. Optional categories may be deferred until a workflow or semantic-layer plan needs them. Core categories may also be deferred, skipped, declined, marked unavailable, or marked not applicable, but only after the user explicitly chooses that known-gap path and onboarding state records the matching `resolution`.
diff --git a/plugins/data-analytics/skills/user-context/scripts/data_analytics_preflight.py b/plugins/data-analytics/skills/user-context/scripts/data_analytics_preflight.py
deleted file mode 100755
index cc4ab89..0000000
--- a/plugins/data-analytics/skills/user-context/scripts/data_analytics_preflight.py
+++ /dev/null
@@ -1,1460 +0,0 @@
-#!/usr/bin/env python3
-"""Read Data Analytics local state and emit a deterministic preflight payload."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import os
-import re
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[3]
-ONBOARDING_REFERENCE = PLUGIN_ROOT / "skills/user-context/references/onboarding.md"
-SOURCE_CATEGORY_CONFIG_REFERENCE = (
-    PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.json"
-)
-APP_MANIFEST_REFERENCE = PLUGIN_ROOT / ".app.json"
-DEFAULT_MAX_CONTEXT_BYTES = 200_000
-CORE_CATEGORY_IDS = ("structured_data", "team_communication", "company_docs")
-ONBOARDING_RESOLVED_SOURCE_STATUSES = {
-    "declined",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "not_applicable",
-    "skipped",
-    "skipped_for_now",
-    "unavailable",
-}
-SOURCE_CLASSIFIED_STATUSES = {
-    "active",
-    "needs_confirmation",
-    "missing",
-    *ONBOARDING_RESOLVED_SOURCE_STATUSES,
-}
-SOURCE_ACTION_REQUIRED_STATUSES = {"needs_confirmation", "missing"}
-CORE_SOURCE_EXPLICIT_FALLBACK_RESOLUTIONS = {
-    "user_continued_with_known_gap",
-    "user_declined",
-    "user_deferred",
-    "user_marked_unavailable",
-    "user_skipped",
-    "user_confirmed_not_applicable",
-}
-SEMANTIC_SETUP_COMPLETE_STATUSES = {
-    "created",
-    "refreshed",
-    "inspected",
-    "repaired",
-    "planned",
-    "skipped",
-    "deferred",
-    "unavailable",
-    "blocked",
-}
-SEMANTIC_SETUP_TARGET_STATUSES = {"created", "refreshed", "inspected", "repaired", "planned"}
-SEMANTIC_REFRESH_COMPLETE_STATUSES = {
-    "accepted",
-    "declined",
-    "deferred",
-    "unavailable",
-    "skipped",
-    "blocked",
-}
-HERO_PROMPT_COMPLETE_STATUSES = {"tried", "skipped", "deferred", "completed"}
-HERO_SKILLS = ("metric-diagnostics", "product-business-analysis", "kpi-reporting")
-SOURCE_ROUTING_PREFERENCES_HEADING = "data analytics source routing preferences"
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Read Data Analytics user-context and onboarding-state files and return "
-            "context plus workflow control obligations as JSON."
-        )
-    )
-    parser.add_argument("--workflow", default="ordinary")
-    parser.add_argument(
-        "--request-mode",
-        choices=("ordinary_workflow", "direct_onboarding_status", "guided_onboarding_workflow"),
-        default="ordinary_workflow",
-    )
-    parser.add_argument("--codex-home", type=Path, default=None)
-    parser.add_argument("--state-dir", type=Path, default=None)
-    parser.add_argument("--max-context-bytes", type=int, default=DEFAULT_MAX_CONTEXT_BYTES)
-    parser.add_argument("--section", action="append", default=[])
-    return parser.parse_args()
-
-
-def default_codex_home() -> Path:
-    env_home = os.environ.get("CODEX_HOME")
-    if env_home:
-        return Path(env_home).expanduser()
-    return Path.home() / ".codex"
-
-
-def state_dir_from_args(args: argparse.Namespace) -> Path:
-    if args.state_dir:
-        return args.state_dir.expanduser()
-    codex_home = args.codex_home.expanduser() if args.codex_home else default_codex_home()
-    return codex_home / "state/plugins/data-analytics"
-
-
-def sha256_text(text: str) -> str:
-    return hashlib.sha256(text.encode("utf-8")).hexdigest()
-
-
-def file_mtime(path: Path) -> str | None:
-    try:
-        return datetime.fromtimestamp(path.stat().st_mtime, tz=timezone.utc).isoformat()
-    except OSError:
-        return None
-
-
-def section_bounds(markdown: str) -> dict[str, tuple[int, int, str]]:
-    matches = list(re.finditer(r"(?m)^(#{1,6})\s+(.+?)\s*$", markdown))
-    bounds: dict[str, tuple[int, int, str]] = {}
-    for index, match in enumerate(matches):
-        start = match.start()
-        level = len(match.group(1))
-        end = len(markdown)
-        for next_match in matches[index + 1 :]:
-            if len(next_match.group(1)) <= level:
-                end = next_match.start()
-                break
-        heading = match.group(2).strip()
-        bounds[heading.casefold()] = (start, end, heading)
-    return bounds
-
-
-def extract_sections(markdown: str, requested: list[str]) -> list[dict[str, str]]:
-    if not requested:
-        return []
-    bounds = section_bounds(markdown)
-    sections: list[dict[str, str]] = []
-    for heading in requested:
-        bound = bounds.get(heading.casefold())
-        if not bound:
-            sections.append({"heading": heading, "status": "missing", "content": ""})
-            continue
-        start, end, canonical_heading = bound
-        sections.append(
-            {
-                "heading": canonical_heading,
-                "status": "present",
-                "content": markdown[start:end].strip(),
-            }
-        )
-    return sections
-
-
-def normalize_source_name(value: Any) -> str:
-    return re.sub(r"[^a-z0-9]+", "", str(value or "").casefold())
-
-
-def parse_source_preference_values(raw_value: str) -> list[str]:
-    return [value.strip() for value in raw_value.split(",") if value.strip()]
-
-
-def extract_saved_source_preferences(
-    markdown: str | None,
-    source_category_config: dict[str, dict[str, Any]],
-) -> dict[str, dict[str, list[str]]]:
-    """Read durable Data Analytics source-routing choices from user-context.md."""
-    if not markdown:
-        return {}
-    bounds = section_bounds(markdown)
-    source_routing_section = bounds.get(SOURCE_ROUTING_PREFERENCES_HEADING)
-    if not source_routing_section:
-        return {}
-    start, end, _ = source_routing_section
-    section = markdown[start:end]
-    preferences: dict[str, dict[str, list[str]]] = {}
-    current_category: str | None = None
-    for raw_line in section.splitlines():
-        line = raw_line.strip()
-        category_match = re.match(r"^##\s+([a-z0-9_]+)\s*$", line)
-        if category_match:
-            category_id = category_match.group(1)
-            current_category = category_id if category_id in source_category_config else None
-            continue
-        if not current_category:
-            continue
-        preference_match = re.match(r"^-\s*(Prefer|Avoid):\s*(.*?)\s*$", line)
-        if not preference_match:
-            continue
-        disposition = "preferred" if preference_match.group(1) == "Prefer" else "avoid"
-        configured_sources = {
-            normalize_source_name(str(source)): str(source)
-            for source in preferred_source_routes(source_category_config[current_category])
-        }
-        for source_name in parse_source_preference_values(preference_match.group(2)):
-            configured_source = configured_sources.get(normalize_source_name(source_name))
-            if not configured_source:
-                continue
-            category_preferences = preferences.setdefault(current_category, {})
-            values = category_preferences.setdefault(disposition, [])
-            if configured_source not in values:
-                values.append(configured_source)
-    return preferences
-
-
-def summarize_user_context(
-    markdown: str | None,
-    source_category_config: dict[str, dict[str, Any]],
-) -> dict[str, Any]:
-    """Return the narrow normalized Data Analytics user-context surface."""
-    return {
-        "scope": "source_routing_preferences_and_semantic_layer_registry",
-        "source_routing_preferences": extract_saved_source_preferences(
-            markdown,
-            source_category_config,
-        ),
-        "semantic_layer_count": len(semantic_layer_registry(markdown)),
-        "normalization_complete": markdown is not None,
-    }
-
-
-def read_text_payload(path: Path, max_context_bytes: int, sections: list[str]) -> dict[str, Any]:
-    payload: dict[str, Any] = {
-        "path": str(path),
-        "status": "missing",
-        "bytes": 0,
-        "sha256": None,
-        "mtime": None,
-        "content": None,
-        "sections": [],
-        "omitted": False,
-        "error": None,
-    }
-    if not path.exists():
-        return payload
-    try:
-        text = path.read_text(encoding="utf-8")
-    except (OSError, UnicodeDecodeError) as exc:
-        payload["status"] = "unreadable"
-        payload["error"] = str(exc)
-        return payload
-
-    encoded_len = len(text.encode("utf-8"))
-    payload.update(
-        {
-            "status": "present",
-            "bytes": encoded_len,
-            "sha256": sha256_text(text),
-            "mtime": file_mtime(path),
-            "sections": extract_sections(text, sections),
-            "_content": text,
-        }
-    )
-    if encoded_len <= max_context_bytes:
-        payload["content"] = text
-    else:
-        payload["omitted"] = True
-        payload["omission_reason"] = (
-            f"file exceeds max_context_bytes ({encoded_len} > {max_context_bytes})"
-        )
-    return payload
-
-
-def payload_text(payload: dict[str, Any]) -> str | None:
-    text = payload.get("_content")
-    if isinstance(text, str):
-        return text
-    content = payload.get("content")
-    return content if isinstance(content, str) else None
-
-
-def public_file_payload(
-    payload: dict[str, Any],
-    *,
-    include_content: bool,
-) -> dict[str, Any]:
-    public = {
-        key: value for key, value in payload.items() if key not in {"_content", "data"}
-    }
-    if not include_content:
-        public.pop("content", None)
-    return public
-
-
-def read_json_payload(path: Path, max_context_bytes: int) -> dict[str, Any]:
-    payload = read_text_payload(path, max_context_bytes=max_context_bytes, sections=[])
-    payload["data"] = None
-    if payload["status"] != "present":
-        return payload
-    try:
-        payload["data"] = json.loads(payload_text(payload) or path.read_text(encoding="utf-8"))
-    except json.JSONDecodeError as exc:
-        payload["status"] = "unreadable"
-        payload["error"] = f"invalid json: {exc}"
-    return payload
-
-
-def markdown_template_from_onboarding_reference(heading_name: str) -> str:
-    text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-    heading = re.search(rf"(?m)^## {re.escape(heading_name)}\s*$", text)
-    if not heading:
-        raise RuntimeError(f"{heading_name} heading not found")
-    following = text[heading.end() :]
-    code_block = re.search(r"```md\n(.*?)\n```", following, flags=re.DOTALL)
-    if not code_block:
-        raise RuntimeError(f"{heading_name} markdown block not found")
-    return code_block.group(1).strip()
-
-
-def load_source_category_config() -> dict[str, Any]:
-    return json.loads(SOURCE_CATEGORY_CONFIG_REFERENCE.read_text(encoding="utf-8"))["categories"]
-
-
-def unique_strings(values: list[Any]) -> list[str]:
-    return list(dict.fromkeys(str(value) for value in values if isinstance(value, str) and value))
-
-
-def preferred_source_routes(metadata: dict[str, Any]) -> list[str]:
-    return unique_strings(
-        [
-            *(metadata.get("preferred_plugins") or []),
-            *(metadata.get("preferred_apps") or []),
-        ]
-    )
-
-
-def normalize_source_kind(value: Any) -> str | None:
-    source_kind = str(value or "").casefold()
-    if source_kind in {"plugin", "app", "connector", "manual"}:
-        return source_kind
-    return None
-
-
-PLUGIN_INSTALL_EVIDENCE = {
-    "installed_plugin_skill_visible",
-    "plugin_in_available_plugins",
-    "plugin_owned_skill_visible",
-    "request_plugin_install_completed",
-    "user_confirmed_installed_plugin",
-}
-
-PLUGIN_SKILL_SURFACE_EVIDENCE = {
-    "plugin_skills_visible",
-    "plugin_owned_skill_visible",
-    "plugin_owned_skills_visible",
-}
-
-PLUGIN_TOOL_SURFACE_EVIDENCE = {
-    "callable_app_tools_visible",
-    "callable_plugin_tools_visible",
-    "plugin_tools_visible",
-}
-
-PLUGIN_FIRST_SETUP_STATUSES = {
-    "active",
-    "missing",
-    "needs_confirmation",
-    "unavailable",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "skipped_for_now",
-}
-
-
-def compact_mapping(value: Any) -> dict[str, Any] | None:
-    if not isinstance(value, dict):
-        return None
-    compact = {
-        str(key): item
-        for key, item in value.items()
-        if isinstance(key, str) and (isinstance(item, (str, int, float, bool)) or item is None)
-    }
-    return compact or None
-
-
-def default_skill_surface(source_kind: str | None, metadata: dict[str, Any]) -> str | None:
-    if source_kind == "plugin":
-        return "plugin_owned"
-    if source_kind in {"app", "connector"}:
-        if metadata.get("relevant_skills"):
-            return "data_analytics_vendored_helper"
-        return "direct_tool"
-    if source_kind == "manual":
-        return "manual"
-    return None
-
-
-def default_runtime_action(source_kind: str | None, skill_surface: str | None) -> str | None:
-    if source_kind == "plugin":
-        return "use_plugin_owned_skill_or_tools"
-    if source_kind in {"app", "connector"}:
-        if skill_surface == "data_analytics_vendored_helper":
-            return "use_connector_or_app_with_data_analytics_helper_guidance"
-        return "use_direct_tool_when_workflow_needs_source"
-    if source_kind == "manual":
-        return "use_manual_or_exported_context"
-    return None
-
-
-def copy_configured_route_metadata(
-    entry: dict[str, Any],
-    raw_entry: dict[str, Any],
-    metadata: dict[str, Any],
-) -> None:
-    source_kind = normalize_source_kind(raw_entry.get("source_kind"))
-    if source_kind is None:
-        if isinstance(raw_entry.get("plugin"), dict):
-            source_kind = "plugin"
-        elif isinstance(raw_entry.get("connector"), dict):
-            source_kind = "connector"
-        elif isinstance(raw_entry.get("app"), dict):
-            source_kind = "app"
-        elif isinstance(raw_entry.get("manual"), dict) or raw_entry.get("manual_source"):
-            source_kind = "manual"
-        elif entry.get("status") == "active" and entry.get("preferred"):
-            source_kind = "connector"
-
-    if source_kind:
-        entry["source_kind"] = source_kind
-
-    for key in ("plugin", "app", "connector", "manual"):
-        compact = compact_mapping(raw_entry.get(key))
-        if compact is not None:
-            entry[key] = compact
-
-    manual_source = raw_entry.get("manual_source")
-    if isinstance(manual_source, str) and manual_source:
-        entry["manual_source"] = manual_source
-
-    skill_surface = str(raw_entry.get("skill_surface") or "") or default_skill_surface(
-        source_kind,
-        metadata,
-    )
-    if skill_surface:
-        entry["skill_surface"] = skill_surface
-
-    runtime_action = str(raw_entry.get("runtime_action") or "") or default_runtime_action(
-        source_kind,
-        skill_surface,
-    )
-    if runtime_action:
-        entry["runtime_action"] = runtime_action
-
-    for evidence_key in (
-        "installed_evidence",
-        "plugin_install_evidence",
-        "app_surface_evidence",
-        "connector_surface_evidence",
-        "skill_surface_evidence",
-        "tool_surface_evidence",
-    ):
-        evidence_value = raw_entry.get(evidence_key)
-        if isinstance(evidence_value, str) and evidence_value:
-            entry[evidence_key] = evidence_value
-
-
-def copy_source_resolution_metadata(
-    entry: dict[str, Any],
-    raw_entry: dict[str, Any],
-) -> None:
-    for key in ("resolution", "resolved_at", "resolution_note"):
-        value = raw_entry.get(key)
-        if isinstance(value, str) and value:
-            entry[key] = value
-
-
-def core_source_fallback_is_explicit(entry: dict[str, Any]) -> bool:
-    return (
-        str(entry.get("resolution") or "").casefold()
-        in CORE_SOURCE_EXPLICIT_FALLBACK_RESOLUTIONS
-    )
-
-
-def normalize_unacknowledged_core_source_fallback(
-    category_id: str,
-    entry: dict[str, Any],
-    metadata: dict[str, Any],
-) -> None:
-    if category_id not in CORE_CATEGORY_IDS:
-        return
-    if entry.get("status") not in ONBOARDING_RESOLVED_SOURCE_STATUSES:
-        return
-    if core_source_fallback_is_explicit(entry):
-        return
-
-    recorded_status = str(entry["status"])
-    previous_setup_action = str(
-        entry.get("fallback_setup_action") or entry.get("setup_action") or ""
-    )
-    preferred_routes = preferred_source_routes(metadata)
-    entry["recorded_status"] = recorded_status
-    entry["status"] = "needs_confirmation"
-    entry["candidates"] = preferred_routes
-    entry["setup_action"] = "run_plugin_first_source_setup"
-    if previous_setup_action and previous_setup_action != entry["setup_action"]:
-        entry["fallback_setup_action"] = previous_setup_action
-    entry["setup_note"] = "core_source_requires_explicit_user_resolution_before_fallback"
-    entry["plugin_preference_order"] = preferred_routes
-    entry["setup_recovery"] = plugin_first_setup_recovery(metadata, previous_setup_action)
-
-
-def compact_source_route(raw_route: Any, metadata: dict[str, Any]) -> dict[str, Any] | None:
-    if not isinstance(raw_route, dict):
-        return None
-    route: dict[str, Any] = {}
-    for key in ("status", "preferred", "name", "label", "fallback", "setup_action"):
-        value = raw_route.get(key)
-        if isinstance(value, str) and value:
-            route[key] = value
-    copy_configured_route_metadata(route, raw_route, metadata)
-    if "preferred" not in route:
-        for route_key in ("plugin", "app", "connector", "manual"):
-            raw_value = raw_route.get(route_key)
-            if isinstance(raw_value, str) and raw_value:
-                route["preferred"] = raw_value
-                break
-            if isinstance(raw_value, dict):
-                for name_key in ("name", "display_name", "id"):
-                    name_value = raw_value.get(name_key)
-                    if isinstance(name_value, str) and name_value:
-                        route["preferred"] = name_value
-                        break
-                if "preferred" in route:
-                    break
-    if "runtime_action" not in route and route.get("source_kind"):
-        runtime_action = default_runtime_action(
-            str(route.get("source_kind") or ""),
-            str(route.get("skill_surface") or ""),
-        )
-        if runtime_action:
-            route["runtime_action"] = runtime_action
-    return route or None
-
-
-def active_plugin_route_present(entry: dict[str, Any]) -> bool:
-    if entry.get("source_kind") == "plugin":
-        return True
-    routes = entry.get("routes")
-    if not isinstance(routes, list):
-        return False
-    return any(isinstance(route, dict) and route.get("source_kind") == "plugin" for route in routes)
-
-
-def unproven_plugin_route(entry: dict[str, Any]) -> bool:
-    if entry.get("source_kind") != "plugin":
-        return False
-    installed_evidence = str(entry.get("installed_evidence") or "")
-    plugin_install_evidence = str(entry.get("plugin_install_evidence") or "")
-    if installed_evidence in PLUGIN_INSTALL_EVIDENCE:
-        return False
-    if plugin_install_evidence not in PLUGIN_INSTALL_EVIDENCE:
-        return True
-    if entry.get("skill_surface") == "plugin_owned":
-        skill_surface_evidence = str(entry.get("skill_surface_evidence") or "")
-        if skill_surface_evidence not in PLUGIN_SKILL_SURFACE_EVIDENCE:
-            return True
-    surface_evidence = {
-        str(entry.get("app_surface_evidence") or ""),
-        str(entry.get("tool_surface_evidence") or ""),
-        str(entry.get("skill_surface_evidence") or ""),
-    }
-    return not surface_evidence.intersection(
-        PLUGIN_TOOL_SURFACE_EVIDENCE | PLUGIN_SKILL_SURFACE_EVIDENCE
-    )
-
-
-def list_from_inventory(inventory: dict[str, Any], *keys: str) -> list[str]:
-    values: list[str] = []
-    for key in keys:
-        raw = inventory.get(key)
-        if isinstance(raw, list):
-            values.extend(str(item) for item in raw if isinstance(item, str) and item)
-    return values
-
-
-def matching_configured_sources(preferred_sources: list[str], candidates: set[str]) -> list[str]:
-    return [
-        source for source in preferred_sources if normalize_source_name(source) in candidates
-    ]
-
-
-def missing_source_it_options(preferred_sources: list[str]) -> list[str]:
-    source_list = ", ".join(preferred_sources) if preferred_sources else "a matching source"
-    return [
-        f"Ask IT or a workspace admin to install or enable one of: {source_list}.",
-        "Use exported, pasted, or manually supplied context for the current workflow.",
-    ]
-
-
-def load_app_connector_ids() -> dict[str, str]:
-    manifest = json.loads(APP_MANIFEST_REFERENCE.read_text(encoding="utf-8"))
-    apps = manifest.get("apps") if isinstance(manifest, dict) else None
-    if not isinstance(apps, dict):
-        return {}
-    return {
-        normalize_source_name(app_name): str(metadata["id"])
-        for app_name, metadata in apps.items()
-        if isinstance(app_name, str)
-        and isinstance(metadata, dict)
-        and isinstance(metadata.get("id"), str)
-        and metadata["id"]
-    }
-
-
-def plugin_connector_ids(
-    plugin_name: str,
-    metadata: dict[str, Any],
-    app_connector_ids: dict[str, str],
-) -> set[str]:
-    plugin_alias = normalize_source_name(plugin_name)
-    return {
-        connector_id
-        for source_name in preferred_source_routes(metadata)
-        if normalize_source_name(source_name) == plugin_alias
-        for connector_id in [app_connector_ids.get(normalize_source_name(source_name))]
-        if connector_id
-    }
-
-
-def compact_install_candidate(candidate: dict[str, Any]) -> dict[str, Any]:
-    return {
-        key: candidate[key]
-        for key in (
-            "id",
-            "name",
-            "display_name",
-            "tool_type",
-            "has_skills",
-            "app_connector_ids",
-        )
-        if key in candidate
-    }
-
-
-def match_installable_plugin_candidates(
-    source_category_config: dict[str, Any],
-    candidates: list[dict[str, Any]] | dict[str, Any],
-    app_connector_ids: dict[str, str] | None = None,
-) -> dict[str, list[dict[str, Any]]]:
-    """Rank installable plugins using config order, plugin slugs, and connector ids."""
-    if isinstance(candidates, dict):
-        raw_candidates = candidates.get("tools")
-        candidates = raw_candidates if isinstance(raw_candidates, list) else []
-    connector_ids = app_connector_ids if app_connector_ids is not None else load_app_connector_ids()
-    matches: dict[str, list[dict[str, Any]]] = {}
-    for category_id, metadata in source_category_config.items():
-        category_matches: list[dict[str, Any]] = []
-        for rank, plugin_name in enumerate(metadata.get("preferred_plugins") or []):
-            plugin_alias = normalize_source_name(plugin_name)
-            configured_connector_ids = plugin_connector_ids(plugin_name, metadata, connector_ids)
-            for candidate in candidates:
-                if not isinstance(candidate, dict) or candidate.get("tool_type") != "plugin":
-                    continue
-                candidate_aliases = {
-                    normalize_source_name(candidate.get("name")),
-                    normalize_source_name(candidate.get("display_name")),
-                    normalize_source_name(str(candidate.get("id") or "").split("@", 1)[0]),
-                }
-                candidate_connector_ids = {
-                    str(connector_id)
-                    for connector_id in candidate.get("app_connector_ids") or []
-                    if isinstance(connector_id, str)
-                }
-                reasons: list[str] = []
-                if plugin_alias and plugin_alias in candidate_aliases:
-                    reasons.append("plugin_name")
-                if configured_connector_ids & candidate_connector_ids:
-                    reasons.append("app_connector_id")
-                if not reasons:
-                    continue
-                category_matches.append(
-                    {
-                        "preferred_plugin": plugin_name,
-                        "preference_rank": rank,
-                        "match_reasons": reasons,
-                        "candidate": compact_install_candidate(candidate),
-                    }
-                )
-                break
-        if category_matches:
-            matches[category_id] = category_matches
-    return matches
-
-
-def plugin_first_setup_recovery(
-    metadata: dict[str, Any],
-    fallback_setup_action: str,
-) -> dict[str, Any]:
-    recovery = {
-        "type": "plugin_first_source_setup",
-        "preferred_plugins": unique_strings(metadata.get("preferred_plugins") or []),
-        "configured_source_routes": preferred_source_routes(metadata),
-        "candidate_lookup": "functions.list_available_plugins_to_install",
-        "candidate_match": "plugin_name_slug_or_app_connector_id_intersection",
-        "install_request": "functions.request_plugin_install",
-        "install_requires_user_approval": True,
-        "fallback_setup_action": fallback_setup_action,
-    }
-    legacy_app_fallbacks = unique_strings(metadata.get("preferred_apps") or [])
-    if legacy_app_fallbacks:
-        recovery["legacy_app_fallbacks"] = legacy_app_fallbacks
-    return recovery
-
-
-def connector_confirmation_base_entry(
-    category_id: str,
-    metadata: dict[str, Any],
-) -> dict[str, Any]:
-    preferred_plugins = unique_strings(metadata.get("preferred_plugins") or [])
-    preferred_apps = unique_strings(metadata.get("preferred_apps") or [])
-    entry: dict[str, Any] = {
-        "id": category_id,
-        "label": metadata["label"],
-        "state_scope": "onboarding_only_not_durable_connector_readiness",
-        "workflow_time_behavior": "attempt_actual_reads_only_when_a_workflow_needs_the_source",
-        "eager_read": False,
-    }
-    if preferred_plugins:
-        entry["preferred_plugins"] = preferred_plugins
-    if preferred_apps:
-        entry["preferred_apps"] = preferred_apps
-    return entry
-
-
-def onboarding_status(onboarding_state: dict[str, Any] | None) -> str:
-    if not onboarding_state:
-        return "missing"
-    return str(onboarding_state.get("status") or "active")
-
-
-def connector_categories(onboarding_state: dict[str, Any] | None) -> dict[str, dict[str, Any]]:
-    state = onboarding_state or {}
-    connector_confirmation = state.get("connector_confirmation") or {}
-    if not isinstance(connector_confirmation, dict):
-        return {}
-    categories = connector_confirmation.get("categories")
-    if isinstance(categories, dict):
-        return categories
-    return {
-        str(category_id): entry
-        for category_id, entry in connector_confirmation.items()
-        if category_id not in {"status", "last_shown", "preferences"}
-        and isinstance(entry, dict)
-    }
-
-
-def source_status(entry: dict[str, Any] | None) -> str:
-    raw_status = str((entry or {}).get("status") or "needs_confirmation").casefold()
-    aliases = {
-        "available": "needs_confirmation",
-        "blocked": "unavailable",
-        "choose_source": "needs_confirmation",
-        "connected": "active",
-        "manual": "active",
-        "needs_setup_probe": "needs_confirmation",
-        "not_available": "missing",
-        "selected": "needs_confirmation",
-        "selected_for_verification": "needs_confirmation",
-        "selected_needs_auth": "needs_confirmation",
-    }
-    return aliases.get(raw_status, raw_status)
-
-
-def source_setup_action(status: str) -> str:
-    actions = {
-        "active": "none_try_on_use",
-        "needs_confirmation": "ask_user_to_choose_or_confirm_source",
-        "missing": "explain_workflow_impact_and_it_admin_options",
-        "unavailable": "ask_admin_or_manual_fallback",
-        "declined": "none_declined",
-        "skipped": "none_skipped",
-        "skipped_for_now": "none_skipped",
-        "deferred": "none_deferred",
-        "deferred_environment_api_limitations": "none_deferred",
-        "not_applicable": "none_not_applicable",
-    }
-    return actions.get(status, "ask_user_to_choose_or_confirm_source")
-
-
-def normalized_connector_confirmation(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> dict[str, dict[str, Any]]:
-    raw_categories = connector_categories(onboarding_state)
-    state = onboarding_state or {}
-    raw_confirmation = state.get("connector_confirmation") or {}
-    raw_preferences = raw_confirmation.get("preferences") if isinstance(raw_confirmation, dict) else {}
-    if not isinstance(raw_preferences, dict):
-        raw_preferences = {}
-    inventory = state.get("connector_inventory") or {}
-    if not isinstance(inventory, dict):
-        inventory = {}
-    confirmation: dict[str, dict[str, Any]] = {}
-    for category_id, metadata in source_category_config.items():
-        raw_entry = raw_categories.get(category_id)
-        if raw_entry is None:
-            raw_entry = raw_preferences.get(category_id)
-        raw_entry = raw_entry if isinstance(raw_entry, dict) else {}
-        preferred_plugins = unique_strings(metadata.get("preferred_plugins") or [])
-        preferred_routes = preferred_source_routes(metadata)
-        entry = connector_confirmation_base_entry(category_id, metadata)
-
-        if raw_entry:
-            status = source_status(raw_entry)
-            if status in ONBOARDING_RESOLVED_SOURCE_STATUSES:
-                entry["status"] = status
-                entry["setup_action"] = source_setup_action(status)
-                copy_configured_route_metadata(entry, raw_entry, metadata)
-                copy_source_resolution_metadata(entry, raw_entry)
-            elif status in {"active", "needs_confirmation", "missing"}:
-                entry["status"] = status
-                preferred = raw_entry.get("preferred") or raw_entry.get("app")
-                if isinstance(preferred, str) and preferred:
-                    entry["preferred"] = preferred
-                fallback = raw_entry.get("fallback") or raw_entry.get("backup")
-                if isinstance(fallback, str) and fallback:
-                    entry["fallback"] = fallback
-                if status == "active":
-                    raw_routes = raw_entry.get("routes")
-                    if isinstance(raw_routes, list):
-                        compacted_routes = [
-                            route
-                            for route in (
-                                compact_source_route(raw_route, metadata) for raw_route in raw_routes
-                            )
-                            if route is not None
-                        ]
-                        routes = [
-                            route
-                            for route in compacted_routes
-                            if not unproven_plugin_route(route)
-                        ]
-                        if routes:
-                            entry["routes"] = routes
-                            if "preferred" not in entry:
-                                first_preferred = routes[0].get("preferred")
-                                if isinstance(first_preferred, str) and first_preferred:
-                                    entry["preferred"] = first_preferred
-                        elif compacted_routes:
-                            entry = connector_confirmation_base_entry(category_id, metadata)
-                            entry.update(
-                                {
-                                    "status": "needs_confirmation",
-                                    "candidates": preferred_routes,
-                                    "setup_action": "run_plugin_first_source_setup",
-                                    "setup_note": (
-                                        "plugin_route_requires_plugin_install_and_surface_evidence_before_active"
-                                    ),
-                                }
-                            )
-                    if entry.get("status") == "active":
-                        if raw_entry.get("confirmed_at"):
-                            entry["confirmed_at"] = raw_entry["confirmed_at"]
-                        entry["setup_action"] = "none_try_on_use"
-                        copy_configured_route_metadata(entry, raw_entry, metadata)
-                        copy_source_resolution_metadata(entry, raw_entry)
-                        if unproven_plugin_route(entry):
-                            entry = connector_confirmation_base_entry(category_id, metadata)
-                            entry.update(
-                                {
-                                    "status": "needs_confirmation",
-                                    "candidates": preferred_routes,
-                                    "setup_action": "run_plugin_first_source_setup",
-                                    "setup_note": (
-                                        "plugin_route_requires_plugin_install_and_surface_evidence_before_active"
-                                    ),
-                                }
-                            )
-                elif status == "needs_confirmation":
-                    candidates = raw_entry.get("candidates")
-                    if not isinstance(candidates, list) or not candidates:
-                        if preferred:
-                            candidates = [preferred]
-                        else:
-                            candidates = preferred_routes
-                    entry["candidates"] = unique_strings(candidates)
-                    entry["setup_action"] = source_setup_action(status)
-                    copy_configured_route_metadata(entry, raw_entry, metadata)
-                    copy_source_resolution_metadata(entry, raw_entry)
-                else:
-                    options = raw_entry.get("options")
-                    if not isinstance(options, list) or not options:
-                        options = preferred_routes
-                    entry["options"] = unique_strings(options)
-                    entry["it_admin_options"] = raw_entry.get(
-                        "it_admin_options"
-                    ) or missing_source_it_options(entry["options"])
-                    entry["setup_action"] = source_setup_action(status)
-                    copy_configured_route_metadata(entry, raw_entry, metadata)
-                    copy_source_resolution_metadata(entry, raw_entry)
-            else:
-                entry["status"] = "needs_confirmation"
-                entry["candidates"] = preferred_routes
-                entry["setup_action"] = source_setup_action("needs_confirmation")
-        else:
-            configured_apps = {
-                normalize_source_name(app)
-                for app in list_from_inventory(
-                    inventory,
-                    "active_apps",
-                    "active",
-                    "installed_and_active",
-                    "connected_apps",
-                    "available_apps",
-                    "available",
-                    "installed_apps",
-                    "surfaced_apps",
-                )
-            }
-            available_matches = matching_configured_sources(preferred_routes, configured_apps)
-            if available_matches:
-                entry["status"] = "needs_confirmation"
-                entry["candidates"] = available_matches
-                entry["setup_action"] = source_setup_action("needs_confirmation")
-            elif inventory:
-                entry["status"] = "missing"
-                entry["options"] = preferred_routes
-                entry["it_admin_options"] = missing_source_it_options(preferred_routes)
-                entry["setup_action"] = source_setup_action("missing")
-            else:
-                entry["status"] = "needs_confirmation"
-                entry["candidates"] = preferred_routes
-                entry["setup_action"] = source_setup_action("needs_confirmation")
-
-        if entry.get("status") == "active" and raw_entry.get("status") == "manual":
-            entry.setdefault("source_kind", "manual")
-            entry.setdefault("skill_surface", "manual")
-            entry.setdefault("runtime_action", "use_manual_or_exported_context")
-
-        if "preferred" not in entry:
-            entry["preferred"] = raw_entry.get("preferred")
-
-        relevant_skills = metadata.get("relevant_skills") or []
-        if relevant_skills and str(entry.get("source_kind") or "").casefold() != "plugin":
-            entry["helper_skills"] = relevant_skills
-            entry["helper_skills_apply_when"] = ["app", "connector"]
-
-        plugin_preference_order = preferred_source_routes(metadata)
-        if (
-            preferred_plugins
-            and plugin_preference_order
-            and entry["status"] in PLUGIN_FIRST_SETUP_STATUSES
-        ):
-            entry["plugin_preference_order"] = plugin_preference_order
-            if not active_plugin_route_present(entry):
-                previous_setup_action = entry.get("setup_action")
-                entry["setup_action"] = "run_plugin_first_source_setup"
-                if previous_setup_action and previous_setup_action != entry["setup_action"]:
-                    entry["fallback_setup_action"] = previous_setup_action
-                entry["setup_recovery"] = plugin_first_setup_recovery(
-                    metadata,
-                    str(previous_setup_action or source_setup_action(entry["status"])),
-                )
-        normalize_unacknowledged_core_source_fallback(category_id, entry, metadata)
-        confirmation[category_id] = entry
-    return confirmation
-
-
-def connector_confirmation_status(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> str:
-    state = onboarding_state or {}
-    if not connector_categories(onboarding_state) and not state.get("connector_inventory"):
-        return "pending"
-    confirmation = normalized_connector_confirmation(source_category_config, onboarding_state)
-    statuses = [confirmation[category_id]["status"] for category_id in CORE_CATEGORY_IDS]
-    if statuses and all(status in SOURCE_CLASSIFIED_STATUSES for status in statuses):
-        return "completed"
-    if any(status for status in statuses):
-        return "in_progress"
-    return "pending"
-
-
-def connector_confirmation_action_required(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> bool:
-    if connector_confirmation_status(source_category_config, onboarding_state) == "pending":
-        return False
-    confirmation = normalized_connector_confirmation(source_category_config, onboarding_state)
-    return any(
-        confirmation[category_id]["status"] in SOURCE_ACTION_REQUIRED_STATUSES
-        for category_id in CORE_CATEGORY_IDS
-    )
-
-
-def connector_setup_confirmation_status(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> str:
-    confirmation_status = connector_confirmation_status(source_category_config, onboarding_state)
-    if confirmation_status == "pending":
-        return "pending"
-    if connector_confirmation_action_required(source_category_config, onboarding_state):
-        return "in_progress"
-    return "completed"
-
-
-def compact_connector_setup_summary_entry(entry: dict[str, Any]) -> dict[str, Any]:
-    compact = {
-        key: entry[key]
-        for key in (
-            "id",
-            "label",
-            "status",
-            "preferred",
-            "setup_action",
-            "fallback_setup_action",
-            "source_kind",
-        )
-        if key in entry
-    }
-    for key in ("candidates", "options"):
-        values = entry.get(key)
-        if isinstance(values, list) and values:
-            compact[key] = unique_strings(values)
-    return compact
-
-
-def build_connector_setup_summary(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> dict[str, Any]:
-    confirmation = normalized_connector_confirmation(source_category_config, onboarding_state)
-    ready: list[dict[str, Any]] = []
-    needs_attention: list[dict[str, Any]] = []
-    fallback_or_closed: list[dict[str, Any]] = []
-    plugin_setup_opportunities: list[dict[str, Any]] = []
-    unresolved_core_ids: list[str] = []
-    for category_id, entry in confirmation.items():
-        status = entry["status"]
-        summary_entry = compact_connector_setup_summary_entry(entry)
-        if status == "active":
-            ready.append(summary_entry)
-        elif status in ONBOARDING_RESOLVED_SOURCE_STATUSES:
-            fallback_or_closed.append(summary_entry)
-        else:
-            needs_attention.append(summary_entry)
-        if entry.get("setup_action") == "run_plugin_first_source_setup":
-            plugin_setup_opportunities.append(summary_entry)
-        if category_id in CORE_CATEGORY_IDS and status in SOURCE_ACTION_REQUIRED_STATUSES:
-            unresolved_core_ids.append(category_id)
-    next_entry = needs_attention[0] if needs_attention else None
-    return {
-        "status": connector_setup_confirmation_status(source_category_config, onboarding_state),
-        "classification_status": connector_confirmation_status(
-            source_category_config,
-            onboarding_state,
-        ),
-        "action_required": connector_confirmation_action_required(
-            source_category_config,
-            onboarding_state,
-        ),
-        "has_setup_gaps": bool(needs_attention or plugin_setup_opportunities),
-        "ready": ready,
-        "active": ready,
-        "needs_attention": needs_attention,
-        "needs_choice": needs_attention,
-        "fallback_or_closed": fallback_or_closed,
-        "not_set_up": fallback_or_closed,
-        "plugin_setup_opportunities": plugin_setup_opportunities,
-        "unresolved_core_ids": unresolved_core_ids,
-        "core_complete": connector_setup_confirmation_status(
-            source_category_config,
-            onboarding_state,
-        )
-        == "completed",
-        "next_action": (
-            {
-                "category_id": next_entry["id"],
-                "label": next_entry["label"],
-                "status": next_entry["status"],
-                "preferred": next_entry["preferred"],
-                "setup_action": next_entry["setup_action"],
-            }
-            if next_entry
-            else None
-        ),
-        "user_facing_guidance": (
-            "Use this summary for capability or setup-status answers. During onboarding, "
-            "resolve one highest-value source action at a time. Active sources are FYI only; "
-            "attempt actual reads only when a workflow needs the source."
-        ),
-    }
-
-
-def semantic_layer_setup_status(onboarding_state: dict[str, Any] | None) -> str:
-    state = onboarding_state or {}
-    return str((state.get("semantic_layer_setup") or {}).get("status") or "pending")
-
-
-def semantic_layer_refresh_status(onboarding_state: dict[str, Any] | None) -> str:
-    state = onboarding_state or {}
-    return str((state.get("semantic_layer_refresh") or {}).get("status") or "pending")
-
-
-def semantic_layer_setup_resolved(onboarding_state: dict[str, Any] | None) -> bool:
-    setup_status = semantic_layer_setup_status(onboarding_state)
-    if setup_status not in SEMANTIC_SETUP_COMPLETE_STATUSES:
-        return False
-    if setup_status in SEMANTIC_SETUP_TARGET_STATUSES:
-        return semantic_layer_refresh_status(onboarding_state) in SEMANTIC_REFRESH_COMPLETE_STATUSES
-    return True
-
-
-def hero_prompt_state(onboarding_state: dict[str, Any] | None) -> dict[str, Any]:
-    state = onboarding_state or {}
-    choice = state.get("hero_prompt_choice")
-    if isinstance(choice, dict):
-        return choice
-    legacy = state.get("hero_prompts")
-    return legacy if isinstance(legacy, dict) else {}
-
-
-def first_hero_prompt_status(onboarding_state: dict[str, Any] | None) -> str:
-    choice = hero_prompt_state(onboarding_state)
-    status = str(choice.get("status") or "")
-    if status in HERO_PROMPT_COMPLETE_STATUSES:
-        return "completed"
-    if status == "selected":
-        return "in_progress"
-    skill_experience = (onboarding_state or {}).get("skill_experience") or {}
-    if any(
-        isinstance(skill_experience.get(skill), dict)
-        and skill_experience[skill].get("first_tried_at")
-        for skill in HERO_SKILLS
-    ):
-        return "completed"
-    if (onboarding_state or {}).get("completed_hero_prompts"):
-        return "completed"
-    return "pending"
-
-
-def core_onboarding_complete(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> bool:
-    if not onboarding_state:
-        return False
-    explicit_status = str(
-        (onboarding_state.get("core_onboarding") or {}).get("status") or ""
-    ).casefold()
-    if explicit_status in {"completed", "complete", "complete_with_fallback"}:
-        return True
-    return (
-        connector_setup_confirmation_status(source_category_config, onboarding_state)
-        == "completed"
-        and semantic_layer_setup_resolved(onboarding_state)
-    )
-
-
-def progress_task_list(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> list[dict[str, str]]:
-    state = onboarding_state or {}
-    status = onboarding_status(onboarding_state)
-    task_specs = (
-        (
-            "orientation",
-            "Orientation",
-            (state.get("orientation") or {}).get("status") in {"shown", "completed"},
-        ),
-        (
-            "source_setup_confirmation",
-            "Check main analytics sources",
-            connector_setup_confirmation_status(source_category_config, onboarding_state)
-            == "completed",
-        ),
-        (
-            "semantic_layer_setup",
-            "Set Up Data Context",
-            semantic_layer_setup_resolved(onboarding_state),
-        ),
-        ("hero_prompt", "Hero prompt", first_hero_prompt_status(onboarding_state) == "completed"),
-    )
-    found_in_progress = False
-    task_list: list[dict[str, str]] = []
-    for task_id, label, completed in task_specs:
-        if completed:
-            task_status = "completed"
-        elif not found_in_progress and status not in {"complete", "quiet"}:
-            task_status = "in_progress"
-            found_in_progress = True
-        else:
-            task_status = "pending"
-        task_list.append({"id": task_id, "label": label, "status": task_status})
-    return task_list
-
-
-def next_onboarding_action(
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> dict[str, str] | None:
-    tasks = progress_task_list(source_category_config, onboarding_state)
-    for task in tasks:
-        if task["status"] == "completed":
-            continue
-        mapping = {
-            "orientation": ("start_data_analytics_onboarding", "show orientation"),
-            "source_setup_confirmation": (
-                "confirm_analytics_sources",
-                "confirm active or missing sources",
-            ),
-            "semantic_layer_setup": (
-                "introduce_semantic_layer_setup",
-                "set up data context",
-            ),
-            "hero_prompt": ("offer_first_hero_prompt", "run or resolve the first hero prompt"),
-        }
-        action_id, label = mapping[task["id"]]
-        return {"id": action_id, "label": label}
-    return None
-
-
-def semantic_layer_registry(user_context_text: str | None) -> list[dict[str, str]]:
-    if not user_context_text:
-        return []
-    bounds = section_bounds(user_context_text)
-    semantic_section = bounds.get("semantic layers")
-    if not semantic_section:
-        return []
-    start, end, _ = semantic_section
-    section = user_context_text[start:end]
-    entries: list[dict[str, str]] = []
-    for block in re.split(r"(?m)^- Area:\s*", section)[1:]:
-        lines = block.splitlines()
-        area = lines[0].strip()
-        entry: dict[str, str] = {"area": area}
-        for line in lines[1:]:
-            match = re.match(
-                r"\s*-\s*(Skill Name|Skill Path|Source Inventory Path|Last Updated):\s*(.+?)\s*$",
-                line,
-            )
-            if match:
-                entry[match.group(1).lower().replace(" ", "_")] = match.group(2)
-        if "skill_path" in entry:
-            entries.append(entry)
-    return entries
-
-
-def saved_anchor_values(user_context_text: str | None) -> dict[str, list[str]]:
-    if not user_context_text:
-        return {"areas": [], "metrics": [], "dashboards": [], "tables": [], "goals": []}
-    return {
-        "areas": [entry["area"] for entry in semantic_layer_registry(user_context_text)],
-        "metrics": [],
-        "dashboards": [],
-        "tables": [],
-        "goals": [],
-    }
-
-
-def build_hero_prompt_candidates(
-    user_context_text: str | None,
-    onboarding_state: dict[str, Any] | None,
-) -> list[dict[str, str]]:
-    anchors = saved_anchor_values(user_context_text)
-    semantic_setup = (onboarding_state or {}).get("semantic_layer_setup") or {}
-    area = semantic_setup.get("area") or (anchors["areas"][0] if anchors["areas"] else None)
-    metric = anchors["metrics"][0] if anchors["metrics"] else None
-    dashboard = anchors["dashboards"][0] if anchors["dashboards"] else None
-    goal = anchors["goals"][0] if anchors["goals"] else None
-    target = area or goal or dashboard or metric
-    if not target:
-        return []
-
-    return [
-        {
-            "skill": "product-business-analysis",
-            "label": "Build a decision-ready report",
-            "prompt": (
-                f"Build a decision-ready report for {target}: explain what is happening, "
-                "identify the main drivers, recommend where the team should focus next, "
-                "and include sources, caveats, and useful charts."
-            ),
-        }
-    ]
-
-
-def reorder_hero_prompt_candidates(
-    candidates: list[dict[str, str]],
-    onboarding_state: dict[str, Any] | None,
-) -> list[dict[str, str]]:
-    selected_skill = str(hero_prompt_state(onboarding_state).get("selected_skill") or "")
-    if not selected_skill:
-        return candidates
-    selected = [candidate for candidate in candidates if candidate["skill"] == selected_skill]
-    remaining = [candidate for candidate in candidates if candidate["skill"] != selected_skill]
-    return selected + remaining
-
-
-def current_skill_experience(
-    workflow: str,
-    request_mode: str,
-    onboarding_state: dict[str, Any] | None,
-) -> dict[str, Any]:
-    skill_experience = (onboarding_state or {}).get("skill_experience") or {}
-    entry = skill_experience.get(workflow) if isinstance(skill_experience, dict) else None
-    entry = entry if isinstance(entry, dict) else {}
-    introduced = bool(entry.get("introduced_at"))
-    tried = bool(entry.get("first_tried_at"))
-    dismissed = bool(entry.get("dismissed_at"))
-    return {
-        "skill": workflow,
-        "introduced": introduced,
-        "tried": tried,
-        "dismissed": dismissed,
-        "intro_eligible": workflow in HERO_SKILLS and not introduced and not dismissed,
-        "cta_owner": "onboarding" if request_mode == "guided_onboarding_workflow" else "skill",
-    }
-
-
-def final_obligations(
-    request_mode: str,
-    user_context_status: str,
-    source_category_config: dict[str, Any],
-    onboarding_state: dict[str, Any] | None,
-) -> list[dict[str, Any]]:
-    if request_mode != "ordinary_workflow":
-        return []
-    status = onboarding_status(onboarding_state)
-    if status in {"complete", "quiet"}:
-        return []
-    if user_context_status in {"missing", "unreadable"}:
-        return [
-            {
-                "id": "offer_data_analytics_onboarding_next_step",
-                "timing": "append_after_main_answer",
-                "template": markdown_template_from_onboarding_reference(
-                    "Ordinary Workflow Onboarding CTA"
-                ),
-                "next_action": {"id": "start_data_analytics_onboarding"},
-                "skip_when": "the response requires a clarification or the user asked for quiet behavior",
-            }
-        ]
-    if not core_onboarding_complete(source_category_config, onboarding_state):
-        return [
-            {
-                "id": "complete_data_analytics_core_onboarding",
-                "timing": "append_after_main_answer",
-                "template": markdown_template_from_onboarding_reference(
-                    "Active Core Onboarding Reminder"
-                ),
-                "requirement": "core onboarding is not complete",
-                "next_action": next_onboarding_action(source_category_config, onboarding_state),
-                "skip_when": "the response requires a clarification or the user asked for quiet behavior",
-            }
-        ]
-    return []
-
-
-def conditional_guidance(request_mode: str, user_context_status: str) -> list[dict[str, Any]]:
-    if request_mode != "ordinary_workflow":
-        return []
-    if user_context_status not in {"missing", "unreadable"}:
-        return []
-    return [
-        {
-            "id": "context_gap_note",
-            "template": markdown_template_from_onboarding_reference("Context Gap Note"),
-            "potential_gaps": [
-                "Data Analytics source-routing preferences or semantic-layer registry are missing or unreadable"
-            ],
-        }
-    ]
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir = state_dir_from_args(args)
-    user_context_path = state_dir / "user-context.md"
-    onboarding_state_path = state_dir / "onboarding-state.json"
-
-    user_context = read_text_payload(
-        user_context_path,
-        max_context_bytes=args.max_context_bytes,
-        sections=args.section,
-    )
-    onboarding_state = read_json_payload(
-        onboarding_state_path,
-        max_context_bytes=args.max_context_bytes,
-    )
-    onboarding_data = (
-        onboarding_state.get("data") if onboarding_state["status"] == "present" else None
-    )
-    source_category_config = load_source_category_config()
-    user_context_text = payload_text(user_context)
-    saved_source_preferences = extract_saved_source_preferences(
-        user_context_text,
-        source_category_config,
-    )
-    connector_confirmation = normalized_connector_confirmation(
-        source_category_config, onboarding_data
-    )
-    connector_setup_summary = build_connector_setup_summary(source_category_config, onboarding_data)
-    hero_prompt_candidates = reorder_hero_prompt_candidates(
-        build_hero_prompt_candidates(user_context_text, onboarding_data),
-        onboarding_data,
-    )
-    task_list = progress_task_list(source_category_config, onboarding_data)
-    payload = {
-        "schema": "data_analytics_preflight.v1",
-        "read_only": True,
-        "workflow": args.workflow,
-        "request_mode": args.request_mode,
-        "state": {
-            "state_dir": str(state_dir),
-            "user_context_status": user_context["status"],
-            "onboarding_state_status": onboarding_state["status"],
-        },
-        "files": {
-            "user_context": public_file_payload(user_context, include_content=False),
-            "onboarding_state": public_file_payload(onboarding_state, include_content=False),
-        },
-        "current_skill_experience": current_skill_experience(
-            args.workflow,
-            args.request_mode,
-            onboarding_data,
-        ),
-        "context": {
-            "user_context": summarize_user_context(user_context_text, source_category_config),
-            "source_preferences": saved_source_preferences,
-            "source_category_config": source_category_config,
-            "connector_confirmation": connector_confirmation,
-            "connector_setup_summary": connector_setup_summary,
-            "semantic_layers": semantic_layer_registry(user_context_text),
-            "hero_prompt_candidates": hero_prompt_candidates,
-            "primary_hero_prompt": hero_prompt_candidates[0] if hero_prompt_candidates else None,
-            "extra_hero_prompt_candidates": hero_prompt_candidates[1:3],
-        },
-        "control": {
-            "response_mode": args.request_mode,
-            "final_obligations": final_obligations(
-                args.request_mode,
-                user_context["status"],
-                source_category_config,
-                onboarding_data,
-            ),
-            "conditional_guidance": conditional_guidance(args.request_mode, user_context["status"]),
-            "onboarding_progress": {
-                "status": onboarding_status(onboarding_data),
-                "core_onboarding": {
-                    "complete": core_onboarding_complete(source_category_config, onboarding_data),
-                    "remaining_step_ids": [
-                        item["id"] for item in task_list if item["status"] != "completed"
-                    ],
-                    "next_action": next_onboarding_action(source_category_config, onboarding_data),
-                },
-                "task_list": task_list,
-            },
-        },
-    }
-    print(json.dumps(payload, indent=2))
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/data-analytics/skills/user-context/scripts/init_user_context_state.py b/plugins/data-analytics/skills/user-context/scripts/init_user_context_state.py
deleted file mode 100755
index 1f3db6f..0000000
--- a/plugins/data-analytics/skills/user-context/scripts/init_user_context_state.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-"""Initialize local Data Analytics source-routing and onboarding state files."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-USER_CONTEXT_TEMPLATE = SKILL_ROOT / "plugin-author-config/user-context-config.md"
-ONBOARDING_STATE_TEMPLATE = SKILL_ROOT / "references/onboarding-state-template.json"
-
-USER_CONTEXT_NOTE = """<!--
-Data Analytics user-context scaffold. This file is user-editable.
-Store only durable source-routing choices explicitly selected for future use
-plus semantic-layer registry entries. Do not store general memory, output
-preferences, onboarding state, or connector readiness here.
--->
-
-"""
-
-DEFAULT_USER_CONTEXT_MARKER = "## Default User Context"
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Create missing Data Analytics local state files from bundled templates. "
-            "Existing files are preserved unless --overwrite is passed."
-        )
-    )
-    parser.add_argument("--codex-home", type=Path, default=None)
-    parser.add_argument("--state-dir", type=Path, default=None)
-    parser.add_argument("--overwrite", action="store_true")
-    return parser.parse_args()
-
-
-def default_codex_home() -> Path:
-    env_home = os.environ.get("CODEX_HOME")
-    if env_home:
-        return Path(env_home).expanduser()
-    return Path.home() / ".codex"
-
-
-def validate_json(path: Path) -> None:
-    with path.open("r", encoding="utf-8") as handle:
-        json.load(handle)
-
-
-def build_user_context_scaffold() -> str:
-    template_text = USER_CONTEXT_TEMPLATE.read_text(encoding="utf-8")
-    if DEFAULT_USER_CONTEXT_MARKER not in template_text:
-        raise ValueError("user-context-config.md must include a Default User Context section")
-    user_context_text = template_text.rsplit(DEFAULT_USER_CONTEXT_MARKER, 1)[1].strip()
-    if not user_context_text.startswith("# Data Analytics Source Routing Preferences"):
-        raise ValueError(
-            "user-context-config.md default user context must start with source routing"
-        )
-    if "# Semantic Layers" not in user_context_text:
-        raise ValueError("user-context-config.md default user context must include Semantic Layers")
-    if "## Saved Links And Context" in user_context_text:
-        raise ValueError("user-context-config.md must not include saved-context sections")
-    return user_context_text + "\n"
-
-
-def write_text_if_needed(path: Path, text: str, overwrite: bool) -> str:
-    existed = path.exists()
-    if existed and not overwrite:
-        return "preserved"
-    path.write_text(text, encoding="utf-8")
-    return "overwritten" if existed else "created"
-
-
-def main() -> int:
-    args = parse_args()
-    codex_home = args.codex_home.expanduser() if args.codex_home else default_codex_home()
-    state_dir = (
-        args.state_dir.expanduser()
-        if args.state_dir
-        else codex_home / "state/plugins/data-analytics"
-    )
-
-    missing_templates = [
-        str(path.relative_to(SKILL_ROOT))
-        for path in (USER_CONTEXT_TEMPLATE, ONBOARDING_STATE_TEMPLATE)
-        if not path.exists()
-    ]
-    if missing_templates:
-        print("Missing Data Analytics bundled state source(s):", file=sys.stderr)
-        for missing in missing_templates:
-            print(f"- {missing}", file=sys.stderr)
-        return 1
-
-    try:
-        user_context_scaffold = build_user_context_scaffold()
-        validate_json(ONBOARDING_STATE_TEMPLATE)
-    except (json.JSONDecodeError, ValueError) as exc:
-        print(f"Invalid Data Analytics bundled state source: {exc}", file=sys.stderr)
-        return 1
-
-    state_dir.mkdir(parents=True, exist_ok=True)
-    user_context_path = state_dir / "user-context.md"
-    onboarding_state_path = state_dir / "onboarding-state.json"
-
-    user_context_result = write_text_if_needed(
-        user_context_path,
-        USER_CONTEXT_NOTE + user_context_scaffold,
-        args.overwrite,
-    )
-    onboarding_state = json.loads(ONBOARDING_STATE_TEMPLATE.read_text(encoding="utf-8"))
-    onboarding_state_result = write_text_if_needed(
-        onboarding_state_path,
-        json.dumps(onboarding_state, indent=2) + "\n",
-        args.overwrite,
-    )
-    validate_json(onboarding_state_path)
-
-    print(f"Data Analytics state directory: {state_dir}")
-    print(f"user-context.md: {user_context_result}")
-    print(f"onboarding-state.json: {onboarding_state_result}")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/data-analytics/skills/user-context/scripts/reset_user_context_state.py b/plugins/data-analytics/skills/user-context/scripts/reset_user_context_state.py
deleted file mode 100755
index e4c9f19..0000000
--- a/plugins/data-analytics/skills/user-context/scripts/reset_user_context_state.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-"""Back up and clear local Data Analytics user-context and onboarding state files."""
-
-from __future__ import annotations
-
-import argparse
-import os
-import shutil
-import sys
-from datetime import datetime
-from pathlib import Path
-
-STATE_FILENAMES = (
-    "user-context.md",
-    "onboarding-state.json",
-)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Move active Data Analytics local state files into a timestamped sibling "
-            "backup directory. This leaves Data Analytics ready for fresh onboarding."
-        )
-    )
-    parser.add_argument("--codex-home", type=Path, default=None)
-    parser.add_argument("--state-dir", type=Path, default=None)
-    parser.add_argument("--backup-dir", type=Path, default=None)
-    parser.add_argument("--dry-run", action="store_true")
-    return parser.parse_args()
-
-
-def default_codex_home() -> Path:
-    env_home = os.environ.get("CODEX_HOME")
-    if env_home:
-        return Path(env_home).expanduser()
-    return Path.home() / ".codex"
-
-
-def resolve_paths(args: argparse.Namespace) -> tuple[Path, Path]:
-    codex_home = args.codex_home.expanduser() if args.codex_home else default_codex_home()
-    state_dir = (
-        args.state_dir.expanduser()
-        if args.state_dir
-        else codex_home / "state/plugins/data-analytics"
-    )
-    if args.backup_dir:
-        backup_dir = args.backup_dir.expanduser()
-    else:
-        timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-        backup_dir = state_dir.parent / f"{state_dir.name}-backup-{timestamp}"
-    return state_dir, backup_dir
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir, backup_dir = resolve_paths(args)
-    state_files = [state_dir / filename for filename in STATE_FILENAMES]
-    existing_state_files = [path for path in state_files if path.exists()]
-
-    if not existing_state_files:
-        print(f"No active Data Analytics state files found in: {state_dir}")
-        return 0
-    if backup_dir.exists():
-        print(f"Backup directory already exists: {backup_dir}", file=sys.stderr)
-        return 1
-    if args.dry_run:
-        print(f"Data Analytics state directory: {state_dir}")
-        print(f"Backup directory: {backup_dir}")
-        for path in existing_state_files:
-            print(f"would move: {path.name}")
-        print("Dry run only; no files were moved.")
-        return 0
-
-    backup_dir.mkdir(parents=True)
-    moved: list[str] = []
-    for path in existing_state_files:
-        shutil.move(str(path), str(backup_dir / path.name))
-        moved.append(path.name)
-
-    removed_state_dir = False
-    try:
-        state_dir.rmdir()
-        removed_state_dir = True
-    except OSError:
-        removed_state_dir = False
-
-    print(f"Data Analytics state directory: {state_dir}")
-    print(f"Backup directory: {backup_dir}")
-    for filename in moved:
-        print(f"moved: {filename}")
-    if removed_state_dir:
-        print("state directory: removed because it was empty")
-    else:
-        print("state directory: kept because it still contains other files")
-    print("Restore by moving the backed-up files back into the Data Analytics state directory.")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/data-analytics/skills/user-context/scripts/validate_user_context_preflight.py b/plugins/data-analytics/skills/user-context/scripts/validate_user_context_preflight.py
deleted file mode 100755
index 934632d..0000000
--- a/plugins/data-analytics/skills/user-context/scripts/validate_user_context_preflight.py
+++ /dev/null
@@ -1,499 +0,0 @@
-#!/usr/bin/env python3
-"""Validate the Data Analytics user-context preflight invariant."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-from pathlib import Path
-
-MANDATORY_GATE_PHRASES = (
-    "Mandatory pre-answer gate:",
-    "Invoke `data-analytics:user-context` in preflight mode",
-    "Use the returned `data_analytics_preflight` envelope as the source of truth",
-    "not as substitutes for workflow-time reads from connected or provided sources",
-)
-USER_CONTEXT_MANDATORY_GATE_PHRASES = (
-    "set the tool's `max_output_tokens` to at least `25000`",
-    "warn the user that Data Analytics could not load all source-routing preferences and semantic-layer registry entries in one pass",
-    "do not use the payload until the complete output is visible",
-    "reports read status for `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md`",
-)
-PREFLIGHT_HELPER_PHRASES = (
-    "summarize_user_context",
-    "extract_saved_source_preferences",
-    '"user_context": summarize_user_context(user_context_text, source_category_config)',
-    '"source_preferences": saved_source_preferences',
-    "semantic_layer_registry",
-    "preferred_source_routes",
-    "match_installable_plugin_candidates",
-    "plugin_first_setup_recovery",
-    "functions.list_available_plugins_to_install",
-    "functions.request_plugin_install",
-    "plugin_setup_opportunities",
-)
-
-
-def normalize_route_name(value: str) -> str:
-    return re.sub(r"[^a-z0-9]+", "", value.casefold())
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("plugin_path", type=Path)
-    parser.add_argument("--plugin-id", default="data-analytics")
-    return parser.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    plugin_root = args.plugin_path.resolve()
-    skills_root = plugin_root / "skills"
-    failures: list[str] = []
-
-    required_paths = (
-        skills_root / "user-context/SKILL.md",
-        skills_root / "user-context/agents/openai.yaml",
-        skills_root / "user-context/plugin-author-config/user-context-config.md",
-        skills_root / "user-context/plugin-author-config/source-category-config.json",
-        skills_root / "user-context/plugin-author-config/automation-config.md",
-        skills_root / "user-context/references/onboarding.md",
-        skills_root / "user-context/references/onboarding-examples.md",
-        skills_root / "user-context/references/onboarding-state-template.json",
-        skills_root / "user-context/references/source-category-runtime.md",
-        skills_root / "user-context/references/automation.md",
-        skills_root / "user-context/references/semantic-layer/setup.md",
-        skills_root / "user-context/references/semantic-layer/source-intake.md",
-        skills_root / "user-context/references/semantic-layer/connector-playbook.md",
-        skills_root / "user-context/references/semantic-layer/skill-template.md",
-        skills_root / "user-context/references/semantic-layer/weekly-polling-automation.md",
-        skills_root / "user-context/scripts/data_analytics_preflight.py",
-        skills_root / "user-context/scripts/init_user_context_state.py",
-        skills_root / "user-context/scripts/reset_user_context_state.py",
-        skills_root / "user-context/tests/test_state_helpers.py",
-        skills_root / "index/SKILL.md",
-    )
-    for path in required_paths:
-        if not path.exists():
-            failures.append(f"Missing required user-context path: {path.relative_to(plugin_root)}")
-
-    unexpected_semantic_layer_skill_dir = skills_root / ("create" + "-semantic-layer")
-    if unexpected_semantic_layer_skill_dir.exists():
-        failures.append(
-            "Unexpected top-level semantic-layer setup skill: "
-            f"{unexpected_semantic_layer_skill_dir.relative_to(plugin_root)}"
-        )
-
-    skill_files = sorted(skills_root.rglob("SKILL.md"))
-    for skill_file in skill_files:
-        text = skill_file.read_text(encoding="utf-8")
-        rel = skill_file.relative_to(plugin_root)
-        if rel == Path("skills/user-context/SKILL.md"):
-            mandatory_gate = text.split("## Mandatory Pre-Answer Gate", 1)[-1].split(
-                "\n## ",
-                1,
-            )[0]
-            for phrase in (
-                "## Mandatory Pre-Answer Gate",
-                "scripts/data_analytics_preflight.py",
-                "follow `references/onboarding.md`",
-                "## First Run Setup",
-                "Never run parallel writes against the same Data Analytics state files",
-                "Do not use `user-context.md` as a general memory file",
-                "If the user asks Data Analytics to remember arbitrary context, do not save it.",
-            ):
-                if phrase not in text:
-                    failures.append(f"{rel} missing required phrase: {phrase}")
-            for phrase in USER_CONTEXT_MANDATORY_GATE_PHRASES:
-                if phrase not in mandatory_gate:
-                    failures.append(f"{rel} mandatory gate missing required phrase: {phrase}")
-            continue
-        for phrase in MANDATORY_GATE_PHRASES:
-            if phrase not in text:
-                failures.append(f"{rel} missing mandatory preflight phrase: {phrase}")
-
-    hero_skills = (
-        skills_root / "metric-diagnostics/SKILL.md",
-        skills_root / "product-business-analysis/SKILL.md",
-        skills_root / "kpi-reporting/SKILL.md",
-    )
-    for hero_skill in hero_skills:
-        text = hero_skill.read_text(encoding="utf-8")
-        rel = hero_skill.relative_to(plugin_root)
-        for phrase in ("data-analytics:user-context",):
-            if phrase not in text:
-                failures.append(f"{rel} missing user-context preflight handoff: {phrase}")
-
-    preflight_script = skills_root / "user-context/scripts/data_analytics_preflight.py"
-    if preflight_script.exists():
-        preflight_text = preflight_script.read_text(encoding="utf-8")
-        for phrase in PREFLIGHT_HELPER_PHRASES:
-            if phrase not in preflight_text:
-                failures.append(
-                    "skills/user-context/scripts/data_analytics_preflight.py "
-                    f"missing compact-context helper wording: {phrase}"
-                )
-        if "user_context_markdown" in preflight_text:
-            failures.append(
-                "skills/user-context/scripts/data_analytics_preflight.py must not emit "
-                "legacy context.user_context_markdown"
-            )
-
-    onboarding_text = (skills_root / "user-context/references/onboarding.md").read_text(
-        encoding="utf-8"
-    )
-    for phrase in (
-        "## Step 2: Check Main Analytics Sources",
-        "### Step 2A: Resolve Source Questions",
-        "Do not preflight-read installed or active sources just to prove they work.",
-        "transition without a proof read",
-        "Source setup has covered each required source type as `active`",
-        "Before Data Analytics sets up data context",
-        "resolution: user_deferred",
-        "### Step 4A: Offer First Hero Prompt",
-        "Do not: show three hero prompts initially",
-        "write the prompt you would rather try instead",
-        "`skip` to finish onboarding",
-        "Step 5, `complete_or_quiet`, when the user skips or defers",
-        "## CTA Arbitration",
-        "## Completion Criteria",
-    ):
-        if phrase not in onboarding_text:
-            failures.append(
-                f"skills/user-context/references/onboarding.md missing required phrase: {phrase}"
-            )
-
-    source_runtime_text = (
-        skills_root / "user-context/references/source-category-runtime.md"
-    ).read_text(encoding="utf-8")
-    for phrase in (
-        "## Setup-Owned Source Routes",
-        "Preflight is a reader.",
-        "functions.list_available_plugins_to_install",
-        "## Plugin-First Setup For Missing Sources",
-        "Do not proactively suggest every installable plugin",
-        "## Missing Sources And Fallbacks",
-        "`needs_confirmation`",
-        "Do not perform an eager read just to prove an installed source works",
-        "Do not run a proof query during onboarding",
-        "attempt the smallest useful app or connector read",
-        "core onboarding categories, not optional setup extras",
-        "resolution: user_deferred",
-    ):
-        if phrase not in source_runtime_text:
-            failures.append(
-                "skills/user-context/references/source-category-runtime.md "
-                f"missing required phrase: {phrase}"
-            )
-
-    automation_text = (skills_root / "user-context/references/automation.md").read_text(
-        encoding="utf-8"
-    )
-    for phrase in (
-        "Ask for explicit user approval",
-        "Only say the default automation setup is complete",
-        "Do not mark setup complete",
-    ):
-        if phrase not in automation_text:
-            failures.append(
-                f"skills/user-context/references/automation.md missing required phrase: {phrase}"
-            )
-
-    semantic_layer_setup_text = (
-        skills_root / "user-context/references/semantic-layer/setup.md"
-    ).read_text(encoding="utf-8")
-    for phrase in (
-        "source-use checkpoint",
-        "Infer expected source lanes",
-        "nearby but not exact",
-        "rejected or lower-confidence candidates",
-        "authoritative data documentation",
-        "source-backed semantic-layer skills",
-        "user-trusted canonical data skills",
-        "treat that as authoritative data documentation",
-    ):
-        if phrase not in semantic_layer_setup_text:
-            failures.append(
-                "skills/user-context/references/semantic-layer/setup.md "
-                f"missing required phrase: {phrase}"
-            )
-
-    semantic_layer_weekly_polling_text = (
-        skills_root / "user-context/references/semantic-layer/weekly-polling-automation.md"
-    ).read_text(encoding="utf-8")
-    for phrase in (
-        "authoritative data documentation",
-        "source-backed semantic-layer skills",
-        "user-trusted canonical data skills",
-        "generic-local-skill-only evidence",
-    ):
-        if phrase not in semantic_layer_weekly_polling_text:
-            failures.append(
-                "skills/user-context/references/semantic-layer/weekly-polling-automation.md "
-                f"missing required phrase: {phrase}"
-            )
-
-    semantic_layer_source_intake_text = (
-        skills_root / "user-context/references/semantic-layer/source-intake.md"
-    ).read_text(encoding="utf-8")
-    for phrase in (
-        "Infer useful source families",
-        "do not treat ambient app availability as user approval",
-    ):
-        if phrase not in semantic_layer_source_intake_text:
-            failures.append(
-                "skills/user-context/references/semantic-layer/source-intake.md "
-                f"missing required phrase: {phrase}"
-            )
-
-    semantic_layer_template_text = (
-        skills_root / "user-context/references/semantic-layer/skill-template.md"
-    ).read_text(encoding="utf-8")
-    for phrase in (
-        "## Start Here",
-        "## References",
-        "## Answering Rules",
-        "## Quick Reference",
-        "## Entity Clarification",
-        "## Standard Filters And Dimensions",
-        "only when the layer needs separate evidence tracking",
-        "Do not put setup-time policy or full evidence procedures",
-    ):
-        if phrase not in semantic_layer_template_text:
-            failures.append(
-                "skills/user-context/references/semantic-layer/skill-template.md "
-                f"missing required phrase: {phrase}"
-            )
-    for phrase in ("### Source Order", "### Evidence Standard"):
-        if phrase in semantic_layer_template_text:
-            failures.append(
-                "skills/user-context/references/semantic-layer/skill-template.md "
-                f"retains heavy generated-skill phrase: {phrase}"
-            )
-
-    app_manifest_path = plugin_root / ".app.json"
-    manifest_app_aliases: set[str] = set()
-    try:
-        app_manifest = json.loads(app_manifest_path.read_text(encoding="utf-8"))
-    except (OSError, json.JSONDecodeError) as exc:
-        failures.append(f"Invalid Data Analytics app manifest: {exc}")
-    else:
-        apps = app_manifest.get("apps") if isinstance(app_manifest, dict) else None
-        if not isinstance(apps, dict) or not apps:
-            failures.append("Data Analytics .app.json must define a non-empty apps object")
-        else:
-            manifest_app_aliases = {
-                normalize_route_name(app_name)
-                for app_name, metadata in apps.items()
-                if isinstance(app_name, str)
-                and isinstance(metadata, dict)
-                and isinstance(metadata.get("id"), str)
-                and metadata["id"]
-            }
-
-    try:
-        source_config = json.loads(
-            (
-                skills_root / "user-context/plugin-author-config/source-category-config.json"
-            ).read_text(encoding="utf-8")
-        )
-        json.loads(
-            (skills_root / "user-context/references/onboarding-state-template.json").read_text(
-                encoding="utf-8"
-            )
-        )
-    except json.JSONDecodeError as exc:
-        failures.append(f"Invalid user-context JSON: {exc}")
-    else:
-        if not isinstance(source_config, dict):
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json "
-                "must define a JSON object"
-            )
-            source_config = {}
-        if source_config.get("schema_version") != "data_analytics_source_category_config.v1":
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json "
-                "missing schema_version data_analytics_source_category_config.v1"
-            )
-        if not isinstance(source_config.get("description"), str):
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json "
-                "must define a description"
-            )
-        elif "preferred plugin routing hints" not in source_config["description"]:
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json "
-                "description must document preferred plugin routing hints"
-            )
-        categories = source_config.get("categories")
-        if not isinstance(categories, dict) or not categories:
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json "
-                "must define a non-empty categories object"
-            )
-        else:
-            for category_id, metadata in categories.items():
-                if not isinstance(category_id, str) or not re.fullmatch(
-                    r"[a-z0-9_]+",
-                    category_id,
-                ):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"has invalid category id: {category_id}"
-                    )
-                    continue
-                if not isinstance(metadata, dict):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} must be an object"
-                    )
-                    continue
-                allowed_fields = {
-                    "label",
-                    "preferred_plugins",
-                    "preferred_apps",
-                    "relevant_skills",
-                }
-                unexpected_fields = sorted(set(metadata) - allowed_fields)
-                if unexpected_fields:
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} has unexpected fields: {unexpected_fields}"
-                    )
-                if not isinstance(metadata.get("label"), str) or not metadata["label"]:
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} must define a non-empty label"
-                    )
-                preferred_plugins = metadata.get("preferred_plugins")
-                if not isinstance(preferred_plugins, list) or not preferred_plugins:
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} preferred_plugins must be a non-empty list"
-                    )
-                    preferred_plugins = []
-                elif not all(
-                    isinstance(plugin, str) and plugin for plugin in preferred_plugins
-                ):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} preferred_plugins must contain non-empty strings"
-                    )
-                else:
-                    for preferred_plugin in preferred_plugins:
-                        if normalize_route_name(preferred_plugin) not in manifest_app_aliases:
-                            failures.append(
-                                "skills/user-context/plugin-author-config/"
-                                "source-category-config.json "
-                                f"category {category_id} preferred plugin {preferred_plugin} "
-                                "must be declared in .app.json"
-                            )
-                preferred_apps = metadata.get("preferred_apps", [])
-                if not isinstance(preferred_apps, list):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} preferred_apps must be a list"
-                    )
-                    preferred_apps = []
-                elif not all(isinstance(app, str) and app for app in preferred_apps):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} preferred_apps must contain non-empty strings"
-                    )
-                else:
-                    for preferred_app in preferred_apps:
-                        if normalize_route_name(preferred_app) not in manifest_app_aliases:
-                            failures.append(
-                                "skills/user-context/plugin-author-config/"
-                                "source-category-config.json "
-                                f"category {category_id} preferred app {preferred_app} "
-                                "must be declared in .app.json"
-                            )
-                relevant_skills = metadata.get("relevant_skills", [])
-                if not isinstance(relevant_skills, list):
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json "
-                        f"category {category_id} relevant_skills must be a list"
-                    )
-                else:
-                    for index, entry in enumerate(relevant_skills):
-                        if not isinstance(entry, dict):
-                            failures.append(
-                                "skills/user-context/plugin-author-config/source-category-config.json "
-                                f"category {category_id} relevant_skills[{index}] must be an object"
-                            )
-                            continue
-                        for field in ("app", "skill"):
-                            if not isinstance(entry.get(field), str) or not entry[field]:
-                                failures.append(
-                                    "skills/user-context/plugin-author-config/"
-                                    "source-category-config.json "
-                                    f"category {category_id} relevant_skills[{index}] "
-                                    f"missing non-empty {field}"
-                                )
-
-    user_context_config_path = (
-        skills_root / "user-context/plugin-author-config/user-context-config.md"
-    )
-    if user_context_config_path.exists():
-        user_context_config = user_context_config_path.read_text(encoding="utf-8")
-        for phrase in (
-            "# Data Analytics User Context Config",
-            "## User Context Shape",
-            "## Default User Context",
-            "Store durable Data Analytics source-routing choices explicitly selected for future use.",
-            "Do not add general memory",
-            "# Data Analytics Source Routing Preferences",
-            "# Semantic Layers",
-        ):
-            if phrase not in user_context_config:
-                failures.append(
-                    "skills/user-context/plugin-author-config/user-context-config.md "
-                    f"missing required phrase: {phrase}"
-                )
-        default_user_context = user_context_config.rsplit("## Default User Context", 1)[-1]
-        for disallowed in ("- Priority:", "- Use When:", "## Saved Links And Context"):
-            if disallowed in default_user_context:
-                failures.append(
-                    "skills/user-context/plugin-author-config/user-context-config.md "
-                    f"default user context must not contain: {disallowed}"
-                )
-        source_category_ids = set((source_config.get("categories") or {}).keys())
-        source_routing_section = default_user_context.split("# Semantic Layers", 1)[0]
-        configured_sections = set(re.findall(r"(?m)^##\s+([a-z0-9_]+)\s*$", source_routing_section))
-        if configured_sections != source_category_ids:
-            failures.append(
-                "skills/user-context/plugin-author-config/user-context-config.md "
-                "source routing sections must match source-category-config.json"
-            )
-        for category_id in configured_sections:
-            category_match = re.search(
-                rf"(?ms)^##\s+{re.escape(category_id)}\s*$.*?(?=^##\s+|^#\s+Semantic Layers|\Z)",
-                source_routing_section,
-            )
-            category_section = category_match.group(0) if category_match else ""
-            for field in ("Prefer", "Avoid"):
-                if len(re.findall(rf"(?m)^-\s+{field}:\s*$", category_section)) != 1:
-                    failures.append(
-                        "skills/user-context/plugin-author-config/user-context-config.md "
-                        f"source routing category {category_id} must define one blank {field} row"
-                    )
-        if "status: not provided" not in default_user_context:
-            failures.append(
-                "skills/user-context/plugin-author-config/user-context-config.md "
-                "semantic layer registry must start with status: not provided"
-            )
-
-    if failures:
-        for failure in failures:
-            print(f"- {failure}", file=sys.stderr)
-        return 1
-    print(f"Validated {len(skill_files)} Data Analytics skill preflight surfaces.")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/data-analytics/skills/user-context/tests/test_state_helpers.py b/plugins/data-analytics/skills/user-context/tests/test_state_helpers.py
deleted file mode 100644
index c35c97a..0000000
--- a/plugins/data-analytics/skills/user-context/tests/test_state_helpers.py
+++ /dev/null
@@ -1,978 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-import json
-import shutil
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-SCRIPT_DIR = SKILL_ROOT / "scripts"
-PLUGIN_ROOT = SKILL_ROOT.parents[1]
-ONBOARDING_REFERENCE = SKILL_ROOT / "references/onboarding.md"
-SOURCE_RUNTIME_REFERENCE = SKILL_ROOT / "references/source-category-runtime.md"
-AUTOMATION_REFERENCE = SKILL_ROOT / "references/automation.md"
-SOURCE_CATEGORY_CONFIG = SKILL_ROOT / "plugin-author-config/source-category-config.json"
-APP_MANIFEST = PLUGIN_ROOT / ".app.json"
-SEMANTIC_LAYER_SETUP_REFERENCE = SKILL_ROOT / "references/semantic-layer/setup.md"
-SEMANTIC_LAYER_SOURCE_INTAKE_REFERENCE = SKILL_ROOT / "references/semantic-layer/source-intake.md"
-SEMANTIC_LAYER_SKILL_TEMPLATE_REFERENCE = SKILL_ROOT / "references/semantic-layer/skill-template.md"
-SEMANTIC_LAYER_WEEKLY_POLLING_REFERENCE = (
-    SKILL_ROOT / "references/semantic-layer/weekly-polling-automation.md"
-)
-HERO_SKILLS = (
-    PLUGIN_ROOT / "skills/metric-diagnostics/SKILL.md",
-    PLUGIN_ROOT / "skills/product-business-analysis/SKILL.md",
-    PLUGIN_ROOT / "skills/kpi-reporting/SKILL.md",
-)
-PREFLIGHT_SPEC = importlib.util.spec_from_file_location(
-    "data_analytics_preflight",
-    SCRIPT_DIR / "data_analytics_preflight.py",
-)
-assert PREFLIGHT_SPEC and PREFLIGHT_SPEC.loader
-PREFLIGHT_MODULE = importlib.util.module_from_spec(PREFLIGHT_SPEC)
-PREFLIGHT_SPEC.loader.exec_module(PREFLIGHT_MODULE)
-
-# Representative functions.list_available_plugins_to_install fixture for plugin-first routing.
-LIVE_INSTALLABLE_PLUGIN_CANDIDATES = [
-    {
-        "id": "databricks@openai-curated",
-        "name": "databricks",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["templated_apps_Databricks"],
-    },
-    {
-        "id": "bigquery@openai-curated",
-        "name": "bigquery",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["connector_7c3f2c8cdbf64bb0b183ff52f527a06e"],
-    },
-    {
-        "id": "snowflake@openai-curated",
-        "name": "snowflake",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["templated_apps_Snowflake"],
-    },
-    {
-        "id": "outlook-calendar@openai-curated",
-        "name": "outlook-calendar",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["connector_e6a7394682e24467ac68c60696f275a4"],
-    },
-    {
-        "id": "outlook-email@openai-curated",
-        "name": "outlook-email",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["connector_4aaab2856305417b993eca9a216aaf6e"],
-    },
-    {
-        "id": "sharepoint@openai-curated",
-        "name": "sharepoint",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["connector_1e4f6a44acf14e3ca1d96672f8c945bc"],
-    },
-    {
-        "id": "slack@openai-curated",
-        "name": "slack",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "teams@openai-curated",
-        "name": "teams",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["connector_246af0940da3457da0e751171dc1ce60"],
-    },
-    {
-        "id": "thoughtspot@openai-curated",
-        "name": "thoughtspot",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_THOUGHTSPOT_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "omni-analytics@openai-curated",
-        "name": "omni-analytics",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_OMNI_ANALYTICS_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "metabase@openai-curated",
-        "name": "metabase",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["templated_apps_Metabase"],
-    },
-    {
-        "id": "amplitude@openai-curated",
-        "name": "amplitude",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_AMPLITUDE_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "mixpanel@openai-curated",
-        "name": "mixpanel",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_MIXPANEL_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "hex@openai-curated",
-        "name": "hex",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_HEX_APP_OR_CONNECTOR_ID"],
-    },
-    {
-        "id": "deepnote@openai-curated",
-        "name": "deepnote",
-        "tool_type": "plugin",
-        "has_skills": True,
-        "app_connector_ids": ["REPLACE_WITH_DEEPNOTE_APP_OR_CONNECTOR_ID"],
-    },
-]
-SESSION_ENABLED_PLUGIN_CANDIDATES = [
-    {"id": "github@openai-curated", "name": "github", "tool_type": "plugin"},
-    {"id": "gmail@openai-curated", "name": "gmail", "tool_type": "plugin"},
-    {"id": "google-calendar@openai-curated", "name": "google-calendar", "tool_type": "plugin"},
-    {"id": "google-drive@openai-curated", "name": "google-drive", "tool_type": "plugin"},
-    {"id": "notion@openai-curated", "name": "notion", "tool_type": "plugin"},
-]
-
-
-class DataAnalyticsStateHelperTests(unittest.TestCase):
-    def setUp(self) -> None:
-        self._tmp = tempfile.TemporaryDirectory(
-            prefix="data-analytics-user-context-tests-",
-            dir="/private/tmp",
-        )
-        self.tmp_path = Path(self._tmp.name)
-        self.state_dir = self.tmp_path / "state"
-        self.maxDiff = None
-
-    def tearDown(self) -> None:
-        self._tmp.cleanup()
-
-    def run_json(self, args: list[str], *, expect: int = 0) -> dict:
-        proc = subprocess.run(args, text=True, capture_output=True)
-        self.assertEqual(
-            proc.returncode,
-            expect,
-            msg=f"command failed: {' '.join(args)}\nSTDOUT:\n{proc.stdout}\nSTDERR:\n{proc.stderr}",
-        )
-        return json.loads(proc.stdout)
-
-    def preflight(self, *extra: str, state_dir: Path | None = None) -> dict:
-        return self.run_json(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "data_analytics_preflight.py"),
-                "--workflow",
-                "metric-diagnostics",
-                "--state-dir",
-                str(state_dir or self.state_dir),
-                *extra,
-            ]
-        )
-
-    def write_state(
-        self,
-        *,
-        user_context: str | None = None,
-        onboarding_state: dict | None = None,
-        state_dir: Path | None = None,
-    ) -> Path:
-        target = state_dir or self.state_dir
-        target.mkdir(parents=True, exist_ok=True)
-        if user_context is not None:
-            (target / "user-context.md").write_text(user_context, encoding="utf-8")
-        if onboarding_state is not None:
-            (target / "onboarding-state.json").write_text(
-                json.dumps(onboarding_state),
-                encoding="utf-8",
-            )
-        return target
-
-    def active_core_categories(self) -> dict[str, dict[str, str]]:
-        return {
-            "structured_data": {"status": "active", "preferred": "Databricks"},
-            "team_communication": {"status": "active", "preferred": "Slack"},
-            "company_docs": {"status": "active", "preferred": "Google Drive"},
-        }
-
-    def source_category_config(self) -> dict:
-        return json.loads(SOURCE_CATEGORY_CONFIG.read_text(encoding="utf-8"))["categories"]
-
-    def test_missing_preflight_returns_setup_obligation_context_gap_and_empty_hero_prompt(
-        self,
-    ) -> None:
-        payload = self.preflight(state_dir=self.tmp_path / "missing")
-        self.assertTrue(payload["read_only"])
-        self.assertEqual(payload["state"]["user_context_status"], "missing")
-        self.assertEqual(payload["state"]["onboarding_state_status"], "missing")
-        self.assertTrue(payload["current_skill_experience"]["intro_eligible"])
-        obligation = payload["control"]["final_obligations"][0]
-        self.assertEqual(obligation["id"], "offer_data_analytics_onboarding_next_step")
-        self.assertIn("## Data Analytics Setup Required", obligation["template"])
-        self.assertEqual(payload["control"]["conditional_guidance"][0]["id"], "context_gap_note")
-        self.assertEqual(payload["context"]["hero_prompt_candidates"], [])
-        self.assertIsNone(payload["context"]["primary_hero_prompt"])
-        self.assertEqual(
-            payload["context"]["connector_setup_summary"]["needs_attention"][0]["status"],
-            "needs_confirmation",
-        )
-
-    def test_direct_and_guided_modes_suppress_ordinary_setup_obligation(self) -> None:
-        direct_payload = self.preflight("--request-mode", "direct_onboarding_status")
-        guided_payload = self.preflight("--request-mode", "guided_onboarding_workflow")
-        self.assertEqual(direct_payload["control"]["final_obligations"], [])
-        self.assertEqual(guided_payload["control"]["final_obligations"], [])
-        self.assertEqual(direct_payload["control"]["conditional_guidance"], [])
-        self.assertEqual(guided_payload["control"]["conditional_guidance"], [])
-        self.assertEqual(guided_payload["current_skill_experience"]["cta_owner"], "onboarding")
-
-    def test_legacy_verified_connector_status_maps_to_active_try_on_use(self) -> None:
-        self.assertEqual(
-            PREFLIGHT_MODULE.source_status({"status": "connected"}),
-            "active",
-        )
-        self.assertEqual(PREFLIGHT_MODULE.source_status({"status": "manual"}), "active")
-        self.assertEqual(
-            PREFLIGHT_MODULE.source_status({"status": "selected_needs_auth"}),
-            "needs_confirmation",
-        )
-        self.assertEqual(PREFLIGHT_MODULE.source_status({"status": "choose_source"}), "needs_confirmation")
-
-    def test_configured_plugin_routes_are_manifest_backed_and_add_google_calendar(
-        self,
-    ) -> None:
-        categories = self.source_category_config()
-        self.assertEqual(categories["team_communication"]["preferred_plugins"], ["Slack", "Teams"])
-        self.assertEqual(
-            categories["company_docs"]["preferred_plugins"],
-            ["Google Drive", "SharePoint", "Notion"],
-        )
-        self.assertEqual(
-            categories["email_context"]["preferred_plugins"],
-            ["Gmail", "Outlook Email"],
-        )
-        self.assertEqual(
-            categories["calendar_context"]["preferred_plugins"],
-            ["Google Calendar", "Outlook Calendar"],
-        )
-        self.assertNotIn("preferred_apps", categories["calendar_context"])
-        self.assertEqual(categories["code_repository"]["preferred_plugins"], ["GitHub"])
-        app_manifest = json.loads(APP_MANIFEST.read_text(encoding="utf-8"))
-        self.assertEqual(
-            app_manifest["apps"]["google_calendar"]["id"],
-            "connector_947e0d954944416db111db556030eea6",
-        )
-        self.assertEqual(
-            PREFLIGHT_MODULE.load_app_connector_ids()["googlecalendar"],
-            "connector_947e0d954944416db111db556030eea6",
-        )
-
-    def test_live_install_listing_fixture_matches_every_installable_data_analytics_plugin(
-        self,
-    ) -> None:
-        matches = PREFLIGHT_MODULE.match_installable_plugin_candidates(
-            self.source_category_config(),
-            {"tools": LIVE_INSTALLABLE_PLUGIN_CANDIDATES},
-        )
-        self.assertEqual(
-            {
-                category_id: [entry["preferred_plugin"] for entry in entries]
-                for category_id, entries in matches.items()
-            },
-            {
-                "structured_data": ["Databricks", "BigQuery", "Snowflake"],
-                "team_communication": ["Slack", "Teams"],
-                "company_docs": ["SharePoint"],
-                "dashboards_bi": ["ThoughtSpot", "Omni Analytics", "Metabase"],
-                "behavior_signals": ["Amplitude", "Mixpanel"],
-                "notebook_lab": ["Hex", "Deepnote"],
-                "email_context": ["Outlook Email"],
-                "calendar_context": ["Outlook Calendar"],
-            },
-        )
-
-    def test_configured_plugins_match_installable_or_enabled_plugin_surfaces(self) -> None:
-        categories = self.source_category_config()
-        matches = PREFLIGHT_MODULE.match_installable_plugin_candidates(
-            categories,
-            [*LIVE_INSTALLABLE_PLUGIN_CANDIDATES, *SESSION_ENABLED_PLUGIN_CANDIDATES],
-        )
-        self.assertEqual(
-            {
-                category_id: [entry["preferred_plugin"] for entry in entries]
-                for category_id, entries in matches.items()
-            },
-            {
-                category_id: metadata["preferred_plugins"]
-                for category_id, metadata in categories.items()
-                if metadata.get("preferred_plugins")
-            },
-        )
-
-    def test_plugin_candidate_matching_uses_connector_id_when_slug_changes(self) -> None:
-        matches = PREFLIGHT_MODULE.match_installable_plugin_candidates(
-            self.source_category_config(),
-            [
-                {
-                    "id": "renamed-plugin@openai-curated",
-                    "name": "renamed-plugin",
-                    "tool_type": "plugin",
-                    "app_connector_ids": ["REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"],
-                }
-            ],
-        )
-        slack_match = matches["team_communication"][0]
-        self.assertEqual(slack_match["preferred_plugin"], "Slack")
-        self.assertEqual(slack_match["match_reasons"], ["app_connector_id"])
-
-    def test_plugin_candidate_matching_ignores_description_only_category_similarity(self) -> None:
-        matches = PREFLIGHT_MODULE.match_installable_plugin_candidates(
-            self.source_category_config(),
-            [
-                {
-                    "id": "broad-search@openai-curated",
-                    "name": "broad-search",
-                    "display_name": "Broad Search",
-                    "description": "Indexes Slack and Teams for team communication.",
-                    "tool_type": "plugin",
-                    "has_skills": True,
-                }
-            ],
-        )
-        self.assertNotIn("team_communication", matches)
-
-    def test_preflight_exposes_plugin_setup_before_connector_fallback(self) -> None:
-        payload = self.preflight(state_dir=self.tmp_path / "missing")
-        confirmation = payload["context"]["connector_confirmation"]
-        slack = confirmation["team_communication"]
-        self.assertEqual(slack["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(
-            slack["fallback_setup_action"],
-            "ask_user_to_choose_or_confirm_source",
-        )
-        self.assertEqual(slack["plugin_preference_order"], ["Slack", "Teams"])
-        self.assertEqual(
-            slack["setup_recovery"]["candidate_lookup"],
-            "functions.list_available_plugins_to_install",
-        )
-        self.assertEqual(
-            slack["setup_recovery"]["install_request"],
-            "functions.request_plugin_install",
-        )
-        calendar = confirmation["calendar_context"]
-        self.assertEqual(
-            calendar["plugin_preference_order"],
-            ["Google Calendar", "Outlook Calendar"],
-        )
-        self.assertIn(
-            "team_communication",
-            {
-                entry["id"]
-                for entry in payload["context"]["connector_setup_summary"][
-                    "plugin_setup_opportunities"
-                ]
-            },
-        )
-
-    def test_preflight_keeps_onboarding_state_provenance_without_raw_state_echo(self) -> None:
-        self.write_state(onboarding_state={"status": "active", "connector_confirmation": {}})
-        payload = self.preflight()
-        onboarding_state = payload["files"]["onboarding_state"]
-        self.assertEqual(onboarding_state["status"], "present")
-        self.assertFalse(onboarding_state["omitted"])
-        self.assertNotIn("content", onboarding_state)
-        self.assertNotIn("data", onboarding_state)
-        self.assertNotIn(
-            "setup_recovery",
-            payload["context"]["connector_setup_summary"]["needs_attention"][0],
-        )
-        self.assertIn(
-            "setup_recovery",
-            payload["context"]["connector_confirmation"]["structured_data"],
-        )
-
-    def test_connector_inventory_classifies_routes_without_eager_reads(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "connector_inventory": {
-                    "active_apps": ["Slack"],
-                    "available_apps": ["Databricks"],
-                },
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["connector_confirmation"]
-
-        self.assertEqual(confirmation["structured_data"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["structured_data"]["candidates"], ["Databricks"])
-        self.assertEqual(confirmation["team_communication"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["team_communication"]["candidates"], ["Slack"])
-        self.assertEqual(confirmation["company_docs"]["status"], "missing")
-        self.assertFalse(confirmation["structured_data"]["eager_read"])
-        self.assertEqual(
-            confirmation["structured_data"]["workflow_time_behavior"],
-            "attempt_actual_reads_only_when_a_workflow_needs_the_source",
-        )
-
-    def test_helper_skills_apply_to_app_fallback_but_not_active_plugin_routes(self) -> None:
-        source_config = {
-            "team_communication": {
-                "label": "Team communication",
-                "preferred_plugins": ["slack"],
-                "preferred_apps": ["Slack"],
-                "relevant_skills": [{"app": "Slack", "skill": "slack-helper"}],
-            }
-        }
-        connector_fallback = PREFLIGHT_MODULE.normalized_connector_confirmation(
-            source_config,
-            {
-                "connector_confirmation": {
-                    "categories": {
-                        "team_communication": {
-                            "status": "active",
-                            "preferred": "Slack",
-                            "source_kind": "connector",
-                        }
-                    }
-                }
-            },
-        )["team_communication"]
-        self.assertEqual(connector_fallback["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(connector_fallback["fallback_setup_action"], "none_try_on_use")
-        self.assertEqual(connector_fallback["helper_skills_apply_when"], ["app", "connector"])
-
-        plugin_route = PREFLIGHT_MODULE.normalized_connector_confirmation(
-            source_config,
-            {
-                "connector_confirmation": {
-                    "categories": {
-                        "team_communication": {
-                            "status": "active",
-                            "preferred": "slack",
-                            "source_kind": "plugin",
-                            "plugin_install_evidence": "plugin_in_available_plugins",
-                            "skill_surface_evidence": "plugin_skills_visible",
-                        }
-                    }
-                }
-            },
-        )["team_communication"]
-        self.assertEqual(plugin_route["setup_action"], "none_try_on_use")
-        self.assertNotIn("fallback_setup_action", plugin_route)
-        self.assertNotIn("setup_recovery", plugin_route)
-        self.assertNotIn("helper_skills", plugin_route)
-
-    def test_validator_rejects_preferred_plugin_without_manifest_dependency(self) -> None:
-        plugin_copy = self.tmp_path / "data-analytics"
-        shutil.copytree(PLUGIN_ROOT, plugin_copy)
-        config_path = plugin_copy / "skills/user-context/plugin-author-config/source-category-config.json"
-        config = json.loads(config_path.read_text(encoding="utf-8"))
-        config["categories"]["calendar_context"]["preferred_plugins"].append("missing-calendar")
-        config_path.write_text(json.dumps(config, indent=2) + "\n", encoding="utf-8")
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "validate_user_context_preflight.py"),
-                str(plugin_copy),
-                "--plugin-id",
-                "data-analytics",
-            ],
-            text=True,
-            capture_output=True,
-        )
-        self.assertNotEqual(proc.returncode, 0)
-        self.assertIn(
-            "preferred plugin missing-calendar must be declared in .app.json",
-            proc.stderr,
-        )
-
-    def test_validator_rejects_preferred_app_without_manifest_dependency(self) -> None:
-        plugin_copy = self.tmp_path / "data-analytics"
-        shutil.copytree(PLUGIN_ROOT, plugin_copy)
-        config_path = plugin_copy / "skills/user-context/plugin-author-config/source-category-config.json"
-        config = json.loads(config_path.read_text(encoding="utf-8"))
-        config["categories"]["calendar_context"]["preferred_apps"] = ["Missing Calendar"]
-        config_path.write_text(json.dumps(config, indent=2) + "\n", encoding="utf-8")
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "validate_user_context_preflight.py"),
-                str(plugin_copy),
-                "--plugin-id",
-                "data-analytics",
-            ],
-            text=True,
-            capture_output=True,
-        )
-        self.assertNotEqual(proc.returncode, 0)
-        self.assertIn(
-            "preferred app Missing Calendar must be declared in .app.json",
-            proc.stderr,
-        )
-
-    def test_validator_rejects_saved_context_heading_in_author_config(self) -> None:
-        plugin_copy = self.tmp_path / "data-analytics"
-        shutil.copytree(PLUGIN_ROOT, plugin_copy)
-        config_path = plugin_copy / "skills/user-context/plugin-author-config/user-context-config.md"
-        config = config_path.read_text(encoding="utf-8")
-        config_path.write_text(
-            config.replace(
-                "# Semantic Layers\n\n",
-                "# Semantic Layers\n\n## Saved Links And Context\n\n",
-                1,
-            ),
-            encoding="utf-8",
-        )
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "validate_user_context_preflight.py"),
-                str(plugin_copy),
-                "--plugin-id",
-                "data-analytics",
-            ],
-            text=True,
-            capture_output=True,
-        )
-        self.assertNotEqual(proc.returncode, 0)
-        self.assertIn("default user context must not contain: ## Saved Links And Context", proc.stderr)
-
-    def test_selected_source_needs_confirmation_stays_unresolved_until_user_resolves_it(
-        self,
-    ) -> None:
-        self.write_state(
-            user_context="""# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer:
-- Avoid:
-
-# Semantic Layers
-
-status: not provided
-""",
-            onboarding_state={
-                "status": "active",
-                "orientation": {"status": "shown"},
-                "connector_confirmation": {
-                    "structured_data": {
-                        "status": "needs_confirmation",
-                        "preferred": "Databricks",
-                    },
-                    "team_communication": {"status": "active", "preferred": "Slack"},
-                    "company_docs": {"status": "active", "preferred": "Google Drive"},
-                },
-            },
-        )
-        payload = self.preflight()
-        summary = payload["context"]["connector_setup_summary"]
-        self.assertEqual(payload["context"]["user_context"]["semantic_layer_count"], 0)
-        self.assertFalse(summary["core_complete"])
-        self.assertEqual(summary["next_action"]["category_id"], "structured_data")
-        self.assertEqual(
-            summary["next_action"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(summary["needs_attention"][0]["status"], "needs_confirmation")
-        self.assertNotIn(
-            "structured_data",
-            {entry["id"] for entry in summary["ready"]},
-        )
-        self.assertEqual(
-            payload["control"]["final_obligations"][0]["id"],
-            "complete_data_analytics_core_onboarding",
-        )
-
-    def test_unacknowledged_deferred_core_sources_reopen_as_needs_confirmation(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "orientation": {"status": "shown"},
-                "connector_confirmation": {
-                    "status": "completed",
-                    "structured_data": {"status": "deferred"},
-                    "team_communication": {"status": "deferred"},
-                    "company_docs": {"status": "active", "preferred": "Google Drive"},
-                },
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["connector_confirmation"]
-        summary = payload["context"]["connector_setup_summary"]
-        self.assertEqual(confirmation["structured_data"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["structured_data"]["recorded_status"], "deferred")
-        self.assertEqual(
-            confirmation["structured_data"]["setup_note"],
-            "core_source_requires_explicit_user_resolution_before_fallback",
-        )
-        self.assertEqual(confirmation["team_communication"]["status"], "needs_confirmation")
-        self.assertFalse(summary["core_complete"])
-        self.assertEqual(
-            summary["unresolved_core_ids"],
-            ["structured_data", "team_communication"],
-        )
-        self.assertEqual(
-            [entry["id"] for entry in summary["needs_attention"][:2]],
-            ["structured_data", "team_communication"],
-        )
-        self.assertEqual(summary["next_action"]["category_id"], "structured_data")
-        self.assertEqual(
-            payload["control"]["onboarding_progress"]["task_list"][1]["status"],
-            "in_progress",
-        )
-
-    def test_connector_confirmation_status_without_core_categories_stays_pending(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "orientation": {"status": "shown"},
-                "connector_confirmation": {"status": "completed"},
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        summary = payload["context"]["connector_setup_summary"]
-        self.assertEqual(summary["classification_status"], "pending")
-        self.assertFalse(summary["core_complete"])
-        self.assertEqual(
-            payload["control"]["onboarding_progress"]["task_list"][1]["status"],
-            "in_progress",
-        )
-
-    def test_explicit_core_source_defer_counts_as_recorded_known_gap(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "connector_confirmation": {
-                    "structured_data": {"status": "deferred", "resolution": "user_deferred"},
-                    "team_communication": {
-                        "status": "deferred",
-                        "resolution": "user_continued_with_known_gap",
-                    },
-                    "company_docs": {"status": "active", "preferred": "Google Drive"},
-                },
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["connector_confirmation"]
-        summary = payload["context"]["connector_setup_summary"]
-        self.assertEqual(confirmation["structured_data"]["status"], "deferred")
-        self.assertEqual(confirmation["structured_data"]["resolution"], "user_deferred")
-        self.assertTrue(summary["core_complete"])
-        self.assertEqual(summary["unresolved_core_ids"], [])
-        self.assertEqual(
-            {entry["id"] for entry in summary["fallback_or_closed"]},
-            {"structured_data", "team_communication"},
-        )
-
-    def test_core_onboarding_complete_requires_active_or_explicit_fallback_sources(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "orientation": {"status": "shown"},
-                "connector_confirmation": {
-                    **self.active_core_categories(),
-                },
-                "semantic_layer_setup": {"status": "created"},
-                "semantic_layer_refresh": {"status": "declined"},
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]
-        task_statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-        self.assertTrue(progress["core_onboarding"]["complete"])
-        self.assertEqual(task_statuses["source_setup_confirmation"], "completed")
-        self.assertEqual(task_statuses["semantic_layer_setup"], "completed")
-        self.assertEqual(task_statuses["hero_prompt"], "in_progress")
-
-    def test_core_onboarding_complete_allows_semantic_layer_skip_without_refresh(self) -> None:
-        self.write_state(
-            onboarding_state={
-                "status": "active",
-                "orientation": {"status": "shown"},
-                "connector_confirmation": {
-                    "status": "completed",
-                    **self.active_core_categories(),
-                },
-                "semantic_layer_setup": {"status": "skipped"},
-            }
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]
-        task_statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-        self.assertTrue(progress["core_onboarding"]["complete"])
-        self.assertEqual(task_statuses["semantic_layer_setup"], "completed")
-        self.assertEqual(task_statuses["hero_prompt"], "in_progress")
-
-    def test_active_core_incomplete_returns_single_reminder_with_next_action(self) -> None:
-        self.write_state(
-            user_context="""# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer:
-- Avoid:
-
-# Semantic Layers
-
-status: not provided
-""",
-            onboarding_state={"status": "active", "orientation": {"status": "shown"}},
-        )
-        payload = self.preflight()
-        obligation = payload["control"]["final_obligations"][0]
-        self.assertEqual(obligation["id"], "complete_data_analytics_core_onboarding")
-        self.assertEqual(obligation["next_action"]["id"], "confirm_analytics_sources")
-        self.assertIn("**Next Step**", obligation["template"])
-        self.assertIn("Reply `continue onboarding`", obligation["template"])
-        self.assertEqual(len(payload["control"]["final_obligations"]), 1)
-
-    def test_complete_and_quiet_onboarding_suppress_auto_obligations(self) -> None:
-        for status in ("complete", "quiet"):
-            with self.subTest(status=status):
-                state_dir = self.tmp_path / status
-                self.write_state(onboarding_state={"status": status}, state_dir=state_dir)
-                payload = self.preflight(state_dir=state_dir)
-                self.assertEqual(payload["control"]["final_obligations"], [])
-
-    def test_semantic_layer_registry_and_hero_prompt_candidates_come_from_context(
-        self,
-    ) -> None:
-        self.write_state(
-            user_context="""# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer: Databricks
-- Avoid: Snowflake
-
-# Semantic Layers
-
-- Area: API
-  - Skill Name: api-semantic-layer
-  - Skill Path: /tmp/api-semantic-layer
-  - Source Inventory Path: /tmp/api-semantic-layer/references/source-inventory.md
-  - Last Updated: 2026-05-29
-""",
-            onboarding_state={"status": "active", "semantic_layer_setup": {"area": "API"}},
-        )
-        payload = self.preflight()
-        self.assertNotIn("user_context_markdown", payload["context"])
-        self.assertNotIn("content", payload["files"]["user_context"])
-        self.assertTrue(payload["context"]["user_context"]["normalization_complete"])
-        self.assertEqual(payload["context"]["semantic_layers"][0]["area"], "API")
-        self.assertEqual(payload["context"]["source_preferences"]["structured_data"], {
-            "preferred": ["Databricks"],
-            "avoid": ["Snowflake"],
-        })
-        self.assertEqual(len(payload["context"]["hero_prompt_candidates"]), 1)
-        self.assertEqual(payload["context"]["primary_hero_prompt"]["skill"], "product-business-analysis")
-        self.assertEqual(
-            payload["context"]["primary_hero_prompt"]["label"],
-            "Build a decision-ready report",
-        )
-        self.assertEqual(
-            payload["context"]["primary_hero_prompt"]["prompt"],
-            (
-                "Build a decision-ready report for API: explain what is happening, "
-                "identify the main drivers, recommend where the team should focus next, "
-                "and include sources, caveats, and useful charts."
-            ),
-        )
-        self.assertEqual(payload["context"]["extra_hero_prompt_candidates"], [])
-
-    def test_large_user_context_renders_source_preferences_and_requested_section(self) -> None:
-        self.write_state(
-            user_context="""# Data Analytics Source Routing Preferences
-
-Store durable Data Analytics source-routing choices explicitly selected for future use.
-
-## structured_data
-
-- Prefer: Databricks
-- Avoid: Snowflake
-
-## company_docs
-
-- Prefer: Google Drive
-- Avoid:
-
-# Semantic Layers
-
-""" + ("x" * 2048),
-        )
-        payload = self.preflight(
-            "--max-context-bytes",
-            "80",
-            "--section",
-            "Data Analytics Source Routing Preferences",
-        )
-        self.assertNotIn("user_context_markdown", payload["context"])
-        self.assertNotIn("content", payload["files"]["user_context"])
-        self.assertTrue(payload["files"]["user_context"]["omitted"])
-        self.assertEqual(payload["files"]["user_context"]["sections"][0]["status"], "present")
-        self.assertTrue(payload["context"]["user_context"]["normalization_complete"])
-        self.assertEqual(
-            payload["context"]["source_preferences"],
-            {
-                "structured_data": {
-                    "preferred": ["Databricks"],
-                    "avoid": ["Snowflake"],
-                },
-                "company_docs": {"preferred": ["Google Drive"]},
-            },
-        )
-        self.assertEqual(
-            payload["context"]["user_context"]["source_routing_preferences"],
-            payload["context"]["source_preferences"],
-        )
-
-    def test_reset_moves_active_state_files(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        user_context_path = self.state_dir / "user-context.md"
-        onboarding_state_path = self.state_dir / "onboarding-state.json"
-        user_context_path.write_text("# Context\n", encoding="utf-8")
-        onboarding_state_path.write_text(json.dumps({"status": "active"}), encoding="utf-8")
-        backup_dir = self.tmp_path / "backup"
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "reset_user_context_state.py"),
-                "--state-dir",
-                str(self.state_dir),
-                "--backup-dir",
-                str(backup_dir),
-            ],
-            text=True,
-            capture_output=True,
-        )
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertFalse(user_context_path.exists())
-        self.assertFalse(onboarding_state_path.exists())
-        self.assertTrue((backup_dir / "user-context.md").exists())
-        self.assertTrue((backup_dir / "onboarding-state.json").exists())
-
-    def test_initializer_creates_user_context_and_onboarding_state(self) -> None:
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "init_user_context_state.py"),
-                "--state-dir",
-                str(self.state_dir),
-            ],
-            text=True,
-            capture_output=True,
-        )
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        user_context = (self.state_dir / "user-context.md").read_text(encoding="utf-8")
-        self.assertNotIn("# Data Analytics User Context Config", user_context)
-        self.assertNotIn("- Priority:", user_context)
-        self.assertNotIn("- Use When:", user_context)
-        self.assertIn(
-            "Store durable Data Analytics source-routing choices explicitly selected for future use.",
-            user_context,
-        )
-        self.assertEqual(user_context.count("## "), 9)
-        self.assertEqual(user_context.count("- Prefer:"), 9)
-        self.assertEqual(user_context.count("- Avoid:"), 9)
-        self.assertEqual(user_context.count("status: not provided"), 1)
-        onboarding_state = json.loads((self.state_dir / "onboarding-state.json").read_text())
-        self.assertEqual(onboarding_state["status"], "active")
-        self.assertNotIn("connector_inventory", onboarding_state)
-        self.assertNotIn("seed_sources", onboarding_state["semantic_layer_setup"])
-        self.assertEqual(onboarding_state["semantic_layer_setup"]["seed_source_count"], 0)
-        self.assertIn("hero_prompt_choice", onboarding_state)
-        self.assertIn("skill_experience", onboarding_state)
-
-    def test_onboarding_contract_mentions_active_sources_and_one_first_hero_prompt(
-        self,
-    ) -> None:
-        onboarding_text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        source_runtime_text = SOURCE_RUNTIME_REFERENCE.read_text(encoding="utf-8")
-        automation_text = AUTOMATION_REFERENCE.read_text(encoding="utf-8")
-        self.assertIn("Check Main Analytics Sources", onboarding_text)
-        self.assertIn("Resolve Source Questions", onboarding_text)
-        self.assertIn("transition without a proof read", onboarding_text)
-        self.assertIn("Set Up Data Context", onboarding_text)
-        self.assertIn("Codex gets better at data work", onboarding_text)
-        self.assertIn("Send anything you would point a new analyst to", onboarding_text)
-        self.assertIn("Reply with any starting points, or say `skip for now`", onboarding_text)
-        self.assertIn("Do not: require the user to name a product or business area up front", onboarding_text)
-        self.assertNotIn("smallest useful seed", onboarding_text)
-        self.assertNotIn("first semantic layer", onboarding_text)
-        self.assertNotIn("main source categories", onboarding_text)
-        self.assertIn("Show one strong prompt first", onboarding_text)
-        self.assertIn("Do not: show three hero prompts initially", onboarding_text)
-        self.assertIn("write the prompt you would rather try instead", onboarding_text)
-        self.assertIn("`skip` to finish onboarding", onboarding_text)
-        self.assertIn("Step 5, `complete_or_quiet`, when the user skips or defers", onboarding_text)
-        self.assertIn("`needs_confirmation`", source_runtime_text)
-        self.assertIn("Do not run a proof query during onboarding", source_runtime_text)
-        self.assertIn("Do not mark setup complete", automation_text)
-        for hero_skill in HERO_SKILLS:
-            text = hero_skill.read_text(encoding="utf-8")
-            self.assertIn("data-analytics:user-context", text)
-
-    def test_semantic_layer_setup_uses_lightweight_generated_skills_and_source_checkpoint(
-        self,
-    ) -> None:
-        setup_text = SEMANTIC_LAYER_SETUP_REFERENCE.read_text(encoding="utf-8")
-        source_intake_text = SEMANTIC_LAYER_SOURCE_INTAKE_REFERENCE.read_text(encoding="utf-8")
-        template_text = SEMANTIC_LAYER_SKILL_TEMPLATE_REFERENCE.read_text(encoding="utf-8")
-        weekly_polling_text = SEMANTIC_LAYER_WEEKLY_POLLING_REFERENCE.read_text(
-            encoding="utf-8"
-        )
-        self.assertIn("source-use checkpoint", setup_text)
-        self.assertIn("rejected or lower-confidence candidates", setup_text)
-        self.assertIn("Infer expected source lanes", setup_text)
-        self.assertIn("nearby but not exact", setup_text)
-        self.assertIn("authoritative data documentation", setup_text)
-        self.assertIn("source-backed semantic-layer skills", setup_text)
-        self.assertIn("user-trusted canonical data skills", setup_text)
-        self.assertIn("treat that as authoritative data documentation", setup_text)
-        self.assertIn("authoritative data documentation", weekly_polling_text)
-        self.assertIn("source-backed semantic-layer skills", weekly_polling_text)
-        self.assertIn("user-trusted canonical data skills", weekly_polling_text)
-        self.assertIn("Infer useful source families", source_intake_text)
-        self.assertIn("do not treat ambient app availability as user approval", source_intake_text)
-        self.assertIn("Send anything you would point a new analyst to", source_intake_text)
-        self.assertIn("Reply with any starting points, or say `skip for now`", source_intake_text)
-        self.assertIn("## Start Here", template_text)
-        self.assertIn("## References", template_text)
-        self.assertIn("Answering Rules", template_text)
-        self.assertIn("## Quick Reference", template_text)
-        self.assertIn("## Entity Clarification", template_text)
-        self.assertIn("## Standard Filters And Dimensions", template_text)
-        self.assertIn("evidence.md`: detailed provenance, only when the layer needs separate evidence tracking", template_text)
-        self.assertIn("Do not put setup-time policy or full evidence procedures", template_text)
-        self.assertNotIn("### Source Order", template_text)
-        self.assertNotIn("### Evidence Standard", template_text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/data-analytics/skills/validate-data/SKILL.md b/plugins/data-analytics/skills/validate-data/SKILL.md
index 23addf9..00a0e0b 100644
--- a/plugins/data-analytics/skills/validate-data/SKILL.md
+++ b/plugins/data-analytics/skills/validate-data/SKILL.md
@@ -1,7 +1,12 @@
 ---
 name: validate-data
-description: "QA an analysis before sharing: review methodology, metric definitions, SQL/query logic, calculation checks, chart integrity, bias risks, caveats, reproducibility, and whether conclusions are supported by evidence. Use when reviewing a report, notebook, spreadsheet, SQL query/results, dashboard, chart, recommendation, or stakeholder-ready analysis before presentation or publication."
+description: "Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions."
 ---
+## Related Skills
+
+Use $analyze-data-quality when validation depends on whether the underlying data is trustworthy, comparable, fresh, or at the right grain.
+
+Use $product-business-analysis when the task asks for a recommendation or decision after the validation pass.
 
 # Validate Data Analysis
 
@@ -9,12 +14,6 @@ Validate an analysis before it is shared with stakeholders. Focus on whether the
 This skill is for analysis QA, not raw dataset profiling alone. When validation depends on dataset reliability checks such as freshness, grain, missingness,
 duplicates, join coverage, or source mismatches, use $analyze-data-quality as a companion.
 
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
-
 ## Workflow
 
 1. Inventory the artifact and claims.
@@ -136,7 +135,7 @@ Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mod
 
 ### Visualization Checks
 
-- Bar charts should generally start at zero.
+- Bar charts should generally start at zero; waterfall, bridge, variance, and other delta-focused charts may use a non-zero or narrowed value axis when zero would materially compress the intended movement, provided exact values, units, and the focused scale are clear.
 - Comparison charts should use consistent scales unless the scale difference is explicit and justified.
 - Axes, units, legends, and date ranges should be labeled.
 - Category ordering should match the comparison the reader should make.
diff --git a/plugins/data-analytics/skills/validate-data/agents/openai.yaml b/plugins/data-analytics/skills/validate-data/agents/openai.yaml
index f7b3fb8..b03fe6c 100644
--- a/plugins/data-analytics/skills/validate-data/agents/openai.yaml
+++ b/plugins/data-analytics/skills/validate-data/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "QA analysis logic, metrics, charts, and conclusions"
   default_prompt: "Validate an analysis result, metric definition, chart, or conclusion for correctness and evidence quality."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/data-analytics/skills/visualize-data/SKILL.md b/plugins/data-analytics/skills/visualize-data/SKILL.md
index 5236879..775003c 100644
--- a/plugins/data-analytics/skills/visualize-data/SKILL.md
+++ b/plugins/data-analytics/skills/visualize-data/SKILL.md
@@ -1,17 +1,25 @@
 ---
 name: visualize-data
-description: "Design, specify, implement, revise, and QA quantitative visuals and chart choices. Use when an analytical answer needs visual judgment; for example comparing values, showing how a total breaks apart, reading concentration in a ranking, or understanding movement over time. This may mean rendering a chart for a report or dashboard, or simply choosing, rendering and QAing the right chart form for an inline answer."
+description: "Design, build, revise, or QA quantitative charts and figures. Use when an analytical answer needs visual judgment, whether for an inline answer, report, dashboard, notebook, or artifact."
 ---
 
+## Related Skills
+
+Use $metric-diagnostics when the visual needs an explanation of a metric movement.
+
+Use $build-report when the visual is part of a durable analytical report.
+
 # Data Visualization
 
 Create quantitative visuals that are analytically sound, immediately readable, and polished enough to ship in a report, memo, slide, dashboard, notebook, widget, or HTML artifact. Treat charts as evidence for a takeaway. Redesign charts that are visually attractive but analytically weak, and revise charts that are technically correct but hard to interpret.
 
-## Skill Configuration
+## Runtime Delivery Routing
+
+If `surface = chatgpt_web` and `mode = work_mode` are both positively identified, do not render with Data Analytics MCP chart or table widgets, do not call `render_chart` or `render_table` for inline delivery, and do not select an MCP artifact as the visual surface. This branch applies only after another workflow has already selected inline delivery; it does not choose inline over a report, dashboard, HTML, notebook, or other surface. For an already-selected inline visual, prefer native Work Mode rendering and read [native-inline-visualizations.md](references/native-inline-visualizations.md) to choose and invoke `charts_widget_v2` or `app_block`. For one supported `bar`, `line`, `pie`, or `scatter` chart, treat `charts_widget_v2` as directly surfaced in this runtime and emit its live `genui` content reference before considering fallback; do not self-declare it unavailable or search for it. The complete outer shape is `genui{"charts_widget_v2":{"content":{...}}}`: replace `{...}` with the real chart spec and emit the reference without Markdown backticks. The inner chart spec alone is not a response; do not print bare JSON, plain HTML, or a code fence. Keep `app_block` conditional on the host surfacing it. If the Visualize plugin is installed, also consult and follow its current visualization guidance for native chart or app-block authoring, while keeping this skill's delivery gate and fallback order authoritative. If an emitted native visual is rejected or fails to render, use a reproducible static/Matplotlib chart that fits a non-MCP surface; use a compact table only if no visual renderer can be delivered. Use static or Matplotlib charting for this native-render failure fallback or when the user explicitly asks for Python, a static image/file, notebook-oriented output, or export. A successful Data Analytics MCP tool result is not delivery confirmation in web Work Mode, so do not say a visual was plotted or rendered above unless the selected native or non-MCP surface actually rendered. MCP servers and other callable tools remain valid data sources.
 
-### User Context
+Whenever the selected report or dashboard delivery surface is HTML, read [recharts-html.md](references/recharts-html.md) and use its packaged Recharts-in-HTML path with same-data static fallbacks. This includes Codex explicit HTML, ChatGPT web Work Mode, and HTML used for PDF, Google Docs, or Google Slides conversion.
 
-Mandatory pre-answer gate: Invoke `data-analytics:user-context` in preflight mode by loading [data-analytics:user-context](../user-context/SKILL.md) and running its preflight script before answering, searching connectors, retrieving evidence, creating artifacts, or drafting output. Do not look for a callable MCP tool named `data-analytics:user-context`. Use the returned `data_analytics_preflight` envelope as the source of truth for saved context, source-category mapping, semantic-layer registry, onboarding/final-response obligations, and conditional guidance; use saved context and semantic layers as source-selection inputs, not as substitutes for workflow-time reads from connected or provided sources. Do not read or reinterpret raw plugin state files unless preflight fails, declares required content omitted, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Outside positively identified web Work Mode, keep the existing surface selection rules unchanged.
 
 ## Chart Selection
 
@@ -39,18 +47,20 @@ Common surface forms should be named in the chart contract when the final surfac
    - analytical question and takeaway
    - canonical family and concrete variant
    - data sufficiency for the chosen visual: expected row count, temporal point count for trend views, scatter observation count and grain, requested date range and grain, and fallback if the first query is too sparse
-   - surface-native chart type or Seaborn template variant when a concrete renderer has been selected
+   - surface-native chart type, Recharts HTML chart type, or explicit static renderer when a concrete renderer has been selected
    - delivery-specific constraints only after the delivery surface is chosen; for MCP widgets or app artifacts, use the shared MCP specification
    - palette policy, approved palette roots, and non-color distinction plan
    - output footprint, final container, export paths or delivery target, and the final QA surface
 4. Select the delivery path that matches the final surface.
-   - Use MCP inline widgets or the Data Analytics MCP artifact app only when that surface has been selected and the user has not waived widget/app rendering. Before shaping MCP-specific artifacts, read `../../src/analytics-app-core.md`.
-   - Use the selected report, dashboard, BI, notebook, slide, or static HTML surface's native chart primitives when they can communicate the point.
-   - Read `references/seaborn-templates.md` during the current task before every static Python implementation, revision, review, QA pass, or HTML report visual. Adapt the closest template and export the format expected by the selected delivery surface. Portable static HTML reports use PNG chart images.
+   - In positively identified web Work Mode, only after the active workflow has already selected inline or chat-visible delivery, read `references/native-inline-visualizations.md` and prefer native Work Mode rendering; do not call `render_chart` or `render_table` for inline delivery. For one supported `bar`, `line`, `pie`, or `scatter` chart, emit the directly surfaced `charts_widget_v2` live content reference before fallback. Use a reproducible static/Matplotlib chart only after that emitted reference is rejected or fails to render, or when no suitable native renderer exists for the requested chart family; use a compact table only if no visual renderer can be delivered, and do not describe a Data Analytics MCP tool result as rendered above. Use static or Matplotlib charting for this native-render failure fallback or for an explicit Python, static image/file, notebook, or export request.
+   - Outside web Work Mode, default to Data Analytics MCP chart widgets for ad hoc, inline, chat-visible, or otherwise unspecified chart requests. Treat "make/show/render this chart" as an MCP widget request unless the user explicitly asks for Python, a static image/file, notebook code, HTML, BI/dashboard editing, slides/docs export, or no rendered artifact.
+   - Use the selected report, dashboard, BI, notebook, slide, or static HTML surface's native chart primitives only when the user explicitly selected that surface or the active report/dashboard workflow selected it before chart rendering.
+   - Whenever the selected report or dashboard surface is HTML, read `references/recharts-html.md` and use its packaged Recharts runtime plus same-data static fallback.
+   - Outside the web Work Mode native-render failure fallback, use static Python charting only when the user explicitly asks for Python, a standalone static image/file, or notebook-oriented output. Choose a reproducible local renderer, export the requested format, and inspect the output. Do not use that path as the default for HTML reports or dashboards.
 - Use governed BI or dashboard-native widgets when that surface owns rendering; this skill should supply the visual spec and QA bar.
 - Implement local HTML/CSS/SVG/canvas/JavaScript visuals only when the final surface truly requires local rendering, custom interaction, offline portability, or a larger artifact than a widget can carry.
 5. Build in this order: format, structure, color, then QA. Choose the family, variant, and delivery surface first; decide labels, annotations, benchmarks, and retained information second; choose palette policy and explicit colors last.
-6. Render or export the chart in its real context. Save the image format the selected delivery surface expects. For delivered portable static HTML artifacts, use PNG chart images.
+6. Render or export the chart in its real context. For HTML reports and dashboards, inspect both the live Recharts mount and its same-data static fallback. For explicit standalone static output, save and inspect the image format the user requested.
 7. Inspect the visual in the final report, slide, dashboard, notebook, widget, or HTML layout. Revise before delivery when the chart, labels, color system, or container fails final-context QA.
 8. For shipped reports or dashboards with multiple visuals, maintain a compact chart map in notes or source material. Name the section, analytical question, selected family and chart type, fields, supported takeaway, palette policy, and delivery artifact, image path, or provenance source.
 
@@ -96,19 +106,18 @@ Concrete forms such as `highlighted multi-series line`, `Likert`, `pie`, `Pareto
 
 ### Surface And Implementation
 
-- Do not treat "route through visualize-data" as "always write chart HTML." This skill may produce a visual spec and QA bar rather than local chart code.
-- Do not assume widgets or the MCP artifact app are required. If the user wants chat, notebook, HTML, BI, Streamlit, slides, static files, or no rendered artifact, keep this skill's output to chart selection, data planning, implementation guidance, and QA for that surface.
+- For data visualization requests outside web Work Mode where the user has not chosen a file, notebook, BI/dashboard, report, slide, docs, HTML, or no-render output surface, render with Data Analytics MCP chart widgets by default when the MCP widget tools are available. In web Work Mode, only after inline delivery has already been selected, read `references/native-inline-visualizations.md` and prefer native Work Mode rendering; for one supported `bar`, `line`, `pie`, or `scatter` chart, emit the directly surfaced `charts_widget_v2` live content reference before fallback. Use a reproducible static/Matplotlib chart before a compact table fallback only after that emitted reference is rejected or fails to render, or when no suitable native renderer exists for the requested chart family. Do not call `render_chart` or `render_table` for web Work Mode delivery, do not treat an `ok:true` Data Analytics MCP tool result as proof that the browser rendered the visual, and use static or Matplotlib charting only for that native-render failure fallback or when the user explicitly asks for Python, a static image/file, notebook-oriented output, or export. When the user explicitly chooses another surface, keep this skill's output to chart selection, data planning, implementation guidance, and QA for that surface.
 - When the selected delivery surface is an MCP Apps widget or Data Analytics MCP report/dashboard artifact, read `../../src/analytics-app-core.md` after selecting the chart family. Do not let an MCP-supported type list drive general chart selection for non-MCP surfaces.
+- When rendering an MCP inline chart from reviewed query results, call `render_chart` with source metadata, an exploration-ready table, chart encodings, and display settings. Do not create an intermediate Python chart image for the same result unless the user explicitly asked for Python/static output or the widget renderer is unavailable.
 - Any table that powers a chart must be richer than the minimum fields needed to draw that chart. Do not ship source-data tables containing only `x`, `y`, and optional series/color fields when the reviewed result can safely include contextual dimensions, numerator/denominator fields, date or cohort fields, ranks, benchmarks, comparison-period values, or adjacent measures. If privacy, query cost, source limits, or metric definitions prevent a richer table, state that limitation in the chart/report notes.
 - Before rendering or exporting, inspect the reviewed result table for information density. If the chart would render fewer than 8 temporal points, fewer than 4 meaningful categories, fewer than 8 scatter observations, or a nearly straight two/three-point path, make one targeted attempt to improve the supporting data by querying a finer grain, extending the time range, or adding a relevant breakdown that supports the same claim. If that is blocked by source limits, query cost, privacy, or metric definition, record the limitation and switch to a more honest form such as KPI cards, grouped bars for discrete periods, a table, or prose with exact values.
 - Include more than one meaningful quantitative field when the user may reasonably compare measures, swap axes, inspect target versus actual, or choose a relationship view. Do not fabricate auxiliary measures or ship raw detail rows merely to make extra chart types appear possible.
 - Shape chart-ready data for realistic alternatives, not arbitrary ones. A trend chart should usually retain its temporal field, value field, and meaningful segment/comparator fields; a category comparison should retain the category, value, useful grouping candidates, and rank/sort context; composition charts should retain the denominator or share context when available. A scatter chart should retain a stable point identity or label, numeric x and y measures, any denominator or sample-size fields, a useful volume/size candidate, and one or two interpretable grouping or filter fields. Do not promote a retained grouping candidate into a color/series encoding unless it adds information not already carried by the axis, facet, or table labels.
 - Treat custom local visual implementations as report-complete only after they match the selected delivery mode and pass final-context QA.
 - Use the selected surface's native chart block when one is available. Do not add an extra decorative outline shell around charts.
-- For HTML reports, keep portable static delivery on PNG chart images. For portable static HTML reports, static report-adjacent doc prep, or other static Python chart paths, use Seaborn templates and export PNG files; SVG may remain as supporting source material, and the HTML report should use PNG chart images.
-- If `references/seaborn-templates.md` cannot be read when needed, do not claim template compliance or delivery readiness for a static Python chart. Surface the blocker or label the result rough/exploratory.
-- When revising the template library itself or auditing multiple charts at once, fix shared helper stack, palette-root, and layout drift before chart-specific exceptions.
-- Assume local environments can be minimal or offline. When needed before importing Seaborn, establish a writable cache such as `MPLCONFIGDIR=/tmp/matplotlib`. Expect Python with Seaborn plus writable temp and output directories; do not assume network installs will succeed.
+- For every HTML report or dashboard, use `references/recharts-html.md`: embed the packaged Recharts runtime, keep static same-data fallbacks, and do not install dependencies or rely on remote scripts.
+- For explicit standalone static/Python output, use a reproducible local renderer and inspect the exported result. Do not let a static-image path replace the packaged Recharts HTML default.
+- Assume local environments can be minimal or offline. Do not assume network installs will succeed.
 
 ### Visual Design
 
@@ -125,6 +134,7 @@ Concrete forms such as `highlighted multi-series line`, `Likert`, `pie`, `Pareto
 - Generate explicit palette maps from declared colors. Do not let plotting library categorical defaults choose shipped chart colors.
 - For signed values, avoid green/red by default. Use dark versus open/light fills, direct signed labels, and clear zero-line context unless documented domain semantics require an exception.
 - For waterfall charts, use matched neutral start and end anchors plus exactly two non-neutral delta colors: one positive and one negative. Do not introduce extra hues or darker/lighter non-neutral keylines for individual drivers.
+- For waterfall, bridge, variance, and other delta-focused charts where the takeaway is movement against a large base, do not force the value axis to zero when zero would materially compress the intended movement. Use a focused value-axis domain around the cumulative path or delta range with padding; preserve honesty with exact start/end/change labels, visible units, and an explicit focused-scale cue in the subtitle, caption, or axis when needed. Keep zero in view when values cross zero or the question is absolute magnitude.
 - Keep one stroke-width system for marks, keylines, guides, and reference lines. Use dark-neutral styling for benchmark, calibration, or ideal lines.
 - Keep left and bottom axis anchors visible when labels depend on them. Remove ticks, guides, or connectors that do not improve the intended comparison.
 - Prefer direct labels when they reduce legend lookups; use a compact top legend when direct labels create clutter.
@@ -140,8 +150,9 @@ Concrete forms such as `highlighted multi-series line`, `Likert`, `pie`, `Pareto
 - Chart choice should follow `Chart Selection`.
 - Choose and honor an explicit delivery surface. Use MCP widgets only when that surface is available, safe, useful, and not contrary to the user's requested output mode.
 - Keep chart contracts, omitted-chart explanations, validator notes, and QA rationale out of visible executive report bodies unless the user asks for methodology or the detail changes the reader-facing takeaway.
-- Static plotting code should visibly adapt the relevant Seaborn template or a minimal skeleton. Generic ad hoc snippets are acceptable only for exploratory charts.
+- Explicit standalone static/Python plotting code should be reproducible and visibly adapted to the requested chart contract. Generic ad hoc snippets are acceptable only for exploratory charts.
 - The form must match the analytical comparison, and scales must stay honest and consistent across comparable charts.
+- Standard bar charts that compare absolute magnitude should start at zero. Delta-focused waterfalls, bridges, variance bars, and small-multiple movement charts may use non-zero or narrowed domains only when the zero baseline would materially hide the intended change and the chart carries exact labels plus a clear scale cue.
 - Data grain, filters, date range, denominator, and units must match the claim the chart supports.
 - Every shipped chart must have a visible title appropriate to its surface and a subtitle that carries needed units, time/cohort context, denominator, sample size, or volume.
 - Labels, ticks, titles, legends, callouts, benchmark labels, and annotations must not collide, clip, or detach from the evidence in the exported chart or final container.
diff --git a/plugins/data-analytics/skills/visualize-data/references/native-inline-visualizations.md b/plugins/data-analytics/skills/visualize-data/references/native-inline-visualizations.md
new file mode 100644
index 0000000..b4834d1
--- /dev/null
+++ b/plugins/data-analytics/skills/visualize-data/references/native-inline-visualizations.md
@@ -0,0 +1,81 @@
+# Native Inline Visualizations
+
+Use this reference only after the active workflow has already selected an inline, chat-visible visual in ChatGPT web Work Mode. It changes the renderer for that inline result; it does not choose inline delivery.
+
+`charts_widget_v2` and `app_block` are host-native Work Mode surfaces.
+
+Do not use this reference to waive or replace `$build-report`, downgrade a selected report or dashboard to an inline answer, replace selected HTML/notebook/BI/slide/document output, or change the plugin's default report routing. If a report, dashboard, or HTML surface was selected, keep that surface and follow its existing rendering contract.
+
+Data Analytics MCP servers and other callable tools can still supply reviewed data, but their chart, table, and artifact widgets are not the web Work Mode delivery surface. Do not call `render_chart`, `render_table`, or `render_artifact` to deliver the inline visual in this runtime.
+
+## Native Invocation
+
+In positively identified ChatGPT web Work Mode, `charts_widget_v2` is the directly surfaced native UI element for exactly one supported `bar`, `line`, `pie`, or `scatter` chart. For that case, invoke it with the exact host-provided `genui` content-reference syntax before considering fallback; do not search for it, self-declare it unavailable, or substitute a static image merely because no separate callable tool is visible. The payloads in this reference are widget arguments, not standalone assistant response text. Never emit a renderer payload as bare JSON, plain HTML, Markdown, or a code fence.
+
+For a directly surfaced `charts_widget_v2`, the current Work Mode shape is `genui{"charts_widget_v2":{"content":{...}}}`: the chart spec belongs inside the widget's `content` argument. Emit the live content reference without Markdown backticks or a fence.
+
+For `app_block` when it is surfaced, use the equivalent outer shape `genui{"app_block":{"content":"<section>...</section>"}}`. Unlike `charts_widget_v2`, keep `app_block` conditional on the host surfacing it. Use the exact invocation syntax the host provides if it differs from these examples.
+
+## Renderer Choice
+
+Choose the renderer that fits the already-selected inline result:
+
+| Inline result needed | Renderer | Decision rule |
+|---|---|---|
+| Exactly one compact bar, line, pie, or scatter chart | `charts_widget_v2` | Use the host-rendered JSON chart when one chart answers the question without controls, KPI cards, or coordinated views. |
+| KPI cards, filters, multiple coordinated views, a compact dashboard-like layout, or a chart family outside the JSON chart surface | `app_block` | Use one self-contained HTML fragment with local data and local interaction. This is the default for rich inline analytical visualizations. |
+| A live `charts_widget_v2` reference was emitted and rejected or failed to render, or no suitable native renderer exists for the requested chart family | Reproducible static/Matplotlib chart | Preserve the requested visual using the same reviewed rows, and inspect the generated image before delivery. |
+| No visual renderer can be produced or delivered, or the answer is inherently table-shaped | Compact table or prose | Preserve the answer and exact values without claiming that a visual rendered. |
+| User explicitly asks for Python, Matplotlib, a notebook, a standalone static image/file, or an export | Static renderer | Honor that explicit surface request without attempting native inline rendering first. |
+
+Mermaid is not a quantitative data-chart renderer for this path. Do not use it as a substitute for `charts_widget_v2`, `app_block`, or the static visual fallback.
+
+When a native inline visual fails, prefer a static visual before a compact table. Do not switch a report to an app block merely because an inline app block would look better.
+
+### JSON Charts Versus Custom Interactive HTML
+
+Use the JSON `charts_widget_v2` path only for its supported simple chart families: `bar`, `line`, `pie`, and ordinary fixed-size `scatter`. A scatter spec can position points by x and y, but it cannot encode a third quantitative variable as point area; a true bubble chart is therefore not a `charts_widget_v2` scatter chart.
+
+For bubble charts, funnel charts, and any other inline chart family outside that JSON surface, use `app_block` custom interactive HTML when the host surfaces it. Do not decline the request, ask the user to repeat it, emit an unsupported JSON shape, or fall back to Matplotlib merely because `charts_widget_v2` does not support the family. Build the custom HTML visual in the same answer, wrapped as a live `app_block` `genui` content reference. Use the static/Matplotlib path only if `app_block` is not surfaced, its emitted reference is rejected or fails to render after one targeted correction, or the user explicitly requested static/Python/export output.
+
+For a bubble chart, follow the compact custom-HTML pattern: embed bounded reviewed rows; use inline SVG for axes, grid, labels, and circles; map x and y to position and the third measure to circle area/radius; add a concise size legend; and use a small labeled filter, hover/focus tooltip, and `aria-live` status only when they materially improve exploration. For a funnel, use inline SVG or semantic HTML/CSS for ordered stages, label each stage and value directly, show adjacent or step-to-step conversion where useful, and keep any filter or highlight interaction local and simple. Keep CSS app-scoped, use host design tokens, use vanilla JavaScript with `addEventListener`, and keep all data local; do not use external libraries, network requests, or raster screenshots.
+
+## `charts_widget_v2`
+
+Use `charts_widget_v2` only for exactly one polished `bar`, `line`, `pie`, or `scatter` chart. Inside the required `charts_widget_v2` invocation, put one valid chart-spec JSON object directly in its `content` argument; do not stringify it, nest it under another inner key, or add prose, Markdown, JavaScript, JSX, imports, comments, or code fences. Do not print the chart spec as the assistant response.
+
+The JSON spec should contain `chartType`, `meta`, the relevant field mappings, and `data` last:
+
+- `meta` contains a concise `title`, a reader-facing `description`, and optional `footer`.
+- Bar, line, and scatter charts use `xKey`, optional `xAxisLabel`, and `series`; pie charts use `nameKey`, `valueKey`, and `series`.
+- Each series uses a stable `dataKey` and may add `label`, `axisLabel`, `valueFormat`, `valuePrefix`, or `valueSuffix`.
+- For percentages with `valueSuffix: "%"`, pass percentage points such as `42` for `42%`, not `0.42`.
+- Use `layout: "vertical"` for horizontal bars when long labels or six or more categories make that easier to read.
+- Use friendly date labels in `data` rather than raw ISO strings unless exact source labels are required.
+
+Let the host renderer own the card, spacing, axes, grid, tooltip, legend, colors, hover states, responsive layout, compact formatting, and dark mode. Do not add an outer card or custom color system. If the answer needs shared filters, KPI cards, a coherent multi-chart layout, a true bubble chart, or an unsupported family such as heatmap, waterfall, funnel, histogram, box plot, or cohort matrix, use `app_block` instead.
+
+## `app_block`
+
+Use `app_block` for a richer but still compact inline analytical surface. Inside the required `app_block` invocation, provide one self-contained HTML fragment in its `content` argument; do not print that HTML as plain response text or a code block:
+
+- Include only app markup, optional app-scoped `<style>`, and one optional final `<script>` block.
+- Do not include `<!doctype>`, `<html>`, `<head>`, `<body>`, an outer `<main>`, a Tailwind CDN script, imports, exports, frameworks, JSX, custom elements, iframes, external scripts or stylesheets, network requests, storage APIs, or permission-gated APIs.
+- Use plain HTML, app-scoped CSS, Tailwind utilities when helpful, and vanilla JavaScript with `addEventListener`; do not use inline event handlers.
+- Keep all reviewed data embedded and bounded. Do not fetch data at render time or imply live refresh.
+- Prefer responsive grids and stacks that fit chat-message width. The top-level fragment should not add a decorative outer card, border, shadow, background, or padding; use those treatments only for meaningful internal controls, KPI cards, chart regions, and result boxes.
+- Use semantic controls with visible labels, local state, focus-safe spacing, and `aria-live` for dynamic results.
+- Prefer crisp inline SVG for chart marks, axes, labels, and annotations; use HTML/CSS for cards, legends, filters, and tables. Avoid raster images and do not recreate a full-page app.
+
+A strong analytical app block usually has a short heading, one or two clearly labeled controls only when they materially improve exploration, a compact KPI row when headline values matter, one primary chart, optional supporting comparison, and a concise takeaway or caveat. Keep interaction focused: filter, metric switch, series toggle, or highlighted comparison is enough. Do not build a long dashboard, multi-screen application, file upload flow, or remote-data experience.
+
+## Data And Visual Quality
+
+- Use only reviewed values and keep the visual grain, time window, units, denominator, and filters consistent with the surrounding analysis.
+- Use the simplest chart family that answers the question, readable labels, honest axes, restrained color, compact number formatting, and direct labels or a clear legend when grouping matters.
+- Keep source names, methodology, and material caveats in the surrounding response or concise visible notes; do not hide them in hover-only interaction.
+- Do not claim that a chart, app block, or filter rendered unless the selected native surface actually rendered.
+
+## Failure Path
+
+For exactly one supported `bar`, `line`, `pie`, or `scatter` chart, do not classify `charts_widget_v2` as unsurfaced before attempting it: emit the live `genui` content reference first. If that emitted reference is rejected or fails to render after one targeted correction, render a reproducible static/Matplotlib chart from the same reviewed rows, inspect it, and deliver it on the selected non-MCP surface. For a richer composition, keep `app_block` conditional on the host surfacing it; if it is not surfaced and no other suitable native renderer exists, use the same static visual fallback. Use a compact table or prose only when a static visual also cannot be produced or delivered, or when the answer is inherently table-shaped.
diff --git a/plugins/data-analytics/skills/visualize-data/references/recharts-html.md b/plugins/data-analytics/skills/visualize-data/references/recharts-html.md
new file mode 100644
index 0000000..d8b9da0
--- /dev/null
+++ b/plugins/data-analytics/skills/visualize-data/references/recharts-html.md
@@ -0,0 +1,97 @@
+# Recharts HTML
+
+Use this reference whenever the selected delivery surface is a self-contained HTML report or dashboard, including Codex explicit HTML, ChatGPT web Work Mode, and HTML used for PDF, Google Docs, or Google Slides conversion. It does not apply to MCP app artifacts or explicit standalone Python/static-image output.
+
+Resolve `<DATA_ANALYTICS_PLUGIN_DIR>` to the installed Data Analytics plugin root, three directories above this reference. Relative paths below are from this reference.
+
+## Delivery Contract
+
+- Build a self-contained HTML file from the app-style shell at ../../../assets/html-report-shell.html. Replace `{{REPORT_AUDIENCE}}` with the selected audience before handoff.
+- Keep narrative, KPI cards, tables, caveats, and source text as static semantic HTML. Keep table cards on the shell's shared reading width; reserve `.wide` for chart cards. Wrap every semantic table directly in `.table-scroll` and preserve the shell's table patterns so narrow tables fill their card, wide tables scroll inside it, and all tables remain readable without JavaScript.
+- Upgrade chart placeholders with the packaged Recharts runtime by running ../../build-report/scripts/embed_html_report_runtime.py; the helper keeps the precompiled runtime compressed and defers the live upgrade until the static fallback has painted. Do not paste the raw runtime into the report; do not install npm packages, run Vite, use a CDN, copy the full MCP artifact bundle, patch minified symbols, import mcp-host.js, or depend on /api/*, fetch, or required localStorage.
+- Every chart needs a readable data-recharts-fallback rendered directly in the authored HTML from the same reviewed rows as the Recharts payload. Prefer a compact inline SVG for line, bar, area, scatter, and composition charts; use a compact semantic table when a faithful static SVG would be risky. The live Recharts mount replaces the fallback only after it produces a supported rendered chart surface, including non-SVG leaderboard and heatmap panels, so the report remains readable when scripts are suppressed or a conversion helper reads static HTML.
+- Keep fallback labels inside the SVG viewBox with edge padding. On wide layouts, keep at least 96px of left gutter around live charts so exact currency ticks remain inside clipped report cards. On narrow layouts, use compact symmetric chart padding so the plot keeps a readable width. For signed horizontal bars, keep negative value labels inside the bar or on its zero side, never in the category-label lane.
+- For waterfall, bridge, variance, and other delta-focused charts where zero would materially compress the intended movement, use the renderer's focused value-axis domain and preserve honesty with exact start/end/change labels plus a clear scale cue.
+- Give every live chart an explicit height of at least 280; use 320 by default and more for dense labels or many horizontal bars.
+- Keep the delivered file self-contained: no sibling chart images, local runtime paths, remote scripts, or remote stylesheets.
+
+## Report Section Maps
+
+The bundled shell is the stakeholder/executive scaffold. When the selected report specification is technical, replace its `<main>` section map before authoring; do not merely rename the executive summary. Preserve these `data-contract-section` roles in order: `title`, `technical-summary`, `key-findings`, `scope-data-and-metric-definitions`, `methodology`, `limitations-uncertainty-and-robustness-checks`, `recommended-next-steps`, and `further-questions`. Set `data-report-audience="technical"`.
+
+## HTML Placeholder Shape
+
+Each chart card needs one host whose data-recharts-chart value matches a payload chart id:
+
+~~~html
+<div data-recharts-chart="weekly-growth">
+  <div class="chart-fallback" data-recharts-fallback>
+    <svg viewBox="0 0 960 420" role="img" aria-label="Weekly growth trend">
+      <!-- Static marks generated from the same rows as the payload. -->
+    </svg>
+  </div>
+  <div data-recharts-live aria-hidden="true"></div>
+</div>
+~~~
+
+Leave exactly one <!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME --> marker before </body>. The embedding helper replaces that marker with safely escaped payload JSON plus the precompiled runtime.
+
+## Payload Shape
+
+Write a separate JSON file with one entry per chart. Use reviewed row objects in dataset.data and the same canonical chart contract used by the app renderer. encodings.y must reference numeric data; percent formats use fractional values such as 0.193 for 19.3%. For multi-series charts, prefer long rows with one numeric y field and one color field so the legend uses reader-facing series values.
+
+~~~json
+{
+  "charts": [
+    {
+      "id": "weekly-growth",
+      "height": 320,
+      "type": "line",
+      "dataset": {
+        "id": "weekly-growth",
+        "title": "Weekly growth",
+        "data": [
+          { "week": "2026-04-06", "series": "Signups", "value": 1080 },
+          { "week": "2026-04-06", "series": "Paid conversions", "value": 214 },
+          { "week": "2026-04-13", "series": "Signups", "value": 1122 },
+          { "week": "2026-04-13", "series": "Paid conversions", "value": 224 }
+        ],
+        "chart_spec": {
+          "id": "weekly-growth",
+          "dataset": "weekly-growth",
+          "title": "Weekly growth",
+          "type": "line",
+          "encodings": {
+            "x": { "field": "week", "type": "temporal" },
+            "y": { "field": "value", "label": "Weekly count", "type": "quantitative" },
+            "color": { "field": "series", "type": "nominal" }
+          },
+          "xAxisTitle": "",
+          "yAxisTitle": "",
+          "valueFormat": "number"
+        }
+      }
+    }
+  ]
+}
+~~~
+
+For horizontal bars, keep the category in encodings.x, the numeric measure in encodings.y, and set settings: { "orientation": "horizontal", "groupMode": "grouped" }. Keep tooltip encodings as an array of encoding objects when they are needed. Set axis titles explicitly; use empty strings when the visible header/subtitle already carries the unit and field-name titles would add clutter.
+
+## Build And QA
+
+1. Copy and adapt ../../../assets/html-report-shell.html into a scratch/output HTML shell. Keep the selected audience specification's required data-contract-section attributes when the artifact is a report; for technical reports, use the complete technical section map above.
+2. Author the static narrative, metric cards, semantic tables, and same-data chart fallbacks. Add one data-recharts-chart host and one payload entry per chart.
+3. Run:
+
+   ~~~bash
+   python3 <DATA_ANALYTICS_PLUGIN_DIR>/skills/build-report/scripts/embed_html_report_runtime.py \
+     --input report-shell.html \
+     --payload report-payload.json \
+     --output report.html
+   ~~~
+
+4. Open the delivered HTML itself. With scripts enabled, confirm each live mount contains an SVG, the fallback is replaced, chart labels fit, and there are no console errors. Keep exactly one host per payload chart; counting hosts is not a rendered-chart check, so visually confirm that each host has only one visible chart after the fallback/live swap. Check that live text stays inside its card and that bar value labels do not intersect category-axis labels. With scripts disabled or removed, confirm the fallback, tables, narrative, and source context remain readable.
+5. Inspect desktop and narrow widths. Confirm every table card aligns to the reading column, narrow tables fill their card, wide tables scroll inside `.table-scroll`, and the document itself never gains horizontal overflow. Fix clipping, overflow, long labels, inconsistent scales, or mismatched fallback/payload values before handoff.
+
+If the packaged runtime parts or embedding helper are unavailable, do not install dependencies or improvise a CDN runtime. Deliver the same semantic HTML with its same-data static fallbacks and label the missing live Recharts upgrade as a delivery limitation.
diff --git a/plugins/data-analytics/skills/visualize-data/references/seaborn-templates.md b/plugins/data-analytics/skills/visualize-data/references/seaborn-templates.md
deleted file mode 100644
index 9f5435b..0000000
--- a/plugins/data-analytics/skills/visualize-data/references/seaborn-templates.md
+++ /dev/null
@@ -1,774 +0,0 @@
-# Seaborn Templates
-
-Use this when implementing static charts in Python. Seaborn owns marks;
-Matplotlib owns layout, axes, annotations, and export.
-
-## How To Use This File
-
-1. Choose the family from `../SKILL.md` `Chart Selection`.
-2. Pick the lightest variant below that answers the question.
-3. Apply shared style before chart-specific tweaks.
-4. Export SVG and PNG.
-5. Inspect in the final notebook, report, dashboard, or slide.
-
-## Canonical Family Map
-
-- `Tables & Scorecards`: HTML layer, not Seaborn.
-- `Trend`: Trend Line, Highlighted Multi-Series Trend, Stacked Area,
-  Overlapping Area, Small Multiples.
-- `Comparison & Ranking`: Grouped Column Bars, Ranked Horizontal Bars,
-  Diverging Horizontal Bars, Dot Reference Comparison, Concentration / Pareto.
-- `Composition`: Stacked Horizontal Bars, Pie Chart.
-- `Distribution`: Histogram / KDE, Grouped Distribution Comparison.
-- `Relationship`: Scatter, Bubble Chart, Scatter / Density.
-- `Uncertainty & Benchmark`: Ordered Line & Dot, Faceted Dot & Interval,
-  Experiment Lift With Confidence Intervals.
-- `Matrix & Cohort`: Heatmap.
-- `Decomposition & Progression`: Funnel Stage Bars, Waterfall Bridge.
-
-## Shared Style Guidance
-
-Use this section for every chart. The recipes name form; this section owns visual decisions.
-
-### Build Sequence
-
-Use this order:
-
-1. `Format`: family and delivery surface.
-2. `Structure`: title, subtitle, axes, labels, annotations, removals.
-3. `Colors`: smallest readable set of palette roots.
-
-If the chart feels busy, revisit `Format` or `Structure`; do not add colors,
-heavier strokes, or extra decoration.
-
-### Palette
-
-Choose a palette policy before plotting. Use only approved roots, explicit maps, neutrals, and shades. Never use default categorical colors for shipped charts.
-
-Policy classes:
-
-- `Single-root preferred`: one non-neutral root plus shades and neutrals. Use for simple trends, ranks, distributions, relationships,
-  heatmaps, intervals, experiment lift, and comparable small multiples.
-- `Hard two-root cap`: at most two non-neutral roots plus neutrals. Use for binary, signed, focal-vs-context, benchmark, Pareto, overlapping area,
-  highlighted trend, ordered line-dot, and waterfall charts.
-- `Relaxed multi-category`: up to five approved roots when category identity matters. Use for pie, stacked bars/area, grouped bars, and stage bars.
-  With more than five categories, group to top-N plus Other or change form.
-
-Shades and neutral comparator fills do not count as roots. Separate series with tone, open fill, marker fill, line style, direct labels, ordering, or faceting.
-Do not ship visually identical fills for adjacent or overlapping marks.
-Use each selected root's `base` tone, the middle swatch of the five-color row,
-as the default non-neutral mark color for shipped charts. Use `dark` for keylines, outlines, labels, or reference strokes; use `light`, `xlight`, or `open` for supporting fills.
-
-| Family | XLight | Light | Base | Mid | Dark |
-|---|---|---|---|---|---|
-| Blue | `#EAF1FE` | `#CEDFFE` | `#A3BEFA` | `#5477C4` | `#2E4780` |
-| Gold | `#FFF4C2` | `#FFEA8F` | `#FFE15B` | `#B8A037` | `#736422` |
-| Orange | `#FFEDDE` | `#FFBDA1` | `#F0986E` | `#CC6F47` | `#804126` |
-| Olive | `#D8ECBD` | `#BEEB96` | `#A3D576` | `#71B436` | `#386411` |
-| Pink | `#FCDAD6` | `#F5BACC` | `#F390CA` | `#BD569B` | `#8A3A6F` |
-
-- Use `Blue` for general product or KPI charts.
-- Use `Gold` for model/eval examples with no subject color.
-- Use `Orange`, `Olive`, or `Pink` when the subject or report points there.
-- Calls with `hue`, grouped marks, multiple lines, `stackplot`, `pie`, grouped distributions, or colormaps must pass explicit palette/color maps.
-- Prefer `base` for primary fills, lines, dots, and legend swatches. Treat `mid` as an exception for cases that need extra contrast.
-- For positive/negative states, use dark vs open/light fills, signed labels,
-  and zero-line context; use green/red only for documented domain semantics.
-
-### Semantic Tokens
-
-| Token | Value | Use |
-|---|---:|---|
-| `surface` | `#FCFCFD` | page or figure background |
-| `panel` | `#FFFFFF` | chart panel and axes background |
-| `ink` | `#1F2430` | titles, primary labels, reference lines |
-| `muted` | `#6F768A` | subtitles, notes, secondary ticks |
-| `grid` | `#E6E8F0` | quiet grid lines and dotted plot areas |
-| `axis` | `#D7DBE7` | visible left/bottom axis bars |
-
-Use dark-neutral calibration, benchmark, reference, and ideal lines.
-
-Neutral comparator fills:
-
-| Token | Value | Use |
-|---|---:|---|
-| `neutral_xlight` | `#F4F5F7` | open comparator fills |
-| `neutral_light` | `#E2E5EA` | secondary comparator fills |
-| `neutral_base` | `#C5CAD3` | dense comparator fills or disabled states |
-| `neutral_mid` | `#7A828F` | comparator outlines and labels |
-| `neutral_dark` | `#464C55` | high-contrast comparator keylines |
-
-### Typography And Naming
-
-- Use a sans stack for titles, subtitles, axis titles, legends, and notes.
-- Use a mono stack for numeric tick labels, direct value labels, benchmark labels, and compact callouts.
-- Every shipped chart must have a visible title and subtitle.
-- Use sentence case for short takeaway titles.
-- Put metric scope in subtitles: unit, denominator, date range, cohort, filters,
-  confidence level, or benchmark.
-- Use title case for short axis labels.
-- Use sentence case for subtitles, notes, and analytical questions.
-- Preserve model names exactly: `GPT-4o`, `o3-mini`, `o1`, `o1-mini`, `Sora`.
-- Do not shrink type to fix layout. Widen, wrap, facet, or split.
-
-### Labels, Legends, And Callouts
-
-- Align title, subtitle, and top legend to the same left edge.
-- Prefer direct labels when they reduce lookup.
-- Use a compact top legend when direct labels collide.
-- Keep value labels near marks with a clear gutter.
-- Put pie labels outside. If labels collide, use a right-side list.
-- Use callouts sparingly; anchor them to data points.
-
-### Axes, Grids, And Plot Area
-
-- Use one stroke width per chart; do not encode emphasis with thickness.
-- Web/static chart default: `1px` visual stroke.
-- Deck export default: `1.5px` visual stroke.
-- Keep keylines on filled marks and shapes.
-- Do not use gradients inside chart marks.
-- Use white or near-white backgrounds unless the whole artifact is dark.
-- For line, area, and line-dot charts, keep left and bottom axis bars visible.
-- Use dotted plot areas only for line, area, or line-dot charts.
-- For bar charts, keep grid lines quiet and only on the value axis.
-- For datetime x-axes, use `format_date_axis(ax)` after plotting so tick labels stay concise and do not overlap.
-
-### Mark Styling
-
-- Bars: filled marks with keylines; sort unless order matters; use horizontal bars for long labels.
-- Use the root `base` tone for primary fills and line marks. Use `dark` as the keyline or high-contrast reference stroke, not as the default fill.
-- Diverging bars: reserve separate zones for category labels, negative value labels, zero line, positive bars, and positive value labels.
-- Lines: use declared roots, line style, tone, or labels; remove extra guides.
-- Line-dot and interval charts: make dots and whiskers primary; keep connector lines quiet; use dark-neutral benchmark lines.
-- Bubbles: area-encode size only when the third metric materially changes the interpretation; include a compact size legend.
-- Areas: keep boundary lines above fills, but do not outline the sides or bottom of filled area polygons.
-- Pie charts: sort slices, use outside labels, avoid tiny inside labels, and keep labels readable.
-- Heatmaps: use a declared sequential scale; annotate only readable cells.
-
-### Layout And Export
-
-- Reserve header space for title and subtitle plus label gutters before plotting.
-- Use `add_chart_header(fig, ax, title, subtitle)` for every shipped static chart. Do not mix `ax.set_title(...)` with separate subtitle text.
-- Use an 8-column footprint for most standalone HTML charts; widen to 10 or 12 for long labels, dense series, or annotations. Treat 6 columns as the minimum.
-- Stack side-by-side charts on mobile; do not invent off-grid widths.
-- For shipped branded web outputs, inspect light and dark variants.
-- Never compress a chart into a narrow card if labels clip.
-- Inspect the exported PNG and final embedded context, not only the raw SVG.
-
-## Minimal Implementation Skeleton
-
-Adapt this skeleton.
-
-```python
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-import matplotlib.ticker as mticker
-import numpy as np
-import seaborn as sns
-import textwrap
-from matplotlib.patches import Patch
-
-FONT_FAMILY = ["Aptos", "Inter", "Segoe UI", "DejaVu Sans", "Arial", "sans-serif"]
-MONO_FONT_FAMILY = ["SF Mono", "Menlo", "Consolas", "DejaVu Sans Mono", "monospace"]
-
-TOKENS = {
-    "surface": "#FCFCFD",
-    "panel": "#FFFFFF",
-    "ink": "#1F2430",
-    "muted": "#6F768A",
-    "grid": "#E6E8F0",
-    "axis": "#D7DBE7",
-}
-
-NEUTRAL_MARKS = {
-    "open": TOKENS["panel"],
-    "xlight": "#F4F5F7",
-    "light": "#E2E5EA",
-    "base": "#C5CAD3",
-    "mid": "#7A828F",
-    "dark": "#464C55",
-}
-
-COLOR_FAMILIES = {
-    "blue": {
-        "open": TOKENS["panel"],
-        "xlight": "#EAF1FE",
-        "light": "#CEDFFE",
-        "base": "#A3BEFA",
-        "mid": "#5477C4",
-        "dark": "#2E4780",
-    },
-    "gold": {
-        "open": TOKENS["panel"],
-        "xlight": "#FFF4C2",
-        "light": "#FFEA8F",
-        "base": "#FFE15B",
-        "mid": "#B8A037",
-        "dark": "#736422",
-    },
-    "orange": {
-        "open": TOKENS["panel"],
-        "xlight": "#FFEDDE",
-        "light": "#FFBDA1",
-        "base": "#F0986E",
-        "mid": "#CC6F47",
-        "dark": "#804126",
-    },
-    "olive": {
-        "open": TOKENS["panel"],
-        "xlight": "#D8ECBD",
-        "light": "#BEEB96",
-        "base": "#A3D576",
-        "mid": "#71B436",
-        "dark": "#386411",
-    },
-    "pink": {
-        "open": TOKENS["panel"],
-        "xlight": "#FCDAD6",
-        "light": "#F5BACC",
-        "base": "#F390CA",
-        "mid": "#BD569B",
-        "dark": "#8A3A6F",
-    },
-}
-
-def palette_from_family(labels, family, tones=("base", "light", "xlight", "open", "base")):
-    """Return an explicit palette map from one declared palette root."""
-    labels = list(labels)
-    tone_list = list(tones)
-    return {
-        label: family[tone_list[i % len(tone_list)]]
-        for i, label in enumerate(labels)
-    }
-
-
-def add_chart_header(fig, ax, title, subtitle, *, title_width=78, subtitle_width=112) -> None:
-    """Add a wrapped figure-level title/subtitle and reserve plot space."""
-    title = str(title).strip()
-    subtitle = str(subtitle).strip()
-    if not title or not subtitle:
-        raise ValueError("Every shipped chart needs a non-empty title and subtitle.")
-
-    title = textwrap.fill(title, width=title_width, break_long_words=False)
-    subtitle = textwrap.fill(subtitle, width=subtitle_width, break_long_words=False)
-    title_lines = title.count("\n") + 1
-    subtitle_lines = subtitle.count("\n") + 1
-
-    ax.set_title("")
-    fig.subplots_adjust(top=max(0.62, 0.86 - 0.045 * (title_lines - 1) - 0.032 * (subtitle_lines - 1)))
-
-    left = ax.get_position().x0
-    fig.text(left, 0.985, title, ha="left", va="top", fontsize=13, fontweight="semibold", color=TOKENS["ink"], linespacing=1.08)
-    fig.text(left, 0.93 - 0.045 * (title_lines - 1), subtitle, ha="left", va="top", fontsize=9, color=TOKENS["muted"], linespacing=1.18)
-    sns.despine(ax=ax)
-
-
-def format_date_axis(ax, *, max_ticks=6) -> None:
-    """Keep time axes readable by limiting ticks and using concise labels."""
-    locator = mdates.AutoDateLocator(minticks=3, maxticks=max_ticks)
-    ax.xaxis.set_major_locator(locator)
-    ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator))
-    ax.tick_params(axis="x", labelrotation=0)
-
-
-def use_chart_theme() -> None:
-    sns.set_theme(
-        style="whitegrid",
-        rc={
-            "figure.facecolor": TOKENS["surface"],
-            "figure.edgecolor": "none",
-            "savefig.facecolor": "none",
-            "savefig.edgecolor": "none",
-            "axes.facecolor": TOKENS["panel"],
-            "axes.edgecolor": TOKENS["axis"],
-            "axes.labelcolor": TOKENS["ink"],
-            "axes.grid": True,
-            "axes.spines.top": False,
-            "axes.spines.right": False,
-            "grid.color": TOKENS["grid"],
-            "grid.linewidth": 0.8,
-            "font.family": "sans-serif",
-            "font.sans-serif": FONT_FAMILY,
-            "font.monospace": MONO_FONT_FAMILY,
-            "patch.linewidth": 1.0,
-        },
-    )
-```
-
-## Visualization Templates
-
-Each template inherits shared style. Snippets assume `fig`, `ax`, `family`,
-`title`, `subtitle`, and labels exist; adapt names and sizing. Finish every shipped chart with `add_chart_header(fig, ax, title, subtitle)`. For FacetGrid or multi-axis charts, pass the figure and first primary axis.
-
-### Trend Line
-
-- Use: continuous metric over ordered x.
-- Data: sorted x, y.
-- Snippet:
-```python
-plot_df = df.sort_values(x_col)
-sns.lineplot(data=plot_df, x=x_col, y=y_col, ax=ax, color=family["base"], linewidth=1.0)
-if np.issubdtype(plot_df[x_col].dtype, np.datetime64):
-    format_date_axis(ax)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Highlighted Multi-Series Trend
-
-- Use: focal series with peer context.
-- Data: x, y, series, focal flag.
-- Snippet:
-```python
-peer_families = [NEUTRAL_MARKS, COLOR_FAMILIES["gold"], NEUTRAL_MARKS]
-for i, (series, part) in enumerate(df.loc[~df[focal_col]].groupby(series_col)):
-    peer_family = peer_families[i % len(peer_families)]
-    sns.lineplot(data=part, x=x_col, y=y_col, ax=ax, color=peer_family["base"], linewidth=1.0, linestyle=["--", ":", "-."][i % 3], label=series)
-focal_label = str(df.loc[df[focal_col], series_col].drop_duplicates().iloc[0])
-sns.lineplot(data=df.loc[df[focal_col]], x=x_col, y=y_col, ax=ax, color=family["base"], linewidth=1.0, label=focal_label)
-if np.issubdtype(df[x_col].dtype, np.datetime64):
-    format_date_axis(ax)
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=min(df[series_col].nunique(), 4), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Stacked Area
-
-- Use: additive composition over time.
-- Data: x plus additive components, or long data pivoted wide.
-- Snippet:
-```python
-wide = df.pivot(index=x_col, columns=segment_col, values=value_col).fillna(0).sort_index()
-family_cycle = [COLOR_FAMILIES[name] for name in ["blue", "gold", "olive", "pink", "orange"]]
-area_families = [family_cycle[i % len(family_cycle)] for i in range(wide.shape[1])]
-fills = [area_families[i]["base"] for i in range(wide.shape[1])]
-areas = ax.stackplot(wide.index, wide.T.to_numpy(), labels=list(wide.columns), colors=fills, linewidth=0)
-lower = np.zeros(len(wide), dtype=float)
-for area, values, area_family in zip(areas, wide.T.to_numpy(), area_families):
-    area.set_edgecolor("none")
-    area.set_linewidth(0)
-    upper = lower + values
-    lower = upper
-cumulative = wide.T.to_numpy().cumsum(axis=0)
-for boundary, area_family in zip(cumulative, area_families):
-    ax.plot(wide.index, boundary, color=area_family["dark"], linewidth=1.0, zorder=3.2)
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=min(wide.shape[1], 4), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Overlapping Area
-
-- Use: overlapping continuous envelopes.
-- Data: x plus 2-3 y series.
-- Snippet:
-```python
-if not 2 <= len(y_cols) <= 3:
-    raise ValueError("Overlapping area expects two or three y series.")
-area_styles = [
-    # Draw rear envelopes first and the low/base envelope last.
-    (str(y_cols[1]), y_cols[1], COLOR_FAMILIES["olive"]["base"], COLOR_FAMILIES["olive"]["dark"], COLOR_FAMILIES["olive"]["base"], "-", 0.36, 1.1),
-]
-if len(y_cols) == 3:
-    area_styles.append(
-        (str(y_cols[2]), y_cols[2], COLOR_FAMILIES["blue"]["base"], COLOR_FAMILIES["blue"]["dark"], COLOR_FAMILIES["blue"]["base"], "-", 0.66, 1.8)
-    )
-area_styles.append(
-    (str(y_cols[0]), y_cols[0], COLOR_FAMILIES["olive"]["base"], COLOR_FAMILIES["olive"]["dark"], COLOR_FAMILIES["olive"]["base"], "-", 0.72, 3.0)
-)
-for name, y, fill_color, edge_color, line_color, line_style, fill_alpha, zorder in area_styles:
-    area = ax.fill_between(
-        df[x_col], 0, df[y],
-        color=fill_color, alpha=fill_alpha,
-        edgecolor="none",
-        linewidth=0,
-        zorder=zorder,
-    )
-for name, y, _, _, line_color, line_style, _, _ in area_styles:
-    ax.plot(df[x_col], df[y], color=line_color, linestyle=line_style, linewidth=1.05, label=name, zorder=5.2)
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=min(len(area_styles), 4), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Small Multiples
-
-- Use: many trend lines.
-- Data: x, y, facet.
-- Snippet:
-```python
-g = sns.FacetGrid(df, col=facet_col, col_wrap=3, sharex=True, sharey=True)
-facet_order = list(df[facet_col].drop_duplicates())
-facet_palette = {facet: COLOR_FAMILIES["blue"]["base"] for facet in facet_order}
-g.map_dataframe(sns.lineplot, x=x_col, y=y_col, hue=facet_col, palette=facet_palette, legend=False, linewidth=1.0)
-g.set_titles("{col_name}", size=8.5)
-add_chart_header(g.figure, g.axes.flat[0], title, subtitle)
-```
-
-### Grouped Column Bars
-
-- Use: side-by-side category comparison.
-- Data: category, series, value; few categories.
-- Snippet:
-```python
-series_order = list(df[series_col].drop_duplicates())
-category_order = list(df[category_col].drop_duplicates())
-x = np.arange(len(category_order))
-width = min(0.24, 0.72 / max(len(series_order), 1))
-series_families = [COLOR_FAMILIES[name] for name in ["orange", "blue", "olive", "gold", "pink"]]
-for i, series in enumerate(series_order):
-    series_family = series_families[i % len(series_families)]
-    values = df.loc[df[series_col] == series].set_index(category_col).loc[category_order, value_col].to_numpy()
-    offset = (i - (len(series_order) - 1) / 2) * width
-    bars = ax.bar(
-        x + offset,
-        values,
-        width=width,
-        label=series,
-        color=series_family["base"],
-        edgecolor=series_family["dark"],
-        linewidth=1.0,
-    )
-ax.set_xticks(x, category_order)
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=min(len(series_order), 4), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Ranked Horizontal Bars
-
-- Use: rank, top-N, long labels.
-- Data: category, value; sort unless order matters.
-- Snippet:
-```python
-plot_df = df.sort_values(value_col, ascending=True)
-bar_family = COLOR_FAMILIES["orange"]
-palette = palette_from_family(plot_df[category_col], bar_family, tones=("base", "light", "xlight", "open", "base"))
-edge_colors = {category: bar_family["dark"] for category in plot_df[category_col]}
-sns.barplot(data=plot_df, x=value_col, y=category_col, hue=category_col, palette=palette, legend=False, dodge=False, ax=ax, edgecolor=TOKENS["ink"], linewidth=1.0)
-for patch, category in zip(ax.patches, plot_df[category_col]):
-    patch.set_edgecolor(edge_colors[category])
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Diverging Horizontal Bars
-
-- Use: signed deltas or drivers around zero.
-- Data: category, signed value.
-- Snippet:
-```python
-plot_df = df.sort_values(value_col)
-positive_family = COLOR_FAMILIES["olive"]
-negative_family = COLOR_FAMILIES["orange"]
-colors = np.where(plot_df[value_col] >= 0, positive_family["base"], negative_family["base"])
-edge_colors = np.where(plot_df[value_col] >= 0, positive_family["dark"], negative_family["dark"])
-bars = ax.barh(plot_df[category_col], plot_df[value_col], color=colors, edgecolor=edge_colors, linewidth=1.0)
-neg_min = plot_df.loc[plot_df[value_col] < 0, value_col].min()
-pos_max = plot_df.loc[plot_df[value_col] > 0, value_col].max()
-neg_label_x = neg_min * 0.74
-ax.set_xlim(neg_min * 1.36, pos_max * 1.12)
-for bar, value in zip(bars, plot_df[value_col]):
-    bar.set_edgecolor(positive_family["dark"] if value >= 0 else negative_family["dark"])
-    label_x = value + 0.25 if value >= 0 else neg_label_x
-    ax.text(label_x, bar.get_y() + bar.get_height() / 2, f"{value:+.1f}", ha="left", va="center", fontsize=8, color=TOKENS["ink"])
-ax.axvline(0, color=TOKENS["ink"], linewidth=1.0)
-ax.legend(
-    handles=[
-        Patch(facecolor=positive_family["base"], edgecolor=positive_family["dark"], label="Positive"),
-        Patch(facecolor=negative_family["base"], edgecolor=negative_family["dark"], label="Negative"),
-    ],
-    loc="lower left",
-    bbox_to_anchor=(0, 1.02),
-    frameon=False,
-    ncol=2,
-    borderaxespad=0,
-)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Dot Reference Comparison
-
-- Use: compact target, before/after, or benchmark comparison.
-- Data: category, value, optional reference.
-- Snippet:
-```python
-value_family = COLOR_FAMILIES["orange"]
-ax.hlines(df[category_col], df[reference_col], df[value_col], color=NEUTRAL_MARKS["base"], linewidth=1.0, label="Reference gap")
-ax.scatter(df[value_col], df[category_col], facecolors=value_family["base"], edgecolors=value_family["dark"], linewidths=1.0, label="Actual")
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=2, borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Concentration / Pareto
-
-- Use: concentration of volume or impact.
-- Data: entity, value; derive cumulative share.
-- Snippet:
-```python
-plot_df = df.sort_values(value_col, ascending=False).head(top_n).assign(cum_share=lambda d: d[value_col].cumsum() / d[value_col].sum())
-bar_family = COLOR_FAMILIES["orange"]
-line_family = COLOR_FAMILIES["blue"]
-sns.barplot(data=plot_df, x=category_col, y=value_col, ax=ax, color=bar_family["base"], edgecolor=bar_family["dark"], linewidth=1.0)
-ax2 = ax.twinx()
-ax2.grid(False)
-ax2.plot(plot_df[category_col], plot_df["cum_share"], color=line_family["base"], linewidth=1.0, marker="o", label="Cumulative share")
-ax2.set_ylim(0, 1.05)
-ax2.yaxis.set_major_formatter(mticker.PercentFormatter(1.0))
-ax2.tick_params(axis="y", colors=TOKENS["muted"], labelsize=8, length=0)
-ax2.spines["right"].set_visible(False)
-ax.legend(
-    handles=[
-        Patch(facecolor=bar_family["base"], edgecolor=bar_family["dark"], label=str(value_col).replace("_", " ").title()),
-        ax2.lines[0],
-    ],
-    loc="lower left",
-    bbox_to_anchor=(0, 1.02),
-    frameon=False,
-    ncol=2,
-    borderaxespad=0,
-)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Stacked Horizontal Bars
-
-- Use: share-of-total, survey, or Likert buckets.
-- Data: group, ordered segment, value or percent.
-- Snippet:
-```python
-wide = df.pivot_table(index=group_col, columns=segment_col, values=value_col, fill_value=0).reindex(index=group_order, columns=segment_order)
-left = np.zeros(len(wide))
-segment_families = [COLOR_FAMILIES[name] for name in ["blue", "gold", "olive", "pink", "orange"]]
-for i, segment in enumerate(segment_order):
-    segment_family = segment_families[i % len(segment_families)]
-    bars = ax.barh(wide.index, wide[segment], left=left, label=segment, color=segment_family["base"], edgecolor=segment_family["dark"], linewidth=1.0)
-    left += wide[segment].to_numpy()
-ax.xaxis.set_major_formatter(mticker.PercentFormatter(100))
-ax.legend(loc="lower left", bbox_to_anchor=(0, 1.04), frameon=False, ncol=min(len(segment_order), 4), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Pie Chart
-
-- Use: rough part-to-whole with few slices.
-- Data: category, value.
-- Snippet:
-```python
-plot_df = df.sort_values(value_col, ascending=False)
-slice_families = [COLOR_FAMILIES[name] for name in ["olive", "gold", "blue", "pink", "orange"]]
-colors = [slice_families[i % len(slice_families)]["base"] for i in range(len(plot_df))]
-wedges, _ = ax.pie(plot_df[value_col], startangle=90, counterclock=False, colors=colors, wedgeprops={"edgecolor": TOKENS["ink"], "linewidth": 1.0})
-for i, (wedge, label, value) in enumerate(zip(wedges, plot_df[category_col], plot_df[value_col])):
-    slice_family = slice_families[i % len(slice_families)]
-    wedge.set_edgecolor(slice_family["dark"])
-    angle = np.deg2rad((wedge.theta1 + wedge.theta2) / 2)
-    ax.text(1.08 * np.cos(angle), 1.08 * np.sin(angle), f"{label}: {value:,.0f}", ha="left" if np.cos(angle) > 0 else "right", va="center")
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Histogram / KDE
-
-- Use: spread, skew, tail, or threshold.
-- Data: one numeric value.
-- Snippet:
-```python
-sns.histplot(data=df, x=value_col, bins=bins, ax=ax, color=family["base"], edgecolor=family["dark"], linewidth=1.0)
-ax.axvline(df[value_col].median(), color=TOKENS["ink"], linestyle=":", linewidth=1.0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Grouped Distribution Comparison
-
-- Use: spread, skew, or outliers across cohorts.
-- Data: group, numeric value.
-- Snippet:
-```python
-group_order = list(df[group_col].drop_duplicates())
-group_family = COLOR_FAMILIES["pink"]
-box_tones = ["base", "light", "xlight"]
-point_tones = ["base", "base", "base"]
-box_palette = {group: group_family[box_tones[i % len(box_tones)]] for i, group in enumerate(group_order)}
-point_palette = {group: group_family[point_tones[i % len(point_tones)]] for i, group in enumerate(group_order)}
-edge_colors = {group: group_family["dark"] for group in group_order}
-sns.boxplot(data=df, x=group_col, y=value_col, hue=group_col, palette=box_palette, legend=False, linewidth=1.0)
-for patch, group in zip(ax.patches, group_order):
-    patch.set_edgecolor(edge_colors[group])
-sns.stripplot(data=df.sample(min(len(df), 500)), x=group_col, y=value_col, hue=group_col, palette=point_palette, legend=False, alpha=0.25, size=2)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Scatter
-
-- Use: two-variable relationship.
-- Data: numeric x/y, optional small group.
-- Snippet:
-```python
-sns.scatterplot(data=df, x=x_col, y=y_col, ax=ax, color=family["base"], edgecolor=family["dark"], linewidth=0.6, alpha=0.65)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Bubble Chart
-
-- Use: two-variable relationship where a third numeric metric materially changes interpretation.
-- Data: numeric x/y, numeric size.
-- Snippet:
-```python
-size_min = df[size_col].min()
-size_max = df[size_col].max()
-size_values = np.quantile(df[size_col], [0.25, 0.5, 0.75])
-
-def marker_size(value):
-    if size_max == size_min:
-        return 240
-    return 80 + (value - size_min) / (size_max - size_min) * 420
-
-sns.scatterplot(
-    data=df,
-    x=x_col,
-    y=y_col,
-    size=size_col,
-    sizes=(80, 500),
-    legend=False,
-    ax=ax,
-    color=family["base"],
-    edgecolor=family["dark"],
-    linewidth=0.8,
-    alpha=0.55,
-)
-handles = [
-    ax.scatter([], [], s=marker_size(value), facecolors=family["base"], edgecolors=family["dark"], linewidths=0.8, alpha=0.55, label=f"{value:,.0f}")
-    for value in size_values
-]
-ax.legend(handles=handles, title=str(size_col).replace("_", " ").title(), loc="lower left", bbox_to_anchor=(0, 1.02), frameon=False, ncol=len(handles), borderaxespad=0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Scatter / Density
-
-- Use: dense point cloud.
-- Data: numeric x/y.
-- Snippet:
-```python
-ax.hexbin(df[x_col], df[y_col], gridsize=28, mincnt=1, cmap=sns.blend_palette([TOKENS["panel"], family["light"], family["base"]], as_cmap=True))
-fit = np.polyfit(df[x_col], df[y_col], deg=1)
-x_fit = np.linspace(df[x_col].min(), df[x_col].max(), 100)
-sns.lineplot(x=x_fit, y=fit[0] * x_fit + fit[1], ax=ax, color=family["base"], linewidth=1.0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Ordered Line & Dot
-
-- Use: ordered bins, calibration, or uncertainty comparisons.
-- Data: ordered x/bin, estimate, optional bounds/series.
-- Snippet:
-```python
-ax.errorbar(
-    df[x_col], df[estimate_col],
-    yerr=[df[estimate_col] - df[lower_col], df[upper_col] - df[estimate_col]],
-    fmt="o", color=family["base"], linewidth=1.0, capsize=3,
-)
-ax.plot(df[x_col], df[estimate_col], color=family["light"], linewidth=1.0)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Faceted Dot & Interval
-
-- Use: subgroup estimates against benchmark.
-- Data: facet, estimate, lower, upper, label.
-- Snippet:
-```python
-for ax, (facet, part) in zip(axes, df.groupby(facet_col)):
-    facet_order = list(df[facet_col].drop_duplicates())
-    facet_tones = ["base", "light", "base"]
-    facet_color = COLOR_FAMILIES["gold"][facet_tones[facet_order.index(facet) % len(facet_tones)]]
-    ax.errorbar(
-        part[estimate_col], part[label_col],
-        xerr=[part[estimate_col] - part[lower_col], part[upper_col] - part[estimate_col]],
-        fmt="o", color=facet_color, linewidth=1.0,
-    )
-    ax.axvline(benchmark, color=TOKENS["ink"], linestyle=":", linewidth=1.0)
-add_chart_header(fig, axes[0], title, subtitle)
-```
-
-### Experiment Lift With Confidence Intervals
-
-- Use: lift, treatment effects, or model intervals.
-- Data: label, estimate, lower, upper.
-- Snippet:
-```python
-ax.axvline(0, color=TOKENS["ink"], linestyle=":", linewidth=1.0)
-metric_family = COLOR_FAMILIES["gold"]
-for _, row in df.iterrows():
-    marker_face = TOKENS["panel"] if row[estimate_col] < 0 else metric_family["base"]
-    ax.errorbar(
-        row[estimate_col], row[label_col],
-        xerr=np.array([[row[estimate_col] - row[lower_col]], [row[upper_col] - row[estimate_col]]]),
-        fmt="o", color=metric_family["base"], markerfacecolor=marker_face, markeredgecolor=metric_family["dark"], linewidth=1.0, capsize=3,
-    )
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Heatmap
-
-- Use: two-dimensional structure or cohort matrix.
-- Data: ordered pivot matrix.
-- Snippet:
-```python
-cmap = sns.blend_palette([TOKENS["panel"], family["xlight"], family["light"], family["base"]], as_cmap=True)
-sns.heatmap(matrix, ax=ax, cmap=cmap, linewidths=1.0, linecolor=TOKENS["panel"], annot=annot, fmt=value_format)
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Funnel Stage Bars
-
-- Use: ordered stages, counts, conversion.
-- Data: stage order, count, optional rate.
-- Snippet:
-```python
-plot_df = df.set_index(stage_col).loc[stage_order].reset_index()
-stage_families = [COLOR_FAMILIES[name] for name in ["olive", "gold", "blue", "pink", "orange"]]
-palette = {stage: stage_families[i % len(stage_families)]["base"] for i, stage in enumerate(stage_order)}
-edge_colors = {stage: stage_families[i % len(stage_families)]["dark"] for i, stage in enumerate(stage_order)}
-sns.barplot(data=plot_df, x=stage_col, y=count_col, hue=stage_col, palette=palette, legend=False, dodge=False, ax=ax, edgecolor=TOKENS["ink"], linewidth=1.0)
-for patch, stage in zip(ax.patches, plot_df[stage_col]):
-    patch.set_edgecolor(edge_colors[stage])
-add_chart_header(fig, ax, title, subtitle)
-```
-
-### Waterfall Bridge
-
-- Use: additive bridge from start to end.
-- Data: ordered drivers, signed deltas, start/end.
-- Snippet:
-```python
-positive_family = COLOR_FAMILIES["olive"]
-negative_family = COLOR_FAMILIES["orange"]
-positive_fill = positive_family["base"]
-negative_fill = negative_family["base"]
-total_fill = NEUTRAL_MARKS["light"]
-total_edge = NEUTRAL_MARKS["dark"]
-x = np.arange(len(df) + 2)
-driver_labels = ["Start", *df[label_col], "End"]
-running = start_value + df[delta_col].cumsum().shift(fill_value=0)
-ending = start_value + df[delta_col].cumsum()
-end_value = ending.iloc[-1]
-bottom = np.minimum(running, ending)
-colors = np.where(df[delta_col] >= 0, positive_fill, negative_fill)
-edge_colors = np.where(df[delta_col] >= 0, positive_family["dark"], negative_family["dark"])
-ax.bar(x[0], start_value, color=total_fill, edgecolor=total_edge, linewidth=1.0)
-bars = ax.bar(x[1:-1], df[delta_col].abs(), bottom=bottom, color=colors, edgecolor=edge_colors, linewidth=1.0)
-for bar, delta in zip(bars, df[delta_col]):
-    bar.set_edgecolor(positive_family["dark"] if delta >= 0 else negative_family["dark"])
-    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_y() + bar.get_height() + 2, f"{delta:+.0f}", ha="center", fontsize=8, color=TOKENS["ink"])
-ax.bar(x[-1], end_value, color=total_fill, edgecolor=total_edge, linewidth=1.0)
-ax.set_xticks(x, driver_labels)
-ax.legend(
-    handles=[
-        Patch(facecolor=total_fill, edgecolor=total_edge, label="Start/end"),
-        Patch(facecolor=positive_fill, edgecolor=positive_family["dark"], label="Positive delta"),
-        Patch(facecolor=negative_fill, edgecolor=negative_family["dark"], label="Negative delta"),
-    ],
-    loc="lower left",
-    bbox_to_anchor=(0, 1.02),
-    frameon=False,
-    ncol=3,
-    borderaxespad=0,
-)
-add_chart_header(fig, ax, title, subtitle)
-```
diff --git a/plugins/data-analytics/src/DESIGN.md b/plugins/data-analytics/src/DESIGN.md
index 57f072b..ec848db 100644
--- a/plugins/data-analytics/src/DESIGN.md
+++ b/plugins/data-analytics/src/DESIGN.md
@@ -47,7 +47,7 @@ surfaces:
   dashboard: { shell_width: "1140px", content_width: "full", outer_padding_x: "24px", role: "visualization-first workspace" }
 components:
   top-bar: { height: "48px", padding: "8px 12px", typography: "text/sm/medium" }
-  metric-card: { width: "186px in report rows", padding: "16px 24px", rounded: "corner-radius/cr-24", stroke: "border/light" }
+  metric-card: { min-width: "280px", padding: "20px", rounded: "corner-radius/cr-24", stroke: "border/light" }
   report-block: { width: "800px default", rounded: "corner-radius/cr-24", stroke: "border/light" }
   chart-block: { width: "full when needed", title: "text/sm/medium", body: "text/sm/normal" }
   table-list: { width: "800px in report context", cell: "text/sm/normal", header: "text/xs/semibold" }
@@ -158,8 +158,8 @@ Top bar:
 
 Metric card:
 
-- Reference report size: 186px wide by 114px tall in a four-card row.
-- Padding: 16px vertical, 24px horizontal.
+- Use a 280px content-safe minimum width and match the tallest card in each row. Dashboard and report strips balance any declared card count responsively; four columns are allowed only when four cards fit without wrapping, and four is neither a target nor a maximum.
+- Padding: 20px on all sides.
 - Radius: `corner-radius/cr-24`.
 - Stroke: `border/light`.
 - Label: `text/xs/normal` or `text/xs/semibold`.
diff --git a/plugins/data-analytics/src/analytics-app-core.md b/plugins/data-analytics/src/analytics-app-core.md
index 2f80253..0176846 100644
--- a/plugins/data-analytics/src/analytics-app-core.md
+++ b/plugins/data-analytics/src/analytics-app-core.md
@@ -2,7 +2,7 @@
 
 Use this shared foundation for generated Data Analytics dashboard and report artifacts. Public skills should keep separate intent contracts, but reuse this core for bounded snapshots, source safety, app runtime behavior, and validation helpers.
 
-Use this file after the selected delivery surface is an MCP Apps inline chart/table widget or a Data Analytics MCP report/dashboard artifact. Root skills remain delivery-mode neutral: if the user asks for chat, notebook, HTML, BI, Streamlit, slides, or no rendered artifact, do not apply MCP widget/app mechanics except for the general safety principles.
+Use this file after the selected delivery surface is an MCP Apps inline chart/table widget or a Data Analytics MCP report/dashboard artifact. Root skills remain delivery-mode neutral: if the user asks for chat, notebook, HTML, BI, Streamlit, slides, or no rendered artifact, do not apply MCP widget/app mechanics except for the general safety principles. Do not use this file to justify MCP UI delivery when `mode = work_mode` is positive and `surface` is not positively `codex_desktop`; select the HTML, connected BI, native Work Mode, or other non-MCP surface instead.
 
 ## Shared Contract
 
@@ -13,28 +13,33 @@ Use this file after the selected delivery surface is an MCP Apps inline chart/ta
 - Declare source artifacts in the manifest when the reader needs provenance,
   query text, or citation metadata.
 - Native chart specs are defined by MCP tool descriptions and enforced by artifact validation. Shared guidance should not duplicate field-level chart schema.
+- Give each native artifact table an explicit `defaultSort` chosen to make the initial view answer the table's question. Sort ranked magnitudes or movements descending, chronological sequences in the reading direction that best supports the analysis, and use a field declared in `columns`.
 - Use `ready`, `partial`, `blocked`, and `fixture` snapshot statuses across dashboard and report artifacts.
 - Reports must expose a visible in-report title. Set `manifest.title` to the reader-facing title, and make the first report markdown block a matching `#` heading.
+- Give every independently laid out or editable major report section its own markdown block. Do not put multiple peer `##` section headings in one markdown body; use `###` headings only for subordinate content that should remain in the same card.
 - Treat `partial` and `blocked` as first-class states with visible access notices, not buried caveats.
 - Reserve snapshot-level access issues for missing required data in `partial`
   or `blocked` artifacts. Optional source limitations in an otherwise `ready`
   artifact belong in caveat sections, source metadata, or report notes.
 - Every native chart block must expose the actual SQL or source path used to generate its chart dataset through the current MCP artifact schema.
 - Native chart source datasets should remain useful beyond visible encodings when safe reviewed context is available. Preserve candidate grouping fields, numerators, denominators, ranks, baselines, comparison periods, and adjacent measures for auditability and realistic chart switching.
-- Use the same canonical `source` structure for inline charts, inline tables, artifact charts, artifact tables, and `manifest.sources[]`: put runnable SQL/code in `source.query.sql`, put the human-readable query summary in `source.query.description`, and put durable query metadata in `source.query.engine`, `source.query.id`, `source.query.url`, `source.query.executed_at`, and `source.query.language`.
+- Use the same canonical `source` structure for inline charts, inline tables, artifact metric cards, artifact charts, artifact tables, and `manifest.sources[]`: put runnable SQL/code in `source.query.sql`, put the human-readable query summary in `source.query.description`, and put durable query metadata in `source.query.engine`, `source.query.id`, `source.query.url`, `source.query.executed_at`, and `source.query.language`.
 - Source metadata must be specific enough to validate: use actual table identifiers such as `example.analytics.fact_revenue`, not query nicknames or semantic labels, and put those identifiers in `source.query.tables_used` when they cannot be inferred from SQL.
 - Declare static source predicates in `source.query.filters`, including date windows, cohort/population rules, material exclusions, and sampling/limit rules. Use artifact `manifest.filters` only for interactive viewer controls.
 - Metric definitions must describe calculations, not only source columns. For ARR, include the aggregation/window, currency/unit, date grain, and material exclusions such as internal orgs, test traffic, true-up rows, or other accounting adjustments.
-- KPI and metric cards must use accurate display semantics and scale. Set `format` to the value type being rendered, such as `percent` only for rates/shares and `currency` only for money. Prefer passing unscaled numeric values and using compact formatting so the artifact renders `K`, `M`, or similar scale suffixes accurately. When a query intentionally pre-scales a value, make that scale visible in the metric label, such as `B tokens` or `revenue, $M`; do not rely on SQL aliases, field names, or query comments to imply it. Do not use a percent or currency format just because the metric is a change; format the actual value's unit and label the comparison separately.
+- KPI and metric cards must use accurate display semantics and scale. Set `format` to the value type being rendered, such as `percent` only for rates/shares and `currency` only for money. Percent format always expects fractional-rate values: pass `0.98` for `98%`, `1` for `100%`, and `2` for `200%`. If the reviewed data is already in percentage points such as `98`, use `format: "number"` with `unit: "%"`, and make that scale clear in the metric label or definition. Prefer passing unscaled numeric values and using compact formatting so the artifact renders `K`, `M`, or similar scale suffixes accurately. When a query intentionally pre-scales a value, make that scale visible in the metric label, such as `B tokens` or `revenue, $M`; do not rely on SQL aliases, field names, or query comments to imply it. Do not use a percent or currency format just because the metric is a change; format the actual value's unit and label the comparison separately.
 - Reader-facing numbers in cards, chart labels, axes, tooltips, headings, and narrative text should use compact shorthand for large values, such as `1.2k` instead of `1,201`, unless the surface is an exact lookup table or audit detail where precise values are the point.
-- KPI and metric cards must be interpretable without reading the SQL. If a metric label depends on business logic rather than a plain count or sum, define the metric in reader-facing terms and preserve the exact calculation in `source.query.metric_definitions`.
+- KPI and metric cards must be interpretable without reading the SQL. If a metric label depends on business logic rather than a plain count or sum, define the metric in reader-facing terms and preserve the exact calculation in `source.query.metric_definitions`. Every card used by a dashboard or report `metric-strip` must declare `sourceId` or inline `source` provenance so its top-right data-source action can open the shared source modal with the exact query, tables, filters, metric definitions, snapshot, and reviewed data rows for that card.
+- Metric-strip cardinality is decision-driven, not layout-driven. Do not pad or cap a strip at four cards. Include every distinct headline metric needed for the decision and omit redundant or vanity cards; the shared dashboard/report renderer chooses a balanced responsive column count for any number of cards.
+- Give each metric card exactly one headline metric. One headline does not mean one `metrics[]` entry. Later entries add short, directly comparable context for that headline, such as its prior-period value, target, or delta. Preserve a decision-relevant directional comparison as a later metric when it is already used in the executive summary or findings; move independent secondary measures to their own cards, charts, tables, or narrative. Keep the card description specific to the headline metric and its comparison context.
 - Definitions should clarify the population being measured, the event or state being counted, the time window, the aggregation method, and any important thresholds, exclusions, numerator/denominator logic, or source-specific meaning. This is especially important for rates, averages, shares, lifecycle or stage metrics, eligibility or activity metrics, quality outcomes, and version/source-attribution metrics.
 
 ## Inline Widgets
 
 - Inline widgets are for compact, reviewed intermediate or final values in non-report runs. Do not render inline chart or table widgets during report-mode work; report charts, tables, and previews belong in the selected report surface.
-- Use `render_chart` for compact inline visuals from reviewed query results in non-report runs when the widget surface is available, safe, and useful for the user. Use `render_table` for row previews, exact lookup tables, or table-shaped data.
+- Use `render_chart` for compact inline visuals from reviewed query results in non-report runs when the widget surface is available, safe, and useful for the user. Use `render_table` for row previews, exact lookup tables, or table-shaped data. In positively identified ChatGPT web Work Mode, and in any run where `mode = work_mode` is positive without a positive `surface = codex_desktop` signal, the Data Analytics MCP widget surface is not available for delivery even if these tools are visible; preserve the delivery mode already selected by the owning workflow. For an already-selected inline visual with exactly one supported `bar`, `line`, `pie`, or `scatter` chart, treat `charts_widget_v2` as directly surfaced: emit its live `genui` content reference in the outer shape `genui{"charts_widget_v2":{"content":{...}}}` before fallback, do not self-declare it unavailable or search for it, and do not print its payload as bare JSON. Keep `app_block` conditional on the host surfacing it for a richer composition. Use image-based/static charting only after an emitted native reference is rejected or fails to render, or when no suitable native renderer exists for the requested chart family; then use compact table/non-MCP output only when no visual renderer can be delivered. Use image-based/static charting for that native-render failure fallback or when the user explicitly asks for Python, a static image/file, notebook-oriented output, or export. Do not treat an `ok:true` MCP tool result as proof that the browser rendered the visual.
 - Chart and table widget payloads are query-shaped and pass the same canonical `source` object. The source query must contain the durable original SQL used to materially produce the reviewed rows, plus any durable query ID or URL as metadata. If a later query only reshapes, sorts, limits, or reselects from already computed rows for widget rendering, keep that later query out of `source.query.sql`; record it as transformation context instead of replacing the original provenance.
+- Treat `source.query.sql` as provenance for the widget's source experience, not as visible answer content. Do not duplicate that SQL in the surrounding final chat response or chart narrative unless the user explicitly asks to see, write, review, debug, or receive SQL or query methodology.
 - The source query text must be runnable SQL in the named engine. Do not pass prose such as "Revenue bridge SQL executed in Databricks..." as `source.query.sql`; put that prose in `source.query.description`.
 - The chart subtitle should add a reader-facing insight not already covered by the title. Put source names, query IDs, table names, SQL intent, metric definitions, and provenance in source metadata or notes instead.
 - Make widget tables exploration-ready with useful dimensions, measures, time columns, candidate grouping fields, ranks, benchmarks, comparison-period values, or adjacent measures from reviewed results. Customer, account, company, segment, and product names are valid analytical dimensions when they are relevant. For scatter widgets, prefer one row per meaningful observation rather than a handful of broad aggregates; retain a stable point label, numeric x and y measures at the same grain, denominator or sample-size fields, one volume/size candidate, and one interpretable grouping or filter field when safe. Retaining a field for exploration does not mean the shipped chart should bind it as color, series, or grouping.
@@ -82,4 +87,4 @@ Use this file after the selected delivery surface is an MCP Apps inline chart/ta
 
 ## Report Mode Boundary
 
-MCP app reports and HTML reports are separate delivery modes. An MCP app report should use `render_artifact` for the in-Codex reader handoff and should not also generate a canonical static `report.html` as part of the same report run. When Google Docs conversion or Google Slides conversion is required, choose the HTML report mode in $build-report instead. HTML report charts should be generated through the Seaborn template workflow and embedded as PNG images.
+MCP app reports and HTML reports are separate delivery modes. An MCP app report should use `render_artifact` for the in-Codex reader handoff and should not also generate a canonical static `report.html` as part of the same report run. When Google Docs conversion or Google Slides conversion is required, choose the HTML report mode in $build-report instead. HTML report charts should use the packaged standalone Recharts runtime with readable same-data static SVG/table fallbacks in `report.html`, not sidecar chart files.
diff --git a/plugins/data-analytics/src/analytics-app/App.tsx b/plugins/data-analytics/src/analytics-app/App.tsx
index 6ba2180..44f5ee1 100644
--- a/plugins/data-analytics/src/analytics-app/App.tsx
+++ b/plugins/data-analytics/src/analytics-app/App.tsx
@@ -1,4 +1,4 @@
-import { ArrowDown, ArrowUp, Boxes, Camera, ChartArea, ChartBar, ChartColumn, ChartColumnStacked, ChartLine, ChartNoAxesColumn, ChartNoAxesCombined, ChartScatter, ChartSpline, Check, ChevronDown, ChevronLeft, ChevronRight, Copy, Database, Ellipsis, Expand, Filter as FunnelIcon, FileDown, FileText, Globe, Pencil, Presentation, RefreshCw, Table2, Tally3, Trash2, X, } from "lucide-react";
+import { ArrowDown, ArrowUp, Boxes, Camera, ChartArea, ChartBar, ChartColumn, ChartColumnStacked, ChartLine, ChartNoAxesColumn, ChartNoAxesCombined, ChartScatter, ChartSpline, Check, ChevronDown, ChevronLeft, ChevronRight, Copy, Database, Ellipsis, Expand, Filter as FunnelIcon, FileDown, FileText, Globe, Pencil, Presentation, Table2, Tally3, Trash2, X, } from "lucide-react";
 import { useCallback, useEffect, useId, useLayoutEffect, useMemo, useRef, useState } from "react";
 import { createPortal } from "react-dom";
 import { AnalyticsLayoutCanvas } from "./layout/AnalyticsLayoutCanvas";
@@ -6,13 +6,8 @@ import { isChartType as sharedIsChartType } from "./charting/chart-compatibility
 import { applyChartSpecOverride, chartEncoding, chartEncodingField, chartEncodingFields, chartEncodingLabel, chartHasEncodingSpec, chartSpecOverrideFromWidgetSpec, chartUsedFields, compatibleChartTypesFor, compatibleChartTypesForArtifactCard, withChartType } from "./charting/chart-app-helpers";
 import { ChartRenderer } from "./charting/ChartRenderer";
 import { RichMarkdown } from "./layout/RichMarkdown";
+import { calculateHorizontalScrollEdges, calculateTableSizing } from "./tables/tableSizing";
 import { copyElementAsImage, copyTextToClipboard, imageCopySuccessMessage, shouldOfferImageClipboardCopy, usePreparedImageExport } from "./imageExport";
-function resizeEditableTextarea(element) {
-if (!element)
-return;
-element.style.height = "auto";
-element.style.height = `${element.scrollHeight}px`;
-}
 function cloneSerializable(value) {
 return JSON.parse(JSON.stringify(value));
 }
@@ -21,6 +16,7 @@ const DEFAULT_REPORT_CARD_LAYOUT = "full";
 const CARD_DRAG_CANCEL_SELECTOR = ".viz-card__no-drag, button, a, input, textarea, select, [contenteditable='true'], .rich-markdown-editor, [role='button'], [role='menu'], [role='menuitem'], [role='menuitemradio']";
 const CHART_FULLSCREEN_HEIGHT = 520;
 const TABLE_CARD_PAGE_SIZE = 15;
+const SOURCE_DATA_PREVIEW_PAGE_SIZE = 10;
 const TABLE_COLUMN_DEFAULT_WIDTH = 144;
 const TABLE_COLUMN_DENSE_HORIZONTAL_PADDING = 16;
 const TABLE_COLUMN_HEADER_CHROME_WIDTH = 24;
@@ -31,17 +27,21 @@ const TABLE_COLUMN_NUMERIC_MAX_WIDTH = 136;
 const TABLE_COLUMN_SAMPLE_SIZE = 50;
 const TABLE_COLUMN_TEXT_MAX_WIDTH = 220;
 const TABLE_COLUMN_LONG_TEXT_MAX_WIDTH = 260;
+const TABLE_COLUMN_CONTENT_FIT_MAX_WIDTH = 240;
+const TABLE_COLUMN_CONTENT_FIT_TARGET_LENGTH = 28;
+const EMPTY_HORIZONTAL_SCROLL_EDGES = {
+canScrollLeft: false,
+canScrollRight: false,
+hasOverflow: false,
+scrollbarBlockSize: 0,
+scrollbarInlineSize: 0
+};
 const EMPTY_ACCESS_ISSUES = [];
 const EMPTY_CARDS = [];
 const EMPTY_CHARTS = [];
 const EMPTY_FILTERS = [];
 const EMPTY_REPORT_BLOCKS = [];
 const EMPTY_TABLES = [];
-const KPI_COLUMN_BREAKPOINTS = [
-{ minWidth: 760, columns: 3 },
-{ minWidth: 520, columns: 2 },
-{ minWidth: 392, columns: 2 }
-];
 function getHeatmapFill(intensity) {
 const index = clamp(Math.floor(intensity * heatmapBlueScale.length), 0, heatmapBlueScale.length - 1);
 return heatmapBlueScale[index];
@@ -165,12 +165,35 @@ return (<svg aria-hidden="true" className={className} fill="none" height="21" vi
 <path clipRule="evenodd" d="M10.6 2.53955C14.9713 2.53955 18.515 6.08326 18.515 10.4546C18.515 14.8259 14.9713 18.3696 10.6 18.3696C6.22864 18.3696 2.68494 14.8259 2.68494 10.4546C2.68494 6.08326 6.22864 2.53955 10.6 2.53955ZM10.6 3.86963C6.96318 3.86963 4.01501 6.81779 4.01501 10.4546C4.01501 14.0914 6.96318 17.0396 10.6 17.0396C14.2368 17.0396 17.1849 14.0914 17.1849 10.4546C17.1849 6.81779 14.2368 3.86963 10.6 3.86963Z" fill="currentColor" fillRule="evenodd"/>
 </svg>);
 }
+const TOOLTIP_OPEN_DELAY_MS = 300;
 function MetricDescriptionPopover({ description, label }) {
 const tooltipId = useId();
 const buttonRef = useRef(null);
 const popoverRef = useRef(null);
+const openTimerRef = useRef(null);
 const [isOpen, setIsOpen] = useState(false);
 const [popoverStyle, setPopoverStyle] = useState(null);
+const clearOpenTimer = useCallback(() => {
+if (openTimerRef.current == null)
+return;
+window.clearTimeout(openTimerRef.current);
+openTimerRef.current = null;
+}, []);
+const openImmediately = useCallback(() => {
+clearOpenTimer();
+setIsOpen(true);
+}, [clearOpenTimer]);
+const openAfterDelay = useCallback(() => {
+clearOpenTimer();
+openTimerRef.current = window.setTimeout(() => {
+openTimerRef.current = null;
+setIsOpen(true);
+}, TOOLTIP_OPEN_DELAY_MS);
+}, [clearOpenTimer]);
+const closePopover = useCallback(() => {
+clearOpenTimer();
+setIsOpen(false);
+}, [clearOpenTimer]);
 const updatePopoverPosition = useCallback(() => {
 if (typeof window === "undefined")
 return;
@@ -213,10 +236,11 @@ window.removeEventListener("resize", updatePopoverPosition);
 window.removeEventListener("scroll", updatePopoverPosition, true);
 };
 }, [isOpen, updatePopoverPosition]);
+useEffect(() => clearOpenTimer, [clearOpenTimer]);
 const renderedPopoverStyle = popoverStyle
 ? { left: popoverStyle.left, maxWidth: popoverStyle.maxWidth, top: popoverStyle.top }
 : { left: 0, maxWidth: "min(320px, calc(100vw - 24px))", top: 0, visibility: "hidden" };
-return (<span className="kpi-info-wrap" onBlur={() => setIsOpen(false)} onFocus={() => setIsOpen(true)} onMouseEnter={() => setIsOpen(true)} onMouseLeave={() => setIsOpen(false)}>
+return (<span className="kpi-info-wrap" onBlur={closePopover} onFocus={openImmediately} onMouseEnter={openAfterDelay} onMouseLeave={closePopover}>
 <button aria-describedby={isOpen ? tooltipId : undefined} aria-label={`${label}: ${description}`} className="kpi-info" ref={buttonRef} type="button">
 <MetricInfoIcon className="kpi-info-icon"/>
 </button>
@@ -334,6 +358,11 @@ const isNumericColumn = isNumericTableColumn(column, sampleRows);
 const isDateColumn = /(date|day|month|quarter|week)/.test(columnKey);
 const horizontalPadding = TABLE_COLUMN_HEADER_CHROME_WIDTH + (density === "dense" ? TABLE_COLUMN_DENSE_HORIZONTAL_PADDING : 0);
 const charWidth = isNumericColumn ? 7.2 : 7.4;
+if (column.sizing === "content") {
+const targetLength = Math.min(Math.max(labelLength, p90Length), TABLE_COLUMN_CONTENT_FIT_TARGET_LENGTH);
+const measuredContentWidth = Math.ceil(targetLength * charWidth + horizontalPadding);
+return clamp(measuredContentWidth, TABLE_COLUMN_MIN_WIDTH, TABLE_COLUMN_CONTENT_FIT_MAX_WIDTH);
+}
 const targetLength = isLongTextColumn
 ? Math.min(Math.max(labelLength + 4, p90Length), 44)
 : Math.max(labelLength, p90Length);
@@ -526,11 +555,14 @@ return layout ?? DEFAULT_DASHBOARD_CARD_LAYOUT;
 function reportCardLayout(layout) {
 return layout ?? DEFAULT_REPORT_CARD_LAYOUT;
 }
+function metricCardBlockId(blockId, cardId) {
+return `metric:${blockId}:${cardId}`;
+}
 function contentLayoutKey(manifest) {
 if (!manifest)
 return null;
 const base = `${manifest.title}:${manifest.generatedAt}`;
-return `datascience-dashboard:content-layout:${base}`;
+return `datascience-dashboard:content-layout:v2:${base}`;
 }
 function chartTextKey(manifest) {
 if (!manifest)
@@ -603,15 +635,20 @@ if (!manifest)
 return null;
 const base = `${manifest.title}:${manifest.generatedAt}`;
 const blockSignature = (manifest.blocks ?? [])
-.map((block) => `${block.id}:${reportCardLayout(block.layout)}`)
+.map((block) => `${block.id}:${reportCardLayout(block.layout)}:${(block.cardIds ?? []).join("|")}`)
 .join(",");
-return `datascience-report:content-layout:v3:${base}:${blockSignature}`;
+return `datascience-report:content-layout:v4:${base}:${blockSignature}`;
 }
 function sourceForChart(chart, sources) {
 if (chart?.source && typeof chart.source === "object")
 return chart.source;
 return sources.find((source) => source.id === chart.sourceId) ?? null;
 }
+function sourceForCard(card, sources) {
+if (card?.source && typeof card.source === "object")
+return card.source;
+return sources.find((source) => source.id === card?.sourceId) ?? null;
+}
 function sourceForTable(table, sources) {
 if (table?.source && typeof table.source === "object")
 return table.source;
@@ -727,37 +764,69 @@ const fallbackMetrics = metricDefinitions.length
 .filter((column) => ["number", "percent", "currency"].includes(column.format ?? column.type ?? ""))
 .map((column) => `${column.label ?? column.field}: displayed from ${column.field}`)
 .slice(0, 6);
-const filterRows = filters.length ? filters : activeFilters && activeFilters !== "None" ? [activeFilters] : ["None declared"];
+const selectedFilterRows = typeof activeFilters === "string"
+? activeFilters.split(/,\s+(?=[^,:]+:\s)/).map((filter) => filter.trim()).filter(Boolean)
+: [];
+const filterRows = filters.length
+? filters
+: activeFilters && activeFilters !== "None" && selectedFilterRows.length
+? selectedFilterRows
+: ["None declared"];
 const tableRows = tables.length ? tables : ["Not declared"];
 const snapshotValue = sourceQuery?.executed_at ?? sourceQueryFromSourceSpec(source)?.executed_at ?? snapshot?.generatedAt ?? "Not declared";
-const primarySource = tableRows[0] === "Not declared" ? dataset ?? "reviewed rows" : tableRows[0];
-const summary = `This block uses ${primarySource} for dataset ${dataset ?? "unknown"}.${filterRows[0] !== "None declared" ? ` Filters: ${filterRows.slice(0, 2).join("; ")}.` : ""} Use the source query below to inspect the exact logic.`;
 return {
 dataset: dataset ?? "Not declared",
 fields: columns
 .map((column) => column.field ?? column.key ?? column.label)
 .filter(Boolean),
 filters: filterRows,
-metricDefinitions: fallbackMetrics.length ? fallbackMetrics : ["Displayed directly from source columns"],
+metricDefinitions: fallbackMetrics.length ? fallbackMetrics : ["Metric values: displayed directly from source columns"],
 snapshot: snapshotValue,
-summary,
 tables: tableRows
 };
 }
-function detailText(values, fallback = "Not declared") {
-const items = Array.isArray(values) ? values.filter(Boolean) : [values].filter(Boolean);
-return items.length ? items.join("; ") : fallback;
+function Badge({ children, className = "" }) {
+return <span className={`chip ${className}`.trim()}>{children}</span>;
+}
+function sourceMetadataValue(value, fallback = "Not declared") {
+return String(value ?? "").trim() || fallback;
 }
-function detailList(values, fallback = "Not declared") {
+function sourceMetadataChips(values, { fallback = "Not declared" } = {}) {
 const items = Array.isArray(values) ? values.filter(Boolean) : [values].filter(Boolean);
-if (!items.length)
-return fallback;
-return (<div className="source-detail-list">
-{items.map((item, index) => <div className="source-detail-list-item" key={`${index}-${item}`}>
+const visibleItems = items.length ? items : [fallback];
+return (<div className="source-metadata-chip-list">
+{visibleItems.map((item, index) => <Badge className="source-metadata-chip" key={`${index}-${item}`}>
 {item}
-</div>)}
+</Badge>)}
 </div>);
 }
+function metricDefinitionParts(value) {
+const text = String(value ?? "").trim();
+const delimiterMatch = text.match(/^(.+?)(:\s*|\s+[=\u2013\u2014]\s+)(.+)$/);
+if (delimiterMatch) {
+return { definition: delimiterMatch[3], term: delimiterMatch[1] };
+}
+const proseMatch = text.match(/^(.+?)(\s+(?:are|is|equals|uses?|means|measures|represents|counts|includes|excludes)\s+)(.+)$/i);
+if (proseMatch) {
+return { definition: `${proseMatch[2].trim()} ${proseMatch[3]}`, term: proseMatch[1] };
+}
+return { definition: text, term: "Definition" };
+}
+function metricDefinitionRows(values, fallback = "Not declared") {
+const items = Array.isArray(values) ? values.filter(Boolean) : [values].filter(Boolean);
+return (items.length ? items : [fallback]).map((item) => {
+const definition = metricDefinitionParts(item);
+return { definition: definition.definition, metric: definition.term };
+});
+}
+const SOURCE_METRIC_DEFINITION_COLUMNS = [
+{ field: "metric", label: "Metric", sizing: "content", type: "text" },
+{ field: "definition", label: "Definition", type: "text" }
+];
+function sourceSnapshotDate(value) {
+const formatted = formatDate(value);
+return formatted === "Unknown" || formatted === "Not declared" ? "Not declared" : formatted;
+}
 function compareTableValues(a, b, field, direction) {
 const aValue = a[field];
 const bValue = b[field];
@@ -779,25 +848,35 @@ sensitivity: "base"
 }
 return direction === "asc" ? result : -result;
 }
+function tableDefaultSort(table) {
+const defaultSort = table?.defaultSort;
+if (!defaultSort || !table.columns.some((column) => column.field === defaultSort.field))
+return null;
+return {
+field: defaultSort.field,
+direction: defaultSort.direction === "desc" ? "desc" : "asc"
+};
+}
 function MetricBadge({ metric, row }) {
 const value = row[metric.field];
 const renderedValue = formatMetricValue(value, metric);
 if (!renderedValue)
 return null;
 const direction = metricMovementDirection(value, metric.signed === true);
-return (<div className={`metric-badge ${direction ?? "neutral"}`}>
+return (<Badge className={`metric-badge ${direction ?? "neutral"}`}>
 <span className="metric-badge-label">{metric.label}</span>
 <span className="metric-badge-value">{renderedValue}</span>
-</div>);
+</Badge>);
 }
-function KpiCard({ card, filters, selectedFilters, snapshot }) {
+function KpiCard({ action, card, className = "", filters, id, selectedFilters, snapshot }) {
 const metrics = cardMetrics(card);
 const [primaryMetric, ...supportingMetrics] = metrics;
 const rows = filterRowsForDataset(getRows(snapshot, card.dataset), card.dataset, filters, selectedFilters, metrics.map((metric) => metric.field));
 const row = rows.find((candidate) => rowMatchesFilter(candidate, card.filter)) ?? {};
 const label = cardLabel(card);
 const description = card.description?.trim();
-return (<section className="kpi-card">
+return (<section className={`kpi-card ${className}`.trim()} id={id}>
+{action}
 <div className="kpi-label-row">
 <div className="kpi-label">{label}</div>
 {description ? <MetricDescriptionPopover description={description} label={label}/> : null}
@@ -808,10 +887,6 @@ return (<section className="kpi-card">
 </div>) : null}
 </section>);
 }
-function getKpiColumnCount(width, cardCount) {
-const breakpoint = KPI_COLUMN_BREAKPOINTS.find((item) => width >= item.minWidth);
-return Math.max(1, Math.min(cardCount, breakpoint?.columns ?? 1));
-}
 function useMeasuredElementSize() {
 const ref = useRef(null);
 const [size, setSize] = useState({ height: 0, width: 0 });
@@ -820,7 +895,7 @@ const element = ref.current;
 if (!element)
 return;
 let frame = 0;
-const updateWidth = () => {
+const updateSize = () => {
 window.cancelAnimationFrame(frame);
 frame = window.requestAnimationFrame(() => {
 const rect = element.getBoundingClientRect();
@@ -830,31 +905,18 @@ width: Math.floor(rect.width)
 });
 });
 };
-updateWidth();
-const observer = new ResizeObserver(updateWidth);
+updateSize();
+const observer = new ResizeObserver(updateSize);
 observer.observe(element);
-window.addEventListener("resize", updateWidth);
+window.addEventListener("resize", updateSize);
 return () => {
 window.cancelAnimationFrame(frame);
 observer.disconnect();
-window.removeEventListener("resize", updateWidth);
+window.removeEventListener("resize", updateSize);
 };
 }, []);
 return [ref, size];
 }
-function KpiStrip({ cards, filters, selectedFilters, snapshot }) {
-const [stripRef, stripSize] = useMeasuredElementSize();
-const fallbackWidth = typeof window === "undefined" ? 0 : window.innerWidth;
-const columns = getKpiColumnCount(stripSize.width || fallbackWidth, cards.length);
-const placeholderCount = (columns - (cards.length % columns)) % columns;
-const stripStyle = { "--kpi-columns": columns };
-if (!cards.length)
-return null;
-return (<section className={`kpi-strip kpi-columns-${columns}`} style={stripStyle} aria-label="Key metrics" ref={stripRef}>
-{cards.map((card) => (<KpiCard card={card} filters={filters} key={card.id} selectedFilters={selectedFilters} snapshot={snapshot}/>))}
-{Array.from({ length: placeholderCount }, (_, index) => (<div aria-hidden="true" className="kpi-card kpi-card-placeholder" key={`kpi-placeholder-${index}`}/>))}
-</section>);
-}
 const MENU_CLOSE_ANIMATION_MS = 100;
 const NARROW_FIXED_MENU_QUERY = "(max-width: 560px)";
 function useDashboardMenu(isOpen, onOpenChange) {
@@ -1095,12 +1157,7 @@ return (<div className="panel-header">
 }
 function EditablePageTitle({ ariaLabel, isEditMode, onChange, onRequestEditMode, placeholder, readOnly = false, title }) {
 const [isEditing, setIsEditing] = useState(false);
-const textareaRef = useRef(null);
 const displayTitle = title.trim() ? title : placeholder;
-useEffect(() => {
-if (isEditing)
-resizeEditableTextarea(textareaRef.current);
-}, [isEditing, title]);
 function startEditing() {
 if (readOnly)
 return;
@@ -1114,10 +1171,12 @@ return (<div className="page-title-edit-target page-title-readonly">
 </div>);
 }
 if (isEditing) {
-return (<textarea aria-label={ariaLabel} autoFocus className="page-title-editor viz-card__no-drag" onBlur={() => setIsEditing(false)} onChange={(event) => {
-resizeEditableTextarea(event.currentTarget);
-onChange(event.currentTarget.value);
-}} placeholder={placeholder} ref={textareaRef} rows={1} value={title}/>);
+return (<input aria-label={ariaLabel} autoFocus className="page-title-editor viz-card__no-drag" onBlur={() => setIsEditing(false)} onChange={(event) => onChange(event.currentTarget.value)} onKeyDown={(event) => {
+if (event.key === "Enter") {
+event.preventDefault();
+event.currentTarget.blur();
+}
+}} placeholder={placeholder} type="text" value={title}/>);
 }
 return (<div aria-label={ariaLabel} className="page-title-edit-target viz-card__no-drag" data-page-title-edit-mode={isEditMode ? "true" : "false"} onClick={() => {
 if (isEditMode)
@@ -1203,6 +1262,7 @@ return first?.trim() || fallback;
 }
 function ChartBody({ accessIssue, chart, filters, isFullscreen = false, selectedFilters, snapshot }) {
 const [visibleSeries, setVisibleSeries] = useState();
+const [chartBodyRef, chartBodySize] = useMeasuredElementSize();
 const rawRows = filterRowsForDataset(getRows(snapshot, chart.dataset), chart.dataset, filters, selectedFilters, chartUsedFields(chart));
 if (accessIssue) {
 return (<div className="permission-card">
@@ -1216,7 +1276,9 @@ return (<div className="permission-card">
 if (!rawRows.length) {
 return <div className="empty-state">No rows match the selected filters.</div>;
 }
-return (<ChartRenderer chart={chart} height={isFullscreen ? CHART_FULLSCREEN_HEIGHT : undefined} onVisibleSeriesChange={setVisibleSeries} rows={rawRows} surface={isFullscreen ? "explorer" : "card"} visibleSeries={visibleSeries}/>);
+return (<div className="chart-body-measure" ref={chartBodyRef}>
+<ChartRenderer chart={chart} height={isFullscreen ? CHART_FULLSCREEN_HEIGHT : undefined} onVisibleSeriesChange={setVisibleSeries} rows={rawRows} surface={isFullscreen ? "explorer" : "card"} visibleSeries={visibleSeries} width={chartBodySize.width || undefined}/>
+</div>);
 }
 function VizCard({ accessIssue, chart, children, isEditMode, isMenuOpen, layout, onCopyResult, onDeleteBlock, onRequestEditMode, onTextChange, onMenuOpenChange, onModalOpen, textOverride }) {
 const cardRef = useRef(null);
@@ -1547,41 +1609,112 @@ nodes.push(sql.slice(cursor));
 return nodes;
 }
 const SOURCE_FETCH_TIMEOUT_MS = 10000;
-function DataSourceDetails({ children, details, source, sourceQuery }) {
-return (<div className="source-modal-sections">
-<section className="source-modal-section">
-<h3>Details</h3>
-<dl className="details-grid source-details-grid">
+function Tabs({ ariaLabel, onSelect, selectedKey, tabs }) {
+const listRef = useRef(null);
+const indicatorRef = useRef(null);
+const updateIndicator = useCallback(() => {
+const list = listRef.current;
+const indicator = indicatorRef.current;
+const activeTab = list?.querySelector('.source-modal-tab[aria-selected="true"]');
+if (!(list instanceof HTMLElement) || !(activeTab instanceof HTMLElement) || !(indicator instanceof HTMLElement))
+return;
+const listRect = list.getBoundingClientRect();
+const tabRect = activeTab.getBoundingClientRect();
+indicator.style.width = `${tabRect.width}px`;
+indicator.style.transform = `translate3d(${tabRect.left - listRect.left + list.scrollLeft}px, 0, 0)`;
+indicator.dataset.ready = "true";
+}, [selectedKey]);
+useLayoutEffect(() => {
+updateIndicator();
+const list = listRef.current;
+if (!(list instanceof HTMLElement) || typeof ResizeObserver !== "function")
+return undefined;
+const observer = new ResizeObserver(updateIndicator);
+observer.observe(list);
+list.querySelectorAll(".source-modal-tab").forEach((tab) => observer.observe(tab));
+return () => observer.disconnect();
+}, [tabs, updateIndicator]);
+function handleKeyDown(event) {
+const currentIndex = tabs.findIndex((tab) => tab.id === selectedKey);
+let nextIndex = currentIndex;
+if (event.key === "ArrowRight")
+nextIndex = (currentIndex + 1) % tabs.length;
+else if (event.key === "ArrowLeft")
+nextIndex = (currentIndex - 1 + tabs.length) % tabs.length;
+else if (event.key === "Home")
+nextIndex = 0;
+else if (event.key === "End")
+nextIndex = tabs.length - 1;
+else
+return;
+event.preventDefault();
+const nextTab = tabs[nextIndex];
+if (!nextTab)
+return;
+onSelect(nextTab.id);
+window.requestAnimationFrame(() => document.getElementById(nextTab.tabId)?.focus());
+}
+return (<div aria-label={ariaLabel} aria-orientation="horizontal" className="source-modal-tabs" onKeyDown={handleKeyDown} ref={listRef} role="tablist">
+{tabs.map((tab) => {
+const isSelected = selectedKey === tab.id;
+return (<div className="source-modal-tab-item" key={tab.id}>
+<button aria-controls={tab.panelId} aria-pressed={isSelected} aria-selected={isSelected} className="source-modal-tab" id={tab.tabId} onClick={() => onSelect(tab.id)} role="tab" tabIndex={isSelected ? 0 : -1} type="button">
+{tab.label}
+</button>
+</div>);
+})}
+<span aria-hidden="true" className="source-modal-tab-indicator" ref={indicatorRef}/>
+</div>);
+}
+function DataSourceDetails({ children, details, itemLabel, itemType, source, sourceQuery }) {
+const [activeTab, setActiveTab] = useState("overview");
+const tabId = useId();
+const tabs = [
+{ id: "overview", label: "Overview", panelId: `${tabId}-overview-panel`, tabId: `${tabId}-overview-tab` },
+{ id: "data", label: "Data preview", panelId: `${tabId}-data-panel`, tabId: `${tabId}-data-tab` },
+{ id: "sql", label: "SQL query", panelId: `${tabId}-sql-panel`, tabId: `${tabId}-sql-tab` }
+];
+return (<div className="source-modal-content">
+<Tabs ariaLabel="Data source sections" onSelect={setActiveTab} selectedKey={activeTab} tabs={tabs}/>
+<div className="source-modal-body">
+{activeTab === "overview" ? (<section aria-labelledby={`${tabId}-overview-tab`} className="source-modal-tab-panel source-overview" id={`${tabId}-overview-panel`} role="tabpanel">
+<div className="source-details-layout">
+<dl className="source-details-summary">
 <div>
-<dt>Dataset</dt>
-<dd>{details.dataset}</dd>
+<dt>{itemType}</dt>
+<dd>{itemLabel}</dd>
 </div>
 <div>
-<dt>Tables used</dt>
-<dd>{detailList(details.tables)}</dd>
+<dt>Dataset</dt>
+<dd>{sourceMetadataValue(details.dataset)}</dd>
 </div>
 <div>
-<dt>Filters</dt>
-<dd>{detailText(details.filters)}</dd>
+<dt>Data snapshot</dt>
+<dd>{sourceSnapshotDate(details.snapshot)}</dd>
 </div>
+</dl>
+<dl className="source-details-stack">
 <div>
-<dt>Metric definitions</dt>
-<dd>{detailList(details.metricDefinitions)}</dd>
+<dt>Tables used</dt>
+<dd>{sourceMetadataChips(details.tables)}</dd>
 </div>
 <div>
-<dt>Snapshot</dt>
-<dd>{formatDate(details.snapshot)}</dd>
+<dt>Filters</dt>
+<dd>{sourceMetadataChips(details.filters)}</dd>
 </div>
 </dl>
-</section>
-<section className="source-modal-section">
-<h3>Source query</h3>
-<SourceQueryBlock source={source} sourceQuery={sourceQuery}/>
-</section>
-<section className="source-modal-section">
-<h3>Data table</h3>
+<div className="source-metric-definitions">
+<SourceDataTable columns={SOURCE_METRIC_DEFINITION_COLUMNS} dataset="__source_metric_definitions" density="spacious" fillAvailableWidth rows={metricDefinitionRows(details.metricDefinitions)}/>
+</div>
+</div>
+</section>) : null}
+{activeTab === "data" ? (<section aria-labelledby={`${tabId}-data-tab`} className="source-modal-tab-panel source-data" id={`${tabId}-data-panel`} role="tabpanel">
 {children}
-</section>
+</section>) : null}
+{activeTab === "sql" ? (<section aria-labelledby={`${tabId}-sql-tab`} className="source-modal-tab-panel source-sql" id={`${tabId}-sql-panel`} role="tabpanel">
+<SourceQueryBlock source={source} sourceQuery={sourceQuery}/>
+</section>) : null}
+</div>
 </div>);
 }
 function SourceQueryBlock({ source, sourceQuery }) {
@@ -1667,7 +1800,7 @@ return (<div className="source-query-shell">
 {source.path ?? source.label}
 </a>) : (source?.path ?? source?.label ?? sourceQuery?.id ?? sourceQuery?.engine)}
 </span>
-<button className="source-query-copy" onClick={() => void handleCopyQuery(displaySourceText || sourceContent.text)} type="button">
+<button aria-live="polite" className="source-query-copy" onClick={() => void handleCopyQuery(displaySourceText || sourceContent.text)} type="button">
 <Copy aria-hidden="true" size={14} strokeWidth={2}/>
 {copyStatus === "copied"
 ? "Copied"
@@ -1712,7 +1845,7 @@ fields.push(field);
 }
 return fields.map((field) => ({ field, label: field }));
 }
-function SourceDataTable({ columns = [], dataset, density = "dense", rows = [] }) {
+function SourceDataTable({ columns = [], dataset, density = "dense", fillAvailableWidth = true, pageSize = TABLE_CARD_PAGE_SIZE, rows = [] }) {
 const previewRows = useMemo(() => asArray(rows).filter((row) => row && typeof row === "object"), [rows]);
 const previewDataset = dataset ?? "__source_preview_rows";
 const table = useMemo(() => ({
@@ -1726,7 +1859,7 @@ useEffect(() => {
 setColumnWidths({});
 }, [table.id]);
 return (<div className="source-data-table">
-<TableContent allowColumnResize={false} columnWidths={columnWidths} density={density} filters={EMPTY_FILTERS} onColumnWidthsChange={(_tableId, nextWidths) => setColumnWidths(nextWidths)} selectedFilters={{}} snapshot={snapshot} table={table}/>
+<TableContent allowColumnResize={false} columnWidths={columnWidths} density={density} fillAvailableWidth={fillAvailableWidth} filters={EMPTY_FILTERS} onColumnWidthsChange={(_tableId, nextWidths) => setColumnWidths(nextWidths)} pageSize={pageSize} selectedFilters={{}} snapshot={snapshot} table={table}/>
 </div>);
 }
 function querySourceForChart(chart, sources) {
@@ -1782,14 +1915,56 @@ event.currentTarget.close();
 <div className="modal-header">
 <div>
 <h2 id="chart-source-modal-title">Data source</h2>
-<p>{chart.title}</p>
 </div>
 <button aria-label="Close data source" className="modal-close-button" onClick={() => dialogRef.current?.close()} type="button">
 <X aria-hidden="true" size={20} strokeWidth={2}/>
 </button>
 </div>
-<DataSourceDetails details={buildDetails} source={source} sourceQuery={sourceQuery}>
-<SourceDataTable dataset={chart.dataset} rows={rows}/>
+<DataSourceDetails details={buildDetails} itemLabel={chart.title} itemType="Chart" source={source} sourceQuery={sourceQuery}>
+<SourceDataTable dataset={chart.dataset} pageSize={SOURCE_DATA_PREVIEW_PAGE_SIZE} rows={rows}/>
+</DataSourceDetails>
+</section>
+</dialog>);
+}
+function CardSourceModalDialog({ activeFilters, card, filters = EMPTY_FILTERS, manifest, onClose, rows: providedRows, selectedFilters = {}, snapshot }) {
+const dialogRef = useRef(null);
+const metrics = cardMetrics(card);
+const source = sourceForCard(card, manifest?.sources ?? []);
+const sourceQuery = sourceQueryFromSourceSpec(source);
+const rows = providedRows ?? filterRowsForDataset(getRows(snapshot, card.dataset), card.dataset, filters, selectedFilters, metrics.map((metric) => metric.field))
+.filter((row) => rowMatchesFilter(row, card.filter));
+const buildDetails = sourceBuildDetails({
+activeFilters,
+dataset: card.dataset,
+metrics,
+source,
+sourceQuery,
+snapshot
+});
+useModalScrollLock(true);
+useEffect(() => {
+const dialog = dialogRef.current;
+if (dialog && !dialog.open) {
+dialog.showModal();
+}
+}, []);
+const label = cardLabel(card);
+return (<dialog aria-labelledby="card-source-modal-title" className="native-modal source-modal" onCancel={onClose} onClick={(event) => {
+if (event.target === event.currentTarget) {
+event.currentTarget.close();
+}
+}} onClose={onClose} ref={dialogRef}>
+<section className="modal-panel source-modal-panel">
+<div className="modal-header">
+<div>
+<h2 id="card-source-modal-title">Data source</h2>
+</div>
+<button aria-label="Close data source" className="modal-close-button" onClick={() => dialogRef.current?.close()} type="button">
+<X aria-hidden="true" size={20} strokeWidth={2}/>
+</button>
+</div>
+<DataSourceDetails details={buildDetails} itemLabel={label} itemType="Metric" source={source} sourceQuery={sourceQuery}>
+<SourceDataTable dataset={card.dataset} pageSize={SOURCE_DATA_PREVIEW_PAGE_SIZE} rows={rows}/>
 </DataSourceDetails>
 </section>
 </dialog>);
@@ -1957,7 +2132,7 @@ if (data.type === "datascience-chart-widget-spec-change" && data.widgetInstanceI
 onChartSpecChange(chart.id, data.visualization_spec);
 }
 if (data.type === "datascience-chart-widget-codex-prompt" && typeof data.prompt === "string") {
-sendCodexPromptToHost(data.prompt, null);
+void sendCodexPromptToHost(data.prompt, "Continue chart analysis");
 }
 }
 window.addEventListener("message", handleWidgetMessage);
@@ -1994,10 +2169,12 @@ const field = chartEncodingField(chart, role);
 if (!field)
 return null;
 const encoding = chartEncoding(chart, role);
+const format = encoding.format ?? (role === "y" ? chart.valueFormat : undefined);
 return {
 key: field,
 label: chartEncodingLabel(chart, role, field),
 type: encoding.type === "temporal" ? "date" : encoding.type === "quantitative" ? "number" : chartWidgetColumnType(encoding, rows[0]?.[field]),
+...(format ? { format } : {}),
 unit: encoding.unit
 };
 }
@@ -2032,7 +2209,10 @@ const sourceQuery = chartSourceQuery ?? sourceSpecQuery;
 const queryText = queryTextFromSourceQuery(chartSourceQuery) ||
 queryTextFromSourceQuery(sourceSpecQuery) ||
 sourceQueryText?.trim();
-const unit = chart.unit ?? chartEncoding(chart, "y").unit ?? (chart.valueFormat === "currency" ? "USD" : chart.valueFormat === "percent" ? "%" : undefined);
+const yEncoding = chartEncoding(chart, "y");
+const yFormat = yEncoding.format ?? chart.valueFormat;
+const yFormatSpec = yFormat ? { format: yFormat } : {};
+const unit = chart.unit ?? yEncoding.unit ?? (yFormat === "currency" ? "USD" : yFormat === "percent" ? "%" : undefined);
 const settings = chartWidgetSettings(chart);
 if (xField && yField) {
 const columns = [
@@ -2058,7 +2238,7 @@ intent: chart.intent ?? "custom",
 visualization_type: chart.type,
 encodings: {
 x: { field: xField, type: chartEncoding(chart, "x").type ?? "nominal" },
-y: { aggregate: chartEncoding(chart, "y").aggregate ?? "sum", field: yField, type: "quantitative", unit },
+y: { aggregate: yEncoding.aggregate ?? "sum", field: yField, type: "quantitative", ...yFormatSpec, unit },
 ...(colorField ? { color: { field: colorField, type: chartEncoding(chart, "color").type ?? "nominal" } } : {})
 },
 presentation: {
@@ -2086,7 +2266,7 @@ result_table: {
 columns: [
 { key: xField, label: chartEncodingLabel(chart, "x", xField), type: chartWidgetColumnType(chartEncoding(chart, "x"), rows[0]?.[xField]) },
 { key: "series", label: "Series", type: "text" },
-{ key: "value", label: chartEncodingLabel(chart, "y", "Value"), type: "number", unit }
+{ key: "value", label: chartEncodingLabel(chart, "y", "Value"), type: "number", ...yFormatSpec, unit }
 ],
 row_count: longRows.length,
 rows: longRows,
@@ -2098,7 +2278,7 @@ intent: chart.intent ?? "custom",
 visualization_type: chart.type,
 encodings: {
 x: { field: xField, type: chartEncoding(chart, "x").type ?? "nominal" },
-y: { aggregate: "sum", field: "value", type: "quantitative", unit },
+y: { aggregate: "sum", field: "value", type: "quantitative", ...yFormatSpec, unit },
 color: { field: "series", type: "nominal" }
 },
 presentation: {
@@ -2136,19 +2316,23 @@ if (encodedPayload)
 return encodedPayload;
 return null;
 }
-function TableContent({ allowColumnResize = true, columnWidths, density, filters, onColumnWidthsChange, selectedFilters, snapshot, table, isFullscreen = false }) {
+function TableContent({ allowColumnResize = true, columnWidths, density, fillAvailableWidth = true, filters, onColumnWidthsChange, pageSize: requestedPageSize = TABLE_CARD_PAGE_SIZE, selectedFilters, snapshot, table, isFullscreen = false }) {
 const rows = filterRowsForDataset(getRows(snapshot, table.dataset), table.dataset, filters, selectedFilters, table.columns.map((column) => column.field));
 const [page, setPage] = useState(0);
-const [sortState, setSortState] = useState(null);
+const [sortState, setSortState] = useState(() => tableDefaultSort(table));
 const [isColumnResizing, setIsColumnResizing] = useState(false);
+const [tableViewportWidth, setTableViewportWidth] = useState(0);
+const [horizontalScrollEdges, setHorizontalScrollEdges] = useState(EMPTY_HORIZONTAL_SCROLL_EDGES);
 const headerCellRefs = useRef({});
+const tableElementRef = useRef(null);
 const tableWrapRef = useRef(null);
+const tableScrollContentRef = useRef(null);
 const sortedRows = useMemo(() => {
 if (!sortState)
 return rows;
 return [...rows].sort((a, b) => compareTableValues(a, b, sortState.field, sortState.direction));
 }, [rows, sortState]);
-const pageSize = isFullscreen ? Math.max(1, sortedRows.length) : TABLE_CARD_PAGE_SIZE;
+const pageSize = isFullscreen ? Math.max(1, sortedRows.length) : Math.max(1, requestedPageSize);
 const totalPages = Math.max(1, Math.ceil(sortedRows.length / pageSize));
 const currentPage = Math.min(page, totalPages - 1);
 const shouldShowPagination = rows.length > 0 && !isFullscreen && totalPages > 1;
@@ -2165,20 +2349,96 @@ column.field,
 clamp(Math.round(columnWidths[column.field] ?? estimatedColumnWidths[column.field] ?? TABLE_COLUMN_DEFAULT_WIDTH), TABLE_COLUMN_MIN_WIDTH, TABLE_COLUMN_MAX_WIDTH)
 ]));
 }, [columnWidths, estimatedColumnWidths, table.columns]);
-const tablePixelWidth = table.columns.reduce((total, column) => total + activeColumnWidths[column.field], 0);
+const hasContentSizedColumns = table.columns.some((column) => column.sizing === "content");
+const shouldFillAvailableWidth = isFullscreen || fillAvailableWidth;
+const tableSizing = calculateTableSizing(table.columns, activeColumnWidths, tableViewportWidth, shouldFillAvailableWidth);
+const renderedColumnWidths = tableSizing.columnWidths;
 const tableStyle = {
-minWidth: `${tablePixelWidth}px`,
+minWidth: `${tableSizing.minimumTableWidth}px`,
 tableLayout: "fixed",
-width: `${tablePixelWidth}px`
+width: `${tableSizing.tableWidth}px`
 };
+const updateHorizontalScrollEdges = useCallback(() => {
+const tableWrap = tableWrapRef.current;
+const nextEdges = tableWrap
+? {
+...calculateHorizontalScrollEdges({
+clientWidth: tableWrap.clientWidth,
+scrollLeft: tableWrap.scrollLeft,
+scrollWidth: tableElementRef.current?.offsetWidth ?? tableWrap.scrollWidth
+}),
+scrollbarBlockSize: Math.max(0, tableWrap.offsetHeight - tableWrap.clientHeight),
+scrollbarInlineSize: Math.max(0, tableWrap.offsetWidth - tableWrap.clientWidth)
+}
+: EMPTY_HORIZONTAL_SCROLL_EDGES;
+setHorizontalScrollEdges((currentEdges) => currentEdges.canScrollLeft === nextEdges.canScrollLeft &&
+currentEdges.canScrollRight === nextEdges.canScrollRight &&
+currentEdges.hasOverflow === nextEdges.hasOverflow &&
+currentEdges.scrollbarBlockSize === nextEdges.scrollbarBlockSize &&
+currentEdges.scrollbarInlineSize === nextEdges.scrollbarInlineSize
+? currentEdges
+: nextEdges);
+}, []);
+function columnWidthStyle(column) {
+return {
+...(column.sizing === "content" ? { maxWidth: `${TABLE_COLUMN_CONTENT_FIT_MAX_WIDTH}px` } : {}),
+width: `${renderedColumnWidths[column.field]}px`
+};
+}
 useEffect(() => {
 setPage(0);
-}, [isFullscreen, rows.length, sortState]);
+}, [isFullscreen, requestedPageSize, rows.length, sortState]);
+useEffect(() => {
+setSortState(tableDefaultSort(table));
+}, [table.defaultSort?.direction, table.defaultSort?.field, table.id]);
 useLayoutEffect(() => {
 if (tableWrapRef.current) {
 tableWrapRef.current.scrollLeft = 0;
+updateHorizontalScrollEdges();
+}
+}, [isFullscreen, rows.length, table.id, updateHorizontalScrollEdges]);
+useLayoutEffect(() => {
+if (!shouldFillAvailableWidth || !tableWrapRef.current) {
+setTableViewportWidth(0);
+return undefined;
+}
+const tableWrap = tableWrapRef.current;
+const updateViewportWidth = () => setTableViewportWidth(Math.floor(tableWrap.clientWidth));
+updateViewportWidth();
+if (typeof ResizeObserver !== "function")
+return undefined;
+const observer = new ResizeObserver(updateViewportWidth);
+observer.observe(tableWrap);
+return () => observer.disconnect();
+}, [shouldFillAvailableWidth, table.id]);
+useLayoutEffect(() => {
+const tableWrap = tableWrapRef.current;
+const tableScrollContent = tableScrollContentRef.current;
+const tableElement = tableElementRef.current;
+if (!tableWrap) {
+setHorizontalScrollEdges(EMPTY_HORIZONTAL_SCROLL_EDGES);
+return undefined;
+}
+updateHorizontalScrollEdges();
+tableWrap.addEventListener("scroll", updateHorizontalScrollEdges, { passive: true });
+if (typeof ResizeObserver !== "function") {
+window.addEventListener("resize", updateHorizontalScrollEdges);
+return () => {
+tableWrap.removeEventListener("scroll", updateHorizontalScrollEdges);
+window.removeEventListener("resize", updateHorizontalScrollEdges);
+};
 }
-}, [isFullscreen, rows.length, table.id]);
+const observer = new ResizeObserver(updateHorizontalScrollEdges);
+observer.observe(tableWrap);
+if (tableScrollContent)
+observer.observe(tableScrollContent);
+if (tableElement)
+observer.observe(tableElement);
+return () => {
+observer.disconnect();
+tableWrap.removeEventListener("scroll", updateHorizontalScrollEdges);
+};
+}, [isFullscreen, rows.length, table.id, tableSizing.tableWidth, updateHorizontalScrollEdges]);
 function toggleSort(field) {
 setSortState((current) => {
 if (current?.field !== field)
@@ -2240,12 +2500,18 @@ event.preventDefault();
 event.stopPropagation();
 resizeColumnBy(field, event.key === "ArrowRight" ? TABLE_COLUMN_KEYBOARD_STEP : -TABLE_COLUMN_KEYBOARD_STEP, true);
 }
+const isScrollableTableRegion = isFullscreen || horizontalScrollEdges.hasOverflow;
+const tableScrollShellStyle = {
+"--table-scrollbar-block-size": `${horizontalScrollEdges.scrollbarBlockSize}px`,
+"--table-scrollbar-inline-size": `${horizontalScrollEdges.scrollbarInlineSize}px`
+};
 return (<>
-{rows.length ? (<div className={`table-wrap table-density-${density} ${isFullscreen ? "fullscreen" : ""}`} ref={tableWrapRef}>
-<div className="table-scroll-content">
-<table className={`data-table data-table-${density} data-table-resizable ${isColumnResizing ? "is-column-resizing" : ""}`} style={tableStyle}>
+{rows.length ? (<div className={`table-scroll-shell ${isFullscreen ? "is-fullscreen" : ""}`} data-can-scroll-left={horizontalScrollEdges.canScrollLeft ? "true" : "false"} data-can-scroll-right={horizontalScrollEdges.canScrollRight ? "true" : "false"} style={tableScrollShellStyle}>
+<div aria-label={isScrollableTableRegion ? `Scrollable table: ${table.title}` : undefined} className={`table-wrap table-density-${density} ${isFullscreen ? "fullscreen" : ""}`} ref={tableWrapRef} role={isScrollableTableRegion ? "region" : undefined} tabIndex={isScrollableTableRegion ? 0 : undefined}>
+<div className="table-scroll-content" ref={tableScrollContentRef}>
+<table className={`data-table data-table-${density} data-table-resizable ${hasContentSizedColumns ? "data-table-smart-layout" : ""} ${isColumnResizing ? "is-column-resizing" : ""}`} ref={tableElementRef} style={tableStyle}>
 <colgroup>
-{table.columns.map((column) => (<col key={column.field} style={{ width: `${activeColumnWidths[column.field]}px` }}/>))}
+{table.columns.map((column) => (<col className={column.sizing === "content" ? "table-column-content-fit" : undefined} key={column.field} style={columnWidthStyle(column)}/>))}
 </colgroup>
 <thead>
 <tr>
@@ -2255,13 +2521,18 @@ const format = tableColumnFormat(column);
 const isNumericColumn = isNumericTableColumn(column, rows);
 const isCenteredColumn = column.align === "center";
 const SortIcon = isSorted ? (sortState.direction === "asc" ? ArrowUp : ArrowDown) : null;
+const headerClassName = [
+isNumericColumn ? "table-header-number" : "",
+isCenteredColumn ? "center" : "",
+column.sizing === "content" ? "table-column-content-fit" : ""
+].filter(Boolean).join(" ") || undefined;
 return (<th aria-sort={isSorted
 ? sortState.direction === "asc"
 ? "ascending"
 : "descending"
 : "none"} key={column.field} ref={(element) => {
 headerCellRefs.current[column.field] = element;
-}} className={isNumericColumn ? "table-header-number" : isCenteredColumn ? "center" : undefined} style={{ width: `${activeColumnWidths[column.field]}px` }}>
+}} className={headerClassName} style={columnWidthStyle(column)}>
 <button className="table-sort-button" onClick={() => toggleSort(column.field)} type="button">
 <span>{column.label}</span>
 {SortIcon ? <SortIcon aria-hidden="true" size={14} strokeWidth={2}/> : null}
@@ -2281,6 +2552,7 @@ const isCenteredColumn = column.align === "center";
 const className = [
 isNumericColumn ? "table-cell-number" : "",
 isCenteredColumn ? "center" : "",
+column.sizing === "content" ? "table-column-content-fit" : "",
 tableCellMovementClass(column, value)
 ].filter(Boolean).join(" ") || undefined;
 return (<td className={className} key={column.field}>
@@ -2291,6 +2563,9 @@ return (<td className={className} key={column.field}>
 </tbody>
 </table>
 </div>
+</div>
+<span aria-hidden="true" className="table-scroll-edge table-scroll-edge-left" data-image-export-exclude="true"/>
+<span aria-hidden="true" className="table-scroll-edge table-scroll-edge-right" data-image-export-exclude="true"/>
 </div>) : (<div className="empty-state">No rows match the selected filters.</div>)}
 {shouldShowFooter ? (<div className="table-pagination">
 {shouldShowCount ? <span className="table-result-count">{resultCountLabel}</span> : null}
@@ -2357,19 +2632,19 @@ source,
 sourceQuery,
 snapshot
 });
-useModalScrollLock(kind === "source");
+useModalScrollLock(true);
 useEffect(() => {
 const dialog = dialogRef.current;
 if (dialog && !dialog.open) {
 dialog.showModal();
 }
 }, []);
-return (<dialog aria-labelledby="table-modal-title" className={`native-modal ${kind === "source" ? "source-modal" : ""}`.trim()} onCancel={onClose} onClick={(event) => {
+return (<dialog aria-labelledby="table-modal-title" className={`native-modal ${kind === "source" ? "source-modal" : "table-fullscreen-modal"}`.trim()} onCancel={onClose} onClick={(event) => {
 if (event.target === event.currentTarget) {
 event.currentTarget.close();
 }
 }} onClose={onClose} ref={dialogRef}>
-<section className={`modal-panel ${kind === "source" ? "source-modal-panel" : ""}`.trim()}>
+<section className={`modal-panel ${kind === "source" ? "source-modal-panel" : "table-fullscreen-panel"}`.trim()}>
 <div className="modal-header">
 <div>
 <h2 id="table-modal-title">{title}</h2>
@@ -2379,8 +2654,8 @@ event.currentTarget.close();
 <X aria-hidden="true" size={20} strokeWidth={2}/>
 </button>
 </div>
-{kind === "fullscreen" ? (<TableContent columnWidths={columnWidths} density={table.density ?? (manifest?.surface === "report" ? "spacious" : "dense")} filters={filters} isFullscreen onColumnWidthsChange={onColumnWidthsChange} selectedFilters={selectedFilters} snapshot={snapshot} table={table}/>) : (<DataSourceDetails details={buildDetails} source={source} sourceQuery={sourceQuery}>
-<SourceDataTable columns={table.columns} dataset={table.dataset} density={table.density ?? (manifest?.surface === "report" ? "spacious" : "dense")} rows={previewRows}/>
+{kind === "fullscreen" ? (<TableContent columnWidths={columnWidths} density={table.density ?? (manifest?.surface === "report" ? "spacious" : "dense")} filters={filters} isFullscreen onColumnWidthsChange={onColumnWidthsChange} selectedFilters={selectedFilters} snapshot={snapshot} table={table}/>) : (<DataSourceDetails details={buildDetails} itemLabel={table.title} itemType="Table" source={source} sourceQuery={sourceQuery}>
+<SourceDataTable columns={table.columns} dataset={table.dataset} density={table.density ?? (manifest?.surface === "report" ? "spacious" : "dense")} pageSize={SOURCE_DATA_PREVIEW_PAGE_SIZE} rows={previewRows}/>
 </DataSourceDetails>)}
 </section>
 </dialog>);
@@ -2520,61 +2795,31 @@ toggleMenu();
 {isEditMode ? (<textarea aria-label={`Edit HTML for ${title}`} className="report-html-editor" onChange={(event) => onHtmlChange(block.id, { html: event.target.value })} spellCheck={false} value={html}/>) : (<iframe className="report-html-frame" onLoad={handleFrameLoad} ref={frameRef} sandbox="allow-same-origin" srcDoc={sandboxedReportHtml(html)} style={frameHeight ? { height: `${frameHeight}px` } : undefined} title={title}/>)}
 </section>);
 }
-function ReportMetricStripBlock({ cards, filters, id, isMenuOpen, onCopyResult, onDeleteBlock, onMenuOpenChange, selectedFilters, snapshot }) {
-const cardRef = useRef(null);
+function ArtifactMetricCard({ card, filters, id, isMenuOpen, onDeleteBlock, onMenuOpenChange, onSourceOpen, selectedFilters, snapshot }) {
 const { closeMenu, fixedMenuStyle, handleMenuButtonKeyDown, handleMenuKeyDown, menuButtonRef, menuMotionClass, menuRef, toggleMenu, shouldRenderMenu } = useDashboardMenu(isMenuOpen, onMenuOpenChange);
-const { getPreparedImageBlob, preparedImageExportStatus, prepareImageExport, resetPreparedImageExport } = usePreparedImageExport(cardRef);
-function prepareImageExportQuietly(force = false) {
-if (!shouldOfferImageClipboardCopy())
-return;
-const prepared = prepareImageExport({ force });
-if (prepared)
-void prepared.promise.catch(() => undefined);
-}
-async function handleCopyAsImage() {
-if (!cardRef.current)
-return;
-try {
-const copyResult = await copyElementAsImage(cardRef.current, getPreparedImageBlob());
-resetPreparedImageExport();
-onCopyResult(imageCopySuccessMessage("Copied key metrics as image.", copyResult));
-}
-catch (error) {
-onCopyResult(error instanceof Error ? error.message : "Failed to copy image.", true);
-}
-}
-const menuItem = (label, icon, onClick, tone = "default", onPrepare, disabled = false) => (<button className={`viz-card-menu-item ${tone === "danger" ? "viz-card-menu-item-danger" : ""}`.trim()} disabled={disabled} key={label} onFocus={onPrepare} onClick={() => {
+const label = cardLabel(card);
+const menuItem = (itemLabel, icon, onClick, tone = "default") => (<button className={`viz-card-menu-item ${tone === "danger" ? "viz-card-menu-item-danger" : ""}`.trim()} key={itemLabel} onClick={() => {
 void onClick();
 closeMenu();
-}} onPointerEnter={onPrepare} role="menuitem" type="button">
+}} role="menuitem" type="button">
 <span aria-hidden="true" className="viz-card-menu-icon">
 {icon}
 </span>
-<span>{label}</span>
+<span>{itemLabel}</span>
 </button>);
-return (<section className="report-metric-strip-block" id={id} ref={cardRef}>
-<div className="viz-card-actions" data-image-export-exclude="true" ref={menuRef}>
-<button aria-expanded={isMenuOpen} aria-label="Open options for key metrics" className="viz-card-menu-button viz-card__no-drag" onClick={(event) => {
+const action = (<div className="viz-card-actions" data-image-export-exclude="true" ref={menuRef}>
+<button aria-expanded={isMenuOpen} aria-label={`Open options for ${label}`} className="viz-card-menu-button viz-card__no-drag" onClick={(event) => {
 event.stopPropagation();
-const nextIsMenuOpen = toggleMenu();
-if (nextIsMenuOpen) {
-prepareImageExportQuietly(true);
-}
-else {
-resetPreparedImageExport();
-}
-}} onFocus={() => prepareImageExportQuietly()} onKeyDown={handleMenuButtonKeyDown} onPointerEnter={() => prepareImageExportQuietly()} ref={menuButtonRef} type="button">
+toggleMenu();
+}} onKeyDown={handleMenuButtonKeyDown} ref={menuButtonRef} type="button">
 <Ellipsis aria-hidden="true" size={18} strokeWidth={2}/>
 </button>
 {shouldRenderMenu ? (<div className={`viz-card-menu viz-card__no-drag menu-surface ${menuMotionClass}`} onKeyDown={handleMenuKeyDown} role="menu" style={fixedMenuStyle}>
-{shouldOfferImageClipboardCopy()
-? menuItem("Copy as image", <Camera size={18} strokeWidth={2}/>, handleCopyAsImage, "default", prepareImageExportQuietly, preparedImageExportStatus === "pending")
-: null}
-{menuItem("Delete", <Trash2 size={18} strokeWidth={2}/>, onDeleteBlock, "danger")}
+{menuItem("View data source", <Database size={18} strokeWidth={2}/>, onSourceOpen)}
+{onDeleteBlock ? menuItem("Delete", <Trash2 size={18} strokeWidth={2}/>, onDeleteBlock, "danger") : null}
 </div>) : null}
-</div>
-<KpiStrip cards={cards} filters={filters} selectedFilters={selectedFilters} snapshot={snapshot}/>
-</section>);
+</div>);
+return (<KpiCard action={action} card={card} className="report-metric-card" filters={filters} id={id} selectedFilters={selectedFilters} snapshot={snapshot}/>);
 }
 function ReportBlockCard({ accessIssues, block, blockTextOverride, chart, chartSpecOverride, chartTextOverride, chartTypeOverride, columnWidths, filters, isBlockMenuOpen, isChartMenuOpen, isEditMode, isTableMenuOpen, layout, manifest, onBlockMenuOpenChange, onBlockTextChange, onChartTypeChange, onChartMenuOpenChange, onColumnWidthsChange, onDeleteBlock, onChartModalOpen, onCopyResult, onRequestEditMode, onTableMenuOpenChange, onTableModalOpen, onTableTextChange, onTextChange, selectedFilters, snapshot, table, tableTextOverride }) {
 if (block.type === "html") {
@@ -2730,29 +2975,37 @@ setOptionalCodexSearchParam(url, "originUrl", packageInfo?.originUrl);
 setOptionalCodexSearchParam(url, "path", packageInfo?.root);
 return url.toString();
 }
-function codexHostPromptPayload(prompt, packageInfo) {
+function codexHostPromptPayload(prompt, title) {
+return title ? { prompt, title } : { prompt };
+}
+function codexMessagePayload(prompt) {
 return {
-originUrl: packageInfo?.originUrl,
-path: packageInfo?.root,
-prompt
+role: "user",
+content: [{ type: "text", text: prompt }]
 };
 }
-function sendCodexPromptToHost(prompt, packageInfo) {
+async function sendCodexPromptToHost(prompt, title) {
 const hostApi = window.openai;
-const payload = codexHostPromptPayload(prompt, packageInfo);
-const launchers = [hostApi?.sendFollowUpMessage, hostApi?.openCodexPrompt];
-for (const launch of launchers) {
-if (typeof launch !== "function")
-continue;
+if (typeof hostApi?.sendFollowUpMessage === "function") {
 try {
-void Promise.resolve(launch.call(hostApi, payload)).catch(() => { });
-return true;
+const result = await hostApi.sendFollowUpMessage(codexHostPromptPayload(prompt, title));
+return result?.isError !== true;
 }
 catch {
+return false;
+}
 }
+if (typeof hostApi?.sendMessage === "function") {
+try {
+const result = await hostApi.sendMessage(codexMessagePayload(prompt));
+return result?.isError !== true;
 }
+catch {
 return false;
 }
+}
+return null;
+}
 function isLocalPreviewHost() {
 return ["localhost", "127.0.0.1", "::1"].includes(window.location.hostname);
 }
@@ -2783,36 +3036,70 @@ catch {
 }
 return linkClicked;
 }
-function openCodexPrompt(prompt, packageInfo, onCopyResult) {
-const url = codexPromptUrl(prompt, packageInfo);
-const launched = launchCodexDeepLink(url);
-const sentToHost = sendCodexPromptToHost(prompt, packageInfo);
-const openedCodex = sentToHost || launched;
-void copyTextToClipboard(prompt)
-.then(() => {
-onCopyResult(sentToHost
-? "Started in this chat."
-: openedCodex
-? "Codex prompt copied. Opening Codex if supported."
+function isPublishedArtifactSite(packageInfo) {
+return packageInfo?.hostedReadOnly === true || packageInfo?.deliveryMode === "site_creator";
+}
+async function submitCodexPrompt(prompt, title, packageInfo, onResult) {
+const sentToHost = await sendCodexPromptToHost(prompt, title);
+if (sentToHost === true) {
+onResult(`Sent to Codex: ${title}.`, false);
+return;
+}
+if (sentToHost === false) {
+onResult("Codex did not accept the request.", true);
+return;
+}
+const clipboardFallback = isPublishedArtifactSite(packageInfo)
+? copyTextToClipboard(prompt)
+: null;
+const launched = launchCodexDeepLink(codexPromptUrl(prompt, packageInfo));
+if (clipboardFallback) {
+try {
+await clipboardFallback;
+onResult(launched
+? `Prompt copied. Opening Codex: ${title}.`
 : "Prompt copied. Open Codex and paste it to continue.", false);
-})
-.catch(() => {
-onCopyResult(openedCodex
-? "Opening Codex if supported, but prompt copy was blocked."
-: "Could not copy the Codex prompt from this preview.", true);
-});
 }
-function AnalyticsTopBarFreshness({ dateLabel, onRefresh, status, statusLabel }) {
-const refreshTitle = dateLabel === "Unknown"
-? "Refresh using latest available data."
-: `Refresh using latest available data. Last updated ${dateLabel}.`;
+catch {
+onResult(launched
+? `Opening Codex: ${title}. Prompt copy was blocked.`
+: "Could not copy the prompt from this published site.", !launched);
+}
+return;
+}
+onResult(launched
+? `Opening Codex: ${title}.`
+: "Open this app inside Codex to continue.", !launched);
+}
+function AnalyticsTopBarFreshness({ dateLabel, onRefresh, status, statusLabel, surfaceLabel }) {
+const tooltipId = useId();
+const refreshButtonRef = useRef(null);
+const [tooltipLeft, setTooltipLeft] = useState(12);
 const refreshLabel = dateLabel === "Unknown" ? "Refresh" : dateLabel;
+const statusDescription = statusLabel ? ` Status: ${statusLabel}.` : "";
+const refreshDescription = dateLabel === "Unknown"
+? `Last refresh date unavailable. Click to ask Codex to refresh this ${surfaceLabel}.${statusDescription}`
+: `Last refreshed ${dateLabel}. Click to ask Codex to refresh this ${surfaceLabel}.${statusDescription}`;
+const positionRefreshTooltip = useCallback(() => {
+const buttonRect = refreshButtonRef.current?.getBoundingClientRect();
+if (!buttonRect) return;
+const viewportPadding = 12;
+const tooltipWidth = Math.min(320, Math.max(0, window.innerWidth - (viewportPadding * 2)));
+const maxLeft = Math.max(viewportPadding, window.innerWidth - tooltipWidth - viewportPadding);
+setTooltipLeft(Math.min(Math.max(buttonRect.left, viewportPadding), maxLeft));
+}, []);
+useEffect(() => {
+window.addEventListener("resize", positionRefreshTooltip);
+return () => window.removeEventListener("resize", positionRefreshTooltip);
+}, [positionRefreshTooltip]);
 return (<div className="analytics-top-bar-freshness">
-<button aria-label={refreshTitle} className="top-bar-button top-bar-button-ghost top-bar-refresh-button" onClick={onRefresh} title={refreshTitle} type="button">
-<RefreshCw aria-hidden="true" size={14} strokeWidth={2}/>
-<span>{refreshLabel}</span>
-{statusLabel ? <span className={`snapshot-status ${status}`}>{statusLabel}</span> : null}
+<span className="top-bar-refresh-tooltip-anchor" onFocus={positionRefreshTooltip} onMouseEnter={positionRefreshTooltip}>
+<button ref={refreshButtonRef} aria-describedby={tooltipId} aria-label={`Refresh ${surfaceLabel}`} className="top-bar-refresh-datetime" onClick={onRefresh} type="button">
+<span className="top-bar-refresh-text">{refreshLabel}</span>
 </button>
+<span className="top-bar-refresh-tooltip" id={tooltipId} role="tooltip" style={{ left: tooltipLeft }}>{refreshDescription}</span>
+</span>
+{statusLabel ? <span className={`snapshot-status ${status}`}>{statusLabel}</span> : null}
 </div>);
 }
 function AnalyticsTopBar({ chrome = "full", isEditMode, onCancelEdit, manifest, onCopyResult, onEdit, onRequestFullscreen, onSaveEdit, onTitleChange, packageInfo, snapshot, title }) {
@@ -2827,23 +3114,35 @@ const showStatusLabel = manifest?.surface !== "report" && statusLabel;
 const showActions = chrome === "full";
 const showInlineExpand = chrome === "inline" && typeof onRequestFullscreen === "function";
 function requestRefresh() {
-void openCodexPrompt(refreshPrompt(manifest, snapshot, packageInfo), packageInfo, onCopyResult);
+void submitCodexPrompt(
+refreshPrompt(manifest, snapshot, packageInfo),
+`Refresh ${appSurfaceLabel(manifest)}`,
+packageInfo,
+onCopyResult
+);
 }
 function requestExport(target) {
 closeMenu();
-void openCodexPrompt(exportPrompt(target, manifest, snapshot, packageInfo), packageInfo, onCopyResult);
+void submitCodexPrompt(
+exportPrompt(target, manifest, snapshot, packageInfo),
+`Export ${appSurfaceLabel(manifest)} as ${EXPORT_TARGET_LABELS[target]}`,
+packageInfo,
+onCopyResult
+);
 }
 return (<div className="analytics-top-bar" aria-label={`${appSurfaceLabel(manifest)} actions`}>
+<div className="analytics-top-bar-leading">
 <div className="analytics-top-bar-title">
 <EditablePageTitle ariaLabel={`Edit ${appSurfaceLabel(manifest)} title`} isEditMode={isEditMode} onChange={onTitleChange} onRequestEditMode={onEdit} placeholder={composePageTitle(manifest)} readOnly={!showActions || !capabilities.canEdit} title={title}/>
 </div>
+{showActions ? <AnalyticsTopBarFreshness dateLabel={dateLabel} onRefresh={requestRefresh} status={snapshot?.status} statusLabel={showStatusLabel ? statusLabel : null} surfaceLabel={appSurfaceLabel(manifest)}/> : null}
+</div>
 {showInlineExpand ? (<div className="analytics-top-bar-actions">
 <button aria-label={`Expand ${appSurfaceLabel(manifest)} fullscreen`} className="top-bar-button" onClick={onRequestFullscreen} title={`Expand ${appSurfaceLabel(manifest)} fullscreen`} type="button">
 <Expand aria-hidden="true" size={14} strokeWidth={2}/>
 <span>Expand</span>
 </button>
 </div>) : showActions ? (<div className="analytics-top-bar-actions">
-<AnalyticsTopBarFreshness dateLabel={dateLabel} onRefresh={requestRefresh} status={snapshot?.status} statusLabel={showStatusLabel ? statusLabel : null}/>
 {isEditMode && capabilities.canEdit ? (<>
 <button className="top-bar-button" onClick={onCancelEdit} type="button">
 <span>Cancel</span>
@@ -2852,17 +3151,10 @@ return (<div className="analytics-top-bar" aria-label={`${appSurfaceLabel(manife
 <span>Save changes</span>
 </button>
 </>) : (<>
-{capabilities.canEdit ? (
-<button className="top-bar-button top-bar-edit-button" onClick={onEdit} type="button">
-<Pencil aria-hidden="true" size={14} strokeWidth={2}/>
-<span>Edit</span>
-</button>
-): null}
 {exportTargets.length ? (
 <div className="export-menu">
-<button ref={menuButtonRef} aria-expanded={isExportMenuOpen} aria-haspopup="menu" className="top-bar-button" onClick={toggleMenu} onKeyDown={handleMenuButtonKeyDown} type="button">
-<span>Export</span>
-<ChevronDown aria-hidden="true" size={14} strokeWidth={2}/>
+<button ref={menuButtonRef} aria-expanded={isExportMenuOpen} aria-haspopup="menu" aria-label={`More ${appSurfaceLabel(manifest)} actions`} className="top-bar-button top-bar-button-ghost top-bar-overflow-button" onClick={toggleMenu} onKeyDown={handleMenuButtonKeyDown} type="button">
+<Ellipsis aria-hidden="true" size={18} strokeWidth={2}/>
 </button>
 {shouldRenderMenu ? (<div ref={menuRef} className={`export-menu-list menu-surface ${menuMotionClass}`} onKeyDown={handleMenuKeyDown} role="menu" style={fixedMenuStyle}>
 {exportTargets.map((target) => (<button className="export-menu-item" key={target} onClick={() => requestExport(target)} role="menuitem" type="button">
@@ -2872,6 +3164,12 @@ return (<div className="analytics-top-bar" aria-label={`${appSurfaceLabel(manife
 </div>) : null}
 </div>
 ): null}
+{capabilities.canEdit ? (
+<button className="top-bar-button top-bar-edit-button" onClick={onEdit} type="button">
+<Pencil aria-hidden="true" size={16} strokeWidth={2}/>
+<span>Edit</span>
+</button>
+): null}
 </>)}
 </div>) : null}
 </div>);
@@ -2893,6 +3191,7 @@ const [deletedReportBlockIds, setDeletedReportBlockIds] = useState([]);
 const [openMenuChartId, setOpenMenuChartId] = useState(null);
 const [openMenuTableId, setOpenMenuTableId] = useState(null);
 const [openMenuBlockId, setOpenMenuBlockId] = useState(null);
+const [cardSourceModal, setCardSourceModal] = useState(null);
 const [chartModal, setChartModal] = useState(null);
 const [tableModal, setTableModal] = useState(null);
 const [copyMessage, setCopyMessage] = useState(null);
@@ -2952,6 +3251,18 @@ const cardsById = useMemo(() => new Map(cards.map((card) => [card.id, card])), [
 const chartsById = useMemo(() => new Map(charts.map((chart) => [chart.id, chart])), [charts]);
 const tablesById = useMemo(() => new Map(tables.map((table) => [table.id, table])), [tables]);
 const reportGridBlocks = reportBlocks;
+const knownDeletableReportIds = useMemo(() => {
+const ids = [];
+for (const block of reportGridBlocks) {
+ids.push(block.id);
+if (block.type === "metric-strip") {
+for (const cardId of block.cardIds ?? []) {
+ids.push(metricCardBlockId(block.id, cardId));
+}
+}
+}
+return new Set(ids);
+}, [reportGridBlocks]);
 const visibleReportGridBlocks = useMemo(() => {
 if (!deletedReportBlockIds.length)
 return reportGridBlocks;
@@ -2970,16 +3281,15 @@ return;
 }
 try {
 const parsed = JSON.parse(stored);
-const knownIds = new Set(reportGridBlocks.map((block) => block.id));
 const nextIds = Array.isArray(parsed)
-? parsed.filter((id) => typeof id === "string" && knownIds.has(id))
+? parsed.filter((id) => typeof id === "string" && knownDeletableReportIds.has(id))
 : [];
 setDeletedReportBlockIds(nextIds);
 }
 catch {
 setDeletedReportBlockIds([]);
 }
-}, [deletedReportBlockStorageKey, reportGridBlocks]);
+}, [deletedReportBlockStorageKey, knownDeletableReportIds, reportGridBlocks]);
 useEffect(() => {
 if (!tables.length || !tableColumnWidthStorageKey) {
 setTableColumnWidths({});
@@ -3466,59 +3776,70 @@ function deleteReportBlock(blockId) {
 deleteReportBlocks([blockId]);
 }
 const dashboardContentBlocks = useMemo(() => {
-const chartBlocks = charts.map((chart) => ({
-className: "analytics-layout-item-chart",
-defaultLayout: dashboardCardLayout(chart.layout),
-id: `chart:${chart.id}`,
-render: (layout, { setLayout }) => {
-const overriddenChart = applyChartSpecOverride(chart, chartSpecOverrides[chart.id]);
-const chartRows = filterRowsForDataset(getRows(snapshot, overriddenChart.dataset), overriddenChart.dataset, activeSurfaceFilters, selectedFilters, chartUsedFields(overriddenChart));
-const chartTypeOptions = compatibleChartTypesForArtifactCard(overriddenChart, chartRows);
-const requestedType = chartTypeOverrides[chart.id] ?? overriddenChart.type;
-const activeType = chartTypeOptions.some((option) => option.type === requestedType)
-? requestedType
-: overriddenChart.type;
-const displayChart = withChartType(overriddenChart, activeType);
-const accessIssue = accessIssueForChart(displayChart, accessIssues);
-return (<VizCard accessIssue={accessIssue} chart={displayChart} chartTypeOptions={chartTypeOptions} isEditMode={activeEditMode} isMenuOpen={openMenuChartId === chart.id} layout={layout} onChartTypeChange={updateChartType} onCopyResult={(message, isError = false) => setCopyMessage({ isError, message })} onRequestEditMode={requestEditMode} onTextChange={updateChartText} onMenuOpenChange={(nextOpen) => {
+const deletedIds = new Set(deletedReportBlockIds);
+return visibleReportGridBlocks.flatMap((block) => {
+if (block.type === "metric-strip") {
+const metricCards = (block.cardIds ?? [])
+.map((cardId) => cardsById.get(cardId))
+.filter((card) => Boolean(card) && !deletedIds.has(metricCardBlockId(block.id, card.id)));
+return metricCards.map((card) => {
+const itemId = metricCardBlockId(block.id, card.id);
+return {
+className: "report-stack-item report-stack-item-metric-card",
+compactGroup: `metric-strip:${block.id}`,
+defaultLayout: "half",
+id: itemId,
+render: () => (<ArtifactMetricCard card={card} filters={activeSurfaceFilters} id={itemId} isMenuOpen={openMenuBlockId === itemId} onDeleteBlock={capabilities.canEdit ? () => deleteReportBlock(itemId) : undefined} onMenuOpenChange={(nextOpen) => {
+setOpenMenuChartId(null);
 setOpenMenuTableId(null);
-setOpenMenuChartId(nextOpen ? chart.id : null);
-}} onModalOpen={setChartModal} textOverride={chartTextOverrides[chart.id]}>
-<ChartBody accessIssue={accessIssue} chart={displayChart} filters={activeSurfaceFilters} layout={layout} selectedFilters={selectedFilters} snapshot={snapshot}/>
-</VizCard>);
+setOpenMenuBlockId(nextOpen ? itemId : null);
+}} onSourceOpen={() => setCardSourceModal(card)} selectedFilters={selectedFilters} snapshot={snapshot}/>)
+};
+});
 }
-}));
-const tableBlocks = tables.map((table) => ({
-className: "analytics-layout-item-table",
-defaultLayout: dashboardCardLayout(table.layout ?? "full"),
-id: `table:${table.id}`,
-render: (layout) => (<DataTable columnWidths={tableColumnWidths[table.id] ?? {}} filters={activeSurfaceFilters} isEditMode={activeEditMode} isMenuOpen={openMenuTableId === table.id} layout={layout} manifest={manifest} onColumnWidthsChange={updateTableColumnWidths} onMenuOpenChange={(nextOpen) => {
-setOpenMenuChartId(null);
-setOpenMenuTableId(nextOpen ? table.id : null);
-}} onModalOpen={setTableModal} onRequestEditMode={requestEditMode} onTextChange={updateTableText} selectedFilters={selectedFilters} snapshot={snapshot} table={table} textOverride={tableTextOverrides[table.id]}/>)
-}));
-const htmlBlocks = visibleReportGridBlocks
-.filter((block) => block.type === "html")
-.map((block) => ({
-className: "analytics-layout-item-html",
-defaultLayout: dashboardCardLayout(block.layout ?? "full"),
-id: `html:${block.id}`,
-render: () => (<ReportHtmlBlock block={block} htmlOverride={blockTextOverrides[block.id]} isEditMode={activeEditMode} isMenuOpen={openMenuBlockId === block.id} onHtmlChange={updateBlockText} onCopyResult={(message, isError = false) => setCopyMessage({ isError, message })} onDeleteBlock={capabilities.canEdit ? () => deleteReportBlock(block.id) : undefined} onMenuOpenChange={(nextOpen) => {
+const chart = block.chartId ? chartsById.get(block.chartId) : undefined;
+const table = block.tableId ? tablesById.get(block.tableId) : undefined;
+const className = block.type === "chart"
+? "analytics-layout-item-chart"
+: block.type === "table"
+? "analytics-layout-item-table"
+: block.type === "html"
+? "analytics-layout-item-html"
+: `report-stack-item report-stack-item-${block.type}`;
+const defaultLayout = block.type === "chart"
+? dashboardCardLayout(block.layout ?? chart?.layout)
+: dashboardCardLayout(block.layout ?? table?.layout ?? "full");
+return [{
+className,
+defaultLayout,
+id: block.id,
+render: (layout) => (<ReportBlockCard accessIssues={accessIssues} block={block} blockTextOverride={blockTextOverrides[block.id]} chart={chart} chartSpecOverride={chart ? chartSpecOverrides[chart.id] : undefined} chartTextOverride={chart ? chartTextOverrides[chart.id] : undefined} chartTypeOverride={chart ? chartTypeOverrides[chart.id] : undefined} columnWidths={table ? tableColumnWidths[table.id] ?? {} : {}} filters={activeSurfaceFilters} isBlockMenuOpen={openMenuBlockId === block.id} isChartMenuOpen={Boolean(chart && openMenuChartId === chart.id)} isEditMode={activeEditMode} isTableMenuOpen={Boolean(table && openMenuTableId === table.id)} layout={layout} manifest={manifest} onBlockMenuOpenChange={(nextOpen) => {
 setOpenMenuChartId(null);
 setOpenMenuTableId(null);
 setOpenMenuBlockId(nextOpen ? block.id : null);
-}}/>)
-}));
-return [...chartBlocks, ...tableBlocks, ...htmlBlocks];
+}} onBlockTextChange={updateBlockText} onChartTypeChange={updateChartType} onChartMenuOpenChange={(nextOpen) => {
+setOpenMenuTableId(null);
+setOpenMenuBlockId(null);
+setOpenMenuChartId(chart && nextOpen ? chart.id : null);
+}} onChartModalOpen={setChartModal} onColumnWidthsChange={updateTableColumnWidths} onCopyResult={(message, isError = false) => setCopyMessage({ isError, message })} onDeleteBlock={capabilities.canEdit ? () => deleteReportBlock(block.id) : undefined} onRequestEditMode={requestEditMode} onTableMenuOpenChange={(nextOpen) => {
+setOpenMenuChartId(null);
+setOpenMenuBlockId(null);
+setOpenMenuTableId(table && nextOpen ? table.id : null);
+}} onTableModalOpen={setTableModal} onTableTextChange={updateTableText} onTextChange={updateChartText} selectedFilters={selectedFilters} snapshot={snapshot} table={table} tableTextOverride={table ? tableTextOverrides[table.id] : undefined}/>)
+}];
+});
 }, [
 accessIssues,
 activeSurfaceFilters,
 activeEditMode,
+blockTextOverrides,
+cardsById,
 capabilities.canEdit,
 chartTextOverrides,
 chartSpecOverrides,
 chartTypeOverrides,
-charts,
+chartsById,
+deletedReportBlockIds,
 manifest,
 openMenuBlockId,
 openMenuChartId,
@@ -3528,29 +3849,34 @@ selectedFilters,
 snapshot,
 tableColumnWidths,
 tableTextOverrides,
-tables,
+tablesById,
 visibleReportGridBlocks
 ]);
 const reportContentBlocks = useMemo(() => {
-return visibleReportGridBlocks.map((block) => {
+const deletedIds = new Set(deletedReportBlockIds);
+return visibleReportGridBlocks.flatMap((block) => {
 if (block.type === "metric-strip") {
 const metricCards = (block.cardIds ?? [])
 .map((cardId) => cardsById.get(cardId))
-.filter((card) => Boolean(card));
+.filter((card) => Boolean(card) && !deletedIds.has(metricCardBlockId(block.id, card.id)));
+return metricCards.map((card) => {
+const itemId = metricCardBlockId(block.id, card.id);
 return {
-className: "report-stack-item report-stack-item-metric-strip",
-defaultLayout: reportCardLayout(block.layout),
-id: block.id,
-render: () => (<ReportMetricStripBlock cards={metricCards} filters={activeSurfaceFilters} id={block.id} isMenuOpen={openMenuBlockId === block.id} onCopyResult={(message, isError = false) => setCopyMessage({ isError, message })} onDeleteBlock={capabilities.canEdit ? () => deleteReportBlock(block.id) : undefined} onMenuOpenChange={(nextOpen) => {
+className: "report-stack-item report-stack-item-metric-card",
+compactGroup: `metric-strip:${block.id}`,
+defaultLayout: "half",
+id: itemId,
+render: () => (<ArtifactMetricCard card={card} filters={activeSurfaceFilters} id={itemId} isMenuOpen={openMenuBlockId === itemId} onDeleteBlock={capabilities.canEdit ? () => deleteReportBlock(itemId) : undefined} onMenuOpenChange={(nextOpen) => {
 setOpenMenuChartId(null);
 setOpenMenuTableId(null);
-setOpenMenuBlockId(nextOpen ? block.id : null);
-}} selectedFilters={selectedFilters} snapshot={snapshot}/>)
+setOpenMenuBlockId(nextOpen ? itemId : null);
+}} onSourceOpen={() => setCardSourceModal(card)} selectedFilters={selectedFilters} snapshot={snapshot}/>)
 };
+});
 }
 const chart = block.chartId ? chartsById.get(block.chartId) : undefined;
 const table = block.tableId ? tablesById.get(block.tableId) : undefined;
-return {
+return [{
 className: `report-stack-item report-stack-item-${block.type}`,
 defaultLayout: reportCardLayout(block.layout),
 id: block.id,
@@ -3567,7 +3893,7 @@ setOpenMenuChartId(null);
 setOpenMenuBlockId(null);
 setOpenMenuTableId(table && nextOpen ? table.id : null);
 }} onTableModalOpen={setTableModal} onTableTextChange={updateTableText} onTextChange={updateChartText} selectedFilters={selectedFilters} snapshot={snapshot} table={table} tableTextOverride={table ? tableTextOverrides[table.id] : undefined}/>)
-};
+}];
 });
 }, [
 accessIssues,
@@ -3580,6 +3906,7 @@ chartSpecOverrides,
 chartTextOverrides,
 chartTypeOverrides,
 chartsById,
+deletedReportBlockIds,
 manifest,
 openMenuBlockId,
 openMenuChartId,
@@ -3633,13 +3960,14 @@ return (<DashboardShell isEditMode={activeEditMode} surface="report">
 {copyMessage.message}
 </div>) : null}
 
-<AnalyticsLayoutCanvas ariaLabel="Report blocks" blocks={reportContentBlocks} cancelSelector={CARD_DRAG_CANCEL_SELECTOR} className="report-content-grid report-block-stack" isEditMode={activeEditMode} layoutResetKey={layoutResetKey} onLayoutChange={recordReportLayoutDraft} storageKey={reportStorageKey}/>
+<AnalyticsLayoutCanvas ariaLabel="Report blocks" blocks={reportContentBlocks} cancelSelector={CARD_DRAG_CANCEL_SELECTOR} className="report-content-grid report-block-stack metric-card-layout" isEditMode={activeEditMode} layoutResetKey={layoutResetKey} onLayoutChange={recordReportLayoutDraft} storageKey={reportStorageKey}/>
 {activeChartModal ? (<ChartDetailPage accessIssue={accessIssueForChart(activeChartModal.chart, accessIssues)} chart={activeChartModal.chart} filters={activeSurfaceFilters} manifest={manifest} onChartSpecChange={updateChartSpec} onClose={() => setChartModal(null)} packageInfo={packageInfo} rows={chartModalRows} selectedFilters={selectedFilters} snapshot={snapshot}/>) : null}
 {chartModal?.kind === "source" && chartModalBaseChart ? (<ChartSourceModalDialog activeFilters={activeFilters} chart={{
 ...chartModalBaseChart,
 subtitle: chartModal.chart.subtitle,
 title: chartModal.chart.title
 }} manifest={manifest} onClose={() => setChartModal(null)} rows={chartModalRows} snapshot={snapshot}/>) : null}
+{cardSourceModal ? (<CardSourceModalDialog activeFilters={activeFilters} card={cardSourceModal} filters={activeSurfaceFilters} manifest={manifest} onClose={() => setCardSourceModal(null)} selectedFilters={selectedFilters} snapshot={snapshot}/>) : null}
 {tableModal ? (<TableModalDialog activeFilters={activeFilters} columnWidths={tableColumnWidths[tableModal.table.id] ?? {}} filters={activeSurfaceFilters} kind={tableModal.kind} manifest={manifest} onColumnWidthsChange={updateTableColumnWidths} onClose={() => setTableModal(null)} selectedFilters={selectedFilters} snapshot={snapshot} table={tableModal.table}/>) : null}
 </DashboardShell>);
 }
@@ -3652,15 +3980,14 @@ return (<DashboardShell isEditMode={activeEditMode} surface={manifest?.surface}>
 
 <FilterToolbar filters={activeSurfaceFilters} onChange={setSelectedFilters} selectedFilters={selectedFilters} snapshot={snapshot}/>
 
-<KpiStrip cards={cards} filters={activeSurfaceFilters} selectedFilters={selectedFilters} snapshot={snapshot}/>
-
-<AnalyticsLayoutCanvas ariaLabel="Dashboard content" blocks={dashboardContentBlocks} cancelSelector={CARD_DRAG_CANCEL_SELECTOR} className="dashboard-content-grid" isEditMode={activeEditMode} layoutResetKey={layoutResetKey} onLayoutChange={recordDashboardLayoutDraft} storageKey={storageKey}/>
+<AnalyticsLayoutCanvas ariaLabel="Dashboard content" blocks={dashboardContentBlocks} cancelSelector={CARD_DRAG_CANCEL_SELECTOR} className="dashboard-content-grid metric-card-layout" isEditMode={activeEditMode} layoutResetKey={layoutResetKey} onLayoutChange={recordDashboardLayoutDraft} storageKey={storageKey}/>
 {activeChartModal ? (<ChartDetailPage accessIssue={accessIssueForChart(activeChartModal.chart, accessIssues)} chart={activeChartModal.chart} filters={activeSurfaceFilters} manifest={manifest} onChartSpecChange={updateChartSpec} onClose={() => setChartModal(null)} packageInfo={packageInfo} rows={chartModalRows} selectedFilters={selectedFilters} snapshot={snapshot}/>) : null}
 {chartModal?.kind === "source" && chartModalBaseChart ? (<ChartSourceModalDialog activeFilters={activeFilters} chart={{
 ...chartModalBaseChart,
 subtitle: chartModal.chart.subtitle,
 title: chartModal.chart.title
 }} manifest={manifest} onClose={() => setChartModal(null)} rows={chartModalRows} snapshot={snapshot}/>) : null}
+{cardSourceModal ? (<CardSourceModalDialog activeFilters={activeFilters} card={cardSourceModal} filters={activeSurfaceFilters} manifest={manifest} onClose={() => setCardSourceModal(null)} selectedFilters={selectedFilters} snapshot={snapshot}/>) : null}
 {tableModal ? (<TableModalDialog activeFilters={activeFilters} columnWidths={tableColumnWidths[tableModal.table.id] ?? {}} filters={activeSurfaceFilters} kind={tableModal.kind} manifest={manifest} onColumnWidthsChange={updateTableColumnWidths} onClose={() => setTableModal(null)} selectedFilters={selectedFilters} snapshot={snapshot} table={tableModal.table}/>) : null}
 </DashboardShell>);
 }
diff --git a/plugins/data-analytics/src/analytics-app/charting/ChartRenderer.tsx b/plugins/data-analytics/src/analytics-app/charting/ChartRenderer.tsx
index 7768181..7b0e5bd 100644
--- a/plugins/data-analytics/src/analytics-app/charting/ChartRenderer.tsx
+++ b/plugins/data-analytics/src/analytics-app/charting/ChartRenderer.tsx
@@ -39,6 +39,7 @@ import {
   formatValue,
   getBoxPlotXAxisScale,
   getDateAxisTicks,
+  getWaterfallYAxisScale,
   getYAxisScaleForSeries,
   type ChartDataRow,
   type WaterfallDataRow,
@@ -57,6 +58,7 @@ const AXIS_LABEL_GAP = 8;
 const RECHARTS_AXIS_TICK_SIZE = 6;
 const RECHARTS_AXIS_TICK_MARGIN = AXIS_LABEL_GAP - RECHARTS_AXIS_TICK_SIZE;
 const Y_AXIS_TICK_LABEL_OVERHANG = 8;
+const HTML_REPORT_NUMERIC_Y_AXIS_WIDTH = 96;
 const CHART_COMPACT_HEIGHT = 240;
 const CHART_CARD_DEFAULT_HEIGHT = 320;
 const CHART_CARD_ROTATED_CATEGORY_HEIGHT = 400;
@@ -81,9 +83,9 @@ const CATEGORY_X_AXIS_ROTATED_TICK_MARGIN = 10;
 const CATEGORY_X_AXIS_WRAPPED_HEIGHT = 38;
 const CATEGORY_X_AXIS_WRAPPED_HEIGHT_WITH_TITLE = 48;
 const CATEGORY_X_AXIS_WRAPPED_TICK_MARGIN = 6;
-const CATEGORY_X_AXIS_MIN_LINE_CHARACTERS = 8;
 const CATEGORY_X_AXIS_ESTIMATED_CHAR_WIDTH = 7;
 const CATEGORY_X_AXIS_LABEL_SLOT_GAP = 16;
+const CATEGORY_X_AXIS_MIN_VISIBLE_TICK_SPACING = 72;
 const BAR_AUTO_VALUE_LABEL_LIMIT = 8;
 const BAR_VALUE_LABEL_OFFSET = 8;
 const BAR_NEGATIVE_VALUE_LABEL_OFFSET = 18;
@@ -324,30 +326,32 @@ function XAxisEndpointTick({
 }
 
 function truncateCategoryAxisLabel(value: string, maxLength: number): string {
-  if (value.length <= maxLength) return value;
-  const contentLength = Math.max(1, maxLength - 3);
-  return `${value.slice(0, contentLength).trimEnd()}...`;
+  const safeMaxLength = Math.max(1, Math.floor(maxLength));
+  if (value.length <= safeMaxLength) return value;
+  if (safeMaxLength === 1) return "…";
+  return `${value.slice(0, safeMaxLength - 1).trimEnd()}…`;
+}
+
+function balancedCategoryAxisLabelSplit(value: string): [string, string] {
+  const midpoint = value.length / 2;
+  const wordBreaks = [...value.matchAll(/\s+/g)]
+    .map((match) => match.index)
+    .filter((index): index is number => index > 0 && index < value.length);
+  const splitIndex = wordBreaks.length
+    ? wordBreaks.reduce((best, index) =>
+        Math.abs(index - midpoint) < Math.abs(best - midpoint) ? index : best,
+      )
+    : Math.ceil(midpoint);
+  return [value.slice(0, splitIndex).trimEnd(), value.slice(splitIndex).trimStart()];
 }
 
 function wrapCategoryAxisLabel(value: string, maxLength: number): string[] {
   const normalized = value.trim();
-  if (!normalized || normalized.length <= maxLength) return [normalized];
-  const words = normalized.split(/\s+/);
-  if (words.length === 1) {
-    return [
-      normalized.slice(0, maxLength),
-      truncateCategoryAxisLabel(normalized.slice(maxLength), maxLength),
-    ].filter(Boolean);
-  }
-
-  let firstLine = words.shift() ?? "";
-  while (words.length && `${firstLine} ${words[0]}`.length <= maxLength) {
-    firstLine = `${firstLine} ${words.shift()}`;
-  }
-  return [
-    truncateCategoryAxisLabel(firstLine, maxLength),
-    truncateCategoryAxisLabel(words.join(" "), maxLength),
-  ].filter(Boolean);
+  const safeMaxLength = Math.max(1, Math.floor(maxLength));
+  if (!normalized || normalized.length <= safeMaxLength) return [normalized];
+  return balancedCategoryAxisLabelSplit(normalized)
+    .map((line) => truncateCategoryAxisLabel(line, safeMaxLength))
+    .filter(Boolean);
 }
 
 function WrappedCategoryXAxisTick({
@@ -357,7 +361,8 @@ function WrappedCategoryXAxisTick({
   y = 0,
 }: AxisTickProps & { maxLineLength: number }) {
   const numericX = typeof x === "number" ? x : Number(x) || 0;
-  const lines = wrapCategoryAxisLabel(String(payload?.value ?? ""), maxLineLength);
+  const value = String(payload?.value ?? "");
+  const lines = wrapCategoryAxisLabel(value, maxLineLength);
   return (
     <text
       className="recharts-cartesian-axis-tick-value"
@@ -366,6 +371,7 @@ function WrappedCategoryXAxisTick({
       x={numericX}
       y={y}
     >
+      {lines.some((line) => line.includes("…")) ? <title>{value}</title> : null}
       {lines.map((line, index) => (
         <tspan dy={index === 0 ? 0 : "1.2em"} key={`${line}-${index}`} x={numericX}>
           {line}
@@ -441,7 +447,40 @@ function shouldWrapCategoryXAxisLabels({
 
 function categoryXAxisWrappedLineLength(chart: ChartSpec, rows: ChartDataRow[], availableWidth: number): number {
   const slotWidth = categoryXAxisLabelSlotWidth(categoryXAxisLabels(chart, rows), availableWidth);
-  return Math.max(CATEGORY_X_AXIS_MIN_LINE_CHARACTERS, Math.floor(slotWidth / CATEGORY_X_AXIS_ESTIMATED_CHAR_WIDTH));
+  return Math.max(1, Math.floor(slotWidth / CATEGORY_X_AXIS_ESTIMATED_CHAR_WIDTH));
+}
+
+function sparseCategoryXAxisTicks({
+  availableWidth,
+  chart,
+  horizontal,
+  rotateCategoryXAxisLabels,
+  rows,
+  wrapCategoryXAxisLabels,
+  xAxisTicks,
+}: {
+  availableWidth: number;
+  chart: ChartSpec;
+  horizontal: boolean;
+  rotateCategoryXAxisLabels: boolean;
+  rows: ChartDataRow[];
+  wrapCategoryXAxisLabels: boolean;
+  xAxisTicks?: string[];
+}): string[] | undefined {
+  if (horizontal || xAxisTicks?.length || (!rotateCategoryXAxisLabels && !wrapCategoryXAxisLabels)) {
+    return xAxisTicks;
+  }
+  const labels = categoryXAxisLabels(chart, rows);
+  if (labels.length <= 2) return undefined;
+  const maxVisibleTicks = Math.max(2, Math.floor(availableWidth / CATEGORY_X_AXIS_MIN_VISIBLE_TICK_SPACING));
+  if (labels.length <= maxVisibleTicks) return undefined;
+
+  const lastIndex = labels.length - 1;
+  const step = Math.max(1, Math.ceil(lastIndex / Math.max(1, maxVisibleTicks - 1)));
+  const ticks = labels.filter((_, index) => index % step === 0);
+  const last = labels[lastIndex];
+  if (ticks[ticks.length - 1] !== last) ticks.push(last);
+  return ticks;
 }
 
 function getFunnelStageColor(index: number, rowCount: number): string {
@@ -862,12 +901,18 @@ function renderBarValueLabel({
   chart,
   height,
   horizontal,
+  placeNegativeHorizontalLabelsAtZeroSide = false,
   showPositiveSign = false,
   value,
   width,
   x,
   y,
-}: BarValueLabelProps & { chart: ChartSpec; horizontal: boolean; showPositiveSign?: boolean }) {
+}: BarValueLabelProps & {
+  chart: ChartSpec;
+  horizontal: boolean;
+  placeNegativeHorizontalLabelsAtZeroSide?: boolean;
+  showPositiveSign?: boolean;
+}) {
   const numericValue = asNumber(value);
   const numericX = asFiniteNumber(x, Number.NaN);
   const numericY = asFiniteNumber(y, Number.NaN);
@@ -885,12 +930,13 @@ function renderBarValueLabel({
   if (horizontal) {
     const left = Math.min(numericX, numericX + numericWidth);
     const right = Math.max(numericX, numericX + numericWidth);
+    const zeroSideNegativeLabel = isNegative && placeNegativeHorizontalLabelsAtZeroSide;
     return (
       <text
         className="chart-bar-value-label"
         dominantBaseline="middle"
-        textAnchor={isNegative ? "end" : "start"}
-        x={isNegative ? left - BAR_VALUE_LABEL_OFFSET : right + BAR_VALUE_LABEL_OFFSET}
+        textAnchor={zeroSideNegativeLabel ? "start" : isNegative ? "end" : "start"}
+        x={zeroSideNegativeLabel ? right + BAR_VALUE_LABEL_OFFSET : isNegative ? left - BAR_VALUE_LABEL_OFFSET : right + BAR_VALUE_LABEL_OFFSET}
         y={numericY + numericHeight / 2}
       >
         {label}
@@ -1131,6 +1177,7 @@ function CustomPanelFrame({
 export function ChartRenderer({
   chart: chartSpec,
   height,
+  htmlReport = false,
   onVisibleSeriesChange,
   responsive = true,
   rows: rawRows,
@@ -1141,6 +1188,7 @@ export function ChartRenderer({
 }: {
   chart: ChartSpec;
   height?: number | string;
+  htmlReport?: boolean;
   onVisibleSeriesChange?: (visible: Set<string>) => void;
   responsive?: boolean;
   rows: ChartDataRow[];
@@ -1187,6 +1235,15 @@ export function ChartRenderer({
     rows,
     xAxisTicks,
   });
+  const categoryXAxisTicks = sparseCategoryXAxisTicks({
+    availableWidth: fixedChartWidth,
+    chart,
+    horizontal,
+    rotateCategoryXAxisLabels,
+    rows,
+    wrapCategoryXAxisLabels,
+    xAxisTicks,
+  });
   const wrappedCategoryXAxisLineLength = categoryXAxisWrappedLineLength(chart, rows, fixedChartWidth);
   const stacked = isStackedChartType(chart.type, barGroupMode);
   const normalized = isNormalizedStackedChartType(chart.type, barGroupMode);
@@ -1260,7 +1317,7 @@ export function ChartRenderer({
 
   const xAxisLabel = chart.xAxisTitle ? bottomAxisLabel(chart.xAxisTitle) : undefined;
   const yAxisLabel = chart.yAxisTitle ? leftAxisLabel(chart.yAxisTitle) : undefined;
-  const horizontalValueAxisLabel = chart.yAxisTitle ? bottomAxisLabel(chart.yAxisTitle) : undefined;
+  const horizontalValueAxisLabel = chart.xAxisTitle ? bottomAxisLabel(chart.xAxisTitle) : undefined;
   // Horizontal bars already show category names as row labels; an extra axis title competes for left-side space.
   const horizontalCategoryAxisLabel = undefined;
   const bottomMarginAxisLabel = horizontal ? horizontalValueAxisLabel : xAxisLabel;
@@ -1276,7 +1333,10 @@ export function ChartRenderer({
       horizontal && barValueLabelSides.hasNegative
         ? Math.max(baseLeftMargin, BAR_VALUE_LABEL_HORIZONTAL_GUTTER)
         : baseLeftMargin,
-    right: horizontal && barValueLabelSides.hasNonNegative ? BAR_VALUE_LABEL_HORIZONTAL_GUTTER : 0,
+    right:
+      horizontal && (barValueLabelSides.hasNonNegative || (htmlReport && barValueLabelSides.hasNegative))
+        ? BAR_VALUE_LABEL_HORIZONTAL_GUTTER
+        : 0,
     top:
       !horizontal && barValueLabelSides.hasNonNegative
         ? Math.max(Y_AXIS_TICK_LABEL_OVERHANG, BAR_VALUE_LABEL_VERTICAL_GUTTER)
@@ -1326,7 +1386,7 @@ export function ChartRenderer({
         tickMargin={RECHARTS_AXIS_TICK_MARGIN}
         tickSize={RECHARTS_AXIS_TICK_SIZE}
         ticks={yAxisScale.ticks}
-        width="auto"
+        width={htmlReport ? HTML_REPORT_NUMERIC_Y_AXIS_WIDTH : "auto"}
       />
     );
   }
@@ -1376,7 +1436,13 @@ export function ChartRenderer({
             {!isStacked ? renderCategoryBarCells(chart, series, rows, colorBarsBySign) : null}
             {showBarValueLabels ? (
               <LabelList
-                content={(props) => renderBarValueLabel({ ...props, chart, horizontal: isHorizontal, showPositiveSign: colorBarsBySign })}
+                content={(props) => renderBarValueLabel({
+                  ...props,
+                  chart,
+                  horizontal: isHorizontal,
+                  placeNegativeHorizontalLabelsAtZeroSide: htmlReport,
+                  showPositiveSign: colorBarsBySign,
+                })}
                 dataKey={series.field}
               />
             ) : null}
@@ -1432,7 +1498,7 @@ export function ChartRenderer({
           ? CATEGORY_X_AXIS_WRAPPED_HEIGHT_WITH_TITLE
           : CATEGORY_X_AXIS_WRAPPED_HEIGHT
       : undefined,
-    interval: rotateCategoryXAxisLabels || wrapCategoryXAxisLabels || xAxisTicks ? 0 : undefined,
+    interval: rotateCategoryXAxisLabels || wrapCategoryXAxisLabels || categoryXAxisTicks ? 0 : undefined,
     label: xAxisLabel,
     tick: xAxisTicks
       ? ((props: AxisTickProps) => (
@@ -1457,13 +1523,14 @@ export function ChartRenderer({
         ? CATEGORY_X_AXIS_WRAPPED_TICK_MARGIN
         : RECHARTS_AXIS_TICK_MARGIN,
     tickSize: RECHARTS_AXIS_TICK_SIZE,
-    ticks: xAxisTicks,
+    ticks: categoryXAxisTicks,
     textAnchor: rotateCategoryXAxisLabels ? "end" : undefined,
   };
 
   const boxPlotRows = chart.type === "boxPlot" ? buildBoxPlotRows(chart, rows) : [];
   const boxPlotXAxisScale = chart.type === "boxPlot" ? getBoxPlotXAxisScale(boxPlotRows) : null;
   const waterfallRows = chart.type === "waterfall" ? buildWaterfallRows(chart, rows) : [];
+  const waterfallYAxisScale = chart.type === "waterfall" ? getWaterfallYAxisScale(waterfallRows) : undefined;
   const funnelRows = chart.type === "funnel" ? buildFunnelRows(chart, rows, getFunnelStageColor) : [];
   const pieRows = chart.type === "pie"
     ? buildPieRows(chart, rows).filter((row) => chart.series.length <= 1 || visibleIds.has(String(row.__pieField ?? "")))
@@ -1493,7 +1560,7 @@ export function ChartRenderer({
       <BarChart {...chartProps} data={waterfallRows} margin={cartesianMargin}>
         <CartesianGrid vertical={false} />
         <XAxis {...xAxisProps} />
-        <YAxis axisLine={false} interval={0} label={yAxisLabel} tick={(props: AxisTickProps) => (<NumericYAxisTick {...props} valueFormat={chart.valueFormat ?? "compact"} />)} tickFormatter={(value) => formatValue(value, chart.valueFormat)} tickLine={false} tickMargin={RECHARTS_AXIS_TICK_MARGIN} tickSize={RECHARTS_AXIS_TICK_SIZE} width="auto" />
+        <YAxis axisLine={false} domain={waterfallYAxisScale?.domain} interval={0} label={yAxisLabel} tick={(props: AxisTickProps) => (<NumericYAxisTick {...props} valueFormat={chart.valueFormat ?? "compact"} />)} tickFormatter={(value) => formatValue(value, chart.valueFormat)} tickLine={false} tickMargin={RECHARTS_AXIS_TICK_MARGIN} tickSize={RECHARTS_AXIS_TICK_SIZE} ticks={waterfallYAxisScale?.ticks} width={htmlReport ? HTML_REPORT_NUMERIC_Y_AXIS_WIDTH : "auto"} />
         <ReferenceLine y={0} stroke="var(--ds-chart-reference-line)" strokeWidth={1} />
         <Tooltip {...fastTooltipProps} content={<WaterfallTooltip chart={tooltipChart} />} cursor={subtleTooltipCursor} />
         <Bar
diff --git a/plugins/data-analytics/src/analytics-app/charting/ChartTooltip.tsx b/plugins/data-analytics/src/analytics-app/charting/ChartTooltip.tsx
index e69f2ae..edeba98 100644
--- a/plugins/data-analytics/src/analytics-app/charting/ChartTooltip.tsx
+++ b/plugins/data-analytics/src/analytics-app/charting/ChartTooltip.tsx
@@ -58,8 +58,13 @@ function tooltipEncoding(chart: ChartSpec, item: ChartTooltipPayloadItem): Chart
   return null;
 }
 
-function encodingValueFormat(encoding: ChartEncodingSpec): ValueFormat {
+function isPercentSymbolUnit(unit: string | undefined): boolean {
+  return unit?.trim() === "%";
+}
+
+function tooltipEncodingValueFormat(chart: ChartSpec, encoding: ChartEncodingSpec): ValueFormat {
   if (isValueFormat(encoding.format)) return encoding.format;
+  if (chart.valueFormat === "percent" && isPercentSymbolUnit(encoding.unit)) return "percent";
   return "number";
 }
 
@@ -95,7 +100,8 @@ function tooltipItemColor(chart: ChartSpec, item: ChartTooltipPayloadItem, itemI
 function tooltipItemValue(chart: ChartSpec, item: ChartTooltipPayloadItem, visibleTotal: number): string {
   const encoding = tooltipEncoding(chart, item);
   if (encoding) {
-    return formatValue(item.value, encodingValueFormat(encoding), encoding.unit);
+    const valueFormat = tooltipEncodingValueFormat(chart, encoding);
+    return formatValue(item.value, valueFormat, encoding.unit);
   }
   const numeric = asNumber(item.value);
   if (isNormalizedStackedChartType(chart.type, chart.settings?.groupMode) && numeric != null && visibleTotal > 0) {
diff --git a/plugins/data-analytics/src/analytics-app/charting/chart-app-helpers.tsx b/plugins/data-analytics/src/analytics-app/charting/chart-app-helpers.tsx
index 2f8facf..4f5863b 100644
--- a/plugins/data-analytics/src/analytics-app/charting/chart-app-helpers.tsx
+++ b/plugins/data-analytics/src/analytics-app/charting/chart-app-helpers.tsx
@@ -318,6 +318,25 @@ function shouldKeepSignedBarAsSingleSeries(chart, rows, colorField) {
   return observed;
 }
 
+function isHorizontalBarChart(chart) {
+  return (
+    chart.type === "horizontalBar" ||
+    chart.type === "horizontalStackedBar" ||
+    chart.type === "horizontalStackedBar100" ||
+    (chart.type === "bar" && chart.settings?.orientation === "horizontal")
+  );
+}
+
+function axisTitlesForEncodedChart(chart, xField, yField) {
+  const horizontal = isHorizontalBarChart(chart);
+  const encodedXAxisTitle = chartEncodingAxisTitle(chart, "x", xField);
+  const encodedYAxisTitle = chartEncodingAxisTitle(chart, "y", yField);
+  return {
+    xAxisTitle: chart.xAxisTitle ?? (horizontal ? encodedYAxisTitle : encodedXAxisTitle),
+    yAxisTitle: chart.yAxisTitle ?? (horizontal ? encodedXAxisTitle : encodedYAxisTitle),
+  };
+}
+
 export function rechartsChartFromEncodedSpec(chart, rows) {
   if (!chartHasEncodingSpec(chart)) return { chart, rows };
   const xField = chartEncodingField(chart, "x");
@@ -326,12 +345,15 @@ export function rechartsChartFromEncodedSpec(chart, rows) {
   const colorField = chartEncodingField(chart, "color");
   const lineStyleField = chartEncodingField(chart, "lineStyle");
   if (yFields.length) {
+    const horizontal = isHorizontalBarChart(chart);
+    const xAxisTitle = chartEncodingAxisTitle(chart, "x", xField);
+    const yAxisTitle = chartEncodingAxisTitle(chart, "y", "Value");
     return {
       chart: {
         ...chart,
         xField,
-        xAxisTitle: chart.xAxisTitle ?? chartEncodingAxisTitle(chart, "x", xField),
-        yAxisTitle: chart.yAxisTitle ?? chartEncodingAxisTitle(chart, "y", "Value"),
+        xAxisTitle: chart.xAxisTitle ?? (horizontal ? yAxisTitle : xAxisTitle),
+        yAxisTitle: chart.yAxisTitle ?? (horizontal ? xAxisTitle : yAxisTitle),
         series: yFields.map((field) => ({ field, label: field, semanticRole: inferredSeriesRole(field) })),
       },
       rows,
@@ -340,12 +362,12 @@ export function rechartsChartFromEncodedSpec(chart, rows) {
   if (!yField) return { chart, rows };
   if (chart.type === "funnel" || !colorField || shouldKeepSignedBarAsSingleSeries(chart, rows, colorField)) {
     const lineStyle = rowLineStyle(rows.find((row) => rowLineStyle(row, lineStyleField)), lineStyleField);
+    const axisTitles = axisTitlesForEncodedChart(chart, xField, yField);
     return {
       chart: {
         ...chart,
         xField,
-        xAxisTitle: chart.xAxisTitle ?? chartEncodingAxisTitle(chart, "x", xField),
-        yAxisTitle: chart.yAxisTitle ?? chartEncodingAxisTitle(chart, "y", yField),
+        ...axisTitles,
         series: [{ field: yField, label: chartEncodingLabel(chart, "y", yField), lineStyle, semanticRole: inferredSeriesRole(chartEncodingLabel(chart, "y", yField)) }],
       },
       rows,
@@ -363,12 +385,12 @@ export function rechartsChartFromEncodedSpec(chart, rows) {
   }
   const seriesFields = new Map(seriesValues.map((value, index) => [value, syntheticSeriesField(value, index)]));
   if (chart.type === "scatter") {
+    const axisTitles = axisTitlesForEncodedChart(chart, xField, yField);
     return {
       chart: {
         ...chart,
         xField,
-        xAxisTitle: chart.xAxisTitle ?? chartEncodingAxisTitle(chart, "x", xField),
-        yAxisTitle: chart.yAxisTitle ?? chartEncodingAxisTitle(chart, "y", yField),
+        ...axisTitles,
         series: seriesValues.map((value) => ({ field: seriesFields.get(value), label: value, lineStyle: seriesLineStyles.get(value), semanticRole: inferredSeriesRole(value) })),
       },
       rows: rows.map((row) => {
@@ -385,12 +407,12 @@ export function rechartsChartFromEncodedSpec(chart, rows) {
     const seriesField = seriesFields.get(String(row[colorField] ?? ""));
     if (seriesField) rowsByX.get(xKey)[seriesField] = row[yField];
   }
+  const axisTitles = axisTitlesForEncodedChart(chart, xField, yField);
   return {
     chart: {
       ...chart,
       xField,
-      xAxisTitle: chart.xAxisTitle ?? chartEncodingAxisTitle(chart, "x", xField),
-      yAxisTitle: chart.yAxisTitle ?? chartEncodingAxisTitle(chart, "y", yField),
+      ...axisTitles,
       series: seriesValues.map((value) => ({ field: seriesFields.get(value), label: value, lineStyle: seriesLineStyles.get(value), semanticRole: inferredSeriesRole(value) })),
     },
     rows: [...rowsByX.values()],
diff --git a/plugins/data-analytics/src/analytics-app/charting/chart-tokens.css b/plugins/data-analytics/src/analytics-app/charting/chart-tokens.css
index cc27cd8..3356f4d 100644
--- a/plugins/data-analytics/src/analytics-app/charting/chart-tokens.css
+++ b/plugins/data-analytics/src/analytics-app/charting/chart-tokens.css
@@ -154,11 +154,11 @@
 
 .chart-frame--heatmap {
   min-height: 0;
-  overflow: hidden;
+  overflow: visible;
 }
 
 .chart-frame--heatmap > .chart-plot {
-  overflow: hidden;
+  overflow: visible;
 }
 
 .chart-frame--custom > .chart-plot > * {
@@ -228,6 +228,20 @@
   color: var(--ds-chart-text);
 }
 
+@keyframes chart-tooltip-reveal {
+  from {
+    opacity: 0;
+  }
+
+  to {
+    opacity: 1;
+  }
+}
+
+.recharts-tooltip-wrapper .chart-tooltip {
+  animation: chart-tooltip-reveal 80ms ease both;
+}
+
 .chart-tooltip-label {
   margin-bottom: 8px;
   color: var(--ds-chart-text-muted);
@@ -506,7 +520,7 @@ button.leaderboard-caption:focus-visible {
   height: 100%;
   min-height: 0;
   max-height: 100%;
-  overflow: hidden;
+  overflow: visible;
   overscroll-behavior: contain;
 }
 
diff --git a/plugins/data-analytics/src/analytics-app/charting/chart-transforms.ts b/plugins/data-analytics/src/analytics-app/charting/chart-transforms.ts
index 47bb585..2ee9ab6 100644
--- a/plugins/data-analytics/src/analytics-app/charting/chart-transforms.ts
+++ b/plugins/data-analytics/src/analytics-app/charting/chart-transforms.ts
@@ -72,6 +72,12 @@ export function formatValue(value: unknown, format: ValueFormat = "compact", uni
   const numeric = asNumber(value);
   if (numeric == null) return value == null ? "n/a" : String(value);
   const cleanUnit = unit?.trim();
+  if (format === "percent" && cleanUnit === "%") {
+    return new Intl.NumberFormat(undefined, {
+      maximumFractionDigits: 1,
+      style: "percent",
+    }).format(numeric);
+  }
   if (cleanUnit) {
     if (isPercentWordUnit(cleanUnit)) {
       return new Intl.NumberFormat(undefined, {
@@ -342,6 +348,47 @@ export function buildWaterfallRows(chart: ChartSpec, rows: ChartDataRow[]): Wate
   });
 }
 
+const WATERFALL_NARROW_AXIS_RATIO = 4;
+const WATERFALL_AXIS_PADDING_RATIO = 0.12;
+
+export function getWaterfallYAxisScale(
+  rows: WaterfallDataRow[],
+): { domain: [number, number]; ticks: number[] } | undefined {
+  const points = rows
+    .flatMap((row) => [row.__waterfallStart, row.__waterfallEnd])
+    .filter((value) => Number.isFinite(value));
+  const nonZeroPoints = points.filter((value) => value !== 0);
+  if (nonZeroPoints.length < 2) return undefined;
+
+  const min = Math.min(...points, 0);
+  const max = Math.max(...points, 0);
+  const focusedMin = Math.min(...nonZeroPoints);
+  const focusedMax = Math.max(...nonZeroPoints);
+  const focusedSpan = focusedMax - focusedMin;
+  const fullSpan = max - min;
+  const sameSideOfZero = focusedMin > 0 || focusedMax < 0;
+  if (!sameSideOfZero || focusedSpan <= 0 || fullSpan / focusedSpan < WATERFALL_NARROW_AXIS_RATIO) {
+    return undefined;
+  }
+
+  const padding = Math.max(
+    focusedSpan * WATERFALL_AXIS_PADDING_RATIO,
+    niceAxisStep(focusedSpan / Math.max(1, Y_AXIS_TICK_COUNT - 1)) / 2,
+  );
+  const paddedMin = focusedMin - padding;
+  const paddedMax = focusedMax + padding;
+  const tickStep = niceAxisStep((paddedMax - paddedMin) / Math.max(1, Y_AXIS_TICK_COUNT - 1));
+  const axisMin = Math.floor(paddedMin / tickStep) * tickStep;
+  const axisMax = Math.ceil(paddedMax / tickStep) * tickStep;
+  if (!Number.isFinite(axisMin) || !Number.isFinite(axisMax) || axisMin === axisMax) return undefined;
+
+  const tickSlots = Math.max(1, Math.round((axisMax - axisMin) / tickStep));
+  return {
+    domain: [axisMin, axisMax],
+    ticks: Array.from({ length: tickSlots + 1 }, (_, index) => axisMin + tickStep * index),
+  };
+}
+
 export function buildPieRows(chart: ChartSpec, rows: ChartDataRow[]): ChartDataRow[] {
   if (chart.series.length > 1) {
     return chart.series
diff --git a/plugins/data-analytics/src/analytics-app/layout/AnalyticsLayoutCanvas.tsx b/plugins/data-analytics/src/analytics-app/layout/AnalyticsLayoutCanvas.tsx
index 7179a1a..c18eedc 100644
--- a/plugins/data-analytics/src/analytics-app/layout/AnalyticsLayoutCanvas.tsx
+++ b/plugins/data-analytics/src/analytics-app/layout/AnalyticsLayoutCanvas.tsx
@@ -27,11 +27,17 @@ export type AnalyticsLayoutRenderActions = {
 
 export type AnalyticsLayoutBlock = {
   className?: string;
+  compactGroup?: string;
   defaultLayout?: AnalyticsLayoutWidth;
   id: string;
   render: (layout: AnalyticsLayoutWidth, actions: AnalyticsLayoutRenderActions) => ReactNode;
 };
 
+type AnalyticsLayoutDisplayRow = {
+  compactGroup: string | null;
+  items: AnalyticsLayoutItemState[];
+};
+
 type BlockRect = {
   bottom: number;
   height: number;
@@ -60,6 +66,8 @@ const AUTO_SCROLL_STEP_PX = 18;
 const REORDER_INSERT_BAND_RATIO = 0.3;
 const SPLIT_SIDE_ZONE_RATIO = 0.4;
 const SPLIT_INDICATOR_OUTSET_PX = 8;
+const COMPACT_ITEM_MIN_WIDTH_PX = 280;
+const COMPACT_ITEM_GAP_PX = 8;
 
 function parseStoredLayout(storageKey: string | null): unknown {
   if (!storageKey) return undefined;
@@ -79,6 +87,69 @@ function sameItems(first: AnalyticsLayoutItemState[], second: AnalyticsLayoutIte
   );
 }
 
+function compactGroupForItem(
+  item: AnalyticsLayoutItemState,
+  blockById: Map<string, AnalyticsLayoutBlock>
+) {
+  return blockById.get(item.id)?.compactGroup ?? null;
+}
+
+function buildDisplayRows(
+  items: AnalyticsLayoutItemState[],
+  blockById: Map<string, AnalyticsLayoutBlock>
+): AnalyticsLayoutDisplayRow[] {
+  const displayRows: AnalyticsLayoutDisplayRow[] = [];
+  let index = 0;
+  while (index < items.length) {
+    const compactGroup = compactGroupForItem(items[index], blockById);
+    if (compactGroup) {
+      const compactItems: AnalyticsLayoutItemState[] = [];
+      while (
+        index < items.length
+        && compactGroupForItem(items[index], blockById) === compactGroup
+      ) {
+        compactItems.push(items[index]);
+        index += 1;
+      }
+      displayRows.push({ compactGroup, items: compactItems });
+      continue;
+    }
+
+    const standardItems: AnalyticsLayoutItemState[] = [];
+    while (index < items.length && !compactGroupForItem(items[index], blockById)) {
+      standardItems.push(items[index]);
+      index += 1;
+    }
+    for (const packedRow of packRows(standardItems)) {
+      displayRows.push({ compactGroup: null, items: [...packedRow] });
+    }
+  }
+  return displayRows;
+}
+
+function balancedCompactColumnCount(containerWidth: number, itemCount: number) {
+  if (itemCount <= 1 || containerWidth <= 0) return 1;
+  const fittingColumns = Math.max(
+    1,
+    Math.min(
+      itemCount,
+      Math.floor((containerWidth + COMPACT_ITEM_GAP_PX) / (COMPACT_ITEM_MIN_WIDTH_PX + COMPACT_ITEM_GAP_PX))
+    )
+  );
+  if (fittingColumns <= 1 || fittingColumns === itemCount) return fittingColumns;
+
+  let balancedColumns = fittingColumns;
+  let fewestEmptyCells = (fittingColumns - (itemCount % fittingColumns)) % fittingColumns;
+  for (let candidate = fittingColumns - 1; candidate >= 2; candidate -= 1) {
+    const emptyCells = (candidate - (itemCount % candidate)) % candidate;
+    if (emptyCells < fewestEmptyCells) {
+      balancedColumns = candidate;
+      fewestEmptyCells = emptyCells;
+    }
+  }
+  return balancedColumns;
+}
+
 function rectFromElement(element: HTMLElement): BlockRect {
   const rect = element.getBoundingClientRect();
   return {
@@ -147,6 +218,7 @@ export function AnalyticsLayoutCanvas({
 }) {
   const [items, setItems] = useState<AnalyticsLayoutItemState[]>(() => normalize(blocks));
   const [dragSession, setDragSession] = useState<DragSession | null>(null);
+  const [canvasWidth, setCanvasWidth] = useState(0);
   const blockById = useMemo(() => new Map(blocks.map((block) => [block.id, block])), [blocks]);
   const canvasRef = useRef<HTMLDivElement | null>(null);
   const itemRefs = useRef(new Map<string, HTMLElement>());
@@ -160,6 +232,23 @@ export function AnalyticsLayoutCanvas({
     onLayoutChangeRef.current = onLayoutChange;
   }, [onLayoutChange]);
 
+  useLayoutEffect(() => {
+    const canvas = canvasRef.current;
+    if (!canvas) return;
+    const updateWidth = () => {
+      const nextWidth = Math.floor(canvas.getBoundingClientRect().width);
+      setCanvasWidth((currentWidth) => currentWidth === nextWidth ? currentWidth : nextWidth);
+    };
+    updateWidth();
+    if (typeof ResizeObserver === "undefined") {
+      window.addEventListener("resize", updateWidth);
+      return () => window.removeEventListener("resize", updateWidth);
+    }
+    const observer = new ResizeObserver(updateWidth);
+    observer.observe(canvas);
+    return () => observer.disconnect();
+  }, []);
+
   useLayoutEffect(() => {
     const previousLoad = layoutLoadRef.current;
     const shouldReloadStoredLayout =
@@ -209,7 +298,7 @@ export function AnalyticsLayoutCanvas({
     };
   }, []);
 
-  const rows = packRows(items);
+  const rows = buildDisplayRows(items, blockById);
 
   function measureBlockRects() {
     const rects = new Map<string, BlockRect>();
@@ -473,51 +562,62 @@ export function AnalyticsLayoutCanvas({
           style={dropIndicator.style}
         />
       ) : null}
-      {rows.map((row, rowIndex) => (
-        <div
-          className={`analytics-layout-row ${row.length === 2 ? "is-half-row" : "is-full-row"}`}
-          key={row.map((item) => item.id).join(":")}
-        >
-          {row.map((item) => {
-            const block = blockById.get(item.id);
-            if (!block) return null;
-            const isDragged = dragSession?.draggedId === item.id;
-            const isTarget = dragSession?.targetId === item.id;
-            const rendered = block.render(item.layout, {
-              setLayout: (nextLayout) => setBlockLayout(item.id, nextLayout)
-            });
-            return (
-              <div
-                className={`analytics-layout-item layout-${item.layout} ${block.className ?? ""} ${itemClassName?.(block, item.layout) ?? ""} ${isDragged ? "is-dragging" : ""} ${isTarget ? `is-drop-target intent-zone-${dragSession?.intent}` : ""}`.trim()}
-                data-analytics-layout-item
-                data-layout-block-id={item.id}
-                data-layout-row-index={rowIndex}
-                data-layout-width={item.layout}
-                key={item.id}
-                ref={(node) => {
-                  if (node) itemRefs.current.set(item.id, node);
-                  else itemRefs.current.delete(item.id);
-                }}
-              >
-                {isEditMode ? (
-                  <div
-                    aria-hidden="true"
-                    className="analytics-layout-grab-handle"
-                    data-image-export-exclude="true"
-                    onPointerDown={(event) => handlePointerDown(event, item.id)}
-                    title="Drag to reorder"
-                  >
-                    <DragHandleIcon />
+      {rows.map((row, rowIndex) => {
+        const compactColumns = row.compactGroup
+          ? balancedCompactColumnCount(canvasWidth, row.items.length)
+          : null;
+        const rowStyle = compactColumns
+          ? { "--analytics-compact-columns": compactColumns } as CSSProperties
+          : undefined;
+        return (
+          <div
+            className={`analytics-layout-row ${row.compactGroup ? "is-compact-row" : row.items.length === 2 ? "is-half-row" : "is-full-row"}`}
+            data-compact-columns={compactColumns ?? undefined}
+            data-compact-group={row.compactGroup ?? undefined}
+            key={row.items.map((item) => item.id).join(":")}
+            style={rowStyle}
+          >
+            {row.items.map((item) => {
+              const block = blockById.get(item.id);
+              if (!block) return null;
+              const isDragged = dragSession?.draggedId === item.id;
+              const isTarget = dragSession?.targetId === item.id;
+              const rendered = block.render(item.layout, {
+                setLayout: (nextLayout) => setBlockLayout(item.id, nextLayout)
+              });
+              return (
+                <div
+                  className={`analytics-layout-item layout-${item.layout} ${block.className ?? ""} ${itemClassName?.(block, item.layout) ?? ""} ${isDragged ? "is-dragging" : ""} ${isTarget ? `is-drop-target intent-zone-${dragSession?.intent}` : ""}`.trim()}
+                  data-analytics-layout-item
+                  data-layout-block-id={item.id}
+                  data-layout-row-index={rowIndex}
+                  data-layout-width={item.layout}
+                  key={item.id}
+                  ref={(node) => {
+                    if (node) itemRefs.current.set(item.id, node);
+                    else itemRefs.current.delete(item.id);
+                  }}
+                >
+                  {isEditMode ? (
+                    <div
+                      aria-hidden="true"
+                      className="analytics-layout-grab-handle"
+                      data-image-export-exclude="true"
+                      onPointerDown={(event) => handlePointerDown(event, item.id)}
+                      title="Drag to reorder"
+                    >
+                      <DragHandleIcon />
+                    </div>
+                  ) : null}
+                  <div className="analytics-layout-item-shell">
+                    {rendered}
                   </div>
-                ) : null}
-                <div className="analytics-layout-item-shell">
-                  {rendered}
                 </div>
-              </div>
-            );
-          })}
-        </div>
-      ))}
+              );
+            })}
+          </div>
+        );
+      })}
       {dragSession ? (
         <div
           aria-hidden="true"
diff --git a/plugins/data-analytics/src/analytics-app/styles.css b/plugins/data-analytics/src/analytics-app/styles.css
index 305457f..100cae8 100644
--- a/plugins/data-analytics/src/analytics-app/styles.css
+++ b/plugins/data-analytics/src/analytics-app/styles.css
@@ -164,34 +164,26 @@ select:focus-visible {
   background: var(--ds-bg);
 }
 
-.analytics-top-bar-title {
+.analytics-top-bar-leading {
+  display: flex;
   flex: 1 1 auto;
+  align-items: center;
+  height: 32px;
   min-width: 0;
 }
 
-.analytics-top-bar-refresh {
-  display: inline-flex;
+.analytics-top-bar-title {
+  flex: 0 1 auto;
   min-width: 0;
-  align-items: center;
-  gap: 8px;
+  margin-right: 8px;
 }
 
-.analytics-top-bar-refresh-label {
+.analytics-top-bar-freshness {
   display: inline-flex;
+  flex: 0 1 auto;
   min-width: 0;
   align-items: center;
   gap: 6px;
-  margin: 0;
-  padding: 0;
-  color: var(--ds-text-tertiary);
-  font-size: 14px;
-  font-weight: 500;
-  line-height: 22px;
-}
-
-.analytics-top-bar-refresh-label span {
-  overflow: hidden;
-  text-overflow: ellipsis;
 }
 
 .analytics-top-bar-actions {
@@ -199,27 +191,105 @@ select:focus-visible {
   flex: 0 0 auto;
   gap: 8px;
   align-items: center;
+  margin-left: auto;
 }
 
-.analytics-top-bar-freshness {
+.top-bar-refresh-tooltip-anchor {
+  position: relative;
   display: inline-flex;
+  flex: 0 1 auto;
   min-width: 0;
+}
+
+.top-bar-refresh-datetime {
+  display: inline-flex;
   align-items: center;
+  justify-content: center;
+  height: 32px;
+  min-width: 0;
+  max-width: min(260px, 42vw);
+  min-height: 32px;
+  padding: 0 12px;
+  border: 0;
+  border-radius: var(--ds-radius-pill);
+  background: transparent;
+  color: var(--ds-text-tertiary);
+  font-size: 14px;
+  font-weight: 400;
+  line-height: 20px;
+  letter-spacing: -0.13px;
+  white-space: nowrap;
+}
+
+.top-bar-refresh-datetime:hover {
+  background: var(--ds-menu-hover-bg);
+  color: var(--ds-text-secondary);
+}
+
+.top-bar-refresh-text {
+  min-width: 0;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.top-bar-refresh-tooltip {
+  position: fixed;
+  z-index: 1000;
+  top: 48px;
+  left: 12px;
+  width: max-content;
+  max-width: min(320px, calc(100vw - 24px));
+  padding: 8px 10px;
+  border: 1px solid var(--ds-menu-border);
+  border-radius: var(--ds-radius-sm);
+  background: var(--ds-menu-bg);
+  box-shadow: var(--ds-shadow);
+  color: var(--ds-menu-text);
+  font-size: 12px;
+  font-weight: 400;
+  line-height: 16px;
+  opacity: 0;
+  overflow-wrap: anywhere;
+  pointer-events: none;
+  transform: translateY(-4px);
+  transition: opacity 120ms ease, transform 120ms ease, visibility 120ms ease;
+  visibility: hidden;
+  white-space: normal;
+}
+
+.top-bar-refresh-tooltip-anchor:hover .top-bar-refresh-tooltip,
+.top-bar-refresh-tooltip-anchor:focus-within .top-bar-refresh-tooltip {
+  opacity: 1;
+  transform: translateY(0);
+  visibility: visible;
+}
+
+.top-bar-refresh-tooltip-anchor:hover .top-bar-refresh-tooltip {
+  transition-delay: 300ms;
+}
+
+.top-bar-refresh-tooltip-anchor:focus-within .top-bar-refresh-tooltip {
+  transition-delay: 0ms;
 }
 
 .analytics-top-bar .page-title-edit-target {
   display: inline-flex;
-  max-width: 100%;
-  width: auto;
+  box-sizing: border-box;
+  width: calc(100% + 16px);
+  min-width: 0;
+  max-width: calc(100% + 16px);
   min-height: 32px;
-  margin: -4px -6px;
+  margin: -4px -8px;
   align-items: center;
-  padding: 4px 6px;
+  padding: 4px 8px;
   border-radius: 8px;
 }
 
 .analytics-top-bar .page-title-edit-target h1 {
-  overflow: visible;
+  min-width: 0;
+  max-width: 100%;
+  overflow: hidden;
   margin: 0;
   color: var(--ds-text-primary);
   font-family: var(--ds-font);
@@ -232,14 +302,23 @@ select:focus-visible {
 }
 
 .analytics-top-bar .page-title-editor {
+  width: calc(100% + 16px);
+  height: 32px;
+  max-width: calc(100% + 16px);
+  max-height: 32px;
   min-height: 32px;
-  padding: 4px 6px;
+  margin: -4px -8px;
+  overflow-x: auto;
+  overflow-y: hidden;
+  padding: 4px 8px;
+  border: 0;
   color: var(--ds-text-primary);
   font-family: var(--ds-font);
   font-size: 14px;
   font-weight: 500;
   line-height: 20px;
   letter-spacing: -0.13px;
+  white-space: nowrap;
 }
 
 .top-bar-button {
@@ -275,6 +354,16 @@ select:focus-visible {
   color: var(--ds-text);
 }
 
+.top-bar-overflow-button {
+  width: 32px;
+  min-width: 32px;
+  padding: 0;
+}
+
+.top-bar-edit-button {
+  min-width: 74px;
+}
+
 .top-bar-button-primary {
   border-color: var(--ds-text-primary);
   background: var(--ds-text-primary);
@@ -771,64 +860,24 @@ select:focus-visible {
   color: var(--ds-green);
 }
 
-.kpi-strip {
-  --kpi-columns: 1;
-  display: grid;
-  grid-template-columns: repeat(var(--kpi-columns), minmax(0, 1fr));
-  align-items: stretch;
-  overflow: hidden;
-  margin: 18px 0;
-  border: 1px solid var(--ds-border-subtle);
-  border-radius: var(--ds-radius-panel);
-  background: var(--ds-surface);
-}
-
 .kpi-card {
-  padding: 16px 24px;
-  border-top: 1px solid var(--ds-border-subtle);
-  background: var(--ds-surface);
-}
-
-.kpi-card:first-child {
-  border-top: 0;
-}
-
-.kpi-card-placeholder {
-  pointer-events: none;
-}
-
-.kpi-strip.kpi-columns-2 .kpi-card:nth-child(n),
-.kpi-strip.kpi-columns-3 .kpi-card:nth-child(n),
-.kpi-strip.kpi-columns-4 .kpi-card:nth-child(n),
-.kpi-strip.kpi-columns-5 .kpi-card:nth-child(n) {
-  border-top: 1px solid var(--ds-border-subtle);
-  border-left: 1px solid var(--ds-border-subtle);
-}
-
-.kpi-strip.kpi-columns-2 .kpi-card:nth-child(-n + 2),
-.kpi-strip.kpi-columns-3 .kpi-card:nth-child(-n + 3),
-.kpi-strip.kpi-columns-4 .kpi-card:nth-child(-n + 4),
-.kpi-strip.kpi-columns-5 .kpi-card:nth-child(-n + 5) {
-  border-top: 0;
-}
-
-.kpi-strip.kpi-columns-2 .kpi-card:nth-child(2n + 1),
-.kpi-strip.kpi-columns-3 .kpi-card:nth-child(3n + 1),
-.kpi-strip.kpi-columns-4 .kpi-card:nth-child(4n + 1),
-.kpi-strip.kpi-columns-5 .kpi-card:nth-child(5n + 1) {
-  border-left: 0;
+  position: relative;
+  background: transparent;
 }
 
 .kpi-label {
+  overflow: hidden;
   color: var(--ds-text-muted);
   font-size: 14px;
   line-height: 20px;
-  overflow-wrap: anywhere;
-  white-space: normal;
+  overflow-wrap: normal;
+  text-overflow: ellipsis;
+  white-space: nowrap;
 }
 
 .kpi-label-row {
   display: flex;
+  flex: 1 1 auto;
   gap: 6px;
   align-items: center;
   min-width: 0;
@@ -910,12 +959,17 @@ select:focus-visible {
 
 .metric-badge-row {
   display: flex;
-  flex-wrap: wrap;
+  flex-wrap: nowrap;
   gap: 8px;
   margin-top: 8px;
 }
 
-.metric-badge {
+.report-metric-card .metric-badge {
+  flex-wrap: nowrap;
+  white-space: nowrap;
+}
+
+.chip {
   display: inline-flex;
   flex-wrap: wrap;
   gap: 4px;
@@ -1175,6 +1229,10 @@ select:focus-visible {
   overflow: hidden;
 }
 
+.chart-frame--custom.chart-frame--heatmap {
+  overflow: visible;
+}
+
 .chart-frame--custom > * {
   width: 100%;
   height: 100%;
@@ -1341,6 +1399,10 @@ select:focus-visible {
   height: 100%;
 }
 
+.analytics-layout-item-shell > .report-metric-card {
+  height: 100%;
+}
+
 .analytics-layout-item.is-dragging {
   opacity: 1;
 }
@@ -1422,6 +1484,22 @@ select:focus-visible {
   flex-direction: column;
 }
 
+.chart-body-measure {
+  display: flex;
+  flex: 1 1 0;
+  width: 100%;
+  min-width: 0;
+  min-height: 0;
+  flex-direction: column;
+}
+
+.chart-body-measure > .chart-frame {
+  flex: 1 1 0;
+  width: 100%;
+  min-width: 0;
+  min-height: 0;
+}
+
 .viz-card .chart-frame {
   flex: 1 1 0;
   width: 100%;
@@ -1873,6 +1951,12 @@ select:focus-visible {
   border: 0;
   border-radius: 24px;
   background: var(--ds-overlay-bg);
+  color: var(--ds-text-primary);
+  font-family: var(--ds-font);
+  font-size: var(--codex-font-size-base);
+  font-weight: 400;
+  letter-spacing: 0;
+  line-height: 20px;
   animation: modal-enter 160ms cubic-bezier(0.16, 1, 0.3, 1);
 }
 
@@ -1883,6 +1967,8 @@ select:focus-visible {
 
 .native-modal.source-modal {
   width: min(800px, calc(100vw - 48px));
+  height: fit-content;
+  max-height: min(760px, calc(100dvh - 48px));
 }
 
 .modal-panel {
@@ -1896,11 +1982,47 @@ select:focus-visible {
 }
 
 .source-modal-panel {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+  height: auto;
+  max-height: min(760px, calc(100dvh - 48px));
+  min-width: 0;
+  min-height: 0;
+  overflow: hidden;
   background: var(--ds-overlay-bg);
-  padding: 20px;
+  padding: 0;
   overscroll-behavior: contain;
 }
 
+.source-modal-panel > .modal-header {
+  margin: 0;
+  padding: 20px 24px 4px;
+}
+
+.native-modal.table-fullscreen-modal {
+  box-sizing: border-box;
+  width: min(1200px, calc(100dvw - 32px));
+  max-width: calc(100dvw - 32px);
+  height: fit-content;
+  max-height: calc(100dvh - 32px);
+}
+
+.table-fullscreen-panel {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+  height: auto;
+  max-height: calc(100dvh - 32px);
+  min-width: 0;
+  min-height: 0;
+  overflow: hidden;
+}
+
+.table-fullscreen-panel > .modal-header {
+  flex: 0 0 auto;
+}
+
 .native-modal.chart-explore-modal {
   width: min(1180px, calc(100vw - 96px));
   max-width: calc(100vw - 96px);
@@ -1979,7 +2101,9 @@ select:focus-visible {
 
 .modal-header h2 {
   margin: 0;
-  font-size: 20px;
+  color: var(--ds-text-primary);
+  font-size: var(--codex-heading-md);
+  font-weight: 500;
   line-height: 26px;
 }
 
@@ -2063,43 +2187,169 @@ select:focus-visible {
   max-width: 100%;
 }
 
-.source-modal-sections {
+.source-modal-content {
+  display: flex;
+  flex: 0 1 auto;
+  flex-direction: column;
+  min-width: 0;
+  min-height: 0;
+}
+
+.source-modal-tabs {
+  position: relative;
+  display: flex;
+  gap: 32px;
+  align-items: flex-end;
+  flex: 0 0 auto;
+  min-width: 0;
+  padding: 0 24px;
+  border-bottom: 1px solid var(--ds-border-subtle);
+}
+
+.source-modal-tab-item {
+  position: relative;
+  display: flex;
+  flex: 0 0 auto;
+  align-items: stretch;
+}
+
+.source-modal-tab {
+  display: flex;
+  min-width: 0;
+  min-height: 40px;
+  align-items: flex-end;
+  padding: 0 0 13px;
+  border: 0;
+  border-radius: 0;
+  background: transparent;
+  color: var(--ds-text-muted);
+  font-size: var(--codex-font-size-base);
+  font-weight: 500;
+  line-height: 20px;
+  transition: color 160ms cubic-bezier(0.16, 1, 0.3, 1);
+}
+
+.source-modal-tab:hover {
+  background: transparent;
+  color: var(--ds-text);
+}
+
+.source-modal-tab[aria-selected="true"] {
+  background: transparent;
+  color: var(--ds-text);
+}
+
+.source-modal-tab-indicator {
+  position: absolute;
+  bottom: -1px;
+  left: 0;
+  width: 0;
+  height: 2px;
+  background: var(--ds-text-primary);
+  opacity: 0;
+  pointer-events: none;
+  transform: translate3d(0, 0, 0);
+  transition:
+    transform 180ms cubic-bezier(0.16, 1, 0.3, 1),
+    width 180ms cubic-bezier(0.16, 1, 0.3, 1),
+    opacity 80ms ease;
+  will-change: transform, width;
+}
+
+.source-modal-tab-indicator[data-ready="true"] {
+  opacity: 1;
+}
+
+.source-modal-tab:focus-visible {
+  outline: 2px solid var(--ds-blue);
+  outline-offset: 2px;
+}
+
+.source-modal-body {
+  flex: 0 1 auto;
+  min-width: 0;
+  min-height: 0;
+  overflow-x: hidden;
+  overflow-y: auto;
+  padding: 20px 24px 24px;
+  overscroll-behavior: contain;
+}
+
+.source-modal-tab-panel {
+  min-width: 0;
+}
+
+.source-overview {
   display: grid;
-  gap: 20px;
 }
 
-.source-modal-section {
+.source-details-layout {
   display: grid;
-  gap: 12px;
+  gap: 24px;
   min-width: 0;
 }
 
-.source-modal-section h3 {
+.source-details-summary,
+.source-details-stack {
+  min-width: 0;
   margin: 0;
-  color: var(--ds-text);
-  font-size: 14px;
-  font-weight: 600;
-  line-height: 20px;
 }
 
-.source-details-grid {
-  grid-template-columns: repeat(2, minmax(0, 1fr));
-  gap: 12px 20px;
+.source-details-summary {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 16px;
+  align-items: start;
 }
 
-.source-details-grid > div {
+.source-details-stack {
+  display: grid;
+  gap: 16px;
+}
+
+.source-details-summary > div,
+.source-details-stack > div {
   display: grid;
   gap: 4px;
   min-width: 0;
 }
 
-.source-detail-list {
-  display: grid;
+.source-details-summary dt,
+.source-details-stack dt {
+  color: var(--ds-text-secondary);
+  font-size: var(--codex-font-size-sm);
+  font-weight: 500;
+  line-height: 16px;
+}
+
+.source-details-summary dd,
+.source-details-stack dd {
+  min-width: 0;
+  margin: 0;
+  color: var(--ds-text-primary);
+  font-size: var(--codex-font-size-base);
+  font-weight: 400;
+  line-height: 20px;
+  overflow-wrap: anywhere;
+}
+
+.source-metadata-chip-list {
+  display: flex;
+  flex-wrap: wrap;
   gap: 6px;
 }
 
-.source-detail-list-item {
+.source-metadata-chip {
+  border-color: var(--ds-border);
+  background: transparent;
+  color: var(--ds-text-primary);
+  font-family: var(--ds-font);
+  font-size: var(--codex-font-size-base);
+  font-weight: 400;
+  line-height: 18px;
+  padding-block: 4px;
   overflow-wrap: anywhere;
+  word-break: break-word;
 }
 
 .source-data-table {
@@ -2112,6 +2362,23 @@ select:focus-visible {
   min-width: 100%;
 }
 
+.source-modal-panel .data-table th {
+  color: var(--ds-text-secondary);
+  font-weight: 500;
+}
+
+.source-modal-panel .data-table td {
+  color: var(--ds-text-primary);
+}
+
+.source-metric-definitions {
+  min-width: 0;
+}
+
+.source-metric-definitions tbody .table-column-content-fit {
+  font-weight: 500;
+}
+
 .source-query-shell {
   width: 100%;
   min-width: 0;
@@ -2165,9 +2432,8 @@ select:focus-visible {
   width: 100%;
   min-width: 0;
   max-width: 100%;
-  max-height: 430px;
-  overflow-x: hidden;
-  overflow-y: auto;
+  max-height: none;
+  overflow: visible;
   margin: 0;
   padding: 14px;
   background: var(--ds-overlay-bg);
@@ -2583,7 +2849,6 @@ button.leaderboard-caption:focus-visible {
 .heatmap-tooltip {
   position: absolute;
   z-index: 40;
-  animation: dashboard-menu-in 120ms cubic-bezier(0.16, 1, 0.3, 1) both;
   pointer-events: none;
 }
 
@@ -2617,33 +2882,69 @@ button.leaderboard-caption:focus-visible {
   min-width: 0;
 }
 
-.report-block-stack {
-  margin-top: 24px;
+.metric-card-layout {
+  --metric-card-gap: 8px;
+  --metric-card-min-width: 280px;
 }
 
-.report-stack-metric-strip .kpi-strip {
-  margin: 0;
+.report-block-stack {
+  margin-top: 24px;
 }
 
-.report-metric-strip-block {
+.report-metric-card {
   position: relative;
+  display: flex;
   min-width: 0;
+  flex-direction: column;
+  justify-content: flex-start;
   overflow: visible;
+  padding: 20px;
+  border: 1px solid var(--ds-border-subtle);
+  border-radius: var(--ds-radius-panel);
+  background: transparent;
 }
 
-.report-metric-strip-block .kpi-strip {
-  margin: 0;
+.report-metric-card .kpi-label-row {
+  flex: 0 0 auto;
 }
 
-.report-metric-strip-block .viz-card-actions {
+.report-metric-card .kpi-info-wrap {
+  width: 18px;
+  height: 20px;
+}
+
+.report-metric-card .kpi-info {
   position: absolute;
-  top: 0;
-  right: 0;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+}
+
+.metric-card-layout .analytics-layout-row:has(> .report-stack-item-metric-card) {
+  gap: var(--metric-card-gap);
+}
+
+.metric-card-layout .analytics-layout-row.is-compact-row {
+  grid-template-columns: repeat(
+    var(--analytics-compact-columns, auto-fit),
+    minmax(min(100%, var(--metric-card-min-width)), 1fr)
+  );
+  gap: var(--metric-card-gap);
+}
+
+.metric-card-layout .analytics-layout-row.is-compact-row > .analytics-layout-item.layout-full {
+  grid-column: auto;
+}
+
+.report-metric-card .viz-card-actions {
+  position: absolute;
+  top: 12px;
+  right: 12px;
   z-index: 10;
 }
 
-.report-metric-strip-block .viz-card-menu-button {
-  margin: 4px;
+.report-metric-card .viz-card-menu {
+  top: 38px;
 }
 
 .report-stack-item {
@@ -2697,6 +2998,13 @@ button.leaderboard-caption:focus-visible {
   max-height: 320px;
 }
 
+.report-block-stack .viz-card .chart-body-measure {
+  flex: 0 0 320px;
+  height: 320px;
+  min-height: 320px;
+  max-height: 320px;
+}
+
 .report-block {
   position: relative;
   display: flex;
@@ -2869,6 +3177,20 @@ button.leaderboard-caption:focus-visible {
   min-width: 0;
 }
 
+.table-scroll-shell {
+  --table-scroll-edge-surface: var(--ds-bg);
+  --table-scrollbar-block-size: 0px;
+  --table-scrollbar-inline-size: 0px;
+  position: relative;
+  width: 100%;
+  max-width: 100%;
+  min-width: 0;
+}
+
+.native-modal .table-scroll-shell {
+  --table-scroll-edge-surface: var(--ds-overlay-bg);
+}
+
 .table-wrap {
   box-sizing: border-box;
   width: 100%;
@@ -2877,7 +3199,42 @@ button.leaderboard-caption:focus-visible {
   overflow-x: auto;
   overflow-y: hidden;
   padding-bottom: 10px;
-  scrollbar-gutter: stable;
+}
+
+.table-scroll-edge {
+  position: absolute;
+  z-index: 4;
+  top: 0;
+  bottom: var(--table-scrollbar-block-size);
+  width: 28px;
+  opacity: 0;
+  pointer-events: none;
+  transition: opacity 150ms ease;
+}
+
+.table-scroll-edge-left {
+  left: 0;
+  background: linear-gradient(
+    to right,
+    var(--table-scroll-edge-surface) 0%,
+    color-mix(in srgb, var(--table-scroll-edge-surface) 82%, transparent) 42%,
+    transparent 100%
+  );
+}
+
+.table-scroll-edge-right {
+  right: var(--table-scrollbar-inline-size);
+  background: linear-gradient(
+    to left,
+    var(--table-scroll-edge-surface) 0%,
+    color-mix(in srgb, var(--table-scroll-edge-surface) 82%, transparent) 42%,
+    transparent 100%
+  );
+}
+
+.table-scroll-shell[data-can-scroll-left="true"] .table-scroll-edge-left,
+.table-scroll-shell[data-can-scroll-right="true"] .table-scroll-edge-right {
+  opacity: 1;
 }
 
 .table-scroll-content {
@@ -2899,8 +3256,36 @@ button.leaderboard-caption:focus-visible {
 }
 
 .table-wrap.fullscreen {
-  max-height: min(68vh, 720px);
+  flex: 0 1 auto;
+  min-height: 0;
+  max-height: min(700px, calc(100dvh - 138px));
   overflow: auto;
+  padding-bottom: 0;
+  outline: none;
+  scrollbar-gutter: stable;
+}
+
+.table-wrap:focus-visible {
+  outline: 2px solid var(--ds-blue);
+  outline-offset: -2px;
+}
+
+.table-wrap.fullscreen .table-scroll-content {
+  width: 100%;
+  min-width: 100%;
+}
+
+.table-wrap.fullscreen th {
+  position: sticky;
+  z-index: 2;
+  top: 0;
+  background: var(--ds-overlay-bg);
+}
+
+@media print, (forced-colors: active) {
+  .table-scroll-edge {
+    display: none;
+  }
 }
 
 table {
@@ -2914,6 +3299,11 @@ table {
   table-layout: fixed;
 }
 
+.data-table-smart-layout .table-column-content-fit {
+  overflow-wrap: anywhere;
+  white-space: normal;
+}
+
 th,
 td {
   border-bottom: 1px solid var(--ds-border-subtle);
@@ -3318,6 +3708,16 @@ td:first-child {
 }
 
 @media (max-width: 760px) {
+  .top-bar-refresh-datetime {
+    max-width: min(180px, 34vw);
+    padding-right: 8px;
+    padding-left: 8px;
+  }
+
+  .analytics-top-bar-freshness .snapshot-status {
+    display: none;
+  }
+
   .report-block-stack {
     grid-template-columns: 1fr;
   }
@@ -3371,12 +3771,19 @@ td:first-child {
   }
 
   .analytics-top-bar {
-    flex-wrap: wrap;
+    gap: 8px;
+    flex-wrap: nowrap;
+  }
+
+  .top-bar-refresh-datetime {
+    max-width: 96px;
+    padding-right: 6px;
+    padding-left: 6px;
   }
 
   .analytics-top-bar-actions {
-    width: 100%;
-    justify-content: flex-start;
+    width: auto;
+    justify-content: flex-end;
   }
 
   .export-menu-list,
@@ -3448,7 +3855,16 @@ td:first-child {
   }
 
   .native-modal.source-modal {
-    width: calc(100vw - 32px);
+    width: calc(100dvw - 24px);
+    height: fit-content;
+    max-height: min(760px, calc(100dvh - 24px));
+  }
+
+  .native-modal.table-fullscreen-modal {
+    width: calc(100dvw - 24px);
+    max-width: calc(100dvw - 24px);
+    height: fit-content;
+    max-height: calc(100dvh - 24px);
   }
 
   .modal-panel {
@@ -3458,11 +3874,24 @@ td:first-child {
   }
 
   .source-modal-panel {
-    padding: 20px;
+    max-height: min(760px, calc(100dvh - 24px));
+    padding: 0;
   }
 
-  .source-details-grid {
-    grid-template-columns: 1fr;
+  .source-modal-panel > .modal-header {
+    padding: 18px 18px 4px;
+  }
+
+  .source-modal-tabs {
+    padding: 0 18px;
+  }
+
+  .source-modal-body {
+    padding: 18px;
+  }
+
+  .source-details-summary {
+    grid-template-columns: minmax(0, 1fr);
   }
 
 }
diff --git a/plugins/data-analytics/src/analytics-app/tables/tableSizing.ts b/plugins/data-analytics/src/analytics-app/tables/tableSizing.ts
new file mode 100644
index 0000000..bd977f3
--- /dev/null
+++ b/plugins/data-analytics/src/analytics-app/tables/tableSizing.ts
@@ -0,0 +1,61 @@
+export type TableSizingColumn = {
+  field: string;
+  sizing?: string;
+};
+
+export type HorizontalScrollMetrics = {
+  clientWidth: number;
+  scrollLeft: number;
+  scrollWidth: number;
+};
+
+const HORIZONTAL_SCROLL_EDGE_TOLERANCE_PX = 1;
+
+export function calculateHorizontalScrollEdges({
+  clientWidth,
+  scrollLeft,
+  scrollWidth,
+}: HorizontalScrollMetrics) {
+  const maximumScrollLeft = Math.max(0, scrollWidth - clientWidth);
+  const hasOverflow = maximumScrollLeft > HORIZONTAL_SCROLL_EDGE_TOLERANCE_PX;
+  const normalizedScrollLeft = Math.min(maximumScrollLeft, Math.max(0, scrollLeft));
+
+  return {
+    canScrollLeft: hasOverflow && normalizedScrollLeft > HORIZONTAL_SCROLL_EDGE_TOLERANCE_PX,
+    canScrollRight:
+      hasOverflow && normalizedScrollLeft < maximumScrollLeft - HORIZONTAL_SCROLL_EDGE_TOLERANCE_PX,
+    hasOverflow,
+  };
+}
+
+export function calculateTableSizing(
+  columns: TableSizingColumn[],
+  activeColumnWidths: Record<string, number>,
+  viewportWidth: number,
+  fillAvailableWidth: boolean,
+) {
+  const flexibleColumns = columns.filter((column) => column.sizing !== "content");
+  const flexibleColumnIndexes = new Map(flexibleColumns.map((column, index) => [column.field, index]));
+  const baseTableWidth = columns.reduce((total, column) => total + (activeColumnWidths[column.field] ?? 0), 0);
+  const normalizedViewportWidth = Math.max(0, Math.round(viewportWidth));
+  const fillExtraWidth = fillAvailableWidth
+    ? Math.max(0, normalizedViewportWidth - baseTableWidth)
+    : 0;
+  const extraWidthPerFlexibleColumn = flexibleColumns.length
+    ? Math.floor(fillExtraWidth / flexibleColumns.length)
+    : 0;
+  const extraWidthRemainder = flexibleColumns.length ? fillExtraWidth % flexibleColumns.length : 0;
+  const columnWidths = Object.fromEntries(columns.map((column) => {
+    const flexibleIndex = flexibleColumnIndexes.get(column.field);
+    const extraWidth = flexibleIndex == null
+      ? 0
+      : extraWidthPerFlexibleColumn + (flexibleIndex < extraWidthRemainder ? 1 : 0);
+    return [column.field, (activeColumnWidths[column.field] ?? 0) + extraWidth];
+  }));
+  const minimumTableWidth = columns.reduce((total, column) => total + (columnWidths[column.field] ?? 0), 0);
+  const tableWidth = fillAvailableWidth
+    ? Math.max(normalizedViewportWidth, minimumTableWidth)
+    : minimumTableWidth;
+
+  return { columnWidths, minimumTableWidth, tableWidth };
+}
diff --git a/plugins/data-analytics/src/analytics-app/tokens.css b/plugins/data-analytics/src/analytics-app/tokens.css
index 488d6ce..0e6cd5e 100644
--- a/plugins/data-analytics/src/analytics-app/tokens.css
+++ b/plugins/data-analytics/src/analytics-app/tokens.css
@@ -54,7 +54,7 @@
   --ds-edit-hover-bg: rgb(232 232 232 / 20%);
   --ds-edit-active-border: var(--color-border-primary, var(--ds-text-primary));
   --ds-menu-button-border: rgba(13, 13, 13, 0.05);
-  --ds-overlay-bg: #ffffff;
+  --ds-overlay-bg: var(--color-token-dropdown-background, #ffffff);
   --ds-menu-bg: var(--ds-overlay-bg);
   --ds-menu-text: var(--color-token-dropdown-foreground, var(--color-text-foreground, var(--ds-text)));
   --ds-menu-border: var(--color-token-dropdown-border, var(--color-border, var(--ds-border-subtle)));
@@ -171,7 +171,7 @@
     --ds-neutral: var(--color-text-tertiary, #afafaf);
     --ds-edit-hover-bg: var(--color-background-primary-ghost-hover, rgb(255 255 255 / 8%));
     --ds-menu-button-border: var(--color-border-subtle, rgba(255, 255, 255, 0.05));
-    --ds-overlay-bg: var(--codex-panel, var(--color-background-primary, #212121));
+    --ds-overlay-bg: var(--color-token-dropdown-background, var(--ds-surface, #212121));
     --ds-menu-bg: var(--ds-overlay-bg);
     --ds-menu-text: var(--color-token-dropdown-foreground, var(--color-text-foreground, var(--ds-text-primary)));
     --ds-menu-border: var(--color-token-dropdown-border, var(--color-border, rgba(255, 255, 255, 0.1)));
@@ -212,7 +212,7 @@
   --ds-neutral: var(--color-text-tertiary, #afafaf);
   --ds-edit-hover-bg: var(--color-background-primary-ghost-hover, rgb(255 255 255 / 8%));
   --ds-menu-button-border: var(--color-border-subtle, rgba(255, 255, 255, 0.05));
-  --ds-overlay-bg: #212121;
+  --ds-overlay-bg: var(--color-token-dropdown-background, #212121);
   --ds-menu-bg: var(--ds-overlay-bg);
   --ds-menu-text: var(--color-token-dropdown-foreground, var(--color-text-foreground, var(--ds-text-primary)));
   --ds-menu-border: var(--color-token-dropdown-border, var(--color-border, rgba(255, 255, 255, 0.1)));
diff --git a/plugins/data-analytics/src/analytics-app/types.ts b/plugins/data-analytics/src/analytics-app/types.ts
index fc7b84e..9839034 100644
--- a/plugins/data-analytics/src/analytics-app/types.ts
+++ b/plugins/data-analytics/src/analytics-app/types.ts
@@ -88,6 +88,8 @@ export type CardSpec = {
   id: string;
   description?: string;
   dataset: string;
+  sourceId?: string;
+  source?: SourceSpec;
   filter?: Record<string, string | number | boolean | null>;
   metrics: CardMetricSpec[];
 };
@@ -170,6 +172,10 @@ export type TableSpec = {
   showDescription?: boolean;
   headerMarkdown?: string;
   dataset: string;
+  defaultSort?: {
+    field: string;
+    direction: "asc" | "desc";
+  };
   density?: TableDensity;
   sourceId?: string;
   source?: SourceSpec;
diff --git a/plugins/data-analytics/src/datascience-artifact-widget.jsx b/plugins/data-analytics/src/datascience-artifact-widget.jsx
index 7aed2f2..87c9285 100644
--- a/plugins/data-analytics/src/datascience-artifact-widget.jsx
+++ b/plugins/data-analytics/src/datascience-artifact-widget.jsx
@@ -26,22 +26,35 @@ const fallbackPayload = {
   manifest: {
     version: 1,
     surface: "report",
-    title: "Core Business Metrics Trend Readout",
-    description: "Fallback report payload for local widget development.",
-    generatedAt: "2026-05-08T00:00:00Z",
+    title: "Weekly Revenue Performance",
+    description: "Local regression showcase for metric cards, chart tooltips, and responsive tables.",
+    generatedAt: "2026-07-08T00:00:00Z",
     blocks: [
+      {
+        id: "report_title",
+        type: "markdown",
+        layout: "full",
+        body: "# Weekly Revenue Performance",
+      },
       {
         id: "summary_text",
         type: "markdown",
         layout: "full",
         body:
-          "## Executive Summary\n\nThis fallback verifies that MCP artifact payloads render with the full report app shell.",
+          "## Executive Summary\n\nNet revenue reached **$1.05M** in the latest week, **3.8% above** the prior week and **4.8% above target**. Orders also reached a period high while refund rate stayed within its recent range.",
       },
       {
         id: "revenue_metrics",
         type: "metric-strip",
         layout: "full",
-        cardIds: ["revenue_card"],
+        cardIds: ["net_revenue_card", "target_attainment_card", "refund_rate_card", "completed_orders_card"],
+      },
+      {
+        id: "trend_context",
+        type: "markdown",
+        layout: "full",
+        body:
+          "## Revenue stayed ahead of plan\n\nThe latest week extended the upward trend and preserved a positive gap to target. The chart separates actual revenue from the weekly plan so the comparison remains explicit.",
       },
       {
         id: "revenue_chart_block",
@@ -50,32 +63,84 @@ const fallbackPayload = {
         chartId: "revenue_chart",
       },
       {
-        id: "revenue_table_block",
+        id: "summary_table_context",
+        type: "markdown",
+        layout: "full",
+        body:
+          "## Weekly summary\n\nThis compact table intentionally has fewer columns than the report viewport. It should stretch to the available width instead of ending at its intrinsic content width.",
+      },
+      {
+        id: "revenue_summary_table_block",
+        type: "table",
+        layout: "full",
+        tableId: "revenue_summary_table",
+      },
+      {
+        id: "detail_table_context",
+        type: "markdown",
+        layout: "full",
+        body:
+          "## Weekly audit detail\n\nThe detailed table keeps every reconciliation field. On narrower screens it should preserve readable columns and use contained horizontal scrolling.",
+      },
+      {
+        id: "revenue_detail_table_block",
         type: "table",
         layout: "full",
-        tableId: "revenue_table",
+        tableId: "revenue_detail_table",
       },
     ],
     cards: [
       {
-        id: "revenue_card",
-        dataset: "weekly_revenue",
+        id: "net_revenue_card",
+        description: "Settled revenue after discounts and refunds for the week starting June 29, 2026.",
+        dataset: "latest_week",
+        sourceId: "snapshot",
+        metrics: [
+          { label: "Net revenue · latest week", field: "net_revenue", format: "currency" },
+          { label: "WoW", field: "revenue_wow", format: "percent", signed: true },
+        ],
+      },
+      {
+        id: "target_attainment_card",
+        description: "Net revenue divided by the approved weekly target; the chip shows the dollar variance to that same target.",
+        dataset: "latest_week",
+        sourceId: "snapshot",
         metrics: [
-          { label: "Revenue", field: "revenue_m", format: "currency" },
-          { label: "WoW", field: "wow", format: "percent", signed: true },
+          { label: "Target attainment", field: "attainment", format: "percent" },
+          { label: "Variance", field: "variance", format: "currency", signed: true },
+        ],
+      },
+      {
+        id: "refund_rate_card",
+        description: "Settled refund dollars divided by gross billings for the latest week.",
+        dataset: "latest_week",
+        sourceId: "snapshot",
+        metrics: [
+          { label: "Refund rate", field: "refund_rate", format: "percent" },
+        ],
+      },
+      {
+        id: "completed_orders_card",
+        description: "Orders completed during the latest reporting week; the chip compares the same measure with the prior week.",
+        dataset: "latest_week",
+        sourceId: "snapshot",
+        metrics: [
+          { label: "Completed orders", field: "orders", format: "number" },
+          { label: "WoW", field: "orders_wow", format: "percent", signed: true },
         ],
       },
     ],
     charts: [
       {
         id: "revenue_chart",
-        title: "Revenue Trend",
+        title: "Net revenue vs target",
+        subtitle: "Weekly net revenue remained above plan throughout the period",
         type: "line",
         dataset: "weekly_revenue_trend",
         sourceId: "snapshot",
         encodings: {
-          x: { field: "week", type: "ordinal" },
-          y: { field: "revenue_m", type: "quantitative", label: "Revenue", format: "currency" },
+          x: { field: "week", type: "temporal" },
+          y: { field: "revenue", type: "quantitative", label: "Revenue", format: "currency" },
           color: { field: "series", type: "nominal" },
         },
         valueFormat: "currency",
@@ -84,41 +149,98 @@ const fallbackPayload = {
     ],
     tables: [
       {
-        id: "revenue_table",
-        title: "Revenue Segment Detail",
-        dataset: "revenue_segment_detail",
+        id: "revenue_summary_table",
+        title: "Weekly revenue summary",
+        dataset: "weekly_detail",
+        sourceId: "snapshot",
+        defaultSort: { field: "week", direction: "desc" },
+        columns: [
+          { field: "week", label: "Week starting", type: "date" },
+          { field: "net_revenue", label: "Net revenue", format: "currency" },
+          { field: "attainment", label: "Attainment", format: "percent" },
+          { field: "refund_rate", label: "Refund rate", format: "percent" },
+        ],
+      },
+      {
+        id: "revenue_detail_table",
+        title: "Weekly revenue detail",
+        dataset: "weekly_detail",
         sourceId: "snapshot",
+        defaultSort: { field: "week", direction: "desc" },
         columns: [
-          { field: "reporting_week", label: "Reporting week" },
-          { field: "segment", label: "Customer segment" },
-          { field: "gross_revenue_m", label: "Gross revenue", format: "currency" },
-          { field: "year_over_year_growth_rate", label: "Year-over-year growth rate", format: "percent" },
-          { field: "estimated_pipeline_coverage_ratio", label: "Estimated pipeline coverage ratio" },
-          { field: "customer_activation_rate", label: "Customer activation rate", format: "percent" },
-          { field: "operating_margin_rate", label: "Operating margin rate", format: "percent" },
+          { field: "week", label: "Week starting", type: "date" },
+          { field: "gross_billings", label: "Gross billings", format: "currency" },
+          { field: "discounts", label: "Discounts", format: "currency" },
+          { field: "settled_refunds", label: "Settled refunds", format: "currency" },
+          { field: "net_revenue", label: "Net revenue", format: "currency" },
+          { field: "target", label: "Target", format: "currency" },
+          { field: "variance", label: "Variance", format: "currency", movement: true },
+          { field: "attainment", label: "Attainment", format: "percent" },
+          { field: "orders", label: "Orders", format: "number" },
+          { field: "revenue_per_order", label: "Revenue / order", format: "currency" },
+          { field: "refund_rate", label: "Refund rate", format: "percent" },
+          { field: "revenue_wow", label: "WoW growth", format: "percent", movement: true },
         ],
       },
     ],
-    sources: [{ id: "snapshot", label: "Fallback snapshot", path: "data/snapshot.json" }],
+    sources: [{ id: "snapshot", label: "Synthetic weekly revenue fixture", path: "data/weekly-revenue.json" }],
   },
   snapshot: {
     version: 1,
-    generatedAt: "2026-05-08T00:00:00Z",
+    generatedAt: "2026-07-08T00:00:00Z",
     status: "fixture",
     datasets: {
-      weekly_revenue: [
-        { week: "Apr 27", revenue_m: 12, wow: "+8% w/w" },
-        { week: "May 04", revenue_m: 18, wow: "+16% w/w" },
+      latest_week: [
+        {
+          attainment: 1.048,
+          net_revenue: 1048000,
+          orders: 5120,
+          orders_wow: 0.0396,
+          refund_rate: 0.032,
+          revenue_wow: 0.038,
+          variance: 48000,
+          week: "2026-06-29",
+        },
       ],
       weekly_revenue_trend: [
-        { week: "Apr 27", series: "Actual", revenue_m: 12 },
-        { week: "May 04", series: "Actual", revenue_m: 18 },
-        { week: "Apr 27", series: "Target", revenue_m: 14 },
-        { week: "May 04", series: "Target", revenue_m: 16 },
+        { week: "2026-04-13", series: "Actual", revenue: 790000 },
+        { week: "2026-04-20", series: "Actual", revenue: 820000 },
+        { week: "2026-04-27", series: "Actual", revenue: 845000 },
+        { week: "2026-05-04", series: "Actual", revenue: 862000 },
+        { week: "2026-05-11", series: "Actual", revenue: 890000 },
+        { week: "2026-05-18", series: "Actual", revenue: 905000 },
+        { week: "2026-05-25", series: "Actual", revenue: 918000 },
+        { week: "2026-06-01", series: "Actual", revenue: 940000 },
+        { week: "2026-06-08", series: "Actual", revenue: 955000 },
+        { week: "2026-06-15", series: "Actual", revenue: 972000 },
+        { week: "2026-06-22", series: "Actual", revenue: 1010000 },
+        { week: "2026-06-29", series: "Actual", revenue: 1048000 },
+        { week: "2026-04-13", series: "Target", revenue: 780000 },
+        { week: "2026-04-20", series: "Target", revenue: 800000 },
+        { week: "2026-04-27", series: "Target", revenue: 820000 },
+        { week: "2026-05-04", series: "Target", revenue: 840000 },
+        { week: "2026-05-11", series: "Target", revenue: 860000 },
+        { week: "2026-05-18", series: "Target", revenue: 880000 },
+        { week: "2026-05-25", series: "Target", revenue: 900000 },
+        { week: "2026-06-01", series: "Target", revenue: 920000 },
+        { week: "2026-06-08", series: "Target", revenue: 940000 },
+        { week: "2026-06-15", series: "Target", revenue: 960000 },
+        { week: "2026-06-22", series: "Target", revenue: 980000 },
+        { week: "2026-06-29", series: "Target", revenue: 1000000 },
       ],
-      revenue_segment_detail: [
-        { reporting_week: "2026-04-27", segment: "Enterprise", gross_revenue_m: 8.2, year_over_year_growth_rate: 0.31, estimated_pipeline_coverage_ratio: "4.3x", customer_activation_rate: 0.67, operating_margin_rate: 0.42 },
-        { reporting_week: "2026-05-04", segment: "Self-serve", gross_revenue_m: 9.8, year_over_year_growth_rate: 0.28, estimated_pipeline_coverage_ratio: "3.8x", customer_activation_rate: 0.61, operating_margin_rate: 0.39 },
+      weekly_detail: [
+        { week: "2026-06-29", gross_billings: 1112000, discounts: 29000, settled_refunds: 35000, net_revenue: 1048000, target: 1000000, variance: 48000, attainment: 1.048, orders: 5120, revenue_per_order: 204.69, refund_rate: 0.032, revenue_wow: 0.038 },
+        { week: "2026-06-22", gross_billings: 1068000, discounts: 28000, settled_refunds: 30000, net_revenue: 1010000, target: 980000, variance: 30000, attainment: 1.031, orders: 4925, revenue_per_order: 205.08, refund_rate: 0.029, revenue_wow: 0.039 },
+        { week: "2026-06-15", gross_billings: 1029000, discounts: 27000, settled_refunds: 30000, net_revenue: 972000, target: 960000, variance: 12000, attainment: 1.013, orders: 4740, revenue_per_order: 205.06, refund_rate: 0.03, revenue_wow: 0.018 },
+        { week: "2026-06-08", gross_billings: 1012000, discounts: 26000, settled_refunds: 31000, net_revenue: 955000, target: 940000, variance: 15000, attainment: 1.016, orders: 4660, revenue_per_order: 204.94, refund_rate: 0.031, revenue_wow: 0.016 },
+        { week: "2026-06-01", gross_billings: 995000, discounts: 26000, settled_refunds: 29000, net_revenue: 940000, target: 920000, variance: 20000, attainment: 1.022, orders: 4575, revenue_per_order: 205.46, refund_rate: 0.03, revenue_wow: 0.024 },
+        { week: "2026-05-25", gross_billings: 970000, discounts: 25000, settled_refunds: 27000, net_revenue: 918000, target: 900000, variance: 18000, attainment: 1.02, orders: 4460, revenue_per_order: 205.83, refund_rate: 0.029, revenue_wow: 0.014 },
+        { week: "2026-05-18", gross_billings: 958000, discounts: 24000, settled_refunds: 29000, net_revenue: 905000, target: 880000, variance: 25000, attainment: 1.028, orders: 4380, revenue_per_order: 206.62, refund_rate: 0.031, revenue_wow: 0.017 },
+        { week: "2026-05-11", gross_billings: 941000, discounts: 24000, settled_refunds: 27000, net_revenue: 890000, target: 860000, variance: 30000, attainment: 1.035, orders: 4315, revenue_per_order: 206.26, refund_rate: 0.029, revenue_wow: 0.032 },
+        { week: "2026-05-04", gross_billings: 909000, discounts: 23000, settled_refunds: 24000, net_revenue: 862000, target: 840000, variance: 22000, attainment: 1.026, orders: 4210, revenue_per_order: 204.75, refund_rate: 0.027, revenue_wow: 0.02 },
+        { week: "2026-04-27", gross_billings: 891000, discounts: 23000, settled_refunds: 23000, net_revenue: 845000, target: 820000, variance: 25000, attainment: 1.03, orders: 4140, revenue_per_order: 204.11, refund_rate: 0.026, revenue_wow: 0.03 },
+        { week: "2026-04-20", gross_billings: 867000, discounts: 22000, settled_refunds: 25000, net_revenue: 820000, target: 800000, variance: 20000, attainment: 1.025, orders: 4050, revenue_per_order: 202.47, refund_rate: 0.03, revenue_wow: 0.038 },
+        { week: "2026-04-13", gross_billings: 836000, discounts: 21000, settled_refunds: 25000, net_revenue: 790000, target: 780000, variance: 10000, attainment: 1.013, orders: 3970, revenue_per_order: 198.99, refund_rate: 0.031, revenue_wow: null },
       ],
     },
   },
@@ -514,13 +636,29 @@ function validateArtifactPayload(payload) {
       issues.push(`${path}.columns must be a non-empty array.`);
       return;
     }
+    const columnFields = new Set();
     table.columns.forEach((column, columnIndex) => {
       if (!isPlainObject(column)) {
         issues.push(`${path}.columns[${columnIndex}] must be an object.`);
         return;
       }
       validateFieldReference(rows, column.field, `${path}.columns[${columnIndex}].field`, issues);
+      if (typeof column.field === "string" && column.field) {
+        columnFields.add(column.field);
+      }
     });
+    if (table.defaultSort != null) {
+      if (!isPlainObject(table.defaultSort)) {
+        issues.push(`${path}.defaultSort must be an object.`);
+      } else {
+        if (!columnFields.has(table.defaultSort.field)) {
+          issues.push(`${path}.defaultSort.field must reference a declared table column.`);
+        }
+        if (!["asc", "desc"].includes(table.defaultSort.direction)) {
+          issues.push(`${path}.defaultSort.direction must be asc or desc.`);
+        }
+      }
+    }
   });
 
   if (Array.isArray(blocks)) {
diff --git a/plugins/data-analytics/src/datascience-chart-widget.css b/plugins/data-analytics/src/datascience-chart-widget.css
index 7af4949..92888e7 100644
--- a/plugins/data-analytics/src/datascience-chart-widget.css
+++ b/plugins/data-analytics/src/datascience-chart-widget.css
@@ -176,14 +176,25 @@
 
       .display-mode-button svg {
         display: block;
-        width: 14px;
-        height: 14px;
-        fill: currentColor;
+        width: 16px;
+        height: 16px;
+        fill: none;
         stroke: currentColor;
+        stroke-width: 1.5;
+        stroke-linecap: round;
+        stroke-linejoin: round;
       }
 
-      .display-mode-button-label {
-        white-space: nowrap;
+      .display-mode-button {
+        aspect-ratio: 1 / 1;
+        width: 32px;
+        height: 32px;
+        min-width: 32px;
+        min-height: 32px;
+        padding: 0;
+        border-radius: 50%;
+        background: var(--ds-surface);
+        color: var(--ds-text-secondary);
       }
 
       .top-bar-button:hover,
diff --git a/plugins/data-analytics/src/datascience-chart-widget.html b/plugins/data-analytics/src/datascience-chart-widget.html
index bb1e7b7..7636670 100644
--- a/plugins/data-analytics/src/datascience-chart-widget.html
+++ b/plugins/data-analytics/src/datascience-chart-widget.html
@@ -39,10 +39,17 @@ <h1 id="title">Data Analytics chart</h1>
             class="display-mode-button"
             id="display-mode-button"
             type="button"
-            title="Edit chart"
-            aria-label="Edit chart"
+            title="Expand chart"
+            aria-label="Expand chart"
             hidden
-          ></button>
+          >
+            <svg aria-hidden="true" viewBox="0 0 24 24" fill="none" focusable="false">
+              <path d="m21 21-6-6m6 6v-4.8m0 4.8h-4.8" />
+              <path d="M3 16.2V21m0 0h4.8M3 21l6-6" />
+              <path d="M21 7.8V3m0 0h-4.8M21 3l-6 6" />
+              <path d="M3 7.8V3m0 0h4.8M3 3l6 6" />
+            </svg>
+          </button>
         </div>
         <p class="subtitle" id="subtitle" hidden></p>
       </header>
diff --git a/plugins/data-analytics/src/datascience-chart-widget.js b/plugins/data-analytics/src/datascience-chart-widget.js
index 8af5bbc..4d40091 100644
--- a/plugins/data-analytics/src/datascience-chart-widget.js
+++ b/plugins/data-analytics/src/datascience-chart-widget.js
@@ -420,40 +420,15 @@ document.body.dataset.displayMode = mode;
 applySplitLayout();
 const button = document.getElementById("display-mode-button");
 if (!button) return;
-button.hidden = !hostSupportsDisplayMode();
-const label = isDetailDisplayMode(mode) ? "Done" : "Edit";
-button.title = isDetailDisplayMode(mode) ? "Return chart inline" : "Edit chart";
-button.setAttribute("aria-label", isDetailDisplayMode(mode) ? "Return chart inline" : "Edit chart");
-const labelElement = document.createElement("span");
-labelElement.className = "display-mode-button-label";
-labelElement.textContent = label;
-if (isDetailDisplayMode(mode)) {
-button.replaceChildren(labelElement);
-} else {
-button.replaceChildren(labelElement, exploreIcon());
-}
+const showInlineExpand = mode === "inline" && hostSupportsDisplayMode();
+button.hidden = !showInlineExpand;
+button.title = "Expand chart";
+button.setAttribute("aria-label", "Expand chart");
 const modalCloseButton = document.getElementById("modal-title-close-button");
 if (modalCloseButton) modalCloseButton.hidden = mode !== "modal";
 if (changed) render();
 }
 
-function exploreIcon() {
-const icon = document.createElement("span");
-icon.className = "display-mode-button-icon";
-const svg = svgEl("svg", {
-viewBox: "0 0 16 16",
-"aria-hidden": "true",
-focusable: "false",
-});
-const path = svgEl("path", {
-d: "M11.949 3.47949C12.0997 3.46465 12.2553 3.51279 12.3709 3.62793C12.4863 3.74328 12.5338 3.89898 12.5193 4.0498C12.5206 4.06633 12.5251 4.08275 12.5252 4.09961V10.667C12.525 10.9565 12.2902 11.191 12.0007 11.1914C11.7109 11.1914 11.4755 10.9568 11.4754 10.667V5.2666L4.37184 12.376C4.16684 12.5807 3.83365 12.5808 3.62867 12.376C3.42385 12.1711 3.42396 11.8388 3.62867 11.6338L10.7332 4.52539H5.33375C5.0438 4.52539 4.80836 4.28995 4.80836 4C4.80836 3.71005 5.0438 3.47461 5.33375 3.47461H11.9002C11.9167 3.47462 11.9328 3.47822 11.949 3.47949Z",
-fill: "currentColor",
-});
-svg.appendChild(path);
-icon.appendChild(svg);
-return icon;
-}
-
 function setViewMode(mode) {
 viewMode = canonicalViewMode(mode);
 const widget = document.querySelector(".widget");
@@ -892,8 +867,27 @@ const presentation = specPresentation(dataset);
 return presentation[key] != null ? presentation[key] : valueFor(dataset, key, fallback);
 }
 
+function selectedMeasureColumn(dataset) {
+return asArray(resultTableFor(dataset)?.columns).find(
+(column) => column && column.key === chartConfig.yField,
+);
+}
+
+function columnValueFormat(column) {
+const format = text(column?.format);
+if (["compact", "number", "percent", "currency"].includes(format)) return format;
+const type = text(column?.type);
+if (["number", "percent", "currency"].includes(type)) return type;
+return null;
+}
+
+function valueFormatFor(dataset, existing = chartSpecFor(dataset)) {
+return existing.valueFormat || columnValueFormat(selectedMeasureColumn(dataset));
+}
+
 function currentWidgetSpec() {
 const dataset = activeDataset();
+const valueFormat = valueFormatFor(dataset);
 return {
 version: "1",
 visualization_type: activeVisualizationType,
@@ -907,6 +901,8 @@ y: {
 aggregate: chartConfig.yAggregation,
 field: chartConfig.yField,
 type: "quantitative",
+...(valueFormat ? { format: valueFormat } : {}),
+...(unitFor(dataset) ? { unit: unitFor(dataset) } : {}),
 },
 ...(chartConfig.colorField ? { color: { field: chartConfig.colorField, type: "nominal" } } : {}),
 ...(chartConfig.lineStyleField ? { lineStyle: { field: chartConfig.lineStyleField, type: "nominal" } } : {}),
@@ -958,6 +954,7 @@ return output;
 }
 
 function projectedRenderColumns(rows, dataset) {
+const valueFormat = valueFormatFor(dataset);
 const columns = [
 {
 key: "x",
@@ -968,6 +965,7 @@ type: fieldLooksDateLike(dataset, chartConfig.xField) ? "date" : "text",
 key: "y",
 label: fieldLabel(dataset, chartConfig.yField, "Value"),
 type: "number",
+...(valueFormat ? { format: valueFormat } : {}),
 unit: unitFor(dataset),
 },
 ];
@@ -992,6 +990,7 @@ const sourceSettings = specSettings(dataset);
 const useColor = projectedRowsUseColorEncoding(rows);
 const xAxisTitle = specValue(dataset, "x_axis_title", existing.xAxisTitle);
 const yAxisTitle = specValue(dataset, "y_axis_title", existing.yAxisTitle);
+const valueFormat = valueFormatFor(dataset, existing);
 return {
 id: text(existing.id || valueFor(dataset, "id", "default")) || "default",
 title: text(valueFor(dataset, "title", payload.title || "Data Analytics chart")),
@@ -1010,6 +1009,7 @@ aggregate: "none",
 field: "y",
 label: fieldLabel(dataset, chartConfig.yField, "Value"),
 type: "quantitative",
+...(valueFormat ? { format: valueFormat } : {}),
 unit: unitFor(dataset),
 },
 ...(useColor ? { color: { field: "series", label: "Series", type: "nominal" } } : {}),
@@ -1026,7 +1026,7 @@ unit: unitFor(dataset),
 ...(xAxisTitle != null ? { xAxisTitle } : {}),
 ...(yAxisTitle != null ? { yAxisTitle } : {}),
 unit: unitFor(dataset),
-valueFormat: existing.valueFormat,
+valueFormat,
 settings: {
 orientation: chartSettings.barOrientation,
 groupMode: chartSettings.barGroupMode,
@@ -1121,9 +1121,7 @@ widget.dataset.barGroupMode = chartSettings.barGroupMode;
 }
 
 function unitFor(dataset) {
-const selectedMeasure = asArray(resultTableFor(dataset)?.columns).find(
-(column) => column && column.key === chartConfig.yField,
-);
+const selectedMeasure = selectedMeasureColumn(dataset);
 if (selectedMeasure?.unit != null && text(selectedMeasure.unit).trim()) {
 return text(selectedMeasure.unit).trim();
 }
diff --git a/plugins/data-analytics/src/html-report-runtime.html b/plugins/data-analytics/src/html-report-runtime.html
new file mode 100644
index 0000000..25195a1
--- /dev/null
+++ b/plugins/data-analytics/src/html-report-runtime.html
@@ -0,0 +1,10 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Data Analytics HTML report runtime</title>
+    <script type="module" src="./html-report-runtime.jsx"></script>
+  </head>
+  <body></body>
+</html>
diff --git a/plugins/data-analytics/src/html-report-runtime.jsx b/plugins/data-analytics/src/html-report-runtime.jsx
new file mode 100644
index 0000000..62f5a07
--- /dev/null
+++ b/plugins/data-analytics/src/html-report-runtime.jsx
@@ -0,0 +1,217 @@
+import "./html-report-source-tooltips.js";
+import { renderRechartsChart } from "./recharts-renderer.jsx";
+
+const PAYLOAD_ID = "analytics-payload";
+const READY_ATTRIBUTE = "rechartsReady";
+const RENDERED_CHART_SELECTOR = "svg, .leaderboard, .heatmap-grid-panel";
+const SERIALIZED_SVG_SIZE_TOLERANCE = 0.08;
+const SERIALIZED_SVG_SIZE_TOLERANCE_PX = 4;
+const SERIALIZED_SVG_WATCHER = "__dataAnalyticsSerializedSvgWatcher";
+
+function asObject(value) {
+  return value && typeof value === "object" && !Array.isArray(value) ? value : {};
+}
+
+function asArray(value) {
+  return Array.isArray(value) ? value : [];
+}
+
+function asPositiveNumber(value) {
+  const parsed = Number(value);
+  return Number.isFinite(parsed) && parsed > 0 ? parsed : undefined;
+}
+
+function selectorEscape(value) {
+  if (typeof CSS !== "undefined" && typeof CSS.escape === "function") {
+    return CSS.escape(value);
+  }
+  return value.replace(/[^a-zA-Z0-9_-]/g, "\\$&");
+}
+
+function measuredSize(element) {
+  if (!element || typeof element.getBoundingClientRect !== "function") return {};
+  const rect = element.getBoundingClientRect();
+  const height = Number(rect.height);
+  const width = Number(rect.width);
+  return {
+    height: Number.isFinite(height) && height > 0 ? height : undefined,
+    width: Number.isFinite(width) && width > 0 ? width : undefined,
+  };
+}
+
+function serializedSvgSize(svg) {
+  if (!svg || typeof svg.getAttribute !== "function") return {};
+  const viewBox = String(svg.getAttribute("viewBox") ?? "").trim();
+  if (viewBox) {
+    const values = viewBox.split(/[\s,]+/).map(Number);
+    if (
+      values.length === 4 &&
+      Number.isFinite(values[2]) &&
+      values[2] > 0 &&
+      Number.isFinite(values[3]) &&
+      values[3] > 0
+    ) {
+      return { height: values[3], width: values[2] };
+    }
+  }
+  return {
+    height: asPositiveNumber(svg.getAttribute("height")),
+    width: asPositiveNumber(svg.getAttribute("width")),
+  };
+}
+
+function sizeIsStale(current, serialized) {
+  if (!current || !serialized) return false;
+  return (
+    Math.abs(current - serialized) >
+    Math.max(
+      SERIALIZED_SVG_SIZE_TOLERANCE_PX,
+      Math.max(current, serialized) * SERIALIZED_SVG_SIZE_TOLERANCE,
+    )
+  );
+}
+
+function serializedSvgIsStale(host, mount, svg) {
+  const mountSize = measuredSize(mount);
+  const hostSize = measuredSize(host);
+  const currentWidth = mountSize.width ?? hostSize.width;
+  const svgSize = serializedSvgSize(svg);
+  if (!currentWidth || !svgSize.width) return true;
+  if (sizeIsStale(currentWidth, svgSize.width)) return true;
+  // The live mount is hidden before readiness, so only compare its height
+  // after it is measurable. Host height can include the static fallback.
+  return sizeIsStale(mountSize.height, svgSize.height);
+}
+
+function stopSerializedSvgWatcher(host) {
+  host[SERIALIZED_SVG_WATCHER]?.disconnect?.();
+  delete host[SERIALIZED_SVG_WATCHER];
+}
+
+function resetReady(host, mount) {
+  stopSerializedSvgWatcher(host);
+  delete host.dataset[READY_ATTRIBUTE];
+  mount.setAttribute("aria-hidden", "true");
+}
+
+function markReady(host, mount) {
+  if (!mount.querySelector(RENDERED_CHART_SELECTOR)) return false;
+  host.dataset[READY_ATTRIBUTE] = "true";
+  mount.removeAttribute("aria-hidden");
+  return true;
+}
+
+function watchSerializedSvg(host, mount, svg, remount) {
+  stopSerializedSvgWatcher(host);
+  let finished = false;
+  const verify = () => {
+    if (finished || !serializedSvgIsStale(host, mount, svg)) return;
+    finished = true;
+    stopSerializedSvgWatcher(host);
+    remount();
+  };
+  if (typeof ResizeObserver === "function") {
+    const observer = new ResizeObserver(verify);
+    host[SERIALIZED_SVG_WATCHER] = observer;
+    observer.observe(host);
+    observer.observe(mount);
+  }
+  // markReady makes a previously hidden live mount measurable. Check once
+  // immediately so a stale height is repaired even before a resize event.
+  verify();
+}
+
+function mountChart(entry) {
+  const id = String(entry.id ?? "").trim();
+  if (!id) return;
+
+  const host = document.querySelector('[data-recharts-chart="' + selectorEscape(id) + '"]');
+  const mount = host?.querySelector("[data-recharts-live]");
+  if (!(host instanceof HTMLElement) || !(mount instanceof HTMLElement)) return;
+
+  const dataset = asObject(entry.dataset);
+  const chartSpec = asObject(dataset.chart_spec);
+  const type = String(entry.type ?? chartSpec.type ?? "bar");
+  const options = {
+    height: asPositiveNumber(entry.height) ?? 320,
+    htmlReport: true,
+    surface: "compact",
+    width: asPositiveNumber(entry.width),
+  };
+
+  let remounted = false;
+  const renderFresh = () => {
+    if (remounted) return;
+    remounted = true;
+    resetReady(host, mount);
+    try {
+      renderRechartsChart(mount, dataset, type, options);
+    } catch (error) {
+      host.dataset.rechartsError = error instanceof Error ? error.message : "render failed";
+      return;
+    }
+
+    let observer;
+    const verify = () => {
+      if (!markReady(host, mount)) return false;
+      observer?.disconnect();
+      return true;
+    };
+    if (typeof MutationObserver === "function") {
+      observer = new MutationObserver(verify);
+      observer.observe(mount, { childList: true, subtree: true });
+      window.setTimeout(() => observer?.disconnect(), 5000);
+    }
+    if (typeof window.requestAnimationFrame === "function") {
+      window.requestAnimationFrame(() => {
+        if (!verify()) window.setTimeout(verify, 0);
+      });
+    } else {
+      window.setTimeout(verify, 0);
+    }
+  };
+
+  // ChatGPT previews can serialize a completed live chart and execute this
+  // module again. Reuse that work only while its SVG still matches this
+  // viewport. A desktop-sized viewBox reused in a phone-width card gets
+  // letterboxed into a tiny plot, so remount it once to restore Recharts'
+  // normal resize path.
+  const serializedSvg = mount.querySelector("svg");
+  if (serializedSvg) {
+    if (serializedSvgIsStale(host, mount, serializedSvg)) {
+      renderFresh();
+      return;
+    } else if (markReady(host, mount)) {
+      watchSerializedSvg(host, mount, serializedSvg, renderFresh);
+      return;
+    }
+  } else if (markReady(host, mount)) {
+    // Non-SVG renderers such as leaderboard and heatmap panels remain
+    // responsive through their own CSS and do not need an SVG geometry check.
+    return;
+  }
+
+  renderFresh();
+}
+
+function boot() {
+  const payloadNode = document.getElementById(PAYLOAD_ID);
+  if (!payloadNode) return;
+
+  let payload;
+  try {
+    payload = JSON.parse(payloadNode.textContent || "{}");
+  } catch {
+    return;
+  }
+
+  for (const chart of asArray(asObject(payload).charts)) {
+    mountChart(asObject(chart));
+  }
+}
+
+if (document.readyState === "loading") {
+  document.addEventListener("DOMContentLoaded", boot, { once: true });
+} else {
+  boot();
+}
diff --git a/plugins/data-analytics/src/html-report-source-tooltips.js b/plugins/data-analytics/src/html-report-source-tooltips.js
new file mode 100644
index 0000000..47060f3
--- /dev/null
+++ b/plugins/data-analytics/src/html-report-source-tooltips.js
@@ -0,0 +1,260 @@
+(() => {
+  const RUNTIME_FLAG = "__dataAnalyticsSourceTooltipsInitialized";
+  if (window[RUNTIME_FLAG]) return;
+  window[RUNTIME_FLAG] = true;
+  document.documentElement.setAttribute("data-source-tooltips-ready", "true");
+
+  const TRIGGER_SELECTOR = ".source-tooltip";
+  const CONTENT_SELECTOR = ".source-tooltip-content";
+  const GAP = 8;
+  const MOBILE_MAX_WIDTH = 600;
+  const SCROLL_DISMISS_THRESHOLD = 24;
+  const VIEWPORT_PADDING = 8;
+  const FIGURE_TRIGGER_SELECTOR = ".source-figure > .source-tooltip";
+  const FIGURE_BUTTON_ATTRIBUTE = "data-source-tooltip-figure-button";
+  const FIGURE_STYLE_ID = "data-source-tooltip-figure-styles";
+
+  let activeTrigger = null;
+  let mobileTrayScrollY = 0;
+  let placementFrame = 0;
+
+  function viewportBounds() {
+    const viewport = window.visualViewport;
+    return {
+      bottom: (viewport?.offsetTop ?? 0) + (viewport?.height ?? window.innerHeight),
+      left: viewport?.offsetLeft ?? 0,
+      right: (viewport?.offsetLeft ?? 0) + (viewport?.width ?? window.innerWidth),
+      top: viewport?.offsetTop ?? 0,
+    };
+  }
+
+  function usesMobileTray() {
+    return (window.visualViewport?.width ?? window.innerWidth) <= MOBILE_MAX_WIDTH;
+  }
+
+  function upgradeFigureSourceButtons() {
+    document.querySelectorAll(FIGURE_TRIGGER_SELECTOR).forEach((originalTrigger) => {
+      if (!(originalTrigger instanceof HTMLElement) || originalTrigger instanceof HTMLButtonElement) return;
+      const button = document.createElement("button");
+      for (const attribute of Array.from(originalTrigger.attributes)) {
+        if (attribute.name !== "tabindex" && attribute.name !== "role") {
+          button.setAttribute(attribute.name, attribute.value);
+        }
+      }
+      button.type = "button";
+      button.setAttribute(FIGURE_BUTTON_ATTRIBUTE, "true");
+      while (originalTrigger.firstChild) button.append(originalTrigger.firstChild);
+      originalTrigger.replaceWith(button);
+    });
+  }
+
+  function installFigureSourceStyles() {
+    if (document.getElementById(FIGURE_STYLE_ID)) return;
+    const style = document.createElement("style");
+    style.id = FIGURE_STYLE_ID;
+    style.textContent = `
+      @media (max-width: 800px) {
+        .chart-grid {
+          grid-template-columns: minmax(0, 1fr);
+          min-width: 0;
+        }
+        .source-figure {
+          min-width: 0;
+          max-width: 100%;
+          overflow: hidden;
+        }
+        .source-figure .chart-wrap {
+          max-width: 100%;
+          overflow-x: auto;
+        }
+      }
+    `;
+    document.head.append(style);
+  }
+
+  function clampAxis(value, size, start, end) {
+    const minimum = start + VIEWPORT_PADDING;
+    const maximum = Math.max(minimum, end - size - VIEWPORT_PADDING);
+    return Math.min(Math.max(value, minimum), maximum);
+  }
+
+  function placeTooltip(trigger) {
+    if (usesMobileTray() || !(trigger instanceof HTMLElement)) return;
+    const tooltip = trigger.querySelector(CONTENT_SELECTOR);
+    if (!(tooltip instanceof HTMLElement)) return;
+
+    const triggerRect = trigger.getBoundingClientRect();
+    const tooltipRect = tooltip.getBoundingClientRect();
+    const viewport = viewportBounds();
+    const isFigureTooltip = trigger.parentElement?.classList.contains("source-figure");
+    const centeredLeft = triggerRect.left + (triggerRect.width - tooltipRect.width) / 2;
+    const aboveTop = triggerRect.top - tooltipRect.height - GAP;
+    const belowTop = triggerRect.bottom + GAP;
+    const preferredTop = isFigureTooltip || aboveTop < viewport.top + VIEWPORT_PADDING
+      ? belowTop
+      : aboveTop;
+    const left = clampAxis(centeredLeft, tooltipRect.width, viewport.left, viewport.right);
+    const top = clampAxis(preferredTop, tooltipRect.height, viewport.top, viewport.bottom);
+
+    tooltip.style.setProperty("--source-tooltip-left", `${Math.round(left)}px`);
+    tooltip.style.setProperty("--source-tooltip-top", `${Math.round(top)}px`);
+  }
+
+  function triggerValue(trigger, tooltip) {
+    return Array.from(trigger.childNodes)
+      .filter((node) => node !== tooltip)
+      .map((node) => node.textContent ?? "")
+      .join("")
+      .trim();
+  }
+
+  function prepareMobileTray(trigger) {
+    const tooltip = trigger.querySelector(CONTENT_SELECTOR);
+    if (!(tooltip instanceof HTMLElement)) return false;
+
+    let heading = tooltip.querySelector(".source-tooltip-heading");
+    if (!(heading instanceof HTMLElement)) {
+      heading = document.createElement("span");
+      heading.className = "source-tooltip-heading";
+      tooltip.prepend(heading);
+    }
+    const value = triggerValue(trigger, tooltip);
+    heading.textContent = value ? `Source for ${value}` : "Source details";
+    return true;
+  }
+
+  function closeMobileTray({ restoreFocus = false } = {}) {
+    const trigger = activeTrigger;
+    if (!(trigger instanceof HTMLElement)) return;
+    trigger.removeAttribute("data-source-tooltip-open");
+    trigger.setAttribute("aria-expanded", "false");
+    activeTrigger = null;
+    if (restoreFocus) trigger.focus({ preventScroll: true });
+  }
+
+  function openMobileTray(trigger) {
+    if (!(trigger instanceof HTMLElement) || !prepareMobileTray(trigger)) return;
+    if (activeTrigger && activeTrigger !== trigger) closeMobileTray();
+    activeTrigger = trigger;
+    mobileTrayScrollY = window.scrollY;
+    trigger.setAttribute("data-source-tooltip-open", "true");
+    trigger.setAttribute("aria-expanded", "true");
+  }
+
+  function toggleMobileTray(trigger) {
+    if (activeTrigger === trigger && trigger.getAttribute("data-source-tooltip-open") === "true") {
+      closeMobileTray();
+    } else {
+      openMobileTray(trigger);
+    }
+  }
+
+  function syncSemantics() {
+    const isMobile = usesMobileTray();
+    document.querySelectorAll(TRIGGER_SELECTOR).forEach((trigger) => {
+      if (!(trigger instanceof HTMLElement)) return;
+      if (isMobile) {
+        if (!trigger.hasAttribute("role")) {
+          trigger.setAttribute("role", "button");
+          trigger.setAttribute("data-source-tooltip-runtime-role", "true");
+        }
+        trigger.setAttribute("aria-expanded", trigger.getAttribute("data-source-tooltip-open") === "true" ? "true" : "false");
+      } else {
+        if (trigger.getAttribute("data-source-tooltip-runtime-role") === "true") {
+          trigger.removeAttribute("role");
+          trigger.removeAttribute("data-source-tooltip-runtime-role");
+        }
+        trigger.removeAttribute("aria-expanded");
+      }
+    });
+  }
+
+  function schedulePlacement(trigger = activeTrigger) {
+    if (!(trigger instanceof HTMLElement)) return;
+    activeTrigger = trigger;
+    if (placementFrame) window.cancelAnimationFrame(placementFrame);
+    placementFrame = window.requestAnimationFrame(() => {
+      placementFrame = 0;
+      placeTooltip(trigger);
+    });
+  }
+
+  function eventTrigger(event) {
+    const target = event.target;
+    return target instanceof HTMLElement ? target.closest(TRIGGER_SELECTOR) : null;
+  }
+
+  function handleClick(event) {
+    if (!usesMobileTray()) return;
+    const target = event.target;
+    if (!(target instanceof HTMLElement)) return;
+    if (target.closest(CONTENT_SELECTOR)) return;
+    const trigger = target.closest(TRIGGER_SELECTOR);
+    if (trigger instanceof HTMLElement) {
+      event.preventDefault();
+      event.stopPropagation();
+      toggleMobileTray(trigger);
+      return;
+    }
+    closeMobileTray();
+  }
+
+  function handleKeydown(event) {
+    if (!usesMobileTray()) return;
+    if (event.key === "Escape") {
+      closeMobileTray({ restoreFocus: true });
+      return;
+    }
+    if (event.key !== "Enter" && event.key !== " ") return;
+    const trigger = eventTrigger(event);
+    if (!(trigger instanceof HTMLElement)) return;
+    event.preventDefault();
+    toggleMobileTray(trigger);
+  }
+
+  function handleScroll() {
+    if (usesMobileTray()) {
+      if (activeTrigger && Math.abs(window.scrollY - mobileTrayScrollY) >= SCROLL_DISMISS_THRESHOLD) {
+        closeMobileTray();
+      }
+      return;
+    }
+    schedulePlacement();
+  }
+
+  function handleViewportChange() {
+    syncSemantics();
+    if (usesMobileTray()) {
+      if (activeTrigger?.getAttribute("data-source-tooltip-open") !== "true") activeTrigger = null;
+      return;
+    }
+    if (activeTrigger?.getAttribute("data-source-tooltip-open") === "true") {
+      closeMobileTray();
+      return;
+    }
+    schedulePlacement();
+  }
+
+  function setup() {
+    upgradeFigureSourceButtons();
+    installFigureSourceStyles();
+    syncSemantics();
+    document.addEventListener("pointerover", (event) => {
+      if (!usesMobileTray()) schedulePlacement(eventTrigger(event));
+    }, true);
+    document.addEventListener("focusin", (event) => {
+      if (!usesMobileTray()) schedulePlacement(eventTrigger(event));
+    });
+    document.addEventListener("click", handleClick, true);
+    document.addEventListener("keydown", handleKeydown);
+    document.addEventListener("scroll", handleScroll, true);
+    window.addEventListener?.("resize", handleViewportChange);
+    window.visualViewport?.addEventListener("resize", handleViewportChange);
+    window.visualViewport?.addEventListener("scroll", handleScroll);
+
+    const currentTrigger = document.querySelector(`${TRIGGER_SELECTOR}:hover, ${TRIGGER_SELECTOR}:focus`);
+    if (!usesMobileTray() && currentTrigger instanceof HTMLElement) schedulePlacement(currentTrigger);
+  }
+
+  setup();
+})();
diff --git a/plugins/data-analytics/src/mcp-host.js b/plugins/data-analytics/src/mcp-host.js
index 716fc1d..c4a4890 100644
--- a/plugins/data-analytics/src/mcp-host.js
+++ b/plugins/data-analytics/src/mcp-host.js
@@ -101,6 +101,18 @@ function installHostCompatibilityShim(app, name) {
       return result;
     };
   }
+
+  if (typeof existing.sendMessage !== "function") {
+    let supportsMessages = false;
+    try {
+      supportsMessages = Boolean(app.getHostCapabilities()?.message?.text);
+    } catch {
+      supportsMessages = false;
+    }
+    if (supportsMessages) {
+      existing.sendMessage = (message) => app.sendMessage(message);
+    }
+  }
 }
 
 export function connectMcpWidgetHost({
diff --git a/plugins/data-analytics/src/recharts-renderer.jsx b/plugins/data-analytics/src/recharts-renderer.jsx
index 1df1034..c0886bb 100644
--- a/plugins/data-analytics/src/recharts-renderer.jsx
+++ b/plugins/data-analytics/src/recharts-renderer.jsx
@@ -255,6 +255,7 @@ function MeasuredChartRenderer({
     <ChartRenderer
       chart={chart}
       height={dimensions.height}
+      htmlReport={options.htmlReport}
       onVisibleSeriesChange={options.onVisibleSeriesChange}
       responsive={false}
       rows={rowsForType(dataset, type)}
@@ -267,9 +268,14 @@ function MeasuredChartRenderer({
 }
 
 export function destroyRechartsChart(container) {
-  if (!container || !container.__datascienceRechartsRoot) return;
-  container.__datascienceRechartsRoot.unmount();
-  container.__datascienceRechartsRoot = null;
+  if (!container) return;
+  if (container.__datascienceRechartsRoot) {
+    container.__datascienceRechartsRoot.unmount();
+    container.__datascienceRechartsRoot = null;
+  }
+  // Uploaded or serialized HTML can contain a prior live mount without its
+  // in-memory React root. Clear it before remounting so boot stays idempotent.
+  container.replaceChildren();
 }
 
 export function renderRechartsChart(container, dataset, type, options = {}) {
diff --git a/plugins/data-analytics/tests/chart-axis-domain-contract.test.mjs b/plugins/data-analytics/tests/chart-axis-domain-contract.test.mjs
new file mode 100644
index 0000000..338ef4f
--- /dev/null
+++ b/plugins/data-analytics/tests/chart-axis-domain-contract.test.mjs
@@ -0,0 +1,31 @@
+import assert from "node:assert/strict";
+import { readFileSync } from "node:fs";
+import { test } from "node:test";
+
+function read(path) {
+  return readFileSync(new URL(path, import.meta.url), "utf8");
+}
+
+test("visualization guidance allows focused domains for compressed delta charts", () => {
+  const visualize = read("../skills/visualize-data/SKILL.md");
+  const validate = read("../skills/validate-data/SKILL.md");
+  const rechartsHtml = read("../skills/visualize-data/references/recharts-html.md");
+
+  for (const source of [visualize, validate, rechartsHtml]) {
+    assert.match(source, /waterfall, bridge, variance, and other delta-focused charts/);
+    assert.match(source, /zero would materially compress/);
+    assert.match(source, /exact (?:start\/end\/change labels|value labels|values)/);
+  }
+
+  assert.match(visualize, /Standard bar charts that compare absolute magnitude should start at zero/);
+  assert.match(visualize, /Keep zero in view when values cross zero or the question is absolute magnitude/);
+  assert.match(validate, /Bar charts should generally start at zero/);
+});
+
+test("waterfall Recharts transform sets a focused y-axis when the zero baseline hides the bridge", () => {
+  const transforms = read("../src/analytics-app/charting/chart-transforms.ts");
+
+  assert.match(transforms, /const WATERFALL_NARROW_AXIS_RATIO = 4;/);
+  assert.match(transforms, /fullSpan \/ focusedSpan < WATERFALL_NARROW_AXIS_RATIO/);
+  assert.match(transforms, /domain: \[axisMin, axisMax\]/);
+});
diff --git a/plugins/data-analytics/tests/chart-tooltip.test.mjs b/plugins/data-analytics/tests/chart-tooltip.test.mjs
new file mode 100644
index 0000000..c69ce92
--- /dev/null
+++ b/plugins/data-analytics/tests/chart-tooltip.test.mjs
@@ -0,0 +1,25 @@
+import assert from "node:assert/strict";
+import { readFile } from "node:fs/promises";
+import test from "node:test";
+
+test("tooltip percent encodings inherit chart percent semantics for literal percent units", async () => {
+  const source = await readFile(new URL("../src/analytics-app/charting/ChartTooltip.tsx", import.meta.url), "utf8");
+
+  assert.match(source, /function tooltipEncodingValueFormat\(chart: ChartSpec, encoding: ChartEncodingSpec\): ValueFormat/);
+  assert.match(source, /chart\.valueFormat === "percent" && isPercentSymbolUnit\(encoding\.unit\)/);
+  assert.match(source, /formatValue\(item\.value, valueFormat, encoding\.unit\)/);
+});
+
+test("chart and heatmap data tooltips reveal immediately without clipping", async () => {
+  const styles = await readFile(new URL("../src/analytics-app/charting/chart-tokens.css", import.meta.url), "utf8");
+  const appStyles = await readFile(new URL("../src/analytics-app/styles.css", import.meta.url), "utf8");
+
+  assert.match(styles, /@keyframes chart-tooltip-reveal/);
+  assert.match(styles, /\.recharts-tooltip-wrapper \.chart-tooltip \{[\s\S]*animation: chart-tooltip-reveal 80ms ease both;/);
+  assert.doesNotMatch(styles, /\.recharts-tooltip-wrapper \.chart-tooltip \{[^}]*300ms/);
+  assert.doesNotMatch(styles, /\.heatmap-cell:hover \.heatmap-tooltip \{[^}]*transition-delay/);
+  assert.match(styles, /\.chart-frame--heatmap \{[^}]*overflow: visible;/);
+  assert.match(styles, /\.chart-frame--heatmap > \.chart-plot \{[^}]*overflow: visible;/);
+  assert.match(styles, /\.heatmap-grid-panel \{[^}]*overflow: visible;/);
+  assert.match(appStyles, /\.chart-frame--custom\.chart-frame--heatmap \{[^}]*overflow: visible;/);
+});
diff --git a/plugins/data-analytics/tests/chart-transforms.test.mjs b/plugins/data-analytics/tests/chart-transforms.test.mjs
index 83ec44b..790d8b5 100644
--- a/plugins/data-analytics/tests/chart-transforms.test.mjs
+++ b/plugins/data-analytics/tests/chart-transforms.test.mjs
@@ -85,3 +85,45 @@ test("word percent units format semantic rates", async () => {
   assert.equal(formatValue(0.625, "number", "percentage"), "62.5%");
   assert.equal(formatValue(60, "compact", "%"), "60%");
 });
+
+test("percent valueFormat with literal percent units uses fractional-rate scale", async () => {
+  const { formatValue } = await loadChartTransforms();
+
+  assert.equal(formatValue(0.98, "percent", "%"), "98%");
+  assert.equal(formatValue(0.006, "percent", "%"), "0.6%");
+  assert.equal(formatValue(2, "percent", "%"), "200%");
+  assert.equal(formatValue(98, "percent", "%"), "9,800%");
+  assert.equal(formatValue(60, "number", "%"), "60%");
+  assert.equal(formatValue(0.98, "number", "%"), "0.98%");
+});
+
+test("waterfall axes narrow when a zero baseline compresses bridge movement", async () => {
+  const { buildWaterfallRows, getWaterfallYAxisScale } = await loadChartTransforms();
+  const chart = { series: [{ field: "value" }] };
+  const rows = [
+    { step: "May", value: 1271.5 },
+    { step: "Plus", value: 12.6 },
+    { step: "Pro", value: 28.2 },
+    { step: "Go", value: 6.8 },
+    { step: "Other", value: 0 },
+  ];
+
+  const scale = getWaterfallYAxisScale(buildWaterfallRows(chart, rows));
+
+  assert.ok(scale, "expected a focused non-zero domain");
+  assert.ok(scale.domain[0] > 0);
+  assert.ok(scale.domain[0] < 1271.5);
+  assert.ok(scale.domain[1] > 1319.1);
+  assert.equal(scale.ticks.includes(0), false);
+});
+
+test("waterfall axes keep the default domain when the bridge crosses zero", async () => {
+  const { buildWaterfallRows, getWaterfallYAxisScale } = await loadChartTransforms();
+  const chart = { series: [{ field: "value" }] };
+  const rows = [
+    { step: "Loss", value: -10 },
+    { step: "Recovery", value: 20 },
+  ];
+
+  assert.equal(getWaterfallYAxisScale(buildWaterfallRows(chart, rows)), undefined);
+});
diff --git a/plugins/data-analytics/tests/html-report-layout.test.mjs b/plugins/data-analytics/tests/html-report-layout.test.mjs
new file mode 100644
index 0000000..0f0011d
--- /dev/null
+++ b/plugins/data-analytics/tests/html-report-layout.test.mjs
@@ -0,0 +1,44 @@
+import assert from "node:assert/strict";
+import { readFileSync } from "node:fs";
+import { test } from "node:test";
+
+const html = readFileSync(new URL("../assets/html-report-shell.html", import.meta.url), "utf8");
+const reference = readFileSync(
+  new URL("../skills/visualize-data/references/recharts-html.md", import.meta.url),
+  "utf8",
+);
+
+function cssRule(selector) {
+  const escapedSelector = selector.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+  const match = html.match(new RegExp(`${escapedSelector}\\s*\\{([^}]*)\\}`));
+  assert.ok(match, `Expected HTML report shell to define ${selector}`);
+  return match[1];
+}
+
+test("HTML report table cards share the narrative reading width", () => {
+  assert.match(cssRule(":root"), /--report-reading-width:\s*800px;/);
+  assert.match(cssRule(".reading"), /width:\s*min\(var\(--report-reading-width\),\s*100%\);/);
+
+  const tableCard = cssRule(".table-card");
+  assert.match(tableCard, /width:\s*min\(var\(--report-reading-width\),\s*100%\);/);
+  assert.match(tableCard, /max-width:\s*100%;/);
+  assert.match(tableCard, /margin:\s*12px auto 46px;/);
+});
+
+test("HTML report tables fill their card and contain horizontal overflow", () => {
+  const tableScroll = cssRule(".table-scroll");
+  assert.match(tableScroll, /width:\s*100%;/);
+  assert.match(tableScroll, /max-width:\s*100%;/);
+  assert.match(tableScroll, /overflow-x:\s*auto;/);
+
+  const table = cssRule(".table-scroll > table");
+  assert.match(table, /width:\s*max-content;/);
+  assert.match(table, /min-width:\s*100%;/);
+  assert.match(html, /<div class="table-scroll">\{\{SEMANTIC_TABLE\}\}<\/div>/);
+});
+
+test("HTML report guidance preserves contained table sizing", () => {
+  assert.match(reference, /Keep table cards on the shell's shared reading width; reserve `\.wide` for chart cards\./);
+  assert.match(reference, /narrow tables fill their card, wide tables scroll inside it/);
+  assert.match(reference, /the document itself never gains horizontal overflow/);
+});
diff --git a/plugins/data-analytics/tests/html-report-runtime.test.mjs b/plugins/data-analytics/tests/html-report-runtime.test.mjs
new file mode 100644
index 0000000..fe39585
--- /dev/null
+++ b/plugins/data-analytics/tests/html-report-runtime.test.mjs
@@ -0,0 +1,323 @@
+import assert from "node:assert/strict";
+import { execFileSync } from "node:child_process";
+import { mkdtempSync, readFileSync, writeFileSync } from "node:fs";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+import { fileURLToPath } from "node:url";
+import { test } from "node:test";
+import { gunzipSync } from "node:zlib";
+
+const helper = fileURLToPath(
+  new URL("../skills/build-report/scripts/embed_html_report_runtime.py", import.meta.url),
+);
+
+function assertShellRejected(shell, charts, expectedError) {
+  const directory = mkdtempSync(join(tmpdir(), "data-analytics-html-report-"));
+  const shellPath = join(directory, "shell.html");
+  const payloadPath = join(directory, "payload.json");
+  const runtimePath = join(directory, "runtime.html");
+  const outputPath = join(directory, "report.html");
+
+  writeFileSync(shellPath, shell);
+  writeFileSync(payloadPath, JSON.stringify({ charts }));
+  writeFileSync(
+    runtimePath,
+    '<!doctype html><script type="module">document.body.dataset.runtime = "ok";</script>',
+  );
+
+  assert.throws(() => {
+    execFileSync("python3", [
+      helper,
+      "--input",
+      shellPath,
+      "--payload",
+      payloadPath,
+      "--runtime-html",
+      runtimePath,
+      "--output",
+      outputPath,
+    ], { encoding: "utf8", stdio: ["ignore", "pipe", "pipe"] });
+  }, (error) => {
+    assert.match(error.stderr, expectedError);
+    return true;
+  });
+}
+
+function loaderBootCount(loaderSource, {
+  height = 320,
+  ready = true,
+  renderedPanel = false,
+  viewBox = "0 0 316 320",
+  width = 316,
+} = {}) {
+  class FakeElement {}
+  class FakeTemplate {}
+  class FakeDecompressionStream {}
+  const source = new FakeTemplate();
+  const svg = viewBox
+    ? { getAttribute: (name) => name === "viewBox" ? viewBox : null }
+    : null;
+  const mount = new FakeElement();
+  mount.getBoundingClientRect = () => ({ height, width });
+  mount.querySelector = (selector) => {
+    if (selector === "svg") return svg;
+    if (selector === ".leaderboard, .heatmap-grid-panel") return renderedPanel ? {} : null;
+    return null;
+  };
+  const host = new FakeElement();
+  host.getAttribute = (name) => name === "data-recharts-ready" && ready ? "true" : null;
+  host.querySelector = (selector) => selector === "[data-recharts-live]" ? mount : null;
+  const document = {
+    getElementById: () => source,
+    querySelectorAll: () => [host],
+    readyState: "complete",
+  };
+  const window = {
+    requestIdleCallback: (callback) => callback(),
+    setTimeout: () => {},
+  };
+  const globalThis = { boots: 0 };
+  const executableSource = loaderSource.replace("void boot();", "globalThis.boots += 1;");
+
+  new Function(
+    "document",
+    "window",
+    "HTMLTemplateElement",
+    "HTMLElement",
+    "DecompressionStream",
+    "globalThis",
+    executableSource,
+  )(
+    document,
+    window,
+    FakeTemplate,
+    FakeElement,
+    FakeDecompressionStream,
+    globalThis,
+  );
+  return globalThis.boots;
+}
+
+test("HTML report runtime helper owns fallback swap and live chart sizing", () => {
+  const directory = mkdtempSync(join(tmpdir(), "data-analytics-html-report-"));
+  const shellPath = join(directory, "shell.html");
+  const payloadPath = join(directory, "payload.json");
+  const runtimePath = join(directory, "runtime.html");
+  const outputPath = join(directory, "report.html");
+
+  writeFileSync(
+    shellPath,
+    [
+      '<!doctype html><style>svg { height: auto !important; min-width: 700px !important; }</style><body>',
+      '<div data-recharts-chart="chart-1">',
+      '<div data-recharts-fallback><svg role="img"><path d="M0 0h1"></path></svg></div>',
+      '<div data-recharts-live aria-hidden="true"></div>',
+      "</div>",
+      "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->",
+      "</body>",
+    ].join("\n"),
+  );
+  writeFileSync(
+    payloadPath,
+    JSON.stringify({
+      charts: [
+        {
+          id: "chart-1",
+          dataset: {
+            chart_spec: { type: "line" },
+            data: [{ label: "</script><script>alert(1)</script>", x: "2026-01-01", y: 1 }],
+          },
+          type: "line",
+        },
+      ],
+    }),
+  );
+  writeFileSync(
+    runtimePath,
+    [
+      "<!doctype html><head>",
+      "<style>.runtime-style { color: blue; }</style>",
+      '<script type="module">document.body.dataset.runtime = "ok";</script>',
+      "</head><body></body>",
+    ].join("\n"),
+  );
+
+  execFileSync("python3", [
+    helper,
+    "--input",
+    shellPath,
+    "--payload",
+    payloadPath,
+    "--runtime-html",
+    runtimePath,
+    "--output",
+    outputPath,
+  ]);
+
+  const output = readFileSync(outputPath, "utf8");
+  const encodedRuntime = output
+    .match(/<template id="data-analytics-html-report-runtime-source"[^>]*>([\s\S]*?)<\/template>/)?.[1]
+    .replace(/\s/g, "");
+  const loaderSource = output
+    .match(/<script data-data-analytics-html-report-runtime-loader="true">([\s\S]*?)<\/script>/)?.[1];
+  const runtimeSource = gunzipSync(Buffer.from(encodedRuntime, "base64")).toString("utf8");
+
+  assert.match(output, /id="analytics-payload"/);
+  assert.match(output, /data-data-analytics-html-report-runtime="true"/);
+  assert.match(output, /data-data-analytics-html-report-source-tooltips="true"/);
+  assert.match(output, /__dataAnalyticsSourceTooltipsInitialized/);
+  assert.match(output, /data-data-analytics-html-report-runtime-source="true"/);
+  assert.match(output, /data-data-analytics-html-report-runtime-loader="true"/);
+  assert.match(
+    output,
+    /\[data-recharts-chart\] \[data-recharts-live\] \{ display: none !important; \}/,
+  );
+  assert.match(
+    output,
+    /\[data-recharts-chart\]\[data-recharts-ready="true"\] \[data-recharts-fallback\] \{ display: none !important; \}/,
+  );
+  assert.match(
+    output,
+    /\[data-recharts-chart\]\[data-recharts-ready="true"\] \[data-recharts-live\] \{ display: block !important; \}/,
+  );
+  assert.match(
+    output,
+    /\[data-recharts-chart\] \[data-recharts-live\] svg \{[\s\S]*height: 100% !important;[\s\S]*min-width: 0 !important;[\s\S]*width: 100% !important;/,
+  );
+  assert.ok(
+    output.indexOf("svg { height: auto !important;") <
+      output.indexOf("[data-recharts-chart] [data-recharts-live] svg {"),
+    "runtime chart sizing guard should follow authored shell CSS",
+  );
+  assert.match(output, /DecompressionStream\("gzip"\)/);
+  assert.match(output, /requestIdleCallback/);
+  assert.doesNotMatch(
+    output,
+    /source\.remove\(\)/,
+    "serialized previews need the compressed source to replay at a new viewport",
+  );
+  assert.doesNotMatch(
+    output,
+    /loader\?\.remove\(\)/,
+    "serialized previews need the tiny loader to replay at a new viewport",
+  );
+  assert.equal(
+    loaderBootCount(loaderSource, { viewBox: "0 0 316 320" }),
+    0,
+    "matching serialized charts should not unpack the full runtime again",
+  );
+  assert.equal(
+    loaderBootCount(loaderSource, { viewBox: "0 0 888 320" }),
+    1,
+    "desktop SVGs serialized into mobile cards should replay the runtime",
+  );
+  assert.equal(
+    loaderBootCount(loaderSource, { ready: false, viewBox: null }),
+    1,
+    "raw reports with empty live mounts should boot the runtime",
+  );
+  assert.match(output, /runtime\.remove\(\)/);
+  assert.match(runtimeSource, /document\.body\.dataset\.runtime = "ok"/);
+  assert.doesNotMatch(output, /<script[^>]*>document\.body\.dataset\.runtime = "ok"/);
+  assert.match(output, /data-recharts-fallback/);
+  assert.match(output, /\\u003c\/script\\u003e/);
+  assert.doesNotMatch(output, /<\/script><script>alert/);
+  assert.doesNotMatch(output, /DATA_ANALYTICS_HTML_REPORT_RUNTIME/);
+});
+
+test("packaged HTML report runtime stays compact for ChatGPT previews", () => {
+  const directory = mkdtempSync(join(tmpdir(), "data-analytics-html-report-"));
+  const shellPath = join(directory, "shell.html");
+  const payloadPath = join(directory, "payload.json");
+  const outputPath = join(directory, "report.html");
+
+  writeFileSync(
+    shellPath,
+    [
+      "<!doctype html><body>",
+      '<div data-recharts-chart="chart-1">',
+      '<div data-recharts-fallback><svg role="img"><path d="M0 0h1"></path></svg></div>',
+      '<div data-recharts-live aria-hidden="true"></div>',
+      "</div>",
+      "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->",
+      "</body>",
+    ].join("\n"),
+  );
+  writeFileSync(
+    payloadPath,
+    JSON.stringify({
+      charts: [{ id: "chart-1", dataset: { chart_spec: { type: "line" }, data: [] }, type: "line" }],
+    }),
+  );
+
+  execFileSync("python3", [
+    helper,
+    "--input",
+    shellPath,
+    "--payload",
+    payloadPath,
+    "--output",
+    outputPath,
+  ]);
+
+  const output = readFileSync(outputPath, "utf8");
+  const encodedRuntime = output
+    .match(/<template id="data-analytics-html-report-runtime-source"[^>]*>([\s\S]*?)<\/template>/)?.[1]
+    .replace(/\s/g, "");
+  const runtimeSource = gunzipSync(Buffer.from(encodedRuntime, "base64")).toString("utf8");
+  const maxLineLength = Math.max(...output.split("\n").map((line) => line.length));
+  assert.match(output, /data-analytics-html-report-runtime-source/);
+  assert.match(runtimeSource, /visualViewport/);
+  assert.match(runtimeSource, /--source-tooltip-left/);
+  assert.match(runtimeSource, /Source for /);
+  assert.match(runtimeSource, /data-source-tooltip-open/);
+  assert.match(runtimeSource, /data-source-tooltip-runtime-role/);
+  assert.match(runtimeSource, /stopPropagation/);
+  assert.ok(Buffer.byteLength(output) < 400_000, "packaged report should not inline the raw runtime");
+  assert.ok(maxLineLength < 10_000, "packaged report should not contain giant minified JS lines");
+});
+
+test("HTML report runtime helper rejects a chart without its own readable fallback", () => {
+  assertShellRejected(
+    [
+      "<!doctype html><body>",
+      '<div data-recharts-chart="chart-1"><div data-recharts-fallback><svg><path d="M0 0h1"></path></svg></div><div data-recharts-live></div></div>',
+      '<div data-recharts-chart="chart-2"><div data-recharts-live></div></div>',
+      "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->",
+      "</body>",
+    ].join("\n"),
+    [
+      { id: "chart-1", dataset: { data: [] }, type: "line" },
+      { id: "chart-2", dataset: { data: [] }, type: "line" },
+    ],
+    /Chart chart-2 needs its own data-recharts-fallback content/,
+  );
+});
+
+test("HTML report runtime helper rejects duplicate chart hosts", () => {
+  assertShellRejected(
+    [
+      "<!doctype html><body>",
+      '<div data-recharts-chart="chart-1"><div data-recharts-fallback><svg><path d="M0 0h1"></path></svg></div><div data-recharts-live></div></div>',
+      '<div data-recharts-chart="chart-1"><div data-recharts-fallback><svg><path d="M0 0h1"></path></svg></div><div data-recharts-live></div></div>',
+      "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->",
+      "</body>",
+    ].join("\n"),
+    [{ id: "chart-1", dataset: { data: [] }, type: "line" }],
+    /exactly one chart host for chart-1/,
+  );
+});
+
+test("HTML report runtime helper rejects chart hosts without payload entries", () => {
+  assertShellRejected(
+    [
+      "<!doctype html><body>",
+      '<div data-recharts-chart="chart-1"><div data-recharts-fallback><svg><path d="M0 0h1"></path></svg></div><div data-recharts-live></div></div>',
+      '<div data-recharts-chart="chart-2"><div data-recharts-fallback><svg><path d="M0 0h1"></path></svg></div><div data-recharts-live></div></div>',
+      "<!-- DATA_ANALYTICS_HTML_REPORT_RUNTIME -->",
+      "</body>",
+    ].join("\n"),
+    [{ id: "chart-1", dataset: { data: [] }, type: "line" }],
+    /chart hosts without payload entries: chart-2/,
+  );
+});
diff --git a/plugins/data-analytics/tests/html-report-source-tooltip.test.mjs b/plugins/data-analytics/tests/html-report-source-tooltip.test.mjs
new file mode 100644
index 0000000..1970f05
--- /dev/null
+++ b/plugins/data-analytics/tests/html-report-source-tooltip.test.mjs
@@ -0,0 +1,95 @@
+import assert from "node:assert/strict";
+import { readFileSync } from "node:fs";
+import { test } from "node:test";
+
+function read(path) {
+  return readFileSync(new URL(path, import.meta.url), "utf8");
+}
+
+test("HTML reports attach exact source provenance to quantitative values", () => {
+  const reportSkill = read("../skills/build-report/SKILL.md");
+
+  assert.match(reportSkill, /every source-backed quantitative value in an HTML report a directly associated source tooltip/);
+  assert.match(reportSkill, /Source: <source system, connector, or artifact>/);
+  assert.match(reportSkill, /Table: <fully qualified table or view>/);
+  assert.match(reportSkill, /list every material contributing source and table/);
+  assert.match(reportSkill, /Never invent provenance/);
+  assert.match(reportSkill, /real hover and keyboard-focus affordances/);
+  assert.match(reportSkill, /Live Recharts charts and their same-data static fallbacks cannot expose source provenance for every mark through HTML alone/);
+  assert.match(reportSkill, /every chart figure has a visible source affordance plus tooltip content/);
+  assert.match(reportSkill, /the actual interactive `report\.html` file is the primary deliverable/);
+  assert.match(reportSkill, /never use `image\.png`, a screenshot, or another flattened image as the only report handoff/);
+  assert.match(reportSkill, /inspect the generated `report\.html` itself, not only the shell or a screenshot/);
+  assert.match(reportSkill, /each tooltip contains real non-placeholder provenance/);
+  assert.match(reportSkill, /visible source affordance/);
+  assert.match(reportSkill, /hover and focus trigger on the visible `Source` affordance only/);
+  assert.match(reportSkill, /rather than the whole chart image/);
+  assert.match(reportSkill, /same shared `source-tooltip-content` style/);
+  assert.match(reportSkill, /Do not create card-, narrative-, table-, or chart-specific tooltip variants/);
+  assert.match(reportSkill, /same computed foreground, background, typography, padding, radius, and shadow/);
+});
+
+test("the Recharts HTML report shell provides the accessible source-tooltip pattern", () => {
+  const html = read("../assets/html-report-shell.html");
+
+  assert.match(html, /\.source-tooltip/);
+  assert.match(html, /\.source-tooltip \{[^\n]+appearance: none;[^\n]+font: inherit;/);
+  assert.match(html, /\.source-tooltip:focus-visible/);
+  assert.match(html, /class="source-tooltip" tabindex="0" aria-describedby="number-source-tooltip-1"/);
+  assert.match(html, /class="source-tooltip-content" id="number-source-tooltip-1" role="tooltip"/);
+  assert.match(html, /<figure class="card source-figure">/);
+  assert.match(html, /<button type="button" class="source-tooltip" aria-describedby="chart-source-tooltip-1">Source<span class="source-tooltip-content" id="chart-source-tooltip-1" role="tooltip">/);
+  assert.match(html, /class="source-tooltip-content" id="chart-source-tooltip-1" role="tooltip"/);
+  assert.match(html, /\.source-figure > \.source-tooltip \{/);
+  assert.match(html, /\.shell \{[^\n]+overflow: hidden;/);
+  assert.match(html, /\.card \{ overflow: hidden;/);
+  assert.match(html, /\.source-figure \{ position: relative; \}/);
+  assert.doesNotMatch(html, /\.source-figure \{[^\n]+overflow: visible;/);
+  assert.match(html, /html, body \{ max-width: 100%; overflow-x: clip; \}/);
+  assert.match(html, /\.source-tooltip-content \{ position: fixed;[^\n]+display: none;[^\n]+max-width: min\(360px, calc\(100vw - 16px\)\);/);
+  assert.match(html, /\.source-tooltip-content \{[^\n]+overflow-wrap: anywhere; white-space: normal;/);
+  assert.match(html, /html:not\(\[data-source-tooltips-ready\]\) \.source-tooltip-content \{ position: absolute;/);
+  assert.match(html, /\.source-tooltip:hover \.source-tooltip-content[^\n]+\{ display: block;/);
+  assert.match(html, /@media \(max-width: 600px\)/);
+  assert.match(html, /\.source-tooltip\[data-source-tooltip-open\] \.source-tooltip-content, html:not\(\[data-source-tooltips-ready\]\) \.source-tooltip:focus \.source-tooltip-content \{[^\n]+bottom: calc\(16px \+ env\(safe-area-inset-bottom, 0px\)\);/);
+  assert.match(html, /\.source-tooltip-heading \{ display: block;/);
+  assert.doesNotMatch(html, /\.source-tooltip:hover \+ \.source-tooltip-content/);
+  assert.doesNotMatch(html, /figure\.source-tooltip/);
+  assert.match(html, /A screenshot\/image preview is non-interactive and cannot replace report\.html/);
+  assert.match(html, /Wrap every source-backed visible number in this source-tooltip pattern/);
+  assert.match(html, /Source: \{\{source system or connector\}\}/);
+  assert.match(html, /Table: \{\{fully qualified table or view; list all material tables\}\}/);
+  assert.match(html, /\.source-tooltip-content, \.source-tooltip-content \* \{ color: #fff !important; font-style: normal !important; text-decoration: none !important; \}/);
+  assert.match(html, /    \.source-tooltip-content \{[^\n]+\}/);
+});
+
+test("the HTML report runtime clamps desktop tooltips and toggles a mobile source tray", () => {
+  const runtime = read("../src/html-report-source-tooltips.js");
+
+  assert.match(runtime, /__dataAnalyticsSourceTooltipsInitialized/);
+  assert.match(runtime, /data-source-tooltips-ready/);
+  assert.match(runtime, /function viewportBounds\(\)/);
+  assert.match(runtime, /window\.visualViewport/);
+  assert.match(runtime, /function clampAxis\(/);
+  assert.match(runtime, /tooltip\.style\.setProperty\("--source-tooltip-left"/);
+  assert.match(runtime, /tooltip\.style\.setProperty\("--source-tooltip-top"/);
+  assert.match(runtime, /function prepareMobileTray\(/);
+  assert.match(runtime, /function upgradeFigureSourceButtons\(/);
+  assert.match(runtime, /originalTrigger instanceof HTMLButtonElement/);
+  assert.match(runtime, /originalTrigger\.replaceWith\(button\)/);
+  assert.match(runtime, /function installFigureSourceStyles\(/);
+  assert.match(runtime, /grid-template-columns: minmax\(0, 1fr\)/);
+  assert.match(runtime, /upgradeFigureSourceButtons\(\);\n    installFigureSourceStyles\(\);\n    syncSemantics\(\);/);
+  assert.match(runtime, /`Source for \$\{value\}`/);
+  assert.match(runtime, /function toggleMobileTray\(/);
+  assert.match(runtime, /function syncSemantics\(/);
+  assert.match(runtime, /trigger\.setAttribute\("role", "button"\)/);
+  assert.match(runtime, /trigger\.setAttribute\("aria-expanded"/);
+  assert.match(runtime, /event\.stopPropagation\(\);\n      toggleMobileTray\(trigger\)/);
+  assert.match(runtime, /document\.addEventListener\("click", handleClick, true\)/);
+  assert.match(runtime, /document\.addEventListener\("keydown", handleKeydown\)/);
+  assert.match(runtime, /document\.addEventListener\("scroll", handleScroll, true\)/);
+  assert.match(runtime, /event\.key === "Escape"/);
+  assert.match(runtime, /SCROLL_DISMISS_THRESHOLD/);
+  assert.match(runtime, /window\.visualViewport\?\.addEventListener\("resize"/);
+});
diff --git a/plugins/data-analytics/tests/mcp-server.test.mjs b/plugins/data-analytics/tests/mcp-server.test.mjs
index 7e1b831..fa8425e 100644
--- a/plugins/data-analytics/tests/mcp-server.test.mjs
+++ b/plugins/data-analytics/tests/mcp-server.test.mjs
@@ -67,6 +67,7 @@ function artifactPayload(surface = "dashboard") {
       {
         id: "revenue_card",
         dataset: "weekly_revenue",
+        sourceId: "weekly_revenue_sql",
         metrics: [{ label: "Revenue", field: "revenue_m", format: "currency" }],
       },
     ],
@@ -185,6 +186,40 @@ test("MCP widget resources serve bundled apps, not local development redirects",
   assert.match(artifactHtml, /Data Analytics Artifact App/);
 });
 
+test("artifact widget URI changes with the plugin version", () => {
+  assert.equal(
+    server.ARTIFACT_WIDGET_URI,
+    `ui://widget/datascience-artifact-${encodeURIComponent(server.SERVER_VERSION)}.html`,
+  );
+  assert.doesNotMatch(server.ARTIFACT_WIDGET_URI, /[?#]/);
+});
+
+test("artifact widget keeps the unversioned resource URI as a compatibility alias", async () => {
+  const legacyUri = "ui://widget/datascience-artifact.html";
+  const response = await server.handleRpc({
+    jsonrpc: "2.0",
+    id: 1,
+    method: "resources/read",
+    params: { uri: legacyUri },
+  });
+
+  assert.equal(response.result.contents[0].uri, legacyUri);
+  assert.match(response.result.contents[0].text, /Data Analytics Artifact App/);
+});
+
+test("artifact widget keeps query-versioned resource URIs as compatibility aliases", async () => {
+  const legacyUri = `ui://widget/datascience-artifact.html?v=${encodeURIComponent(server.SERVER_VERSION)}`;
+  const response = await server.handleRpc({
+    jsonrpc: "2.0",
+    id: 1,
+    method: "resources/read",
+    params: { uri: legacyUri },
+  });
+
+  assert.equal(response.result.contents[0].uri, legacyUri);
+  assert.match(response.result.contents[0].text, /Data Analytics Artifact App/);
+});
+
 test("JavaScript MCP server renders hosted artifact payloads", () => {
   const payload = server.callTool("render_artifact", artifactPayload("report"));
 
@@ -210,6 +245,41 @@ test("JavaScript MCP server exposes only canonical tool names", () => {
   );
 });
 
+test("JavaScript MCP UI tool instructions guard web Work Mode delivery", () => {
+  const tools = Object.fromEntries(server.toolDefinitions().map((tool) => [tool.name, tool]));
+
+  assert.match(server.SERVER_INSTRUCTIONS, /do not call render_artifact, render_chart, or render_table/);
+  assert.match(server.SERVER_INSTRUCTIONS, /mode = work_mode is positive and surface is unknown/);
+  assert.match(server.SERVER_INSTRUCTIONS, /lack appContext/);
+  assert.match(server.SERVER_INSTRUCTIONS, /Preserve the delivery mode already selected/);
+  assert.match(server.SERVER_INSTRUCTIONS, /treat charts_widget_v2 as directly surfaced/);
+  assert.match(server.SERVER_INSTRUCTIONS, /emit its live genui content reference before fallback/);
+  assert.match(server.SERVER_INSTRUCTIONS, /do not self-declare it unavailable, search for it, or print its payload as bare JSON/);
+  assert.match(server.SERVER_INSTRUCTIONS, /Keep app_block conditional on/);
+  assert.ok(server.SERVER_INSTRUCTIONS.includes('genui{"charts_widget_v2":{"content":{...}}}'));
+  assert.match(server.SERVER_INSTRUCTIONS, /not standalone assistant text/);
+  assert.match(server.SERVER_INSTRUCTIONS, /Use image-based\/static charting only after an emitted native reference is rejected or fails to render/);
+  assert.match(server.SERVER_INSTRUCTIONS, /Use image-based\/static charting for that native-render failure fallback/);
+  assert.match(server.SERVER_INSTRUCTIONS, /Do not say a visual rendered above/);
+  assert.match(tools.render_artifact.description, /positively identified ChatGPT web Work Mode/);
+  assert.match(tools.render_artifact.description, /mode = work_mode is positive and surface is/);
+  assert.match(tools.render_artifact.description, /not delivery confirmation/);
+  assert.match(tools.render_chart.description, /Do not call this tool for inline visual delivery/);
+  assert.match(tools.render_chart.description, /otherwise not positively codex_desktop/);
+  assert.match(tools.render_chart.description, /treat charts_widget_v2 as directly surfaced/);
+  assert.match(tools.render_chart.description, /emit its live genui content reference before fallback/);
+  assert.match(tools.render_chart.description, /do not self-declare it unavailable, search for it, or print its payload as bare JSON/);
+  assert.match(tools.render_chart.description, /Keep app_block conditional on/);
+  assert.ok(tools.render_chart.description.includes('genui{"charts_widget_v2":{"content":{...}}}'));
+  assert.match(tools.render_chart.description, /not standalone assistant text/);
+  assert.match(tools.render_chart.description, /Use image-based\/static charting only after an emitted native reference is rejected or fails to render/);
+  assert.match(tools.render_chart.description, /Use image-based\/static charting for that native-render failure fallback/);
+  assert.match(tools.render_chart.description, /not delivery confirmation/);
+  assert.match(tools.render_table.description, /Do not call this tool for inline table delivery/);
+  assert.match(tools.render_table.description, /otherwise not positively codex_desktop/);
+  assert.match(tools.render_table.description, /not delivery confirmation/);
+});
+
 test("JavaScript MCP server advertises Data Analytics icons on server info", async () => {
   assert.equal(server.DATA_ANALYTICS_ICONS[0].mimeType, "image/svg+xml");
   assert.deepEqual(server.DATA_ANALYTICS_ICONS[0].sizes, ["24x24"]);
@@ -228,7 +298,7 @@ test("JavaScript MCP server advertises Data Analytics icons on server info", asy
     method: "initialize",
     params: {},
   });
-  assert.equal(response.result.serverInfo.title, "Data Analytics Widgets");
+  assert.equal(response.result.serverInfo.title, "Data Analytics");
   assert.equal(
     response.result.serverInfo.description,
     "Render Data Analytics charts, tables, dashboards, and report artifacts.",
@@ -323,6 +393,40 @@ test("JavaScript MCP server rejects key aliases for report table columns", () =>
   );
 });
 
+test("JavaScript MCP server accepts and validates artifact table default sorts", () => {
+  const args = artifactPayload("report");
+  args.manifest.tables[0].defaultSort = {
+    field: "revenue_m",
+    direction: "desc",
+  };
+
+  const payload = server.callTool("render_artifact", args);
+  assert.deepEqual(payload.manifest.tables[0].defaultSort, {
+    field: "revenue_m",
+    direction: "desc",
+  });
+
+  const unknownField = artifactPayload("report");
+  unknownField.manifest.tables[0].defaultSort = {
+    field: "missing_metric",
+    direction: "desc",
+  };
+  assert.throws(
+    () => server.callTool("validate_artifact", unknownField),
+    /defaultSort\.field must reference a declared table column/,
+  );
+
+  const invalidDirection = artifactPayload("report");
+  invalidDirection.manifest.tables[0].defaultSort = {
+    field: "revenue_m",
+    direction: "descending",
+  };
+  assert.throws(
+    () => server.callTool("validate_artifact", invalidDirection),
+    /defaultSort\.direction must be asc or desc/,
+  );
+});
+
 test("JavaScript MCP server accepts body strings in report markdown blocks", () => {
   const args = artifactPayload("report");
   args.manifest.blocks[0] = {
@@ -417,6 +521,56 @@ test("JavaScript MCP server leaves percent chart scale to the manifest data cont
   assert.equal(server.callTool("validate_artifact", args).ok, true);
 });
 
+test("JavaScript MCP server preserves percent format and percent unit for rate chart tooltips", () => {
+  const payload = server.callTool("render_chart", {
+    title: "Daily no-deployment rate",
+    source: sourceQueryForTest(),
+    table: {
+      columns: [
+        { key: "ds", label: "Date", type: "date" },
+        { key: "segment", label: "Segment", type: "text" },
+        { key: "no_deployment_rate", label: "No-deployment rate", type: "number", format: "percent", unit: "%" },
+      ],
+      rows: [
+        { ds: "2026-05-25", segment: "Broad", no_deployment_rate: 0.98 },
+        { ds: "2026-05-25", segment: "Narrow", no_deployment_rate: 0.97 },
+        { ds: "2026-05-26", segment: "Broad", no_deployment_rate: 0.965 },
+        { ds: "2026-05-26", segment: "Narrow", no_deployment_rate: 0.955 },
+      ],
+      row_count: 4,
+      truncated: false,
+    },
+    chart: {
+      type: "line",
+      fields: {
+        x: { field: "ds", type: "temporal" },
+        y: { field: "no_deployment_rate", type: "quantitative", unit: "%" },
+        color: { field: "segment", type: "nominal" },
+      },
+      options: { points: "always" },
+    },
+    display: {
+      unit: "%",
+      y_axis_title: "no_deployment_rate (%)",
+    },
+  });
+
+  assert.equal(payload.chart_spec.valueFormat, "percent");
+  assert.equal(payload.chart_spec.unit, "%");
+  assert.equal(payload.chart_spec.encodings.y.format, "percent");
+  assert.equal(payload.chart_spec.encodings.y.unit, "%");
+  assert.deepEqual(payload.data.map((row) => row.y), [0.98, 0.97, 0.965, 0.955]);
+});
+
+test("artifact chart widget bridge preserves y value formats for synthetic value fields", () => {
+  const app = readFileSync(new URL("../src/analytics-app/App.tsx", import.meta.url), "utf8");
+
+  assert.match(app, /const yFormat = yEncoding\.format \?\? chart\.valueFormat;/);
+  assert.match(app, /\.\.\.\(format \? \{ format \} : \{\}\),/);
+  assert.match(app, /\{ key: "value", label: chartEncodingLabel\(chart, "y", "Value"\), type: "number", \.\.\.yFormatSpec, unit \}/);
+  assert.match(app, /y: \{ aggregate: "sum", field: "value", type: "quantitative", \.\.\.yFormatSpec, unit \}/);
+});
+
 test("JavaScript MCP server requires explicit artifact chart source links", () => {
   const args = artifactPayload("report");
   delete args.manifest.charts[0].sourceId;
@@ -509,6 +663,48 @@ test("JavaScript MCP server validates metric-strip blocks", () => {
     () => server.callTool("validate_artifact", missingCard),
     /cardIds\[0\] does not match a manifest card/,
   );
+
+  const missingSource = artifactPayload("report");
+  missingSource.manifest.blocks.unshift({
+    id: "headline_metrics",
+    type: "metric-strip",
+    cardIds: ["revenue_card"],
+  });
+  delete missingSource.manifest.cards[0].sourceId;
+  assert.throws(
+    () => server.callTool("validate_artifact", missingSource),
+    /manifest\.cards\[0\]\.source must include the actual SQL query text/,
+  );
+
+  const missingDashboardSource = artifactPayload("dashboard");
+  delete missingDashboardSource.manifest.cards[0].sourceId;
+  assert.throws(
+    () => server.callTool("validate_artifact", missingDashboardSource),
+    /manifest\.cards\[0\]\.source must include the actual SQL query text/,
+  );
+
+  const inlineSource = artifactPayload("report");
+  inlineSource.manifest.blocks.unshift({
+    id: "headline_metrics",
+    type: "metric-strip",
+    cardIds: ["revenue_card"],
+  });
+  delete inlineSource.manifest.cards[0].sourceId;
+  inlineSource.manifest.cards[0].source = sourceQueryForTest();
+  assert.equal(server.callTool("validate_artifact", inlineSource).ok, true);
+
+  for (const cardCount of [5, 7]) {
+    const manyCards = artifactPayload("dashboard");
+    manyCards.manifest.cards = Array.from({ length: cardCount }, (_, index) => ({
+      ...manyCards.manifest.cards[0],
+      id: `revenue_card_${index + 1}`,
+      metrics: [{ label: `Revenue ${index + 1}`, field: "revenue_m", format: "currency" }],
+    }));
+    manyCards.manifest.blocks[0].cardIds = manyCards.manifest.cards.map((card) => card.id);
+    const rendered = server.callTool("render_artifact", manyCards);
+    assert.deepEqual(rendered.manifest.blocks[0].cardIds, manyCards.manifest.blocks[0].cardIds);
+    assert.equal(rendered.manifest.cards.length, cardCount);
+  }
 });
 
 test("funnel labels use an explicit high-contrast fill", () => {
@@ -569,6 +765,25 @@ test("signed vertical bar labels reserve compact axis clearance", () => {
   assert.match(labelRenderer, /const BAR_NEGATIVE_VALUE_LABEL_AXIS_GUTTER = 40;/);
 });
 
+test("signed horizontal bar labels stay out of the category-label lane", () => {
+  const renderer = readFileSync(
+    new URL("../src/analytics-app/charting/ChartRenderer.tsx", import.meta.url),
+    "utf8",
+  );
+  const labelRenderer = renderer
+    .split("function renderBarValueLabel", 2)[1]
+    .split("function categoryBarTooltipColor", 1)[0];
+
+  assert.match(labelRenderer, /const zeroSideNegativeLabel = isNegative && placeNegativeHorizontalLabelsAtZeroSide;/);
+  assert.match(labelRenderer, /zeroSideNegativeLabel \? "start" : isNegative \? "end" : "start"/);
+  assert.match(labelRenderer, /zeroSideNegativeLabel \? right \+ BAR_VALUE_LABEL_OFFSET : isNegative \? left - BAR_VALUE_LABEL_OFFSET : right \+ BAR_VALUE_LABEL_OFFSET/);
+  assert.match(renderer, /placeNegativeHorizontalLabelsAtZeroSide: htmlReport/);
+  assert.match(
+    renderer,
+    /horizontal && \(barValueLabelSides\.hasNonNegative \|\| \(htmlReport && barValueLabelSides\.hasNegative\)\)/,
+  );
+});
+
 test("encoded funnel charts keep a single series even when color is present", () => {
   const helpers = readFileSync(
     new URL("../src/analytics-app/charting/chart-app-helpers.tsx", import.meta.url),
@@ -667,6 +882,35 @@ test("category x-axis labels wrap when crowded and rotate only when requested",
   assert.match(wrappingHeuristic, /longestLabelWidth <= labelSlotWidth\) return false/);
   assert.match(wrappingHeuristic, /if \(maxLength >= CATEGORY_X_AXIS_LONG_LABEL_LENGTH\) return true/);
   assert.match(renderer, /function WrappedCategoryXAxisTick/);
+  assert.match(renderer, /function sparseCategoryXAxisTicks/);
+  assert.match(renderer, /CATEGORY_X_AXIS_MIN_VISIBLE_TICK_SPACING/);
+  assert.match(renderer, /if \(ticks\[ticks\.length - 1\] !== last\) ticks\.push\(last\)/);
+  assert.match(renderer, /ticks: categoryXAxisTicks/);
+});
+
+test("category x-axis wrapping respects narrow slots and balances long labels", () => {
+  const renderer = readFileSync(
+    new URL("../src/analytics-app/charting/ChartRenderer.tsx", import.meta.url),
+    "utf8",
+  );
+  const lineLengthBudget = renderer
+    .split("function categoryXAxisWrappedLineLength", 2)[1]
+    .split("function sparseCategoryXAxisTicks", 1)[0];
+  const balancedSplit = renderer
+    .split("function balancedCategoryAxisLabelSplit", 2)[1]
+    .split("function wrapCategoryAxisLabel", 1)[0];
+  const wrappedTick = renderer
+    .split("function WrappedCategoryXAxisTick", 2)[1]
+    .split("function categoryXAxisLabels", 1)[0];
+
+  assert.doesNotMatch(renderer, /CATEGORY_X_AXIS_MIN_LINE_CHARACTERS/);
+  assert.match(lineLengthBudget, /Math\.max\(1, Math\.floor\(slotWidth \/ CATEGORY_X_AXIS_ESTIMATED_CHAR_WIDTH\)\)/);
+  assert.match(balancedSplit, /const midpoint = value\.length \/ 2/);
+  assert.match(balancedSplit, /value\.matchAll\(\/\\s\+\/g\)/);
+  assert.match(balancedSplit, /Math\.abs\(index - midpoint\) < Math\.abs\(best - midpoint\)/);
+  assert.match(balancedSplit, /Math\.ceil\(midpoint\)/);
+  assert.match(renderer, /if \(safeMaxLength === 1\) return "…"/);
+  assert.match(wrappedTick, /<title>\{value\}<\/title>/);
 });
 
 test("numeric axis ticks omit repeated units while axis titles carry unit context", () => {
@@ -694,7 +938,10 @@ test("numeric axis ticks omit repeated units while axis titles carry unit contex
   assert.doesNotMatch(renderer, /tickFormatter=\{\(value\) => formatValue\(value, [^}\n]*chart\.unit/);
   assert.match(transforms, /labels: ticks\.map\(\(value\) => formatValue\(value, chart\.valueFormat\)\)/);
   assert.match(helpers, /function chartEncodingAxisTitle/);
+  assert.match(helpers, /function axisTitlesForEncodedChart/);
+  assert.match(helpers, /horizontal \? encodedYAxisTitle : encodedXAxisTitle/);
   assert.match(helpers, /return `\$\{label\} \(\$\{unit\}\)`/);
+  assert.match(renderer, /const horizontalValueAxisLabel = chart\.xAxisTitle \? bottomAxisLabel\(chart\.xAxisTitle\) : undefined;/);
   assert.match(horizontalBar, /<XAxis[^>]*label=\{horizontalValueAxisLabel\}/);
   assert.match(horizontalBar, /<YAxis[^>]*label=\{horizontalCategoryAxisLabel\}/);
   assert.doesNotMatch(horizontalBar, /label=\{yAxisLabel\}/);
@@ -723,6 +970,20 @@ test("artifact table movement cells render explicit positive and negative signs"
   assert.match(app, /tableCellMovementClass\(column, value\)/);
 });
 
+test("artifact tables initialize interactive sorting from the manifest default", () => {
+  const app = readFileSync(new URL("../src/analytics-app/App.tsx", import.meta.url), "utf8");
+  const artifactWidget = readFileSync(
+    new URL("../src/datascience-artifact-widget.jsx", import.meta.url),
+    "utf8",
+  );
+
+  assert.match(app, /function tableDefaultSort\(table\)/);
+  assert.match(app, /useState\(\(\) => tableDefaultSort\(table\)\)/);
+  assert.match(app, /setSortState\(tableDefaultSort\(table\)\)/);
+  assert.match(artifactWidget, /defaultSort\.field must reference a declared table column/);
+  assert.match(artifactWidget, /defaultSort\.direction must be asc or desc/);
+});
+
 test("artifact card chart type menu is limited by data shape", () => {
   const compatibility = readFileSync(
     new URL("../src/analytics-app/charting/chart-compatibility.ts", import.meta.url),
@@ -740,26 +1001,105 @@ test("artifact card chart type menu is limited by data shape", () => {
   assert.match(compatibility, /const HIDDEN_BAR_VARIANT_TYPE_LIST: ChartType\[\] = \[/);
   assert.match(compatibility, /if \(chart\.type === "funnel" \|\| chart\.intent === "funnel"\) return FUNNEL_SHAPE_TYPES;/);
   assert.match(helpers, /export function compatibleChartTypesForArtifactCard/);
-  assert.equal((app.match(/compatibleChartTypesForArtifactCard\(overriddenChart, chartRows\)/g) || []).length, 2);
-  assert.match(app, /applyChartSpecOverride\(chart, chartSpecOverrides\[chart\.id\]\)/);
+  assert.equal((app.match(/compatibleChartTypesForArtifactCard\(overriddenChart, chartRows\)/g) || []).length, 1);
+  assert.match(app, /const dashboardContentBlocks = useMemo\([\s\S]*<ReportBlockCard/);
+  assert.match(app, /applyChartSpecOverride\(chart, chartSpecOverride\)/);
+  assert.match(app, /chartSpecOverride=\{chart \? chartSpecOverrides\[chart\.id\] : undefined\}/);
   assert.match(app, /const chartModalTypeOptions = chartModalBaseChart\s+\?\s+compatibleChartTypesFor\(chartModalBaseChart, chartModalRows\)/);
 });
 
-test("artifact data source dialogs use sectioned shared table content", () => {
+test("artifact data source dialogs use three accessible tabs with one scroll body", () => {
   const app = readFileSync(new URL("../src/analytics-app/App.tsx", import.meta.url), "utf8");
+  const tableSizing = readFileSync(new URL("../src/analytics-app/tables/tableSizing.ts", import.meta.url), "utf8");
   const styles = readFileSync(new URL("../src/analytics-app/styles.css", import.meta.url), "utf8");
 
-  assert.match(app, /<h3>Details<\/h3>/);
-  assert.match(app, /<h3>Source query<\/h3>/);
-  assert.match(app, /<h3>Data table<\/h3>/);
+  assert.match(app, /function Tabs\(\{ ariaLabel, onSelect, selectedKey, tabs \}\)/);
+  assert.match(app, /role="tablist"/);
+  assert.match(app, /\{ id: "overview", label: "Overview", panelId:/);
+  assert.match(app, /\{ id: "data", label: "Data preview", panelId:/);
+  assert.match(app, /\{ id: "sql", label: "SQL query", panelId:/);
+  assert.match(app, /className="source-modal-tab-indicator"/);
+  assert.equal((app.match(/className="source-modal-tab-indicator"/g) || []).length, 1);
+  assert.match(app, /const updateIndicator = useCallback/);
+  assert.match(app, /indicator\.style\.transform = `translate3d/);
+  assert.match(app, /new ResizeObserver\(updateIndicator\)/);
+  assert.match(app, /event\.key === "ArrowRight"/);
+  assert.match(app, /event\.key === "Home"/);
+  assert.match(app, /activeTab === "data"/);
+  assert.match(app, /id=\{`\$\{tabId\}-data-panel`\}/);
+  assert.doesNotMatch(app, /<h3>Data preview<\/h3>/);
+  assert.match(app, /function Badge\(\{ children, className = "" \}\)/);
+  assert.match(app, /function sourceMetadataValue/);
+  assert.match(app, /function sourceMetadataChips/);
+  assert.match(app, /<Badge className="source-metadata-chip"/);
+  assert.doesNotMatch(app, /function sourceMetadataBadge/);
+  assert.doesNotMatch(app, /className="source-metadata-code"/);
+  assert.match(app, /<dd>\{sourceMetadataValue\(details\.dataset\)\}<\/dd>/);
+  assert.match(app, /sourceMetadataChips\(details\.tables\)/);
+  assert.match(app, /sourceMetadataChips\(details\.filters\)/);
+  assert.match(app, /function metricDefinitionRows/);
+  assert.match(app, /const SOURCE_METRIC_DEFINITION_COLUMNS = \[/);
+  assert.match(app, /dataset="__source_metric_definitions" density="spacious" fillAvailableWidth rows=\{metricDefinitionRows/);
+  assert.doesNotMatch(app, /<dt>Metric definitions<\/dt>/);
+  assert.doesNotMatch(app, /className="source-metric-definition-table"/);
+  assert.match(app, /are\|is\|equals\|uses\?\|means\|measures/);
+  assert.match(app, /itemLabel=\{chart\.title\} itemType="Chart"/);
+  assert.match(app, /itemLabel=\{label\} itemType="Metric"/);
+  assert.match(app, /itemLabel=\{table\.title\} itemType="Table"/);
+  assert.match(app, /function sourceSnapshotDate/);
+  assert.match(app, /<dt>Data snapshot<\/dt>/);
+  assert.match(app, /sourceSnapshotDate\(details\.snapshot\)/);
+  assert.doesNotMatch(app, /sourceSnapshotSubtitle/);
+  assert.doesNotMatch(app, /source-overview-summary/);
+  assert.doesNotMatch(app, /const summary =/);
+  assert.match(app, /className="source-modal-body"/);
   assert.match(app, /function SourceDataTable/);
+  assert.match(app, /function CardSourceModalDialog/);
+  assert.match(app, /function sourceForCard/);
+  assert.doesNotMatch(app, /kpi-source-button|kpi-source-icon|FileSearch2/);
+  assert.match(app, /<CardSourceModalDialog activeFilters=/);
   assert.match(app, /className="source-data-table"/);
   assert.match(app, /<TableContent allowColumnResize=\{false\} columnWidths=/);
+  assert.match(app, /const SOURCE_DATA_PREVIEW_PAGE_SIZE = 10;/);
+  assert.match(app, /pageSize: requestedPageSize = TABLE_CARD_PAGE_SIZE/);
+  assert.match(app, /pageSize=\{SOURCE_DATA_PREVIEW_PAGE_SIZE\}/);
+  assert.equal((app.match(/pageSize=\{SOURCE_DATA_PREVIEW_PAGE_SIZE\}/g) || []).length, 3);
   assert.doesNotMatch(app, /function SourcePreviewRows/);
   assert.match(app, /className="modal-close-button"/);
   assert.match(app, /useModalScrollLock\(true\)/);
   assert.match(styles, /\.native-modal\.source-modal/);
-  assert.match(styles, /\.source-query \{[\s\S]*?overflow-x: hidden;[\s\S]*?white-space: pre-wrap;/);
+  assert.doesNotMatch(styles, /\.kpi-source-button|\.kpi-source-icon/);
+  assert.match(styles, /\.source-query \{[\s\S]*?max-height: none;[\s\S]*?overflow: visible;/);
+  assert.match(styles, /\.source-query \{[\s\S]*?white-space: pre-wrap;/);
+  assert.match(styles, /\.source-modal-body \{[\s\S]*?overflow-y: auto;/);
+  assert.match(styles, /\.source-details-summary \{[\s\S]*?grid-template-columns: repeat\(3, minmax\(0, 1fr\)\);/);
+  assert.match(styles, /\.source-details-stack \{[\s\S]*?display: grid;[\s\S]*?gap: 16px;/);
+  assert.match(styles, /\.chip \{[\s\S]*?border-radius: var\(--ds-radius-pill\);/);
+  assert.match(styles, /\.source-metadata-chip \{[\s\S]*?background: transparent;[\s\S]*?color: var\(--ds-text-primary\);[\s\S]*?font-family: var\(--ds-font\);[\s\S]*?font-size: var\(--codex-font-size-base\);[\s\S]*?font-weight: 400;[\s\S]*?padding-block: 4px;/);
+  assert.doesNotMatch(styles, /\.source-metadata-code \{/);
+  assert.doesNotMatch(styles, /\.source-metric-definitions > dt \{/);
+  assert.match(styles, /\.source-metric-definitions \{[\s\S]*?min-width: 0;/);
+  assert.match(styles, /\.source-metric-definitions tbody \.table-column-content-fit \{[\s\S]*?font-weight: 500;/);
+  assert.doesNotMatch(styles, /\.source-metric-definition-table \{/);
+  assert.match(styles, /\.source-modal-tab \{[\s\S]*?padding: 0 0 13px;/);
+  assert.match(styles, /\.source-modal-panel > \.modal-header \{[\s\S]*?padding: 20px 24px 4px;/);
+  assert.match(styles, /\.native-modal\.source-modal \{[\s\S]*?height: fit-content;[\s\S]*?max-height: min\(760px, calc\(100dvh - 48px\)\);/);
+  assert.match(styles, /\.source-modal-panel \{[\s\S]*?height: auto;[\s\S]*?max-height: min\(760px, calc\(100dvh - 48px\)\);/);
+  assert.match(styles, /\.source-modal-tabs \{[\s\S]*?border-bottom: 1px solid var\(--ds-border-subtle\);/);
+  assert.match(styles, /\.source-modal-tab-indicator \{[\s\S]*?height: 2px;[\s\S]*?transform 180ms/);
+  assert.match(styles, /\.source-modal-tab-indicator\[data-ready="true"\] \{[\s\S]*?opacity: 1;/);
+  assert.doesNotMatch(styles, /\.source-modal-panel > \.modal-header \{[^}]*border(?:-bottom)?:/);
+  assert.doesNotMatch(styles, /\.source-filter-chip \{/);
+  assert.match(app, /\{ field: "metric", label: "Metric", sizing: "content", type: "text" \}/);
+  assert.match(app, /column\.sizing === "content"/);
+  assert.match(app, /calculateTableSizing\(table\.columns, activeColumnWidths, tableViewportWidth, shouldFillAvailableWidth\)/);
+  assert.match(tableSizing, /column\.sizing !== "content"/);
+  assert.match(tableSizing, /normalizedViewportWidth - baseTableWidth/);
+  assert.match(tableSizing, /extraWidthPerFlexibleColumn/);
+  assert.match(app, /new ResizeObserver\(updateViewportWidth\)/);
+  assert.match(styles, /\.data-table-smart-layout \.table-column-content-fit/);
+  assert.match(styles, /\.source-query \{[\s\S]*?max-height: none;[\s\S]*?overflow: visible;/);
+  assert.match(styles, /\.source-query \{[\s\S]*?white-space: pre-wrap;/);
 });
 
 test("JavaScript MCP server rejects ready artifacts with access issues", () => {
@@ -880,6 +1220,10 @@ test("JavaScript MCP server advertises artifact handoff before fallback widgets"
   assert.match(server.SERVER_INSTRUCTIONS, /^Before rendering a report or dashboard artifact/);
   assert.match(server.SERVER_INSTRUCTIONS, /manifest\.blocks/);
   assert.match(server.SERVER_INSTRUCTIONS, /first markdown block whose body is a # heading matching manifest\.title/);
+  assert.match(server.SERVER_INSTRUCTIONS, /independently editable major report section/);
+  assert.match(server.SERVER_INSTRUCTIONS, /multiple peer ## headings/);
+  assert.match(server.SERVER_INSTRUCTIONS, /One headline metric does not mean one metrics\[\] entry/);
+  assert.match(server.SERVER_INSTRUCTIONS, /defaultSort/);
   assert.match(validateTool.description, /without rendering a hosted widget/);
   assert.equal(validateTool._meta, undefined);
   assert.match(artifactTool.description, /dashboard or report artifact/);
@@ -887,20 +1231,37 @@ test("JavaScript MCP server advertises artifact handoff before fallback widgets"
   assert.match(artifactTool.inputSchema.properties.manifest.description, /top-level manifest\.blocks/);
   assert.match(artifactTool.inputSchema.properties.manifest.description, /first content heading/);
   assert.match(artifactTool.inputSchema.properties.manifest.properties.blocks.description, /type "html"/);
+  assert.match(artifactTool.inputSchema.properties.manifest.properties.blocks.description, /independently editable major report section/);
   assert.match(artifactTool.inputSchema.properties.manifest.properties.title.description, /first content heading/);
   const manifestSchema = artifactTool.inputSchema.properties.manifest;
   const cardSchema = manifestSchema.properties.cards.items;
   assert.ok(cardSchema.required.includes("metrics"));
   assert.equal(cardSchema.properties.valueField, undefined);
+  assert.equal(cardSchema.properties.sourceId.type[0], "string");
+  assert.ok(cardSchema.properties.source);
+  assert.match(cardSchema.properties.sourceId.description, /data-source modal/);
+  assert.match(cardSchema.properties.metrics.description, /One headline metric/);
+  assert.match(cardSchema.properties.metrics.description, /One headline does not mean one metrics\[\] entry/);
+  assert.match(cardSchema.properties.metrics.description, /executive summary or findings/);
+  assert.match(cardSchema.properties.metrics.description, /independent secondary metrics/);
   assert.equal(cardSchema.properties.metrics.items.properties.field.type, "string");
+  assert.match(cardSchema.properties.metrics.items.properties.format.description, /Use "percent" only for fractional-rate values/);
 
-  const tableColumnSchema = manifestSchema.properties.tables.items.properties.columns.items;
+  const tableSchema = manifestSchema.properties.tables.items;
+  const tableColumnSchema = tableSchema.properties.columns.items;
   assert.ok(tableColumnSchema.required.includes("field"));
   assert.equal(tableColumnSchema.properties.key, undefined);
+  assert.deepEqual(tableSchema.properties.defaultSort.required, ["field", "direction"]);
+  assert.deepEqual(tableSchema.properties.defaultSort.properties.direction.enum, ["asc", "desc"]);
+  assert.match(tableColumnSchema.properties.format.description, /98.*use "number" with unit "%"/);
+
+  const chartSchema = manifestSchema.properties.charts.items;
+  assert.match(chartSchema.properties.valueFormat.description, /0\.98 for 98%/);
 
   const blockSchema = manifestSchema.properties.blocks.items;
   assert.ok(blockSchema.required.includes("id"));
   assert.equal(blockSchema.properties.markdown, undefined);
+  assert.match(blockSchema.properties.body.description, /one peer ## heading/);
   assert.deepEqual(artifactTool._meta.ui, {
     resourceUri: server.ARTIFACT_WIDGET_URI,
     visibility: ["model"],
@@ -1004,6 +1365,41 @@ test("JavaScript MCP server accepts custom chart axis titles", () => {
   assert.equal("y_label" in payload, false);
 });
 
+test("JavaScript MCP server preserves physical axis titles for horizontal bars", () => {
+  const payload = server.callTool("render_chart", {
+    title: "Horizontal activation milestones",
+    source: sourceQueryForTest(),
+    table: {
+      columns: [
+        { key: "milestone", label: "Activation milestone", type: "text" },
+        { key: "workspaces", label: "Activated workspaces", type: "number", format: "number" },
+      ],
+      rows: [
+        { milestone: "Admin invited teammates", workspaces: 1820 },
+        { milestone: "Slack integration connected", workspaces: 1428 },
+      ],
+      row_count: 2,
+      truncated: false,
+    },
+    chart: {
+      type: "bar",
+      fields: {
+        x: { field: "milestone", type: "nominal" },
+        y: { field: "workspaces", type: "quantitative" },
+      },
+      options: { orientation: "horizontal" },
+    },
+    display: {
+      x_axis_title: "Activated workspaces",
+      y_axis_title: "Activation milestone",
+    },
+  });
+
+  assert.equal(payload.chart_spec.settings.orientation, "horizontal");
+  assert.equal(payload.chart_spec.xAxisTitle, "Activated workspaces");
+  assert.equal(payload.chart_spec.yAxisTitle, "Activation milestone");
+});
+
 test("JavaScript MCP server normalizes heatmap encodings to widget field roles", () => {
   const payload = server.callTool("render_chart", {
     title: "Heatmap",
@@ -1174,6 +1570,8 @@ test("JavaScript MCP server exposes chart v1 settings in the chart schema", () =
   assert.equal("labels" in display, false);
   assert.equal("number_format" in display, false);
   assert.deepEqual(tableColumn.format.enum, ["compact", "number", "percent", "currency", null]);
+  assert.match(tableColumn.format.description, /Use "percent" only for fractional-rate values/);
+  assert.match(tableColumn.format.description, /98.*use "number" with unit "%"/);
   assert.equal("version" in chart, false);
   assert.equal("comparisonContext" in chart, false);
   assert.equal("notes" in properties, false);
diff --git a/plugins/data-analytics/tests/native-style-contract.test.mjs b/plugins/data-analytics/tests/native-style-contract.test.mjs
index a300d3e..60160ae 100644
--- a/plugins/data-analytics/tests/native-style-contract.test.mjs
+++ b/plugins/data-analytics/tests/native-style-contract.test.mjs
@@ -56,29 +56,57 @@ test("report tables retain contained horizontal scrolling and Codex-aligned read
   assert.match(styles, /\.report-stack-item-table \.table-wrap \{[\s\S]*margin-left: 0;/);
   assert.match(styles, /\.report-stack-item-table \.table-scroll-content \{[\s\S]*padding-left: 0;/);
   assert.match(styles, /\.table-density-spacious table:not\(\.data-table-resizable\) \{/);
-  assert.match(app, /minWidth: `\$\{tablePixelWidth\}px`/);
-  assert.match(app, /width: `\$\{tablePixelWidth\}px`/);
+  assert.match(app, /calculateTableSizing\(table\.columns, activeColumnWidths, tableViewportWidth, shouldFillAvailableWidth\)/);
+  assert.match(app, /const shouldFillAvailableWidth = isFullscreen \|\| fillAvailableWidth;/);
+  assert.match(app, /minWidth: `\$\{tableSizing\.minimumTableWidth\}px`/);
+  assert.match(app, /width: `\$\{tableSizing\.tableWidth\}px`/);
+  assert.match(app, /function TableContent\(\{[\s\S]*fillAvailableWidth = true,/);
+  assert.match(app, /function SourceDataTable\(\{[\s\S]*fillAvailableWidth = true,/);
+  assert.match(app, /calculateHorizontalScrollEdges\(\{[\s\S]*clientWidth: tableWrap\.clientWidth,[\s\S]*scrollLeft: tableWrap\.scrollLeft,[\s\S]*scrollWidth: tableElementRef\.current\?\.offsetWidth \?\? tableWrap\.scrollWidth/);
+  assert.match(app, /scrollbarBlockSize: Math\.max\(0, tableWrap\.offsetHeight - tableWrap\.clientHeight\)/);
+  assert.match(app, /scrollbarInlineSize: Math\.max\(0, tableWrap\.offsetWidth - tableWrap\.clientWidth\)/);
+  assert.match(app, /tableWrap\.addEventListener\("scroll", updateHorizontalScrollEdges, \{ passive: true \}\)/);
+  assert.match(app, /observer\.observe\(tableScrollContent\)/);
+  assert.match(app, /observer\.observe\(tableElement\)/);
+  assert.match(app, /data-can-scroll-left=\{horizontalScrollEdges\.canScrollLeft/);
+  assert.match(app, /data-can-scroll-right=\{horizontalScrollEdges\.canScrollRight/);
+  assert.match(app, /role=\{isScrollableTableRegion \? "region" : undefined\}/);
+  assert.match(app, /tabIndex=\{isScrollableTableRegion \? 0 : undefined\}/);
+  assert.match(app, /className="table-scroll-edge table-scroll-edge-left" data-image-export-exclude="true"/);
+  assert.match(app, /className="table-scroll-edge table-scroll-edge-right" data-image-export-exclude="true"/);
+  assert.match(styles, /\.table-scroll-shell \{[\s\S]*--table-scroll-edge-surface: var\(--ds-bg\);[\s\S]*--table-scrollbar-block-size: 0px;[\s\S]*--table-scrollbar-inline-size: 0px;[\s\S]*position: relative;/);
+  assert.match(styles, /\.native-modal \.table-scroll-shell \{[\s\S]*--table-scroll-edge-surface: var\(--ds-overlay-bg\);/);
+  assert.match(styles, /\.table-scroll-edge \{[\s\S]*width: 28px;[\s\S]*opacity: 0;[\s\S]*pointer-events: none;[\s\S]*transition: opacity 150ms ease;/);
+  assert.match(styles, /\.table-scroll-edge \{[\s\S]*bottom: var\(--table-scrollbar-block-size\);/);
+  assert.match(styles, /\.table-scroll-edge-right \{[\s\S]*right: var\(--table-scrollbar-inline-size\);/);
+  assert.match(styles, /\.table-scroll-shell\[data-can-scroll-left="true"\] \.table-scroll-edge-left,[\s\S]*\.table-scroll-shell\[data-can-scroll-right="true"\] \.table-scroll-edge-right \{[\s\S]*opacity: 1;/);
+  assert.match(styles, /@media print, \(forced-colors: active\) \{[\s\S]*\.table-scroll-edge \{[\s\S]*display: none;/);
   assert.match(styles, /--ds-menu-hover-bg: #2b2b2b;/);
   assert.doesNotMatch(styles, /\.report-stack-item-table \.table-wrap \{[^}]*padding-left:/);
   assert.match(styles, /\.modal-panel \{[\s\S]*background: var\(--ds-overlay-bg\);/);
   assert.match(styles, /\.native-modal\.source-modal \{[\s\S]*width: min\(800px, calc\(100vw - 48px\)\);/);
-  assert.match(styles, /\.source-modal-panel \{[\s\S]*background: var\(--ds-overlay-bg\);[\s\S]*padding: 20px;/);
+  assert.match(styles, /\.source-modal-panel \{[\s\S]*overflow: hidden;[\s\S]*background: var\(--ds-overlay-bg\);[\s\S]*padding: 0;/);
+  assert.match(styles, /\.native-modal\.table-fullscreen-modal \{[\s\S]*width: min\(1200px, calc\(100dvw - 32px\)\);[\s\S]*height: fit-content;/);
+  assert.match(styles, /\.table-fullscreen-panel \{[\s\S]*height: auto;[\s\S]*max-height: calc\(100dvh - 32px\);/);
+  assert.match(styles, /\.table-wrap\.fullscreen \{[\s\S]*flex: 0 1 auto;[\s\S]*max-height: min\(700px, calc\(100dvh - 138px\)\);[\s\S]*overflow: auto;/);
   assert.match(read("../src/analytics-app/tokens.css"), /--ds-menu-bg: var\(--ds-overlay-bg\);/);
-  assert.match(styles, /\.metric-badge \{[\s\S]*background: var\(--ds-surface-tertiary\);/);
+  assert.match(styles, /\.chip \{[\s\S]*background: var\(--ds-surface-tertiary\);/);
   assert.match(styles, /\.metric-badge\.positive \{[\s\S]*color: var\(--ds-green\);/);
   assert.match(styles, /\.metric-badge\.negative \{[\s\S]*color: var\(--ds-red\);/);
   assert.match(tableStyles, /\.data-table td \{[\s\S]*color: var\(--ds-text-secondary\);/);
   assert.match(tableStyles, /\.data-table td:first-child \{[\s\S]*color: var\(--ds-text-primary\);/);
   assert.match(source, /tableWrapRef\.current\.scrollLeft = 0/);
   assert.match(app, /const TABLE_CARD_PAGE_SIZE = 15;/);
-  assert.match(app, /ref=\{tableWrapRef\}>\s*<div className="table-scroll-content">/);
-  assert.match(app, /minWidth: `\$\{tablePixelWidth\}px`/);
-  assert.match(app, /width: `\$\{tablePixelWidth\}px`/);
+  assert.match(app, /minWidth: `\$\{tableSizing\.minimumTableWidth\}px`/);
+  assert.match(app, /width: `\$\{tableSizing\.tableWidth\}px`/);
   assert.match(tableStyles, /\.table-sort-button:hover,\s*\.table-sort-button:focus-visible \{[\s\S]*border-radius: 0;/);
 });
 
 test("report top-bar title uses the compact supported text style", () => {
   const styles = read("../src/analytics-app/styles.css");
+  const target = styles
+    .split(".analytics-top-bar .page-title-edit-target {", 2)[1]
+    .split("}", 1)[0];
   const title = styles
     .split(".analytics-top-bar .page-title-edit-target h1 {", 2)[1]
     .split("}", 1)[0];
@@ -92,26 +120,214 @@ test("report top-bar title uses the compact supported text style", () => {
     assert.match(block, /line-height: 20px;/);
     assert.match(block, /letter-spacing: -0\.13px;/);
   }
+
+  for (const block of [target, editor]) {
+    assert.match(block, /width: calc\(100% \+ 16px\);/);
+    assert.match(block, /max-width: calc\(100% \+ 16px\);/);
+    assert.match(block, /margin: -4px -8px;/);
+    assert.match(block, /padding: 4px 8px;/);
+  }
+  assert.match(editor, /border: 0;/);
 });
 
-test("chart detail actions use edit language", () => {
+test("chart cards use an icon-only inline expand action", () => {
   const app = read("../src/analytics-app/App.tsx");
   const widget = read("../src/datascience-chart-widget.js");
+  const widgetMarkup = read("../src/datascience-chart-widget.html");
+  const widgetStyles = read("../src/datascience-chart-widget.css");
+  const inlineControl = widgetMarkup.match(/<button(?=[^>]*id="display-mode-button")[^>]*>[\s\S]*?<\/button>/)?.[0];
 
   assert.match(app, /menuItem\("Edit chart"/);
   assert.doesNotMatch(app, /Switch chart type/);
   assert.doesNotMatch(app, /<h2>Edit chart<\/h2>/);
   assert.doesNotMatch(app, /Explore chart/);
-  assert.match(widget, /"Edit chart"/);
+  assert.ok(inlineControl);
+  assert.match(inlineControl, /title="Expand chart"/);
+  assert.match(inlineControl, /aria-label="Expand chart"/);
+  assert.match(inlineControl, /<svg/);
+  assert.match(inlineControl, /m21 21-6-6/);
+  assert.doesNotMatch(inlineControl, /M6 2H2v4/);
+  assert.doesNotMatch(inlineControl, /<span|Edit/);
+  assert.match(widget, /const showInlineExpand = mode === "inline" && hostSupportsDisplayMode\(\);/);
+  assert.match(widget, /button\.hidden = !showInlineExpand;/);
+  assert.match(widget, /button\.title = "Expand chart";/);
+  assert.doesNotMatch(widget, /"Edit chart"|display-mode-button-label|exploreIcon/);
   assert.doesNotMatch(widget, /Explore chart/);
+  assert.match(widgetStyles, /\.display-mode-button \{[\s\S]*aspect-ratio: 1 \/ 1;[\s\S]*width: 32px;[\s\S]*height: 32px;[\s\S]*border-radius: 50%;/);
+});
+
+test("report and dashboard metrics render as independent draggable cards in canonical block order", () => {
+  const app = read("../src/analytics-app/App.tsx");
+  const layoutCanvas = read("../src/analytics-app/layout/AnalyticsLayoutCanvas.tsx");
+  const serverSource = read("../mcp/server.cjs");
+  const styles = read("../src/analytics-app/styles.css");
+  const dashboardResolver = app
+    .split("const dashboardContentBlocks = useMemo(() => {", 2)[1]
+    .split("const reportContentBlocks = useMemo(() => {", 1)[0];
+
+  assert.ok(dashboardResolver);
+  assert.match(app, /function metricCardBlockId\(blockId, cardId\)/);
+  assert.match(app, /visibleReportGridBlocks\.flatMap\(\(block\) =>/);
+  assert.match(app, /className: "report-stack-item report-stack-item-metric-card"/);
+  assert.match(app, /compactGroup: `metric-strip:\$\{block\.id\}`/);
+  assert.match(app, /defaultLayout: "half"/);
+  assert.match(layoutCanvas, /function buildDisplayRows/);
+  assert.match(layoutCanvas, /function compactGroupForItem/);
+  assert.match(layoutCanvas, /compactGroupForItem\(items\[index\], blockById\) === compactGroup/);
+  assert.match(layoutCanvas, /while \(index < items\.length && !compactGroupForItem\(items\[index\], blockById\)\)/);
+  assert.match(layoutCanvas, /row\.compactGroup \? "is-compact-row"/);
+  assert.match(layoutCanvas, /function balancedCompactColumnCount/);
+  assert.match(layoutCanvas, /const \[canvasWidth, setCanvasWidth\] = useState\(0\)/);
+  assert.match(layoutCanvas, /new ResizeObserver\(updateWidth\)/);
+  assert.match(layoutCanvas, /fewestEmptyCells/);
+  assert.match(layoutCanvas, /"--analytics-compact-columns": compactColumns/);
+  assert.match(app, /function ArtifactMetricCard/);
+  assert.match(app, /datascience-dashboard:content-layout:v2:/);
+  assert.doesNotMatch(app, /function KpiStrip/);
+  assert.match(dashboardResolver, /return visibleReportGridBlocks\.flatMap\(\(block\) =>/);
+  assert.match(dashboardResolver, /if \(block\.type === "metric-strip"\)/);
+  assert.match(dashboardResolver, /const chart = block\.chartId \? chartsById\.get\(block\.chartId\) : undefined;/);
+  assert.match(dashboardResolver, /const table = block\.tableId \? tablesById\.get\(block\.tableId\) : undefined;/);
+  assert.doesNotMatch(dashboardResolver, /charts\.map|tables\.map|const metricBlocks/);
+  assert.match(app, /className="dashboard-content-grid metric-card-layout"/);
+  assert.match(app, /className="report-content-grid report-block-stack metric-card-layout"/);
+  assert.match(app, /menuItem\("View data source"/);
+  assert.match(app, /onDeleteBlock \? menuItem\("Delete"/);
+  assert.match(app, /knownDeletableReportIds\.has\(id\)/);
+  assert.match(styles, /\.report-metric-card \{[\s\S]*border: 1px solid var\(--ds-border-subtle\);[\s\S]*border-radius: var\(--ds-radius-panel\);/);
+  assert.match(styles, /\.report-metric-card \{[\s\S]*justify-content: flex-start;/);
+  assert.match(styles, /\.report-metric-card \{[\s\S]*padding: 20px;/);
+  assert.match(styles, /\.kpi-card \{[^}]*background: transparent;/);
+  assert.match(styles, /\.report-metric-card \{[^}]*background: transparent;/);
+  assert.doesNotMatch(styles, /\.report-metric-card \{[\s\S]*min-height: 132px;/);
+  assert.match(styles, /\.analytics-layout-item-shell > \.report-metric-card \{[\s\S]*height: 100%;/);
+  assert.match(styles, /\.report-metric-card \.kpi-label-row \{[\s\S]*flex: 0 0 auto;/);
+  assert.match(styles, /\.report-metric-card \.kpi-info-wrap \{[\s\S]*height: 20px;/);
+  assert.match(styles, /\.report-metric-card \.kpi-info \{[\s\S]*position: absolute;[\s\S]*transform: translate\(-50%, -50%\);/);
+  assert.match(styles, /\.report-metric-card \.viz-card-actions \{[\s\S]*position: absolute;[\s\S]*top: 12px;[\s\S]*right: 12px;/);
+  assert.doesNotMatch(styles, /padding: 20px 56px 20px 24px;/);
+  assert.doesNotMatch(styles, /padding: 18px 52px 18px 20px;/);
+  assert.match(styles, /--metric-card-gap: 8px;/);
+  assert.match(styles, /--metric-card-min-width: 280px;/);
+  assert.match(layoutCanvas, /const COMPACT_ITEM_MIN_WIDTH_PX = 280;/);
+  assert.match(styles, /\.kpi-label \{[\s\S]*text-overflow: ellipsis;[\s\S]*white-space: nowrap;/);
+  assert.match(styles, /\.metric-badge-row \{[\s\S]*flex-wrap: nowrap;/);
+  assert.match(styles, /\.report-metric-card \.metric-badge \{[\s\S]*white-space: nowrap;/);
+  assert.match(styles, /\.metric-card-layout \.analytics-layout-row\.is-compact-row \{[\s\S]*grid-template-columns: repeat\([\s\S]*var\(--analytics-compact-columns, auto-fit\),[\s\S]*minmax\(min\(100%, var\(--metric-card-min-width\)\), 1fr\)[\s\S]*\);/);
+  assert.match(styles, /\.metric-card-layout \.analytics-layout-row\.is-compact-row > \.analytics-layout-item\.layout-full \{[\s\S]*grid-column: auto;/);
+  assert.doesNotMatch(styles, /\.kpi-strip|kpi-columns-/);
+  assert.match(serverSource, /cardIds: \{[\s\S]*minItems: 1,[\s\S]*There is no preferred or maximum card count/);
+  assert.doesNotMatch(styles, /@container report-block-stack/);
+  assert.doesNotMatch(styles, /\.report-block-stack \.analytics-layout-row\.is-compact-row \{\s*grid-template-columns: 1fr;/);
+  assert.doesNotMatch(app, /function ReportMetricStripBlock/);
 });
 
-test("artifact top bar shows the snapshot date as the refresh label", () => {
+test("artifact top bar matches the title-date-overflow-edit layout", () => {
   const app = read("../src/analytics-app/App.tsx");
+  const styles = read("../src/analytics-app/styles.css");
+  const host = read("../src/mcp-host.js");
+  const editablePageTitle = app
+    .split("function EditablePageTitle", 2)[1]
+    .split("function composeHeaderMarkdown", 1)[0];
+  const submitPrompt = app
+    .split("function submitCodexPrompt", 2)[1]
+    .split("function AnalyticsTopBarFreshness", 1)[0];
+  const freshness = app
+    .split("function AnalyticsTopBarFreshness", 2)[1]
+    .split("function AnalyticsTopBar(", 1)[0];
+  const topBar = app
+    .split("function AnalyticsTopBar(", 2)[1]
+    .split("export default function App", 1)[0];
+  const topBarStyles = styles
+    .split(".analytics-top-bar {", 2)[1]
+    .split("}", 1)[0];
+  const leadingStyles = styles
+    .split(".analytics-top-bar-leading {", 2)[1]
+    .split("}", 1)[0];
+  const titleStyles = styles
+    .split(".analytics-top-bar-title {", 2)[1]
+    .split("}", 1)[0];
+  const datetimeStyles = styles
+    .split(".top-bar-refresh-datetime {", 2)[1]
+    .split("}", 1)[0];
+  const tooltipStyles = styles
+    .split(".top-bar-refresh-tooltip {", 2)[1]
+    .split("}", 1)[0];
+  const overflowStyles = styles
+    .split(".top-bar-overflow-button {", 2)[1]
+    .split("}", 1)[0];
+  const editStyles = styles
+    .split(".top-bar-edit-button {", 2)[1]
+    .split("}", 1)[0];
 
-  assert.match(app, /const refreshLabel = dateLabel === "Unknown" \? "Refresh" : dateLabel;/);
-  assert.match(app, /<span>\{refreshLabel\}<\/span>/);
-  assert.doesNotMatch(app, /<span>Refresh<\/span>\\n\{statusLabel/);
+  assert.match(freshness, /const refreshLabel = dateLabel === "Unknown" \? "Refresh" : dateLabel;/);
+  assert.match(freshness, /aria-label=\{`Refresh \$\{surfaceLabel\}`\}/);
+  assert.match(freshness, /className="top-bar-refresh-datetime"/);
+  assert.match(freshness, /onFocus=\{positionRefreshTooltip\} onMouseEnter=\{positionRefreshTooltip\}/);
+  assert.match(freshness, /const positionRefreshTooltip = useCallback\(\(\) => \{/);
+  assert.match(freshness, /const tooltipWidth = Math\.min\(320, Math\.max\(0, window\.innerWidth - \(viewportPadding \* 2\)\)\);/);
+  assert.match(freshness, /setTooltipLeft\(Math\.min\(Math\.max\(buttonRect\.left, viewportPadding\), maxLeft\)\);/);
+  assert.match(freshness, /window\.addEventListener\("resize", positionRefreshTooltip\);/);
+  assert.match(freshness, /window\.removeEventListener\("resize", positionRefreshTooltip\);/);
+  assert.match(freshness, /<span className="top-bar-refresh-text">\{refreshLabel\}<\/span>/);
+  assert.match(freshness, /Last refreshed \$\{dateLabel\}\. Click to ask Codex to refresh this \$\{surfaceLabel\}\./);
+  assert.match(freshness, /className="top-bar-refresh-tooltip"[^>]*role="tooltip"/);
+  assert.doesNotMatch(freshness, /RefreshCw|top-bar-button/);
+  assert.ok(topBar.indexOf('className="analytics-top-bar-leading"') < topBar.indexOf('className="analytics-top-bar-actions"'));
+  assert.ok(topBar.indexOf("<AnalyticsTopBarFreshness") < topBar.indexOf('className="analytics-top-bar-actions"'));
+  assert.ok(topBar.indexOf('className="export-menu"') < topBar.indexOf('className="top-bar-button top-bar-edit-button"'));
+  assert.match(topBar, /aria-label=\{`More \$\{appSurfaceLabel\(manifest\)\} actions`\}/);
+  assert.match(topBar, /<Ellipsis aria-hidden="true" size=\{18\} strokeWidth=\{2\}\/>/);
+  assert.match(topBar, /<Pencil aria-hidden="true" size=\{16\} strokeWidth=\{2\}\/>/);
+  assert.doesNotMatch(topBar, /<span>Export<\/span>|<ChevronDown/);
+  assert.match(topBar, /exportTargets\.map\(\(target\) => \(<button className="export-menu-item"/);
+  assert.match(app, /void submitCodexPrompt\([\s\S]*refreshPrompt\(manifest, snapshot, packageInfo\),[\s\S]*`Refresh \$\{appSurfaceLabel\(manifest\)\}`/);
+  assert.ok(submitPrompt);
+  assert.match(submitPrompt, /await sendCodexPromptToHost\(prompt, title\)/);
+  assert.match(submitPrompt, /isPublishedArtifactSite\(packageInfo\)[\s\S]*copyTextToClipboard\(prompt\)/);
+  assert.match(app, /function isPublishedArtifactSite\(packageInfo\) \{[\s\S]*hostedReadOnly === true \|\| packageInfo\?\.deliveryMode === "site_creator";/);
+  assert.match(app, /const result = await hostApi\.sendFollowUpMessage\(codexHostPromptPayload\(prompt, title\)\);[\s\S]*result\?\.isError !== true/);
+  assert.match(app, /const result = await hostApi\.sendMessage\(codexMessagePayload\(prompt\)\);[\s\S]*result\?\.isError !== true/);
+  assert.doesNotMatch(app, /hostApi\?\.openCodexPrompt/);
+  assert.match(host, /app\.getHostCapabilities\(\)\?\.message\?\.text/);
+  assert.match(host, /existing\.sendMessage = \(message\) => app\.sendMessage\(message\)/);
+  assert.match(topBarStyles, /height: 48px;/);
+  assert.match(topBarStyles, /padding: 8px 12px;/);
+  assert.match(leadingStyles, /display: flex;/);
+  assert.match(leadingStyles, /flex: 1 1 auto;/);
+  assert.match(leadingStyles, /height: 32px;/);
+  assert.match(titleStyles, /flex: 0 1 auto;/);
+  assert.match(titleStyles, /margin-right: 8px;/);
+  assert.doesNotMatch(styles, /\.is-app-editing \.analytics-top-bar-title/);
+  assert.match(datetimeStyles, /height: 32px;/);
+  assert.match(datetimeStyles, /padding: 0 12px;/);
+  assert.match(datetimeStyles, /border: 0;/);
+  assert.match(datetimeStyles, /background: transparent;/);
+  assert.match(datetimeStyles, /font-size: 14px;/);
+  assert.match(datetimeStyles, /font-weight: 400;/);
+  assert.match(datetimeStyles, /line-height: 20px;/);
+  assert.match(tooltipStyles, /position: fixed;/);
+  assert.match(tooltipStyles, /top: 48px;/);
+  assert.match(tooltipStyles, /left: 12px;/);
+  assert.doesNotMatch(tooltipStyles, /right:/);
+  assert.match(overflowStyles, /width: 32px;/);
+  assert.match(overflowStyles, /min-width: 32px;/);
+  assert.match(overflowStyles, /padding: 0;/);
+  assert.match(editStyles, /min-width: 74px;/);
+  assert.match(styles, /\.top-bar-refresh-tooltip \{[\s\S]*visibility: hidden;/);
+  assert.match(styles, /\.top-bar-refresh-tooltip-anchor:hover \.top-bar-refresh-tooltip,[\s\S]*visibility: visible;/);
+  assert.match(styles, /\.top-bar-refresh-tooltip-anchor:hover \.top-bar-refresh-tooltip \{[\s\S]*transition-delay: 300ms;/);
+  assert.match(styles, /\.top-bar-refresh-tooltip-anchor:focus-within \.top-bar-refresh-tooltip \{[\s\S]*transition-delay: 0ms;/);
+  assert.match(styles, /\.analytics-top-bar \.page-title-editor \{[\s\S]*height: 32px;[\s\S]*max-height: 32px;[\s\S]*overflow-x: auto;[\s\S]*overflow-y: hidden;[\s\S]*white-space: nowrap;/);
+  assert.match(editablePageTitle, /return \(<input[\s\S]*type="text"/);
+  assert.match(editablePageTitle, /event\.key === "Enter"[\s\S]*event\.preventDefault\(\);[\s\S]*event\.currentTarget\.blur\(\);/);
+  assert.doesNotMatch(editablePageTitle, /<textarea/);
+  assert.match(app, /const TOOLTIP_OPEN_DELAY_MS = 300;/);
+  assert.match(app, /openTimerRef\.current = window\.setTimeout\([\s\S]*TOOLTIP_OPEN_DELAY_MS/);
+  assert.match(app, /onFocus=\{openImmediately\} onMouseEnter=\{openAfterDelay\} onMouseLeave=\{closePopover\}/);
+  assert.match(styles, /@media \(max-width: 760px\) \{[\s\S]*\.top-bar-refresh-datetime \{[\s\S]*max-width: min\(180px, 34vw\);/);
+  assert.match(styles, /@media \(max-width: 560px\) \{[\s\S]*\.analytics-top-bar \{[\s\S]*flex-wrap: nowrap;[\s\S]*\.analytics-top-bar-actions \{[\s\S]*width: auto;/);
+  assert.doesNotMatch(styles, /\.top-bar-refresh-text[^}]*display: none/);
 });
 
 test("artifact chart edit modal uses contained modal chart chrome", () => {
@@ -174,7 +390,10 @@ test("artifact fallback uses encoded chart fields and fullscreen language", () =
   const source = read("../src/datascience-artifact-widget.jsx");
 
   assert.match(source, /x: \{ field: "week", type: "(?:ordinal|temporal)" \}/);
-  assert.match(source, /y: \{ field: "revenue_m", type: "quantitative", label: "Revenue"/);
+  assert.match(source, /y: \{ field: "revenue", type: "quantitative", label: "Revenue"/);
+  assert.match(source, /cardIds: \["net_revenue_card", "target_attainment_card", "refund_rate_card", "completed_orders_card"\]/);
+  assert.match(source, /tableId: "revenue_summary_table"/);
+  assert.match(source, /tableId: "revenue_detail_table"/);
   assert.doesNotMatch(source, /xField: "week"/);
   assert.doesNotMatch(source, /Open in sidebar/);
   assert.match(source, /requestArtifactDisplayMode\("fullscreen"/);
diff --git a/plugins/data-analytics/tests/onboarding-intake.test.mjs b/plugins/data-analytics/tests/onboarding-intake.test.mjs
new file mode 100644
index 0000000..d3f3c02
--- /dev/null
+++ b/plugins/data-analytics/tests/onboarding-intake.test.mjs
@@ -0,0 +1,240 @@
+import assert from "node:assert/strict";
+import { existsSync, readFileSync } from "node:fs";
+import { test } from "node:test";
+
+function sectionBetween(source, startHeading, endHeading) {
+  const start = source.indexOf(startHeading);
+  assert.notEqual(start, -1, `missing section start: ${startHeading}`);
+  const end = source.indexOf(endHeading, start + startHeading.length);
+  assert.notEqual(end, -1, `missing section end: ${endHeading}`);
+  return source.slice(start, end);
+}
+
+function sectionFrom(source, startHeading) {
+  const start = source.indexOf(startHeading);
+  assert.notEqual(start, -1, `missing section start: ${startHeading}`);
+  return source.slice(start);
+}
+
+test("plugin starter copy keeps guided flow separate from reusable context setup", () => {
+  const manifest = JSON.parse(readFileSync(new URL("../.codex-plugin/plugin.json", import.meta.url), "utf8"));
+
+  assert.equal(manifest.description, "Answer product and business questions with data");
+  assert.ok(manifest.keywords.includes("guided-flow"));
+  assert.equal(manifest.interface.shortDescription, "Answer product and business questions with data");
+  assert.match(manifest.interface.longDescription, /The plugin guides you through the right workflow/);
+  assert.doesNotMatch(manifest.interface.longDescription, /Guided onboarding helps|reusable data context/);
+  assert.deepEqual(manifest.interface.defaultPrompt, [
+    "Help me get started with my first data task",
+    "Analyze product or business data and recommend where to focus next",
+    "Diagnose why a key metric changed and identify the biggest drivers",
+  ]);
+});
+
+test("app manifest is the canonical source category mapping", () => {
+  const manifest = JSON.parse(readFileSync(new URL("../.app.json", import.meta.url), "utf8"));
+
+  assert.equal(existsSync(new URL("../DEPENDENCIES.MD", import.meta.url)), false);
+  assert.ok(Object.values(manifest.apps).every((app) => typeof app.category === "string"));
+  assert.ok(Object.values(manifest.apps).every((app) => app.optional === true));
+  assert.equal(manifest.apps.databricks.category, "Data warehouse");
+  assert.equal(manifest.apps.hex.category, "Notebook lab");
+  assert.equal(manifest.apps.amplitude.category, "Behavior signals");
+  assert.equal(manifest.apps.thoughtspot.category, "Dashboards and BI");
+  assert.equal(manifest.apps.slack.category, "Team communication");
+  assert.equal(manifest.apps.google_drive.category, "Company docs");
+});
+
+test("guided intake uses two connector tasks plus upload your own or upload and sample fallbacks", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const guided = sectionBetween(index, "### Guided Flow And Source Setup", "### Source Discovery And Verification");
+  const intake = sectionBetween(guided, "#### First-task intake", "#### Selection handling");
+  const selection = sectionBetween(guided, "#### Selection handling", "#### Custom-question access");
+  const options = sectionBetween(guided, "#### Connected-source option copy", "#### Demo data");
+  const connectedForm = sectionBetween(intake, "Connected-source form:", "No-source form:");
+  const noSourceForm = sectionFrom(intake, "No-source form:");
+
+  assert.match(guided, /owns task selection, setup-adjacent routing, and guided workflow continuation/);
+  assert.match(guided, /stateless first-task flow/);
+  assert.match(guided, /If the user already supplied a concrete task, skip intake and treat it as a custom question/);
+  assert.match(guided, /Do not show a source, project, dashboard, table, SQL, file, connector, or provider picker/);
+  assert.match(guided, /If the user asks to analyze data but provides no data, source, or discoverable callable source/);
+  assert.match(guided, /ask for the smallest useful data artifact or offer the upload\/sample fallback/);
+  assert.match(guided, /Do not create saved data context from this flow/);
+
+  assert.match(intake, /Before presenting connected-source options, identify likely data lanes from the runtime-provided skills inventory, installed Apps and Plugins, recommended-plugin list, callable tools/);
+  assert.match(intake, /discoverable semantic-layer skills/);
+  assert.match(intake, /actively look for source-of-truth lanes such as warehouses or SQL databases, BI dashboards, product analytics systems, spreadsheets, source-definition docs, and relevant communication context/);
+  assert.match(intake, /use callable tools, uploaded or pasted artifacts, local files, and current-run source reads to determine which lanes are immediately usable/);
+  assert.match(intake, /Use recommended plugins and installed plugin metadata to identify setup or install paths when a likely source lane is not immediately callable/);
+  assert.match(intake, /If `tool_search` is unavailable and the runtime exposes `ALL_TOOLS`/);
+  assert.match(intake, /inspect `ALL_TOOLS` names, descriptions, and plugin provenance/);
+  assert.match(intake, /Treat callable tools as stronger evidence of immediate source access than installation recommendations/);
+  assert.match(intake, /When any warehouse or source-system tool is callable, offer a warehouse-backed task first/);
+  assert.match(intake, /runtime-provided installed Apps and Plugins inventory plus callable tools already visible/);
+  assert.match(intake, /including custom MCP servers and other callable tools that can read the needed source/);
+  assert.match(intake, /Do not read connector records, call `functions\.list_available_plugins_to_install`, or treat `.app\.json` declarations as installed/);
+  assert.match(intake, /If tools are deferred, use `tool_search` when available/);
+  assert.match(intake, /otherwise use the runtime's callable-tool registry, such as `ALL_TOOLS`/);
+  assert.match(intake, /Query by the name, source type, or task-relevant capability/);
+  assert.match(intake, /already-visible or user-named connector, custom MCP server, or other callable tool/);
+  assert.match(intake, /do not search only a fixed provider catalog/);
+  assert.match(intake, /exactly three options: two distinct tasks backed by existing installed connectors or callable tools, followed by `Upload your own`/);
+  assert.match(intake, /if fewer than two are useful, derive multiple non-duplicative tasks from the same connected source's capabilities/);
+  assert.match(intake, /Never include sample, installation, or unsupported options/);
+  assert.match(intake, /offer exactly `Upload data` followed by `Use sample data`/);
+  assert.match(intake, /Do not add `Connect a data source`/);
+
+  for (const label of ["{connected task 1}", "{connected task 2}"]) {
+    assert.ok(connectedForm.includes(`label: "${label}"`), `missing connected option ${label}`);
+  }
+  assert.match(connectedForm, /label: "Upload your own"/);
+  assert.match(connectedForm, /Upload or paste your own data and produce an analytics report with findings and next steps/);
+  assert.doesNotMatch(connectedForm, /\{connected task 3\}|Upload data|Use sample data|Connect a data source/);
+  assert.match(noSourceForm, /label: "Upload data"/);
+  assert.match(noSourceForm, /label: "Use sample data"/);
+  assert.doesNotMatch(noSourceForm, /Connect a data source/);
+
+  assert.match(intake, /Use `Dashboard`, `Report`, or `Data task` as the header/);
+  assert.match(intake, /On Codex desktop, call `request_user_input` whenever it is available/);
+  assert.match(intake, /On every other surface or mode, invoke `\$answers-ask-user-input` whenever it is available/);
+  assert.match(intake, /invoke `request_user_input` on Codex desktop or `\$answers-ask-user-input` elsewhere once with exactly three options/);
+  assert.match(intake, /What should the dashboard be about\?/);
+  assert.match(intake, /What should the report be about\?/);
+  assert.match(intake, /The Data Analytics plugin turns data into insights, recommendations, and decision-ready artifacts\. Try it out with one of these prompts:/);
+  assert.match(intake, /built-in free-form Other field for a custom question/);
+  assert.doesNotMatch(intake, /\(Recommended\)/);
+
+  assert.match(selection, /Connected-source task: use its existing connector or callable tool/);
+  assert.match(selection, /For pre-populated connected-source options, always invoke `\$product-business-analysis`, `\$visualize-data`, and `\$build-report`/);
+  assert.match(selection, /do not open plugin installation/);
+  assert.match(selection, /`Upload your own` or `Upload data`: ask for the smallest useful export/);
+  assert.match(selection, /`Use sample data`: start the demo contract immediately/);
+  assert.match(selection, /An explicit request to "use sample data" or "using the sample data" selects `Use sample data`/);
+  assert.match(selection, /resolve the bundled demo immediately instead of searching the workspace or asking for an upload/);
+  assert.match(selection, /Other text: treat it as a user-authored custom question/);
+  assert.match(selection, /Do ask for the actual missing data artifact when no usable data or source is supplied or discoverable/);
+  assert.doesNotMatch(selection, /usually `\$product-business-analysis`/);
+
+  assert.match(options, /warehouse\/source system first, then BI, product analytics, GitHub, tabular Drive\/files/);
+  assert.match(options, /If one connector fills multiple slots, vary the task by real connector capability/);
+  for (const label of [
+    "Analyze business data",
+    "Analyze dashboard trends",
+    "Analyze product usage",
+    "Analyze GitHub activity",
+    "Analyze email trends",
+    "Analyze Drive files",
+    "Analyze meeting patterns",
+    "Analyze Notion content",
+    "Analyze Slack activity",
+    "Analyze Teams messages",
+    "Analyze SharePoint files",
+  ]) {
+    assert.ok(options.includes(`\`${label}\``), `missing option copy for ${label}`);
+  }
+});
+
+test("only user-authored custom questions can trigger connector installation", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const guided = sectionBetween(index, "### Guided Flow And Source Setup", "### Source Discovery And Verification");
+  const access = sectionBetween(guided, "#### Custom-question access", "#### Connected-source option copy");
+
+  assert.match(access, /Only a concrete task supplied in the user's message or the intake form's Other field may trigger installation/);
+  assert.match(access, /custom MCP server, other callable tool, uploaded or pasted artifact/);
+  assert.match(access, /check visible or lazy-loadable custom MCP servers and other callable tools that can read the needed source before native connector installation/);
+  assert.match(access, /server or tool name, description, action schema, or the user-named source/);
+  assert.match(access, /routing hints, not the source universe/);
+  assert.match(access, /call `functions\.list_available_plugins_to_install` once/);
+  assert.match(access, /current-run context already lists exact suppressed plugin-install ids/);
+  assert.match(access, /do not run the legacy preflight just to obtain suppressions/);
+  assert.match(access, /call `functions\.request_plugin_install` sequentially for every exact returned candidate useful to the task/);
+  assert.match(access, /Do not guess ids, narrow to one generic "best" candidate, stop after the first useful candidate, call installs in parallel, or use `request_connector_setup`/);
+  assert.match(access, /record that exact id with `record_plugin_install_suppression\.py`/);
+
+  assert.match(access, /Warehouse exception/);
+  assert.match(access, /keep every returned warehouse candidate/);
+  assert.match(access, /Ask which warehouse to use, naming all of them; install only the selected exact candidate/);
+  assert.match(access, /Never choose by manifest order or configured preference/);
+
+  assert.match(access, /id: "fallback_next_step"/);
+  assert.match(access, /question: "I don't have enough data to complete this task\. How do you want to proceed\?"/);
+  assert.match(access, /header: "Next step"/);
+  assert.match(access, /label: "Upload data"/);
+  assert.match(access, /label: "Use sample data"/);
+  assert.match(access, /After successful setup, start the focused workflow automatically/);
+  assert.match(access, /Do not show post-setup flow-control choices/);
+});
+
+test("missing data always closes with upload or synthetic demo choices", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const guided = sectionBetween(index, "### Guided Flow And Source Setup", "### Source Discovery And Verification");
+  const invariant = sectionBetween(guided, "#### No-source completion invariant", "#### Connected-source option copy");
+  const guardrail = sectionBetween(index, "### Source Access Guardrail", "### Audience And Language");
+
+  assert.match(invariant, /MUST offer `Upload data` and `Use sample data` before the turn ends/);
+  assert.match(invariant, /install prompt being declined, dismissed, canceled, or left unconfirmed/);
+  assert.match(invariant, /successful install that still cannot return usable evidence/);
+  assert.match(invariant, /Do not end with only a missing-source explanation, an installation suggestion, or an instruction to connect a source and retry/);
+  assert.match(invariant, /installation prompt may come before the fallback question, but it never replaces the fallback question/);
+  assert.match(invariant, /bundled demo is synthetic and will demonstrate the workflow rather than answer the user's real-data question/);
+
+  assert.match(guardrail, /apply the mandatory `No-source completion invariant`/);
+  assert.match(guardrail, /offer uploaded data or the clearly labeled synthetic demo before ending the same turn/);
+});
+
+test("demo contract uses the bundled synthetic dataset", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const demoSection = sectionBetween(index, "#### Demo data", "### Source Discovery And Verification");
+  const demo = readFileSync(new URL("../assets/demo-product-growth.csv", import.meta.url), "utf8")
+    .trim()
+    .split(/\r?\n/);
+
+  assert.match(demoSection, /Show `Use sample data` only when no useful connected source exists or a selected workflow still lacks usable evidence/);
+  assert.match(demoSection, /\[demo-product-growth\.csv\]\(\.\.\/\.\.\/assets\/demo-product-growth\.csv\)/);
+  assert.match(demoSection, /label it synthetic/);
+  assert.match(demoSection, /analyze it with reproducible SQL without inventing rows or findings/);
+  assert.equal(demo[0], "week_start,acquisition_channel,signups,activated_users,paid_conversions,revenue_usd,support_tickets");
+  assert.equal(demo.length - 1, 32);
+  assert.ok(demo.slice(1).every((row) => row.split(",").length === 7));
+});
+
+test("Data Analytics uses global guided-flow input without an intake MCP server", () => {
+  const mcpConfig = JSON.parse(readFileSync(new URL("../.mcp.json", import.meta.url), "utf8"));
+
+  assert.equal(mcpConfig.mcpServers?.dataAnalyticsIntake, undefined);
+  assert.equal(mcpConfig.mcpServers?.datascienceIntake, undefined);
+  assert.ok(mcpConfig.mcpServers?.dataAnalyticsWidgets);
+  assert.equal(mcpConfig.mcpServers.dataAnalyticsWidgets.title, "Data Analytics");
+  assert.equal(existsSync(new URL("../mcp/intake-server.mjs", import.meta.url)), false);
+});
+
+test("guided flow remains separate from create-data-context setup", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const dataContext = readFileSync(new URL("../skills/create-data-context/SKILL.md", import.meta.url), "utf8");
+  const guided = sectionBetween(index, "### Guided Flow And Source Setup", "### Source Discovery And Verification");
+
+  assert.doesNotMatch(guided, /future data work|reusable semantic layer/i);
+  assert.match(dataContext, /name: create-data-context/);
+  assert.match(dataContext, /Data-task routing is owned by the Data Analytics `index` skill/);
+  assert.match(dataContext, /Use this skill only when the user asks to save data context or create, update, inspect, or repair a semantic layer/);
+  assert.doesNotMatch(dataContext, /Route to data task|What should Data Analytics set up/);
+});
+
+test("existing semantic-layer skills are used as context", () => {
+  const index = readFileSync(new URL("../skills/index/SKILL.md", import.meta.url), "utf8");
+  const savedContext = sectionBetween(index, "### Saved Data Context And Semantic Layers", "### Guided Flow And Source Setup");
+  const semanticLayers = sectionBetween(index, "## Semantic Layers", "## Evidence And Handoff");
+
+  assert.match(savedContext, /user-created semantic-layer skills discoverable in the current runtime/);
+  assert.match(semanticLayers, /user names a semantic layer, semantic-layer skill, or path/);
+  assert.match(semanticLayers, /relevant semantic-layer skill is already discoverable from the current runtime/);
+  assert.match(semanticLayers, /If a relevant semantic layer exists, read it before selecting tables/);
+  assert.match(semanticLayers, /When no relevant semantic layer exists/);
+});
+
+test("standalone guided-flow skill is absent", () => {
+  assert.equal(existsSync(new URL("../skills/guided-flow", import.meta.url)), false);
+  assert.equal(existsSync(new URL("../skills/onboarding", import.meta.url)), false);
+  assert.equal(existsSync(new URL("../skills/user-context", import.meta.url)), false);
+});
diff --git a/plugins/data-analytics/tests/recharts-renderer.test.mjs b/plugins/data-analytics/tests/recharts-renderer.test.mjs
index a3c9fb5..cb5ea8f 100644
--- a/plugins/data-analytics/tests/recharts-renderer.test.mjs
+++ b/plugins/data-analytics/tests/recharts-renderer.test.mjs
@@ -2,6 +2,131 @@ import assert from "node:assert/strict";
 import { readFile } from "node:fs/promises";
 import test from "node:test";
 
+function executableHtmlReportRuntime(source) {
+  return source
+    .replace('import "./html-report-source-tooltips.js";\n', "")
+    .replace(
+      'import { renderRechartsChart } from "./recharts-renderer.jsx";',
+      "const renderRechartsChart = globalThis.__renderRechartsChart;",
+    );
+}
+
+function executeHtmlReportRuntime(source, {
+  document,
+  HTMLElement,
+  MutationObserver,
+  ResizeObserver,
+  renderRechartsChart,
+  window,
+}) {
+  new Function(
+    "document",
+    "window",
+    "HTMLElement",
+    "CSS",
+    "MutationObserver",
+    "ResizeObserver",
+    "globalThis",
+    executableHtmlReportRuntime(source),
+  )(
+    document,
+    window,
+    HTMLElement,
+    undefined,
+    MutationObserver,
+    ResizeObserver,
+    { __renderRechartsChart: renderRechartsChart },
+  );
+}
+
+function fakeSvg(viewBox) {
+  return {
+    getAttribute(name) {
+      if (name === "viewBox") return viewBox;
+      return null;
+    },
+  };
+}
+
+function serializedChartFixture({
+  hostHeight = 320,
+  hostWidth = 316,
+  mountHeight = 320,
+  mountWidth = hostWidth,
+  renderedSelector = "svg",
+  viewBox = "0 0 316 320",
+}) {
+  class FakeElement {}
+  let rendered = false;
+  let removedAriaHidden = false;
+  const attributes = new Map();
+  const serializedSvg = fakeSvg(viewBox);
+  const liveMount = new FakeElement();
+  liveMount.getBoundingClientRect = () => ({ height: mountHeight, width: mountWidth });
+  liveMount.querySelector = (selector) => {
+    if (!selector.includes(renderedSelector)) return null;
+    if (renderedSelector === "svg") {
+      return rendered ? fakeSvg(`0 0 ${mountWidth || 316} 320`) : serializedSvg;
+    }
+    return {};
+  };
+  liveMount.removeAttribute = (name) => {
+    attributes.delete(name);
+    if (name === "aria-hidden") removedAriaHidden = true;
+  };
+  liveMount.setAttribute = (name, value) => {
+    attributes.set(name, value);
+  };
+  const host = new FakeElement();
+  host.dataset = {};
+  host.getBoundingClientRect = () => ({ height: hostHeight, width: hostWidth });
+  host.querySelector = (selector) => selector === "[data-recharts-live]" ? liveMount : null;
+  const payload = {
+    textContent: JSON.stringify({
+      charts: [{ id: "chart-1", dataset: { chart_spec: {}, data: [] }, type: "line" }],
+    }),
+  };
+  const document = {
+    getElementById: (id) => id === "analytics-payload" ? payload : null,
+    querySelector: () => host,
+    readyState: "complete",
+  };
+  return {
+    attributes,
+    document,
+    FakeElement,
+    host,
+    liveMount,
+    markRendered() {
+      rendered = true;
+    },
+    removedAriaHidden() {
+      return removedAriaHidden;
+    },
+    setHostWidth(width) {
+      hostWidth = width;
+    },
+    setMountWidth(width) {
+      mountWidth = width;
+    },
+    setMountHeight(height) {
+      mountHeight = height;
+    },
+  };
+}
+
+class FakeMutationObserver {
+  observe() {}
+  disconnect() {}
+}
+
+function immediatelyReadyWindow() {
+  return {
+    requestAnimationFrame: (callback) => callback(),
+    setTimeout: () => {},
+  };
+}
+
 test("inline Recharts bridge normalizes word percent units into valueFormat", async () => {
   const source = await readFile(new URL("../src/recharts-renderer.jsx", import.meta.url), "utf8");
 
@@ -9,3 +134,265 @@ test("inline Recharts bridge normalizes word percent units into valueFormat", as
   assert.match(source, /const unit = percentWordUnit \? "" : rawUnit;/);
   assert.match(source, /valueFormat: existing\.valueFormat \|\| \(percentWordUnit \? "percent" : unit === "\$" \? "currency" : "compact"\)/);
 });
+
+test("inline Recharts bridge clears serialized live markup before remounting", async () => {
+  const source = await readFile(new URL("../src/recharts-renderer.jsx", import.meta.url), "utf8");
+  const destroyer = source
+    .split("export function destroyRechartsChart", 2)[1]
+    .split("export function renderRechartsChart", 1)[0];
+  const destroyRechartsChart = new Function(
+    `return function destroyRechartsChart${destroyer}`,
+  )();
+  const serializedContainer = {
+    childNodes: [{}, {}],
+    replaceChildren() {
+      this.childNodes = [];
+    },
+  };
+
+  assert.match(destroyer, /if \(!container\) return;/);
+  assert.match(destroyer, /container\.replaceChildren\(\);/);
+  assert.match(source, /destroyRechartsChart\(container\);\n  const mount = document\.createElement/);
+  destroyRechartsChart(serializedContainer);
+  assert.deepEqual(serializedContainer.childNodes, []);
+});
+
+test("HTML report runtime reuses a matching serialized SVG chart", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const fixture = serializedChartFixture({ hostWidth: 316, mountWidth: 316, viewBox: "0 0 316 320" });
+  let renderCalls = 0;
+
+  assert.match(source, /const RENDERED_CHART_SELECTOR = "svg, \.leaderboard, \.heatmap-grid-panel";/);
+  executeHtmlReportRuntime(source, {
+    document: fixture.document,
+    HTMLElement: fixture.FakeElement,
+    MutationObserver: undefined,
+    ResizeObserver: undefined,
+    renderRechartsChart: () => { renderCalls += 1; },
+    window: {
+      requestAnimationFrame: () => {
+        throw new Error("matching serialized charts should not schedule a remount");
+      },
+      setTimeout: () => {
+        throw new Error("matching serialized charts should not schedule a remount");
+      },
+    },
+  });
+
+  assert.equal(renderCalls, 0);
+  assert.equal(fixture.removedAriaHidden(), true);
+  assert.equal(fixture.host.dataset.rechartsReady, "true");
+});
+
+for (const renderedSelector of [".leaderboard", ".heatmap-grid-panel"]) {
+  test("HTML report runtime reuses a serialized " + renderedSelector + " chart", async () => {
+    const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+    const fixture = serializedChartFixture({ renderedSelector });
+    let renderCalls = 0;
+
+    executeHtmlReportRuntime(source, {
+      document: fixture.document,
+      HTMLElement: fixture.FakeElement,
+      MutationObserver: undefined,
+      ResizeObserver: undefined,
+      renderRechartsChart: () => { renderCalls += 1; },
+      window: {
+        requestAnimationFrame: () => {
+          throw new Error("ready serialized panels should not schedule a remount");
+        },
+        setTimeout: () => {
+          throw new Error("ready serialized panels should not schedule a remount");
+        },
+      },
+    });
+
+    assert.equal(renderCalls, 0);
+    assert.equal(fixture.removedAriaHidden(), true);
+    assert.equal(fixture.host.dataset.rechartsReady, "true");
+  });
+}
+
+test("HTML report runtime remounts a desktop SVG serialized into a mobile card", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const fixture = serializedChartFixture({ hostWidth: 316, mountWidth: 316, viewBox: "0 0 888 320" });
+  fixture.host.dataset.rechartsReady = "true";
+  let renderCalls = 0;
+  let resetBeforeRender = false;
+
+  executeHtmlReportRuntime(source, {
+    document: fixture.document,
+    HTMLElement: fixture.FakeElement,
+    MutationObserver: FakeMutationObserver,
+    ResizeObserver: undefined,
+    renderRechartsChart: () => {
+      renderCalls += 1;
+      resetBeforeRender =
+        fixture.host.dataset.rechartsReady === undefined &&
+        fixture.attributes.get("aria-hidden") === "true";
+      fixture.markRendered();
+    },
+    window: immediatelyReadyWindow(),
+  });
+
+  assert.equal(renderCalls, 1);
+  assert.equal(resetBeforeRender, true);
+  assert.equal(fixture.host.dataset.rechartsReady, "true");
+});
+
+test("HTML report runtime remounts a serialized SVG with stale height", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const fixture = serializedChartFixture({
+    hostWidth: 316,
+    mountHeight: 320,
+    mountWidth: 316,
+    viewBox: "0 0 316 640",
+  });
+  let renderCalls = 0;
+
+  executeHtmlReportRuntime(source, {
+    document: fixture.document,
+    HTMLElement: fixture.FakeElement,
+    MutationObserver: FakeMutationObserver,
+    ResizeObserver: undefined,
+    renderRechartsChart: () => {
+      renderCalls += 1;
+      fixture.markRendered();
+    },
+    window: immediatelyReadyWindow(),
+  });
+
+  assert.equal(renderCalls, 1);
+  assert.equal(fixture.host.dataset.rechartsReady, "true");
+});
+
+test("HTML report runtime remounts a reused SVG after its card narrows", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const fixture = serializedChartFixture({ hostWidth: 888, mountWidth: 888, viewBox: "0 0 888 320" });
+  let renderCalls = 0;
+  let resizeCallback;
+  let disconnectCalls = 0;
+  class FakeResizeObserver {
+    constructor(callback) {
+      resizeCallback = callback;
+    }
+    observe() {}
+    disconnect() {
+      disconnectCalls += 1;
+    }
+  }
+
+  executeHtmlReportRuntime(source, {
+    document: fixture.document,
+    HTMLElement: fixture.FakeElement,
+    MutationObserver: FakeMutationObserver,
+    ResizeObserver: FakeResizeObserver,
+    renderRechartsChart: () => {
+      renderCalls += 1;
+      fixture.markRendered();
+    },
+    window: immediatelyReadyWindow(),
+  });
+
+  assert.equal(renderCalls, 0);
+  fixture.setHostWidth(316);
+  fixture.setMountWidth(316);
+  resizeCallback();
+  assert.equal(renderCalls, 1);
+  resizeCallback();
+  assert.equal(renderCalls, 1);
+  assert.ok(disconnectCalls >= 1);
+  assert.equal(fixture.host.dataset.rechartsReady, "true");
+});
+
+test("HTML report runtime remounts serialized SVGs when current geometry is unknown", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const fixture = serializedChartFixture({ hostWidth: 0, mountWidth: 0, viewBox: "0 0 888 320" });
+  let renderCalls = 0;
+
+  executeHtmlReportRuntime(source, {
+    document: fixture.document,
+    HTMLElement: fixture.FakeElement,
+    MutationObserver: FakeMutationObserver,
+    ResizeObserver: undefined,
+    renderRechartsChart: () => {
+      renderCalls += 1;
+      fixture.markRendered();
+    },
+    window: immediatelyReadyWindow(),
+  });
+
+  assert.equal(renderCalls, 1);
+});
+
+test("HTML report runtime marks a freshly rendered non-SVG panel ready", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  let renderCalls = 0;
+  let rendered = false;
+  let removedAriaHidden = false;
+  class FakeElement {}
+  const liveMount = new FakeElement();
+  liveMount.querySelector = (selector) => rendered && selector.includes(".leaderboard") ? {} : null;
+  liveMount.removeAttribute = (name) => {
+    if (name === "aria-hidden") removedAriaHidden = true;
+  };
+  liveMount.setAttribute = () => {};
+  const host = new FakeElement();
+  host.dataset = {};
+  host.querySelector = (selector) => selector === "[data-recharts-live]" ? liveMount : null;
+  const payload = {
+    textContent: JSON.stringify({
+      charts: [{ id: "chart-1", dataset: { chart_spec: {}, data: [] }, type: "leaderboard" }],
+    }),
+  };
+  const document = {
+    getElementById: (id) => id === "analytics-payload" ? payload : null,
+    querySelector: () => host,
+    readyState: "complete",
+  };
+  const window = immediatelyReadyWindow();
+
+  executeHtmlReportRuntime(source, {
+    document,
+    HTMLElement: FakeElement,
+    MutationObserver: FakeMutationObserver,
+    ResizeObserver: undefined,
+    renderRechartsChart: () => {
+      renderCalls += 1;
+      rendered = true;
+    },
+    window,
+  });
+
+  assert.equal(renderCalls, 1);
+  assert.equal(removedAriaHidden, true);
+  assert.equal(host.dataset.rechartsReady, "true");
+});
+
+test("HTML report runtime opts into report-only chart spacing", async () => {
+  const source = await readFile(new URL("../src/html-report-runtime.jsx", import.meta.url), "utf8");
+  const renderer = await readFile(new URL("../src/recharts-renderer.jsx", import.meta.url), "utf8");
+  const chartRenderer = await readFile(new URL("../src/analytics-app/charting/ChartRenderer.tsx", import.meta.url), "utf8");
+
+  assert.match(source, /htmlReport: true,/);
+  assert.match(renderer, /htmlReport=\{options\.htmlReport\}/);
+  assert.match(chartRenderer, /htmlReport = false,/);
+  assert.match(chartRenderer, /const HTML_REPORT_NUMERIC_Y_AXIS_WIDTH = 96;/);
+  assert.equal(
+    (
+      chartRenderer.match(
+        /width=\{htmlReport \? HTML_REPORT_NUMERIC_Y_AXIS_WIDTH : "auto"\}/g,
+      ) ?? []
+    ).length,
+    2,
+  );
+});
+
+test("inline chart widget projection preserves selected measure value formats", async () => {
+  const source = await readFile(new URL("../src/datascience-chart-widget.js", import.meta.url), "utf8");
+
+  assert.match(source, /function selectedMeasureColumn\(dataset\)/);
+  assert.match(source, /function valueFormatFor\(dataset, existing = chartSpecFor\(dataset\)\)/);
+  assert.match(source, /const valueFormat = valueFormatFor\(dataset, existing\);/);
+  assert.match(source, /\.\.\.\(valueFormat \? \{ format: valueFormat \} : \{\}\),/);
+  assert.match(source, /valueFormat,\nsettings: \{/);
+});
diff --git a/plugins/data-analytics/tests/report-delivery-contract.test.mjs b/plugins/data-analytics/tests/report-delivery-contract.test.mjs
new file mode 100644
index 0000000..d19b9cd
--- /dev/null
+++ b/plugins/data-analytics/tests/report-delivery-contract.test.mjs
@@ -0,0 +1,141 @@
+import assert from "node:assert/strict";
+import fs from "node:fs";
+import path from "node:path";
+import test from "node:test";
+import { fileURLToPath } from "node:url";
+
+const here = path.dirname(fileURLToPath(import.meta.url));
+const read = (relativePath) => fs.readFileSync(path.resolve(here, relativePath), "utf8");
+
+test("Data Analytics defaults completed analytical findings to a durable report", () => {
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(index, /Default to `report` whenever a Data Analytics run analyzes real or sample data and produces findings/);
+  assert.match(index, /The user does not need to say "report"/);
+  assert.match(index, /When uncertain between `inline` and `report`, choose `report`/);
+  assert.match(index, /Use `inline` only when the user explicitly asks for a quick chat answer/);
+  assert.match(index, /the run is incomplete until a rendered MCP app report or HTML report exists/);
+});
+
+test("report delivery always resolves to MCP app or HTML from the runtime", () => {
+  const report = read("../skills/build-report/SKILL.md");
+  const reportSpec = read("../skills/build-report/specifications/mcp-app-report.md");
+  const core = read("../src/analytics-app-core.md");
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(report, /Codex desktop: use `mcp-app` by default/);
+  assert.match(report, /Positively identified ChatGPT web Work Mode: use `html`/);
+  assert.match(report, /Work Mode signal with unknown or non-desktop surface: use `html`/);
+  assert.match(report, /Only a positive `surface = codex_desktop` signal may take the Codex desktop MCP default/);
+  assert.match(report, /Any other runtime where MCP app report rendering is unavailable: use `html`/);
+  assert.match(report, /Unknown or unclassified runtime: default to portable `html` rather than omitting the report/);
+  assert.match(report, /If an MCP app report was already rendered and the current context later identifies Work Mode without a positive Codex desktop surface/);
+  assert.match(report, /fall back to `html` in the same run/);
+  assert.match(report, /A renderer failure changes the delivery mode; it does not waive the report/);
+  assert.match(report, /When this skill is selected directly and the user explicitly asks to "use sample data" or create a report "using the sample data"/);
+  assert.match(report, /\[demo-product-growth\.csv\]\(\.\.\/\.\.\/assets\/demo-product-growth\.csv\)/);
+  assert.match(report, /do not search the workspace for a substitute or invent replacement data/);
+  for (const guidance of [report, reportSpec, core]) {
+    assert.match(guidance, /independently laid out or editable major report (segment|section)/);
+    assert.match(guidance, /multiple peer `##`/);
+    assert.match(guidance, /one headline does not mean one `metrics\[\]` entry/i);
+    assert.match(guidance, /executive summary or findings/);
+  }
+  assert.match(index, /Report-mode work routes through `\$build-report`, which uses `mcp-app` by default/);
+});
+
+test("HTML reports preserve automatic light and dark system appearances", () => {
+  const report = read("../skills/build-report/SKILL.md");
+  const shell = read("../assets/html-report-shell.html");
+
+  assert.match(report, /automatic system appearance support/);
+  assert.match(report, /<meta name="color-scheme" content="light dark">/);
+  assert.match(report, /@media \(prefers-color-scheme: dark\)/);
+  assert.match(report, /both light and dark system modes/);
+  assert.match(shell, /<meta name="color-scheme" content="light dark">/);
+  assert.match(shell, /@media \(prefers-color-scheme: dark\)/);
+  assert.match(shell, /--surface: #212121;/);
+  assert.match(shell, /--text: #fff;/);
+});
+
+test("primary analytical workflows honor their report delivery contracts", () => {
+  const index = read("../skills/index/SKILL.md");
+  const report = read("../skills/build-report/SKILL.md");
+  const product = read("../skills/product-business-analysis/SKILL.md");
+  const diagnostics = read("../skills/metric-diagnostics/SKILL.md");
+  const kpi = read("../skills/kpi-reporting/SKILL.md");
+  const market = read("../skills/market-sizing/SKILL.md");
+  const design = read("../skills/design-kpis/SKILL.md");
+  const quality = read("../skills/analyze-data-quality/SKILL.md");
+  const reportImplications = index.split("Every `report` route includes", 2)[1].split("\n\n", 1)[0];
+
+  assert.match(report, /A user can explicitly waive report creation by requesting an inline, chat-only, brief\/no-artifact answer/);
+  assert.match(report, /Do not infer a waiver from the absence of the word "report"/);
+  assert.match(product, /This handoff is mandatory when no explicit human waiver was given/);
+  assert.match(product, /do not infer a waiver because the user asked a direct question/);
+  assert.match(diagnostics, /This handoff is mandatory when no explicit human waiver was given/);
+  assert.match(diagnostics, /direct diagnostic question/);
+  assert.match(kpi, /When no explicit human waiver was given, the report handoff is mandatory/);
+  assert.doesNotMatch(kpi, /If they did not specify one, ask what format they want/);
+  assert.match(market, /This handoff is mandatory when no explicit human waiver was given/);
+  assert.match(design, /deliver it inline by default/);
+  assert.match(design, /Do not load `\$build-report` merely because the KPI framework uses evidence, compares candidates, or contains several metrics/);
+  assert.match(design, /Use `\$visualize-data` and `\$build-report` when a data visual would materially improve the answer/);
+  assert.match(design, /showing how a proposed target compares with historical performance or benchmarks/);
+  assert.doesNotMatch(design, /Default to \$build-report/);
+  assert.doesNotMatch(reportImplications, /\$design-kpis/);
+  assert.match(quality, /pass the completed findings to \$build-report by default/);
+});
+
+test("driver questions route to diagnostics while bounded lookups remain inline", () => {
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(index, /Do not classify a prompt as bounded merely because its evidence might fit in one table or chart/);
+  assert.match(index, /which dimension, category, cohort, entity, segment, or driver drove a metric's growth, decline, change, gap, or variance is a diagnostic route/);
+  assert.match(index, /Current-value lookups, totals, simple time series, and direct current-value rankings remain inline/);
+  assert.match(index, /A quick KPI status update or brief KPI readout is an inline output choice/);
+  assert.match(index, /Do not infer a waiver from the absence of the word "report"/);
+});
+
+test("KPI and market-sizing workflows pass complete report inputs forward", () => {
+  const kpi = read("../skills/kpi-reporting/SKILL.md");
+  const market = read("../skills/market-sizing/SKILL.md");
+  const slides = read("../skills/build-report/report-to-google-slides/SKILL.md");
+
+  assert.match(kpi, /When the primary KPI is top-line, composite, or otherwise not directly actionable, define its driver decomposition/);
+  assert.match(kpi, /Tell \$build-report this is a KPI readout and pass the status, pacing, driver, uncertainty, audience, cadence, surface, and visual guidance above/);
+  assert.match(kpi, /use \$build-report to create an HTML report first because `\$report-to-google-slides` consumes an existing local HTML analytics report/);
+  assert.match(kpi, /top-line actual, component drivers, largest contributors or non-drivers, and residual or unresolved movement/);
+  assert.match(slides, /This skill consumes an HTML report/);
+
+  assert.match(market, /Before handoff, make the market-sizing conclusion explicit/);
+  assert.match(market, /method and calculation chain/);
+  assert.match(market, /Do not render charts directly from this skill/);
+  assert.match(market, /\$visualize-data owns chart selection and QA/);
+});
+
+test("report-mode visualization ownership stays with visualize-data", () => {
+  const index = read("../skills/index/SKILL.md");
+  const report = read("../skills/build-report/SKILL.md");
+  const kpi = read("../skills/kpi-reporting/SKILL.md");
+
+  assert.match(index, /For report-mode runs, include `\$visualize-data` whenever charts or figures are needed/);
+  assert.match(index, /primary analysis skills pass visual intent and evidence forward rather than rendering charts directly/);
+  assert.match(report, /Route every report visualization through `\$visualize-data`/);
+  assert.match(kpi, /Do not render charts directly from this skill/);
+});
+
+test("query provenance stays out of visible answers unless SQL is requested", () => {
+  const index = read("../skills/index/SKILL.md");
+  const core = read("../src/analytics-app-core.md");
+  const report = read("../skills/build-report/SKILL.md");
+  const semanticTemplate = read("../skills/create-data-context/references/semantic-layer/skill-template.md");
+
+  assert.match(index, /A data question answered by executing SQL, including a query-backed answer, is not by itself a request for SQL/);
+  assert.match(index, /Do not paste or reproduce source SQL in the final chat response or reader-facing report narrative unless the user explicitly asks/);
+  assert.match(index, /Keep full SQL in `source\.query\.sql`, a source modal, a query permalink, a notebook or query file, or supporting notes/);
+  assert.match(core, /Treat `source\.query\.sql` as provenance for the widget's source experience, not as visible answer content/);
+  assert.match(core, /Do not duplicate that SQL in the surrounding final chat response or chart narrative unless the user explicitly asks/);
+  assert.match(report, /Do not paste source SQL into the visible report body or final chat handoff unless the user explicitly asks/);
+  assert.match(semanticTemplate, /a data question answered with SQL is not an SQL-delivery request/);
+});
diff --git a/plugins/data-analytics/tests/source-discovery-contract.test.mjs b/plugins/data-analytics/tests/source-discovery-contract.test.mjs
new file mode 100644
index 0000000..697b95e
--- /dev/null
+++ b/plugins/data-analytics/tests/source-discovery-contract.test.mjs
@@ -0,0 +1,55 @@
+import assert from "node:assert/strict";
+import { readFileSync } from "node:fs";
+import { test } from "node:test";
+
+const skillNames = [
+  "index",
+  "build-dashboard",
+  "design-kpis",
+  "kpi-reporting",
+  "market-sizing",
+  "metric-diagnostics",
+  "product-business-analysis",
+];
+
+function readSkill(name) {
+  return readFileSync(new URL(`../skills/${name}/SKILL.md`, import.meta.url), "utf8");
+}
+
+function sourceDiscoverySection(source) {
+  const startHeading = "### Source Discovery And Verification";
+  const endHeading = "### Source Access Guardrail";
+  const start = source.indexOf(startHeading);
+  assert.notEqual(start, -1, `missing section start: ${startHeading}`);
+  const end = source.indexOf(endHeading, start + startHeading.length);
+  assert.notEqual(end, -1, `missing section end: ${endHeading}`);
+  return source.slice(start, end);
+}
+
+test("analytical preflight exhausts plausible cross-source discovery", () => {
+  for (const name of skillNames) {
+    const preflight = sourceDiscoverySection(readSkill(name));
+
+    assert.match(preflight, /1\. \*\*Explore all possible sources\.\*\*/, name);
+    assert.match(preflight, /Search every connected or provided source that could contain task-relevant data or change the interpretation/, name);
+    assert.match(preflight, /run fresh catalog or metadata discovery/, name);
+    assert.match(preflight, /starting points, not stopping points/, name);
+    assert.match(preflight, /2\. \*\*Compare duplicates and conflicts\.\*\*/, name);
+    assert.match(preflight, /compare ownership, freshness, definition, grain, coverage, and directness/, name);
+    assert.match(preflight, /Use the best authoritative source, or combine complementary sources when needed/, name);
+    assert.match(preflight, /explain why the selected source or sources control the answer/, name);
+    assert.ok(preflight.trim().split(/\s+/).length <= 130, `${name} source preflight is too long`);
+  }
+});
+
+test("business-context retrieval cannot short-circuit comprehensive discovery", () => {
+  const context = readSkill("gather-business-context");
+
+  assert.match(context, /1\. \*\*Explore all possible sources\.\*\*/);
+  assert.match(context, /Search every enabled or provided source family that could contain useful context or task-relevant data/);
+  assert.match(context, /run fresh catalog or metadata discovery/);
+  assert.match(context, /starting points, not stopping points/);
+  assert.match(context, /2\. \*\*Compare duplicates and conflicts\.\*\*/);
+  assert.match(context, /compare authority, freshness, definition, scope, and directness/);
+  assert.match(context, /explain which source or combination should guide downstream analysis/);
+});
diff --git a/plugins/data-analytics/tests/table-sizing.test.mjs b/plugins/data-analytics/tests/table-sizing.test.mjs
new file mode 100644
index 0000000..b67197a
--- /dev/null
+++ b/plugins/data-analytics/tests/table-sizing.test.mjs
@@ -0,0 +1,77 @@
+import assert from "node:assert/strict";
+import { readFile } from "node:fs/promises";
+import { join } from "node:path";
+import { describe, test } from "node:test";
+import ts from "typescript";
+
+async function loadTableSizing() {
+  const source = await readFile(join(process.cwd(), "src", "analytics-app", "tables", "tableSizing.ts"), "utf8");
+  const { outputText } = ts.transpileModule(source, {
+    compilerOptions: {
+      module: ts.ModuleKind.ES2022,
+      target: ts.ScriptTarget.ES2022,
+    },
+  });
+  const url = `data:text/javascript;base64,${Buffer.from(outputText).toString("base64")}`;
+  return import(url);
+}
+
+describe("artifact table sizing", () => {
+  test("reports horizontal scroll affordances only where more content exists", async () => {
+    const { calculateHorizontalScrollEdges } = await loadTableSizing();
+
+    assert.deepEqual(
+      calculateHorizontalScrollEdges({ clientWidth: 768, scrollLeft: 0, scrollWidth: 768 }),
+      { canScrollLeft: false, canScrollRight: false, hasOverflow: false },
+    );
+    assert.deepEqual(
+      calculateHorizontalScrollEdges({ clientWidth: 320, scrollLeft: 0, scrollWidth: 960 }),
+      { canScrollLeft: false, canScrollRight: true, hasOverflow: true },
+    );
+    assert.deepEqual(
+      calculateHorizontalScrollEdges({ clientWidth: 320, scrollLeft: 240, scrollWidth: 960 }),
+      { canScrollLeft: true, canScrollRight: true, hasOverflow: true },
+    );
+    assert.deepEqual(
+      calculateHorizontalScrollEdges({ clientWidth: 320, scrollLeft: 639.4, scrollWidth: 960 }),
+      { canScrollLeft: true, canScrollRight: false, hasOverflow: true },
+    );
+  });
+
+  test("distributes spare viewport width without introducing horizontal overflow", async () => {
+    const { calculateTableSizing } = await loadTableSizing();
+    const columns = ["week", "revenue", "attainment", "refund_rate"].map((field) => ({ field }));
+    const result = calculateTableSizing(
+      columns,
+      { attainment: 96, refund_rate: 96, revenue: 112, week: 125 },
+      768,
+      true,
+    );
+
+    assert.equal(result.minimumTableWidth, 768);
+    assert.equal(result.tableWidth, 768);
+    assert.equal(Object.values(result.columnWidths).reduce((sum, width) => sum + width, 0), 768);
+  });
+
+  test("preserves a wide table width so its container can scroll", async () => {
+    const { calculateTableSizing } = await loadTableSizing();
+    const columns = Array.from({ length: 12 }, (_, index) => ({ field: `column_${index}` }));
+    const widths = Object.fromEntries(columns.map((column) => [column.field, 144]));
+    const result = calculateTableSizing(columns, widths, 768, true);
+
+    assert.equal(result.minimumTableWidth, 1728);
+    assert.equal(result.tableWidth, 1728);
+  });
+
+  test("keeps content-sized columns fixed while flexible columns absorb spare width", async () => {
+    const { calculateTableSizing } = await loadTableSizing();
+    const columns = [
+      { field: "metric", sizing: "content" },
+      { field: "definition" },
+    ];
+    const result = calculateTableSizing(columns, { definition: 100, metric: 120 }, 300, true);
+
+    assert.deepEqual(result.columnWidths, { definition: 180, metric: 120 });
+    assert.equal(result.tableWidth, 300);
+  });
+});
diff --git a/plugins/data-analytics/tests/test_app_dependencies.py b/plugins/data-analytics/tests/test_app_dependencies.py
new file mode 100644
index 0000000..c7c87c4
--- /dev/null
+++ b/plugins/data-analytics/tests/test_app_dependencies.py
@@ -0,0 +1,20 @@
+import json
+from pathlib import Path
+
+
+PLUGIN_ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_data_analytics_declares_no_required_apps() -> None:
+    manifest = json.loads((PLUGIN_ROOT / ".codex-plugin" / "plugin.json").read_text())
+    app_config = json.loads((PLUGIN_ROOT / ".app.json").read_text())
+
+    assert manifest["apps"] == "./.app.json"
+
+    required_apps = [
+        app_name
+        for app_name, config in app_config["apps"].items()
+        if config.get("optional") is not True
+    ]
+
+    assert required_apps == []
diff --git a/plugins/data-analytics/tests/web-work-mode-routing.test.mjs b/plugins/data-analytics/tests/web-work-mode-routing.test.mjs
new file mode 100644
index 0000000..ea71951
--- /dev/null
+++ b/plugins/data-analytics/tests/web-work-mode-routing.test.mjs
@@ -0,0 +1,274 @@
+import assert from "node:assert/strict";
+import { existsSync, readFileSync } from "node:fs";
+import { test } from "node:test";
+
+function read(relativePath) {
+  return readFileSync(new URL(relativePath, import.meta.url), "utf8");
+}
+
+test("preserves desktop data-context and MCP UI capabilities", () => {
+  for (const relativePath of [
+    "../.mcp.json",
+    "../mcp/server.cjs",
+    "../src/analytics-app/App.tsx",
+    "../skills/create-data-context/SKILL.md",
+    "../skills/build-dashboard/specifications/mcp-artifact-dashboard.md",
+    "../skills/build-report/specifications/mcp-app-report.md",
+  ]) {
+    assert.equal(existsSync(new URL(relativePath, import.meta.url)), true, relativePath);
+  }
+});
+
+test("classifies surface and mode independently from positive signals", () => {
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(index, /Classify `surface` and `mode` separately/);
+  assert.match(index, /`surface = codex_desktop`/);
+  assert.match(index, /`surface = chatgpt_web`/);
+  assert.match(index, /`mode = work_mode`/);
+  assert.match(index, /`mode = chat`/);
+  assert.match(index, /Otherwise set the relevant value to `unknown`/);
+  assert.match(index, /Never infer mode from surface, missing tools, tool failure/);
+  assert.match(index, /only when `surface = chatgpt_web` and `mode = work_mode` are both positively identified/);
+  assert.match(index, /Treat `mode = work_mode` with `surface = unknown` as a partial web Work Mode signal for delivery safety/);
+  assert.match(index, /unless `surface = codex_desktop` is positively identified/);
+});
+
+test("ChatGPT web Chat mode recommends Work Mode before doing analytics work", () => {
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(index, /ChatGPT web Chat mode stop gate \(read first\)/);
+  assert.match(index, /both `surface = chatgpt_web` and `mode = chat`/);
+  assert.match(index, /respond only with a concise recommendation to switch to Work Mode/);
+  assert.match(index, /Do not load focused analytics skills, inspect data or sources, call tools, ask intake questions, perform analysis, or create an artifact/);
+  assert.match(index, /only after the recommendation has been shown and the user explicitly says to continue, proceed, or stay in Chat mode/);
+  assert.match(index, /Repeating the original request, adding data, or answering an earlier question does not count as an override/);
+  assert.match(index, /resume the original analytics request without making them restate it/);
+  assert.match(index, /follow the ChatGPT web Work Mode runtime branch below for intake, persistence, and outputs/);
+  assert.match(index, /Keep that override for the current analytics request/);
+  assert.match(index, /ChatGPT web Chat mode \(`surface = chatgpt_web`, `mode = chat`\)/);
+  assert.match(index, /After an explicit override, follow the ChatGPT web Work Mode intake guidance below/);
+  assert.match(index, /After an override, follow the ChatGPT web Work Mode persistence guidance below/);
+  assert.match(index, /After an override, follow the ChatGPT web Work Mode output guidance below/);
+  assert.match(index, /Do not create outputs before an explicit override/);
+});
+
+test("runtime routing uses answers-ask-user-input except on Codex desktop", () => {
+  const index = read("../skills/index/SKILL.md");
+
+  assert.match(index, /After classifying `surface` and `mode`, select the most specific matching runtime branch/);
+  assert.match(index, /Codex desktop \(`surface = codex_desktop`\)/);
+  assert.match(index, /ChatGPT web Work Mode \(`web_work_mode = true`\)/);
+  assert.match(index, /Work Mode with unknown surface \(`mode = work_mode`, `surface = unknown`\)/);
+  assert.match(index, /Else: all other or unknown runtimes/);
+  assert.ok(index.includes("| Codex desktop (`surface = codex_desktop`) | Use `request_user_input` for structured intake when available."));
+  assert.ok(index.includes("| ChatGPT web Work Mode (`web_work_mode = true`) | Use `$answers-ask-user-input` or an equivalent native structured intake action for structured intake when available."));
+  assert.ok(index.includes("| Work Mode with unknown surface (`mode = work_mode`, `surface = unknown`) | Use `$answers-ask-user-input` or an equivalent native structured intake action for structured intake when available."));
+  assert.match(index, /On Codex desktop, call `request_user_input` whenever it is available/);
+  assert.match(index, /On every other surface or mode, invoke `\$answers-ask-user-input` whenever it is available/);
+  assert.match(index, /If the surface-appropriate structured intake action is unavailable but the runtime exposes an equivalent native structured intake action, use that action/);
+  assert.match(index, /Do not fall back to conversational choices merely because the runtime is `chatgpt_web`, `work_mode`, or an unknown environment/);
+  assert.match(index, /present the same task or fallback choices compactly in normal conversation/);
+  assert.doesNotMatch(index, /Use the structured form contract below only when `surface = codex_desktop`/);
+  assert.doesNotMatch(index, /including in `web_work_mode`/);
+});
+
+test("web Work Mode routes explicit semantic-layer setup to persistent skill creation", () => {
+  const index = read("../skills/index/SKILL.md");
+  const dataContext = read("../skills/create-data-context/SKILL.md");
+
+  assert.match(index, /local creations use that skill's existing `\$CODEX_HOME\/skills\/<area>-semantic-layer` default/);
+  assert.match(index, /Use ChatGPT personal Skills persistence when the Skills install surface is available in the run/);
+  assert.match(index, /That skill chooses an exposed ChatGPT Skills, local Codex, or portable package destination/);
+  assert.match(index, /For ordinary analytics work using supplied context for the current answer, keep the context current-session only/);
+  assert.match(index, /it uses the current runtime's classified `surface` and `mode` values/);
+  assert.match(index, /ChatGPT personal Skills, local Codex, or portable package persistence/);
+  assert.match(dataContext, /Use the current runtime's classified `surface` and `mode` values/);
+  assert.match(dataContext, /Use structured intake when a supported form action is available/);
+  assert.match(dataContext, /Build and validate the same canonical semantic-layer skill package before writing it to any destination/);
+  assert.match(dataContext, /Choose one persistence destination by default/);
+  assert.match(dataContext, /Destination branch/);
+  assert.match(dataContext, /Select this branch when/);
+  assert.match(dataContext, /product-backed personal Skills install surface/);
+  assert.match(dataContext, /Install the generated skill into the user's personal Skills library/);
+  assert.match(dataContext, /include it as a Markdown link in the chat response/);
+  assert.match(dataContext, /For ChatGPT personal Skills creations, include the installed skill link in the response/);
+  assert.ok(dataContext.includes("`$CODEX_HOME/skills/<area>-semantic-layer`"));
+  assert.ok(dataContext.includes("`~/.codex/skills/<area>-semantic-layer`"));
+  assert.doesNotMatch(dataContext, /\.agents\/skills/);
+  assert.match(dataContext, /For dual ChatGPT web and local Codex availability/);
+  assert.match(dataContext, /portable skill package or source plan/);
+});
+
+test("semantic-layer create and update results include a weekly refresh automation offer", () => {
+  const dataContext = read("../skills/create-data-context/SKILL.md");
+  const weeklyPolling = read("../skills/create-data-context/references/semantic-layer/weekly-polling-automation.md");
+  const automation = read("../skills/create-data-context/references/automation.md");
+
+  assert.match(dataContext, /After creating or updating a semantic-layer skill/);
+  assert.match(dataContext, /always offer weekly refresh/);
+  assert.match(dataContext, /do not create it without explicit user approval/);
+  assert.match(dataContext, /weekly refresh automation offer or prerequisite blocker/);
+  assert.match(dataContext, /iteration guidance/);
+  assert.match(dataContext, /refine definitions, add sources, update caveats/);
+  assert.match(dataContext, /let the user know they can iterate on the semantic layer/);
+  assert.doesNotMatch(dataContext, /validation result/);
+  assert.doesNotMatch(dataContext, /validation results/);
+  assert.match(weeklyPolling, /Offer weekly polling after semantic-layer creation or refresh/);
+  assert.match(automation, /offers exactly one refresh automation/);
+});
+
+test("web Work Mode selects web output surfaces while retaining MCP data sources", () => {
+  const index = read("../skills/index/SKILL.md");
+  const dashboard = read("../skills/build-dashboard/SKILL.md");
+  const report = read("../skills/build-report/SKILL.md");
+  const visualize = read("../skills/visualize-data/SKILL.md");
+  const nativeInline = read("../skills/visualize-data/references/native-inline-visualizations.md");
+  const core = read("../src/analytics-app-core.md");
+  const dashboardSpec = read("../skills/build-dashboard/specifications/mcp-artifact-dashboard.md");
+  const reportSpec = read("../skills/build-report/specifications/mcp-app-report.md");
+
+  assert.match(index, /Default durable reports and dashboards to HTML; use connected BI or another non-MCP destination when the user selects it/);
+  assert.match(index, /MCP servers and other callable tools remain valid data sources/);
+  assert.match(index, /Only after inline delivery is already selected, use native Work Mode rendering/);
+  assert.match(index, /treat `charts_widget_v2` as directly surfaced and emit its live `genui` content reference before fallback/);
+  assert.match(index, /do not self-declare it unavailable, search for it, or print its payload as bare JSON/);
+  assert.match(index, /Do not call Data Analytics MCP UI tools for delivery in web Work Mode/);
+  assert.match(index, /Do not use an unknown surface classification as permission to render an MCP app artifact/);
+  assert.match(dashboard, /do not select the Data Analytics MCP artifact app/);
+  assert.match(dashboard, /positive Work Mode signal without a positive Codex desktop surface/);
+  assert.match(dashboard, /otherwise build portable HTML/);
+  assert.match(report, /ChatGPT web Work Mode: use `html`/);
+  assert.match(report, /Work Mode signal with unknown or non-desktop surface: use `html`/);
+  assert.match(report, /do not treat an unknown surface classification as permission to render an MCP app report/);
+  assert.match(visualize, /only after another workflow has already selected inline delivery/);
+  assert.match(visualize, /only after the active workflow has already selected inline or chat-visible delivery/);
+  assert.match(visualize, /prefer native Work Mode rendering/);
+  assert.match(visualize, /native-inline-visualizations\.md/);
+  assert.ok(visualize.includes('genui{"charts_widget_v2":{"content":{...}}}'));
+  assert.match(visualize, /The inner chart spec alone is not a response/);
+  assert.match(visualize, /do not print bare JSON, plain HTML, or a code fence/);
+  assert.match(visualize, /treat `charts_widget_v2` as directly surfaced in this runtime/);
+  assert.match(visualize, /do not self-declare it unavailable or search for it/);
+  assert.match(visualize, /If the Visualize plugin is installed, also consult and follow its current visualization guidance/);
+  assert.match(visualize, /do not call `render_chart` or `render_table` for inline delivery/);
+  assert.match(visualize, /Use static or Matplotlib charting for this native-render failure fallback/);
+  assert.match(visualize, /use a compact table only if no visual renderer can be delivered/);
+  assert.match(visualize, /Outside the web Work Mode native-render failure fallback, use static Python charting only when/);
+  assert.match(visualize, /successful Data Analytics MCP tool result is not delivery confirmation/);
+  assert.match(nativeInline, /only after the active workflow has already selected an inline, chat-visible visual/);
+  assert.match(nativeInline, /it does not choose inline delivery/);
+  assert.match(nativeInline, /`charts_widget_v2`/);
+  assert.match(nativeInline, /`app_block`/);
+  assert.match(nativeInline, /Never emit a renderer payload as bare JSON/);
+  assert.match(nativeInline, /`charts_widget_v2` is the directly surfaced native UI element/);
+  assert.match(nativeInline, /do not search for it, self-declare it unavailable/);
+  assert.ok(nativeInline.includes('genui{"charts_widget_v2":{"content":{...}}}'));
+  assert.ok(nativeInline.includes('genui{"app_block":{"content":"<section>...</section>"}}'));
+  assert.match(nativeInline, /do not classify `charts_widget_v2` as unsurfaced before attempting it/);
+  assert.match(nativeInline, /ordinary fixed-size `scatter`/);
+  assert.match(nativeInline, /true bubble chart is therefore not a `charts_widget_v2` scatter chart/);
+  assert.match(nativeInline, /For bubble charts, funnel charts, and any other inline chart family outside that JSON surface, use `app_block` custom interactive HTML/);
+  assert.match(nativeInline, /Do not decline the request, ask the user to repeat it, emit an unsupported JSON shape, or fall back to Matplotlib merely because `charts_widget_v2` does not support the family/);
+  assert.match(nativeInline, /Mermaid is not a quantitative data-chart renderer/);
+  assert.match(nativeInline, /Reproducible static\/Matplotlib chart/);
+  assert.match(nativeInline, /prefer a static visual before a compact table/);
+  assert.doesNotMatch(index, /native-inline-visualizations/);
+  assert.match(core, /Data Analytics MCP widget surface is not available for delivery/);
+  assert.match(core, /without a positive `surface = codex_desktop` signal/);
+  assert.match(core, /preserve the delivery mode already selected/);
+  assert.match(core, /treat `charts_widget_v2` as directly surfaced/);
+  assert.match(core, /emit its live `genui` content reference in the outer shape/);
+  assert.ok(core.includes('genui{"charts_widget_v2":{"content":{...}}}'));
+  assert.match(core, /do not self-declare it unavailable or search for it/);
+  assert.match(core, /Keep `app_block` conditional on the host surfacing it/);
+  assert.match(core, /Use image-based\/static charting only after an emitted native reference is rejected or fails to render/);
+  assert.match(core, /compact table\/non-MCP output only when no visual renderer can be delivered/);
+  assert.match(core, /Do not treat an `ok:true` MCP tool result as proof/);
+  assert.match(dashboardSpec, /Do not select this specification when `surface = chatgpt_web` and `mode = work_mode`/);
+  assert.match(dashboardSpec, /`mode = work_mode` is positive and `surface` is unknown/);
+  assert.match(reportSpec, /Do not select this specification when `surface = chatgpt_web` and `mode = work_mode`/);
+  assert.match(reportSpec, /`mode = work_mode` is positive and `surface` is unknown/);
+});
+
+test("all HTML reports and dashboards use packaged Recharts with readable fallbacks", () => {
+  const index = read("../skills/index/SKILL.md");
+  const report = read("../skills/build-report/SKILL.md");
+  const visualize = read("../skills/visualize-data/SKILL.md");
+  const dashboardHtml = read("../skills/build-dashboard/specifications/html-dashboard.md");
+  const rechartsReference = read("../skills/visualize-data/references/recharts-html.md");
+  const htmlShell = read("../assets/html-report-shell.html");
+  const runtime = read("../src/html-report-runtime.jsx");
+  const helper = read("../skills/build-report/scripts/embed_html_report_runtime.py");
+  const core = read("../src/analytics-app-core.md");
+
+  assert.match(report, /For every selected HTML report path.*precompiled Recharts-in-HTML path/s);
+  assert.match(report, /Codex desktop explicit HTML.*Google Slides conversion/s);
+  assert.match(report, /For every HTML report path.*recharts-html\.md/s);
+  assert.match(visualize, /Whenever the selected report or dashboard delivery surface is HTML.*packaged Recharts-in-HTML path/s);
+  assert.match(visualize, /For every HTML report or dashboard.*references\/recharts-html\.md/s);
+  assert.match(dashboardHtml, /For every portable HTML dashboard.*packaged Recharts runtime plus same-data static fallbacks/s);
+  assert.match(rechartsReference, /including Codex explicit HTML.*ChatGPT web Work Mode.*Google Slides conversion/s);
+  assert.match(rechartsReference, /do not install npm packages, run Vite, use a CDN/);
+  assert.match(rechartsReference, /same reviewed rows as the Recharts payload/);
+  assert.match(rechartsReference, /Keep fallback labels inside the SVG viewBox with edge padding/);
+  assert.match(rechartsReference, /bar value labels do not intersect category-axis labels/);
+  assert.match(rechartsReference, /\.\.\/\.\.\/\.\.\/assets\/html-report-shell\.html/);
+  assert.match(rechartsReference, /\.\.\/\.\.\/build-report\/scripts\/embed_html_report_runtime\.py/);
+  assert.match(rechartsReference, /technical-summary.*scope-data-and-metric-definitions.*methodology.*limitations-uncertainty-and-robustness-checks/s);
+  assert.match(htmlShell, /data-report-audience="\{\{REPORT_AUDIENCE\}\}"/);
+  assert.match(htmlShell, /data-recharts-chart="chart-1"/);
+  assert.match(htmlShell, /data-recharts-fallback/);
+  assert.match(htmlShell, /figure\.card \{ margin: 0; \}/);
+  assert.match(htmlShell, /\.chart-wrap \{ padding: 16px 24px 16px 96px; \}/);
+  assert.match(htmlShell, /\.chart-wrap \{ overflow-x: auto; padding: 16px 18px; \}/);
+  assert.match(rechartsReference, /On wide layouts.*96px.*On narrow layouts.*compact symmetric chart padding/);
+  assert.match(htmlShell, /data-contract-section="further-questions"/);
+  assert.match(htmlShell, /DATA_ANALYTICS_HTML_REPORT_RUNTIME/);
+  assert.match(runtime, /renderRechartsChart/);
+  assert.match(runtime, /data-recharts-live/);
+  assert.doesNotMatch(runtime, /mcp-host|localStorage|fetch\(/);
+  assert.match(helper, /html-report-runtime\.html/);
+  assert.match(helper, /safe_json_script_text/);
+  assert.match(helper, /data-recharts-fallback/);
+  assert.match(index, /Every selected HTML report path.*packaged Recharts runtime contract/s);
+  assert.match(core, /HTML report charts should use the packaged standalone Recharts runtime/);
+  assert.match(core, /same-data static SVG\/table fallbacks/);
+});
+
+test("desktop reports default to MCP and require a concrete HTML fallback", () => {
+  const index = read("../skills/index/SKILL.md");
+  const report = read("../skills/build-report/SKILL.md");
+
+  assert.match(report, /Codex desktop: use `mcp-app` by default/);
+  assert.match(report, /ChatGPT web Work Mode: use `html`/);
+  assert.match(report, /Work Mode signal with unknown or non-desktop surface: use `html`/);
+  assert.match(report, /On Codex desktop, use `html` only when the user explicitly asks/);
+  assert.match(report, /an MCP app report was actually attempted and failed/);
+  assert.match(index, /Codex desktop defaults to `mcp-app`/);
+  assert.match(index, /partial Work Mode signals without a positive Codex desktop surface/);
+  assert.match(index, /after a concrete failed MCP attempt/);
+});
+
+test("conversion handoffs keep routine verification details out of user-facing responses", () => {
+  const createDataContext = read("../skills/create-data-context/SKILL.md");
+  const dashboard = read("../skills/build-dashboard/SKILL.md");
+  const doc = read("../skills/build-report/report-to-google-doc/SKILL.md");
+  const jupyter = read("../skills/jupyter-notebooks/SKILL.md");
+  const pdf = read("../skills/build-report/report-to-pdf/SKILL.md");
+  const slides = read("../skills/build-report/report-to-google-slides/SKILL.md");
+
+  for (const skill of [dashboard, doc, pdf, slides]) {
+    assert.match(skill, /Keep routine check.*support artifacts/);
+    assert.match(skill, /Do not list internal checks in the user-facing handoff/);
+  }
+  assert.match(createDataContext, /Keep routine validation details in support artifacts/);
+  assert.match(jupyter, /do not add a separate routine validation section for a clean run/);
+  assert.doesNotMatch(slides, /verification performed/);
+  assert.doesNotMatch(pdf, /verification performed/);
+  assert.doesNotMatch(doc, /validation performed/);
+  assert.doesNotMatch(dashboard, /what validation was performed/);
+  assert.doesNotMatch(createDataContext, /validation result/);
+  assert.doesNotMatch(createDataContext, /validation results/);
+  assert.doesNotMatch(jupyter, /Include validation status in the final response/);
+});
diff --git a/plugins/data-analytics/vite.config.ts b/plugins/data-analytics/vite.config.ts
index 9a46454..0df6b35 100644
--- a/plugins/data-analytics/vite.config.ts
+++ b/plugins/data-analytics/vite.config.ts
@@ -6,8 +6,10 @@ import { viteSingleFile } from "vite-plugin-singlefile";
 
 const input = process.env.INPUT ?? "datascience-chart-widget.html";
 const isArtifactWidget = input.includes("datascience-artifact-widget");
+const isHtmlReportRuntime = input.includes("html-report-runtime");
+const usesReactApp = isArtifactWidget || isHtmlReportRuntime;
 const pluginRoot = fileURLToPath(new URL(".", import.meta.url));
-const reactAliases = isArtifactWidget
+const reactAliases = usesReactApp
   ? [
       { find: /^react$/, replacement: path.join(pluginRoot, "node_modules/react/index.js") },
       {
@@ -37,7 +39,7 @@ export default defineConfig({
   build: {
     outDir: "../assets",
     emptyOutDir: false,
-    minify: isArtifactWidget ? "oxc" : false,
+    minify: usesReactApp ? "oxc" : false,
     cssMinify: false,
     rollupOptions: {
       input
diff --git a/plugins/financial-markets/.app.json b/plugins/financial-markets/.app.json
deleted file mode 100644
index 4f0b300..0000000
--- a/plugins/financial-markets/.app.json
+++ /dev/null
@@ -1,49 +0,0 @@
-{
-  "apps": {
-    "slack": {
-      "id": "REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"
-    },
-    "pitchbook": {
-      "id": "REPLACE_WITH_PITCHBOOK_APP_OR_CONNECTOR_ID"
-    },
-    "factset": {
-      "id": "REPLACE_WITH_FACTSET_APP_OR_CONNECTOR_ID"
-    },
-    "morningstar": {
-      "id": "REPLACE_WITH_MORNINGSTAR_APP_OR_CONNECTOR_ID"
-    },
-    "lseg": {
-      "id": "REPLACE_WITH_LSEG_APP_OR_CONNECTOR_ID"
-    },
-    "s-p": {
-      "id": "REPLACE_WITH_S_P_APP_OR_CONNECTOR_ID"
-    },
-    "third-bridge": {
-      "id": "REPLACE_WITH_THIRD_BRIDGE_APP_OR_CONNECTOR_ID"
-    },
-    "daloopa": {
-      "id": "REPLACE_WITH_DALOOPA_APP_OR_CONNECTOR_ID"
-    },
-    "quartr": {
-      "id": "REPLACE_WITH_QUARTR_APP_OR_CONNECTOR_ID"
-    },
-    "alpaca": {
-      "id": "REPLACE_WITH_ALPACA_APP_OR_CONNECTOR_ID"
-    },
-    "google_drive": {
-      "id": "connector_5f3c8c41a1e54ad7a76272c89e2554fa"
-    },
-    "gmail_connector": {
-      "id": "connector_2128aebfecb84f64a069897515042a44"
-    },
-    "outlook_email_connector": {
-      "id": "connector_4aaab2856305417b993eca9a216aaf6e"
-    },
-    "sharepoint_connector": {
-      "id": "connector_1e4f6a44acf14e3ca1d96672f8c945bc"
-    },
-    "teams_connector": {
-      "id": "connector_246af0940da3457da0e751171dc1ce60"
-    }
-  }
-}
diff --git a/plugins/financial-markets/.codex-plugin/plugin.json b/plugins/financial-markets/.codex-plugin/plugin.json
deleted file mode 100644
index 9207686..0000000
--- a/plugins/financial-markets/.codex-plugin/plugin.json
+++ /dev/null
@@ -1,56 +0,0 @@
-{
-  "name": "financial-markets",
-  "version": "0.1.27",
-  "description": "Public equity investing workflows for listed-company research, earnings analysis, valuation, model updates, long/short pitches, catalysts, thesis tracking, ETF/index and constituent diligence, sell-side research notes, risk sizing, hedging, dashboards, and investment memos.",
-  "author": {
-    "name": "OpenAI"
-  },
-  "homepage": "https://github.com/openai/role-specific-plugins",
-  "repository": "https://github.com/openai/role-specific-plugins/tree/main/plugins/financial-markets",
-  "license": "MIT",
-  "keywords": [
-    "financial-markets",
-    "public-equity-investing",
-    "public-equity",
-    "listed-equities",
-    "equity-research",
-    "long-only",
-    "hedge-fund",
-    "long-short",
-    "earnings",
-    "valuation",
-    "catalysts",
-    "investment-memo",
-    "etf-index-diligence",
-    "benchmark-relative-equity",
-    "sell-side-research",
-    "public-equity-diligence",
-    "portfolio-manager"
-  ],
-  "skills": "./skills/",
-  "apps": "./.app.json",
-  "interface": {
-    "displayName": "Financial Markets",
-    "shortDescription": "Public equity PM research, long/short, earnings, ETF/index diligence, and memos",
-    "longDescription": "Public equity investing workflows for long-only investors, long/short hedge funds, sell-side equity research teams, ETF/index and constituent diligence, event-driven equity investors, and sector/thematic listed-equity research teams. Outputs should surface what is mispriced, what is priced in, what proves or kills the thesis, what to do next, and what evidence is missing. Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.",
-    "developerName": "OpenAI",
-    "category": "Business",
-    "capabilities": [
-      "Interactive",
-      "Read",
-      "Write"
-    ],
-    "websiteURL": "https://openai.com/",
-    "privacyPolicyURL": "https://openai.com/policies/row-privacy-policy/",
-    "termsOfServiceURL": "https://openai.com/policies/row-terms-of-use/",
-    "defaultPrompt": [
-      "Help me get started",
-      "Analyze Apple's latest earnings: what changed, what is priced in, and what should an investor watch next",
-      "Screen listed-equity beneficiaries of AI data-center power demand and rank the best ideas, risks, and false positives"
-    ],
-    "brandColor": "#04B84C",
-    "composerIcon": "./assets/composerIcon.svg",
-    "logo": "./assets/logo.png",
-    "screenshots": []
-  }
-}
diff --git a/plugins/financial-markets/README.md b/plugins/financial-markets/README.md
deleted file mode 100644
index c5d7666..0000000
--- a/plugins/financial-markets/README.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# Public Equity Investing
-
-Make sense of market and company information, pressure-test investment theses, and track what is changing.
-
-## When to use this plugin
-
-Use Public Equity Investing when you need to review earnings, compare companies, track signals, assess whether an investment thesis is strengthening or weakening, update valuation work, generate ideas, or prepare investor-facing research artifacts. It is designed for long-only investors, long/short teams, sell-side research, event-driven investors, and ETF or index diligence. Use Credit Markets instead when the core question is about debt securities, spreads, yields, covenants, distressed recovery, or creditworthiness.
-
-## Optional onboarding
-
-Ask Codex:
-
-`@Public Equity Investing Help me get started`
-
-Public Equity Investing has an optional onboarding flow that helps you save useful output and audience defaults, choose the tools and sources to use, decide whether to add a weekday source check, and pick a first workflow.
-
-Optional onboarding is an interactive, step-by-step conversation that runs only when you ask for it. Codex will guide you through setup, ask for approval before making changes, and help you try a first workflow.
-
-You can also skip setup and start any workflow directly.
-
-## Example workflows
-
-Hero workflows are bolded.
-
-| Workflow | Try this | Skill | Result |
-| --- | --- | --- | --- |
-| Create a company tearsheet | `Build a public-equity tearsheet for this issuer` | `company-tearsheet` | A source-backed issuer profile for screening, monitoring, or meeting context |
-| Prepare an earnings preview | `Build a pre-earnings preview with the expectation bar, scenarios, and call questions` | `earnings-preview` | A full preview report covering expectations, KPI setup, scenarios, credibility, and decision-relevant questions |
-| **Analyze earnings results** | `Analyze this quarter's results and transcript against the pre-print debate` | `earnings-deep-dive` | A post-earnings deep dive covering the print, guidance, transcript, thesis read-through, and action implications |
-| Build initiating coverage | `Build an initiating-coverage report for this listed company` | `initiating-coverage` | A structured equity-research report with sources, thesis, valuation, risks, and monitoring priorities |
-| Draft an investment memo | `Turn this analysis into a formal public-equity investment memo` | `memo-builder` | An investment memo, PM update, committee note, client note, or research update grounded in the available evidence |
-| **Build a long or short pitch** | `Build a PM-facing long pitch with variant perception, catalysts, and exit rules` | `long-short-pitch` | A trade pitch covering what is mispriced, why now, proof points, thesis killers, sizing considerations, and action thresholds |
-| **Generate ideas** | `Screen this sector for the most interesting public-equity ideas to investigate` | `idea-generation` | A triage report for candidate ideas, market maps, or watchlist review |
-| Track a thesis | `Build a thesis tracker with milestones, risks, and evidence updates` | `thesis-tracker` | A monitoring view of what proves, weakens, or kills the thesis over time |
-| Prepare for a meeting | `Prepare an investor meeting brief for this company` | `meeting-prep` | A focused public-equity meeting brief with source-backed context and questions |
-| Build a DCF | `Build a public-equity DCF workbook for this issuer` | `dcf-model-builder` | A valuation workbook with assumptions, sensitivities, sources, and decision-relevant outputs |
-| Build a three-statement model | `Build a public-company three-statement operating model` | `three-statement-model-builder` | An integrated operating-model workbook with statements, drivers, checks, and scenarios |
-| Build a comps valuation | `Build a public-equity comps analysis and explain the valuation read-through` | `comps-valuation` | A report or workbook covering peers, multiples, implied prices, refreshable tables, and valuation debate |
-| Update an equity model | `Flow these actuals into a copy of the model and show the change log` | `equity-model-update` | An updated XLSX model copy with source-to-model mapping and support logs |
-| Normalize public-company financials | `Normalize these reported financials into model-ready schedules` | `financials-normalizer` | Clean public-company financial schedules with source mapping and QA flags |
-| Audit a model | `Audit this public-equity model and identify the critical readiness issues` | `model-audit-tieout` | A model-audit report covering source tie-outs, blockers, sensitivities, permitted use, and remediation sequence |
-| Build a scenario analysis | `Turn this thesis into upside, base, downside, and breakpoint scenarios` | `scenario-sensitivity-generator` | A scenario and sensitivity package with PM action thresholds |
-| Build a catalyst calendar | `Build a 90-day catalyst calendar for this company` | `catalyst-calendar` | A polished monitoring calendar for dated events, windows, milestones, and thesis implications |
-| Analyze an event path | `Analyze the probability, payoff, and expected-return paths for this event` | `event-driven-analyzer` | An event report covering scenarios, timing, probabilities, payoffs, and decision implications |
-| Translate an external development | `Explain how this policy change affects the sector and the most exposed issuers` | `economic-impact-report` | An issuer and sector impact report covering earnings, valuation, positioning, and portfolio implications |
-| Size a position or hedge | `Build a position-sizing and hedge plan for this listed-equity thesis` | `portfolio-risk-management` | A thesis-aware risk plan covering sizing, hedges, causal risks, and action thresholds |
-| QC a deck or report | `Run a first-pass QC review on this investor report` | `deck-report-qc` | A circulation-focused review of research materials, with issues to resolve before sharing |
-
-## Integrations
-
-Public Equity Investing can use available tools when they are connected:
-
-| Source | Supported integrations | What they unlock |
-| --- | --- | --- |
-| Market and company data | FactSet, LSEG, S&P, Moody's, Morningstar, Daloopa, Quartr | Source-backed company research, market context, financial analysis, valuation, transcripts, and earnings work |
-| Research and diligence context | PitchBook, Third Bridge, Datasite, Hebbia | Additional company, industry, expert, diligence, and source-document context when available |
-| Brokerage context | Alpaca | Available portfolio or market context for supported workflows |
-| Collaboration and documents | Slack, Google Drive, Gmail, Outlook Email, SharePoint, Microsoft Teams | Research inputs, source documents, internal context, and controlled handoff |
-
-Connected sources improve grounding, but you can also begin with uploaded files, exported data, pasted excerpts, existing workbooks, and manually provided assumptions.
diff --git a/plugins/financial-markets/assets/composerIcon.svg b/plugins/financial-markets/assets/composerIcon.svg
deleted file mode 100644
index 541917a..0000000
--- a/plugins/financial-markets/assets/composerIcon.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M20.4994 1.59961C21.2726 1.59961 21.8998 2.2268 21.8998 3V7C21.8998 7.49706 21.4964 7.90039 20.9994 7.90039C20.5025 7.90019 20.099 7.49693 20.099 7V4.54395L14.7289 9.36426C14.199 9.83977 13.397 9.84186 12.8646 9.36914L10.1771 6.98242L3.59802 12.875C3.2279 13.2065 2.65917 13.1755 2.32751 12.8057C1.99592 12.4354 2.0266 11.8658 2.39685 11.5342L9.24158 5.4043C9.73832 4.95944 10.474 4.93033 11.0023 5.31738L11.1058 5.40039L13.7924 7.78613L18.6791 3.40039H16.4994C16.0025 3.40019 15.599 2.99693 15.599 2.5C15.599 2.00307 16.0025 1.59981 16.4994 1.59961H20.4994Z" fill="#04B84C"/>
-<path d="M21 19.0996C21.4971 19.0996 21.9004 19.5029 21.9004 20C21.9004 20.4971 21.4971 20.9004 21 20.9004H3C2.50294 20.9004 2.09961 20.4971 2.09961 20C2.09961 19.5029 2.50294 19.0996 3 19.0996H21Z" fill="#04B84C"/>
-<path d="M3.10303 17V15.5C3.10303 15.0029 3.50636 14.5996 4.00342 14.5996C4.50047 14.5996 4.90381 15.0029 4.90381 15.5V17C4.90381 17.4971 4.50047 17.9004 4.00342 17.9004C3.50636 17.9004 3.10303 17.4971 3.10303 17Z" fill="#04B84C" fill-opacity="0.5"/>
-<path d="M7.09814 17V12.5C7.09814 12.0029 7.50148 11.5996 7.99854 11.5996C8.49559 11.5996 8.89893 12.0029 8.89893 12.5V17C8.89893 17.4971 8.49559 17.9004 7.99854 17.9004C7.50148 17.9004 7.09814 17.4971 7.09814 17Z" fill="#04B84C" fill-opacity="0.5"/>
-<path d="M11.0933 17V12.8125C11.0933 12.3154 11.4966 11.9121 11.9937 11.9121C12.4907 11.9121 12.894 12.3154 12.894 12.8125V17C12.894 17.4971 12.4907 17.9004 11.9937 17.9004C11.4966 17.9004 11.0933 17.4971 11.0933 17Z" fill="#04B84C" fill-opacity="0.5"/>
-<path d="M15.0884 17V11.75C15.0884 11.2529 15.4917 10.8496 15.9888 10.8496C16.4858 10.8496 16.8892 11.2529 16.8892 11.75V17C16.8892 17.4971 16.4858 17.9004 15.9888 17.9004C15.4917 17.9004 15.0884 17.4971 15.0884 17Z" fill="#04B84C" fill-opacity="0.5"/>
-<path d="M19.0835 17V10.5C19.0835 10.0029 19.4868 9.59961 19.9839 9.59961C20.4809 9.59961 20.8843 10.0029 20.8843 10.5V17C20.8843 17.4971 20.4809 17.9004 19.9839 17.9004C19.4868 17.9004 19.0835 17.4971 19.0835 17Z" fill="#04B84C" fill-opacity="0.5"/>
-</svg>
diff --git a/plugins/financial-markets/assets/logo.png b/plugins/financial-markets/assets/logo.png
deleted file mode 100644
index 0b7b370..0000000
Binary files a/plugins/financial-markets/assets/logo.png and /dev/null differ
diff --git a/plugins/financial-markets/shared/__init__.py b/plugins/financial-markets/shared/__init__.py
deleted file mode 100644
index 1ac96b7..0000000
--- a/plugins/financial-markets/shared/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Shared helpers for Public Equity Investing plugin scripts."""
diff --git a/plugins/financial-markets/shared/artifact_packager.py b/plugins/financial-markets/shared/artifact_packager.py
deleted file mode 100644
index aacfa47..0000000
--- a/plugins/financial-markets/shared/artifact_packager.py
+++ /dev/null
@@ -1,465 +0,0 @@
-"""Shared artifact packaging helpers for Public Equity Investing skills.
-
-The helpers enforce the Public Equity Investing output surface: XLSX and HTML are the
-normal reader-facing artifacts, while JSON, CSV, logs, manifests, and support
-notes remain support material unless explicitly requested.
-"""
-
-from __future__ import annotations
-
-import html
-import json
-import re
-import zipfile
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Mapping, Sequence
-
-MANIFEST_VERSION = "1.0"
-SUPPORT_PRIMARY_EXTENSIONS = {".csv", ".json", ".md", ".markdown", ".log", ".txt"}
-HTML_MODES = {"html_dashboard", "html_report"}
-WORKBOOK_MODES = {"workbook", "banker_formula_workbook", "xlsx_update_copy", "xlsx_control_pack"}
-PRIMARY_EXEMPT_MODES = {"blocked", "chat_only", "support_only"}
-
-
-def utc_now_iso() -> str:
-    return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
-
-
-def _string_path(path: str | Path | None) -> str:
-    return "" if path is None else str(path)
-
-
-def artifact_type_from_path(path: str | Path) -> str:
-    suffix = Path(path).suffix.lower()
-    return {
-        ".xlsx": "xlsx",
-        ".xlsm": "xlsm",
-        ".html": "html",
-        ".htm": "html",
-        ".pptx": "native_deck",
-        ".docx": "native_document",
-        ".csv": "csv",
-        ".json": "json",
-        ".md": "markdown",
-        ".markdown": "markdown",
-        ".log": "log",
-        ".txt": "text",
-    }.get(suffix, "file")
-
-
-def artifact_item(
-    path: str | Path,
-    role: str,
-    artifact_type: str | None = None,
-    description: str = "",
-    user_visible_default: bool = False,
-    contains_new_analysis: bool = False,
-    support_reason: str = "",
-    user_requested_machine_readable: bool = False,
-) -> dict[str, Any]:
-    return {
-        "path": _string_path(path),
-        "role": role,
-        "artifact_type": artifact_type or artifact_type_from_path(path),
-        "description": description,
-        "user_visible_default": bool(user_visible_default),
-        "contains_new_analysis": bool(contains_new_analysis),
-        "support_reason": support_reason,
-        "user_requested_machine_readable": bool(user_requested_machine_readable),
-    }
-
-
-def support_dir(output_dir: str | Path) -> Path:
-    path = Path(output_dir) / "support"
-    path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-def logs_dir(output_dir: str | Path) -> Path:
-    path = Path(output_dir) / "logs"
-    path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-def validate_artifact_manifest(manifest: Mapping[str, Any]) -> None:
-    errors: list[str] = []
-    primary = str(manifest.get("primary_human_deliverable") or "")
-    mode = str(manifest.get("artifact_mode") or "")
-    status = manifest.get("blocked_or_partial_status")
-    status_value = status.get("status") if isinstance(status, Mapping) else ""
-    primary_exempt = mode in PRIMARY_EXEMPT_MODES or status_value in {"blocked", "support_only"}
-
-    for field in [
-        "manifest_version",
-        "plugin",
-        "skill",
-        "artifact_mode",
-        "output_dir",
-        "first_read",
-        "human_deliverables",
-        "companion_deliverables",
-        "support_artifacts",
-        "agent_artifacts",
-        "support_artifacts_user_visible_default",
-        "blocked_or_partial_status",
-        "final_response_guidance",
-        "discipline_note",
-    ]:
-        if field not in manifest:
-            errors.append(f"missing required field: {field}")
-    if manifest.get("manifest_version") != MANIFEST_VERSION:
-        errors.append("manifest_version must be 1.0")
-    if not primary and not primary_exempt:
-        errors.append(
-            "primary_human_deliverable is required unless output is blocked/chat/support-only"
-        )
-    if primary:
-        suffix = Path(primary).suffix.lower()
-        user_requested = bool(manifest.get("user_requested_machine_readable"))
-        if suffix in SUPPORT_PRIMARY_EXTENSIONS and not user_requested:
-            errors.append(
-                "support-format files cannot be primary unless user_requested_machine_readable is true"
-            )
-        if mode in HTML_MODES and suffix not in {".html", ".htm"}:
-            errors.append("HTML modes require an .html primary deliverable")
-        if mode in WORKBOOK_MODES and suffix not in {".xlsx", ".xlsm"}:
-            errors.append("workbook modes require an .xlsx or .xlsm primary deliverable")
-    for item in manifest.get("support_artifacts", []) or []:
-        if not isinstance(item, Mapping):
-            errors.append("support_artifacts entries must be objects")
-            continue
-        if not item.get("support_reason"):
-            errors.append(f"support artifact lacks support_reason: {item.get('path', '<unknown>')}")
-        if item.get("user_visible_default") is True:
-            errors.append(
-                f"support artifact cannot be user_visible_default=true: {item.get('path', '<unknown>')}"
-            )
-    if errors:
-        raise ValueError("; ".join(errors))
-
-
-def write_artifact_manifest(
-    output_dir: str | Path,
-    skill: str,
-    artifact_mode: str,
-    primary_human_deliverable: str | Path | None = None,
-    *,
-    human_deliverables: Sequence[Mapping[str, Any]] | None = None,
-    companion_deliverables: Sequence[Mapping[str, Any]] | None = None,
-    support_artifacts: Sequence[Mapping[str, Any]] | None = None,
-    agent_artifacts: Sequence[Mapping[str, Any]] | None = None,
-    first_read: Mapping[str, Any] | None = None,
-    blocked_or_partial_status: Mapping[str, Any] | None = None,
-    final_response_guidance: Mapping[str, Any] | None = None,
-    user_requested_machine_readable: bool = False,
-    manifest_name: str = "manifest.json",
-    extra: Mapping[str, Any] | None = None,
-    validate: bool = True,
-) -> dict[str, Any]:
-    out = Path(output_dir)
-    out.mkdir(parents=True, exist_ok=True)
-    manifest_path = out / manifest_name
-    primary = _string_path(primary_human_deliverable)
-    humans = list(human_deliverables or [])
-    if primary and not humans:
-        humans.append(
-            artifact_item(
-                primary,
-                "human_deliverable",
-                description="Primary Public Equity Investing reader-facing deliverable.",
-                user_visible_default=True,
-                contains_new_analysis=True,
-                user_requested_machine_readable=user_requested_machine_readable,
-            )
-        )
-    agents = list(agent_artifacts or [])
-    if not any(
-        str(item.get("path", "")).endswith(manifest_name)
-        for item in agents
-        if isinstance(item, Mapping)
-    ):
-        agents.append(
-            artifact_item(
-                manifest_path,
-                "agent_artifact",
-                "json",
-                "Artifact manifest used to preserve deliverable hierarchy.",
-                False,
-                False,
-                "Manifest is audit/routing support, not the Public Equity Investing deliverable.",
-            )
-        )
-    status = dict(
-        blocked_or_partial_status
-        or {"status": "complete" if primary else "support_only", "reason": "", "missing_inputs": []}
-    )
-    manifest: dict[str, Any] = {
-        "manifest_version": MANIFEST_VERSION,
-        "plugin": "public-equity-investing",
-        "skill": skill,
-        "artifact_mode": artifact_mode,
-        "output_dir": str(out),
-        "first_read": dict(
-            first_read
-            or {
-                "path": primary,
-                "role": "primary_human_deliverable",
-                "why": "Open this first; support files are audit/import material.",
-            }
-        ),
-        "primary_human_deliverable": primary,
-        "human_deliverables": humans,
-        "companion_deliverables": list(companion_deliverables or []),
-        "support_artifacts": list(support_artifacts or []),
-        "agent_artifacts": agents,
-        "support_artifacts_user_visible_default": False,
-        "blocked_or_partial_status": status,
-        "final_response_guidance": dict(
-            final_response_guidance
-            or {
-                "lead_with": "primary_human_deliverable",
-                "mention_support_artifacts": "only_briefly_unless_requested",
-            }
-        ),
-        "discipline_note": "Use the HTML/XLSX human deliverable as the main output; JSON, CSV, logs, manifests, and support notes are audit support unless requested.",
-        "created_at": utc_now_iso(),
-        "user_requested_machine_readable": bool(user_requested_machine_readable),
-    }
-    if extra:
-        manifest.update(dict(extra))
-    if validate:
-        validate_artifact_manifest(manifest)
-    manifest_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8")
-    return manifest
-
-
-def _column_name(index: int) -> str:
-    index += 1
-    name = ""
-    while index:
-        index, rem = divmod(index - 1, 26)
-        name = chr(65 + rem) + name
-    return name
-
-
-def _sheet_name(name: str, used: set[str]) -> str:
-    cleaned = re.sub(r"[\[\]:*?/\\]", "_", str(name)).strip() or "Sheet"
-    cleaned = cleaned[:31]
-    base = cleaned
-    suffix = 1
-    while cleaned in used:
-        tail = f"_{suffix}"
-        cleaned = f"{base[: 31 - len(tail)]}{tail}"
-        suffix += 1
-    used.add(cleaned)
-    return cleaned
-
-
-def _sheet_xml(rows: Sequence[Sequence[Any]]) -> str:
-    parts = ['<?xml version="1.0" encoding="UTF-8" standalone="yes"?>']
-    parts.append(
-        '<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main"><sheetData>'
-    )
-    for r_idx, row in enumerate(rows, start=1):
-        parts.append(f'<row r="{r_idx}">')
-        for c_idx, value in enumerate(row):
-            if value is None:
-                continue
-            ref = f"{_column_name(c_idx)}{r_idx}"
-            if isinstance(value, bool):
-                parts.append(f'<c r="{ref}" t="b"><v>{1 if value else 0}</v></c>')
-            elif isinstance(value, (int, float)) and not isinstance(value, bool):
-                parts.append(f'<c r="{ref}"><v>{value}</v></c>')
-            else:
-                parts.append(
-                    f'<c r="{ref}" t="inlineStr"><is><t>{html.escape(str(value))}</t></is></c>'
-                )
-        parts.append("</row>")
-    parts.append("</sheetData></worksheet>")
-    return "".join(parts)
-
-
-def write_simple_xlsx_from_tables(
-    path: str | Path, sheets: Mapping[str, Sequence[Sequence[Any]]]
-) -> Path:
-    workbook = Path(path)
-    workbook.parent.mkdir(parents=True, exist_ok=True)
-    used: set[str] = set()
-    normalized = [(_sheet_name(name, used), list(rows)) for name, rows in sheets.items()]
-    if not normalized:
-        normalized = [("Cover", [["Generated workbook", "No tabular data supplied."]])]
-    if normalized[0][0] != "Cover":
-        raise ValueError("first sheet must be Cover")
-    content_types = ['<?xml version="1.0" encoding="UTF-8" standalone="yes"?>']
-    content_types.append(
-        '<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">'
-    )
-    content_types.append(
-        '<Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>'
-    )
-    content_types.append('<Default Extension="xml" ContentType="application/xml"/>')
-    content_types.append(
-        '<Override PartName="/xl/workbook.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.main+xml"/>'
-    )
-    content_types.append(
-        '<Override PartName="/xl/styles.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.styles+xml"/>'
-    )
-    for idx in range(1, len(normalized) + 1):
-        content_types.append(
-            f'<Override PartName="/xl/worksheets/sheet{idx}.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"/>'
-        )
-    content_types.append("</Types>")
-    workbook_xml = ['<?xml version="1.0" encoding="UTF-8" standalone="yes"?>']
-    workbook_xml.append(
-        '<workbook xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"><sheets>'
-    )
-    for idx, (name, _rows) in enumerate(normalized, start=1):
-        workbook_xml.append(f'<sheet name="{html.escape(name)}" sheetId="{idx}" r:id="rId{idx}"/>')
-    workbook_xml.append("</sheets></workbook>")
-    rels = ['<?xml version="1.0" encoding="UTF-8" standalone="yes"?>']
-    rels.append(
-        '<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">'
-    )
-    for idx in range(1, len(normalized) + 1):
-        rels.append(
-            f'<Relationship Id="rId{idx}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" Target="worksheets/sheet{idx}.xml"/>'
-        )
-    rels.append(
-        f'<Relationship Id="rId{len(normalized) + 1}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles" Target="styles.xml"/>'
-    )
-    rels.append("</Relationships>")
-    with zipfile.ZipFile(workbook, "w", zipfile.ZIP_DEFLATED) as zf:
-        zf.writestr("[Content_Types].xml", "".join(content_types))
-        zf.writestr(
-            "_rels/.rels",
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?><Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships"><Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" Target="xl/workbook.xml"/></Relationships>',
-        )
-        zf.writestr("xl/workbook.xml", "".join(workbook_xml))
-        zf.writestr("xl/_rels/workbook.xml.rels", "".join(rels))
-        zf.writestr(
-            "xl/styles.xml",
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?><styleSheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main"><fonts count="1"><font><sz val="11"/><name val="Aptos"/></font></fonts><fills count="1"><fill><patternFill patternType="none"/></fill></fills><borders count="1"><border/></borders><cellStyleXfs count="1"><xf/></cellStyleXfs><cellXfs count="1"><xf xfId="0"/></cellXfs></styleSheet>',
-        )
-        for idx, (_name, rows) in enumerate(normalized, start=1):
-            zf.writestr(f"xl/worksheets/sheet{idx}.xml", _sheet_xml(rows))
-    return workbook
-
-
-def write_cover_first_workbook(
-    path: str | Path,
-    cover_rows: Sequence[Sequence[Any]],
-    tables: Mapping[str, Sequence[Sequence[Any]]] | None = None,
-) -> Path:
-    sheets: dict[str, Sequence[Sequence[Any]]] = {"Cover": cover_rows}
-    sheets.update(dict(tables or {}))
-    return write_simple_xlsx_from_tables(path, sheets)
-
-
-def dict_rows_to_sheet(
-    rows: Sequence[Mapping[str, Any]], headers: Sequence[str] | None = None
-) -> list[list[Any]]:
-    if headers is None:
-        seen: list[str] = []
-        for row in rows:
-            for key in row.keys():
-                if str(key) not in seen:
-                    seen.append(str(key))
-        headers = seen
-    header_list = [str(header) for header in headers]
-    return [header_list, *[[row.get(header, "") for header in header_list] for row in rows]]
-
-
-def write_report_html(
-    path: str | Path,
-    title: str,
-    markdown_text: str,
-    subtitle: str = "Reader-facing Public Equity Investing report. Backing files are support artifacts.",
-) -> Path:
-    target = Path(path)
-    target.parent.mkdir(parents=True, exist_ok=True)
-    html_text = f"""<!doctype html>
-<html lang="en">
-<head>
-  <meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>{html.escape(title)}</title>
-  <style>
-    body {{ margin:0; font-family: Arial, Helvetica, sans-serif; background:#f6f7f9; color:#111827; }}
-    .topbar {{ background:#0f172a; color:#fff; padding:18px 28px; position:sticky; top:0; z-index:10; }}
-    .topbar h1 {{ margin:0; font-size:1.45rem; }}
-    .topbar p {{ margin:6px 0 0; color:#dbeafe; }}
-    main {{ max-width:1120px; margin:0 auto; padding:28px; }}
-    .card {{ background:#fff; border:1px solid #d7dae0; border-radius:8px; padding:24px; box-shadow:0 16px 36px rgba(15,23,42,.08); }}
-    pre {{ white-space:pre-wrap; overflow-wrap:anywhere; margin:0; font:0.94rem/1.58 ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; }}
-    @media (max-width:720px) {{ main {{ padding:16px; }} .card {{ padding:18px; }} }}
-  </style>
-</head>
-<body>
-  <header class="topbar"><h1>{html.escape(title)}</h1><p>{html.escape(subtitle)}</p></header>
-  <main><section class="card"><pre>{html.escape(markdown_text)}</pre></section></main>
-</body>
-</html>"""
-    target.write_text(html_text, encoding="utf-8")
-    return target
-
-
-def write_dashboard_contract(
-    path: str | Path,
-    skill: str,
-    title: str,
-    entity: str,
-    render_mode: str,
-    primary_artifact: str | Path,
-    *,
-    executive_summary: str | Sequence[Any] | Mapping[str, Any] | None = None,
-    hero_actions: Sequence[Mapping[str, Any]] | None = None,
-    supporting_outputs: Sequence[Mapping[str, Any]] | None = None,
-    report_body: Sequence[Mapping[str, Any]] | None = None,
-    readiness_posture: str = "draft",
-    citation_policy: str = "strict",
-    blocked_output_context: Mapping[str, Any] | None = None,
-    extra: Mapping[str, Any] | None = None,
-) -> Path:
-    target = Path(path)
-    target.parent.mkdir(parents=True, exist_ok=True)
-    contract: dict[str, Any] = {
-        "dashboard_title": title,
-        "entity": entity,
-        "skill": skill,
-        "render_mode": render_mode,
-        "posture": readiness_posture,
-        "metadata": {"readiness_posture": readiness_posture, "citation_policy": citation_policy},
-        "deliverable": {
-            "render_mode": render_mode,
-            "primary_artifact": _string_path(primary_artifact),
-            "primary_artifact_type": artifact_type_from_path(primary_artifact),
-            "readiness_posture": readiness_posture,
-            "citation_policy": citation_policy,
-            "executive_summary": executive_summary or [],
-            "hero_actions": list(hero_actions or []),
-            "utility_controls": {
-                "copy_full_report": True,
-                "print_pdf": True,
-                "open_primary_artifact": True,
-            },
-            "table_export_default": True,
-            "hero_callout": "Open the primary artifact first; support files are audit/import material.",
-        },
-        "hero": {
-            "eyebrow": skill,
-            "headline": title,
-            "dek": "Public Equity Investing output with support artifacts kept behind the reader-facing artifact.",
-            "callout_label": "First read",
-            "callout": _string_path(primary_artifact),
-        },
-        "report_body": list(report_body or []),
-        "supporting_outputs": list(supporting_outputs or []),
-        "blocked_output_context": dict(
-            blocked_output_context or {"blocked": False, "reason": "", "missing_inputs": []}
-        ),
-    }
-    if extra:
-        contract.update(dict(extra))
-    target.write_text(json.dumps(contract, indent=2) + "\n", encoding="utf-8")
-    return target
diff --git a/plugins/financial-markets/shared/artifacts.py b/plugins/financial-markets/shared/artifacts.py
deleted file mode 100644
index 1b4d92b..0000000
--- a/plugins/financial-markets/shared/artifacts.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""Shared deterministic artifact helpers for Public Equity Investing scripts.
-
-These helpers standardize the boring but important bookkeeping around run logs,
-output manifests, status labels, source basis, and warning/failure payloads.
-They intentionally do not contain finance logic.
-"""
-
-from __future__ import annotations
-
-import json
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Iterable, Mapping, Sequence
-
-STATUS_FAILED = "failed"
-STATUS_COMPLETED = "completed"
-
-MODEL_STATUS_NOT_DECISION_READY = "not-decision-ready"
-MODEL_STATUS_SCREEN_GRADE = "screen-grade"
-MODEL_STATUS_SENIOR_REVIEW_READY = "senior-review-ready"
-MODEL_STATUS_DECISION_GRADE = "decision-grade"
-
-
-def utc_now_iso() -> str:
-    return datetime.now(timezone.utc).isoformat()
-
-
-def write_json(path: str | Path, obj: Any) -> None:
-    destination = Path(path)
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    destination.write_text(json.dumps(obj, indent=2, sort_keys=False) + "\n", encoding="utf-8")
-
-
-def artifact_paths(output_dir: Path, filenames: Mapping[str, str]) -> dict[str, str]:
-    return {key: str(output_dir / filename) for key, filename in filenames.items()}
-
-
-def output_manifest(
-    paths: Mapping[str, str],
-    *,
-    required: Iterable[str] | None = None,
-    optional: Iterable[str] | None = None,
-    written: Iterable[str] | None = None,
-    descriptions: Mapping[str, str] | None = None,
-    artifact_roles: Mapping[str, str] | None = None,
-    hidden_unless_requested: Iterable[str] | None = None,
-) -> list[dict[str, Any]]:
-    required_keys = set(required or paths.keys())
-    optional_keys = set(optional or [])
-    written_keys = set(written or [])
-    descriptions = descriptions or {}
-    artifact_roles = artifact_roles or {}
-    hidden_keys = set(hidden_unless_requested or [])
-    return [
-        {
-            "key": key,
-            "path": path,
-            "required": key in required_keys and key not in optional_keys,
-            "written": key in written_keys,
-            "artifact_role": artifact_roles.get(key, "support_artifact"),
-            "hidden_unless_requested": key in hidden_keys,
-            "description": descriptions.get(key, "Deterministic Public Equity Investing artifact."),
-        }
-        for key, path in paths.items()
-    ]
-
-
-def normalize_message_items(items: Sequence[Any] | None) -> list[Any]:
-    if not items:
-        return []
-    seen: set[str] = set()
-    normalized: list[Any] = []
-    for item in items:
-        marker = (
-            json.dumps(item, sort_keys=True, default=str) if isinstance(item, dict) else str(item)
-        )
-        if marker in seen:
-            continue
-        seen.add(marker)
-        normalized.append(item)
-    return normalized
-
-
-def status_from_findings(
-    hard_failures: Sequence[Any] | None,
-    warnings: Sequence[Any] | None,
-    *,
-    placeholder_active: bool = False,
-) -> str:
-    if hard_failures:
-        return MODEL_STATUS_NOT_DECISION_READY
-    if placeholder_active:
-        return MODEL_STATUS_SCREEN_GRADE
-    if warnings:
-        return MODEL_STATUS_SCREEN_GRADE
-    return MODEL_STATUS_SENIOR_REVIEW_READY
-
-
-def build_run_log(
-    *,
-    hard_failures: Sequence[Any] | None,
-    warnings: Sequence[Any] | None,
-    outputs: Mapping[str, str],
-    output_manifest_rows: Sequence[Mapping[str, Any]],
-    source_basis: Sequence[Mapping[str, Any]] | None = None,
-    assumptions: Mapping[str, Any] | None = None,
-    checks: Mapping[str, Any] | None = None,
-    p0_handoff: Mapping[str, Any] | None = None,
-    model_status: str | None = None,
-    workbook_mode: str = "deterministic_export",
-    artifact_level: str = "deterministic_export",
-    extra: Mapping[str, Any] | None = None,
-) -> dict[str, Any]:
-    hard = normalize_message_items(hard_failures)
-    warns = normalize_message_items(warnings)
-    run_log: dict[str, Any] = {
-        "status": STATUS_FAILED if hard else STATUS_COMPLETED,
-        "model_status": model_status or status_from_findings(hard, warns),
-        "workbook_mode": workbook_mode,
-        "artifact_level": artifact_level,
-        "generated_at": utc_now_iso(),
-        "source_basis": list(source_basis or []),
-        "hard_failures": hard,
-        "warnings": warns,
-        "assumptions": dict(assumptions or {}),
-        "checks": dict(checks or {}),
-        "outputs": dict(outputs),
-        "output_manifest": [dict(row) for row in output_manifest_rows],
-        "p0_handoff": dict(p0_handoff or {}),
-    }
-    if extra:
-        run_log.update(extra)
-    return run_log
-
-
-def write_run_log_bundle(output_dir: Path, run_log: Mapping[str, Any]) -> None:
-    write_json(output_dir / "run_log.json", dict(run_log))
-    manifest: dict[str, Any] = {"outputs": list(run_log.get("output_manifest", []))}
-    if "primary_human_deliverable" in run_log:
-        manifest["primary_human_deliverable"] = run_log.get("primary_human_deliverable")
-    if "support_artifacts" in run_log:
-        manifest["support_artifacts"] = list(run_log.get("support_artifacts", []))
-    write_json(output_dir / "manifest.json", manifest)
diff --git a/plugins/financial-markets/shared/credit-markets-handoff.md b/plugins/financial-markets/shared/credit-markets-handoff.md
deleted file mode 100644
index 8104690..0000000
--- a/plugins/financial-markets/shared/credit-markets-handoff.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# Credit Markets Handoff
-
-Public Equity Investing owns common-stock, ADR, listed-equity, equity-short, pair-trade, event-driven equity, earnings, valuation, thesis-tracking, and equity portfolio workflows.
-
-Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-
-Keep leverage, maturities, liquidity, ratings, spreads, covenants, debt terms, and recovery considerations in this plugin only when they support a listed-equity decision. If the user's primary decision is a bond, loan, CDS, private-credit / public-credit instruments, distressed exchange, restructuring, bankruptcy, covenant package, recovery waterfall, or debt-security relative-value question, route out to Credit Markets.
-
-## PM/Risk Handoff Boundary
-
-For `portfolio-risk-management`, Public Equity Investing owns sizing and hedge judgment for listed-equity longs, listed-equity shorts, pairs, ETF/index constituents, listed options, equity factor hedges, and macro proxies when the purpose is a public-equity decision.
-
-Use Credit Markets for CDS hedges, bond hedges, loan hedges, high-yield or investment-grade bond selection, leveraged loans, bank loans, spread DV01 / CS01 sizing, credit spread hedges, capital-structure hedges, distressed hedges, recovery waterfalls, covenant hedging, covenant analysis, debt-security valuation, and any credit instrument where the security being sized or hedged is debt or CDS.
-
-Public Equity Investing exception: CDS, credit spreads, ratings, maturity walls, refinancing pressure, covenant headlines, and liquidity stress may appear only as equity-risk context, warning signals, or inputs to common-equity downside. If the next action is to buy, sell, size, hedge, or value a credit security, route to Credit Markets.
-
-Risk-workflow reminder: credit hedge construction belongs in Credit Markets; use here only as public-equity risk context. Route CDS, bonds, loans, spread DV01/CS01, capital-structure, distressed, recovery, and covenant implementation to Credit Markets.
diff --git a/plugins/financial-markets/shared/dashboard/__init__.py b/plugins/financial-markets/shared/dashboard/__init__.py
deleted file mode 100644
index dec5194..0000000
--- a/plugins/financial-markets/shared/dashboard/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Shared Public Equity Investing dashboard rendering utilities."""
-
-from .qa import validate_payload
-from .renderer import render_dashboard
-
-__all__ = ["render_dashboard", "validate_payload"]
diff --git a/plugins/financial-markets/shared/dashboard/assets/dashboard.css b/plugins/financial-markets/shared/dashboard/assets/dashboard.css
deleted file mode 100644
index 2a64091..0000000
--- a/plugins/financial-markets/shared/dashboard/assets/dashboard.css
+++ /dev/null
@@ -1,1383 +0,0 @@
-:root {
-  color-scheme: light;
-  --page: #ffffff;
-  --surface: #ffffff;
-  --surface-muted: #f8fafc;
-  --ink: #111827;
-  --muted: #5b6678;
-  --line: #e2e8f0;
-  --line-strong: #cbd5e1;
-  --issuer-accent: #245f5a;
-  --ticker-badge-bg: #245f5a;
-  --accent-soft: #eef7f5;
-  --data-accent: #256f69;
-  --status-positive: #166534;
-  --status-positive-bg: #f0fdf4;
-  --status-positive-line: #bbf7d0;
-  --status-watch: #92400e;
-  --status-watch-bg: #fffbeb;
-  --status-watch-line: #fde68a;
-  --status-negative: #991b1b;
-  --status-negative-bg: #fef2f2;
-  --status-negative-line: #fecaca;
-  --amber: var(--status-watch);
-  --red: var(--status-negative);
-  --green: var(--status-positive);
-  --narrative-width: 72ch;
-  --font-sans: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
-  --text-xs: 11px;
-  --text-sm: 12px;
-  --text-md: 13px;
-  --text-base: 14px;
-  --text-lg: 16px;
-  --text-xl: 18px;
-  --text-2xl: 21px;
-  --text-3xl: 26px;
-  --text-hero: 40px;
-  --leading-tight: 1.12;
-  --leading-snug: 1.32;
-  --leading-body: 1.45;
-}
-
-* {
-  box-sizing: border-box;
-}
-
-body {
-  margin: 0;
-  background: var(--page);
-  color: var(--ink);
-  font-family: var(--font-sans);
-  font-size: var(--text-base);
-  line-height: var(--leading-body);
-}
-
-html {
-  scroll-behavior: smooth;
-}
-
-h1,
-h2,
-h3,
-h4,
-p,
-button,
-input,
-select,
-textarea,
-table {
-  font-family: var(--font-sans);
-}
-
-button {
-  font: inherit;
-}
-
-.top-band {
-  background: var(--surface);
-  border-bottom: 1px solid var(--line);
-}
-
-.utility {
-  display: flex;
-  gap: 12px;
-  justify-content: flex-start;
-  align-items: center;
-  max-width: 1440px;
-  margin: 0 auto;
-  padding: 12px 24px;
-  color: var(--muted);
-  font-size: var(--text-sm);
-  text-transform: uppercase;
-  letter-spacing: 0.08em;
-}
-
-.utility-meta {
-  display: flex;
-  flex-wrap: wrap;
-  gap: 12px;
-}
-
-.dashboard-utility-bar {
-  position: sticky;
-  top: 0;
-  z-index: 12;
-  display: flex;
-  align-items: center;
-  justify-content: space-between;
-  gap: 16px;
-  width: 100%;
-  margin: 0;
-  padding: 10px max(24px, calc((100vw - 1440px) / 2 + 24px));
-  border-bottom: 1px solid var(--line);
-  background: var(--surface);
-}
-
-.utility-kicker {
-  display: block;
-  color: var(--muted);
-  font-size: var(--text-sm);
-  font-weight: 800;
-  letter-spacing: 0.08em;
-  text-transform: uppercase;
-}
-
-.utility-actions {
-  display: flex;
-  flex-wrap: wrap;
-  gap: 8px;
-  justify-content: flex-end;
-}
-
-.utility-action {
-  display: inline-flex;
-  align-items: center;
-  min-height: 32px;
-  padding: 6px 10px;
-  border: 1px solid var(--line);
-  border-radius: 999px;
-  background: var(--surface-muted);
-  color: var(--ink);
-  cursor: pointer;
-  font-size: var(--text-sm);
-  font-weight: 850;
-  text-decoration: none;
-}
-
-.utility-action:hover,
-.utility-action:focus-visible {
-  border-color: var(--issuer-accent);
-  outline: none;
-}
-
-.utility-action.is-copied {
-  color: var(--issuer-accent);
-  background: color-mix(in srgb, var(--issuer-accent) 8%, #ffffff);
-}
-
-.hero {
-  display: grid;
-  grid-template-columns: 84px minmax(0, 1fr) minmax(248px, 340px);
-  gap: 18px;
-  align-items: center;
-  max-width: 1440px;
-  min-height: 192px;
-  margin: 0 auto;
-  padding: 20px 24px 24px;
-}
-
-.hero.no-callout {
-  grid-template-columns: 84px minmax(0, 1fr);
-}
-
-.identity-lockup {
-  display: grid;
-  place-items: center;
-  width: 84px;
-  height: 84px;
-  background: var(--ticker-badge-bg);
-  color: #ffffff;
-  border: 1px solid color-mix(in srgb, var(--ticker-badge-bg) 82%, #ffffff);
-}
-
-.identity-lockup span {
-  max-width: 88%;
-  overflow-wrap: anywhere;
-  text-align: center;
-  font-weight: 900;
-  font-size: var(--text-2xl);
-  line-height: 0.95;
-  letter-spacing: 0.01em;
-}
-
-.hero-copy h1 {
-  max-width: 900px;
-  margin: 4px 0 8px;
-  font-size: var(--text-hero);
-  line-height: 1.02;
-  letter-spacing: 0;
-}
-
-.hero-copy p {
-  max-width: var(--narrative-width);
-  margin: 0;
-  color: var(--muted);
-  font-size: var(--text-lg);
-}
-
-.eyebrow,
-.label,
-.tile-label {
-  margin: 0;
-  color: var(--issuer-accent);
-  font-size: var(--text-sm);
-  font-weight: 800;
-  letter-spacing: 0.08em;
-  text-transform: uppercase;
-}
-
-.hero-callout {
-  border: 1px solid var(--line);
-  border-left: 4px solid var(--issuer-accent);
-  border-radius: 8px;
-  padding: 12px 14px;
-  background: var(--surface-muted);
-}
-
-.hero-callout strong {
-  display: block;
-  margin-top: 8px;
-  font-size: var(--text-xl);
-  line-height: 1.25;
-}
-
-main {
-  max-width: 1440px;
-  margin: 0 auto;
-  padding: 16px 24px 34px;
-}
-
-.snapshot-grid,
-.metric-grid {
-  display: grid;
-  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
-  gap: 10px;
-  margin-bottom: 12px;
-}
-
-.metric-grid {
-  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
-}
-
-.snapshot-grid:has(> .metric-tile:only-child),
-.metric-grid:has(> .metric-tile:only-child) {
-  grid-template-columns: minmax(220px, 420px);
-}
-
-.metric-tile,
-.decision-box,
-.tab-shell,
-.card,
-.scenario-card,
-.question-item,
-.timeline-item,
-.text-block,
-.executive-summary {
-  background: var(--surface);
-  border: 1px solid var(--line);
-  border-radius: 8px;
-}
-
-.metric-tile {
-  padding: 14px;
-}
-
-.metric-tile strong {
-  display: block;
-  margin: 6px 0 3px;
-  font-size: var(--text-2xl);
-  line-height: 1.1;
-  font-variant-numeric: tabular-nums lining-nums;
-}
-
-.metric-tile small {
-  display: block;
-  color: var(--muted);
-}
-
-.tab-shell {
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-}
-
-.tab-list {
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-  gap: 4px;
-  overflow-x: auto;
-  padding: 10px;
-  background: var(--surface-muted);
-  border-bottom: 1px solid var(--line);
-}
-
-.tab-button {
-  min-height: 44px;
-  border: 1px solid transparent;
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-  background: transparent;
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-  white-space: nowrap;
-  padding: 10px 13px;
-  font-size: var(--text-base);
-  font-weight: 750;
-}
-
-.tab-button.is-active {
-  background: var(--surface);
-  color: var(--ink);
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-}
-
-.tab-panel {
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-  padding: 26px;
-}
-
-.tab-panel.is-active {
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-.print-tab-heading {
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-
-.section-shell {
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-}
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-.toc-list {
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-  padding: 8px;
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-.toc-link {
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-  .table-wrap table {
-    width: 100% !important;
-    min-width: 0 !important;
-    border: 1px solid var(--line) !important;
-    border-collapse: separate !important;
-    border-spacing: 0 !important;
-    border-radius: 8px;
-    box-sizing: border-box;
-  }
-
-  .section-shell,
-  .section-stack,
-  .dashboard-section,
-  .tab-shell,
-  .tab-panel,
-  .tab-panel.is-active {
-    display: block !important;
-  }
-
-  .tab-shell {
-    border: 0;
-    border-radius: 0;
-  }
-
-  .tab-panel,
-  .tab-panel.is-active {
-    padding: 0 0 18px;
-  }
-
-  .section-stack > .dashboard-section + .dashboard-section,
-  .tab-panel + .tab-panel {
-    margin-top: 18px;
-  }
-
-  .print-tab-heading {
-    display: flex;
-    align-items: baseline;
-    gap: 10px;
-    margin: 4px 0 12px;
-    padding-top: 4px;
-    break-after: avoid-page;
-    page-break-after: avoid;
-  }
-
-  .print-tab-heading span {
-    color: var(--issuer-accent);
-    font-size: var(--text-sm);
-    font-weight: 900;
-    letter-spacing: 0.08em;
-    text-transform: uppercase;
-  }
-
-  .print-tab-heading h2 {
-    margin: 0;
-    font-size: var(--text-3xl);
-    line-height: var(--leading-tight);
-  }
-
-  .section-anchor-heading,
-  .section-heading,
-  .print-tab-heading,
-  .module-header {
-    break-after: avoid-page;
-    page-break-after: avoid;
-  }
-
-  .section-anchor-heading + *,
-  .section-heading + *,
-  .print-tab-heading + *,
-  .module-header + * {
-    break-before: avoid-page;
-    page-break-before: avoid;
-  }
-
-  .metric-tile,
-  .decision-box,
-  .table-wrap,
-  .card,
-  .source-list li,
-  .scenario-card,
-  .question-item,
-  .timeline-item,
-  .executive-summary,
-  .bar-chart,
-  .combo-chart,
-  table,
-  tr {
-    break-inside: avoid-page;
-    page-break-inside: avoid;
-  }
-}
diff --git a/plugins/financial-markets/shared/dashboard/assets/dashboard.js b/plugins/financial-markets/shared/dashboard/assets/dashboard.js
deleted file mode 100644
index 84faea6..0000000
--- a/plugins/financial-markets/shared/dashboard/assets/dashboard.js
+++ /dev/null
@@ -1,212 +0,0 @@
-const SOURCE_DATA = __SOURCE_DATA__;
-const tabButtons = document.querySelectorAll(".tab-button");
-const panels = document.querySelectorAll(".tab-panel");
-const tocLinks = document.querySelectorAll(".toc-link");
-const printButtons = document.querySelectorAll("[data-print-dashboard]");
-
-printButtons.forEach((button) => {
-  button.addEventListener("click", () => window.print());
-});
-
-tabButtons.forEach((button) => {
-  button.addEventListener("click", () => {
-    const target = button.dataset.tab;
-    tabButtons.forEach((item) => {
-      const active = item === button;
-      item.classList.toggle("is-active", active);
-      item.setAttribute("aria-selected", String(active));
-    });
-    panels.forEach((panel) => {
-      panel.classList.toggle("is-active", panel.id === target);
-    });
-  });
-});
-
-tocLinks.forEach((link) => {
-  link.addEventListener("click", () => {
-    tocLinks.forEach((item) => item.classList.toggle("is-active", item === link));
-  });
-});
-
-if (tocLinks.length > 0 && "IntersectionObserver" in window) {
-  const sections = Array.from(document.querySelectorAll(".dashboard-section"));
-  const observer = new IntersectionObserver(
-    (entries) => {
-      const visible = entries
-        .filter((entry) => entry.isIntersecting)
-        .sort((a, b) => b.intersectionRatio - a.intersectionRatio)[0];
-      if (!visible) {
-        return;
-      }
-      tocLinks.forEach((link) => {
-        link.classList.toggle("is-active", link.getAttribute("href") === `#${visible.target.id}`);
-      });
-    },
-    {rootMargin: "-18% 0px -70% 0px", threshold: [0.1, 0.35, 0.6]}
-  );
-  sections.forEach((section) => observer.observe(section));
-}
-
-function escapeHtml(value) {
-  return String(value || "")
-    .replace(/&/g, "&amp;")
-    .replace(/</g, "&lt;")
-    .replace(/>/g, "&gt;")
-    .replace(/"/g, "&quot;")
-    .replace(/'/g, "&#039;");
-}
-
-function reportCopyText() {
-  const root = document.querySelector("[data-report-copy-root]") || document.querySelector("main");
-  if (!root) return "";
-  const clone = root.cloneNode(true);
-  clone.querySelectorAll(".toc-list, .tab-list, #sources, [data-report-copy-exclude], .citation-popover, .dashboard-utility-bar, .table-action-bar").forEach((node) => node.remove());
-  clone.querySelectorAll(".citation-badge, .citation-chip, .citation-link, .source-id").forEach((node) => {
-    const label = (node.innerText || node.textContent || "").trim();
-    node.replaceWith(document.createTextNode(label ? ` ${label}` : ""));
-  });
-  return clone.innerText.replace(/\n{3,}/g, "\n\n").trim();
-}
-
-function fallbackCopy(text) {
-  const area = document.createElement("textarea");
-  area.value = text;
-  area.setAttribute("readonly", "");
-  area.style.position = "fixed";
-  area.style.left = "-9999px";
-  document.body.appendChild(area);
-  area.select();
-  const ok = document.execCommand("copy");
-  area.remove();
-  if (!ok) {
-    return Promise.reject(new Error("Clipboard fallback failed"));
-  }
-  return Promise.resolve(true);
-}
-
-function writeClipboard(text) {
-  if (navigator.clipboard && navigator.clipboard.writeText) {
-    return navigator.clipboard.writeText(text).catch(() => fallbackCopy(text));
-  }
-  return fallbackCopy(text);
-}
-
-function markCopied(button) {
-  const original = button.getAttribute("data-label-original") || button.textContent || "Copy";
-  button.setAttribute("data-label-original", original);
-  button.classList.add("is-copied");
-  button.textContent = "Copied";
-  window.setTimeout(() => {
-    button.classList.remove("is-copied");
-    button.textContent = original;
-  }, 1300);
-}
-
-document.addEventListener("click", (event) => {
-  const fullReportButton = event.target.closest("[data-copy-full-report]");
-  if (!fullReportButton) return;
-  const text = reportCopyText();
-  if (!text) return;
-  writeClipboard(text)
-    .then(() => markCopied(fullReportButton))
-    .catch(() => fullReportButton.classList.add("copy-failed"));
-});
-
-let citationPopover;
-let citationHideTimer;
-const CITATION_SELECTOR = ".citation-chip[data-citation-id], .citation-chip[data-citation-title], .citation-link[data-citation-id], .citation-link[data-citation-title]";
-
-function citationField(label, value) {
-  if (!value) return "";
-  return `<div class="citation-popover-label">${escapeHtml(label)}</div><div class="citation-popover-value">${escapeHtml(value)}</div>`;
-}
-
-function ensureCitationPopover() {
-  if (citationPopover) return citationPopover;
-  citationPopover = document.createElement("div");
-  citationPopover.className = "citation-popover";
-  citationPopover.id = "citation-popover";
-  citationPopover.setAttribute("role", "tooltip");
-  document.body.appendChild(citationPopover);
-  return citationPopover;
-}
-
-function sourceFromChip(chip) {
-  const id = chip?.getAttribute("data-citation-id");
-  if (id && SOURCE_DATA[id]) return SOURCE_DATA[id];
-  const title = chip?.getAttribute("data-citation-title");
-  if (!title) return null;
-  return {
-    id: id || "source",
-    title,
-    type: "External source",
-    status: "",
-    date: chip.getAttribute("data-citation-date") || "",
-    detail: chip.getAttribute("data-citation-detail") || "",
-  };
-}
-
-function positionCitationPopover(popover, chip) {
-  const rect = chip.getBoundingClientRect();
-  const popRect = popover.getBoundingClientRect();
-  const margin = 12;
-  let left = rect.left + rect.width / 2 - popRect.width / 2;
-  left = Math.max(margin, Math.min(left, window.innerWidth - popRect.width - margin));
-  let top = rect.bottom + 8;
-  if (top + popRect.height > window.innerHeight - margin) {
-    top = rect.top - popRect.height - 8;
-  }
-  if (top < margin) top = margin;
-  popover.style.left = `${left}px`;
-  popover.style.top = `${top}px`;
-}
-
-function showCitationPopover(chip) {
-  const source = sourceFromChip(chip);
-  if (!source) return;
-  window.clearTimeout(citationHideTimer);
-  const popover = ensureCitationPopover();
-  popover.innerHTML = `
-    <div class="citation-popover-title"><span class="citation-popover-id">${escapeHtml(source.id || "source")}</span>${escapeHtml(source.title || "Source")}</div>
-    <div class="citation-popover-grid">
-      ${citationField("Type", source.type)}
-      ${citationField("Status", source.status)}
-      ${citationField("Date", source.date)}
-      ${citationField("Pinpoint", source.detail)}
-    </div>
-    <div class="citation-popover-note">Click the citation to jump to the source ledger or open the source in a new window.</div>
-  `;
-  chip.setAttribute("aria-describedby", "citation-popover");
-  popover.classList.add("show");
-  positionCitationPopover(popover, chip);
-}
-
-function hideCitationPopover(delay = 100) {
-  window.clearTimeout(citationHideTimer);
-  citationHideTimer = window.setTimeout(() => {
-    document.querySelectorAll("[aria-describedby='citation-popover']").forEach((node) => node.removeAttribute("aria-describedby"));
-    if (citationPopover) citationPopover.classList.remove("show");
-  }, delay);
-}
-
-window.addEventListener("resize", () => hideCitationPopover(0));
-window.addEventListener("scroll", () => hideCitationPopover(0), true);
-document.addEventListener("mouseover", (event) => {
-  const chip = event.target.closest(CITATION_SELECTOR);
-  if (chip) showCitationPopover(chip);
-});
-document.addEventListener("mouseout", (event) => {
-  const chip = event.target.closest(CITATION_SELECTOR);
-  if (chip) hideCitationPopover();
-});
-document.addEventListener("focusin", (event) => {
-  const chip = event.target.closest(CITATION_SELECTOR);
-  if (chip) showCitationPopover(chip);
-});
-document.addEventListener("focusout", (event) => {
-  const chip = event.target.closest(CITATION_SELECTOR);
-  if (chip) hideCitationPopover();
-});
-document.addEventListener("keydown", (event) => {
-  if (event.key === "Escape") hideCitationPopover(0);
-});
diff --git a/plugins/financial-markets/shared/dashboard/module_registry.py b/plugins/financial-markets/shared/dashboard/module_registry.py
deleted file mode 100644
index 4228ed7..0000000
--- a/plugins/financial-markets/shared/dashboard/module_registry.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""Supported dashboard module registry.
-
-The registry intentionally describes presentation modules only. Public Equity Investing
-skills still own the investment logic that populates each module.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-
-@dataclass(frozen=True)
-class ModuleSpec:
-    name: str
-    description: str
-    required_keys: tuple[str, ...] = ()
-
-
-MODULES: dict[str, ModuleSpec] = {
-    "decision_box": ModuleSpec(
-        "decision_box",
-        "Top-line investor view with thesis change, action posture, and key points.",
-        ("stance",),
-    ),
-    "metric_tiles": ModuleSpec(
-        "metric_tiles",
-        "Grid of high-signal metrics such as event date, price, guide, valuation, or risk.",
-        ("items",),
-    ),
-    "highlight_tiles": ModuleSpec(
-        "highlight_tiles",
-        "Alias for high-signal hero metrics when the producer wants to distinguish PM highlights from routine KPI tiles.",
-        ("items",),
-    ),
-    "executive_summary": ModuleSpec(
-        "executive_summary",
-        "Dense PM-style summary paragraph with optional watch items and net read.",
-    ),
-    "key_metrics": ModuleSpec(
-        "key_metrics",
-        "Dense quarterly KPI board with company-specific operating metrics, growth rates, trajectory, and PM read.",
-        ("rows",),
-    ),
-    "growth_trajectory": ModuleSpec(
-        "growth_trajectory",
-        "Acceleration/deceleration map across the metrics that matter for the issuer.",
-        ("rows",),
-    ),
-    "cards": ModuleSpec(
-        "cards",
-        "Reusable card grid for debates, drivers, risks, read-throughs, or thesis pillars.",
-        ("items",),
-    ),
-    "table": ModuleSpec(
-        "table",
-        "Responsive table for KPIs, consensus, guidance, beat/miss, or source posture.",
-        ("columns", "rows"),
-    ),
-    "scenario_map": ModuleSpec(
-        "scenario_map",
-        "Bull/base/bear or probability-case cards with drivers and falsifiers.",
-        ("cases",),
-    ),
-    "question_list": ModuleSpec(
-        "question_list",
-        "Call, diligence, management-meeting, or monitoring questions with listen-fors.",
-        ("questions",),
-    ),
-    "transcript_qa": ModuleSpec(
-        "transcript_qa",
-        "Dense earnings-call Q&A map with analyst, firm, topic, management response, short direct quote, and answer quality.",
-        ("rows",),
-    ),
-    "read_throughs": ModuleSpec(
-        "read_throughs",
-        "Cross-company and industry read-through table covering names, relationships, what was said, implications, and confidence.",
-        ("rows",),
-    ),
-    "market_events": ModuleSpec(
-        "market_events",
-        "Source-backed major news, market events, regulatory items, macro shifts, and upcoming catalysts with investor impact.",
-        ("events",),
-    ),
-    "bar_chart": ModuleSpec(
-        "bar_chart",
-        "Compact native bar chart for segment growth, revenue mix, guidance deltas, exposure, or other small comparable metric sets.",
-        ("items",),
-    ),
-    "financial_trend_chart": ModuleSpec(
-        "financial_trend_chart",
-        "Quarterly grouped bars for revenue, gross profit, and net income with a selected profitability-margin line overlay.",
-        ("periods",),
-    ),
-    "eps_actual_vs_estimate_chart": ModuleSpec(
-        "eps_actual_vs_estimate_chart",
-        "Grouped EPS chart comparing estimated EPS and actual EPS across recent quarters.",
-        ("periods",),
-    ),
-    "equity_price_event_chart": ModuleSpec(
-        "equity_price_event_chart",
-        "Equity price line chart with sourced market-event annotations, gated to robust daily/hourly/minute price tapes.",
-        ("prices", "events"),
-    ),
-    "timeline": ModuleSpec(
-        "timeline",
-        "Catalyst, event, filing, regulatory, or monitoring timeline.",
-        ("events",),
-    ),
-    "text_block": ModuleSpec(
-        "text_block",
-        "Narrative block with optional bullets for compact explanation.",
-    ),
-    "source_list": ModuleSpec(
-        "source_list",
-        "Source ledger with URL, timestamp, confidence, and notes.",
-        ("sources",),
-    ),
-    "missing_evidence": ModuleSpec(
-        "missing_evidence",
-        "Visible refresh gaps, unavailable data, stale inputs, and terminal-only fields.",
-        ("items",),
-    ),
-}
-
-
-def supported_module_names() -> list[str]:
-    return sorted(MODULES)
diff --git a/plugins/financial-markets/shared/dashboard/qa.py b/plugins/financial-markets/shared/dashboard/qa.py
deleted file mode 100644
index aaa842e..0000000
--- a/plugins/financial-markets/shared/dashboard/qa.py
+++ /dev/null
@@ -1,1027 +0,0 @@
-"""Validation for Public Equity Investing dashboard payloads."""
-
-from __future__ import annotations
-
-import json
-import re
-from collections.abc import Mapping, Sequence
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-from .module_registry import MODULES, supported_module_names
-
-EXPECTED_KIND = "public_equity_investing_dashboard.v1"
-VALIDATION_PROFILES = {"draft", "production", "standard"}
-PRODUCTION_READY_POSTURES = {
-    "board_ready",
-    "board-ready",
-    "client_ready",
-    "client-ready",
-    "committee_ready",
-    "committee-ready",
-    "external",
-    "final",
-    "investor_ready",
-    "investor-ready",
-    "pm_ready",
-    "pm-ready",
-    "publication_ready",
-    "publication-ready",
-    "senior_review_ready",
-    "senior-review-ready",
-}
-DRAFT_POSTURES = {
-    "draft",
-    "draft_with_citation_gaps",
-    "draft-with-citation-gaps",
-    "preliminary",
-    "screen_grade",
-    "screen-grade",
-    "working_draft",
-    "working-draft",
-}
-PRODUCTION_METADATA_FIELDS = (
-    "payload_stage",
-    "freeze_time",
-    "source_posture",
-    "readiness_label",
-    "readiness_posture",
-    "citation_policy",
-    "decision_context",
-)
-_ID_RE = re.compile(r"^[a-z][a-z0-9-]*$")
-_BAD_TEXT_PATTERNS = (
-    "TODO",
-    "FIXME",
-    "[TOKEN]",
-    "{PLACEHOLDER}",
-    "undefined",
-)
-_CITATION_RE = re.compile(r"\[([A-Za-z][A-Za-z0-9_.:-]{0,40})\]")
-_NUMERIC_RE = re.compile(r"(?<![A-Za-z])(?:\$?\d[\d,]*(?:\.\d+)?%?|\d{4})(?![A-Za-z])")
-_MATERIAL_KEYS = {
-    "value",
-    "price",
-    "detail",
-    "summary",
-    "body",
-    "text",
-    "headline",
-    "callout",
-    "stance",
-    "thesis_change",
-    "estimate_revision",
-    "stock_skew",
-    "next_catalyst",
-    "event",
-    "impact",
-    "investor_read",
-    "pm_read",
-    "read",
-    "current",
-    "prior",
-    "growth",
-    "compare",
-    "trajectory",
-    "revenue",
-    "gross_profit",
-    "net_income",
-    "net_margin",
-    "operating_margin",
-    "adjusted_operating_margin",
-    "ebitda_margin",
-    "fcf_margin",
-    "margin_rationale",
-    "margin_note",
-    "estimated_eps",
-    "actual_eps",
-    "surprise",
-}
-_SKIP_CITATION_KEYS = {
-    "id",
-    "source_id",
-    "source_ids",
-    "citation",
-    "citations",
-    "citation_ids",
-    "url",
-    "href",
-    "source_url",
-    "local_path",
-    "model_citations",
-    "model_citations_path",
-    "workbook_citations",
-    "workbook_citations_path",
-    "_payload_dir",
-    "accent_color",
-    "brand_color",
-    "ticker_badge_color",
-}
-
-_PRICE_EVENT_GRANULARITY_KEYS = (
-    "price_granularity",
-    "price_frequency",
-    "price_interval",
-    "granularity",
-    "frequency",
-    "interval",
-)
-_PRICE_EVENT_GRANULARITY_ALIASES = {
-    "1d": "daily",
-    "day": "daily",
-    "daily": "daily",
-    "eod": "daily",
-    "endofday": "daily",
-    "close": "daily",
-    "closing": "daily",
-    "1h": "hourly",
-    "60m": "hourly",
-    "60min": "hourly",
-    "60minute": "hourly",
-    "60minutes": "hourly",
-    "hour": "hourly",
-    "hourly": "hourly",
-    "1m": "minute",
-    "1min": "minute",
-    "1minute": "minute",
-    "5m": "minute",
-    "5min": "minute",
-    "5minute": "minute",
-    "15m": "minute",
-    "15min": "minute",
-    "15minute": "minute",
-    "30m": "minute",
-    "30min": "minute",
-    "30minute": "minute",
-    "intraday": "minute",
-    "minute": "minute",
-    "minutely": "minute",
-}
-_PRICE_EVENT_POINT_MINIMUMS = {"daily": 10, "hourly": 24, "minute": 60}
-_PRICE_EVENT_SPAN_MINUTES = {
-    "daily": 7 * 24 * 60,
-    "hourly": 6 * 60,
-    "minute": 45,
-}
-
-
-def load_payload(path: str | Path) -> dict[str, Any]:
-    payload_path = Path(path)
-    with payload_path.open("r", encoding="utf-8") as handle:
-        payload = json.load(handle)
-    if not isinstance(payload, dict):
-        raise ValueError("Dashboard payload must be a JSON object.")
-    payload["_payload_dir"] = str(payload_path.resolve().parent)
-    return payload
-
-
-def validate_payload(payload: Mapping[str, Any], profile: str = "production") -> dict[str, Any]:
-    hard_failures: list[str] = []
-    warnings: list[str] = []
-    validation_profile = _normalise_profile(profile)
-    if validation_profile not in VALIDATION_PROFILES:
-        hard_failures.append(
-            f"profile must be one of {sorted(VALIDATION_PROFILES)}; got {profile!r}."
-        )
-        validation_profile = "draft"
-    readiness_posture = _readiness_posture(payload)
-    production_required = (
-        validation_profile == "production" or readiness_posture in PRODUCTION_READY_POSTURES
-    )
-    citation_strict = _citation_policy(payload) == "strict" or production_required
-    sources = payload.get("sources", [])
-    source_records = _source_records(payload)
-    source_ids = _source_id_set(source_records)
-
-    if payload.get("kind") != EXPECTED_KIND:
-        hard_failures.append(f"kind must be {EXPECTED_KIND!r}; got {payload.get('kind')!r}")
-
-    if production_required:
-        _validate_production_contract(
-            payload,
-            readiness_posture,
-            hard_failures,
-            warnings,
-        )
-
-    _require_string(payload, "title", hard_failures)
-
-    layout = str(payload.get("layout") or "tabs")
-    if layout not in {"tabs", "single_page"}:
-        hard_failures.append("layout must be either 'tabs' or 'single_page' when provided.")
-
-    issuer = payload.get("issuer")
-    if not isinstance(issuer, Mapping):
-        hard_failures.append("issuer must be an object with at least ticker or name.")
-    elif not (issuer.get("ticker") or issuer.get("name")):
-        hard_failures.append("issuer must include ticker or name.")
-
-    tabs = payload.get("tabs")
-    if not isinstance(tabs, Sequence) or isinstance(tabs, (str, bytes)):
-        hard_failures.append("tabs must be a non-empty list.")
-        tabs = []
-    elif not tabs:
-        hard_failures.append("tabs must contain at least one tab.")
-
-    seen_tabs: set[str] = set()
-    for tab_index, tab in enumerate(tabs):
-        if not isinstance(tab, Mapping):
-            hard_failures.append(f"tabs[{tab_index}] must be an object.")
-            continue
-        tab_id = str(tab.get("id", ""))
-        if not _ID_RE.match(tab_id):
-            hard_failures.append(f"tabs[{tab_index}].id must be kebab-case starting with a letter.")
-        if tab_id in seen_tabs:
-            hard_failures.append(f"duplicate tab id: {tab_id}")
-        seen_tabs.add(tab_id)
-        _require_string(tab, "label", hard_failures, f"tabs[{tab_index}]")
-        modules = tab.get("modules", [])
-        if not isinstance(modules, Sequence) or isinstance(modules, (str, bytes)):
-            hard_failures.append(f"tabs[{tab_index}].modules must be a list.")
-            continue
-        if not modules:
-            warnings.append(f"tab {tab_id or tab_index} has no modules.")
-        for module_index, module in enumerate(modules):
-            _validate_module(
-                module,
-                f"tabs[{tab_index}].modules[{module_index}]",
-                hard_failures,
-                warnings,
-                citation_strict,
-            )
-
-    source_ledger_message = _source_ledger_message(source_records, source_ids, citation_strict)
-    if source_ledger_message:
-        target = hard_failures if citation_strict else warnings
-        target.append(source_ledger_message)
-
-    snapshot = payload.get("snapshot", [])
-    if isinstance(snapshot, Sequence) and not isinstance(snapshot, (str, bytes)):
-        for index, item in enumerate(snapshot):
-            if not isinstance(item, Mapping):
-                continue
-            label = str(item.get("label") or "").strip().lower()
-            if label in {"quarter", "period", "fiscal period"}:
-                warnings.append(
-                    f"snapshot[{index}] duplicates period metadata; keep the quarter prominent in the hero/title instead."
-                )
-
-    unresolved = _find_unresolved_text(payload)
-    if unresolved:
-        hard_failures.extend(unresolved[:20])
-        if len(unresolved) > 20:
-            hard_failures.append(f"{len(unresolved) - 20} additional unresolved text hits.")
-
-    unresolved_citations = _find_unresolved_citations(payload, source_ids)
-    if unresolved_citations:
-        target = hard_failures if citation_strict else warnings
-        target.extend(unresolved_citations[:20])
-        if len(unresolved_citations) > 20:
-            target.append(f"{len(unresolved_citations) - 20} additional unresolved citation hits.")
-
-    numeric_gaps = _find_numeric_citation_gaps(payload)
-    if numeric_gaps:
-        target = hard_failures if citation_strict else warnings
-        prefix = "citation gap" if citation_strict else "citation warning"
-        target.extend(f"{prefix}: {gap}" for gap in numeric_gaps[:30])
-        if len(numeric_gaps) > 30:
-            target.append(f"{len(numeric_gaps) - 30} additional numeric citation gaps.")
-
-    return {
-        "status": "failed" if hard_failures else "passed",
-        "hard_failures": hard_failures,
-        "warnings": warnings,
-        "supported_modules": supported_module_names(),
-        "profile": "production" if production_required else validation_profile,
-    }
-
-
-def _validate_module(
-    module: Any,
-    path: str,
-    hard_failures: list[str],
-    warnings: list[str],
-    citation_strict: bool,
-) -> None:
-    if not isinstance(module, Mapping):
-        hard_failures.append(f"{path} must be an object.")
-        return
-
-    module_type = module.get("type")
-    if module_type not in MODULES:
-        hard_failures.append(
-            f"{path}.type {module_type!r} is not supported; use one of {supported_module_names()}."
-        )
-        return
-
-    data = module.get("data", {})
-    if not isinstance(data, Mapping):
-        hard_failures.append(f"{path}.data must be an object.")
-        return
-
-    spec = MODULES[str(module_type)]
-    for key in spec.required_keys:
-        if key not in data:
-            hard_failures.append(f"{path}.data missing required key {key!r}.")
-
-    if module_type == "table":
-        columns = data.get("columns")
-        rows = data.get("rows")
-        if not isinstance(columns, Sequence) or isinstance(columns, (str, bytes)) or not columns:
-            hard_failures.append(f"{path}.data.columns must be a non-empty list.")
-        if not isinstance(rows, Sequence) or isinstance(rows, (str, bytes)):
-            hard_failures.append(f"{path}.data.rows must be a list.")
-    if module_type == "bar_chart":
-        items = data.get("items")
-        if not isinstance(items, Sequence) or isinstance(items, (str, bytes)) or not items:
-            hard_failures.append(f"{path}.data.items must be a non-empty list.")
-    if module_type == "scenario_map":
-        cases = data.get("cases")
-        if not isinstance(cases, Sequence) or isinstance(cases, (str, bytes)) or not cases:
-            hard_failures.append(f"{path}.data.cases must be a non-empty list.")
-        else:
-            for index, case in enumerate(cases):
-                if not isinstance(case, Mapping):
-                    hard_failures.append(f"{path}.data.cases[{index}] must be an object.")
-                    continue
-                text_fields = ("title", "headline", "body", "summary")
-                has_text = any(str(case.get(key) or "").strip() for key in text_fields)
-                bullets = case.get("bullets")
-                has_bullets = (
-                    isinstance(bullets, Sequence)
-                    and not isinstance(bullets, (str, bytes))
-                    and any(str(item or "").strip() for item in bullets)
-                )
-                if not has_text and not has_bullets:
-                    hard_failures.append(
-                        f"{path}.data.cases[{index}] has no substantive title, summary, body, or bullets; omit empty scenario cards."
-                    )
-    if module_type == "market_events":
-        events = data.get("events")
-        if not isinstance(events, Sequence) or isinstance(events, (str, bytes)) or not events:
-            hard_failures.append(f"{path}.data.events must be a non-empty list.")
-    if module_type in {"financial_trend_chart", "eps_actual_vs_estimate_chart"}:
-        periods = data.get("periods") or data.get("rows")
-        if not isinstance(periods, Sequence) or isinstance(periods, (str, bytes)) or not periods:
-            hard_failures.append(f"{path}.data.periods must be a non-empty list.")
-        else:
-            _validate_chart_ready_rows(
-                str(module_type), data, path, hard_failures, warnings, citation_strict
-            )
-    if module_type == "equity_price_event_chart":
-        prices = data.get("prices")
-        events = data.get("events")
-        prices_valid = (
-            isinstance(prices, Sequence) and not isinstance(prices, (str, bytes)) and bool(prices)
-        )
-        events_valid = (
-            isinstance(events, Sequence) and not isinstance(events, (str, bytes)) and bool(events)
-        )
-        if not prices_valid:
-            hard_failures.append(
-                f"{path}.data.prices must be a non-empty list of sourced price points."
-            )
-        if not isinstance(events, Sequence) or isinstance(events, (str, bytes)) or not events:
-            hard_failures.append(f"{path}.data.events must be a non-empty list.")
-        if prices_valid and events_valid:
-            _validate_chart_ready_rows(
-                str(module_type), data, path, hard_failures, warnings, citation_strict
-            )
-
-
-def _validate_chart_ready_rows(
-    module_type: str,
-    data: Mapping[str, Any],
-    path: str,
-    hard_failures: list[str],
-    warnings: list[str],
-    citation_strict: bool,
-) -> None:
-    if module_type == "financial_trend_chart":
-        ready_count = len(_financial_trend_ready_rows(data))
-        if ready_count < 2:
-            _append_chart_readiness_issue(
-                module_type,
-                path,
-                "has fewer than 2 chart-ready rows",
-                hard_failures,
-                warnings,
-                citation_strict,
-            )
-        return
-
-    if module_type == "eps_actual_vs_estimate_chart":
-        ready_count = len(_eps_ready_rows(data))
-        if ready_count < 2:
-            _append_chart_readiness_issue(
-                module_type,
-                path,
-                "has fewer than 2 chart-ready rows",
-                hard_failures,
-                warnings,
-                citation_strict,
-            )
-        return
-
-    if module_type == "equity_price_event_chart":
-        readiness = equity_price_event_readiness(data)
-        for issue in readiness["issues"]:
-            _append_chart_readiness_issue(
-                module_type,
-                path,
-                str(issue),
-                hard_failures,
-                warnings,
-                citation_strict,
-            )
-
-
-def _append_chart_readiness_issue(
-    module_type: str,
-    path: str,
-    problem: str,
-    hard_failures: list[str],
-    warnings: list[str],
-    citation_strict: bool,
-) -> None:
-    message = (
-        f"{path} {module_type} {problem}; omit the chart and add missing_evidence "
-        "for unavailable series."
-    )
-    target = hard_failures if citation_strict else warnings
-    target.append(message)
-
-
-def _financial_trend_ready_rows(data: Mapping[str, Any]) -> list[Mapping[str, Any]]:
-    periods = data.get("periods") or data.get("rows") or []
-    rows = [row for row in periods if isinstance(row, Mapping)]
-    margin_key = _selected_margin_metric_for_validation(data, rows)
-    return [
-        row
-        for row in rows
-        if row.get("period")
-        and _to_float(row.get("revenue")) is not None
-        and _to_float(row.get("gross_profit")) is not None
-        and _to_float(row.get("net_income")) is not None
-        and _to_ratio(row.get(margin_key)) is not None
-    ]
-
-
-def _eps_ready_rows(data: Mapping[str, Any]) -> list[Mapping[str, Any]]:
-    periods = data.get("periods") or data.get("rows") or []
-    rows = [row for row in periods if isinstance(row, Mapping)]
-    return [
-        row
-        for row in rows
-        if row.get("period")
-        and _to_float(row.get("estimated_eps")) is not None
-        and _to_float(row.get("actual_eps")) is not None
-    ]
-
-
-def equity_price_event_readiness(data: Mapping[str, Any]) -> dict[str, Any]:
-    """Return readiness details shared by QA and renderer gating."""
-    price_rows = _price_event_ready_price_rows(data)
-    distinct_price_rows = _distinct_price_rows(price_rows)
-    event_rows = _price_event_ready_event_rows(data)
-    granularity = _normalise_price_granularity(data) or _infer_price_granularity(
-        distinct_price_rows
-    )
-    issues: list[str] = []
-
-    if granularity not in _PRICE_EVENT_POINT_MINIMUMS:
-        if granularity:
-            issues.append(
-                f"uses unsupported price granularity {granularity!r}; use daily, hourly, or minute data"
-            )
-        else:
-            issues.append(
-                "must include enough parseable daily, hourly, or minute price data to infer granularity"
-            )
-        granularity = "daily"
-
-    required_points = _PRICE_EVENT_POINT_MINIMUMS[granularity]
-    ready_price_count = len(distinct_price_rows)
-    if ready_price_count < required_points:
-        issues.append(
-            f"needs at least {required_points} {granularity} chart-ready price rows; got {ready_price_count}"
-        )
-
-    if granularity in {"hourly", "minute"}:
-        timed_price_count = len(
-            {
-                _price_timestamp_key(row)
-                for row in distinct_price_rows
-                if _price_timestamp_has_time(row)
-            }
-        )
-        if timed_price_count < required_points:
-            issues.append(
-                f"needs at least {required_points} time-stamped {granularity} price rows; got {timed_price_count}"
-            )
-
-    if ready_price_count >= 2:
-        span_minutes = _price_span_minutes(distinct_price_rows)
-        required_span = _PRICE_EVENT_SPAN_MINUTES[granularity]
-        if span_minutes is None:
-            issues.append("needs parseable price timestamps to prove the price-window coverage")
-        elif span_minutes < required_span:
-            issues.append(
-                f"needs a {granularity} price window spanning at least {_format_minutes(required_span)}; got {_format_minutes(span_minutes)}"
-            )
-
-    if len(event_rows) < 1:
-        issues.append("needs at least 1 chart-ready event row with date and event text")
-
-    missing_source_count = _price_source_gap_count(distinct_price_rows, data)
-    if missing_source_count:
-        issues.append(
-            f"has {missing_source_count} price rows without direct price-source citations or module-level price source"
-        )
-
-    return {
-        "ready": not issues,
-        "granularity": granularity,
-        "required_price_points": required_points,
-        "ready_price_count": ready_price_count,
-        "price_rows": distinct_price_rows,
-        "event_rows": event_rows,
-        "issues": issues,
-    }
-
-
-def _price_event_ready_price_rows(data: Mapping[str, Any]) -> list[Mapping[str, Any]]:
-    prices = data.get("prices") or []
-    if not isinstance(prices, Sequence) or isinstance(prices, (str, bytes)):
-        return []
-    rows = [
-        row
-        for row in prices
-        if isinstance(row, Mapping)
-        and _price_timestamp_raw(row)
-        and _to_float(row.get("price")) is not None
-    ]
-    return sorted(rows, key=lambda row: _price_timestamp_parts(row)[0] or datetime.max)
-
-
-def _price_event_ready_event_rows(data: Mapping[str, Any]) -> list[Mapping[str, Any]]:
-    events = data.get("events") or []
-    if not isinstance(events, Sequence) or isinstance(events, (str, bytes)):
-        return []
-    return [
-        row
-        for row in events
-        if isinstance(row, Mapping) and row.get("date") and (row.get("event") or row.get("title"))
-    ]
-
-
-def _distinct_price_rows(rows: Sequence[Mapping[str, Any]]) -> list[Mapping[str, Any]]:
-    seen: set[str] = set()
-    distinct: list[Mapping[str, Any]] = []
-    for row in rows:
-        key = _price_timestamp_key(row)
-        if key in seen:
-            continue
-        seen.add(key)
-        distinct.append(row)
-    return distinct
-
-
-def _normalise_price_granularity(data: Mapping[str, Any]) -> str | None:
-    for key in _PRICE_EVENT_GRANULARITY_KEYS:
-        raw = data.get(key)
-        if raw is None:
-            continue
-        compact = re.sub(r"[^a-z0-9]+", "", str(raw).strip().lower())
-        if compact in _PRICE_EVENT_GRANULARITY_ALIASES:
-            return _PRICE_EVENT_GRANULARITY_ALIASES[compact]
-        if compact.endswith(("min", "minute", "minutes")):
-            return "minute"
-        if compact.endswith(("h", "hour", "hours")):
-            return "hourly"
-        if compact:
-            return compact
-    return None
-
-
-def _infer_price_granularity(rows: Sequence[Mapping[str, Any]]) -> str | None:
-    parsed = [_price_timestamp_parts(row) for row in rows]
-    parsed_dates = sorted(timestamp for timestamp, _ in parsed if timestamp is not None)
-    if len(parsed_dates) < 2:
-        return None
-    timed_count = len([1 for timestamp, has_time in parsed if timestamp is not None and has_time])
-    if timed_count < max(2, len(parsed_dates) // 2):
-        return "daily"
-    diffs = sorted(
-        (parsed_dates[index] - parsed_dates[index - 1]).total_seconds() / 60
-        for index in range(1, len(parsed_dates))
-        if parsed_dates[index] > parsed_dates[index - 1]
-    )
-    if not diffs:
-        return None
-    median_gap = diffs[len(diffs) // 2]
-    if median_gap <= 30:
-        return "minute"
-    if median_gap <= 180:
-        return "hourly"
-    return "daily"
-
-
-def _price_span_minutes(rows: Sequence[Mapping[str, Any]]) -> float | None:
-    parsed_dates = sorted(
-        timestamp
-        for timestamp, _ in (_price_timestamp_parts(row) for row in rows)
-        if timestamp is not None
-    )
-    if len(parsed_dates) < 2:
-        return None
-    return (parsed_dates[-1] - parsed_dates[0]).total_seconds() / 60
-
-
-def _price_timestamp_has_time(row: Mapping[str, Any]) -> bool:
-    _, has_time = _price_timestamp_parts(row)
-    return has_time
-
-
-def _price_timestamp_key(row: Mapping[str, Any]) -> str:
-    timestamp, _ = _price_timestamp_parts(row)
-    if timestamp is not None:
-        return timestamp.isoformat()
-    return str(_price_timestamp_raw(row) or "").strip()
-
-
-def _price_timestamp_parts(row: Mapping[str, Any]) -> tuple[datetime | None, bool]:
-    raw = _price_timestamp_raw(row)
-    if raw is None:
-        return None, False
-    text = str(raw).strip()
-    if not text:
-        return None, False
-    has_time = bool(re.search(r"\d{1,2}:\d{2}", text))
-    cleaned = text.replace("Z", "+00:00")
-    try:
-        parsed = datetime.fromisoformat(cleaned)
-    except ValueError:
-        try:
-            parsed = datetime.strptime(text[:10], "%Y-%m-%d")
-        except ValueError:
-            return None, has_time
-    if parsed.tzinfo is not None:
-        parsed = parsed.astimezone(timezone.utc).replace(tzinfo=None)
-    return parsed, has_time
-
-
-def _price_timestamp_raw(row: Mapping[str, Any]) -> Any:
-    return row.get("timestamp") or row.get("datetime") or row.get("date")
-
-
-def _price_source_gap_count(rows: Sequence[Mapping[str, Any]], data: Mapping[str, Any]) -> int:
-    module_has_price_source = _has_direct_citation_support(data) or any(
-        str(data.get(key) or "").strip()
-        for key in ("price_source", "price_source_title", "price_source_url", "price_as_of")
-    )
-    if module_has_price_source:
-        return 0
-    return len([row for row in rows if not _has_direct_citation_support(row)])
-
-
-def _has_direct_citation_support(value: Mapping[str, Any]) -> bool:
-    if value.get("citation") or value.get("source_id") or value.get("source_url"):
-        return True
-    return any(_listify(value.get(key)) for key in ("citations", "citation_ids", "source_ids"))
-
-
-def _format_minutes(minutes: float) -> str:
-    rounded = int(round(minutes))
-    if rounded >= 24 * 60 and rounded % (24 * 60) == 0:
-        days = rounded // (24 * 60)
-        return f"{days} day{'s' if days != 1 else ''}"
-    if rounded >= 60 and rounded % 60 == 0:
-        hours = rounded // 60
-        return f"{hours} hour{'s' if hours != 1 else ''}"
-    return f"{rounded} minute{'s' if rounded != 1 else ''}"
-
-
-def _selected_margin_metric_for_validation(
-    data: Mapping[str, Any],
-    rows: Sequence[Mapping[str, Any]],
-) -> str:
-    explicit = (
-        data.get("margin_metric")
-        or data.get("line_metric")
-        or data.get("profitability_metric")
-        or data.get("margin_key")
-    )
-    if explicit:
-        return str(explicit)
-    for candidate in (
-        "net_margin",
-        "operating_margin",
-        "adjusted_operating_margin",
-        "ebitda_margin",
-        "fcf_margin",
-    ):
-        if any(_to_ratio(row.get(candidate)) is not None for row in rows):
-            return candidate
-    return "net_margin"
-
-
-def _to_float(value: Any) -> float | None:
-    if isinstance(value, (int, float)):
-        return float(value)
-    if isinstance(value, str):
-        cleaned = value.replace("%", "").replace(",", "").replace("$", "").replace("+", "").strip()
-        try:
-            return float(cleaned)
-        except ValueError:
-            return None
-    return None
-
-
-def _to_ratio(value: Any) -> float | None:
-    if isinstance(value, str) and "%" in value:
-        parsed = _to_float(value)
-        return None if parsed is None else parsed / 100.0
-    parsed = _to_float(value)
-    if parsed is None:
-        return None
-    return parsed / 100.0 if abs(parsed) > 2 else parsed
-
-
-def _require_string(
-    obj: Mapping[str, Any],
-    key: str,
-    hard_failures: list[str],
-    path: str = "payload",
-) -> None:
-    value = obj.get(key)
-    if not isinstance(value, str) or not value.strip():
-        hard_failures.append(f"{path}.{key} must be a non-empty string.")
-
-
-def _normalise_profile(profile: str) -> str:
-    value = str(profile or "draft").strip().lower().replace("_", "-")
-    if value in {"standard", "support"}:
-        return "draft"
-    return value.replace("-", "_")
-
-
-def _normalise_posture(value: Any) -> str:
-    return str(value or "").strip().lower().replace(" ", "_").replace("-", "_")
-
-
-def _readiness_posture(payload: Mapping[str, Any]) -> str:
-    metadata = payload.get("metadata")
-    posture = payload.get("readiness_posture")
-    if isinstance(metadata, Mapping):
-        posture = (
-            metadata.get("readiness_posture")
-            or metadata.get("readiness")
-            or metadata.get("readiness_label")
-            or posture
-        )
-    return _normalise_posture(posture)
-
-
-def _validate_production_contract(
-    payload: Mapping[str, Any],
-    readiness_posture: str,
-    hard_failures: list[str],
-    warnings: list[str],
-) -> None:
-    _require_string(payload, "mode", hard_failures)
-
-    if not isinstance(payload.get("layout"), str) or not str(payload.get("layout")).strip():
-        hard_failures.append("production dashboards must include layout.")
-
-    metadata = payload.get("metadata")
-    if not isinstance(metadata, Mapping):
-        hard_failures.append("production dashboards must include metadata.")
-    else:
-        for key in PRODUCTION_METADATA_FIELDS:
-            _require_string(metadata, key, hard_failures, "metadata")
-        if str(metadata.get("payload_stage") or "").strip().lower() != "production":
-            hard_failures.append(
-                "production dashboards must set metadata.payload_stage to 'production'."
-            )
-        if str(metadata.get("citation_policy") or "").strip().lower() != "strict":
-            hard_failures.append(
-                "production dashboards must set metadata.citation_policy to 'strict'."
-            )
-        if readiness_posture in DRAFT_POSTURES:
-            hard_failures.append("production dashboards cannot use a draft readiness_posture.")
-
-    hero = payload.get("hero")
-    if not isinstance(hero, Mapping) or not hero:
-        hard_failures.append("production dashboards must include a non-empty hero.")
-    elif not any(str(hero.get(key) or "").strip() for key in ("headline", "dek", "callout")):
-        hard_failures.append(
-            "production dashboards must include hero headline, dek, or callout text."
-        )
-
-    snapshot = payload.get("snapshot")
-    if not isinstance(snapshot, Sequence) or isinstance(snapshot, (str, bytes)) or not snapshot:
-        hard_failures.append("production dashboards must include non-empty snapshot tiles.")
-
-    _ = warnings
-
-
-def _find_unresolved_text(value: Any, path: str = "$") -> list[str]:
-    hits: list[str] = []
-    if isinstance(value, str):
-        for pattern in _BAD_TEXT_PATTERNS:
-            if pattern in value:
-                hits.append(f"{path} contains unresolved text pattern {pattern!r}.")
-                break
-    elif isinstance(value, Mapping):
-        for key, child in value.items():
-            hits.extend(_find_unresolved_text(child, f"{path}.{key}"))
-    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-        for index, child in enumerate(value):
-            hits.extend(_find_unresolved_text(child, f"{path}[{index}]"))
-    return hits
-
-
-def _citation_policy(payload: Mapping[str, Any]) -> str:
-    metadata = payload.get("metadata")
-    policy = payload.get("citation_policy")
-    if isinstance(metadata, Mapping):
-        policy = metadata.get("citation_policy") or policy
-    return str(policy or "warn").strip().lower()
-
-
-def _source_id_set(sources: Any) -> set[str]:
-    ids: set[str] = set()
-    if not isinstance(sources, Sequence) or isinstance(sources, (str, bytes)):
-        return ids
-    for index, source in enumerate(sources, start=1):
-        if not isinstance(source, Mapping):
-            continue
-        source_id = str(source.get("id") or source.get("source_id") or f"S{index}").strip()
-        if source_id:
-            ids.add(source_id)
-    return ids
-
-
-def _source_records(payload: Mapping[str, Any]) -> list[dict[str, Any]]:
-    records: list[dict[str, Any]] = []
-    for key in ("sources", "model_citations", "workbook_citations"):
-        value = payload.get(key)
-        if isinstance(value, Mapping):
-            records.extend(
-                dict(item, id=record_id)
-                for record_id, item in value.items()
-                if isinstance(item, Mapping)
-            )
-        elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-            records.extend(item for item in value if isinstance(item, Mapping))
-    for key in ("model_citations_path", "workbook_citations_path"):
-        records.extend(_load_source_records_from_path(payload, payload.get(key)))
-    return records
-
-
-def _resolve_payload_path(payload: Mapping[str, Any], path_value: Any) -> Path | None:
-    raw = str(path_value or "").strip()
-    if not raw or re.match(r"^[a-zA-Z][a-zA-Z0-9+.-]*:", raw):
-        return None
-    path = Path(raw)
-    if not path.is_absolute() and payload.get("_payload_dir"):
-        path = Path(str(payload.get("_payload_dir"))) / path
-    return path
-
-
-def _load_source_records_from_path(
-    payload: Mapping[str, Any], path_value: Any
-) -> list[dict[str, Any]]:
-    path = _resolve_payload_path(payload, path_value)
-    if not path or not path.exists():
-        return []
-    try:
-        loaded = json.loads(path.read_text(encoding="utf-8"))
-    except Exception:
-        return []
-    if isinstance(loaded, Mapping):
-        for key in ("model_citations", "workbook_citations", "sources"):
-            if isinstance(loaded.get(key), Sequence) and not isinstance(
-                loaded.get(key), (str, bytes)
-            ):
-                return [item for item in loaded[key] if isinstance(item, Mapping)]
-        return [
-            dict(item, id=record_id)
-            for record_id, item in loaded.items()
-            if isinstance(item, Mapping)
-        ]
-    if isinstance(loaded, Sequence) and not isinstance(loaded, (str, bytes)):
-        return [item for item in loaded if isinstance(item, Mapping)]
-    return []
-
-
-def _source_ledger_message(
-    sources: Any,
-    source_ids: set[str],
-    citation_strict: bool,
-) -> str | None:
-    if not isinstance(sources, Sequence) or isinstance(sources, (str, bytes)):
-        problem = "sources must be a non-empty list"
-    elif not sources or not source_ids:
-        problem = "sources is empty"
-    else:
-        return None
-
-    if citation_strict:
-        return f"{problem}; strict dashboards must include a source ledger."
-    return f"{problem}; final dashboards should include a source ledger."
-
-
-def _find_unresolved_citations(value: Any, source_ids: set[str], path: str = "$") -> list[str]:
-    hits: list[str] = []
-    if isinstance(value, str):
-        for source_id in _CITATION_RE.findall(value):
-            if source_id not in source_ids:
-                hits.append(f"{path} cites unknown source id {source_id!r}.")
-    elif isinstance(value, Mapping):
-        for key in ("citation", "source_id"):
-            source_id = value.get(key)
-            if source_id and str(source_id) not in source_ids:
-                hits.append(f"{path}.{key} references unknown source id {source_id!r}.")
-        for key in ("citations", "citation_ids", "source_ids"):
-            for source_id in _listify(value.get(key)):
-                if source_id and str(source_id) not in source_ids:
-                    hits.append(f"{path}.{key} references unknown source id {source_id!r}.")
-        for key, child in value.items():
-            if key in _SKIP_CITATION_KEYS:
-                continue
-            hits.extend(_find_unresolved_citations(child, source_ids, f"{path}.{key}"))
-    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-        for index, child in enumerate(value):
-            hits.extend(_find_unresolved_citations(child, source_ids, f"{path}[{index}]"))
-    return hits
-
-
-def _find_numeric_citation_gaps(
-    value: Any, path: str = "$", inherited_support: bool = False, key: str = ""
-) -> list[str]:
-    hits: list[str] = []
-    if isinstance(value, Mapping):
-        support = inherited_support or _has_citation_support(value)
-        for child_key, child in value.items():
-            if child_key in _SKIP_CITATION_KEYS or child_key in {"sources", "qa"}:
-                continue
-            child_support = support
-            if child_key in {
-                "rows",
-                "items",
-                "events",
-                "periods",
-                "cases",
-                "questions",
-                "snapshot",
-            }:
-                child_support = support
-            hits.extend(
-                _find_numeric_citation_gaps(child, f"{path}.{child_key}", child_support, child_key)
-            )
-    elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-        for index, child in enumerate(value):
-            hits.extend(
-                _find_numeric_citation_gaps(child, f"{path}[{index}]", inherited_support, key)
-            )
-    elif isinstance(value, str):
-        if (
-            key in _MATERIAL_KEYS
-            and _NUMERIC_RE.search(value)
-            and not (inherited_support or _has_inline_citation(value))
-        ):
-            snippet = re.sub(r"\s+", " ", value.strip())
-            if len(snippet) > 90:
-                snippet = snippet[:87] + "..."
-            hits.append(f"{path} has numeric material without citations: {snippet!r}")
-    return hits
-
-
-def _has_citation_support(value: Mapping[str, Any]) -> bool:
-    if value.get("citation") or value.get("source_id") or value.get("source_url"):
-        return True
-    for key in ("citations", "citation_ids", "source_ids"):
-        if _listify(value.get(key)):
-            return True
-    return any(isinstance(child, str) and _has_inline_citation(child) for child in value.values())
-
-
-def _has_inline_citation(value: str) -> bool:
-    return bool(_CITATION_RE.search(value))
-
-
-def _listify(value: Any) -> list[Any]:
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return value
-    if isinstance(value, tuple):
-        return list(value)
-    if isinstance(value, str):
-        if "," in value or ";" in value:
-            return [item.strip() for item in re.split(r"[,;]", value) if item.strip()]
-        return [value]
-    return [value]
diff --git a/plugins/financial-markets/shared/dashboard/renderer.py b/plugins/financial-markets/shared/dashboard/renderer.py
deleted file mode 100644
index 7467eec..0000000
--- a/plugins/financial-markets/shared/dashboard/renderer.py
+++ /dev/null
@@ -1,2064 +0,0 @@
-"""Render standalone Public Equity Investing HTML dashboards from typed payloads."""
-
-from __future__ import annotations
-
-import html
-import json
-import math
-import re
-from collections.abc import Mapping, Sequence
-from pathlib import Path
-from typing import Any
-from urllib.parse import quote
-
-from .qa import equity_price_event_readiness, validate_payload
-
-PACKAGE_DIR = Path(__file__).resolve().parent
-ASSET_DIR = PACKAGE_DIR / "assets"
-TEMPLATE_PATH = PACKAGE_DIR / "templates" / "base.html"
-
-
-def render_dashboard(
-    payload: Mapping[str, Any],
-    *,
-    validate: bool = True,
-    validation_profile: str = "production",
-) -> str:
-    if validate:
-        report = validate_payload(payload, profile=validation_profile)
-        if report["hard_failures"]:
-            raise ValueError(
-                "Dashboard payload failed validation: " + "; ".join(report["hard_failures"])
-            )
-
-    css = (ASSET_DIR / "dashboard.css").read_text(encoding="utf-8")
-    source_records = _source_records(payload)
-    source_index = {str(source["id"]): source for source in source_records}
-    js = (
-        (ASSET_DIR / "dashboard.js")
-        .read_text(encoding="utf-8")
-        .replace(
-            "__SOURCE_DATA__",
-            _safe_json(_source_tooltip_payload(source_index)),
-        )
-    )
-    template = TEMPLATE_PATH.read_text(encoding="utf-8")
-
-    title = str(payload.get("title", "Public Equity Investing Dashboard"))
-    body = _render_body(payload, source_index)
-    return (
-        template.replace("{{title}}", _escape(title))
-        .replace("{{styles}}", css)
-        .replace("{{body}}", body)
-        .replace("{{script}}", js)
-    )
-
-
-def _render_body(payload: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    issuer = payload.get("issuer") if isinstance(payload.get("issuer"), Mapping) else {}
-    metadata = payload.get("metadata") if isinstance(payload.get("metadata"), Mapping) else {}
-    hero = payload.get("hero") if isinstance(payload.get("hero"), Mapping) else {}
-    ticker = str(issuer.get("ticker") or issuer.get("name") or "PM")
-    issuer_name = str(issuer.get("name") or ticker)
-    mode = str(payload.get("mode") or "public-equity-investing")
-    freeze_time = str(
-        metadata.get("freeze_time") or metadata.get("as_of") or "source freeze not provided"
-    )
-    accent = _safe_color(
-        str(issuer.get("accent_color") or payload.get("accent_color") or "#245f5a")
-    )
-    ticker_badge = _ticker_badge_color(issuer, payload, accent)
-    layout = str(payload.get("layout") or "tabs").lower()
-
-    tabs = list(payload.get("tabs", []))
-    if source_index and not any(
-        tab.get("id") == "sources" for tab in tabs if isinstance(tab, Mapping)
-    ):
-        tabs.append(
-            {
-                "id": "sources",
-                "label": "Sources",
-                "modules": [
-                    {
-                        "type": "source_list",
-                        "title": "Sources and Refresh Gaps",
-                        "eyebrow": "Evidence log",
-                        "data": {"sources": list(source_index.values())},
-                    }
-                ],
-            }
-        )
-
-    if layout == "single_page":
-        section_shell = f"""
-      <section class="section-shell">
-        {_render_toc(tabs)}
-        {_render_single_page_sections(tabs, source_index)}
-      </section>
-        """
-    else:
-        section_shell = f"""
-      <section class="tab-shell">
-        {_render_tab_list(tabs)}
-        {_render_tab_panels(tabs, source_index)}
-      </section>
-        """
-
-    utility_bar = _render_utility_bar()
-    callout = hero.get("callout") or metadata.get("decision_context")
-    callout_html = ""
-    hero_class = "hero"
-    if callout:
-        callout_html = f"""
-        <div class="hero-callout">
-          <span class="label">{_escape(str(hero.get("callout_label") or "Core Debate"))}</span>
-          <strong>{_render_inline_text(callout, source_index, hero.get("citations"), append_non_numeric_citations=True, link_numeric_citations=True)}</strong>
-        </div>"""
-    else:
-        hero_class += " no-callout"
-
-    return f"""
-    <header class="top-band" style="--issuer-accent: {accent}; --ticker-badge-bg: {ticker_badge}">
-      <nav class="utility">
-        <div class="utility-meta">
-          <span>{_escape(mode.replace("_", " ").title())}</span>
-          <span>{_escape(issuer_name)}</span>
-          <span>{_escape(freeze_time)}</span>
-        </div>
-      </nav>
-      <section class="{hero_class}">
-        <div class="identity-lockup" aria-label="Issuer identity tile">
-          <span>{_escape(_identity_text(ticker))}</span>
-        </div>
-        <div class="hero-copy">
-          <p class="eyebrow">{_escape(str(hero.get("eyebrow") or "Public Equity Investing Dashboard"))}</p>
-          <h1>{_escape(str(hero.get("headline") or payload.get("title") or issuer_name))}</h1>
-          <p>{_render_inline_text(hero.get("dek") or payload.get("subtitle") or "Decision-grade public-equity-investing dashboard.", source_index, hero.get("citations"), append_non_numeric_citations=True, link_numeric_citations=True)}</p>
-        </div>
-        {callout_html}
-      </section>
-    </header>
-    {utility_bar}
-    <main data-report-copy-root>
-      {_render_metric_tiles(payload.get("snapshot", []), source_index, class_name="snapshot-grid")}
-      {section_shell}
-    </main>
-    """
-
-
-def _render_utility_bar() -> str:
-    return (
-        '<div class="dashboard-utility-bar" aria-label="Dashboard actions">'
-        '<div><span class="utility-kicker">Reader actions</span></div>'
-        '<div class="utility-actions">'
-        '<button class="utility-action" type="button" data-copy-full-report>Copy Full Report</button>'
-        '<button class="utility-action" type="button" data-print-dashboard>Print / Save PDF</button>'
-        "</div>"
-        "</div>"
-    )
-
-
-def _render_tab_list(tabs: Sequence[Any]) -> str:
-    buttons: list[str] = []
-    for index, tab in enumerate(tabs):
-        if not isinstance(tab, Mapping):
-            continue
-        tab_id = str(tab.get("id") or f"tab-{index + 1}")
-        active = " is-active" if index == 0 else ""
-        selected = "true" if index == 0 else "false"
-        buttons.append(
-            f'<button class="tab-button{active}" data-tab="{_escape_attr(tab_id)}" '
-            f'role="tab" aria-selected="{selected}">{_escape(str(tab.get("label") or tab_id.title()))}</button>'
-        )
-    return f'<div class="tab-list" role="tablist" aria-label="Dashboard sections">{"".join(buttons)}</div>'
-
-
-def _render_tab_panels(tabs: Sequence[Any], source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    panels: list[str] = []
-    for index, tab in enumerate(tabs):
-        if not isinstance(tab, Mapping):
-            continue
-        tab_id = str(tab.get("id") or f"tab-{index + 1}")
-        label = str(tab.get("label") or tab_id.title())
-        active = " is-active" if index == 0 else ""
-        modules = tab.get("modules", [])
-        panels.append(
-            f'<section id="{_escape_attr(tab_id)}" class="tab-panel{active}" role="tabpanel">'
-            f'<div class="print-tab-heading"><span>{index + 1:02d}</span><h2>{_escape(label)}</h2></div>'
-            + "".join(
-                _render_module(module, source_index)
-                for module in modules
-                if isinstance(module, Mapping)
-            )
-            + "</section>"
-        )
-    return "".join(panels)
-
-
-def _render_toc(tabs: Sequence[Any]) -> str:
-    links: list[str] = []
-    for index, tab in enumerate(tabs):
-        if not isinstance(tab, Mapping):
-            continue
-        tab_id = str(tab.get("id") or f"section-{index + 1}")
-        active = " is-active" if index == 0 else ""
-        links.append(
-            f'<a class="toc-link{active}" href="#{_escape_attr(tab_id)}">'
-            f"<span>{index + 1:02d}</span>{_escape(str(tab.get('label') or tab_id.title()))}</a>"
-        )
-    return f'<nav class="toc-list" aria-label="Dashboard contents">{"".join(links)}</nav>'
-
-
-def _render_single_page_sections(
-    tabs: Sequence[Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    sections: list[str] = []
-    for index, tab in enumerate(tabs):
-        if not isinstance(tab, Mapping):
-            continue
-        tab_id = str(tab.get("id") or f"section-{index + 1}")
-        label = str(tab.get("label") or tab_id.title())
-        modules = tab.get("modules", [])
-        sections.append(
-            f'<section id="{_escape_attr(tab_id)}" class="dashboard-section" tabindex="-1">'
-            f'<div class="section-anchor-heading"><span>{index + 1:02d}</span><h2>{_escape(label)}</h2></div>'
-            + "".join(
-                _render_module(module, source_index, parent_heading=label)
-                for module in modules
-                if isinstance(module, Mapping)
-            )
-            + "</section>"
-        )
-    return '<div class="section-stack">' + "".join(sections) + "</div>"
-
-
-def _render_module(
-    module: Mapping[str, Any],
-    source_index: Mapping[str, Mapping[str, Any]],
-    *,
-    parent_heading: str | None = None,
-) -> str:
-    module_type = str(module.get("type") or "")
-    data = module.get("data") if isinstance(module.get("data"), Mapping) else {}
-    heading = _render_heading(module, parent_heading=parent_heading)
-
-    if module_type == "decision_box":
-        return heading + _render_decision_box(data, source_index)
-    if module_type in {"metric_tiles", "highlight_tiles"}:
-        return heading + _render_metric_tiles(data.get("items", []), source_index)
-    if module_type == "executive_summary":
-        return heading + _render_executive_summary(data, source_index)
-    if module_type == "key_metrics":
-        return heading + _render_key_metrics(data, source_index)
-    if module_type == "growth_trajectory":
-        return heading + _render_growth_trajectory(data, source_index)
-    if module_type == "transcript_qa":
-        return heading + _render_transcript_qa(data, source_index)
-    if module_type == "read_throughs":
-        return heading + _render_read_throughs(data, source_index)
-    if module_type == "market_events":
-        return heading + _render_market_events(data, source_index)
-    if module_type == "bar_chart":
-        return heading + _render_bar_chart(data, source_index)
-    if module_type == "financial_trend_chart":
-        return heading + _render_financial_trend_chart(data, source_index)
-    if module_type == "eps_actual_vs_estimate_chart":
-        return heading + _render_eps_actual_vs_estimate_chart(data, source_index)
-    if module_type == "equity_price_event_chart":
-        body = _render_equity_price_event_chart(data, source_index)
-        return heading + body if body else ""
-    if module_type == "cards":
-        return heading + _render_cards(data.get("items", []), source_index)
-    if module_type == "table":
-        return heading + _render_table(data, source_index)
-    if module_type == "scenario_map":
-        return heading + _render_scenarios(data.get("cases", []), source_index)
-    if module_type == "question_list":
-        return heading + _render_questions(data.get("questions", []), source_index)
-    if module_type == "timeline":
-        return heading + _render_timeline(data.get("events", []), source_index)
-    if module_type == "text_block":
-        return heading + _render_text_block(data, source_index)
-    if module_type == "source_list":
-        return heading + _render_sources(data.get("sources", []), source_index)
-    if module_type == "missing_evidence":
-        return heading + _render_missing(data.get("items", []), source_index)
-
-    return f'<div class="notice">Unsupported module: {_escape(module_type)}</div>'
-
-
-def _render_heading(module: Mapping[str, Any], *, parent_heading: str | None = None) -> str:
-    title = module.get("title")
-    eyebrow = module.get("eyebrow")
-    if not title and not eyebrow:
-        return ""
-    if title and parent_heading and not eyebrow:
-        normalized_title = re.sub(r"[\s_-]+", " ", str(title).strip().lower())
-        normalized_parent = re.sub(r"[\s_-]+", " ", parent_heading.strip().lower())
-        if normalized_title == normalized_parent:
-            return ""
-    eyebrow_html = f'<p class="eyebrow">{_escape(str(eyebrow))}</p>' if eyebrow else ""
-    title_html = f"<h2>{_escape(str(title))}</h2>" if title else ""
-    return f'<div class="section-heading">{eyebrow_html}{title_html}</div>'
-
-
-def _render_decision_box(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    bullets = data.get("key_points") or data.get("bullets") or []
-    fallback_citations = _citations_for(data)
-    bullets_html = "".join(
-        f"<li>{_format_cell(item, source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</li>"
-        for item in bullets
-    )
-    meta = []
-    for label in ("thesis_change", "estimate_revision", "stock_skew", "next_catalyst"):
-        if data.get(label):
-            meta.append(
-                f"<span><strong>{_escape(label.replace('_', ' ').title())}</strong>"
-                f"{_format_cell(data[label], source_index, data.get(f'{label}_citations') or fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</span>"
-            )
-    citation_note = _citation_note(fallback_citations, source_index)
-    return f"""
-    <article class="decision-box">
-      <span class="label">{_escape(str(data.get("label") or "PM Bottom Line"))}</span>
-      <h3>{_render_inline_text(data.get("stance") or "Decision view not provided", source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</h3>
-      <p>{_format_cell(data.get("summary") or "", source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>
-      <div class="decision-meta">{"".join(meta)}</div>
-      <ul class="check-list">{bullets_html}</ul>
-      {citation_note}
-    </article>
-    """
-
-
-def _render_metric_tiles(
-    items: Any,
-    source_index: Mapping[str, Mapping[str, Any]],
-    class_name: str = "metric-grid",
-) -> str:
-    if not isinstance(items, Sequence) or isinstance(items, (str, bytes)) or not items:
-        return ""
-    tiles = []
-    for item in items:
-        if not isinstance(item, Mapping):
-            continue
-        status = _status_class(str(item.get("status") or ""))
-        item_citations = _citations_for(item)
-        tiles.append(
-            f'<article class="metric-tile {status}">'
-            f'<span class="tile-label">{_escape(str(item.get("label") or ""))}</span>'
-            f"<strong>{_format_cell(item.get('value') or '', source_index, item_citations, append_non_numeric_citations=False)}</strong>"
-            f"<small>{_format_cell(item.get('detail') or '', source_index, item_citations, append_non_numeric_citations=False)}</small>"
-            "</article>"
-        )
-    return f'<section class="{class_name}">{"".join(tiles)}</section>'
-
-
-def _render_executive_summary(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    headline = data.get("headline")
-    body = data.get("body") or data.get("summary") or data.get("text") or ""
-    bullets = data.get("bullets") or data.get("watch_items") or []
-    net_read = data.get("net_read")
-    fallback_citations = _citations_for(data)
-    body_html = (
-        _format_rich_text(
-            str(body),
-            source_index,
-            fallback_citations,
-            append_non_numeric_citations=False,
-            link_numeric_citations=False,
-        )
-        if body
-        else ""
-    )
-    bullets_html = "".join(
-        f"<li>{_format_cell(item, source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</li>"
-        for item in bullets
-    )
-    citation_note = _citation_note(fallback_citations, source_index)
-    return (
-        '<article class="executive-summary">'
-        + (
-            f"<h3>{_render_inline_text(headline, source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</h3>"
-            if headline
-            else ""
-        )
-        + body_html
-        + (f'<ul class="check-list">{bullets_html}</ul>' if bullets_html else "")
-        + (
-            f'<p class="net-read"><strong>Net read:</strong> {_format_cell(net_read, source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>'
-            if net_read
-            else ""
-        )
-        + citation_note
-        + "</article>"
-    )
-
-
-def _render_key_metrics(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    table_data = dict(data)
-    if "columns" not in table_data:
-        table_data["columns"] = [
-            {"key": "metric", "label": "Metric"},
-            {"key": "current", "label": "Current"},
-            {"key": "growth", "label": "Growth"},
-            {"key": "compare", "label": "Consensus / Guide"},
-            {"key": "trajectory", "label": "Trajectory"},
-            {"key": "read", "label": "PM Read"},
-        ]
-    table_data.setdefault("rows", data.get("items", []))
-    table_data.setdefault("density", "dense")
-    return _render_table(table_data, source_index)
-
-
-def _render_growth_trajectory(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    table_data = dict(data)
-    if "columns" not in table_data:
-        table_data["columns"] = [
-            {"key": "metric", "label": "Metric"},
-            {"key": "prior", "label": "Prior"},
-            {"key": "current", "label": "Current"},
-            {"key": "trajectory", "label": "Trajectory"},
-            {"key": "read", "label": "PM Read"},
-        ]
-    table_data.setdefault("rows", data.get("items", []))
-    table_data.setdefault("density", "dense")
-    return _render_table(table_data, source_index)
-
-
-def _render_transcript_qa(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    table_data = dict(data)
-    if "columns" not in table_data:
-        table_data["columns"] = [
-            {"key": "number", "label": "#"},
-            {"key": "analyst", "label": "Analyst (Firm)"},
-            {"key": "topic", "label": "Topic"},
-            {"key": "response", "label": "Mgmt Response"},
-            {"key": "quote", "label": "Direct Quote"},
-            {"key": "quality", "label": "Quality"},
-        ]
-    table_data.setdefault("rows", data.get("questions", []))
-    table_data.setdefault("density", "dense")
-    return _render_table(table_data, source_index)
-
-
-def _render_read_throughs(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    table_data = dict(data)
-    if "columns" not in table_data:
-        table_data["columns"] = [
-            {"key": "name", "label": "Name"},
-            {"key": "relationship", "label": "Relationship"},
-            {"key": "parent_said", "label": "Parent Said"},
-            {"key": "read_through", "label": "Read-Through"},
-            {"key": "implication", "label": "Implication"},
-            {"key": "confidence", "label": "Confidence"},
-        ]
-    table_data.setdefault("rows", data.get("items", []))
-    table_data.setdefault("density", "dense")
-    return _render_table(table_data, source_index)
-
-
-def _render_market_events(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    events = data.get("events") or data.get("rows") or []
-    if not isinstance(events, Sequence) or isinstance(events, (str, bytes)):
-        return ""
-
-    rows: list[str] = []
-    for event in events:
-        if not isinstance(event, Mapping):
-            continue
-        event_citations = _citations_for(event) or _source_ref_from_fields(event)
-        cited_source = _first_cited_source(event_citations, source_index)
-        source_title = str(
-            event.get("source_title") or event.get("source") or cited_source.get("title") or ""
-        )
-        source_url = str(
-            event.get("source_url") or event.get("url") or cited_source.get("url") or ""
-        )
-        source_as_of = str(
-            event.get("as_of")
-            or event.get("source_as_of")
-            or event.get("published")
-            or cited_source.get("as_of")
-            or cited_source.get("date")
-            or cited_source.get("published")
-            or ""
-        )
-        source_html = (
-            f'<a href="{_escape_attr(source_url)}" target="_blank" rel="noreferrer">{_escape(source_title or "Source")}</a>'
-            if source_url
-            else _escape(source_title or "Not sourced")
-        )
-        if source_as_of:
-            source_html += f"<small>{_escape(source_as_of)}</small>"
-        rows.append(
-            "<tr>"
-            f'<td data-label="Date / Window">{_format_cell(event.get("date") or event.get("window") or "", source_index, event_citations, append_non_numeric_citations=False)}</td>'
-            f'<td data-label="Event">{_format_cell(event.get("event") or event.get("title") or "", source_index, event_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</td>'
-            f'<td data-label="Type">{_escape(str(event.get("type") or event.get("category") or event.get("timing") or ""))}</td>'
-            f'<td data-label="Potential Impact">{_format_cell(event.get("impact") or event.get("potential_impact") or "", source_index, event_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</td>'
-            f'<td data-label="Investor Read">{_format_cell(event.get("investor_read") or event.get("pm_read") or event.get("read") or "", source_index, event_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</td>'
-            f'<td data-label="Source">{source_html}</td>'
-            "</tr>"
-        )
-
-    return f"""
-    <div class="table-wrap dense-table market-events-table" data-table-label="{_escape_attr(str(data.get("mobile_label") or "Market events and news coverage"))}">
-      <table>
-        <thead><tr><th>Date / Window</th><th>Event</th><th>Type</th><th>Potential Impact</th><th>Investor Read</th><th>Source</th></tr></thead>
-        <tbody>{"".join(rows)}</tbody>
-      </table>
-    </div>
-    """
-
-
-def _render_bar_chart(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    items = data.get("items") or data.get("rows") or []
-    if not isinstance(items, Sequence) or isinstance(items, (str, bytes)):
-        return ""
-
-    rows = [item for item in items if isinstance(item, Mapping)]
-    values = [_to_float(row.get("bar_value", row.get("value"))) for row in rows]
-    max_value = max([abs(value) for value in values if value is not None] or [1.0])
-
-    rendered: list[str] = []
-    for row, value in zip(rows, values, strict=False):
-        row_citations = _citations_for(row)
-        width = 0.0 if value is None else min(abs(value) / max_value * 100, 100)
-        label = str(row.get("label") or row.get("metric") or "")
-        detail = row.get("detail") or row.get("subtitle") or ""
-        display_value = row.get("display") or row.get("value") or ""
-        rendered.append(
-            '<div class="bar-row">'
-            f'<div class="bar-label"><strong>{_escape(label)}</strong>'
-            + (
-                f"<span>{_format_cell(detail, source_index, row_citations)}</span>"
-                if detail
-                else ""
-            )
-            + "</div>"
-            f'<div class="bar-track" aria-label="{_escape_attr(label)}">'
-            f'<span class="bar-fill" style="--bar-width: {width:.1f}%"></span>'
-            "</div>"
-            f'<div class="bar-value">{_format_cell(display_value, source_index, row_citations)}</div>'
-            "</div>"
-        )
-    return f'<div class="bar-chart">{"".join(rendered)}</div>'
-
-
-def _render_financial_trend_chart(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    periods = data.get("periods") or data.get("rows") or []
-    rows = [row for row in periods if isinstance(row, Mapping)]
-    margin_key = _selected_margin_metric(data, rows)
-    margin_label = str(
-        data.get("margin_label") or data.get("line_label") or _metric_label(margin_key)
-    )
-    complete_rows = [
-        row
-        for row in rows
-        if row.get("period")
-        and _to_float(row.get("revenue")) is not None
-        and _to_float(row.get("gross_profit")) is not None
-        and _to_float(row.get("net_income")) is not None
-        and _to_ratio(row.get(margin_key)) is not None
-    ]
-    if len(complete_rows) < 2:
-        return ""
-
-    series = [
-        ("revenue", "Revenue", "#2563eb"),
-        ("gross_profit", "Gross Profit", "#14b8a6"),
-        ("net_income", "Net Income", "#7c3aed"),
-    ]
-    bar_values = [_to_float(row.get(key)) for row in complete_rows for key, _, _ in series]
-    margins = [_to_ratio(row.get(margin_key)) for row in complete_rows]
-    svg = _render_grouped_bar_line_svg(
-        complete_rows,
-        series,
-        line_key=margin_key,
-        line_label=margin_label,
-        line_color="#111827",
-        value_min_max=_min_max_with_zero([value for value in bar_values if value is not None]),
-        line_min_max=_margin_min_max_with_zero([value for value in margins if value is not None]),
-        value_formatter=_format_number,
-        line_formatter=lambda value: f"{value * 100:.1f}%",
-    )
-    table_rows = [
-        {
-            "period": row.get("period"),
-            "revenue": _format_number(_to_float(row.get("revenue"))),
-            "gross_profit": _format_number(_to_float(row.get("gross_profit"))),
-            "net_income": _format_number(_to_float(row.get("net_income"))),
-            margin_key: _format_ratio(_to_ratio(row.get(margin_key))),
-            "source": row.get("source") or row.get("source_title") or row.get("as_of") or "",
-            "citations": _citations_for(row) or _source_ref_from_fields(row),
-        }
-        for row in complete_rows
-    ]
-    margin_note = data.get("margin_rationale") or data.get("margin_note") or data.get("line_note")
-    return (
-        '<div class="combo-chart">'
-        + svg
-        + _render_chart_legend(
-            [(label, color) for _, label, color in series] + [(margin_label, "#111827")]
-        )
-        + _render_chart_note(margin_note, source_index, _citations_for(data))
-        + _render_table(
-            {
-                "columns": [
-                    {"key": "period", "label": "Period"},
-                    {"key": "revenue", "label": "Revenue"},
-                    {"key": "gross_profit", "label": "Gross Profit"},
-                    {"key": "net_income", "label": "Net Income"},
-                    {"key": margin_key, "label": margin_label},
-                    {"key": "source", "label": "Source"},
-                ],
-                "rows": table_rows,
-                "density": "dense",
-                "mobile_label": str(
-                    data.get("mobile_label") or "Quarterly financial trend source table"
-                ),
-            },
-            source_index,
-        )
-        + "</div>"
-    )
-
-
-def _render_eps_actual_vs_estimate_chart(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    periods = data.get("periods") or data.get("rows") or []
-    rows = [row for row in periods if isinstance(row, Mapping)]
-    complete_rows = [
-        row
-        for row in rows
-        if row.get("period")
-        and _to_float(row.get("estimated_eps")) is not None
-        and _to_float(row.get("actual_eps")) is not None
-    ]
-    if len(complete_rows) < 2:
-        return ""
-
-    series = [
-        ("estimated_eps", "Estimated EPS", "#94a3b8"),
-        ("actual_eps", "Actual EPS", "#245f5a"),
-    ]
-    values = [_to_float(row.get(key)) for row in complete_rows for key, _, _ in series]
-    svg = _render_grouped_bar_svg(
-        complete_rows,
-        series,
-        value_min_max=_min_max_with_zero([value for value in values if value is not None]),
-        value_formatter=lambda value: f"${value:.2f}",
-    )
-    table_rows = []
-    for row in complete_rows:
-        estimate = _to_float(row.get("estimated_eps"))
-        actual = _to_float(row.get("actual_eps"))
-        surprise = row.get("surprise")
-        if surprise is None and estimate is not None and actual is not None:
-            surprise = actual - estimate
-        table_rows.append(
-            {
-                "period": row.get("period"),
-                "estimated_eps": _format_eps(estimate),
-                "actual_eps": _format_eps(actual),
-                "surprise": _format_eps(_to_float(surprise)),
-                "basis": row.get("basis") or "",
-                "source": row.get("source") or row.get("source_title") or row.get("as_of") or "",
-                "citations": _citations_for(row) or _source_ref_from_fields(row),
-            }
-        )
-    return (
-        '<div class="combo-chart">'
-        + svg
-        + _render_chart_legend([(label, color) for _, label, color in series])
-        + _render_table(
-            {
-                "columns": ["period", "estimated_eps", "actual_eps", "surprise", "basis", "source"],
-                "rows": table_rows,
-                "density": "dense",
-                "mobile_label": str(
-                    data.get("mobile_label") or "EPS estimate versus actual source table"
-                ),
-            },
-            source_index,
-        )
-        + "</div>"
-    )
-
-
-def _render_equity_price_event_chart(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    prices = data.get("prices") or []
-    events = data.get("events") or []
-    _ = prices, events
-    readiness = equity_price_event_readiness(data)
-    if not readiness["ready"]:
-        return ""
-    price_rows = readiness["price_rows"]
-    event_rows = readiness["event_rows"]
-
-    svg = _render_price_event_svg(price_rows, event_rows)
-    event_table = _render_market_events(
-        {
-            "events": event_rows,
-            "mobile_label": data.get("mobile_label") or "Annotated equity events",
-        },
-        source_index,
-    )
-    return '<div class="combo-chart price-event-chart">' + svg + event_table + "</div>"
-
-
-def _render_grouped_bar_line_svg(
-    rows: Sequence[Mapping[str, Any]],
-    series: Sequence[tuple[str, str, str]],
-    *,
-    line_key: str,
-    line_label: str,
-    line_color: str,
-    value_min_max: tuple[float, float],
-    line_min_max: tuple[float, float],
-    value_formatter: Any,
-    line_formatter: Any,
-) -> str:
-    bars_svg, geometry = _grouped_bar_parts(rows, series, value_min_max)
-    line_points = []
-    for index, row in enumerate(rows):
-        value = _to_ratio(row.get(line_key))
-        if value is None:
-            continue
-        x = geometry["left"] + geometry["group_width"] * index + geometry["group_width"] / 2
-        y = _scale_y(
-            value, line_min_max[0], line_min_max[1], geometry["top"], geometry["plot_height"]
-        )
-        line_points.append((x, y, value))
-    path = ""
-    if line_points:
-        path = " ".join(
-            ("M" if idx == 0 else "L") + f"{x:.1f},{y:.1f}"
-            for idx, (x, y, _) in enumerate(line_points)
-        )
-    markers = "".join(
-        f'<circle cx="{x:.1f}" cy="{y:.1f}" r="4.5"><title>{_escape(line_label)}: {_escape(line_formatter(value))}</title></circle>'
-        for x, y, value in line_points
-    )
-    right_axis = _render_right_axis_ticks(
-        geometry, line_min_max[0], line_min_max[1], line_formatter
-    )
-    return (
-        _svg_open(geometry)
-        + bars_svg
-        + f'<path class="combo-line" style="--line-color: {line_color}" d="{path}"></path>'
-        + f'<g class="combo-markers" style="--line-color: {line_color}">{markers}</g>'
-        + right_axis
-        + _svg_close()
-    )
-
-
-def _render_grouped_bar_svg(
-    rows: Sequence[Mapping[str, Any]],
-    series: Sequence[tuple[str, str, str]],
-    *,
-    value_min_max: tuple[float, float],
-    value_formatter: Any,
-) -> str:
-    bars_svg, geometry = _grouped_bar_parts(
-        rows, series, value_min_max, value_formatter=value_formatter
-    )
-    return _svg_open(geometry) + bars_svg + _svg_close()
-
-
-def _grouped_bar_parts(
-    rows: Sequence[Mapping[str, Any]],
-    series: Sequence[tuple[str, str, str]],
-    value_min_max: tuple[float, float],
-    *,
-    value_formatter: Any = None,
-) -> tuple[str, dict[str, float]]:
-    if value_formatter is None:
-        value_formatter = _format_number
-    width = 760.0
-    height = 320.0
-    left = 54.0
-    right = 54.0
-    top = 22.0
-    bottom = 58.0
-    plot_width = width - left - right
-    plot_height = height - top - bottom
-    group_width = plot_width / max(len(rows), 1)
-    bar_width = min(16.0, group_width / (len(series) + 1.5))
-    min_value, max_value = value_min_max
-    zero_y = _scale_y(0.0, min_value, max_value, top, plot_height)
-    parts = [
-        _render_left_axis_ticks(
-            width, right, top, plot_height, left, min_value, max_value, value_formatter
-        )
-    ]
-    for row_index, row in enumerate(rows):
-        center = left + group_width * row_index + group_width / 2
-        label = str(row.get("period") or row.get("label") or "")
-        parts.append(
-            f'<text class="chart-x-label" x="{center:.1f}" y="{height - 20}" text-anchor="middle">{_escape(label)}</text>'
-        )
-        start_x = center - (len(series) * bar_width + (len(series) - 1) * 4) / 2
-        for series_index, (key, series_label, color) in enumerate(series):
-            value = _to_float(row.get(key))
-            if value is None:
-                continue
-            y = _scale_y(value, min_value, max_value, top, plot_height)
-            rect_y = min(y, zero_y)
-            rect_height = max(abs(zero_y - y), 1.0)
-            x = start_x + series_index * (bar_width + 4)
-            parts.append(
-                f'<rect class="combo-bar" x="{x:.1f}" y="{rect_y:.1f}" width="{bar_width:.1f}" height="{rect_height:.1f}" fill="{color}">'
-                f"<title>{_escape(series_label)} {label}: {_escape(value_formatter(value))}</title>"
-                "</rect>"
-            )
-    return "".join(parts), {
-        "width": width,
-        "height": height,
-        "left": left,
-        "right": right,
-        "top": top,
-        "bottom": bottom,
-        "plot_width": plot_width,
-        "plot_height": plot_height,
-        "group_width": group_width,
-    }
-
-
-def _render_price_event_svg(
-    price_rows: Sequence[Mapping[str, Any]], event_rows: Sequence[Mapping[str, Any]]
-) -> str:
-    width = 760.0
-    height = 320.0
-    left = 54.0
-    right = 34.0
-    top = 24.0
-    bottom = 58.0
-    plot_width = width - left - right
-    plot_height = height - top - bottom
-    values = [_to_float(row.get("price")) for row in price_rows]
-    min_value, max_value = _min_max_with_padding([value for value in values if value is not None])
-    x_step = plot_width / max(len(price_rows) - 1, 1)
-    points = []
-    date_to_index = {str(row.get("date")): index for index, row in enumerate(price_rows)}
-    for index, row in enumerate(price_rows):
-        value = _to_float(row.get("price"))
-        if value is None:
-            continue
-        x = left + x_step * index
-        y = _scale_y(value, min_value, max_value, top, plot_height)
-        points.append((x, y, value, str(row.get("date"))))
-    path = " ".join(
-        ("M" if idx == 0 else "L") + f"{x:.1f},{y:.1f}" for idx, (x, y, _, _) in enumerate(points)
-    )
-    marker_parts = []
-    for event_index, event in enumerate(event_rows, start=1):
-        raw_date = str(event.get("date") or event.get("window") or "")
-        index = date_to_index.get(raw_date)
-        if index is None:
-            index = min(
-                round((event_index - 1) / max(len(event_rows) - 1, 1) * (len(price_rows) - 1)),
-                len(price_rows) - 1,
-            )
-        x, y, _, _ = points[index]
-        marker_parts.append(
-            f'<g class="event-marker" transform="translate({x:.1f} {y:.1f})">'
-            '<circle r="7"></circle>'
-            f'<text y="-11" text-anchor="middle">{event_index}</text>'
-            f"<title>{_escape(str(event.get('event') or event.get('title') or raw_date))}</title>"
-            "</g>"
-        )
-    x_labels = ""
-    if points:
-        x_labels = (
-            f'<text class="chart-x-label" x="{points[0][0]:.1f}" y="{height - 20}" text-anchor="middle">{_escape(points[0][3])}</text>'
-            f'<text class="chart-x-label" x="{points[-1][0]:.1f}" y="{height - 20}" text-anchor="middle">{_escape(points[-1][3])}</text>'
-        )
-    geometry = {
-        "width": width,
-        "height": height,
-        "left": left,
-        "top": top,
-        "plot_height": plot_height,
-    }
-    return (
-        _svg_open(geometry)
-        + f'<line class="chart-grid" x1="{left}" x2="{width - right}" y1="{top + plot_height:.1f}" y2="{top + plot_height:.1f}"></line>'
-        + f'<text class="chart-axis-label" x="{left}" y="{top + 4}">{_escape(_format_price(max_value))}</text>'
-        + f'<text class="chart-axis-label" x="{left}" y="{top + plot_height}">{_escape(_format_price(min_value))}</text>'
-        + f'<path class="price-line" d="{path}"></path>'
-        + "".join(marker_parts)
-        + x_labels
-        + _svg_close()
-    )
-
-
-def _render_left_axis_ticks(
-    width: float,
-    right: float,
-    top: float,
-    plot_height: float,
-    left: float,
-    min_value: float,
-    max_value: float,
-    formatter: Any,
-) -> str:
-    parts: list[str] = []
-    for tick in _axis_ticks(min_value, max_value):
-        y = _scale_y(tick, min_value, max_value, top, plot_height)
-        grid_class = "chart-grid chart-zero-line" if abs(tick) < 1e-9 else "chart-grid"
-        parts.append(
-            f'<line class="{grid_class}" x1="{left}" x2="{width - right}" y1="{y:.1f}" y2="{y:.1f}"></line>'
-        )
-        parts.append(
-            f'<text class="chart-axis-label" x="{left - 6}" y="{y + 4:.1f}" text-anchor="end">{_escape(formatter(tick))}</text>'
-        )
-    return "".join(parts)
-
-
-def _render_right_axis_ticks(
-    geometry: Mapping[str, float],
-    min_value: float,
-    max_value: float,
-    formatter: Any,
-) -> str:
-    parts: list[str] = []
-    width = geometry["width"]
-    top = geometry["top"]
-    plot_height = geometry["plot_height"]
-    for tick in _axis_ticks(min_value, max_value):
-        y = _scale_y(tick, min_value, max_value, top, plot_height)
-        parts.append(
-            f'<text class="chart-axis-label right-axis-label" x="{width - 4}" y="{y + 4:.1f}" text-anchor="end">{_escape(formatter(tick))}</text>'
-        )
-    return "".join(parts)
-
-
-def _axis_ticks(min_value: float, max_value: float, target_count: int = 7) -> list[float]:
-    if max_value == min_value:
-        return [min_value]
-    step = _nice_step((max_value - min_value) / max(target_count - 1, 1))
-    start = math.ceil((min_value - 1e-9) / step) * step
-    end = math.floor((max_value + 1e-9) / step) * step
-    ticks: list[float] = []
-    current = start
-    guard = 0
-    while current <= end + step * 0.001 and guard < 20:
-        ticks.append(0.0 if abs(current) < step * 0.001 else current)
-        current += step
-        guard += 1
-    if not ticks:
-        ticks = [min_value, max_value]
-    return ticks
-
-
-def _svg_open(geometry: Mapping[str, float]) -> str:
-    return (
-        f'<svg class="native-chart" viewBox="0 0 {geometry["width"]:.0f} {geometry["height"]:.0f}" role="img" '
-        'aria-label="Dashboard chart" preserveAspectRatio="xMidYMid meet">'
-    )
-
-
-def _svg_close() -> str:
-    return "</svg>"
-
-
-def _render_chart_legend(items: Sequence[tuple[str, str]]) -> str:
-    return (
-        '<div class="chart-legend">'
-        + "".join(
-            f'<span><i style="--legend-color: {color}"></i>{_escape(label)}</span>'
-            for label, color in items
-        )
-        + "</div>"
-    )
-
-
-def _render_chart_note(
-    note: Any, source_index: Mapping[str, Mapping[str, Any]], citations: Any = None
-) -> str:
-    if not note:
-        return ""
-    return (
-        '<p class="chart-note">'
-        + _format_cell(
-            note,
-            source_index,
-            citations,
-            append_non_numeric_citations=False,
-            link_numeric_citations=False,
-        )
-        + "</p>"
-    )
-
-
-def _render_cards(items: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    cards = []
-    for item in (
-        items if isinstance(items, Sequence) and not isinstance(items, (str, bytes)) else []
-    ):
-        if not isinstance(item, Mapping):
-            continue
-        bullets = item.get("bullets") or []
-        item_citations = _citations_for(item)
-        bullets_html = "".join(
-            f"<li>{_format_cell(bullet, source_index, item_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</li>"
-            for bullet in bullets
-        )
-        label = item.get("eyebrow") or item.get("label")
-        citation_note = _citation_note(item_citations, source_index, label="Card sources")
-        cards.append(
-            f'<article class="card {_status_class(str(item.get("status") or ""))}">'
-            + (f'<span class="label">{_escape(str(label))}</span>' if label else "")
-            + f"<strong>{_render_inline_text(item.get('title') or '', source_index, item_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</strong>"
-            + f"<p>{_format_cell(item.get('body') or item.get('text') or '', source_index, item_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>"
-            + (f"<ul>{bullets_html}</ul>" if bullets_html else "")
-            + citation_note
-            + "</article>"
-        )
-    return f'<div class="card-grid">{"".join(cards)}</div>'
-
-
-def _render_table(data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    columns = data.get("columns") if isinstance(data.get("columns"), Sequence) else []
-    rows = data.get("rows") if isinstance(data.get("rows"), Sequence) else []
-    normalized_columns = [
-        (*_normalize_column(column), _column_alignment_class(column)) for column in columns
-    ]
-    header = "".join(
-        f'<th class="{class_name}">{_escape(label)}</th>'
-        if class_name
-        else f"<th>{_escape(label)}</th>"
-        for _, label, class_name in normalized_columns
-    )
-    body_rows = []
-    for row in rows:
-        if not isinstance(row, Mapping):
-            continue
-        cells = []
-        row_citations = _citations_for(row) or _source_ref_from_fields(row)
-        for key, label, class_name in normalized_columns:
-            value = row.get(key, "")
-            cell_citations = row_citations
-            if key in _NO_FALLBACK_CITATION_COLUMNS and not (
-                isinstance(value, Mapping) and _citations_for(value)
-            ):
-                cell_citations = []
-            class_attr = f' class="{class_name}"' if class_name else ""
-            cells.append(
-                f'<td{class_attr} data-label="{_escape_attr(label)}">'
-                f"{_format_cell(value, source_index, cell_citations, append_non_numeric_citations=False)}</td>"
-            )
-        body_rows.append(f"<tr>{''.join(cells)}</tr>")
-    density = " dense-table" if str(data.get("density") or "").lower() == "dense" else ""
-    return f"""
-    <div class="table-wrap{density}" data-table-label="{_escape_attr(str(data.get("mobile_label") or "Dashboard table"))}">
-      <table>
-        <thead><tr>{header}</tr></thead>
-        <tbody>{"".join(body_rows)}</tbody>
-      </table>
-    </div>
-    """
-
-
-def _render_scenarios(cases: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    cards = []
-    for case in (
-        cases if isinstance(cases, Sequence) and not isinstance(cases, (str, bytes)) else []
-    ):
-        if not isinstance(case, Mapping):
-            continue
-        kind = _slug(str(case.get("status") or case.get("type") or case.get("label") or "case"))
-        case_citations = _citations_for(case)
-        bullets = "".join(
-            f"<li>{_format_cell(item, source_index, case_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</li>"
-            for item in case.get("bullets", [])
-        )
-        citation_note = _citation_note(case_citations, source_index, label="Case sources")
-        cards.append(
-            f'<article class="scenario-card {kind}">'
-            f'<span class="label">{_escape(str(case.get("type") or case.get("label") or "Case"))}</span>'
-            f"<strong>{_render_inline_text(case.get('title') or '', source_index, case_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</strong>"
-            f"<p>{_format_cell(case.get('headline') or case.get('body') or case.get('summary') or '', source_index, case_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>"
-            f"<ul>{bullets}</ul>"
-            f"{citation_note}"
-            "</article>"
-        )
-    return f'<div class="scenario-grid">{"".join(cards)}</div>'
-
-
-def _render_questions(questions: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    items = []
-    for index, question in enumerate(
-        questions
-        if isinstance(questions, Sequence) and not isinstance(questions, (str, bytes))
-        else []
-    ):
-        if not isinstance(question, Mapping):
-            continue
-        question_citations = _citations_for(question)
-        citation_note = _citation_note(question_citations, source_index, label="Question sources")
-        items.append(
-            '<article class="question-item">'
-            "<div>"
-            f'<span class="label">Question {index + 1}</span>'
-            f"<h3>{_render_inline_text(question.get('question') or question.get('q') or '', source_index, question_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</h3>"
-            f'<p class="question-meta">{_format_cell(question.get("why") or question.get("why_it_matters") or "", source_index, question_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>'
-            "</div><div>"
-            "<strong>Listen-fors</strong>"
-            f"<p>{_format_cell(question.get('listen_for') or question.get('listen_fors') or '', source_index, question_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>"
-            f"{citation_note}</div></article>"
-        )
-    return f'<div class="question-list">{"".join(items)}</div>'
-
-
-def _render_timeline(events: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    items = []
-    for event in (
-        events if isinstance(events, Sequence) and not isinstance(events, (str, bytes)) else []
-    ):
-        if not isinstance(event, Mapping):
-            continue
-        event_citations = _citations_for(event) or _source_ref_from_fields(event)
-        items.append(
-            '<article class="timeline-item">'
-            f"<time>{_format_cell(event.get('date') or event.get('window') or '', source_index, event_citations)}</time>"
-            f"<strong>{_render_inline_text(event.get('title') or event.get('event') or '', source_index, event_citations)}</strong>"
-            f"<p>{_format_cell(event.get('detail') or event.get('read_through') or '', source_index, event_citations)}</p>"
-            "</article>"
-        )
-    return f'<div class="timeline">{"".join(items)}</div>'
-
-
-def _render_text_block(
-    data: Mapping[str, Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    fallback_citations = _citations_for(data)
-    bullets = "".join(
-        f"<li>{_format_cell(item, source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</li>"
-        for item in data.get("bullets", [])
-    )
-    citation_note = _citation_note(fallback_citations, source_index)
-    return (
-        '<article class="text-block">'
-        f"<p>{_format_cell(data.get('body') or data.get('text') or '', source_index, fallback_citations, append_non_numeric_citations=False, link_numeric_citations=False)}</p>"
-        + (f'<ul class="check-list">{bullets}</ul>' if bullets else "")
-        + citation_note
-        + "</article>"
-    )
-
-
-def _render_sources(sources: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    items = []
-    source_rows = _normalized_sources(sources)
-    if not source_rows and source_index:
-        source_rows = list(source_index.values())
-    for source in source_rows:
-        if not isinstance(source, Mapping):
-            continue
-        source_id = str(source.get("id") or source.get("source_id") or "")
-        workbook_ref = _workbook_ref(source)
-        title = _escape(
-            str(
-                source.get("title")
-                or source.get("name")
-                or source.get("url")
-                or workbook_ref
-                or "Source"
-            )
-        )
-        url = str(source.get("url") or _workbook_href(source) or "")
-        source_type = (
-            source.get("type")
-            or source.get("source_type")
-            or ("model_cell" if _is_workbook_source(source) else "")
-        )
-        status = (
-            source.get("status")
-            or source.get("source_status")
-            or source.get("reliance_status")
-            or ""
-        )
-        timestamp = (
-            source.get("as_of")
-            or source.get("timestamp")
-            or source.get("date")
-            or source.get("accessed")
-        )
-        note = (
-            _workbook_detail(source)
-            or source.get("note")
-            or source.get("description")
-            or source.get("excerpt")
-            or source.get("detail")
-            or ""
-        )
-        link = (
-            f'<a href="{_escape_attr(url)}" target="_blank" rel="noreferrer">{title}</a>'
-            if url
-            else f"<strong>{title}</strong>"
-        )
-        items.append(
-            f'<li id="source-{_slug(source_id)}">'
-            f'<span class="source-id">{_escape(source_id)}</span>{link}'
-            + (f"<small>{_escape(str(source_type))}</small>" if source_type else "")
-            + (f"<small>{_escape(str(status))}</small>" if status else "")
-            + f"<span>{_escape(str(note))}</span>"
-            + (f"<small>{_escape(str(timestamp))}</small>" if timestamp else "")
-            + "</li>"
-        )
-    return f'<ol class="source-list">{"".join(items)}</ol>'
-
-
-def _render_missing(items: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    rows = []
-    for item in (
-        items if isinstance(items, Sequence) and not isinstance(items, (str, bytes)) else []
-    ):
-        if isinstance(item, Mapping):
-            item_citations = _citations_for(item)
-            rows.append(
-                f"<li><strong>{_escape(str(item.get('item') or item.get('field') or 'Missing evidence'))}</strong>"
-                f": {_format_cell(item.get('needed') or item.get('detail') or '', source_index, item_citations)}</li>"
-            )
-        else:
-            rows.append(f"<li>{_format_cell(item, source_index)}</li>")
-    return '<div class="notice"><ul class="check-list">' + "".join(rows) + "</ul></div>"
-
-
-def _normalize_column(column: Any) -> tuple[str, str]:
-    if isinstance(column, Mapping):
-        key = str(column.get("key") or column.get("field") or column.get("label") or "")
-        label = str(column.get("label") or key.replace("_", " ").title())
-        return key, label
-    key = str(column)
-    return key, key.replace("_", " ").title()
-
-
-def _column_alignment_class(column: Any) -> str:
-    key, label = _normalize_column(column)
-    if isinstance(column, Mapping):
-        explicit = str(column.get("align") or column.get("type") or "").strip().lower()
-        if explicit in {"number", "numeric", "currency", "percent", "percentage", "right"}:
-            return "is-numeric"
-        if explicit in {"left", "text", "string"}:
-            return ""
-    numeric_hint = re.compile(
-        r"(?:%|\b(?:amount|arr|bps|capex|cash|debt|ebitda|eps|growth|income|irr|"
-        r"leverage|margin|moic|multiple|opex|price|profit|rate|return|revenue|"
-        r"sales|shares?|value|valuation|variance)\b)",
-        re.IGNORECASE,
-    )
-    return "is-numeric" if numeric_hint.search(f"{key} {label}") else ""
-
-
-def _format_cell(
-    value: Any,
-    source_index: Mapping[str, Mapping[str, Any]],
-    fallback_citations: Any = None,
-    *,
-    append_non_numeric_citations: bool = True,
-    link_numeric_citations: bool = True,
-) -> str:
-    if isinstance(value, Mapping):
-        citations = _citations_for(value) or fallback_citations
-        status = value.get("status")
-        text = _render_inline_text(
-            value.get("text") or value.get("value") or "",
-            source_index,
-            citations,
-            append_non_numeric_citations=bool(_citations_for(value))
-            or append_non_numeric_citations,
-            link_numeric_citations=link_numeric_citations,
-        )
-        if status:
-            return f'{text} <span class="status {_status_class(str(status))}">{_escape(str(status))}</span>'
-        return text
-    if isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-        return (
-            "<ul>"
-            + "".join(
-                f"<li>{_format_cell(item, source_index, fallback_citations, append_non_numeric_citations=append_non_numeric_citations, link_numeric_citations=link_numeric_citations)}</li>"
-                for item in value
-            )
-            + "</ul>"
-        )
-    return _render_inline_text(
-        value,
-        source_index,
-        fallback_citations,
-        append_non_numeric_citations=append_non_numeric_citations,
-        link_numeric_citations=link_numeric_citations,
-    )
-
-
-def _render_inline_text(
-    value: Any,
-    source_index: Mapping[str, Mapping[str, Any]],
-    citations: Any = None,
-    *,
-    append_non_numeric_citations: bool = True,
-    link_numeric_citations: bool = True,
-) -> str:
-    if isinstance(value, Mapping):
-        return _format_cell(value, source_index, citations)
-    text = "" if value is None else str(value)
-    if not text:
-        return ""
-    pattern = re.compile(r"\[([A-Za-z][A-Za-z0-9_.:-]{0,40})\]")
-    pieces: list[str] = []
-    last = 0
-    for match in pattern.finditer(text):
-        source_id = match.group(1)
-        if source_id not in source_index:
-            continue
-        pieces.append(_escape(text[last : match.start()]))
-        pieces.append(_citation_ref(source_id, source_index))
-        last = match.end()
-    rendered = _escape(text) if last == 0 else "".join(pieces + [_escape(text[last:])])
-    if last == 0 and link_numeric_citations:
-        linked = _link_numeric_text(text, citations, source_index)
-        if linked:
-            return linked
-    if append_non_numeric_citations:
-        return rendered + _citation_refs(citations, source_index)
-    return rendered
-
-
-_NUMERIC_CITATION_RE = re.compile(
-    r"(?<![A-Za-z])(?:[+-]?[$€£]?\d[\d,]*(?:\.\d+)?(?:\s?(?:B|M|K|bn|mm|billion|million))?%?|\d{4})(?![A-Za-z])",
-    re.IGNORECASE,
-)
-_MONTH_RE = re.compile(
-    r"\b(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:t(?:ember)?)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)\b",
-    re.IGNORECASE,
-)
-_NO_FALLBACK_CITATION_COLUMNS = {
-    "metric",
-    "name",
-    "relationship",
-    "workstream",
-    "event",
-    "title",
-    "topic",
-    "analyst",
-    "type",
-    "category",
-}
-
-
-def _link_numeric_text(
-    text: str,
-    citations: Any,
-    source_index: Mapping[str, Mapping[str, Any]],
-) -> str:
-    citation = _first_linkable_citation(citations, source_index)
-    if citation is None:
-        return ""
-    if _should_link_entire_value(text):
-        return _citation_link(text, citation, source_index)
-    pieces: list[str] = []
-    last = 0
-    matched = False
-    for match in _NUMERIC_CITATION_RE.finditer(text):
-        matched = True
-        pieces.append(_escape(text[last : match.start()]))
-        pieces.append(_citation_link(match.group(0), citation, source_index))
-        last = match.end()
-    if not matched:
-        return ""
-    pieces.append(_escape(text[last:]))
-    return "".join(pieces)
-
-
-def _should_link_entire_value(text: str) -> bool:
-    compact = re.sub(r"\s+", " ", text.strip())
-    if not compact or not _NUMERIC_CITATION_RE.search(compact):
-        return False
-    if len(compact) <= 28:
-        words = re.findall(r"[A-Za-z]+", compact)
-        return len(words) <= 4
-    if len(compact) <= 64 and _MONTH_RE.search(compact):
-        return True
-    return False
-
-
-def _first_linkable_citation(citations: Any, source_index: Mapping[str, Mapping[str, Any]]) -> Any:
-    for citation in _dedupe_citations(_listify(citations)):
-        if isinstance(citation, Mapping):
-            source_id = str(citation.get("id") or citation.get("source_id") or "").strip()
-            if source_id and source_id in source_index:
-                return source_id
-            if citation.get("url") or citation.get("source_url"):
-                return citation
-            continue
-        source_id = str(citation).strip()
-        if source_id and source_id in source_index:
-            return source_id
-    return None
-
-
-def _citation_link(
-    content: str, citation: Any, source_index: Mapping[str, Mapping[str, Any]]
-) -> str:
-    if isinstance(citation, Mapping):
-        source_id = str(citation.get("id") or citation.get("source_id") or "").strip()
-        if source_id and source_id in source_index:
-            return _citation_link_for_source_id(content, source_id)
-        url = str(citation.get("url") or citation.get("source_url") or "").strip()
-        if url:
-            title = str(
-                citation.get("title")
-                or citation.get("source_title")
-                or citation.get("name")
-                or "Source"
-            )
-            detail = str(
-                citation.get("excerpt")
-                or citation.get("pinpoint")
-                or citation.get("note")
-                or citation.get("detail")
-                or ""
-            )
-            date = str(
-                citation.get("as_of") or citation.get("date") or citation.get("published") or ""
-            )
-            return (
-                f'<a class="citation-link" href="{_escape_attr(url)}" target="_blank" rel="noreferrer" '
-                f'data-citation-title="{_escape_attr(title)}" data-citation-detail="{_escape_attr(detail)}" '
-                f'data-citation-date="{_escape_attr(date)}" aria-label="Source for {_escape_attr(content)}">'
-                f"{_escape(content)}</a>"
-            )
-    source_id = str(citation).strip()
-    if source_id and source_id in source_index:
-        return _citation_link_for_source_id(content, source_id)
-    return _escape(content)
-
-
-def _citation_link_for_source_id(content: str, source_id: str) -> str:
-    source_attr = _escape_attr(source_id)
-    return (
-        f'<a class="citation-link" href="#source-{_slug(source_id)}" '
-        f'data-citation-id="{source_attr}" aria-label="Source {source_attr} for {_escape_attr(content)}">'
-        f"{_escape(content)}</a>"
-    )
-
-
-def _citation_note(
-    citations: Any, source_index: Mapping[str, Mapping[str, Any]], label: str = "Section sources"
-) -> str:
-    refs = _citation_refs(citations, source_index)
-    if not refs:
-        return ""
-    return f'<p class="section-citation-note"><span>{_escape(label)}:</span>{refs}</p>'
-
-
-def _citations_for(value: Any) -> list[Any]:
-    if not isinstance(value, Mapping):
-        return []
-    citations: list[Any] = []
-    for key in ("citations", "citation_ids", "source_ids"):
-        citations.extend(_listify(value.get(key)))
-    for key in ("citation", "source_id"):
-        item = value.get(key)
-        if item:
-            citations.append(item)
-    if value.get("source_ref") or value.get("source_url"):
-        source_ref = (
-            value.get("source_ref") if isinstance(value.get("source_ref"), Mapping) else value
-        )
-        citations.append(source_ref)
-    return _dedupe_citations(citations)
-
-
-def _source_ref_from_fields(value: Mapping[str, Any]) -> list[Any]:
-    if value.get("source_id") or value.get("source_url") or value.get("url"):
-        return [value]
-    return []
-
-
-def _first_cited_source(
-    citations: Sequence[Any], source_index: Mapping[str, Mapping[str, Any]]
-) -> Mapping[str, Any]:
-    for citation in citations:
-        source_id = _citation_key(citation)
-        if source_id in source_index:
-            return source_index[source_id]
-    return {}
-
-
-def _listify(value: Any) -> list[Any]:
-    if value is None:
-        return []
-    if isinstance(value, list):
-        return value
-    if isinstance(value, tuple):
-        return list(value)
-    if isinstance(value, str):
-        if "," in value or ";" in value:
-            return [item.strip() for item in re.split(r"[,;]", value) if item.strip()]
-        return [value]
-    return [value]
-
-
-def _dedupe_citations(citations: Sequence[Any]) -> list[Any]:
-    seen: set[str] = set()
-    result: list[Any] = []
-    for citation in citations:
-        key = _citation_key(citation)
-        if not key or key in seen:
-            continue
-        seen.add(key)
-        result.append(citation)
-    return result
-
-
-def _citation_key(citation: Any) -> str:
-    if isinstance(citation, Mapping):
-        return str(
-            citation.get("id")
-            or citation.get("source_id")
-            or citation.get("url")
-            or citation.get("source_url")
-            or ""
-        ).strip()
-    return str(citation or "").strip()
-
-
-def _citation_refs(citations: Any, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    refs = []
-    for citation in _dedupe_citations(_listify(citations)):
-        if isinstance(citation, Mapping):
-            source_id = str(citation.get("id") or citation.get("source_id") or "").strip()
-            if source_id:
-                refs.append(_citation_ref(source_id, source_index))
-                continue
-            url = str(citation.get("url") or citation.get("source_url") or "").strip()
-            if url:
-                refs.append(_external_citation_ref(citation))
-            continue
-        source_id = str(citation).strip()
-        if source_id:
-            refs.append(_citation_ref(source_id, source_index))
-    if not refs:
-        return ""
-    return '<span class="citation-run" aria-label="Citations">' + "".join(refs) + "</span>"
-
-
-def _citation_ref(source_id: str, source_index: Mapping[str, Mapping[str, Any]]) -> str:
-    label = f"[{source_id}]"
-    if source_id in source_index:
-        source_attr = _escape_attr(source_id)
-        return (
-            f'<a class="citation-chip" href="#source-{_slug(source_id)}" '
-            f'data-citation-id="{source_attr}" aria-label="Citation {source_attr}">{_escape(label)}</a>'
-        )
-    return f'<span class="citation-chip citation-unresolved">{_escape(label)}</span>'
-
-
-def _external_citation_ref(citation: Mapping[str, Any]) -> str:
-    url = str(citation.get("url") or citation.get("source_url") or "")
-    title = str(
-        citation.get("title") or citation.get("source_title") or citation.get("name") or "Source"
-    )
-    detail = str(
-        citation.get("excerpt")
-        or citation.get("pinpoint")
-        or citation.get("note")
-        or citation.get("detail")
-        or ""
-    )
-    date = str(citation.get("as_of") or citation.get("date") or citation.get("published") or "")
-    return (
-        f'<a class="citation-chip" href="{_escape_attr(url)}" target="_blank" rel="noreferrer" '
-        f'data-citation-title="{_escape_attr(title)}" data-citation-detail="{_escape_attr(detail)}" '
-        f'data-citation-date="{_escape_attr(date)}" aria-label="Citation source">[source]</a>'
-    )
-
-
-def _source_records(payload: Mapping[str, Any]) -> list[dict[str, Any]]:
-    records: list[dict[str, Any]] = []
-    for key in ("sources", "model_citations", "workbook_citations"):
-        value = payload.get(key)
-        if isinstance(value, Mapping):
-            records.extend(
-                dict(item, id=record_id)
-                for record_id, item in value.items()
-                if isinstance(item, Mapping)
-            )
-        elif isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-            records.extend(item for item in value if isinstance(item, Mapping))
-    for key in ("model_citations_path", "workbook_citations_path"):
-        records.extend(_load_source_records_from_path(payload, payload.get(key)))
-    return _normalized_sources(records)
-
-
-def _resolve_payload_path(payload: Mapping[str, Any], path_value: Any) -> Path | None:
-    raw = str(path_value or "").strip()
-    if not raw or re.match(r"^[a-zA-Z][a-zA-Z0-9+.-]*:", raw):
-        return None
-    path = Path(raw)
-    if not path.is_absolute() and payload.get("_payload_dir"):
-        path = Path(str(payload.get("_payload_dir"))) / path
-    return path
-
-
-def _load_source_records_from_path(
-    payload: Mapping[str, Any], path_value: Any
-) -> list[dict[str, Any]]:
-    path = _resolve_payload_path(payload, path_value)
-    if not path or not path.exists():
-        return []
-    try:
-        loaded = json.loads(path.read_text(encoding="utf-8"))
-    except Exception:
-        return []
-    if isinstance(loaded, Mapping):
-        for key in ("model_citations", "workbook_citations", "sources"):
-            if isinstance(loaded.get(key), Sequence) and not isinstance(
-                loaded.get(key), (str, bytes)
-            ):
-                return [item for item in loaded[key] if isinstance(item, Mapping)]
-        return [
-            dict(item, id=record_id)
-            for record_id, item in loaded.items()
-            if isinstance(item, Mapping)
-        ]
-    if isinstance(loaded, Sequence) and not isinstance(loaded, (str, bytes)):
-        return [item for item in loaded if isinstance(item, Mapping)]
-    return []
-
-
-def _normalized_sources(sources: Any) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    if not isinstance(sources, Sequence) or isinstance(sources, (str, bytes)):
-        return rows
-    seen: set[str] = set()
-    for index, source in enumerate(sources, start=1):
-        if not isinstance(source, Mapping):
-            continue
-        row = dict(source)
-        source_id = str(row.get("id") or row.get("source_id") or f"S{index}").strip()
-        if not source_id:
-            continue
-        if source_id in seen:
-            source_id = f"{source_id}-{index}"
-        row["id"] = source_id
-        seen.add(source_id)
-        rows.append(row)
-    return rows
-
-
-def _source_tooltip_payload(
-    source_index: Mapping[str, Mapping[str, Any]],
-) -> dict[str, dict[str, str]]:
-    payload: dict[str, dict[str, str]] = {}
-    for source_id, source in source_index.items():
-        date_bits = [
-            str(source.get(key))
-            for key in ("date", "as_of", "timestamp", "accessed", "retrieved_at", "last_checked")
-            if source.get(key)
-        ]
-        workbook_ref = _workbook_ref(source)
-        workbook_detail = _workbook_detail(source)
-        payload[source_id] = {
-            "id": source_id,
-            "title": str(
-                source.get("title")
-                or source.get("name")
-                or source.get("url")
-                or workbook_ref
-                or source_id
-            ),
-            "type": str(source.get("type") or source.get("source_type") or ""),
-            "status": str(
-                source.get("status")
-                or source.get("source_status")
-                or source.get("reliance_status")
-                or ""
-            ),
-            "date": "; ".join(date_bits),
-            "detail": workbook_detail
-            or str(
-                source.get("pinpoint")
-                or source.get("excerpt")
-                or source.get("note")
-                or source.get("detail")
-                or source.get("description")
-                or ""
-            ),
-            "url": str(source.get("url") or _workbook_href(source) or ""),
-        }
-    return payload
-
-
-def _workbook_ref(source: Mapping[str, Any]) -> str:
-    sheet = str(source.get("sheet") or source.get("worksheet") or source.get("tab") or "").strip()
-    cell_range = str(source.get("range") or source.get("cell") or source.get("cells") or "").strip()
-    if sheet and cell_range:
-        return f"{sheet}!{cell_range}"
-    return sheet or cell_range
-
-
-def _is_workbook_source(source: Mapping[str, Any]) -> bool:
-    source_type = str(source.get("type") or source.get("category") or "").lower()
-    return bool(
-        source.get("workbook_path")
-        or source.get("workbook")
-        or source.get("sheet")
-        or source.get("range")
-        or source.get("cell")
-        or source_type in {"model_cell", "workbook_cell", "workbook_range"}
-    )
-
-
-def _file_href(path_value: Any) -> str:
-    raw = str(path_value or "").strip()
-    if not raw:
-        return ""
-    if re.match(r"^[a-zA-Z][a-zA-Z0-9+.-]*:", raw):
-        return raw
-    if raw.startswith("/"):
-        return "file://" + raw
-    return raw
-
-
-def _workbook_href(source: Mapping[str, Any]) -> str:
-    path = (
-        source.get("workbook_path")
-        or source.get("workbook")
-        or source.get("path")
-        or source.get("href")
-    )
-    if not path:
-        return ""
-    href = _file_href(path)
-    if _workbook_ref(source):
-        separator = "&" if "#" in href else "#"
-        href = (
-            f"{href}{separator}sheet={quote(str(source.get('sheet') or source.get('worksheet') or source.get('tab') or ''), safe='')}"
-            f"&range={quote(str(source.get('range') or source.get('cell') or source.get('cells') or ''), safe='')}"
-        )
-    return href
-
-
-def _workbook_detail(source: Mapping[str, Any]) -> str:
-    if not _is_workbook_source(source):
-        return ""
-    parts = []
-    ref = _workbook_ref(source)
-    if ref:
-        parts.append(ref)
-    if source.get("value") not in (None, ""):
-        parts.append(f"value: {source.get('value')}")
-    if source.get("formula"):
-        parts.append(f"formula: {source.get('formula')}")
-    if source.get("source_label"):
-        parts.append(f"source: {source.get('source_label')}")
-    return "; ".join(str(part) for part in parts if part)
-
-
-def _safe_json(value: Any) -> str:
-    return json.dumps(value, ensure_ascii=False).replace("</", "<\\/")
-
-
-def _to_float(value: Any) -> float | None:
-    if isinstance(value, (int, float)):
-        return float(value)
-    if isinstance(value, str):
-        cleaned = value.replace("%", "").replace(",", "").replace("$", "").replace("+", "").strip()
-        try:
-            return float(cleaned)
-        except ValueError:
-            return None
-    return None
-
-
-def _to_ratio(value: Any) -> float | None:
-    if isinstance(value, str) and "%" in value:
-        parsed = _to_float(value)
-        return None if parsed is None else parsed / 100.0
-    parsed = _to_float(value)
-    if parsed is None:
-        return None
-    return parsed / 100.0 if abs(parsed) > 2 else parsed
-
-
-def _selected_margin_metric(data: Mapping[str, Any], rows: Sequence[Mapping[str, Any]]) -> str:
-    explicit = (
-        data.get("margin_metric")
-        or data.get("line_metric")
-        or data.get("profitability_metric")
-        or data.get("margin_key")
-    )
-    if explicit:
-        return str(explicit)
-    for candidate in (
-        "net_margin",
-        "operating_margin",
-        "adjusted_operating_margin",
-        "ebitda_margin",
-        "fcf_margin",
-    ):
-        if any(_to_ratio(row.get(candidate)) is not None for row in rows):
-            return candidate
-    return "net_margin"
-
-
-def _metric_label(metric: str) -> str:
-    labels = {
-        "net_margin": "Net Margin",
-        "operating_margin": "Operating Margin",
-        "adjusted_operating_margin": "Adjusted Operating Margin",
-        "ebitda_margin": "EBITDA Margin",
-        "fcf_margin": "FCF Margin",
-        "gross_margin": "Gross Margin",
-    }
-    if metric in labels:
-        return labels[metric]
-    return metric.replace("_", " ").title()
-
-
-def _min_max_with_zero(values: Sequence[float]) -> tuple[float, float]:
-    if not values:
-        return 0.0, 1.0
-    finite_values = [value for value in values if math.isfinite(value)]
-    if not finite_values:
-        return 0.0, 1.0
-    raw_min = min(finite_values)
-    raw_max = max(finite_values)
-    if raw_min >= 0:
-        return _nice_bounds(0.0, raw_max, keep_zero_floor=True)
-    if raw_max <= 0:
-        return _nice_bounds(raw_min, 0.0, keep_zero_ceiling=True)
-    return _nice_bounds(raw_min, raw_max)
-
-
-def _margin_min_max_with_zero(values: Sequence[float]) -> tuple[float, float]:
-    if not values:
-        return 0.0, 1.0
-    finite_values = [value for value in values if math.isfinite(value)]
-    if not finite_values:
-        return 0.0, 1.0
-    raw_min = min(finite_values)
-    raw_max = max(finite_values)
-    if raw_min >= 0:
-        upper = max(raw_max * 1.08, 0.05)
-        return 0.0, math.ceil(upper / 0.05) * 0.05
-    if raw_max <= 0:
-        lower = min(raw_min * 1.08, -0.05)
-        return math.floor(lower / 0.05) * 0.05, 0.0
-    return _nice_bounds(raw_min, raw_max)
-
-
-def _nice_bounds(
-    min_value: float,
-    max_value: float,
-    *,
-    keep_zero_floor: bool = False,
-    keep_zero_ceiling: bool = False,
-) -> tuple[float, float]:
-    if min_value == max_value:
-        if min_value == 0:
-            return 0.0, 1.0
-        padding = abs(min_value) * 0.1 or 1.0
-        min_value -= padding
-        max_value += padding
-    span = max_value - min_value
-    padding = span * 0.08
-    padded_min = min_value if keep_zero_floor else min_value - padding
-    padded_max = max_value if keep_zero_ceiling else max_value + padding
-    step = _nice_step((padded_max - padded_min) / 5.0)
-    nice_min = 0.0 if keep_zero_floor else math.floor(padded_min / step) * step
-    nice_max = 0.0 if keep_zero_ceiling else math.ceil(padded_max / step) * step
-    if nice_min == nice_max:
-        nice_max = nice_min + step
-    return nice_min, nice_max
-
-
-def _nice_step(raw_step: float) -> float:
-    if raw_step <= 0 or not math.isfinite(raw_step):
-        return 1.0
-    exponent = math.floor(math.log10(raw_step))
-    base = 10**exponent
-    fraction = raw_step / base
-    for nice in (1.0, 1.5, 2.0, 2.5, 5.0, 10.0):
-        if fraction <= nice:
-            return nice * base
-    return 10.0 * base
-
-
-def _min_max_with_padding(values: Sequence[float]) -> tuple[float, float]:
-    if not values:
-        return 0.0, 1.0
-    min_value = min(values)
-    max_value = max(values)
-    if min_value == max_value:
-        padding = abs(max_value) * 0.08 or 1.0
-        return min_value - padding, max_value + padding
-    padding = (max_value - min_value) * 0.08
-    return min_value - padding, max_value + padding
-
-
-def _scale_y(
-    value: float, min_value: float, max_value: float, top: float, plot_height: float
-) -> float:
-    if max_value == min_value:
-        return top + plot_height / 2
-    return top + (max_value - value) / (max_value - min_value) * plot_height
-
-
-def _format_number(value: float | None) -> str:
-    if value is None:
-        return ""
-    if abs(value) < 1e-9:
-        return "0"
-    abs_value = abs(value)
-    if abs_value >= 1000:
-        return f"{value / 1000:.1f}B"
-    return f"{value:.1f}M"
-
-
-def _format_ratio(value: float | None) -> str:
-    return "" if value is None else f"{value * 100:.1f}%"
-
-
-def _format_eps(value: float | None) -> str:
-    return "" if value is None else f"${value:.2f}"
-
-
-def _format_price(value: float | None) -> str:
-    return "" if value is None else f"${value:.2f}"
-
-
-def _format_rich_text(
-    value: str,
-    source_index: Mapping[str, Mapping[str, Any]],
-    citations: Any = None,
-    *,
-    append_non_numeric_citations: bool = True,
-    link_numeric_citations: bool = True,
-) -> str:
-    paragraphs = [part.strip() for part in value.split("\n\n") if part.strip()]
-    return "".join(
-        f"<p>{_render_inline_text(part, source_index, citations, append_non_numeric_citations=append_non_numeric_citations, link_numeric_citations=link_numeric_citations).replace(chr(10), '<br>')}</p>"
-        for part in paragraphs
-    )
-
-
-def _identity_text(ticker: str) -> str:
-    clean = re.sub(r"[^A-Za-z0-9]", "", ticker.upper())
-    return (clean or "PM")[:4]
-
-
-def _safe_color(color: str) -> str:
-    return color if re.match(r"^#[0-9A-Fa-f]{6}$", color) else "#245f5a"
-
-
-def _ticker_badge_color(issuer: Mapping[str, Any], payload: Mapping[str, Any], accent: str) -> str:
-    for field in (
-        "identity_color",
-        "ticker_badge_color",
-        "brand_dark_color",
-        "dark_brand_color",
-        "brand_color",
-        "accent_color",
-    ):
-        value = issuer.get(field) or payload.get(field)
-        if isinstance(value, str) and re.match(r"^#[0-9A-Fa-f]{6}$", value):
-            return _ensure_white_text_readable(value)
-
-    candidates: list[str] = []
-    for field in ("brand_colors", "palette", "colors"):
-        value = issuer.get(field) or payload.get(field)
-        if isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
-            candidates.extend(str(item) for item in value)
-
-    safe_candidates = [
-        str(color) for color in candidates if re.match(r"^#[0-9A-Fa-f]{6}$", str(color))
-    ]
-    readable = [color for color in safe_candidates if _contrast_ratio(color, "#ffffff") >= 4.5]
-    if readable:
-        return min(readable, key=_relative_luminance)
-    if safe_candidates:
-        return _darken_until_readable(safe_candidates[0])
-    return _ensure_white_text_readable(accent)
-
-
-def _ensure_white_text_readable(color: str) -> str:
-    safe = _safe_color(color)
-    if _contrast_ratio(safe, "#ffffff") >= 4.5:
-        return safe
-    return _darken_until_readable(safe)
-
-
-def _darken_until_readable(color: str) -> str:
-    red, green, blue = _hex_to_rgb(color)
-    for factor in (0.88, 0.78, 0.68, 0.58, 0.48, 0.38):
-        candidate = _rgb_to_hex(int(red * factor), int(green * factor), int(blue * factor))
-        if _contrast_ratio(candidate, "#ffffff") >= 4.5:
-            return candidate
-    return "#1f2933"
-
-
-def _contrast_ratio(color_a: str, color_b: str) -> float:
-    lum_a = _relative_luminance(color_a)
-    lum_b = _relative_luminance(color_b)
-    lighter = max(lum_a, lum_b)
-    darker = min(lum_a, lum_b)
-    return (lighter + 0.05) / (darker + 0.05)
-
-
-def _relative_luminance(color: str) -> float:
-    red, green, blue = _hex_to_rgb(color)
-    channels = []
-    for channel in (red, green, blue):
-        value = channel / 255.0
-        channels.append(value / 12.92 if value <= 0.03928 else ((value + 0.055) / 1.055) ** 2.4)
-    return 0.2126 * channels[0] + 0.7152 * channels[1] + 0.0722 * channels[2]
-
-
-def _hex_to_rgb(color: str) -> tuple[int, int, int]:
-    clean = _safe_color(color).lstrip("#")
-    return int(clean[0:2], 16), int(clean[2:4], 16), int(clean[4:6], 16)
-
-
-def _rgb_to_hex(red: int, green: int, blue: int) -> str:
-    return f"#{max(0, min(red, 255)):02x}{max(0, min(green, 255)):02x}{max(0, min(blue, 255)):02x}"
-
-
-def _status_class(status: str) -> str:
-    clean = _slug(status)
-    return clean if clean in {"good", "watch", "risk", "bad", "gap", "bull", "base", "bear"} else ""
-
-
-def _slug(text: str) -> str:
-    return re.sub(r"[^a-z0-9-]+", "-", text.lower()).strip("-")
-
-
-def _escape(value: str) -> str:
-    return html.escape(value, quote=False)
-
-
-def _escape_attr(value: str) -> str:
-    return html.escape(value, quote=True)
diff --git a/plugins/financial-markets/shared/dashboard/schema/dashboard_payload.schema.json b/plugins/financial-markets/shared/dashboard/schema/dashboard_payload.schema.json
deleted file mode 100644
index 5b802a7..0000000
--- a/plugins/financial-markets/shared/dashboard/schema/dashboard_payload.schema.json
+++ /dev/null
@@ -1,153 +0,0 @@
-{
-  "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "$id": "https://openai.com/public-equity-investing/dashboard_payload.schema.json",
-  "title": "Public Equity Investing Dashboard Payload",
-  "type": "object",
-  "required": ["kind", "title", "issuer", "tabs"],
-  "allOf": [
-    {
-      "if": {
-        "required": ["metadata"],
-        "properties": {
-          "metadata": {
-            "type": "object",
-            "required": ["payload_stage"],
-            "properties": {
-              "payload_stage": {"const": "production"}
-            }
-          }
-        }
-      },
-      "then": {
-        "required": ["kind", "mode", "layout", "title", "issuer", "metadata", "hero", "snapshot", "tabs", "sources"],
-        "properties": {
-          "metadata": {
-            "type": "object",
-            "required": [
-              "payload_stage",
-              "freeze_time",
-              "source_posture",
-              "readiness_label",
-              "readiness_posture",
-              "citation_policy",
-              "decision_context"
-            ],
-            "properties": {
-              "citation_policy": {"const": "strict"}
-            }
-          },
-          "hero": {
-            "type": "object",
-            "minProperties": 1
-          },
-          "snapshot": {
-            "type": "array",
-            "minItems": 1
-          },
-          "sources": {
-            "type": "array",
-            "minItems": 1
-          }
-        }
-      }
-    }
-  ],
-  "properties": {
-    "kind": {
-      "const": "public_equity_investing_dashboard.v1"
-    },
-    "mode": {
-      "type": "string"
-    },
-    "layout": {
-      "type": "string",
-      "enum": ["tabs", "single_page"]
-    },
-    "title": {
-      "type": "string",
-      "minLength": 1
-    },
-    "subtitle": {
-      "type": "string"
-    },
-    "issuer": {
-      "type": "object",
-      "properties": {
-        "ticker": {"type": "string"},
-        "name": {"type": "string"},
-        "exchange": {"type": "string"},
-        "sector": {"type": "string"},
-        "accent_color": {"type": "string"}
-      },
-      "additionalProperties": true
-    },
-    "metadata": {
-      "type": "object",
-      "properties": {
-        "payload_stage": {
-          "type": "string",
-          "enum": ["draft", "support", "production"]
-        },
-        "freeze_time": {"type": "string"},
-        "source_posture": {"type": "string"},
-        "readiness_label": {"type": "string"},
-        "readiness_posture": {"type": "string"},
-        "citation_policy": {
-          "type": "string",
-          "enum": ["warn", "strict"]
-        },
-        "decision_context": {"type": "string"}
-      },
-      "additionalProperties": true
-    },
-    "hero": {
-      "type": "object",
-      "additionalProperties": true
-    },
-    "snapshot": {
-      "type": "array",
-      "items": {"type": "object"}
-    },
-    "tabs": {
-      "type": "array",
-      "minItems": 1,
-      "items": {
-        "type": "object",
-        "required": ["id", "label", "modules"],
-        "properties": {
-          "id": {"type": "string", "pattern": "^[a-z][a-z0-9-]*$"},
-          "label": {"type": "string"},
-          "modules": {
-            "type": "array",
-            "items": {"type": "object"}
-          }
-        },
-        "additionalProperties": true
-      }
-    },
-    "sources": {
-      "type": "array",
-      "items": {
-        "type": "object",
-        "properties": {
-          "id": {"type": "string"},
-          "source_id": {"type": "string"},
-          "title": {"type": "string"},
-          "url": {"type": "string"},
-          "as_of": {"type": "string"},
-          "type": {"type": "string"},
-          "status": {"type": "string"},
-          "excerpt": {"type": "string"},
-          "pinpoint": {"type": "string"},
-          "note": {"type": "string"}
-        },
-        "additionalProperties": true
-      }
-    },
-    "qa": {
-      "type": "object",
-      "additionalProperties": true
-    }
-  },
-  "additionalProperties": true
-}
diff --git a/plugins/financial-markets/shared/dashboard/templates/base.html b/plugins/financial-markets/shared/dashboard/templates/base.html
deleted file mode 100644
index 1916f25..0000000
--- a/plugins/financial-markets/shared/dashboard/templates/base.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    <title>{{title}}</title>
-    <style>
-{{styles}}
-    </style>
-  </head>
-  <body>
-{{body}}
-    <footer>
-      <span>Public Equity Investing dashboard. Verify time-sensitive market data before investment use.</span>
-    </footer>
-    <script>
-{{script}}
-    </script>
-  </body>
-</html>
diff --git a/plugins/financial-markets/shared/deliverable-intake-policy.md b/plugins/financial-markets/shared/deliverable-intake-policy.md
deleted file mode 100644
index ccf7eec..0000000
--- a/plugins/financial-markets/shared/deliverable-intake-policy.md
+++ /dev/null
@@ -1,173 +0,0 @@
-# Adaptive Deliverable Intake Policy
-
-Use this policy before a Public Equity Investing skill acting as the lead owner creates a new standalone reader-facing artifact. It applies to research reports, memos, models, valuation packages, event and catalyst artifacts,
-risk/monitoring views, and dashboards. Presentation and support skills consume resolved choices from an owning workflow; they ask only when independently invoked to create a new standalone hero deliverable.
-
-## Presentation-Surface Precedence
-
-Resolve presentation format in this order:
-
-1. Honor an explicit user-requested format.
-2. Preserve the format of an existing artifact for edits and direct reviews unless the user requests conversion. When the workflow creates a separate review report, use that workflow's declared natural artifact without converting the source artifact.
-3. Use the workflow's obvious natural artifact when the job is inherently artifact-specific:
-   - Use an XLSX workbook for models, reusable calculations, schedules, trackers, normalization, and data cleaning.
-   - Use a native deck or document for explicit slide, deck, presentation, or document work.
-4. Apply a saved reader-facing output preference as the default when multiple reader-facing formats remain reasonable.
-5. Otherwise, resolve any new standalone reader-facing output to polished standalone HTML.
-6. Use chat-only output only when the user explicitly requests chat, inline, quick, no-file, or a similarly lightweight response.
-
-Do not silently choose chat because the request appears narrow, conversational, or easy to answer inline. HTML is the normal fallback for a completed reader-facing deliverable, not a signal to compress analysis or force a standardized dashboard.
-
-A direct analytical question, a detail-page hero prompt, or the absence of a requested filename is not an explicit lightweight-response request. Do not choose chat-only output because a concise answer seems sufficient or more useful. When a saved HTML preference applies and the workflow's natural artifact is reader-facing HTML, create the HTML artifact.
-
-Once the presentation surface resolves to HTML, treat that as a committed deliverable decision. Create the HTML artifact before responding; do not later reconsider whether the analysis belongs in chat, split the full deliverable between the artifact and chat, or substitute an inline report. Use chat only as a concise cover note linking to the HTML artifact.
-
-## When To Ask
-
-Before source gathering, substantive analysis, building, or rendering,
-determine whether output format, analysis depth, audience/use, and material analytical focus are already stated or evident from the request.
-
-- Ask only for materially unresolved choices.
-- Do not ask the format question when the user already requests DOCX, HTML,
-  PPTX, XLSX, a dashboard, or inline output.
-- For a substantive single-company 60/90-day `catalyst-calendar` request,
-  treat a polished HTML catalyst calendar as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or a workbook/tracker. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus; present another format as an opt-out rather than a blocking intake decision.
-- For an explicit post-earnings `deep dive`, `full report`, or reusable/source-heavy post-print package, treat a polished standalone HTML post-earnings report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or workbook/model-update output. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For an explicit `pre-earnings preview`, `full preview report`, or reusable/source-heavy pre-print package, treat a polished standalone HTML pre-earnings report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or workbook/model output. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For an explicit `full event analysis`, `full event report`, or reusable/source-heavy special-situations package, treat a polished standalone HTML event report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or model/math output. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For a substantive `idea-generation` screen, market map, watchlist review, or reusable/source-heavy candidate set, treat a polished standalone HTML idea-triage report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or workbook/tracker output. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For an explicit `investment committee memo`, substantive buy-side `investment memo`, or reusable/source-heavy public-equity memo, treat a polished standalone HTML investment committee memo as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, `.docx`, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For a substantive reusable `meeting-prep` packet or explicit HTML meeting brief, treat a polished standalone HTML live-meeting brief as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, meeting type, or focus.
-- For a substantive reusable `long-short-pitch` package or explicit HTML trade pitch, treat a polished standalone HTML trade-pitch report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, trade direction, or focus.
-- For a substantive reusable `economic-impact-report` package or explicit HTML public-equity shock analysis, treat a polished standalone HTML economic-impact report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For a substantive reusable `scenario-sensitivity-generator` package, explicit HTML scenario report, or sourced discrete-event success/delay/break overlay, treat a polished standalone HTML scenario report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, workbook/model output, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For a substantive standalone `model-audit-tieout` review of an existing model or explicit HTML model-audit request, treat a polished standalone HTML model-audit report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, remediation output, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, materiality, or focus.
-- For a substantive standalone `deck-report-qc` review of an existing deck or report with supporting materials, treat a polished standalone HTML senior-review QC report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, circulation stage, audience/use, or review focus. An embedded QC check inherits the owning workflow's resolved surface and does not create a new HTML report by default.
-- For an explicit `initiating coverage` report, substantive `long_only_initiation`, or reusable/source-heavy coverage launch, treat a polished standalone HTML initiation report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, a model/workbook-first deliverable, or a standardized dashboard. In an interactive run, ask only remaining material choices such as depth, audience/use, or focus.
-- For a substantive `integrated_risk_plan`, reusable position-and-hedge package, or explicit HTML risk-plan request, treat a polished standalone HTML risk decision report as the workflow-resolved format unless the user requests another surface, a quick/no-file answer, workbook output, or a standardized dashboard. In an interactive run, ask only remaining material choices such as loss-budget interpretation, depth, audience/use, or focus.
-- A format-only request resolves only the delivery surface. For example,
-  "make a doc" can select a Word document, but does not resolve depth,
-  audience/use, or analytical focus. Do not silently infer those choices.
-- When editing or reviewing an existing deck, workbook, or document, preserve the existing format unless the user asks for conversion. Ask only missing depth, audience/use, or focus questions that change the work.
-- Apply a saved reader-facing output preference as the default when multiple reader-facing formats are reasonable. A saved HTML preference resolves the presentation surface to HTML in those cases; do not silently choose chat or ask a format question. Do not let a saved HTML preference override an obvious workbook, deck, document, or existing-artifact workflow. Models, model updates, trackers, workbook audits, workbook-first calculations, deck requests, document requests, and edits to an existing artifact keep their natural format unless the user explicitly asks for conversion.
-- Reuse choices already confirmed in the current conversation or workflow.
-  Downstream modeling, QC, and dashboard rendering do not ask again unless a new hero artifact creates a consequential unresolved choice.
-- Keep preferences in conversational context only; do not add fields to dashboard payloads, workbooks, manifests, run logs, or schemas.
-
-## Native Prompt Contract
-
-If a material choice remains unresolved and `request_user_input` is callable in an interactive Codex runtime, call `request_user_input` before creating the hero artifact:
-
-- Include only unresolved decisions and no more than three questions.
-- Supply two or three meaningful options for each question.
-- The UI provides a free-form `Other` response automatically; never include an `Other` option in the options list.
-- Filter format choices to surfaces that are available in the active runtime.
-  If a requested format is infeasible, ask for a feasible alternative.
-
-If `request_user_input` is unavailable or errors in an interactive run, the missing material choices still require intake: ask one concise plain-text scoping question in normal chat and wait, covering only the unresolved preferences. Do not present a choice menu when the runtime disallows it, and do not silently select depth or audience. In a non-interactive run, do not attempt an intake exchange: apply the owning workflow's documented recommended defaults and disclose the assumed format and depth in the delivery message or accompanying chat summary, not as visible artifact metadata.
-For a substantive 60/90-day catalyst calendar, those recommended defaults are a polished standalone HTML artifact and `Full working analysis`.
-For an explicit full or reusable/source-heavy post-earnings deep dive, those recommended defaults are a polished standalone HTML post-earnings report and `Full working analysis`.
-For an explicit full or reusable/source-heavy pre-earnings preview, those recommended defaults are a polished standalone HTML pre-earnings report and `Full working analysis`.
-For an explicit full or reusable/source-heavy event-driven analysis, those recommended defaults are a polished standalone HTML event report and `Full working analysis`.
-For a substantive idea-generation screen, market map, watchlist review, or reusable/source-heavy candidate set, those recommended defaults are a polished standalone HTML idea-triage report and `Full working analysis`.
-For an explicit investment committee memo, substantive buy-side investment memo, or reusable/source-heavy public-equity memo, those recommended defaults are a polished standalone HTML investment committee memo and `Full working analysis`.
-For a substantive reusable meeting-prep packet or explicit HTML meeting brief, those recommended defaults are a polished standalone HTML live-meeting brief and `Full working analysis`.
-For a substantive reusable long/short pitch package or explicit HTML trade pitch, those recommended defaults are a polished standalone HTML trade-pitch report and `Full working analysis`.
-For a substantive reusable economic-impact-report package or explicit HTML public-equity shock analysis, those recommended defaults are a polished standalone HTML economic-impact report and `Full working analysis`.
-For a substantive reusable scenario-sensitivity-generator package, explicit HTML scenario report, or sourced discrete-event success/delay/break overlay, those recommended defaults are a polished standalone HTML scenario report and `Full working analysis`.
-For a substantive standalone model-audit-tieout review of an existing model or explicit HTML model-audit request, those recommended defaults are a polished standalone HTML model-audit report and `Full working analysis`.
-For a substantive standalone deck/report QC review with supporting materials, those recommended defaults are a polished standalone HTML senior-review QC report and `Full working analysis`.
-For an explicit initiating coverage report, substantive long-only initiation, or reusable/source-heavy coverage launch, those recommended defaults are a polished standalone HTML initiation report and `Full working analysis`.
-For a substantive integrated risk plan, reusable position-and-hedge package, or explicit HTML risk-plan request, those recommended defaults are a polished standalone HTML risk decision report and `Full working analysis`.
-
-When using the interactive chat fallback in the Codex composer, append this sentence after the blocking question: "For clickable intake options,
-switch to Plan mode with `Shift + Tab` in the Codex composer and resend your request; otherwise answer here and I will continue." This is guidance, not a requirement: do not require a mode change or use this hint instead of asking the unresolved scoping question.
-
-## Format Choices
-
-| Artifact lane | Options |
-| --- | --- |
-| Memo, narrative report, or briefing | `HTML report (Recommended)`, `Word document (.docx)`, `Inline response` |
-| Deck or presentation | `PowerPoint deck (.pptx) (Recommended)`, `HTML storyboard`, `Inline outline` |
-| Model, schedule, tracker, or tabular package | `Excel workbook (.xlsx) (Recommended)`, `HTML summary`, `Inline summary` |
-| Dashboard or monitoring view | `HTML dashboard (Recommended)`, `Excel tracker`, `Inline brief` |
-
-A selected presentation surface does not eliminate required model, evidence,
-or QA companions. For example, a valuation model may retain an XLSX workbook even if the requested first-read presentation is HTML.
-
-For a substantive single-company 60/90-day `catalyst-calendar`, the default presentation surface is a polished HTML catalyst calendar rather than an `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or the requested job no longer fits that calendar default.
-
-For an explicit post-earnings `deep dive`, `full report`, or reusable/source-heavy post-print package, the default presentation surface is a polished standalone HTML post-earnings report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or the requested job no longer fits that deep-dive default.
-
-For an explicit `pre-earnings preview`, `full preview report`, or reusable/source-heavy pre-print package, the default presentation surface is a polished standalone HTML pre-earnings report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or the requested job no longer fits that preview-report default.
-
-For an explicit `full event analysis`, `full event report`, or reusable/source-heavy special-situations package, the default presentation surface is a polished standalone HTML event report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or the requested job no longer fits that event-report default.
-
-For a substantive `idea-generation` screen, market map, watchlist review, or reusable/source-heavy candidate set, the default presentation surface is a polished standalone HTML idea-triage report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or the requested job no longer fits that idea-triage default.
-
-For an explicit `investment committee memo`, substantive buy-side `investment memo`, or reusable/source-heavy public-equity memo, the default presentation surface is a polished standalone HTML investment committee memo rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or explicitly request a standardized dashboard.
-
-For a substantive reusable `meeting-prep` packet or explicit HTML meeting brief, the default presentation surface is a polished standalone HTML live-meeting brief rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or explicitly request a standardized dashboard.
-
-For a substantive reusable `long-short-pitch` package or explicit HTML trade pitch, the default presentation surface is a polished standalone HTML trade-pitch report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or explicitly request a standardized dashboard.
-
-For a substantive reusable `economic-impact-report` package or explicit HTML public-equity shock analysis, the default presentation surface is a polished standalone HTML economic-impact report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or explicitly request a standardized dashboard.
-
-For a substantive reusable `scenario-sensitivity-generator` package, explicit HTML scenario report, or sourced discrete-event success/delay/break overlay, the default presentation surface is a polished standalone HTML scenario report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output, workbook/model output, or explicitly request a standardized dashboard.
-
-For a substantive standalone `model-audit-tieout` review of an existing model or explicit HTML model-audit request, the default presentation surface is a polished standalone HTML model-audit report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output, remediation output, or explicitly request a standardized dashboard.
-
-For a substantive standalone `deck-report-qc` review of an existing deck or report with supporting materials, the default presentation surface is a polished standalone HTML senior-review QC report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output or explicitly request a standardized dashboard.
-
-For an explicit `initiating coverage` report, substantive `long_only_initiation`, or reusable/source-heavy coverage launch, the default presentation surface is a polished standalone HTML initiation report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output, a model/workbook-first deliverable, or explicitly request a standardized dashboard.
-
-For a substantive `integrated_risk_plan`, reusable position-and-hedge package, or explicit HTML risk-plan request, the default presentation surface is a polished standalone HTML risk decision report rather than a standardized `HTML dashboard`. Do not ask the user to select a format unless they request an alternate output, workbook output, or explicitly request a standardized dashboard.
-
-## Depth And Framing
-
-If depth is unresolved, offer:
-
-- `Focused first pass`: top drivers, risks, targeted analysis, and next questions.
-- `Full working analysis (Recommended)`: the normal complete working artifact.
-- `Deep diligence package`: expanded scenarios, supporting tables, evidence requests, and appendices.
-
-Depth is not readiness. Deep analysis does not permit PM-ready, client-ready,
-committee-ready, publication-ready, or decision-grade language without the source and review gates required by the owning skill.
-
-Use the third question slot for audience/use when it could change tone,
-decision framing, disclosure, or delivery:
-
-- `PM or investment team`
-- `Client or research audience`
-- `Internal committee`
-
-If audience is known or immaterial and the requested work remains broad, ask for focus instead:
-
-- `Thesis / catalysts`
-- `Valuation / estimates`
-- `Risk / monitoring`
-
-## Expected Behavior
-
-- A new model or valuation-package request with no stated surface asks for format, depth, and any consequential audience/use preference before building, recommending `Excel workbook (.xlsx)`.
-- An explicit HTML research report request skips the format question.
-- "Make a doc about why Duolingo's stock has plummeted since last May"
-  resolves Word format only and still asks for unresolved depth and audience before research begins.
-- A review of an existing workbook preserves `.xlsx` as its selected surface.
-- An analysis owner that has already collected preferences passes them through to model, QC, and `dashboard-builder` steps without repeated prompts.
-- A substantive single-company 60/90-day catalyst-calendar request with no selected format or depth defaults to a polished HTML catalyst calendar; in an interactive run it asks for unresolved depth without blocking on format,
-  and in a non-interactive run it proceeds with full working analysis and states those assumptions in the delivery message rather than in the investor-facing artifact.
-- An explicit post-earnings deep dive or reusable/source-heavy post-print package with no selected format defaults to a polished standalone HTML post-earnings report; in an interactive run it asks only for unresolved depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- An explicit pre-earnings preview or reusable/source-heavy pre-print package with no selected format defaults to a polished standalone HTML pre-earnings report; in an interactive run it asks only for unresolved depth,
-  audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- An explicit full event analysis or reusable/source-heavy special-situations package with no selected format defaults to a polished standalone HTML event report; in an interactive run it asks only for unresolved depth,
-  audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive idea-generation screen, market map, watchlist review, or reusable/source-heavy candidate set with no selected format defaults to a polished standalone HTML idea-triage report; in an interactive run it asks only for unresolved depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- An explicit investment committee memo, substantive buy-side investment memo, or reusable/source-heavy public-equity memo with no selected format defaults to a polished standalone HTML investment committee memo; in an interactive run it asks only for unresolved depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive reusable meeting-prep packet or explicit HTML meeting brief defaults to a polished standalone HTML live-meeting brief; in an interactive run it asks only for unresolved depth, audience/use, meeting type, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive reusable long/short pitch package or explicit HTML trade pitch defaults to a polished standalone HTML trade-pitch report; in an interactive run it asks only for unresolved depth, audience/use, trade direction, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive reusable economic-impact-report package or explicit HTML public-equity shock analysis defaults to a polished standalone HTML economic-impact report; in an interactive run it asks only for unresolved depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive standalone model-audit-tieout review of an existing model or explicit HTML model-audit request defaults to a polished standalone HTML model-audit report; in an interactive run it asks only for unresolved depth, audience/use, materiality, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive standalone deck/report QC review with supporting materials and no selected format defaults to a polished standalone HTML senior-review QC report; in an interactive run it asks only for unresolved depth, circulation stage, audience/use, or review focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- An explicit initiating coverage report, substantive long-only initiation, or reusable/source-heavy coverage launch with no selected format defaults to a polished standalone HTML initiation report; in an interactive run it asks only for unresolved depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing those assumptions outside the artifact.
-- A substantive integrated risk plan, reusable position-and-hedge package, or explicit HTML risk-plan request with no selected format defaults to a polished standalone HTML risk decision report; in an interactive run it asks only for unresolved loss-budget interpretation, depth, audience/use, or focus, and in a non-interactive run it proceeds with full working analysis while disclosing assumptions outside the artifact.
diff --git a/plugins/financial-markets/shared/equity-research-support-standard.md b/plugins/financial-markets/shared/equity-research-support-standard.md
deleted file mode 100644
index 3243597..0000000
--- a/plugins/financial-markets/shared/equity-research-support-standard.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Equity Research Support Standard
-
-Use this shared standard for `financial-source-of-truth`, `financials-normalizer`, `excel-data-cleaner`, `deck-report-qc`, and `style-guide-adapter` inside Public Equity Investing.
-
-Also load `shared/support-layer-routing-contract.md` whenever these skills are invoked as embedded support services under an owning Public Equity Investing workflow.
-
-## Support-Layer Role
-
-These skills are the research operating layer underneath public-equity work. They own evidence control, source hierarchy, data hygiene, financial normalization, table cleaning, artifact QC, and style preservation for long-only PMs, long/short hedge funds, sell-side equity research, event-driven equity investors, ETF/index diligence, and public-company diligence.
-
-They do not own the investment conclusion. They should feed `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `long-short-pitch`, `memo-builder`, `portfolio-risk-management`, `event-driven-analyzer`, `thesis-tracker`, and `dashboard-builder`.
-
-## Embedded Services Contract
-
-Support skills are usually embedded under the owning workflow, not peer investment owners. When embedded, preserve `owning_workflow`, `decision_impact`, `readiness_effect`, `artifact_role`, and whether support files are `hidden_unless_requested` in internal working context or support/audit artifacts. Do not print these field names in the user-facing hero artifact; surface material implications in plain reader-facing language. Standalone support use is allowed only when the user explicitly asks for source review, normalization, table cleaning, QC, or style adaptation as the task itself.
-
-## Not-Owner Rule
-
-They do not own memos, pitches, valuation, earnings calls, trade construction, credit research, or investment recommendations. If the user asks for the substantive view, route to the owning Public Equity skill after making the source/data/QC posture visible.
-
-## PM Judgment Layer
-
-Evidence quality is an investment input, not a footnote. Stale consensus, unsupported market data, conflicted EPS, missing source IDs, broken fiscal periods, bad units, unsupported chart claims, or style edits that alter caveats should change confidence, readiness, actionability, rating/target support, sizing, hedge posture, or watchlist status.
-
-Every substantial support output should surface:
-
-- evidence posture and source hierarchy;
-- source/as-of dates for market-sensitive data;
-- facts versus issuer claims versus consensus versus analyst assumptions;
-- missing evidence that would change the public-equity decision;
-- data QA issues that affect valuation, EPS, target price, rating, catalyst, benchmark/ETF/index exposure, sizing, or risk;
-- the downstream owning skill and handoff readiness.
-
-## Connector Honesty Rule
-
-Never imply live Bloomberg, FactSet, S&P Capital IQ, CapIQ, LSEG, Refinitiv, Daloopa, PitchBook, Morningstar, broker, email, collaboration-app, or internal data-system access unless that connector/app/tool is actually callable in the current runtime. If unavailable, use user-provided exports, request the export, label the gap as `missing_required_source`, and keep the output `preliminary`, `screen-grade`, or `not supportable` as appropriate.
-
-## Credit Markets Boundary
-
-Credit data may support common-equity risk judgment when it explains refinancing stress, solvency pressure, maturity walls, liquidity risk, ratings pressure, CDS/spread warning signals, or downside equity impairment.
-
-Credit Markets owns CDS, bonds, loans, investment-grade credit, high yield, leveraged loans, bank loans, private-credit / public-credit instruments, creditworthiness, covenant analysis, distressed, restructuring, recovery waterfalls, spread/yield relative value, and debt-security selection. In this plugin, credit terms may appear only as public-equity evidence or a Credit Markets handoff.
-
-## Output Discipline
-
-Support artifacts such as CSV, JSON, run logs, evidence ledgers, profiles, and style profiles are audit layers. For substantial user-facing work, lead with a concise source/data/QC verdict or an HTML/dashboard/workbook/report handoff when the owning skill calls for it. Never hide missing source evidence in sidecars.
-
-When embedded, support artifacts should remain secondary to the owning workflow's HTML dashboard/report, XLSX workbook, memo/report, meeting brief, thesis tracker, or QC report. CSV, JSON, Markdown, logs, manifests, profiles, and ledgers stay behind the curtain unless requested.
diff --git a/plugins/financial-markets/shared/equity-valuation-pm-standard.md b/plugins/financial-markets/shared/equity-valuation-pm-standard.md
deleted file mode 100644
index cf086d1..0000000
--- a/plugins/financial-markets/shared/equity-valuation-pm-standard.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Equity Valuation PM Standard
-
-Use this standard whenever Public Equity Investing work produces a model, valuation, scenario table, model update, comps package, or model audit. Pair it with `shared/pm-judgment-heuristics.md`.
-
-## Required PM Questions
-
-Every valuation artifact should answer these before it asks the reader to trust the math:
-
-1. What does the current stock price imply?
-2. What is the variant estimate path?
-3. Is upside driven by fundamentals, multiple expansion, mix, capital return, sentiment, or a one-time event?
-4. What breaks first in downside?
-5. What changes target, rating, sizing, hedge, trim, exit, or watchlist status?
-6. What evidence is missing?
-
-## Equity Valuation Taste Rules
-
-- Anchor to spot: show current price, market data as-of date, implied value per share, upside/downside to spot, and whether the current stock price already discounts the base case.
-- Separate model correctness from investment usefulness. A mechanically clean workbook can still be useless if the estimate path, multiple, source posture, or downside mechanism is wrong.
-- Bridge the Street: compare model drivers to consensus, guidance, and the market-implied path when available. State where the model is variant and where it is simply consensus repackaged.
-- Explain the rerating mechanism. Multiple expansion requires a reason: faster growth durability, margin confidence, ROIC improvement, capital return, mix shift, risk premium change, or positioning/catalyst reset.
-- Treat debt as an equity input unless the security is credit. Net debt, cost of debt, maturity risk, liquidity, and refinancing can affect common-equity value; bond, loan, CDS, covenant, recovery, spread/yield, and credit-security valuation belongs in Credit Markets.
-- Make downside mechanical. Identify the first driver to fail, the model line it hits, the stock-price effect, and the observable falsifier.
-- Convert output to action. Label the PM implication as `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `watchlist`, `wait for proof`, or `re-underwrite`.
-
-## Minimum Valuation Output
-
-- `Current price / as-of`: price, date, source, and market-data freshness.
-- `Implied value / share`: base, upside, downside, and probability-weighted value when supported.
-- `What is priced in`: market-implied revenue, margin, EPS, FCF, multiple, or event expectation.
-- `Variant estimate path`: the few model lines that must differ from consensus for the stock to work.
-- `Valuation bridge`: peer median or DCF base to selected value, with growth, margin, ROIC/quality, leverage, liquidity, cyclicality, index/ETF/ownership, and source-confidence adjustments where relevant.
-- `Scenario skew`: expected return versus hurdle, downside/upside ratio, break-even probability when applicable, and underwriteable-vs-optical upside label.
-- `Action rules`: add/trim/exit/watchlist thresholds tied to observable evidence.
-- `Missing evidence`: unresolved source, consensus, market-data, model, or diligence gaps.
-
-## Credit Markets Handoff
-
-Use Credit Markets when the user asks for bond comps, loan comps, CDS, spread or yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instruments underwriting, or distressed claim valuation. Public Equity Investing may cite those outputs only as common-equity risk context.
diff --git a/plugins/financial-markets/shared/final-deliverable-framework.md b/plugins/financial-markets/shared/final-deliverable-framework.md
deleted file mode 100644
index 0abc39c..0000000
--- a/plugins/financial-markets/shared/final-deliverable-framework.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# Final Deliverable Framework
-
-Use this standard for all Public Equity Investing skills. The goal is to give the user a human-readable investment artifact first, while keeping machine-readable files available for audit, reruns, and downstream automation. For user-facing HTML, also follow `shared/html-artifact-standard.md`.
-
-Before selecting this plugin for an untagged request, apply `shared/invocation-policy.md`. Activate Public Equity Investing only when it is explicitly named or tagged, or when the prompt is an unmistakable listed-equity investor workflow.
-
-Before an artifact-owning lead skill begins source gathering or analysis for a new standalone reader-facing artifact, read `shared/deliverable-intake-policy.md` and collect any materially missing choices through its adaptive `request_user_input` preflight, subject to the workflow-resolved catalyst-calendar, explicit full earnings-deep-dive,
-explicit pre-earnings-preview, explicit full event-analysis, and substantive idea-generation format defaults documented there. Respect formats already requested, preserve the surface of an existing artifact being reviewed or edited, and have downstream support or presentation skills reuse resolved choices without prompting again.
-
-Use the presentation-surface precedence in `shared/deliverable-intake-policy.md` as the controlling format rule. A new standalone reader-facing output defaults to polished standalone HTML when no explicit format, existing-artifact delivery, obvious workbook, or explicit native deck/document workflow takes precedence.
-
-When HTML is selected or defaulted, create the HTML artifact before responding. Do not reopen the presentation decision after source gathering or analysis, and do not substitute a full inline report for the selected HTML deliverable; use chat as a concise cover note linking to the hero artifact.
-
-The `public-equity-investing` router reads `internal-support/policy.md` before loading bundled internal evidence control,
-generic cleaning, rendering, style, or sector-context support. Those support capabilities do not appear as selectable skill entrypoints. `financials-normalizer` and `model-audit-tieout` remain visible when normalization or model review is the user's requested job, and may also support an owning workflow.
-
-## Hero lanes
-
-Choose one hero lane before drafting or running scripts:
-
-| Lane | Use when | Hero artifact |
-|---|---|---|
-| HTML dashboard or report | Substantial public-equity research, catalyst calendars, earnings previews or deep dives, event-driven work, pipeline or sector diligence, ETF/index constituent diligence, benchmark-relative research, thesis trackers, idea generation, memos, and source-heavy reports. | Polished standalone `.html` tailored to the workflow and following `shared/html-artifact-standard.md`; use internal `dashboard-builder` when a standardized or payload-driven dashboard is selected. |
-| XLSX workbook | DCF, three-statement, comps model, model update, tracker, and other workbook-style outputs. Financial normalization and data cleaning use this lane only for explicit standalone normalization/cleanup asks or when the owning workflow needs a workbook handoff. | `.xlsx` with a first visible `Cover` tab that acts as an insight dashboard. |
-| Concise chat answer | The user explicitly requests chat, inline, quick, no-file, or a similarly lightweight response. | Chat response with source posture and caveats. |
-| Support and audit files | Reproducibility, validation, manifests, logs, deterministic runners, and data interchange. | JSON, CSV, Markdown, logs, and manifests as secondary files only. |
-
-Markdown, raw JSON, CSV, manifests, run logs, and plan files are not the default user-facing deliverable. Use them as intermediate, support, audit, or explicit-request outputs. When a deterministic runner writes a Markdown sidecar by default, name it `support_note.md` or a specific `*_support_note.md`; reserve `report.md` for explicit legacy compatibility flags.
-
-Internal support playbooks follow the router-bundled `skills/public-equity-investing/internal-support/policy.md` and `shared/support-layer-routing-contract.md`: when invoked by an owning workflow, their CSV/XLSX/JSON/Markdown/profiles/ledgers are secondary support unless the user explicitly asked for that support artifact as the deliverable.
-
-## HTML recommendation triggers
-
-Treat a polished HTML artifact as the recommended presentation path when the ask includes:
-
-- 60-day, 90-day, or multi-category catalyst calendars;
-- earnings preview, earnings deep dive, post-print diligence, or full report;
-- event calendar, regulatory/FDA calendar, clinical pipeline map, or investor-day/macro/company catalyst map;
-- substantive idea-generation screen, market map, watchlist review, or reusable/source-heavy candidate set;
-- thesis tracker, diligence map, pipeline map, PM cockpit, source-heavy memo, ETF/index or constituent diligence, benchmark-relative exposure review, or multi-tab/table deliverable;
-- any request with many dates, many sources, multiple tables, or a need to scan and jump between sections.
-
-For substantive single-company 60/90-day catalyst calendars, resolve the format to a polished HTML catalyst calendar unless the user requests another format, a quick/no-file answer, or a workbook/tracker. In interactive runs,
-ask only remaining material choices such as depth; allow another format as a non-blocking opt-out.
-
-For explicit post-earnings deep dives, full reports, or reusable/source-heavy post-print packages, resolve the format to a polished standalone HTML post-earnings report unless the user requests another format, a quick/no-file answer, or workbook/model-update output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus.
-
-For explicit pre-earnings previews, full preview reports, or reusable/source-heavy pre-print packages, resolve the format to a polished standalone HTML pre-earnings report unless the user requests another format, a quick/no-file answer, or workbook/model output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus.
-
-For explicit full event analyses, full event reports, or reusable/source-heavy special-situations packages, resolve the format to a polished standalone HTML event report unless the user requests another format, a quick/no-file answer,
-or model/math output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus.
-
-For substantive idea-generation screens, market maps, watchlist reviews, or reusable/source-heavy candidate sets, resolve the format to a polished standalone HTML idea-triage report unless the user requests another format, a quick/no-file answer, or workbook/tracker output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus.
-
-For other workflows in this list, use `shared/deliverable-intake-policy.md`
-before selecting a materially unresolved format or depth in interactive runs.
-In non-interactive runs, apply the workflow's documented recommended defaults and disclose the assumed format and depth in the delivery message or accompanying summary rather than as visible hero-artifact metadata.
-
-Stay chat-only only when the user selects or explicitly asks for a quick read,
-short answer, summary, no file, no dashboard, or one named event/section as a lightweight response. Do not infer chat-only intent from direct question wording or the absence of a requested file.
-
-## Full-depth default
-
-Full analytical coverage is the recommended depth and the non-interactive fallback. Rendering as HTML is not a reason to compress the substance, and full coverage does not require a fixed section or module inventory. In an interactive run, honor the depth selected during intake. Compress when the user selects or explicitly asks for summary, short, quick, brief, one-pager,
-TL;DR, or a single requested section.
-
-## Citation standard
-
-For HTML dashboard/report outputs:
-
-- Material figures, confirmed dates, time-sensitive facts, and consequential factual claims should be visibly traceable to supporting sources near the point of use.
-- Keep confirmed facts, inferred timing, assumptions, and PM judgment distinguishable.
-- In source-heavy outputs, include a readable source ledger or equivalent audit surface.
-- Prefer unobtrusive citation treatment that supports review without dominating the layout.
-
-Source visibility belongs near the number or claim the reader is evaluating, not only in a final references list.
-
-When the standardized `dashboard-builder` path is selected, production dashboard JSON remains a support artifact, but it must still be complete because the rendered HTML depends on it. Those dashboards require `mode`, `layout`, `metadata`, `hero`, `snapshot`, tabs/modules, source ledger, `metadata.citation_policy: "strict"`, `metadata.payload_stage: "production"`, freeze time, source posture, readiness label/posture, and decision context. Draft/support payloads may use warning-mode validation only when they are clearly marked as draft/support and the final human artifact does not imply PM-ready, client-ready, committee-ready, external, or publication-ready status.
-
-## Workbook cover standard
-
-Every generated workbook should open to a first visible `Cover` tab that functions as an insight dashboard. It should include:
-
-- recommendation, net read, or decision question;
-- model/workbook status and decision-readiness label;
-- key metrics, valuation/model outputs, scenario results, or event pressure;
-- source posture, stale/missing evidence, warnings, hard failures, and unsupported assumptions;
-- major sensitivities, catalyst/risk flags, or thesis breaks;
-- what not to use the workbook for when caveated;
-- workbook map as a secondary navigation block.
-
-A table-of-contents cover is not enough. If the first tab does not answer the investment question, `model-audit-tieout` should flag it.
-
-## Final handoff
-
-Final responses should name the hero artifact first, then supporting files, then limitations. Do not lead with JSON, Markdown, CSV, run logs, or manifests unless the user explicitly requested those formats.
diff --git a/plugins/financial-markets/shared/html-artifact-standard.md b/plugins/financial-markets/shared/html-artifact-standard.md
deleted file mode 100644
index 1b3beb1..0000000
--- a/plugins/financial-markets/shared/html-artifact-standard.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# HTML Artifact Standard
-
-Use this standard whenever a Public Equity Investing workflow creates a user-facing HTML artifact. HTML should improve the reader's ability to understand and act on the analysis, not force every workflow into the same dashboard shape.
-
-## Design Principles
-
-- Produce a polished, investment-professional artifact appropriate for an institutional investor, research analyst, or portfolio manager.
-- Let the requested job determine the hierarchy and layout. A catalyst calendar should feel like a calendar; an earnings preview should lead with the expectation bar; a thesis tracker should lead with what changed.
-- Prefer legibility, restrained color, readable typography, clear visual hierarchy, and scan-friendly sections over bright palettes, decorative complexity, or crowded dashboard panels.
-- Put the most decision-relevant information near the top and make the workflow's primary object easy to find without extensive scrolling.
-- Create a strong first-read layer with the concise investor readout, key decision triggers, and the workflow's primary analytical object. Put detailed registers, monitoring queues, and source ledgers below that scan layer.
-- Use tables, timelines, cards, charts, and callouts only when they reduce interpretation time or improve comparison.
-- Avoid repetitive sections, redundant decision framing, and citation presentation that competes with the analysis.
-
-## Evidence And Integrity
-
-- Cite material figures, confirmed dates, time-sensitive facts, and consequential factual claims near where the reader uses them.
-- Before displaying a market price, return, multiple, or market-derived value in a hero card, headline KPI, or decision conclusion, verify that the cited source identifies the correct issuer, security, or ticker and the applicable as-of date. If that verification fails, omit the figure from the headline view or label it unavailable rather than carry an unverified anchor into the artifact.
-- Keep citations traceable without making the page read like a control log. Cite material figures, derived calculations, dated events, quotes, and consequential disputed claims inline; when adjacent narrative sentences rely on the same source, use a single nearby citation or short section-level source note instead of repeating identical source chips after each clause.
-- Distinguish confirmed facts from inferred windows, monitoring items, assumptions, and PM judgment.
-- Keep important missing evidence and limitations visible, but integrate them proportionately rather than overwhelming the artifact.
-- Express evidence limitations in plain investment-professional language. Do not expose internal workflow, validation, schema, or artifact-tracking fields such as `owning_workflow`, `decision_impact`, `readiness_effect`, `artifact_role`, or `hidden_unless_requested` in a user-facing artifact.
-- A visible source ledger is appropriate when provenance matters to the investment decision, but it should read as research documentation rather than an internal control record.
-- Do not claim decision readiness or precision unsupported by the available sources.
-
-## Output And QA
-
-- Deliver one polished standalone HTML file unless the user asks for another format or the workflow's primary deliverable is a workbook.
-- Keep JSON, Markdown, CSV, manifests, logs, payloads, and intermediate notes as secondary support artifacts unless explicitly requested.
-- Keep generation mechanics and intake/default disclosures out of the visible artifact, including badges such as `Format assumption`; when the workflow must disclose an assumed format or depth, do so in the delivery message or accompanying summary.
-- Before delivering a substantive HTML artifact, render and visually inspect screenshots of the opening viewport and important downstream sections, then iterate on hierarchy, legibility, clipping, crowding, citation noise, and whether the requested job is immediately visible.
-- For standalone local `.html` artifacts opened from a filesystem path or `file://` URL, do not use the in-app Browser plugin for visual inspection because it cannot open local HTML files reliably. Use local headless-browser screenshots instead.
-- For HTML served through an allowed local or hosted URL, the normal browser inspection workflow may be used.
-
-## Standardized Dashboard Option
-
-Use the internal `dashboard-builder` capability when the user asks for a standardized dashboard, a reusable validated template, a structured payload-driven render, or another workflow explicitly selects that rendering path. Its payload schema and validation rules apply only on that standardized dashboard path; bespoke HTML artifacts should follow this standard without being forced through a fixed module inventory.
diff --git a/plugins/financial-markets/shared/invocation-policy.md b/plugins/financial-markets/shared/invocation-policy.md
deleted file mode 100644
index 1bdad6b..0000000
--- a/plugins/financial-markets/shared/invocation-policy.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Public Equity Investing Invocation Policy
-
-## Entry Gate
-
-Activate this plugin only when at least one of these conditions is satisfied:
-
-1. **Explicit invocation.** The user tags or names Public Equity Investing,
-   uses `@public-equity-investing`, supplies its plugin link, or explicitly invokes one of its skills.
-2. **Perfect-fit mandate.** The prompt unambiguously asks for investment work on a listed equity or publicly traded security from an equity-investor perspective.
-
-When a prompt could just as reasonably be ordinary research, corporate finance, banking, private investing, or document creation, do not activate this plugin.
-
-## Perfect-Fit Signals
-
-The untagged prompt must tie a listed issuer, ticker, equity position, or public security directly to a public-equity workflow, such as:
-
-- an earnings preview/deep dive, public-equity investment thesis, initiation,
-  long/short pitch, catalyst calendar, or thesis tracker;
-- a common-equity DCF/comps/model update, price-target debate, risk sizing,
-  hedge view, or add/trim/exit decision;
-- listed-equity event, ETF/index constituent, or benchmark-relative equity diligence for an investment team.
-
-## Non-Triggers
-
-Do not invoke Public Equity Investing automatically for generic requests to research a company, explain a share-price move, create a report or document,
-build a model, value a business, summarize earnings, clean a workbook, or prepare a meeting brief unless the listed-equity investment mandate is unmistakable. A public company name by itself is not sufficient.
-
-## After Activation
-
-Once this gate is met, the router selects the owning workflow from user intent and focused skill descriptions. The selected lead workflow, not the router, applies `final-deliverable-framework.md` and reads `deliverable-intake-policy.md` before a new standalone reader-facing artifact begins to resolve presentation choices. Support and presentation skills inherit this activation decision rather than extending the implicit scope.
diff --git a/plugins/financial-markets/shared/model_citations.py b/plugins/financial-markets/shared/model_citations.py
deleted file mode 100644
index 44d51ad..0000000
--- a/plugins/financial-markets/shared/model_citations.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""Source-to-cell citation helpers for Public Equity Investing model workbooks."""
-
-from __future__ import annotations
-
-import json
-import re
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Mapping, Sequence
-
-REQUIRED_CITATION_FIELDS = {
-    "citation_id",
-    "workbook_path",
-    "sheet",
-    "cell_or_range",
-    "metric_name",
-    "value",
-    "formula",
-    "source_ids",
-    "assumption_flag",
-    "tie_out_status",
-}
-VALID_TIE_OUT_STATUSES = {"tied", "not_tied", "needs_review", "model_generated", "not_applicable"}
-
-
-def _slug(value: Any) -> str:
-    raw = str(value or "").strip().lower()
-    raw = re.sub(r"[^a-z0-9]+", "-", raw).strip("-")
-    return raw or "citation"
-
-
-def _stringify(value: Any) -> str:
-    if value is None:
-        return ""
-    if isinstance(value, (dict, list)):
-        return json.dumps(value, sort_keys=True)
-    return str(value)
-
-
-def citation_item(
-    citation_id: str,
-    workbook_path: str | Path,
-    sheet: str,
-    cell_or_range: str,
-    metric_name: str,
-    value: Any = "",
-    formula: str = "",
-    source_ids: Sequence[str] | None = None,
-    assumption_flag: bool = False,
-    tie_out_status: str = "model_generated",
-    **extra: Any,
-) -> dict[str, Any]:
-    sources = [str(item) for item in (source_ids or ["model-output"]) if str(item)] or [
-        "model-output"
-    ]
-    cid = str(
-        citation_id or f"model-output:{_slug(sheet)}:{_slug(metric_name)}:{_slug(cell_or_range)}"
-    )
-    cell = str(cell_or_range or "A1")
-    item = {
-        "citation_id": cid,
-        "id": cid,
-        "source_id": cid,
-        "parent_source_id": sources[0],
-        "title": str(metric_name or cid),
-        "short_label": f"Model: {sheet}!{cell}",
-        "type": "model_cell",
-        "quality": "model_output",
-        "workbook_path": str(workbook_path),
-        "sheet": str(sheet),
-        "cell_or_range": cell,
-        "cell": cell.split(":", 1)[0],
-        "range": cell,
-        "metric_name": str(metric_name or cid),
-        "value": _stringify(value),
-        "formula": str(formula or ""),
-        "source_ids": sources,
-        "assumption_flag": bool(assumption_flag),
-        "tie_out_status": tie_out_status
-        if tie_out_status in VALID_TIE_OUT_STATUSES
-        else "needs_review",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "aliases": [str(metric_name or cid)],
-        "notes": "Generated workbook-level citation for dashboard/source-gate use.",
-    }
-    item.update(extra)
-    return item
-
-
-def load_model_citations(path: str | Path) -> list[dict[str, Any]]:
-    payload = json.loads(Path(path).read_text(encoding="utf-8"))
-    if isinstance(payload, dict):
-        payload = payload.get("model_citations") or payload.get("workbook_citations") or []
-    return [item for item in payload if isinstance(item, dict)] if isinstance(payload, list) else []
-
-
-def validate_model_citations(
-    payload: Sequence[Mapping[str, Any]] | Mapping[str, Any], strict: bool = False
-) -> list[str]:
-    citations = (
-        (payload.get("model_citations") or payload.get("workbook_citations") or [])
-        if isinstance(payload, Mapping)
-        else payload
-    )
-    errors: list[str] = []
-    if not isinstance(citations, Sequence) or isinstance(citations, (str, bytes)) or not citations:
-        return ["model citations must be a non-empty array"]
-    for idx, citation in enumerate(citations):
-        prefix = f"citations[{idx}]"
-        if not isinstance(citation, Mapping):
-            errors.append(f"{prefix} must be an object")
-            continue
-        missing = sorted(field for field in REQUIRED_CITATION_FIELDS if field not in citation)
-        if missing:
-            errors.append(f"{prefix} missing required fields: {', '.join(missing)}")
-        if citation.get("tie_out_status") not in VALID_TIE_OUT_STATUSES:
-            errors.append(f"{prefix}.tie_out_status invalid: {citation.get('tie_out_status')}")
-        source_ids = citation.get("source_ids")
-        if not isinstance(source_ids, list) or not source_ids:
-            errors.append(f"{prefix}.source_ids must be a non-empty array")
-        if strict:
-            for field in ("citation_id", "workbook_path", "sheet", "cell_or_range", "metric_name"):
-                if str(citation.get(field, "")).strip().lower() in {
-                    "",
-                    "unknown",
-                    "not_provided",
-                    "n/a",
-                }:
-                    errors.append(f"{prefix}.{field} cannot be placeholder in strict mode")
-    return errors
diff --git a/plugins/financial-markets/shared/pm-judgment-heuristics.md b/plugins/financial-markets/shared/pm-judgment-heuristics.md
deleted file mode 100644
index 05ee13a..0000000
--- a/plugins/financial-markets/shared/pm-judgment-heuristics.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Public Equity PM Judgment Heuristics
-
-Use this shared standard for Public Equity Investing skills when the user is diligencing a listed equity, public-company catalyst, public-company model, long/short pitch, sell-side note, ETF/index constituent, or portfolio position.
-
-## Audience Modes
-
-- `long_only_pm`: emphasize thesis durability, downside capture, benchmark-relative risk, liquidity, add/trim discipline, and portfolio role.
-- `long_short_hf`: emphasize variant perception, catalyst path, scenario skew, borrow/crowding, hedge/cover rules, gross/net exposure, and timing risk.
-- `sell_side_research`: emphasize rating and target discipline, estimate revisions, Street debate framing, risk-to-rating, client readability, and compliance-safe wording.
-- `etf_index_diligence`: emphasize index methodology, constituent weight, float/liquidity, ETF ownership, passive flow relevance, factor/sector exposure, rebalance risk, and tracking-error implications.
-- `public_equity_diligence`: emphasize source-backed issuer diligence, business quality, management credibility, governance, accounting quality, capital allocation, valuation risk, and the next diligence source needed.
-
-## Veteran PM Questions
-
-Every substantial output should answer these before it adds background:
-
-1. What is mispriced, if anything?
-2. What is already priced in?
-3. What proves the thesis?
-4. What kills the thesis?
-5. Why now?
-6. What changes sizing, rating, target, hedge, trim, exit, cover, or watchlist status?
-7. What evidence is missing?
-
-## Core Heuristics
-
-- Variant wedge: separate a known fact from the mispriced insight. If there is no variant wedge, say the idea is a monitoring item or pass.
-- What is priced in: state what the current price, consensus, positioning, or benchmark ownership already appears to discount before recommending action.
-- Estimate path: identify the revenue, margin, EPS, FCF, KPI, or multiple path that must move for the stock to work.
-- Valuation skew: show base/upside/downside and whether the current price already pays for the upside case.
-- Downside mechanism: explain how money is lost before explaining how money is made.
-- Catalyst quality: distinguish hard dates, evidence windows, soft narrative catalysts, and no-catalyst setups.
-- Ownership and positioning: flag liquidity, capacity, passive ownership, ETF/index exposure, short interest, borrow, crowding, and factor/sector exposure when relevant.
-- Benchmark and factor exposure: state whether the security is an absolute-return idea, benchmark-relative active bet, hedge, factor expression, or constituent diligence item.
-- Falsifiers: make every thesis test observable, dated, and connected to a source, KPI, model line, or event.
-- Action discipline: classify the next action as `add`, `press`, `hold`, `trim`, `exit`, `cover`, `hedge`, `watchlist`, `pass`, `wait for proof`, or `re-underwrite`.
-
-## Taste Rules
-
-- No generic `good company / cheap stock` conclusions. Name the specific mispricing or say none is evident.
-- Separate company thesis from stock thesis. A good company can be a bad stock; a bad print can create a better setup.
-- Label each important claim as fact, management claim, consensus, market data, model output, assumption, or PM judgment.
-- Use freeze times and as-of dates for consensus, price, ownership, liquidity, borrow, options, index, ETF, and short-interest data.
-- Penalize uncited catalysts, stale market data, false precision, and unsupported claims about what the market believes.
-- Do not smooth over missing data. Put missing evidence into the decision, not just the appendix.
-
-## PM/Risk Workflow Lens
-
-Use this shared lens for `portfolio-risk-management` whenever the user is sizing or hedging a public-equity position.
-
-- Alpha vs unwanted exposure: isolate the idiosyncratic, catalyst, factor, benchmark, or index exposure the PM is intentionally paid to own from the beta, factor, sector, liquidity, borrow, options, FX, commodity, rates, crowding, or gap risk that is accidental.
-- Retained exposure: state what risk remains after the size or hedge recommendation. A hedge that removes the actual stock thesis is usually worse than a smaller position.
-- Binding constraint: name the tightest credible limiter across downside loss, liquidity, borrow, gross/net/beta/factor exposure, active weight, option premium/Greeks, mandate, catalyst timing, and confidence.
-- Hedge-failure scenario: include one case where the hedge, pair, ETF, option, or macro proxy fails when the equity position is under pressure.
-- Size-down/no-hedge alternative: compare the proposed hedge to reducing gross, using a smaller starter size, or doing nothing when basis risk, cost, or alpha destruction is worse than the exposure.
-- Action rules: define add/trim/exit/cover/resize/roll/remove rules tied to observable prices, thesis evidence, liquidity, borrow, option expiry, catalyst path, factor exposure, or hedge basis.
-- Credit signal boundary: CDS levels, credit spreads, ratings, refinancing stress, maturity walls, and covenant headlines may inform public-equity downside or solvency risk. Do not size or recommend CDS, bonds, loans, spread DV01/CS01, capital-structure, distressed, recovery, or covenant trades inside Public Equity Investing; route those to Credit Markets.
diff --git a/plugins/financial-markets/shared/support-layer-routing-contract.md b/plugins/financial-markets/shared/support-layer-routing-contract.md
deleted file mode 100644
index f788560..0000000
--- a/plugins/financial-markets/shared/support-layer-routing-contract.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Support-Layer Routing Contract
-
-Use this contract for `financial-source-of-truth`, `financials-normalizer`,
-`excel-data-cleaner`, `deck-report-qc`, and `style-guide-adapter`. Read `skills/public-equity-investing/internal-support/policy.md` through the `public-equity-investing` router to resolve internal capability labels to their bundled playbooks.
-
-## Embedded Service Rule
-
-These five capabilities are embedded services inside Public Equity Investing.
-`financials-normalizer` and `deck-report-qc` remain visible when explicitly requested as standalone jobs; `financial-source-of-truth`,
-`excel-data-cleaner`, and `style-guide-adapter` load as internal playbooks.
-They normally support an owning workflow rather than becoming the final investment owner. The owning workflow can be `earnings-preview`, `earnings-deep-dive`,
-`company-tearsheet`, `initiating-coverage`, `memo-builder`,
-`long-short-pitch`, `equity-model-update`, `dcf-model-builder`,
-`three-statement-model-builder`, `comps-valuation`,
-`scenario-sensitivity-generator`, `portfolio-risk-management`,
-`event-driven-analyzer`, `economic-impact-report`, `thesis-tracker`,
-`meeting-prep`, or the internal `dashboard-builder` capability.
-
-Standalone use is allowed only when the user explicitly asks for source review, normalization, data cleaning, QC, or style adaptation as the task itself. Even then, the support skill should state what downstream investment workflow it is preparing or de-risking.
-
-## Internal Support Handoff Fields
-
-Every substantial embedded support pass should preserve these fields in working context, a structured handoff, a manifest, or a support/audit artifact when one exists:
-
-- `owning_workflow`: the Public Equity skill that owns the final investment artifact or `standalone_support_request` when the support task is explicit.
-- `decision_impact`: how the source, data, QC, or style issue changes confidence, valuation, target, estimate path, sizing, hedge posture, watchlist status, or circulation readiness.
-- `readiness_effect`: `decision_grade`, `research_grade`, `screen_grade`, `needs_targeted_fixes`, `not_circulable`, `not_decision_ready`, or `blocked`.
-- `artifact_role`: `embedded_support_artifact`, `standalone_support_artifact`, `human_qc_report`, or `primary_workbook_when_explicitly_requested`.
-- `hidden_unless_requested`: `true` for CSV, JSON, Markdown, logs, manifests, profiles, ledgers, and other machine-readable or audit sidecars unless the user explicitly asks for them.
-
-## Hero Artifact Policy
-
-When embedded, support outputs should feed the owning workflow's hero deliverable: HTML dashboard/report, XLSX workbook, memo/report, meeting brief, thesis tracker, or QC report. CSV, JSON, Markdown, run logs, profiles, ledgers, and manifests remain support artifacts behind that hero deliverable unless the user asks for those formats.
-
-Do not display internal handoff field names or implementation values in the owning workflow's user-facing hero artifact. Translate material implications into plain reader-facing language, such as an evidence limitation, readiness caveat, or next source needed, while keeping the metadata available for audit or downstream processing.
-
-When standalone, the support skill may lead with the appropriate human artifact:
-
-- `financial-source-of-truth`: source posture and evidence ledger summary.
-- `financials-normalizer`: normalized workbook or source-index package only when explicitly requested as the deliverable.
-- `excel-data-cleaner`: cleaned workbook only when the task is standalone cleanup.
-- `deck-report-qc`: HTML QC report for explicit QC-only work.
-- `style-guide-adapter`: style profile, edit checklist, or edited artifact only when a safe artifact editor is available.
-
-## Invocation Rules For Owning Skills
-
-Owning skills should invoke support services when the risk is decision-changing:
-
-- Use `financial-source-of-truth` before final claims, citations, source conflicts, stale market data, or issuer/management assertions.
-- Use `financials-normalizer` before model, comps, memo, earnings, or thesis work depends on messy issuer financials, consensus/provider exports, guidance, segment tables, share count, net debt, or capital allocation inputs.
-- Use `excel-data-cleaner` before workbook/model/dashboard work depends on messy tables, duplicate rows, mixed fiscal periods, malformed dates/numbers, or ambiguous identifiers.
-- Use `deck-report-qc` before circulation of decks, reports, model-output packs, source-heavy memos, or dashboard/report packets.
-- Use `style-guide-adapter` after substance is source-supported, when the task is to match institutional style without changing facts, formulas, citations, caveats, or source posture.
-
-## Not-Owner Boundary
-
-Support services do not own recommendations, rating changes, trade construction, valuation conclusions, earnings calls, or public-equity thesis decisions. If support work uncovers a decision-changing issue, it should name the issue, name the owning workflow, and state the handoff needed.
diff --git a/plugins/financial-markets/shared/workbook-artifact-standard.md b/plugins/financial-markets/shared/workbook-artifact-standard.md
deleted file mode 100644
index 3babd85..0000000
--- a/plugins/financial-markets/shared/workbook-artifact-standard.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Workbook Artifact Standard
-
-Use this standard for Public Equity Investing skills that generate an `.xlsx` model, tracker, export pack, or workbook-style deliverable.
-
-## Core rule
-
-Generated workbooks must include a first visible sheet named `Cover`, or a workflow-specific equivalent such as `Update_Cover` for a model-update control pack, unless the task is explicitly a non-destructive edit of a user-provided workbook.
-
-The cover is an analyst and PM insight dashboard, not a raw data table, README, or table of contents. It should answer, within one screen:
-
-- what the workbook is and what decision it supports;
-- the recommendation, net read, or decision question;
-- the headline output or read-through;
-- the key metrics, cases, or events that drive the answer;
-- the major sensitivities, catalyst/risk flags, or thesis breaks that could change the answer;
-- the model/workbook status and decision-readiness label, including whether the output is screen-grade, senior-review-ready, decision-grade, blocked, or not-decision-ready;
-- the source posture, stale or missing evidence, warnings, and hard failures;
-- where the reviewer should go next inside the workbook.
-
-Workbook navigation is secondary. A table-of-contents cover is not enough. A cover that only explains workbook contents is not sufficient.
-
-## Required cover blocks
-
-Include the following blocks whenever the underlying data exists. If a block cannot be supported, include it with a clear `not provided`, `not modeled`, or `needs source support` note rather than silently omitting it.
-
-| Block | Required content |
-|---|---|
-| Header / status strip | Company or issuer, ticker/security, workbook type, as-of or valuation date, currency, units, freeze time, model status, artifact level, workbook mode. |
-| Executive read-through | Two to four concise sentences or rows describing the main implication and how much confidence to place on it. |
-| KPI tiles | The highest-signal metrics for the workbook type: value/share, scenario range, revenue/EBITDA/FCF, liquidity, peer multiples, event counts, or data-quality metrics. |
-| Scenario / output table | Base/upside/downside, bull/base/bear, valuation range, top catalysts, cleaning summary, thesis state, or comparable-company statistics. |
-| Visuals or chart-ready data | At least two chart-ready tables. Add native charts when the writer supports them; otherwise label the chart-ready tables clearly. |
-| Source posture | Primary sources used, source dates, confidence labels, stale items, missing source support, and unsupported assumptions. |
-| QA and caveats | Hard failures, warnings, denominator distortions, formula/value-export caveats, open checks, and recalc limitations. |
-| Workbook map | Tab name, role, what the reviewer should use it for, and notable limitations. |
-
-## Skill-specific mapping
-
-- `comps-valuation`: target snapshot, peer universe, selected multiples, valuation range, peer medians/quartiles, outlier and denominator flags, EV bridge caveats, source gaps, QA status.
-- `dcf-model-builder`: valuation range, value/share by case, WACC and terminal assumptions, terminal value share of EV, PV bridge, key value drivers, sensitivity highlights, source and QA posture.
-- `three-statement-model-builder`: final revenue, EBITDA, FCF, cash, liquidity trough, peak net leverage, covenant or liquidity warnings, driver deltas, source and QA posture.
-- `catalyst-calendar`: top catalysts, urgency/impact counts, next 30/60/90 day calendar pressure, owner/prep status, source confidence, stale or unconfirmed events.
-- `earnings-preview`: consensus bar, whisper deltas, key KPI watch items, scenario framing, source freeze, unsupported live-market sections, call watch items.
-- `thesis-tracker`: thesis status, conviction/rating, base/bull/bear value, pillar health, KPI evidence, upcoming catalysts, estimate revisions, open questions.
-- `excel-data-cleaner`: input and output row counts, inferred grain/domain, cleaning actions, quality issue counts, missingness/duplicate/type-conversion flags, preserved raw tabs, downstream use limitations.
-
-## Formula Workbook Inspection Gates
-
-Formula workbooks must clear model-specific thresholds before the skill may label them `banker_formula_workbook`:
-
-- DCF formula workbooks: at least 800 formulas, required sheets present, required formula-bearing sheets populated, `Cover` first, styles preserved, no external workbook links, and named ranges or a source-to-cell anchor map such as `model_citations.json`.
-- Three-statement formula workbooks: at least 1,100 formulas, required sheets present, required formula-bearing sheets populated, `Cover` first, styles preserved, no external workbook links, and named ranges or a source-to-cell anchor map such as `model_citations.json`.
-
-The inspection threshold should be high enough to catch accidental flattening of the bundled formula template, not just prove that a few formulas survived.
-
-## Audit expectation
-
-`model-audit-tieout` should flag generated or reviewed workbooks that lack a decision-useful first visible cover/dashboard. A workbook can pass mechanical formula checks but still be weak for review if source posture, model status, warnings, and key outputs are buried in later tabs.
diff --git a/plugins/financial-markets/shared/workbook_artifacts.py b/plugins/financial-markets/shared/workbook_artifacts.py
deleted file mode 100644
index b1d411d..0000000
--- a/plugins/financial-markets/shared/workbook_artifacts.py
+++ /dev/null
@@ -1,429 +0,0 @@
-"""Shared workbook cover helpers for Public Equity Investing deterministic artifacts."""
-
-from __future__ import annotations
-
-import math
-from typing import Any, Iterable, Mapping, Sequence
-
-
-def _text(value: Any, default: str = "") -> str:
-    if value is None:
-        return default
-    if isinstance(value, float) and not math.isfinite(value):
-        return default
-    return str(value)
-
-
-def _number(value: Any) -> float | None:
-    if isinstance(value, bool):
-        return None
-    if isinstance(value, (int, float)) and math.isfinite(float(value)):
-        return float(value)
-    return None
-
-
-def _fmt(value: Any, default: str = "n/a") -> str:
-    num = _number(value)
-    if num is None:
-        return _text(value, default)
-    if abs(num) >= 100:
-        return f"{num:,.1f}"
-    if abs(num) >= 10:
-        return f"{num:,.2f}"
-    return f"{num:,.3f}"
-
-
-def _fmt_pct(value: Any, default: str = "n/a") -> str:
-    num = _number(value)
-    if num is None:
-        return _text(value, default)
-    if abs(num) <= 2:
-        return f"{num * 100:,.1f}%"
-    return f"{num:,.1f}%"
-
-
-def _join_messages(items: Sequence[Any] | None, limit: int = 3) -> str:
-    if not items:
-        return "None flagged"
-    out: list[str] = []
-    for item in items[:limit]:
-        if isinstance(item, Mapping):
-            out.append(_text(item.get("message") or item.get("code") or item))
-        else:
-            out.append(_text(item))
-    suffix = "" if len(items) <= limit else f" (+{len(items) - limit} more)"
-    return "; ".join(out) + suffix
-
-
-def cover_dict_rows(rows: Iterable[Sequence[Any]]) -> list[dict[str, Any]]:
-    return [
-        {
-            "section": row[0] if len(row) > 0 else "",
-            "metric": row[1] if len(row) > 1 else "",
-            "value": row[2] if len(row) > 2 else "",
-            "notes": row[3] if len(row) > 3 else "",
-        }
-        for row in rows
-    ]
-
-
-def dcf_cover_rows(
-    plan: Mapping[str, Any],
-    scenario_results: Mapping[str, Mapping[str, Any]],
-    sensitivity_result: Mapping[str, Any],
-    checks: Mapping[str, Any],
-    run_log: Mapping[str, Any],
-) -> list[list[Any]]:
-    meta = plan.get("meta", {}) if isinstance(plan.get("meta"), Mapping) else {}
-    source_basis = (
-        plan.get("source_basis", []) if isinstance(plan.get("source_basis"), list) else []
-    )
-    scenarios = [name for name in ("downside", "base", "upside") if name in scenario_results]
-    values = [
-        _number(scenario_results[name].get("value_per_share"))
-        for name in scenarios
-        if _number(scenario_results[name].get("value_per_share")) is not None
-    ]
-    base = scenario_results.get("base", {})
-    terminal = base.get("terminal", {}) if isinstance(base.get("terminal"), Mapping) else {}
-    pv = base.get("pv", {}) if isinstance(base.get("pv"), Mapping) else {}
-    wacc = base.get("wacc", {}) if isinstance(base.get("wacc"), Mapping) else {}
-    sensitivity_rows = (
-        sensitivity_result.get("rows", [])
-        if isinstance(sensitivity_result.get("rows"), list)
-        else []
-    )
-    drivers: list[tuple[str, float]] = []
-    for sensitivity in sorted(
-        {_text(row.get("sensitivity")) for row in sensitivity_rows if isinstance(row, Mapping)}
-    ):
-        vals = [
-            _number(row.get("value"))
-            for row in sensitivity_rows
-            if isinstance(row, Mapping) and _text(row.get("sensitivity")) == sensitivity
-        ]
-        nums = [v for v in vals if v is not None]
-        if nums:
-            drivers.append((sensitivity, max(nums) - min(nums)))
-    drivers.sort(key=lambda item: item[1], reverse=True)
-    value_range = f"{_fmt(min(values))} - {_fmt(max(values))} per share" if values else "n/a"
-    exit_multiple = _fmt(terminal.get("exit_ebitda_multiple"))
-    exit_multiple_label = f"{exit_multiple}x" if exit_multiple != "n/a" else "n/a"
-
-    rows: list[list[Any]] = [
-        ["Section", "Metric", "Value", "Notes"],
-        ["Header", "Company", meta.get("company", ""), "DCF workbook landing page"],
-        [
-            "Header",
-            "Decision question",
-            "What valuation range is supportable?",
-            "Cover must answer the investment question, not just navigate the workbook",
-        ],
-        ["Header", "Model type", meta.get("model_type", ""), "FCFF/FCFE basis should be explicit"],
-        [
-            "Header",
-            "Valuation date",
-            meta.get("valuation_date", ""),
-            "Use source freeze before PM/IC use",
-        ],
-        [
-            "Header",
-            "Currency / units",
-            f"{meta.get('currency', '')} / {meta.get('units', '')}",
-            "Do not mix share and enterprise metrics",
-        ],
-        ["Status", "Model status", run_log.get("model_status", ""), "Decision-readiness label"],
-        [
-            "Status",
-            "Workbook mode",
-            run_log.get("workbook_mode", ""),
-            "Deterministic value export, not a fully linked banker formula workbook",
-        ],
-        [
-            "Status",
-            "Hard failures",
-            len(run_log.get("hard_failures", []) or []),
-            _join_messages(run_log.get("hard_failures", [])),
-        ],
-        [
-            "Status",
-            "Warnings",
-            len(run_log.get("warnings", []) or []),
-            _join_messages(run_log.get("warnings", [])),
-        ],
-        [
-            "Executive read-through",
-            "Net read",
-            value_range,
-            "Headline output; downside/base/upside scenario range",
-        ],
-        [
-            "KPI tiles",
-            "Base value per share",
-            _fmt(base.get("value_per_share")),
-            "Headline valuation metric",
-        ],
-        ["KPI tiles", "Base enterprise value", _fmt(base.get("enterprise_value")), "Base case"],
-    ]
-    for name in scenarios:
-        result = scenario_results[name]
-        rows.append(
-            [
-                "Scenario summary",
-                name,
-                _fmt(result.get("value_per_share")),
-                f"EV {_fmt(result.get('enterprise_value'))}; equity {_fmt(result.get('equity_value'))}; discount rate {_fmt_pct(result.get('discount_rate'))}",
-            ]
-        )
-    rows.extend(
-        [
-            ["DCF bridge", "PV of FCF", _fmt(pv.get("pv_fcf")), "Base case"],
-            [
-                "DCF bridge",
-                "PV of terminal value",
-                _fmt(pv.get("pv_terminal_value")),
-                f"TV share of EV {_fmt_pct(base.get('tv_percent_ev'))}",
-            ],
-            [
-                "Assumptions",
-                "WACC",
-                _fmt_pct(wacc.get("wacc") or base.get("discount_rate")),
-                "Base case discount rate",
-            ],
-            [
-                "Assumptions",
-                "Terminal method",
-                terminal.get("method", ""),
-                "Exit multiple or perpetuity growth",
-            ],
-            [
-                "Assumptions",
-                "Terminal growth / exit multiple",
-                f"{_fmt_pct(terminal.get('terminal_growth_rate'))} / {exit_multiple_label}",
-                "Show n/a when not applicable",
-            ],
-        ]
-    )
-    for idx, (driver, spread) in enumerate(drivers[:5], start=1):
-        rows.append(
-            ["Sensitivity read", f"Driver {idx}", driver, f"Value/share spread {_fmt(spread)}"]
-        )
-    rows.extend(
-        [
-            [
-                "Chart-ready data",
-                "Scenario values",
-                "Downside / base / upside",
-                "Use Summary for chart-ready valuation range",
-            ],
-            [
-                "Chart-ready data",
-                "DCF bridge",
-                "PV FCF / PV terminal value",
-                "Use Cover rows and Summary for bridge chart",
-            ],
-            [
-                "Chart-ready data",
-                "Sensitivity drivers",
-                ", ".join(driver for driver, _spread in drivers[:3]) or "n/a",
-                "Use Sensitivities for full grid",
-            ],
-            [
-                "Source posture",
-                "Source count",
-                len(source_basis),
-                "Review Assumptions tab for source labels and dates",
-            ],
-            [
-                "Source posture",
-                "Source caveat",
-                "Visible in Assumptions",
-                "Unsupported or stale assumptions should not be buried",
-            ],
-            [
-                "Workbook map",
-                "Summary",
-                "Scenario-level valuation table",
-                "Use with Cover for headline range",
-            ],
-            [
-                "Workbook map",
-                "Model",
-                "Long-format DCF outputs",
-                "Source ids and evidence labels retained",
-            ],
-            [
-                "Workbook map",
-                "Sensitivities",
-                "Chart-ready sensitivity rows",
-                "Native charts not produced by the stdlib writer",
-            ],
-            [
-                "Workbook map",
-                "Checks / Run Log",
-                "QA, warnings, and run status",
-                "Resolve hard failures before decision use",
-            ],
-        ]
-    )
-    return rows
-
-
-def three_statement_cover_rows(
-    plan: Mapping[str, Any],
-    scenario_outputs: Mapping[str, Mapping[str, Any]],
-    run_log: Mapping[str, Any],
-) -> list[dict[str, Any]]:
-    p0 = run_log.get("p0_handoff", {}) if isinstance(run_log.get("p0_handoff"), Mapping) else {}
-    summaries = (
-        p0.get("scenario_outputs", {}) if isinstance(p0.get("scenario_outputs"), Mapping) else {}
-    )
-    meta = plan.get("meta", {}) if isinstance(plan.get("meta"), Mapping) else {}
-    source_basis = (
-        plan.get("source_basis", []) if isinstance(plan.get("source_basis"), list) else []
-    )
-    base = summaries.get("base", {}) if isinstance(summaries.get("base"), Mapping) else {}
-    rows: list[list[Any]] = [
-        [
-            "Header",
-            "Company",
-            meta.get("company_name") or meta.get("company", ""),
-            "Three-statement model landing page",
-        ],
-        [
-            "Header",
-            "Decision question",
-            "What operating path, liquidity, and leverage profile is supportable?",
-            "Cover must answer the PM question, not just navigate the workbook",
-        ],
-        [
-            "Header",
-            "Industry",
-            meta.get("industry", ""),
-            "Use sector context for KPIs and operating drivers",
-        ],
-        ["Header", "As-of date", meta.get("as_of_date", ""), "Refresh before PM/IC use"],
-        [
-            "Header",
-            "Currency / units",
-            f"{meta.get('currency', '')} / {meta.get('units', '')}",
-            "Keep all model outputs on one unit basis",
-        ],
-        ["Status", "Model status", run_log.get("model_status", ""), "Decision-readiness label"],
-        [
-            "Status",
-            "Workbook mode",
-            run_log.get("workbook_mode", ""),
-            "Deterministic value export, not a fully linked banker formula workbook",
-        ],
-        [
-            "Status",
-            "Hard failures",
-            len(run_log.get("hard_failures", []) or []),
-            _join_messages(run_log.get("hard_failures", [])),
-        ],
-        [
-            "Status",
-            "Warnings",
-            len(run_log.get("warnings", []) or []),
-            _join_messages(run_log.get("warnings", [])),
-        ],
-        [
-            "Executive read-through",
-            "Net read",
-            base.get("final_period", ""),
-            "Base case final period used for headline outputs",
-        ],
-        [
-            "Executive read-through",
-            "Base revenue / EBITDA / FCF",
-            f"{_fmt(base.get('final_revenue'))} / {_fmt(base.get('final_ebitda'))} / {_fmt(base.get('final_fcf'))}",
-            "Final-period operating read-through",
-        ],
-        [
-            "Executive read-through",
-            "Liquidity trough",
-            _fmt(p0.get("liquidity_trough")),
-            "Minimum liquidity across scenarios",
-        ],
-        [
-            "KPI tiles",
-            "Base ending cash",
-            _fmt(base.get("ending_cash")),
-            "Base case liquidity ending point",
-        ],
-        [
-            "KPI tiles",
-            "Base peak net leverage",
-            f"{_fmt(base.get('peak_net_leverage'))}x",
-            "Base case balance sheet stress",
-        ],
-    ]
-    for name in ("downside", "base", "upside"):
-        summary = summaries.get(name)
-        if not isinstance(summary, Mapping):
-            continue
-        rows.append(
-            [
-                "Scenario summary",
-                name,
-                f"Revenue {_fmt(summary.get('final_revenue'))}; EBITDA {_fmt(summary.get('final_ebitda'))}; FCF {_fmt(summary.get('final_fcf'))}",
-                f"Ending cash {_fmt(summary.get('ending_cash'))}; peak net leverage {_fmt(summary.get('peak_net_leverage'))}x",
-            ]
-        )
-    rows.extend(
-        [
-            [
-                "Chart-ready data",
-                "Scenario outputs",
-                "Revenue / EBITDA / FCF / cash",
-                "Use Summary for chart-ready scenario comparison",
-            ],
-            [
-                "Chart-ready data",
-                "Sensitivity outputs",
-                "Stress cases",
-                "Use Sensitivities for full grid",
-            ],
-            [
-                "Driver dashboard",
-                "Key operating drivers",
-                ", ".join(p0.get("key_operating_drivers", [])[:6])
-                if isinstance(p0.get("key_operating_drivers"), list)
-                else "revenue growth, margin, working capital, capex, interest",
-                "Use Model tab for period-level detail",
-            ],
-            [
-                "Source posture",
-                "Source count",
-                len(source_basis),
-                "Review Sources and Assumptions tabs for dates and confidence",
-            ],
-            [
-                "QA posture",
-                "Checks passed/failed",
-                _text(p0.get("checks_passed_failed", "")),
-                "Resolve hard failures before decision use",
-            ],
-            [
-                "Workbook map",
-                "Summary",
-                "Scenario-level final-period outputs",
-                "Headline model outputs by scenario",
-            ],
-            [
-                "Workbook map",
-                "Model",
-                "Long-format three-statement rows",
-                "All scenarios and periods",
-            ],
-            ["Workbook map", "Sensitivities", "Chart-ready stress cases", "Use for driver deltas"],
-            [
-                "Workbook map",
-                "Checks / Run_Log",
-                "QA and run status",
-                "Review before relying on output",
-            ],
-        ]
-    )
-    return cover_dict_rows(rows)
diff --git a/plugins/financial-markets/shared/workbook_inspection.py b/plugins/financial-markets/shared/workbook_inspection.py
deleted file mode 100644
index cae63e4..0000000
--- a/plugins/financial-markets/shared/workbook_inspection.py
+++ /dev/null
@@ -1,146 +0,0 @@
-"""Workbook inspection gates for Public Equity Investing formula workbooks."""
-
-from __future__ import annotations
-
-import zipfile
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Sequence
-from xml.etree import ElementTree as ET
-
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-
-
-@dataclass(frozen=True)
-class WorkbookInspectionPolicy:
-    workbook_type: str
-    minimum_formula_count: int
-    required_formula_sheets: tuple[str, ...]
-
-
-POLICIES: dict[str, WorkbookInspectionPolicy] = {
-    "dcf": WorkbookInspectionPolicy(
-        "dcf",
-        800,
-        (
-            "Revenue Build",
-            "Margin Cost Build",
-            "Working Capital",
-            "Capex D&A",
-            "Tax Schedule",
-            "Unlevered FCF",
-            "WACC",
-            "Terminal Value",
-            "DCF Valuation",
-            "Sensitivities",
-            "Checks",
-        ),
-    ),
-    "three_statement": WorkbookInspectionPolicy(
-        "three_statement",
-        1100,
-        (
-            "Revenue Build",
-            "Expense Build",
-            "Income Statement",
-            "Working Capital",
-            "PP&E D&A",
-            "Debt Interest",
-            "Tax",
-            "Balance Sheet",
-            "Cash Flow Statement",
-            "Scenarios",
-            "Checks",
-        ),
-    ),
-}
-
-
-def workbook_sheet_paths(path: str | Path) -> tuple[dict[str, str], dict[str, str]]:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        rels = ET.fromstring(zf.read("xl/_rels/workbook.xml.rels"))
-        relmap = {rel.attrib["Id"]: rel.attrib["Target"] for rel in rels}
-        sheets = workbook.find(f"{{{NS_MAIN}}}sheets")
-        if sheets is None:
-            return {}, {}
-        paths: dict[str, str] = {}
-        states: dict[str, str] = {}
-        for sheet in sheets:
-            rid = sheet.attrib[f"{{{NS_REL}}}id"]
-            target = relmap[rid].lstrip("/")
-            name = sheet.attrib["name"]
-            paths[name] = target if target.startswith("xl/") else f"xl/{target}"
-            states[name] = sheet.attrib.get("state", "visible")
-        return paths, states
-
-
-def defined_names(path: str | Path) -> list[str]:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-    node = workbook.find(f"{{{NS_MAIN}}}definedNames")
-    if node is None:
-        return []
-    return [child.attrib["name"] for child in node if child.attrib.get("name")]
-
-
-def inspect_formula_workbook(
-    path: str | Path,
-    *,
-    required_sheets: Sequence[str],
-    workbook_type: str,
-    minimum_formula_count: int | None = None,
-    required_formula_sheets: Sequence[str] | None = None,
-) -> dict[str, Any]:
-    policy = POLICIES.get(workbook_type)
-    min_formulas = (
-        minimum_formula_count
-        if minimum_formula_count is not None
-        else (policy.minimum_formula_count if policy else 100)
-    )
-    formula_sheet_requirements = tuple(
-        required_formula_sheets or (policy.required_formula_sheets if policy else ())
-    )
-    with zipfile.ZipFile(path) as zf:
-        names = set(zf.namelist())
-        sheet_paths, sheet_states = workbook_sheet_paths(path)
-        formula_counts_by_sheet: dict[str, int] = {}
-        formula_count = 0
-        for sheet_name, sheet_path in sheet_paths.items():
-            root = ET.fromstring(zf.read(sheet_path))
-            count = len(root.findall(f".//{{{NS_MAIN}}}f"))
-            formula_counts_by_sheet[sheet_name] = count
-            formula_count += count
-        external_links = sorted(name for name in names if name.startswith("xl/externalLinks/"))
-        has_styles = "xl/styles.xml" in names and len(zf.read("xl/styles.xml")) > 100
-    sheet_names = list(sheet_paths.keys())
-    visible = [name for name in sheet_names if sheet_states.get(name, "visible") == "visible"]
-    names_defined = defined_names(path)
-    missing_required = [sheet for sheet in required_sheets if sheet not in sheet_paths]
-    missing_formula_sheets = [
-        sheet for sheet in formula_sheet_requirements if formula_counts_by_sheet.get(sheet, 0) <= 0
-    ]
-    return {
-        "workbook_type": workbook_type,
-        "sheet_names": sheet_names,
-        "sheet_states": sheet_states,
-        "first_visible_sheet": visible[0] if visible else "",
-        "cover_first": bool(visible and visible[0] == "Cover"),
-        "required_sheets_present": not missing_required,
-        "missing_required_sheets": missing_required,
-        "formula_count": formula_count,
-        "minimum_formula_count": min_formulas,
-        "formula_count_passes_threshold": formula_count >= min_formulas,
-        "formula_counts_by_sheet": formula_counts_by_sheet,
-        "formula_sheets": sum(1 for count in formula_counts_by_sheet.values() if count > 0),
-        "required_formula_sheets": list(formula_sheet_requirements),
-        "missing_formula_sheets": missing_formula_sheets,
-        "required_formula_sheets_populated": not missing_formula_sheets,
-        "defined_names": names_defined,
-        "named_ranges_count": len(names_defined),
-        "has_named_ranges": bool(names_defined),
-        "has_styles": has_styles,
-        "external_links": external_links,
-        "no_external_links": not external_links,
-    }
diff --git a/plugins/financial-markets/shared/workflow-source-resolution.md b/plugins/financial-markets/shared/workflow-source-resolution.md
deleted file mode 100644
index 35e6966..0000000
--- a/plugins/financial-markets/shared/workflow-source-resolution.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Workflow Source Resolution
-
-Use this reference only during ordinary Public Equity Investing workflows after `skills/user-context/scripts/user_context_preflight.py` returns `source_category_plan`. Source setup remains owned by `skills/user-context/references/source-category-runtime.md`.
-
-Resolve only the categories needed for the current workflow. Prefer a user-named source first, then an active saved `configured_route`. Attempt the smallest useful native read only when the workflow needs that source. If a route needs authentication, connection, entitlement, or setup, state the practical limitation and continue from prompt context, active artifacts, pasted or exported material, and public sources when the workflow can still be useful. Do not inspect unrelated categories, run broad source setup, write connector readiness, or create, read, migrate, or update `category-state.json`.
-
-Treat named providers, internal research stores, calendars, email, messaging, document stores, and portfolio systems as callable only when the runtime exposes a scoped route. Otherwise request a user-provided export or use a clearly labeled fallback. A configured `.app.json` entry is not proof of availability or entitlement.
-
-## Provider Guides
-
-Load a provider guide only after the current workflow attempts a category, selects that provider as its concrete route, and confirms the scoped route is callable. Do not load provider guides merely because `.app.json` declares the dependency or the source-category plan names a preferred provider.
-
-- If the selected callable route is Daloopa, load `skills/public-equity-investing/internal-support/daloopa-provider-guide/INTERNAL.md`.
-- If the selected callable route is Quartr, load `skills/public-equity-investing/internal-support/quartr-provider-guide/INTERNAL.md`.
-
-## Categories
-
-- `company_filings_ir`: filings, IR materials, reported financials, and disclosures.
-- `earnings_transcripts_presentations`: transcripts, presentations, events, and management commentary.
-- `internal_research`: internal notes, expert context, research, and team discussions.
-- `portfolio_models_trackers`: portfolio context, watchlists, models, and thesis trackers.
-- `market_data_estimates`: market data, consensus, estimates, ownership, positioning, and provider exports.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/SKILL.md b/plugins/financial-markets/skills/catalyst-calendar/SKILL.md
deleted file mode 100644
index bfa00b0..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/SKILL.md
+++ /dev/null
@@ -1,115 +0,0 @@
----
-name: catalyst-calendar
-description: Use when building public-equity-investing catalyst calendars. Do not use for full event underwriting; use event-driven-analyzer.
----
-
-# Catalyst Calendar
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML catalyst calendar. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved choices. For a substantive single-company 60/90-day catalyst calendar, the workflow default resolves the presentation surface to a polished HTML catalyst calendar unless the user asks for another format, a quick/no-file answer, or a workbook/tracker. In that case, do not block on a format question; ask only for unresolved depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-Build or refresh source-backed Public Equity Investing catalyst calendars. The output should show timing, confidence, thesis relevance, prep work, owner/status, and likely PM decision, not just dates.
-
-## Use / Do Not Use
-Use for single-name, portfolio, sector, earnings, regulatory, clinical, technical, capital-markets, and special-situation catalyst tracking. Use the script for tracker workbooks, CSV/JSON intake, refresh logs, blank templates, or optional ICS export.
-
-Do not use for merger-arb/spin/tender probability math; route to `event-driven-analyzer`. Do not use for pure pre-print notes, post-event analysis, model refreshes, thesis updates, risk sizing, or hedge design; route to `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `thesis-tracker`, or `portfolio-risk-management`.
-
-## Reference Router
-- `../../shared/html-artifact-standard.md`: shared principles for polished, legible, evidence-aware HTML artifacts.
-- `source-and-data-protocol.md`: source hierarchy, stale-date checks, confidence labels, refresh rules.
-- `catalyst-taxonomy-and-fields.md`: event fields, categories, scoring labels.
-- `event-scoring-framework.md`: impact, confidence, controversy, urgency.
-- `pm-action-playbook.md` / `portfolio-pm-playbook.md`: prep actions, thesis checkpoints, clustering, position context.
-- `sector-and-asset-overlays.md`: sector-specific catalyst logic.
-- `output-templates.md` / `pm-review-checklist.md`: delivery formats and QA.
-
-## Workflow
-1. Classify mode: no-context template, partial-context build, refresh, single-company map, portfolio dashboard, single-event brief, or sector sweep.
-2. Set scope: default to next 30/60/90 days plus thesis-critical longer-dated items. Ask only for destructive or blocking choices.
-3. Build rows with exact date or window, source, confidence, status, thesis link, model/KPI line, prep action, owner, and decision implication.
-4. Rank by impact, confidence, controversy, actionability, portfolio relevance, and same-day clustering.
-5. Convert high-priority events into work items, model updates, questions, risk/sizing reviews, hedge prompts, or post-event follow-up.
-6. Refresh non-destructively: preserve prior rows, append evidence, mark stale/superseded items, and produce a change log.
-7. For a named issuer plus a substantive 60/90-day calendar, default to a polished standalone HTML catalyst calendar when local artifact creation is available unless the user requests another format, a quick/no-file answer, or a workbook/tracker. In interactive runs, ask only for unresolved depth, audience/use, or focus choices; mention that another format is available as an opt-out without making it a blocking question. In non-interactive runs, apply the HTML catalyst calendar plus full working analysis defaults and disclose those assumptions in the delivery message or accompanying chat summary, not as visible artifact metadata.
-8. For other multi-category calendars, PM monitoring calendars, or requests that ask for earnings plus macro/regulatory/company-specific catalysts, treat polished standalone HTML as the recommended presentation path and use `../../shared/deliverable-intake-policy.md` for materially unresolved format or depth choices.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: event/date verification, source freshness, catalyst impact, probability/timing, and follow-up monitoring. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Guardrails
-- Never imply a date is confirmed without reliable evidence.
-- Use windows for guided, estimated, inferred, or street timing; do not turn soft windows into one-day ICS events.
-- Separate facts, assumptions, rumors, restricted/internal notes, and PM judgments.
-- Do not invent earnings dates, trial readouts, approvals, lockups, merger deadlines, conferences, or policy dates.
-- Surface conflicts and prefer company/regulator/exchange sources over aggregators.
-
-## Default Output
-Recommend the full catalyst package: PM summary, top-catalysts table, full catalyst register or clearly scoped subset, prep plan, source/freshness notes, refresh change log when relevant, decision implications, and recommended adjacent work.
-
-For substantive single-company 60/90-day catalyst calendars, default to a concise chat summary plus a polished standalone HTML catalyst calendar when local artifact creation is available. This workflow default resolves presentation format unless the user requests another format, a quick/no-file answer, or a workbook/tracker. In interactive runs, ask for unresolved analysis depth and any consequential audience/use or focus choice, but do not require the user to confirm HTML before proceeding. In non-interactive runs, default to the HTML catalyst calendar and `Full working analysis`, and state the assumed format and depth in the delivery message or accompanying chat summary rather than in the visible HTML artifact.
-
-If the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render, route that optional rendering path through `dashboard-builder` and follow its payload and validation contract. `catalyst-calendar` continues to own event selection, timing, confidence, impact, and PM monitoring judgment. Keep JSON/Markdown/CSV/ICS support files behind the HTML artifact unless explicitly requested.
-
-Use a single-event brief or compressed watchlist only when the user explicitly asks for a summary, quick read, one-pager, brief, TL;DR, or one specific event.
-
-## HTML Guidance
-
-For substantive HTML output, load `../../shared/html-artifact-standard.md` and apply these calendar-specific priorities:
-
-- Use a title that identifies the requested artifact, such as `<Company> (<Ticker>): 90-Day Catalyst Calendar`; do not substitute a generic label such as `Decision Map` for a calendar request.
-- Structure the first read as an investor brief: concise thesis checkpoint, add/hold/trim triggers, then the catalyst schedule. Put the detailed register, preparation queue, and source ledger below that scan layer.
-- Make the catalyst schedule the primary visual object; the reader should reach upcoming dates and timing windows quickly.
-- Before a detailed register table, include a compact month-by-month or time-horizon visual summary of the highest-decision-pressure confirmed dates and inferred windows. Keep it concise: the detailed register should own full explanations, implications, and sourcing.
-- Show confirmed dates distinctly from inferred windows and unscheduled monitoring items.
-- Surface the highest-decision-pressure events prominently, while retaining lower-priority confirmed issuer events in the calendar when they fall inside scope.
-- Include preparation actions and add/hold/trim implications when the user supplies portfolio context.
-- Keep sourcing unobtrusive but auditable, with visible timing confidence and material evidence gaps.
-- A full catalyst register is appropriate for substantive calendars, but do not repeat the same event across overview, register, thesis tests, and prep sections unless each view answers a distinct investment question.
-- Do not surface internal support-contract fields in the HTML artifact; convert relevant evidence gaps and readiness limitations into polished PM-facing prose.
-- Do not show format-selection, intake, or generation-process notes such as `Format assumption` in the HTML artifact; keep any required default disclosure in the delivery message.
-
-## Script Contract
-
-```bash
-python scripts/create_catalyst_calendar_workbook.py --input events.csv --output catalyst_calendar.xlsx --ics catalyst_calendar.ics
-python scripts/create_catalyst_calendar_workbook.py --prior prior_events.csv --input refreshed_events.csv --output refreshed_calendar.xlsx
-```
-
-The script is a materializer, not a live data provider. It preserves source/confidence fields, keeps undated/windowed items in review tables, writes refresh changes, and exports ICS entries only for confirmed exact dates.
-
-Workbook outputs start with a `Cover` tab that serves as the PM dashboard: as-of date, event counts, 30/60/90 day pressure, red/amber urgency, high-impact events, low-confidence events, overdue/missing-owner prep, top catalysts, source posture, and workbook map.
-
-## Public Equity PM Judgment Layer
-
-For substantial catalyst work, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-PM catalyst discipline: a catalyst only matters if it can alter estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-Required PM judgment:
-- Rank by `decision_pressure`, not date proximity alone.
-- Add index/ETF and technical catalysts when relevant: index additions/deletions, Russell/S&P/MSCI rebalance, ETF reconstitutions, lockups, secondary offerings, buyback windows, blackout windows, passive ownership shifts, and float changes.
-- Separate confirmed dates from inferred windows. Do not export inferred windows as exact calendar events.
-- Group low-impact dates as hygiene unless they are tied to a decision.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/agents/openai.yaml b/plugins/financial-markets/skills/catalyst-calendar/agents/openai.yaml
deleted file mode 100644
index 6917459..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: Catalyst Calendar
-  short_description: Build PM-grade public equity markets catalyst calendars.
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/catalyst-taxonomy-and-fields.md b/plugins/financial-markets/skills/catalyst-calendar/references/catalyst-taxonomy-and-fields.md
deleted file mode 100644
index de8de08..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/catalyst-taxonomy-and-fields.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Catalyst Taxonomy And Fields
-
-Use stable field names for workbook rows and refresh comparisons. The workbook helper accepts common aliases such as `company`, `event_type`, `event_window_start`, `event_window_end`, `source`, `confidence`, `importance`, `prep_action`, and `next_decision`, then writes canonical fields.
-
-## Canonical Row Fields
-
-| Group | Fields | Notes |
-|---|---|---|
-| Identity | `event_id`, `ticker`, `issuer`, `security_type`, `sector`, `region`, `portfolio_group`, `position_context` | `event_id` must be stable enough for refresh/change-log tracking. |
-| Event | `event_category`, `event_subcategory`, `event_name`, `event_description`, `reported_period`, `status` | Use concrete labels; avoid generic “update” when the underlying event is earnings, regulatory, trial, financing, legal, etc. |
-| Timing | `date_type`, `event_date`, `window_start`, `window_end`, `time_of_day`, `time_zone` | Exact dates require confirmation. Use windows for guided, estimated, inferred, or statutory timing. |
-| Source | `source_label`, `source_id`, `source_notes` | Keep company/regulator/exchange sources distinct from provider, street-estimated, assumption, PM judgment, rumor, and restricted/internal sources. |
-| Priority | `impact_score`, `confidence_score`, `urgency`, `thesis_relevance`, `kpi_or_model_line`, `market_setup` | Scores are 1-5; missing or weak evidence should downgrade confidence. |
-| PM Action | `expected_outcome`, `variant_watch`, `prep_required`, `owner`, `prep_due_date`, `follow_up_date`, `decision_implication`, `post_event_action`, `notes` | Every high-impact event should create a work item, model/thesis checkpoint, or explicit monitoring rationale. |
-
-## Event Categories
-
-- **Earnings and guidance:** reports, preannouncements, guidance, KPI disclosures, call Q&A, restatements, capital return updates.
-- **Investor events:** investor days, conferences, firesides, roadshows, product demos, teach-ins. Track only when there is disclosure or narrative risk.
-- **Product/customer/GTM:** launches, pricing, user conferences, customer go-lives, channel checks, adoption/ramp milestones.
-- **Regulatory/policy/legal:** agency decisions, comment deadlines, tariffs, rate cases, permits, antitrust, hearings, rulings, appeals.
-- **Clinical/healthcare:** enrollment, topline/full data, PDUFA, advisory committees, label/reimbursement decisions.
-- **Macro/rates/FX/commodities:** CPI, payrolls, central banks, PMIs, auctions, OPEC, inventories, fiscal deadlines. Track exposure relevance, not just the release date.
-- **Technical/capital markets:** lockups, offerings, converts, buybacks, index changes, blackout windows, maturities, covenant tests.
-- **M&A/activism/special situations:** votes, tenders, regulatory approvals, walk dates, go-shops, spins, separations, proxy deadlines.
-
-## Scoring Labels
-
-- **Impact 5:** Thesis, valuation, position size, financing path, or regulatory viability can change.
-- **Impact 4:** Estimates, multiple, sentiment, or risk can move materially.
-- **Impact 3:** Relevant update with model/narrative implications.
-- **Impact 2:** Monitoring item.
-- **Impact 1:** Calendar hygiene.
-
-Confidence labels:
-- **Confirmed:** Primary source confirms exact date.
-- **Guided:** Company/regulator gives a window or expected timing.
-- **Expected:** Reliable aggregator/consensus, no primary confirmation.
-- **Inferred:** Cadence, statutory timeline, or historical pattern.
-- **Rumored:** Press/market chatter only.
-- **Unknown:** Insufficient evidence.
-
-## Public Equity PM Catalyst Fields
-
-Add fields: `benchmark_index`, `index_event_type`, `estimated_passive_flow`, `flow_vs_adv`, `etf_holder_base`, `rebalance_date`, `float_change`, `active_weight_relevance`, `decision_pressure`.
-
-Add technical/capital-markets subcategories: `index_rebalance`, `offering`, `lockup`, `buyback_blackout`, `float_change`, `convert_or_warrant`, `passive_flow`.
-
-Confirmed dates must stay separate from inferred windows. Do not export inferred windows as exact ICS events.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/event-scoring-framework.md b/plugins/financial-markets/skills/catalyst-calendar/references/event-scoring-framework.md
deleted file mode 100644
index 3f49407..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/event-scoring-framework.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# Event Scoring Framework
-
-## Purpose
-Prioritize events by how much they can change price, estimates, thesis confidence, or risk. A PM should see where attention is scarce and what deserves preparation.
-
-## Materiality score, 1 to 5
-Score based on expected effect on enterprise value, earnings power, narrative, financing, or risk.
-
-5 - Thesis-defining or valuation-changing event. Examples: pivotal trial readout, major guidance reset, regulatory approval, antitrust decision, large M&A vote, investor day with new targets, covenant/liquidity event.
-4 - Major estimate or multiple event. Examples: quarterly earnings with contested KPIs, key product launch, large contract award, rate case decision, macro print central to the trade.
-3 - Meaningful update. Examples: conference with possible guidance nuance, monthly KPI release, peer read-through, non-core regulatory step.
-2 - Monitoring event. Examples: routine conference, low-stakes data point, minor product update.
-1 - Administrative or low-impact event.
-
-## Actionability score, 1 to 5
-5 - Requires immediate model, questions, scenario work, hedge/sizing decision, or meeting prep.
-4 - Requires prep within one to two weeks.
-3 - Monitor and prepare if new data arrives.
-2 - Add to watchlist only.
-1 - No action required.
-
-## Date confidence
-- High: primary source confirmed specific date/time or official release calendar.
-- Medium: credible secondary source, company-indicated window, or multiple consistent non-primary sources.
-- Low: historical cadence, rumor, stale source, broad window, or conflicting data.
-
-## Priority assignment
-Use combined judgment, not a rigid formula.
-- Critical: materiality 5, actionability 4-5, or event can force position/thesis change.
-- High: materiality 4-5 or significant portfolio exposure.
-- Medium: materiality 3 or meaningful read-through.
-- Low: materiality 2 with limited action need.
-- Monitor: materiality 1 or date not reliable enough to act.
-
-## Special escalators
-Upgrade priority when any applies:
-- event is within 7 days and prep is incomplete;
-- date confidence is low but event materiality is high;
-- event overlaps with another portfolio-critical event;
-- expected volatility is high or liquidity is poor;
-- market setup is one-sided or crowded;
-- event has asymmetry not reflected in consensus;
-- event can trigger follow-on catalysts;
-- missing data prevents a clean risk decision.
-
-## Red flags
-Flag these explicitly:
-- stale earnings date from secondary source;
-- trial readout window without source or endpoint clarity;
-- lockup expiry date without share count or holder context;
-- regulatory date confused with filing acceptance, adcom, pdufa, or final approval;
-- macro event listed without relevance to the security;
-- conference event listed with no management participation confirmed;
-- consensus or guidance cited without timestamp;
-- model update required but not scheduled;
-- event marked low priority despite direct thesis linkage.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/output-templates.md b/plugins/financial-markets/skills/catalyst-calendar/references/output-templates.md
deleted file mode 100644
index d6dd3e2..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/output-templates.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Output Templates
-
-## No-context kickoff
-Use when the user asks for a catalyst calendar but provides no universe.
-
-```markdown
-## Catalyst Calendar Setup
-I can build this, but I need the universe first. Please provide one of:
-- Ticker list / portfolio export
-- Sector/theme and geography
-- Existing watchlist or model workbook
-- Timeframe and output format
-
-Default output once provided: full next 30/60/90 day catalyst package, full register or scoped subset, top catalyst table, PM prep plan, source map, and optional Excel/ICS export.
-```
-
-## PM summary
-```markdown
-## Catalyst Calendar - PM Summary
-**Scope:** [universe], [timeframe], last refreshed [date/time]
-**Highest-priority catalysts:**
-1. [Ticker] - [date/window] - [event]: [why it matters] | prep: [action]
-2. ...
-
-**Calendar risk:** [same-day clusters, binary events, low-confidence dates, macro overlap]
-**Immediate work:** [this week actions]
-**Source gaps:** [missing/stale/conflicting sources]
-```
-
-## Top catalysts table
-| Date / Window | Ticker | Event | Type | Confidence | Importance | What Could Change | Thesis Link | Prep Action |
-|---|---:|---|---|---|---:|---|---|---|
-| [date/window] | [ticker] | [event] | [type] | [confirmed/guided/inferred] | [1-5] | [estimates/multiple/narrative/risk] | [thesis element] | [owner/action] |
-
-## Single-event brief
-Use only when the user explicitly asks for one event, a brief, or a compressed output.
-
-```markdown
-## [Ticker] [Event] Brief
-**Timing:** [date/window] ([confidence], source: [source/date])
-**Setup:** [investor debate and why event matters]
-**Market expectation:** [guidance/consensus/buy-side bogey/implied move if available]
-**Variant-view relevance:** [what could surprise]
-**Confirming evidence:** [thresholds/signals]
-**Disconfirming evidence:** [thresholds/signals]
-**Prep before event:** [model, questions, checks, calls]
-**Post-event action:** [model update, thesis tracker, risk review, note]
-**Source caveats:** [conflicts/stale/missing]
-```
-
-## Existing calendar refresh log
-```markdown
-## Refresh Log
-**Refreshed:** [date/time]
-**Sources checked:** [list]
-**Rows added:** [count]
-**Rows changed:** [count]
-**Rows marked stale/needs refresh:** [count]
-**Material changes:**
-- [Ticker/Event]: [old date/status/source] -> [new date/status/source], reason [source]
-**Do not rely on:** [unconfirmed/stale items]
-```
-
-The deterministic workbook helper supports refresh mode with `--prior prior_events.csv --input refreshed_events.csv`. It preserves prior events, updates matched `event_id` rows from refreshed evidence, marks prior rows missing from the refreshed file as `needs refresh`, and writes an append-only `Change Log` tab.
-
-ICS exports are exact-date only. Windowed, guided, street-estimated, or speculative catalysts remain in workbook tables and should not be converted into one-day calendar events.
-
-## Weekly PM prep plan
-| Priority | Due | Ticker/Event | Work Product | Owner | Decision It Supports |
-|---:|---|---|---|---|---|
-| 1 | [date] | [event] | [earnings preview/model sensitivity/question list] | [owner] | [add/trim/hold/hedge/update thesis] |
-
-## Source map
-| Event ID | Source | Source Date | Retrieval Date | Confidence | Notes |
-|---|---|---|---|---|---|
-
-## Final response when returning a file
-```markdown
-Built the catalyst calendar and included:
-- [workbook/export details]
-- [top PM findings]
-- [source/freshness caveats]
-- [change log / missing data]
-Download: [file]
-```
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/pm-action-playbook.md b/plugins/financial-markets/skills/catalyst-calendar/references/pm-action-playbook.md
deleted file mode 100644
index 799e7cb..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/pm-action-playbook.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# PM Action Playbook
-
-## The PM questions every catalyst calendar must answer
-1. What could change because of this event?
-2. What does the market appear to expect?
-3. What is our variant view or exposure to surprise?
-4. What work must be completed before the event?
-5. What would confirm, weaken, or break the thesis?
-6. Is this event worth changing position size, hedge, or research priority?
-7. What do we do immediately after the event prints?
-
-## Convert rows into action
-For each high-priority event, produce:
-- **Setup:** Current investor debate and why timing matters.
-- **Key metrics:** The numbers, KPIs, or milestones that will matter.
-- **Expectation frame:** Guidance, consensus, buy-side bogey, implied move, prior company commentary, or internal model.
-- **Variant view:** Where the user's view differs from market or where uncertainty is underpriced.
-- **Confirm/disconfirm signals:** Concrete thresholds or qualitative outcomes.
-- **Prep plan:** Model refresh, question list, management call, expert call, data scrape, channel check, sensitivity, hedge review.
-- **Post-event playbook:** Update model, thesis tracker, risk sizing, memo, deck, or alert list.
-
-## Ranking events for PM attention
-Prioritize events that combine:
-- High impact and high controversy.
-- High position size or exposure contribution.
-- Low confidence but high potential impact.
-- Same-day clustering across correlated positions.
-- Catalysts that can change forward estimates, terminal narrative, balance sheet risk, regulatory path, or liquidity.
-- Events near option expiries, lockups, financing windows, or deal deadlines.
-
-## Public-equity nuance by strategy
-### Long-only
-Focus on thesis durability, quality of compounding, revision risk, downside protection, and whether events create entry/exit opportunities without overtrading.
-
-### Long/short equity
-Focus on crowded expectations, asymmetric setups, pair read-throughs, factor exposures, short squeeze risk, borrow/technical events, and whether catalysts are idiosyncratic enough to underwrite.
-
-### Event-driven
-Focus on path dependency, legal/regulatory milestones, deal-spread math, timing slippage, break risk, downside on break, borrow/financing mechanics, and hard vs soft deadlines.
-
-### Equity research
-Focus on pre-event setup, consensus sensitivity, key questions for management, likely investor debate, and what note/report must be published before and after the event.
-
-### Mutual fund / diversified PM
-Focus on portfolio-level clustering, benchmark-relative risk, sector drawdowns, liquidity, and whether the event can impair long-term thesis.
-
-## Signals that deserve escalation
-- A high-impact event has no source confidence or only stale evidence.
-- Report dates are unconfirmed but treated as hard dates in the user's tracker.
-- Trial/regulatory dates are mixed up between filing, acceptance, advisory committee, approval, and commercial launch.
-- Lockup/secondary/share issuance events are omitted for a recent IPO or de-SPAC.
-- M&A timeline omits key regulatory or shareholder dates.
-- Macro events cluster with high-beta or rate-sensitive positions.
-- Event has high downside but no prep action or hedge/risk review.
-- A thesis-critical catalyst slipped without explanation.
-
-## PM-quality language
-Use crisp, decision-oriented phrasing:
-- "This is a thesis checkpoint, not just a calendar item."
-- "The date is confirmed, but the market impact depends on whether management updates FY guidance."
-- "The key risk is not the print itself; it is whether the company resets the medium-term margin algorithm."
-- "Treat this as a window until primary-source confirmation; do not schedule position decisions around a soft date."
-- "Prep priority is high because the event could force an estimate change and position-size decision within 24 hours."
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/pm-review-checklist.md b/plugins/financial-markets/skills/catalyst-calendar/references/pm-review-checklist.md
deleted file mode 100644
index a1aed1f..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/pm-review-checklist.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# PM Review Checklist
-
-Before delivering, verify:
-
-## Source quality
-- Every high-impact event has a source, source date, and confidence label.
-- Hard dates are not used for inferred windows.
-- Primary/company/regulator sources override aggregators where conflicts exist.
-- Stale events are labeled needs refresh, not silently removed.
-
-## Calendar quality
-- Events are deduplicated by ticker, date/window, and event type.
-- Date windows are visible, not hidden in notes.
-- Time zone/time of day is captured when market-relevant.
-- Same-day clusters and correlated exposures are flagged.
-- Undated but thesis-critical events are not dropped.
-
-## PM usefulness
-- Output identifies what could change, not just what happens.
-- High-priority rows include prep actions and next decisions.
-- Confirm/disconfirm signals are specific enough to update a thesis tracker.
-- Calendar links to model updates, earnings previews, risk/hedge review, or event-driven analysis where needed.
-- The readout highlights low-confidence/high-impact events.
-
-## Sector nuance
-- Sector-specific event types and KPIs were considered.
-- Regulatory, clinical, commodity, rate, and technical events are included when relevant.
-- M&A/special situation path dependencies are broken into milestones.
-
-## File safety
-- Existing user data was not deleted or overwritten without instruction.
-- Workbook formulas, hidden tabs, comments, raw data, and source logs were preserved where applicable.
-- Refreshes include a dated change log.
-- External-clean outputs omit confidential portfolio views, MNPI-sensitive notes, and trade intent unless approved.
-
-## Final delivery
-- Include scope, as-of date/time, sources/freshness caveats, missing data, and recommended next actions.
-- Be explicit about limitations rather than filling gaps with guesses.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/portfolio-pm-playbook.md b/plugins/financial-markets/skills/catalyst-calendar/references/portfolio-pm-playbook.md
deleted file mode 100644
index a708cf9..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/portfolio-pm-playbook.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Portfolio PM Playbook
-
-## What PM-grade catalyst work answers
-For each top event, answer:
-1. What is the decision: add, trim, hold, hedge, avoid, update model, update thesis, or wait?
-2. What can change: estimate line, KPI trajectory, TAM, margin structure, multiple, risk premium, legal/regulatory path, liquidity, credit profile, or thesis confidence?
-3. What is the market expecting: consensus, guidance, whisper numbers, positioning, revisions, implied volatility, short interest, borrow, and valuation setup?
-4. What is our variant perception: where is the user's view different from consensus or current narrative?
-5. What evidence matters: the datapoint, endpoint, management answer, regulatory action, or leading indicator that confirms or disconfirms the thesis?
-6. What is the risk of being wrong: gap risk, liquidity risk, mandate risk, borrow risk, downside scenario, or opportunity cost?
-7. What must be done before the event: model update, consensus refresh, call prep, diligence check, channel read, scenario analysis, or hedge review?
-8. What happens after: follow-up date, update owner, model refresh, thesis log, risk meeting, trade review, or post-mortem?
-
-## Ranking framework
-Rank events using composite judgment, not date sorting:
-- Impact: can the event change estimates, thesis, multiple, or risk limits?
-- Timing proximity: is the event near enough to require work or risk action?
-- Confidence: is timing confirmed or uncertain?
-- Asymmetry: is upside/downside skew large relative to consensus or positioning?
-- Setup: is the bar high/low, crowded/derisked, or underfollowed?
-- Exposure: does the user own, short, benchmark, or hedge the security?
-- Actionability: is there a decision before or after the event?
-- Preparation gap: is the team underprepared or missing sources?
-
-## Event-specific PM lenses
-### Earnings
-Separate reported quarter, guidance, KPI trends, margin bridge, cash flow, and qualitative commentary. Track consensus, whisper, guidance range, prior commentary, and recent revisions.
-
-### Investor days / analyst days
-Focus on medium-term targets, margin roadmap, TAM framing, capital allocation, segment disclosure, and whether targets are credible.
-
-### Regulatory / legal
-Distinguish deadline, hearing, vote, comment period, agency decision, and appeal timeline. Track probability, downside if adverse, sector read-through, and follow-up filings.
-
-### Clinical / biotech
-Separate abstract release, presentation, top-line data, full data, regulatory submission, adcom, and pdufa. Track endpoint, powering, comparator, safety, prior data, competitive data, and statistical risk.
-
-### Macro / rates
-Tie each print to actual securities affected: duration, curve, credit spreads, cyclicals, banks, housing, commodities, FX, or factor exposure.
-
-### Lockups / flows / index events
-Distinguish mechanical supply/demand from fundamentals. Track float added, holders released, average daily volume, borrow/short interest, index size, and likely liquidity impact.
-
-### M&A / special situations
-Break complex situations into filing, financing, shareholder vote, regulatory clearance, court date, tender expiration, outside date, close, and spread catalyst. Route to `event-driven-analyzer` when probability/spread/recovery analysis is needed.
-
-## Next 30/60/90 day framing
-- Next 30 days: high/medium impact events, overdue prep, and source conflicts.
-- 31-60 days: events needing pre-work now, such as investor days, trial data, regulatory decisions, lockups, or high-impact earnings.
-- 61-90 days: high-impact windows and long-lead workstreams.
-- Beyond 90 days: monitor unless binary or high impact.
-
-## PM Catalyst Discipline
-
-Rank by decision pressure, not date proximity. A catalyst matters if it can alter estimate path, narrative, multiple, position size, downside, liquidity, or event probability. Low-impact dates should be hygiene unless tied to a decision.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/sector-and-asset-overlays.md b/plugins/financial-markets/skills/catalyst-calendar/references/sector-and-asset-overlays.md
deleted file mode 100644
index cde5a8e..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/sector-and-asset-overlays.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Sector and Asset Overlays
-
-Use these overlays to avoid generic calendars. Add only the sections relevant to the user's universe.
-
-## Software and SaaS
-Track earnings, ARR/NRR disclosures, price increases, product conferences, AI launches, seat expansion commentary, cloud marketplace events, sales reorg updates, and major customer renewals. Watch billings vs revenue timing, cRPO, net retention, sales efficiency, churn, migration cycles, and AI monetization proof points.
-
-## Usage-based software and internet platforms
-Track usage disclosures, pricing metric changes, developer conferences, API/platform policy changes, traffic reports, take-rate updates, creator/merchant events, and large customer ramps. Separate usage volume, price, mix, and margin effects.
-
-## Semiconductors and hardware
-Track earnings, customer product cycles, wafer starts, foundry capacity, export controls, supply-chain commentary, capex updates, inventory digestion, product launches, and key customer events. Map events to revenue lag and gross margin mix.
-
-## Consumer, retail, restaurants, and staples
-Track monthly/quarterly sales, traffic, pricing, promotions, holiday periods, product launches, distributor/channel updates, commodity costs, inventory markdowns, and competitor read-throughs. Watch comp sales, traffic vs ticket, margin bridge, and inventory quality.
-
-## Banks, brokers, and financials
-Track earnings, NII/NIM updates, deposit betas, credit costs, CCAR/stress tests, regulatory capital changes, loan growth, trading activity, fee revenues, and macro/rate dates. Watch credit migration and balance sheet duration, not just EPS.
-
-## Insurance
-Track catastrophe events, reserve reviews, pricing renewals, regulatory capital, investment income, morbidity/mortality trends, reinsurance renewals, and litigation/regulatory developments. Distinguish current accident year from prior-year development.
-
-## Asset managers and exchanges
-Track AUM flows, market levels, fee-rate changes, product launches, volume statistics, regulatory changes, index rebalances, and capital return. Watch flow mix and operating leverage.
-
-## Fintech and payments
-Track volume metrics, take rate, credit performance, funding costs, regulatory actions, partner bank events, network updates, product launches, and consumer spend prints. Separate growth quality from credit and fraud risk.
-
-## Biotech and pharma
-Track trial enrollment, topline data, full data, conferences, FDA acceptance, PDUFA, advisory committees, label expansions, patent/litigation dates, competitor data, pricing/reimbursement, and launch metrics. Separate clinical, regulatory, commercial, and IP catalysts.
-
-## Healthcare services and medtech
-Track reimbursement updates, CMS rules, utilization trends, hospital capex, procedure volumes, regulatory inspections, product approvals, and payer contract renewals. Watch volume, pricing, mix, and labor cost dynamics.
-
-## Energy and E&P
-Track OPEC, inventory reports, commodity macro, production updates, reserve reports, hedging updates, capex, regulatory permits, midstream constraints, and M&A. Link events to commodity beta and free cash flow sensitivity.
-
-## Utilities and renewables
-Track rate cases, regulatory hearings/orders, wildfire/storm events, project COD dates, tax credit guidance, interconnection milestones, fuel costs, and financing windows. Separate allowed ROE, rate base, capex, and regulatory lag.
-
-## REITs and real estate
-Track earnings, leasing updates, rent spreads, occupancy, cap rates, debt maturities, refinancing dates, asset sales, development deliveries, and macro/rate events. Watch AFFO, leverage, liquidity, and tenant concentration.
-
-## Industrials, aerospace, and transportation
-Track order books, production rates, deliveries, labor contracts, regulatory approvals, commodity costs, supply-chain disruptions, and customer capex cycles. Separate backlog conversion from new demand.
-
-## Media, telecom, and gaming
-Track subscriber updates, content launches, sports rights, spectrum auctions, regulatory approvals, advertising market data, game releases, box office, and streaming profitability milestones. Watch churn, ARPU, content amortization, and capex.
-
-## M&A and special situations
-Track announcement date, HSR/antitrust filings, shareholder vote, financing commitment, regulatory approvals, go-shop expiration, tender deadlines, outside date, walk rights, court dates, and closing window. Keep path-dependent milestones visible.
-
-## Macro/rates/cross-asset overlays
-Track central bank meetings, CPI, payrolls, PMIs, retail sales, auctions, fiscal deadlines, FX policy, commodity reports, and geopolitical deadlines. Map each event to portfolio exposures: rates duration, credit spreads, dollar, oil/gas, cyclicals, defensives, banks, real estate, and high-multiple growth.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/references/source-and-data-protocol.md b/plugins/financial-markets/skills/catalyst-calendar/references/source-and-data-protocol.md
deleted file mode 100644
index f351df8..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/references/source-and-data-protocol.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Source and Data Protocol
-
-## Source hierarchy
-Use the highest-quality available source for each event. Prefer in this order:
-1. User-provided materials, callable connected routes, and exports: portfolio exports, watchlists, models, research notes, internal calendars, CRM/IR notes, Slack/email summaries, Drive files, and approved market-data connectors.
-2. Primary company sources: IR calendars, press releases, SEC/SEDAR/market filings, earnings releases, shareholder materials, prospectuses, registration statements, merger documents, event pages, and company presentation decks.
-3. Regulator, court, exchange, and official calendars: SEC, FDA, EMA, CMS, FTC/DOJ, EU regulators, court dockets, exchange notices, index providers, central banks, treasury, BLS/BEA/Census/FRED, and other official agencies.
-4. Trusted market-data and research routes or exports: FactSet, Bloomberg, LSEG, S&P Capital IQ, Visible Alpha, AlphaSense, Daloopa, Quartr, broker research, exchange calendars, and industry databases, subject to runtime availability and entitlement.
-5. Reputable press and web sources, used as fallback or corroboration. Do not treat press speculation as a confirmed date.
-6. User-provided assumptions, explicitly labeled as assumptions.
-
-## Required source metadata
-For each material event, capture:
-- Source name and link or connector reference when available.
-- Source date or retrieval date.
-- Event date publication date if different from retrieval date.
-- Confidence label: confirmed, guided, expected, inferred, rumored, or unknown.
-- Date type: hard date, soft date, date window, quarter window, half-year window, seasonal window, or unknown.
-- Last checked timestamp.
-
-## Freshness rules
-- Earnings dates, conference schedules, merger dates, regulatory dates, trial readouts, lockup expirations, and macro calendars can change. Treat them as time-sensitive.
-- If the user asks for current or upcoming events, refresh using connected live sources or web where permitted.
-- For recurring events, do not carry forward prior-year dates without labeling them inferred.
-- When an aggregator and primary source conflict, prefer the primary source and show the conflict.
-- If a date is stale, keep it in the tracker with status `Needs Refresh` rather than silently deleting it.
-
-## Evidence labels
-Use these labels in outputs:
-- **Fact:** Source-backed event/date.
-- **Company-guided:** Management has indicated timing but not exact confirmed details.
-- **Consensus/Street:** Based on consensus or sell-side/buy-side expectation.
-- **Inferred:** Derived from historical cadence, statutory timeline, or management commentary.
-- **Assumption:** User or model assumption.
-- **Judgment:** PM interpretation or prioritization.
-
-## Non-destructive update protocol
-When editing an existing file:
-1. Create a backup/copy or new dated version.
-2. Preserve raw source tabs, hidden tabs, formulas, comments, and prior evidence logs.
-3. Append new rows rather than overwriting unless matching on a stable event ID.
-4. If updating matched events, keep old date/status/source in change log.
-5. Do not delete rows. Mark as archived, superseded, canceled, or stale.
-6. Include a clear refresh log: date/time, sources checked, rows added, rows changed, rows requiring review.
-
-## Confidentiality and compliance
-- Treat internal research notes, portfolio holdings, meeting notes, and position-sizing context as confidential unless the user asks for an external-clean version.
-- Do not blend confidential notes into an output intended for external distribution.
-- Flag potential MNPI indicators, including non-public company guidance, non-public process details, private trial or regulatory communications, or private order/customer information.
-- For external-clean materials, use only public or clearly authorized sources and omit internal view, position, cost basis, target prices, and trade intent unless explicitly approved.
-
-## Index And Passive Flow Sources
-
-Use index-provider, exchange, issuer, regulator, and primary company sources before aggregators. Index/ETF events require methodology, official announcement, holdings/rebalance file where available, issuer float/share-count disclosure, price, ADV, liquidity, and as-of times. Separate confirmed dates from inferred windows.
diff --git a/plugins/financial-markets/skills/catalyst-calendar/scripts/create_catalyst_calendar_workbook.py b/plugins/financial-markets/skills/catalyst-calendar/scripts/create_catalyst_calendar_workbook.py
deleted file mode 100755
index aadff24..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/scripts/create_catalyst_calendar_workbook.py
+++ /dev/null
@@ -1,1073 +0,0 @@
-#!/usr/bin/env python3
-"""Create a PM-grade catalyst calendar workbook and optional ICS export.
-
-This script intentionally uses only the Python standard library so it can run in
-restricted Skill environments without external spreadsheet packages. It accepts a
-clean CSV of event records and writes a multi-sheet .xlsx workbook. If no input
-is provided, it creates a blank institutional template with headers, source map,
-change log, and data dictionary.
-
-Example:
-  python scripts/create_catalyst_calendar_workbook.py \
-    --input events.csv \
-    --output catalyst_calendar.xlsx \
-    --ics catalyst_calendar.ics
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import datetime as dt
-import hashlib
-import json
-import re
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Sequence
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from workbook_writer import write_xlsx
-
-EVENT_HEADERS = [
-    "event_id",
-    "ticker",
-    "issuer",
-    "security_type",
-    "portfolio_group",
-    "position_context",
-    "sector",
-    "region",
-    "event_category",
-    "event_subcategory",
-    "event_name",
-    "event_description",
-    "reported_period",
-    "date_type",
-    "event_date",
-    "window_start",
-    "window_end",
-    "time_of_day",
-    "time_zone",
-    "status",
-    "source_label",
-    "source_id",
-    "source_notes",
-    "impact_score",
-    "confidence_score",
-    "urgency",
-    "thesis_relevance",
-    "kpi_or_model_line",
-    "market_setup",
-    "expected_outcome",
-    "variant_watch",
-    "prep_required",
-    "owner",
-    "prep_due_date",
-    "follow_up_date",
-    "decision_implication",
-    "post_event_action",
-    "notes",
-]
-
-FIELD_ALIASES = {
-    "company": "issuer",
-    "event_type": "event_category",
-    "event_window_start": "window_start",
-    "event_window_end": "window_end",
-    "date_window_start": "window_start",
-    "date_window_end": "window_end",
-    "source": "source_label",
-    "source_confidence": "confidence_score",
-    "confidence": "confidence_score",
-    "importance": "impact_score",
-    "materiality": "impact_score",
-    "impact": "impact_score",
-    "impact_path": "kpi_or_model_line",
-    "expected_market_impact": "market_setup",
-    "thesis_link": "thesis_relevance",
-    "prep_action": "prep_required",
-    "next_decision": "decision_implication",
-    "confirm_signal": "variant_watch",
-    "disconfirm_signal": "variant_watch",
-}
-
-SOURCE_HEADERS = [
-    "source_id",
-    "source_name",
-    "source_type",
-    "source_owner_or_provider",
-    "source_date",
-    "retrieved_at",
-    "fields_supported",
-    "event_ids_supported",
-    "confidence",
-    "limitations",
-    "citation_or_location",
-    "restricted_flag",
-    "notes",
-]
-
-CHANGE_HEADERS = [
-    "change_id",
-    "timestamp",
-    "event_id",
-    "field_changed",
-    "old_value",
-    "new_value",
-    "source_id",
-    "confidence_change",
-    "rationale",
-    "changed_by",
-]
-
-PREP_HEADERS = [
-    "event_id",
-    "ticker",
-    "event_name",
-    "prep_workstream",
-    "owner",
-    "due_date",
-    "status",
-    "dependency",
-    "blocker",
-    "next_step",
-    "last_updated",
-]
-
-TOP_EVENT_HEADERS = [
-    "rank",
-    "ticker",
-    "event_name",
-    "date_or_window",
-    "status",
-    "impact_score",
-    "confidence_score",
-    "urgency",
-    "why_it_matters",
-    "market_setup",
-    "owner",
-    "prep_due_date",
-    "decision_implication",
-]
-
-DASHBOARD_HEADERS = ["Metric", "Value", "PM implication"]
-
-CATEGORY_SHEETS = [
-    ("Earnings", ["earnings"]),
-    ("Reg Legal", ["regulatory / legal", "regulatory", "legal"]),
-    (
-        "Clinical Product",
-        ["clinical / scientific", "clinical", "scientific", "company event", "product"],
-    ),
-    ("Macro Rates", ["macro / rates / cross-asset", "macro", "rates", "cross-asset"]),
-    ("CapMkts Flows", ["capital markets", "ownership / flow", "corporate action"]),
-]
-
-DATA_DICTIONARY_ROWS = [
-    ["event_id", "Stable unique identifier used to avoid duplicate events on refresh."],
-    [
-        "date_type",
-        "confirmed date, confirmed window, management-indicated window, street-estimated date/window, statutory deadline, model-derived date, speculative / needs confirmation.",
-    ],
-    [
-        "source_label",
-        "company-confirmed, official-confirmed, data-provider, street-estimated, model-derived, user-provided, assumption, pm-judgment, restricted/internal.",
-    ],
-    [
-        "impact_score",
-        "1-5 expected impact on estimates, valuation, risk, narrative, or thesis status.",
-    ],
-    ["confidence_score", "1-5 confidence in timing and event mechanics."],
-    [
-        "urgency",
-        "red, amber, green, gray based on impact, timing, prep, and source conflict.",
-    ],
-    [
-        "thesis_relevance",
-        "confirmatory, disconfirmatory, asymmetric upside/downside, binary risk, volatility event, monitoring only.",
-    ],
-    [
-        "market_setup",
-        "high bar, low bar, crowded long/short, derisked, underfollowed, catalyst fatigue, high/low implied move, unknown.",
-    ],
-    [
-        "prep_due_date",
-        "Internal deadline before the event date; should not default to the event date.",
-    ],
-    ["follow_up_date", "Date to log actual outcome and update model/thesis/risk work."],
-]
-
-REVIEW_ROWS = [
-    [
-        "Source/date integrity",
-        "Exact dates are sourced or mechanically derived; windows remain windows; source conflicts surfaced.",
-    ],
-    [
-        "Calendar hygiene",
-        "Moved/delayed/canceled/stale events are marked, not deleted; duplicate events consolidated.",
-    ],
-    [
-        "PM usefulness",
-        "Top events explain why they matter, what changes, market setup, prep, owner, and decision implication.",
-    ],
-    [
-        "Sector judgment",
-        "Sector-specific mechanics handled correctly (trial dates, bank CCAR, retail comps, lockups, etc.).",
-    ],
-    [
-        "Compliance",
-        "Restricted/internal sources flagged; external-clean outputs remove trade/position/private-source language.",
-    ],
-    [
-        "Workbook safety",
-        "Original data preserved; source map and change log append-only.",
-    ],
-]
-
-
-@dataclass
-class EventRecord:
-    values: dict[str, str]
-
-    def get(self, key: str, default: str = "") -> str:
-        value = self.values.get(key, default)
-        return "" if value is None else str(value).strip()
-
-    def set(self, key: str, value: object) -> None:
-        self.values[key] = "" if value is None else str(value)
-
-
-def normalize_header(header: str) -> str:
-    return re.sub(r"[^a-z0-9]+", "_", header.strip().lower()).strip("_")
-
-
-def canonicalize_row(row: dict[str, str]) -> dict[str, str]:
-    canonical: dict[str, str] = dict(row)
-    for alias, target in FIELD_ALIASES.items():
-        if alias in row and target not in canonical:
-            canonical[target] = row[alias]
-        elif alias in row and not str(canonical.get(target, "")).strip():
-            canonical[target] = row[alias]
-
-    # Convert legacy confidence labels into numeric timing confidence when needed.
-    confidence = str(row.get("confidence", "") or row.get("source_confidence", "")).strip().lower()
-    if confidence and not str(canonical.get("confidence_score", "")).strip().isdigit():
-        canonical["confidence_score"] = {
-            "confirmed": "5",
-            "guided": "4",
-            "expected": "3",
-            "inferred": "2",
-            "rumored": "1",
-            "unknown": "1",
-        }.get(confidence, canonical.get("confidence_score", ""))
-        if not str(canonical.get("source_notes", "")).strip():
-            canonical["source_notes"] = f"confidence label: {confidence}"
-    return canonical
-
-
-def parse_date(value: str) -> dt.date | None:
-    if not value:
-        return None
-    text = value.strip()
-    for fmt in (
-        "%Y-%m-%d",
-        "%m/%d/%Y",
-        "%m/%d/%y",
-        "%Y/%m/%d",
-        "%d-%b-%Y",
-        "%b %d %Y",
-        "%B %d %Y",
-    ):
-        try:
-            return dt.datetime.strptime(text, fmt).date()
-        except ValueError:
-            pass
-    return None
-
-
-def iso_date(value: dt.date | None) -> str:
-    return value.isoformat() if value else ""
-
-
-def safe_int(value: str, default: int = 0) -> int:
-    try:
-        if value is None or str(value).strip() == "":
-            return default
-        return int(float(str(value).strip()))
-    except ValueError:
-        return default
-
-
-def make_event_id(row: dict[str, str]) -> str:
-    ticker = row.get("ticker") or row.get("issuer") or "unknown"
-    category = row.get("event_category") or "event"
-    name = row.get("event_name") or row.get("event_description") or "unnamed"
-    period = (
-        row.get("reported_period") or row.get("event_date") or row.get("window_start") or "undated"
-    )
-    raw = "|".join([ticker, category, name, period]).lower()
-    digest = hashlib.sha1(raw.encode("utf-8")).hexdigest()[:8]
-    slug = re.sub(r"[^a-z0-9]+", "_", f"{ticker}_{category}_{digest}".lower()).strip("_")
-    return slug[:60] or digest
-
-
-def canonical_category(category: str) -> str:
-    text = (category or "").strip().lower()
-    aliases = {
-        "earnings": "earnings",
-        "print": "earnings",
-        "results": "earnings",
-        "regulatory": "regulatory / legal",
-        "legal": "regulatory / legal",
-        "court": "regulatory / legal",
-        "clinical": "clinical / scientific",
-        "biotech": "clinical / scientific",
-        "trial": "clinical / scientific",
-        "macro": "macro / rates / cross-asset",
-        "rates": "macro / rates / cross-asset",
-        "central bank": "macro / rates / cross-asset",
-        "capital markets": "capital markets",
-        "lockup": "capital markets",
-        "flows": "ownership / flow",
-        "ownership": "ownership / flow",
-        "m&a": "corporate action",
-        "ma": "corporate action",
-        "merger": "corporate action",
-        "conference": "sell-side / access",
-        "broker": "sell-side / access",
-        "investor day": "company event",
-        "product": "company event",
-    }
-    if text in aliases:
-        return aliases[text]
-    return category.strip() if category else "needs classification"
-
-
-def infer_date_type(row: dict[str, str]) -> str:
-    if row.get("date_type"):
-        return row["date_type"]
-    if row.get("event_date"):
-        source = (row.get("source_label") or "").lower()
-        if "company" in source or "official" in source:
-            return "confirmed date"
-        if "street" in source or "provider" in source:
-            return "street-estimated date/window"
-        return "user-provided"
-    if row.get("window_start") or row.get("window_end"):
-        return (
-            "confirmed window"
-            if "confirmed" in (row.get("source_label") or "").lower()
-            else "management-indicated window"
-        )
-    return "speculative / needs confirmation"
-
-
-def date_sort_key(event: EventRecord) -> dt.date:
-    return (
-        parse_date(event.get("event_date"))
-        or parse_date(event.get("window_start"))
-        or dt.date(9999, 12, 31)
-    )
-
-
-def compute_urgency(event: EventRecord, as_of: dt.date) -> str:
-    existing = event.get("urgency").lower()
-    if existing in {"red", "amber", "green", "gray"}:
-        return existing
-    status = event.get("status").lower()
-    if status in {"completed", "canceled", "cancelled", "superseded"}:
-        return "gray"
-    event_date = date_sort_key(event)
-    days = (event_date - as_of).days
-    impact = safe_int(event.get("impact_score"), 0)
-    confidence = safe_int(event.get("confidence_score"), 0)
-    prep_due = parse_date(event.get("prep_due_date"))
-    prep_overdue = (
-        prep_due is not None
-        and prep_due < as_of
-        and status not in {"completed", "canceled", "cancelled"}
-    )
-    source_conflict = (
-        "conflict" in event.get("source_notes").lower()
-        or "needs confirmation" in event.get("status").lower()
-    )
-    if (
-        prep_overdue
-        or (impact >= 4 and days <= 14)
-        or (impact >= 5 and days <= 30)
-        or (impact >= 4 and source_conflict)
-    ):
-        return "red"
-    if (impact >= 3 and days <= 30) or (impact >= 4 and days <= 60) or confidence <= 2:
-        return "amber"
-    if status in {"stale", "completed"} or days < -1:
-        return "gray"
-    return "green"
-
-
-def normalize_event_row(row: dict[str, str], as_of: dt.date) -> EventRecord:
-    row = canonicalize_row(row)
-    normalized = {h: str(row.get(h, "")).strip() for h in EVENT_HEADERS}
-    # Preserve user-provided extra keys in notes if not part of the canonical schema.
-    extras = {k: v for k, v in row.items() if k not in normalized and str(v).strip()}
-    if extras and not normalized.get("notes"):
-        normalized["notes"] = "; ".join(f"{k}: {v}" for k, v in extras.items())
-    if not normalized["event_id"]:
-        normalized["event_id"] = make_event_id(normalized)
-    normalized["event_category"] = canonical_category(normalized.get("event_category", ""))
-    normalized["date_type"] = infer_date_type(normalized)
-    if not normalized["status"]:
-        d = parse_date(normalized.get("event_date", "")) or parse_date(
-            normalized.get("window_end", "")
-        )
-        normalized["status"] = "stale" if d and d < as_of else "upcoming"
-    if not normalized["source_label"]:
-        normalized["source_label"] = "user-provided" if any(row.values()) else "assumption"
-    if not normalized["impact_score"]:
-        normalized["impact_score"] = "3"
-    if not normalized["confidence_score"]:
-        normalized["confidence_score"] = "3" if normalized["event_date"] else "2"
-    rec = EventRecord(normalized)
-    rec.set("urgency", compute_urgency(rec, as_of))
-    return rec
-
-
-def load_events(path: Path | None, as_of: dt.date) -> list[EventRecord]:
-    if not path:
-        return []
-
-    if path.suffix.lower() == ".json":
-        with path.open("r", encoding="utf-8-sig") as f:
-            payload = json.load(f)
-        if isinstance(payload, dict):
-            records = payload.get("events") or payload.get("rows") or []
-        elif isinstance(payload, list):
-            records = payload
-        else:
-            records = []
-        events = []
-        for raw_row in records:
-            if isinstance(raw_row, dict):
-                row = {
-                    normalize_header(str(k)): ("" if v is None else str(v))
-                    for k, v in raw_row.items()
-                }
-                events.append(normalize_event_row(row, as_of))
-        return events
-
-    with path.open("r", encoding="utf-8-sig", newline="") as f:
-        reader = csv.DictReader(f)
-        if not reader.fieldnames:
-            return []
-        normalized_fields = {field: normalize_header(field) for field in reader.fieldnames}
-        events: list[EventRecord] = []
-        for raw_row in reader:
-            row = {normalized_fields[k]: (v or "") for k, v in raw_row.items()}
-            events.append(normalize_event_row(row, as_of))
-        return events
-
-
-def date_or_window(event: EventRecord) -> str:
-    if event.get("event_date"):
-        return event.get("event_date")
-    start = event.get("window_start")
-    end = event.get("window_end")
-    if start and end:
-        return f"{start} to {end}"
-    if start:
-        return f"From {start}"
-    if end:
-        return f"By {end}"
-    return "Needs confirmation"
-
-
-def why_it_matters(event: EventRecord) -> str:
-    explicit = event.get("notes")
-    if explicit:
-        return explicit[:240]
-    thesis = event.get("thesis_relevance") or "thesis confidence"
-    kpi = event.get("kpi_or_model_line") or "estimates / narrative"
-    category = event.get("event_category") or "event"
-    return f"Tests {thesis} through {kpi}; {category} can affect estimates, narrative, risk premium, or position sizing."
-
-
-def ranked_events(
-    events: Sequence[EventRecord], as_of: dt.date, limit: int = 25
-) -> list[EventRecord]:
-    urgency_rank = {"red": 0, "amber": 1, "green": 2, "gray": 3}
-
-    def score(event: EventRecord) -> tuple[int, int, int, dt.date]:
-        urgency = urgency_rank.get(event.get("urgency").lower(), 2)
-        impact = -safe_int(event.get("impact_score"), 0)
-        confidence_penalty = 0 if safe_int(event.get("confidence_score"), 0) >= 3 else -1
-        return (urgency, impact, confidence_penalty, date_sort_key(event))
-
-    return sorted(events, key=score)[:limit]
-
-
-def build_dashboard(events: Sequence[EventRecord], as_of: dt.date) -> list[list[object]]:
-    def within(days: int) -> int:
-        count = 0
-        for event in events:
-            d = date_sort_key(event)
-            delta = (d - as_of).days
-            if 0 <= delta <= days:
-                count += 1
-        return count
-
-    red_count = sum(1 for e in events if e.get("urgency").lower() == "red")
-    amber_count = sum(1 for e in events if e.get("urgency").lower() == "amber")
-    needs_confirmation = sum(
-        1
-        for e in events
-        if "needs confirmation" in (e.get("status") + " " + e.get("date_type")).lower()
-    )
-    stale_count = sum(1 for e in events if e.get("status").lower() == "stale")
-    high_impact = sum(1 for e in events if safe_int(e.get("impact_score"), 0) >= 4)
-    low_confidence = sum(1 for e in events if safe_int(e.get("confidence_score"), 0) <= 2)
-    red_next_30 = 0
-    missing_owner = 0
-    overdue_prep = 0
-    for event in events:
-        d = date_sort_key(event)
-        delta = (d - as_of).days
-        if 0 <= delta <= 30 and event.get("urgency").lower() == "red":
-            red_next_30 += 1
-        if not event.get("owner") and not event.get("prep_owner"):
-            missing_owner += 1
-        due = parse_date(event.get("prep_due_date"))
-        if (
-            due
-            and due < as_of
-            and event.get("status").lower() not in {"completed", "canceled", "cancelled"}
-        ):
-            overdue_prep += 1
-    top = ranked_events(events, as_of, 1)
-    top_desc = (
-        f"{top[0].get('ticker')} - {top[0].get('event_name')} ({date_or_window(top[0])})"
-        if top
-        else "No events loaded"
-    )
-    rows: list[list[object]] = [
-        ["As of", as_of.isoformat(), "Refresh source data before PM/IC use."],
-        [
-            "Total events",
-            len(events),
-            "Use Event Master for full source-backed database.",
-        ],
-        ["Events next 30 days", within(30), "Prioritize red/amber events for prep."],
-        [
-            "Events next 60 days",
-            within(60),
-            "Start long-lead work for high-impact windows.",
-        ],
-        [
-            "Events next 90 days",
-            within(90),
-            "Keep binary/high-impact events above the fold.",
-        ],
-        [
-            "Red urgency",
-            red_count,
-            "High impact, near-term, overdue prep, or source conflict.",
-        ],
-        [
-            "Amber urgency",
-            amber_count,
-            "Moderate/high impact or lower timing confidence.",
-        ],
-        [
-            "Needs confirmation",
-            needs_confirmation,
-            "Do not treat as confirmed until better sourced.",
-        ],
-        [
-            "Stale events",
-            stale_count,
-            "Mark completed/moved/canceled; do not delete history.",
-        ],
-        ["Overdue prep items", overdue_prep, "Escalate owner and workstream."],
-        [
-            "Top event",
-            top_desc,
-            "Review market setup, thesis linkage, and decision implication.",
-        ],
-    ]
-    rows.extend(
-        [
-            [
-                "High-impact events",
-                high_impact,
-                "Impact score >=4; should be explicit in PM/IC prep.",
-            ],
-            [
-                "Red events next 30 days",
-                red_next_30,
-                "Near-term high-urgency items need owner, prep, and source refresh.",
-            ],
-            [
-                "Low-confidence events",
-                low_confidence,
-                "Confidence score <=2; treat timing/read-through as provisional.",
-            ],
-            [
-                "Missing prep owner",
-                missing_owner,
-                "Assign owner before using workbook as a live workplan.",
-            ],
-        ]
-    )
-    for idx, event in enumerate(ranked_events(events, as_of, 5), start=1):
-        rows.append(
-            [
-                f"Top catalyst {idx}",
-                f"{event.get('ticker') or event.get('issuer')} - {event.get('event_name')} ({date_or_window(event)})",
-                why_it_matters(event),
-            ]
-        )
-    rows.extend(
-        [
-            [
-                "Workbook mode",
-                "calendar_dashboard_export",
-                "First tab is the PM cover; Top Events and Event Master contain detail.",
-            ],
-            [
-                "Source posture",
-                "See Source Map",
-                "Confirm dates, confidence, and stale/moved events before external use.",
-            ],
-        ]
-    )
-    return rows
-
-
-def build_top_events(events: Sequence[EventRecord], as_of: dt.date) -> list[list[object]]:
-    rows: list[list[object]] = []
-    for idx, event in enumerate(ranked_events(events, as_of, 30), start=1):
-        rows.append(
-            [
-                idx,
-                event.get("ticker") or event.get("issuer"),
-                event.get("event_name"),
-                date_or_window(event),
-                event.get("status"),
-                safe_int(event.get("impact_score"), 0),
-                safe_int(event.get("confidence_score"), 0),
-                event.get("urgency"),
-                why_it_matters(event),
-                event.get("market_setup"),
-                event.get("owner"),
-                event.get("prep_due_date"),
-                event.get("decision_implication"),
-            ]
-        )
-    return rows
-
-
-def build_prep_rows(events: Sequence[EventRecord], as_of: dt.date) -> list[list[object]]:
-    rows: list[list[object]] = []
-    for event in ranked_events(events, as_of, 100):
-        prep = event.get("prep_required")
-        if not prep and safe_int(event.get("impact_score"), 0) < 4:
-            continue
-        rows.append(
-            [
-                event.get("event_id"),
-                event.get("ticker") or event.get("issuer"),
-                event.get("event_name"),
-                prep or "Confirm source, refresh model/consensus, and prepare PM questions",
-                event.get("owner"),
-                event.get("prep_due_date"),
-                "not started" if event.get("status").lower() == "upcoming" else event.get("status"),
-                event.get("source_id"),
-                "" if event.get("confidence_score") != "1" else "Low timing confidence",
-                event.get("variant_watch") or "Define decision trigger before event",
-                as_of.isoformat(),
-            ]
-        )
-    return rows
-
-
-def category_filter(events: Sequence[EventRecord], categories: Sequence[str]) -> list[list[object]]:
-    cats = {c.lower() for c in categories}
-    rows: list[list[object]] = []
-    for event in events:
-        category = event.get("event_category").lower()
-        subcategory = event.get("event_subcategory").lower()
-        if category in cats or subcategory in cats or any(c in category for c in cats):
-            rows.append([event.get(h) for h in EVENT_HEADERS])
-    return rows
-
-
-def blank_rows(headers: Sequence[str], n: int = 5) -> list[list[str]]:
-    return [["" for _ in headers] for _ in range(n)]
-
-
-def event_rows(events: Sequence[EventRecord]) -> list[list[object]]:
-    return [[event.get(h) for h in EVENT_HEADERS] for event in sorted(events, key=date_sort_key)]
-
-
-def source_rows(events: Sequence[EventRecord], as_of: dt.date) -> list[list[object]]:
-    source_ids = sorted({event.get("source_id") for event in events if event.get("source_id")})
-    if not source_ids:
-        return blank_rows(SOURCE_HEADERS, 5)
-    rows = []
-    for sid in source_ids:
-        supported = [event.get("event_id") for event in events if event.get("source_id") == sid]
-        rows.append(
-            [
-                sid,
-                "",
-                "",
-                "",
-                "",
-                as_of.isoformat(),
-                "",
-                "; ".join(supported),
-                "",
-                "",
-                "",
-                "",
-                "",
-            ]
-        )
-    return rows
-
-
-def change_rows(changes: Sequence[Sequence[object]]) -> list[list[object]]:
-    return list(changes) if changes else blank_rows(CHANGE_HEADERS, 5)
-
-
-def merge_events_for_refresh(
-    prior_events: Sequence[EventRecord],
-    current_events: Sequence[EventRecord],
-    as_of: dt.date,
-) -> tuple[list[EventRecord], list[list[object]]]:
-    """Merge a prior calendar with new evidence and produce an append-only change log."""
-    prior_by_id = {event.get("event_id"): event for event in prior_events if event.get("event_id")}
-    current_by_id = {
-        event.get("event_id"): event for event in current_events if event.get("event_id")
-    }
-    merged: dict[str, EventRecord] = {}
-    changes: list[list[object]] = []
-    timestamp = dt.datetime.combine(as_of, dt.time()).isoformat()
-
-    for event_id, prior in prior_by_id.items():
-        merged[event_id] = EventRecord(dict(prior.values))
-
-    for event_id, current in current_by_id.items():
-        if event_id not in prior_by_id:
-            merged[event_id] = current
-            changes.append(
-                [
-                    hashlib.sha1(f"{event_id}|row_added|{timestamp}".encode("utf-8")).hexdigest()[
-                        :10
-                    ],
-                    timestamp,
-                    event_id,
-                    "row_added",
-                    "",
-                    current.get("event_name") or current.get("event_description"),
-                    current.get("source_id"),
-                    current.get("confidence_score"),
-                    "New event in refreshed input.",
-                    "catalyst-calendar script",
-                ]
-            )
-            continue
-
-        prior = prior_by_id[event_id]
-        updated = dict(prior.values)
-        for field in EVENT_HEADERS:
-            new_value = current.get(field)
-            old_value = prior.get(field)
-            if new_value and new_value != old_value:
-                updated[field] = new_value
-                changes.append(
-                    [
-                        hashlib.sha1(
-                            f"{event_id}|{field}|{old_value}|{new_value}|{timestamp}".encode(
-                                "utf-8"
-                            )
-                        ).hexdigest()[:10],
-                        timestamp,
-                        event_id,
-                        field,
-                        old_value,
-                        new_value,
-                        current.get("source_id") or prior.get("source_id"),
-                        f"{old_value}->{new_value}" if field == "confidence_score" else "",
-                        "Updated from refreshed input.",
-                        "catalyst-calendar script",
-                    ]
-                )
-        merged[event_id] = EventRecord(updated)
-
-    for event_id, prior in prior_by_id.items():
-        if event_id in current_by_id:
-            continue
-        preserved = dict(prior.values)
-        status = str(preserved.get("status", "")).lower()
-        if status not in {"completed", "canceled", "cancelled", "superseded", "stale"}:
-            old_status = preserved.get("status", "")
-            preserved["status"] = "needs refresh"
-            changes.append(
-                [
-                    hashlib.sha1(
-                        f"{event_id}|needs_refresh|{timestamp}".encode("utf-8")
-                    ).hexdigest()[:10],
-                    timestamp,
-                    event_id,
-                    "status",
-                    old_status,
-                    "needs refresh",
-                    preserved.get("source_id", ""),
-                    "",
-                    "Prior event was not present in refreshed input; preserved for review.",
-                    "catalyst-calendar script",
-                ]
-            )
-        merged[event_id] = EventRecord(preserved)
-
-    return sorted(merged.values(), key=date_sort_key), changes
-
-
-def ics_escape(value: str) -> str:
-    return value.replace("\\", "\\\\").replace(",", "\\,").replace(";", "\\;").replace("\n", "\\n")
-
-
-def fold_ics_line(line: str) -> str:
-    # RFC5545 line folding at 75 octets; approximate by chars for simplicity.
-    if len(line) <= 75:
-        return line
-    chunks = [line[:75]]
-    rest = line[75:]
-    while rest:
-        chunks.append(" " + rest[:74])
-        rest = rest[74:]
-    return "\r\n".join(chunks)
-
-
-def is_confirmed_ics_date(event: EventRecord) -> bool:
-    if not event.get("event_date"):
-        return False
-    date_type = event.get("date_type").lower()
-    source = event.get("source_label").lower()
-    if (
-        "window" in date_type
-        or "estimated" in date_type
-        or "speculative" in date_type
-        or "needs confirmation" in date_type
-    ):
-        return False
-    return (
-        date_type in {"confirmed date", "statutory deadline", "model-derived date"}
-        or "confirmed" in source
-        or "official" in source
-    )
-
-
-def write_ics(path: Path, events: Sequence[EventRecord], as_of: dt.date) -> None:
-    lines = [
-        "BEGIN:VCALENDAR",
-        "VERSION:2.0",
-        "PRODID:-//catalyst-calendar skill//EN",
-        "CALSCALE:GREGORIAN",
-        "METHOD:PUBLISH",
-    ]
-    stamp = dt.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
-    for event in events:
-        if event.get("source_label").lower() == "restricted/internal":
-            continue
-        if not is_confirmed_ics_date(event):
-            continue
-        d = parse_date(event.get("event_date"))
-        if not d:
-            continue
-        end = d + dt.timedelta(days=1)
-        uid = f"{event.get('event_id')}@catalyst-calendar-skill"
-        summary = f"{event.get('ticker') or event.get('issuer')} - {event.get('event_name')}"
-        description = (
-            f"Category: {event.get('event_category')}\\n"
-            f"Date type: {event.get('date_type')}\\n"
-            f"Status: {event.get('status')}\\n"
-            f"Impact: {event.get('impact_score')} | Confidence: {event.get('confidence_score')} | Urgency: {event.get('urgency')}\\n"
-            f"Thesis linkage: {event.get('thesis_relevance')}\\n"
-            f"Prep owner: {event.get('owner')} | Prep due: {event.get('prep_due_date')}\\n"
-            f"Decision implication: {event.get('decision_implication')}"
-        )
-        lines.extend(
-            [
-                "BEGIN:VEVENT",
-                f"UID:{ics_escape(uid)}",
-                f"DTSTAMP:{stamp}",
-                f"DTSTART;VALUE=DATE:{d.strftime('%Y%m%d')}",
-                f"DTEND;VALUE=DATE:{end.strftime('%Y%m%d')}",
-                f"SUMMARY:{ics_escape(summary)}",
-                f"DESCRIPTION:{ics_escape(description)}",
-                "END:VEVENT",
-            ]
-        )
-    lines.append("END:VCALENDAR")
-    with path.open("w", encoding="utf-8", newline="") as f:
-        for line in lines:
-            f.write(fold_ics_line(line) + "\r\n")
-
-
-def build_sheets(
-    events: Sequence[EventRecord],
-    as_of: dt.date,
-    changes: Sequence[Sequence[object]] = (),
-) -> list[tuple[str, Sequence[str], Sequence[Sequence[object]], str]]:
-    event_master_rows = event_rows(events) if events else blank_rows(EVENT_HEADERS, 10)
-    sheets: list[tuple[str, Sequence[str], Sequence[Sequence[object]], str]] = [
-        (
-            "Cover",
-            DASHBOARD_HEADERS,
-            build_dashboard(events, as_of),
-            "Catalyst Calendar Dashboard Cover",
-        ),
-        (
-            "Top Events",
-            TOP_EVENT_HEADERS,
-            build_top_events(events, as_of) if events else blank_rows(TOP_EVENT_HEADERS, 10),
-            "PM-Ranked Top Events",
-        ),
-        ("Event Master", EVENT_HEADERS, event_master_rows, "Full Event Master"),
-        (
-            "Prep Tracker",
-            PREP_HEADERS,
-            build_prep_rows(events, as_of) if events else blank_rows(PREP_HEADERS, 10),
-            "Pre-Event Workstreams",
-        ),
-    ]
-    for sheet_name, categories in CATEGORY_SHEETS:
-        rows = category_filter(events, categories) if events else blank_rows(EVENT_HEADERS, 10)
-        sheets.append((sheet_name, EVENT_HEADERS, rows, sheet_name))
-    sheets.extend(
-        [
-            (
-                "Source Map",
-                SOURCE_HEADERS,
-                source_rows(events, as_of),
-                "Append-Only Source Map",
-            ),
-            (
-                "Change Log",
-                CHANGE_HEADERS,
-                change_rows(changes),
-                "Append-Only Change Log",
-            ),
-            (
-                "Data Dictionary",
-                ["Field", "Definition"],
-                DATA_DICTIONARY_ROWS,
-                "Data Dictionary",
-            ),
-            (
-                "Review Checklist",
-                ["Review area", "Check"],
-                REVIEW_ROWS,
-                "PM Review Checklist",
-            ),
-            (
-                "Read Me",
-                ["Topic", "Guidance"],
-                [
-                    [
-                        "Purpose",
-                        "Track sourced, prioritized, PM-actionable public-equity catalysts.",
-                    ],
-                    [
-                        "Safe refresh",
-                        "Preserve prior views; append source map and change log; do not delete stale/moved events.",
-                    ],
-                    [
-                        "Date discipline",
-                        "Use exact dates only when confirmed or mechanically derived; otherwise use windows.",
-                    ],
-                    [
-                        "Actionability",
-                        "Top events should connect to thesis, model line, market setup, prep owner, and decision implication.",
-                    ],
-                    [
-                        "External-clean",
-                        "Remove position/trade/private-source language before broader sharing.",
-                    ],
-                ],
-                "Read Me",
-            ),
-        ]
-    )
-    return sheets
-
-
-def main(argv: Sequence[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(
-        description="Create a catalyst calendar workbook and optional ICS export."
-    )
-    parser.add_argument("--input", type=Path, help="Optional CSV of catalyst events.")
-    parser.add_argument(
-        "--prior",
-        type=Path,
-        help="Optional prior CSV/JSON catalyst calendar for refresh/change-log mode.",
-    )
-    parser.add_argument(
-        "--output",
-        type=Path,
-        default=Path("catalyst_calendar.xlsx"),
-        help="Output .xlsx path.",
-    )
-    parser.add_argument("--ics", type=Path, help="Optional output .ics calendar path.")
-    parser.add_argument(
-        "--as-of",
-        type=str,
-        default=dt.date.today().isoformat(),
-        help="As-of date, YYYY-MM-DD.",
-    )
-    args = parser.parse_args(argv)
-
-    as_of = parse_date(args.as_of)
-    if not as_of:
-        print(f"Invalid --as-of date: {args.as_of}. Use YYYY-MM-DD.", file=sys.stderr)
-        return 2
-
-    if args.input and not args.input.exists():
-        print(f"Input CSV not found: {args.input}", file=sys.stderr)
-        return 2
-    if args.prior and not args.prior.exists():
-        print(f"Prior calendar not found: {args.prior}", file=sys.stderr)
-        return 2
-
-    events = load_events(args.input, as_of)
-    changes: list[list[object]] = []
-    if args.prior:
-        prior_events = load_events(args.prior, as_of)
-        events, changes = merge_events_for_refresh(prior_events, events, as_of)
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    write_xlsx(args.output, build_sheets(events, as_of, changes))
-
-    if args.ics:
-        args.ics.parent.mkdir(parents=True, exist_ok=True)
-        write_ics(args.ics, events, as_of)
-
-    print(f"Created workbook: {args.output}")
-    if args.ics:
-        print(f"Created ICS: {args.ics}")
-    print(f"Events loaded: {len(events)}")
-    if args.prior:
-        print(f"Refresh changes logged: {len(changes)}")
-    print(
-        "Sheets: Cover, Top Events, Event Master, Prep Tracker, category tabs, Source Map, Change Log, Data Dictionary, Review Checklist, Read Me"
-    )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/catalyst-calendar/scripts/workbook_writer.py b/plugins/financial-markets/skills/catalyst-calendar/scripts/workbook_writer.py
deleted file mode 100644
index 84765a2..0000000
--- a/plugins/financial-markets/skills/catalyst-calendar/scripts/workbook_writer.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""Minimal XLSX writer for catalyst-calendar outputs."""
-
-from __future__ import annotations
-
-import datetime as dt
-import zipfile
-from html import escape
-from pathlib import Path
-from typing import Sequence
-
-
-def col_letter(index: int) -> str:
-    result = ""
-    while index:
-        index, rem = divmod(index - 1, 26)
-        result = chr(65 + rem) + result
-    return result
-
-
-def cell_ref(row: int, col: int) -> str:
-    return f"{col_letter(col)}{row}"
-
-
-def xml_cell(value: object, row: int, col: int, style: int = 0) -> str:
-    ref = cell_ref(row, col)
-    s_attr = f' s="{style}"' if style else ""
-    if value is None or value == "":
-        return f'<c r="{ref}"{s_attr}/>'
-    if isinstance(value, bool):
-        return f'<c r="{ref}" t="b"{s_attr}><v>{1 if value else 0}</v></c>'
-    if isinstance(value, (int, float)) and not isinstance(value, bool):
-        return f'<c r="{ref}"{s_attr}><v>{value}</v></c>'
-    text = str(value)
-    if text.startswith("="):
-        return f'<c r="{ref}"{s_attr}><f>{escape(text[1:])}</f></c>'
-    return f'<c r="{ref}" t="inlineStr"{s_attr}><is><t>{escape(text)}</t></is></c>'
-
-
-def sheet_xml(
-    headers: Sequence[str],
-    rows: Sequence[Sequence[object]],
-    title: str = "",
-    freeze: bool = True,
-) -> str:
-    out: list[str] = ['<?xml version="1.0" encoding="UTF-8" standalone="yes"?>']
-    out.append(
-        '<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">'
-    )
-    out.append('<sheetViews><sheetView workbookViewId="0">')
-    if freeze:
-        out.append('<pane ySplit="1" topLeftCell="A2" activePane="bottomLeft" state="frozen"/>')
-    out.append("</sheetView></sheetViews>")
-    # Conservative column widths, broad enough for PM notes but not extreme.
-    col_count = max(len(headers), max((len(r) for r in rows), default=0), 1)
-    out.append("<cols>")
-    for i in range(1, col_count + 1):
-        width = 14
-        if i <= len(headers):
-            header = headers[i - 1].lower()
-            if (
-                "description" in header
-                or "notes" in header
-                or "why" in header
-                or "implication" in header
-                or "source" in header
-            ):
-                width = 32
-            elif "event" in header or "ticker" in header or "owner" in header:
-                width = 18
-            elif "date" in header or "window" in header or "status" in header:
-                width = 16
-        out.append(f'<col min="{i}" max="{i}" width="{width}" customWidth="1"/>')
-    out.append("</cols>")
-    out.append("<sheetData>")
-    current_row = 1
-    if title:
-        out.append(f'<row r="{current_row}">{xml_cell(title, current_row, 1, 1)}</row>')
-        current_row += 1
-    if headers:
-        cells = "".join(xml_cell(h, current_row, c, 2) for c, h in enumerate(headers, start=1))
-        out.append(f'<row r="{current_row}">{cells}</row>')
-        current_row += 1
-    for row_values in rows:
-        cells = "".join(xml_cell(v, current_row, c, 0) for c, v in enumerate(row_values, start=1))
-        out.append(f'<row r="{current_row}">{cells}</row>')
-        current_row += 1
-    out.append("</sheetData>")
-    out.append("</worksheet>")
-    return "".join(out)
-
-
-def workbook_xml(sheet_names: Sequence[str]) -> str:
-    sheets = []
-    for i, name in enumerate(sheet_names, start=1):
-        sheets.append(f'<sheet name="{escape(name)}" sheetId="{i}" r:id="rId{i}"/>')
-    return (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        + '<workbook xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"><sheets>'
-        + "".join(sheets)
-        + "</sheets></workbook>"
-    )
-
-
-def workbook_rels(sheet_count: int) -> str:
-    rels = [
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?><Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">'
-    ]
-    for i in range(1, sheet_count + 1):
-        rels.append(
-            f'<Relationship Id="rId{i}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" Target="worksheets/sheet{i}.xml"/>'
-        )
-    rels.append(
-        f'<Relationship Id="rId{sheet_count + 1}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles" Target="styles.xml"/>'
-    )
-    rels.append("</Relationships>")
-    return "".join(rels)
-
-
-def root_rels() -> str:
-    return """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
-  <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" Target="xl/workbook.xml"/>
-  <Relationship Id="rId2" Type="http://schemas.openxmlformats.org/package/2006/relationships/metadata/core-properties" Target="docProps/core.xml"/>
-  <Relationship Id="rId3" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/extended-properties" Target="docProps/app.xml"/>
-</Relationships>"""
-
-
-def content_types(sheet_count: int) -> str:
-    parts = [
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?><Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">'
-    ]
-    parts.append(
-        '<Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>'
-    )
-    parts.append('<Default Extension="xml" ContentType="application/xml"/>')
-    parts.append(
-        '<Override PartName="/xl/workbook.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.main+xml"/>'
-    )
-    parts.append(
-        '<Override PartName="/xl/styles.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.styles+xml"/>'
-    )
-    parts.append(
-        '<Override PartName="/docProps/core.xml" ContentType="application/vnd.openxmlformats-package.core-properties+xml"/>'
-    )
-    parts.append(
-        '<Override PartName="/docProps/app.xml" ContentType="application/vnd.openxmlformats-officedocument.extended-properties+xml"/>'
-    )
-    for i in range(1, sheet_count + 1):
-        parts.append(
-            f'<Override PartName="/xl/worksheets/sheet{i}.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"/>'
-        )
-    parts.append("</Types>")
-    return "".join(parts)
-
-
-def styles_xml() -> str:
-    return """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<styleSheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main">
-  <fonts count="3">
-    <font><sz val="11"/><name val="Calibri"/></font>
-    <font><b/><sz val="14"/><name val="Calibri"/></font>
-    <font><b/><sz val="11"/><name val="Calibri"/><color rgb="FFFFFFFF"/></font>
-  </fonts>
-  <fills count="3">
-    <fill><patternFill patternType="none"/></fill>
-    <fill><patternFill patternType="gray125"/></fill>
-    <fill><patternFill patternType="solid"><fgColor rgb="FF1F4E79"/><bgColor indexed="64"/></patternFill></fill>
-  </fills>
-  <borders count="2">
-    <border><left/><right/><top/><bottom/><diagonal/></border>
-    <border><left style="thin"/><right style="thin"/><top style="thin"/><bottom style="thin"/><diagonal/></border>
-  </borders>
-  <cellStyleXfs count="1"><xf numFmtId="0" fontId="0" fillId="0" borderId="0"/></cellStyleXfs>
-  <cellXfs count="3">
-    <xf numFmtId="0" fontId="0" fillId="0" borderId="0" xfId="0"/>
-    <xf numFmtId="0" fontId="1" fillId="0" borderId="0" xfId="0" applyFont="1"/>
-    <xf numFmtId="0" fontId="2" fillId="2" borderId="1" xfId="0" applyFont="1" applyFill="1" applyBorder="1"/>
-  </cellXfs>
-  <cellStyles count="1"><cellStyle name="Normal" xfId="0" builtinId="0"/></cellStyles>
-</styleSheet>"""
-
-
-def core_props() -> str:
-    now = dt.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
-    return f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<cp:coreProperties xmlns:cp="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dcmitype="http://purl.org/dc/dcmitype/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
-  <dc:creator>catalyst-calendar skill</dc:creator>
-  <cp:lastModifiedBy>catalyst-calendar skill</cp:lastModifiedBy>
-  <dcterms:created xsi:type="dcterms:W3CDTF">{now}</dcterms:created>
-  <dcterms:modified xsi:type="dcterms:W3CDTF">{now}</dcterms:modified>
-</cp:coreProperties>"""
-
-
-def app_props(sheet_names: Sequence[str]) -> str:
-    titles = "".join(f"<vt:lpstr>{escape(name)}</vt:lpstr>" for name in sheet_names)
-    return f'''<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Properties xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes">
-  <Application>catalyst-calendar skill</Application>
-  <DocSecurity>0</DocSecurity>
-  <ScaleCrop>false</ScaleCrop>
-  <HeadingPairs><vt:vector size="2" baseType="variant"><vt:variant><vt:lpstr>Worksheets</vt:lpstr></vt:variant><vt:variant><vt:i4>{len(sheet_names)}</vt:i4></vt:variant></vt:vector></HeadingPairs>
-  <TitlesOfParts><vt:vector size="{len(sheet_names)}" baseType="lpstr">{titles}</vt:vector></TitlesOfParts>
-</Properties>'''
-
-
-def write_xlsx(
-    path: Path,
-    sheets: Sequence[tuple[str, Sequence[str], Sequence[Sequence[object]], str]],
-) -> None:
-    sheet_names = [name for name, _, _, _ in sheets]
-    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
-        zf.writestr("[Content_Types].xml", content_types(len(sheets)))
-        zf.writestr("_rels/.rels", root_rels())
-        zf.writestr("docProps/core.xml", core_props())
-        zf.writestr("docProps/app.xml", app_props(sheet_names))
-        zf.writestr("xl/workbook.xml", workbook_xml(sheet_names))
-        zf.writestr("xl/_rels/workbook.xml.rels", workbook_rels(len(sheets)))
-        zf.writestr("xl/styles.xml", styles_xml())
-        for idx, (_name, headers, rows, title) in enumerate(sheets, start=1):
-            zf.writestr(f"xl/worksheets/sheet{idx}.xml", sheet_xml(headers, rows, title=title))
diff --git a/plugins/financial-markets/skills/company-tearsheet/SKILL.md b/plugins/financial-markets/skills/company-tearsheet/SKILL.md
deleted file mode 100644
index 974b4c1..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/SKILL.md
+++ /dev/null
@@ -1,139 +0,0 @@
----
-name: company-tearsheet
-description: Use when creating source-backed public issuer tearsheets. Do not use for private diligence, fund diligence, vendors, or market maps.
----
-
-# Company Tearsheet
-
-## Skill Configuration
-
-### User Context Preflight
-
-Invoke `public-equity-investing:user-context` in preflight mode by loading `skills/user-context/SKILL.md` from the plugin root and running `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root before searching connectors, retrieving evidence, or drafting output. Set the working directory before the first attempt; do not probe alternate relative paths. Use the returned envelope as authoritative for `saved_context`, `source_category_plan`, and `next_action`. Apply relevant `saved_context`. Do not read or reinterpret raw plugin state files unless preflight fails or the user explicitly asks for raw state inspection. Missing, malformed, or uninitialized context must not block tearsheet work.
-
-During ordinary tearsheet work, do not initialize state or run onboarding or broad source setup. If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append the router's one-line optional setup offer only once. Leave other onboarding steps to the explicit `user-context` flow.
-
-### Source Resolution
-
-Use `source_category_plan` from preflight to resolve only catalogued source categories needed for the current tearsheet. Prefer a user-named source first, then an active saved route when available. Attempt the smallest useful native read only when the workflow needs that source. If a route needs auth, connection, or setup, state the practical limitation and continue from prompt context, active artifacts, pasted or exported material, and public sources when the tearsheet can still be useful. Do not inspect unrelated source categories, run broad source setup, write connector readiness, or create, read, migrate, or update `category-state.json`.
-
-The source-category plan covers the catalogued Public Equity Investing sources below. Use `references/source-and-evidence.md` for the broader evidence hierarchy and freshness rules.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. These are semantic source categories, not fixed connector names.
-
-- `company_filings_ir`: filings, IR materials, reported financials, and issuer disclosures needed for the factual baseline.
-- `earnings_transcripts_presentations`: transcripts, presentations, events, and recent management commentary when they materially change the issuer read.
-- `internal_research`: internal notes, expert context, prior research, and team discussions when they materially improve the baseline.
-- `portfolio_models_trackers`: portfolio context, watchlists, models, and thesis trackers only when they materially change the investor lens or downstream route.
-- `market_data_estimates`: market data, consensus, estimates, ownership, positioning, and provider exports needed for valuation context and freshness checks.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML issuer tearsheet. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. When the user explicitly requests HTML for a tearsheet, that resolves the presentation surface to a polished standalone HTML tearsheet; ask only remaining material choices and do not treat HTML as a request for a standardized dashboard. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Create a source-backed baseline profile for a public issuer so downstream Public Equity Investing workflows start from the same factual view. The default artifact is an issuer baseline by design, not a memo, model, pitch, or recommendation.
-
-Use this before or inside comps, DCF, 3-statement, earnings, model update, long/short pitch, memo, meeting-prep, thesis tracker, risk/sizing, hedge, event, catalyst, equity-risk credit-signal, and deck/report workflows when a fast public profile is needed.
-
-Produce a compact polished standalone HTML tearsheet following `../../shared/html-artifact-standard.md` unless another format takes precedence; let the issuer's business model and requested review lens determine the hierarchy. Use chat only when the user explicitly requests a lightweight response. If the work expands into detailed thesis construction, scenarios, underwriting, or a full investment view, route the baseline into `initiating-coverage`, `memo-builder`, or the relevant owning workflow. Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render.
-
-## Do Not Use
-
-- Private company, sponsor-owned asset, private fund, LP/GP diligence target, vendor, customer, or commercial counterparty.
-- Fund/asset-manager diligence profiles.
-- Market maps, buyer lists, private-company sourcing screens, or diligence workplans.
-- Full memos, valuation models, trade pitches, or investment recommendations.
-- Do not compress a broader investment, earnings, comps, credit, or pitch request into a tearsheet; route to the owning full-analysis skill.
-
-## Non-Negotiables
-
-- Preserve source materials and user work.
-- Prefer user-provided context/files, callable runtime apps/connectors when actually available, primary sources, user-provided provider exports, credible secondary sources, then labeled assumptions.
-- Use `financial-source-of-truth` for source hierarchy, stale-data checks, citations, conflicts, and fact/assumption discipline.
-- Use `financials-normalizer` or `excel-data-cleaner` first when source financials/tables are messy.
-- Never invent missing facts, metrics, ratings, debt, valuation, ownership, customers, or KPIs.
-- Label facts, calculations, estimates, management claims, user adjustments, assumptions, missing fields, and confidence.
-- In a reader-facing artifact, translate internal evidence labels into plain investor language such as `Reported`, `Company-defined`, `Derived`, or `Not yet sourced`; retain exact evidence labels only in support data or when specifically requested.
-
-## Workflow
-
-1. **Classify profile.** Choose `public_company`, `equity_issuer_profile`, or `public_sector_peer`; identify downstream use case.
-2. **Build source inventory.** Track `source_id`, source name/type, owner/provider, as-of date, retrieved-at date, period, location, freshness, and notes.
-3. **Extract profile-critical facts.** Use only source-supported business, segment, geography, leadership, metrics, valuation, capital structure, KPI, recent event, risk, and evidence-gap fields relevant to the use case.
-4. **Label evidence and confidence.** Use labels from `references/source-and-evidence.md` and confidence values `high`, `medium`, or `low`.
-5. **Compose tearsheet.** For an HTML tearsheet, build a compact issuer baseline with an investor read, four or five decision-useful metrics, core business and earnings drivers, valuation context, concise catalysts/risks, material evidence gaps, source notes, and recommended next analytical route.
-6. **Keep scope at baseline.** Do not allow a live event, long missing-data register, extensive diligence questions, or scenario work to overwhelm the issuer baseline. Escalate to an owning full-analysis workflow when that additional work becomes central.
-7. **Run QC.** Confirm identity, periods, units, currency, sources, confidence, stale/conflicting data, derived-calculation support, and reader-facing legibility.
-
-## Deterministic Helpers
-
-```bash
-python scripts/validate_tearsheet_json.py path/to/tearsheet.json
-python scripts/build_tearsheet_markdown.py path/to/tearsheet.json output.md
-```
-
-The helpers validate/render structured inputs. They do not fetch data or replace source review. Raw JSON and generated Markdown are support or renderer-input artifacts unless the user explicitly asks for those formats.
-
-For standardized dashboard handoffs only, use `references/DASHBOARD_PACK.md`. `company-tearsheet` owns the issuer baseline and source confidence; `dashboard-builder` owns the shared shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown support files behind the HTML artifact unless explicitly requested.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` for source hierarchy, stale data, conflicts, and fact/assumption labels; use `financials-normalizer` or `excel-data-cleaner` before using messy financials, ownership tables, market/security data, or KPI exports. Support artifacts stay secondary to the tearsheet, dashboard/report, or downstream owning workflow.
-
-## Output Contract
-
-User-facing response:
-
-1. Tearsheet created: entity, profile type, scope, sources, as-of date.
-2. Key takeaways: 3-5 bullets with estimates/assumptions marked.
-3. Metric table: decision-useful metrics with period, units, source, confidence.
-4. Risks/gaps: stale data, conflicts, missing facts, evidence flags.
-5. Recommended next step or downstream handoff.
-
-For a standalone HTML tearsheet, keep the first-read structure compact:
-
-1. Investor read and four or five high-signal metrics.
-2. Earnings-driver table or compact driver cards.
-3. Use the visible heading `Valuation Context` or `Trailing Valuation Snapshot` when only historical or derived multiples are supported. Do not include `Debate` in the heading unless forward estimates, peer comparisons, target-price evidence, or explicit market expectations are sourced.
-4. Concise catalyst-and-risk matrix.
-5. Material evidence gaps, source ledger, and next analytical route.
-
-Do not expand the tearsheet into a full initiation report, long diligence-question set, scenario package, or recommendation merely because HTML space is available.
-
-## HTML Guidance
-
-When HTML is requested or selected, load `../../shared/html-artifact-standard.md` and apply these workflow-specific rules:
-
-- Lead with the factual investor read and the core earnings-driver question; the issuer baseline and earnings drivers are the primary objects.
-- Prefer four or five distinct metric tiles, one earnings-driver comparison object, one compact valuation-context object, and one catalyst/risk object over repeated dashboard panels.
-- When a current transaction, rumor, regulatory item, or other live event is material but not the requested focus, feature it in the investor read and catalyst/risk section and identify missing primary evidence in the evidence-gaps block. Do not thread it through earnings drivers, valuation, and multiple summary panels unless it directly changes those analyses.
-- Keep missing ownership, positioning, factor, or consensus fields visible through a compact evidence-gaps block; do not render a long table of unsourced fields unless those fields are the requested diligence focus.
-- Render visible evidence posture in plain language rather than internal labels such as `fact_source_reported` or `missing_required_source`.
-- Keep citations traceable but readable: do not fragment tickers, years, dates, numeric ranges, metric names, or product labels into separately linked tokens.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, density, clipping, citation rendering, and whitespace before delivery.
-
-## Reference Map
-
-- `references/source-and-evidence.md`: source hierarchy, citations, stale-data, labels, conflicts.
-- `references/profile-templates.md`: one-page and profile-specific templates.
-- `references/metric-library.md`: metric categories, `sector-context-overlay` guidance, KPI guidance.
-- `references/quality-checks.md`: profile QC checks.
-- `references/integration-guide.md`: Public Equity Investing handoffs.
-
-## Public Equity PM Judgment Layer
-
-For substantial tearsheets, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Use-case modes: `long_only_baseline`, `hf_baseline`, `sell_side_coverage_starter`, `etf_index_constituent_profile`, `public_diligence_baseline`.
-
-Required PM judgment:
-- Keep the tearsheet factual, but make the next analytical route obvious.
-- Include investor-useful fields when available: market cap, enterprise value, float, ADV/liquidity, index membership, ETF/passive ownership and flow relevance, top holders, short interest, borrow/crowding, factor exposure, ownership concentration, governance, capital allocation, sell-side coverage, consensus setup, balance-sheet risk, and key operating KPIs.
-- Always identify unavailable or stale ownership, positioning, borrow, liquidity, factor, and consensus fields with an as-of/source requirement rather than silently omitting them. In reader-facing HTML, consolidate non-central missing fields into a compact evidence-gaps block instead of displaying a long low-information register.
-- Do not turn a tearsheet into a recommendation; route investment decisions to initiating coverage, memo-builder, long-short-pitch, or thesis-tracker.
diff --git a/plugins/financial-markets/skills/company-tearsheet/agents/openai.yaml b/plugins/financial-markets/skills/company-tearsheet/agents/openai.yaml
deleted file mode 100644
index 6c37af6..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Company Tearsheet"
-  short_description: "Create cited public-company, public-issuer, and public sector-peer profiles."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/company-tearsheet/assets/icon.svg b/plugins/financial-markets/skills/company-tearsheet/assets/icon.svg
deleted file mode 100644
index f52440e..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/assets/icon.svg
+++ /dev/null
@@ -1,8 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" role="img" aria-label="Company tearsheet icon">
-  <rect x="10" y="6" width="44" height="52" rx="6" fill="#0F172A"/>
-  <rect x="16" y="14" width="32" height="5" rx="2" fill="#E2E8F0"/>
-  <rect x="16" y="25" width="18" height="4" rx="2" fill="#38BDF8"/>
-  <rect x="16" y="34" width="32" height="3" rx="1.5" fill="#CBD5E1"/>
-  <rect x="16" y="42" width="26" height="3" rx="1.5" fill="#CBD5E1"/>
-  <circle cx="44" cy="27" r="5" fill="#22C55E"/>
-</svg>
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/company-tearsheet/references/DASHBOARD_PACK.md
deleted file mode 100644
index d583c26..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Company Tearsheet Dashboard Pack
-
-Use this pack only for an explicitly selected standardized dashboard, reusable dashboard template, or structured payload-driven render for a public issuer baseline, diligence starter, pipeline card, or source-heavy profile artifact. For an ordinary standalone HTML tearsheet, follow the flexible HTML artifact standard and the owning skill's compact profile guidance instead of this fixed module map.
-
-## Producer Role
-
-`company-tearsheet` owns issuer identity, baseline facts, metric selection, source confidence, and downstream handoff. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `company_tearsheet`
-- `layout`: `single_page` for reusable issuer profiles unless the user explicitly requests tabs
-- `hero.callout`: why this issuer matters for the next workflow
-- `snapshot`: ticker/security, profile type, market/financial snapshot if sourced, key KPI, latest material event, confidence label
-- `sources`: primary filings/releases first, trusted providers next, and assumptions or missing fields clearly labeled
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `issuer-baseline`
-   - `decision_box`: profile posture, confidence, recommended next workflow, and most important source gap
-   - `metric_tiles`: ticker/security, sector, fiscal year, market snapshot, and source confidence
-2. `business-snapshot`
-   - `cards`: business model, segment mix, geography, customer/end-market exposure, and management claims with citations
-   - `table`: segment, revenue, growth, margin, KPI, or geography rows when source-backed
-3. `key-metrics`
-   - `key_metrics`: source-backed operating, financial, valuation, capital-structure, and issuer-specific KPIs
-4. `recent-developments`
-   - `market_events`: filings, earnings, management changes, capital allocation, regulatory/legal/product events, and PM relevance
-5. `risks-and-handoffs`
-   - `cards`: risks, stale/conflicting data, confidence limits, and recommended downstream skill
-   - `missing_evidence`: unsupported facts, stale values, missing debt/ownership/segment/KPI fields
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every factual issuer claim, metric, market-data value, leadership item, segment/geography figure, and event date.
-- Label profile type and confidence for every major section.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not turn the tearsheet into a recommendation, trade pitch, full memo, or model.
-- Do not compress a broader investment request into a profile when another Public Equity Investing skill should own it.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-- Do not let a live event, extensive diligence-question list, or long register of unavailable fields turn a baseline profile into initiation coverage.
-- Do not render internal evidence labels as investor-facing prose or fragment tickers, years, dates, numeric ranges, metric names, or product labels with citations.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm identity, periods, units, currency, source confidence, stale/conflicting data, and missing evidence are visible.
-- Confirm the issuer baseline and core earnings drivers remain primary, missing fields are proportionate, and citation rendering remains readable.
-- Confirm every next-step handoff points to the correct owning skill.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/integration-guide.md b/plugins/financial-markets/skills/company-tearsheet/references/integration-guide.md
deleted file mode 100644
index d00286d..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/integration-guide.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Integration Guide
-
-## Position in the shared-core stack
-`company-tearsheet` is a baseline profile skill. It should be fast, cited, and compact. It should not duplicate deeper workflows.
-
-Recommended order:
-1. `financial-source-of-truth` for source routing and evidence hierarchy.
-2. `financials-normalizer` or `excel-data-cleaner` if the underlying data is messy.
-3. `company-tearsheet` to create the baseline profile.
-4. Downstream role skill for analysis, modeling, memo, deck, or meeting prep.
-5. `deck-report-qc` or `model-audit-tieout` for final QA where relevant.
-
-## Handoff rules
-
-### Public Equity Investing
-- `earnings-preview`: use profile fields to frame company, segment/KPI context, and current debate.
-- `earnings-deep-dive`: use profile if company context is missing before post-print analysis.
-- `equity-model-update`: use only as context; model updates require normalized financial inputs.
-- `long-short-pitch`: use as the factual baseline before thesis, variant perception, valuation, and risk/reward.
-- `comps-valuation`, or `dcf-model-builder`: hand off only after key financial metrics are source-backed and normalized.
-- Credit Markets: use for Credit Markets, distressed, maturity wall, liquidity, capital structure, or recovery questions.
-- `event-driven-analyzer`: use when the company profile is a baseline for M&A, spin, activism, litigation, regulatory, restructuring, or other special situations.
-- `economic-impact-report`: use only when macro, policy, commodity, FX, rate, or cross-asset context is needed; do not turn a company profile into macro analysis.
-- `thesis-tracker` or `portfolio-risk-management`: use when the profile feeds ongoing monitoring, sizing, or hedge work.
-- `memo-builder`, `meeting-prep`, or `deck-report-qc`: use when the profile becomes a memo, call prep, or circulated deck/report.
-
-### Shared core
-- `financial-source-of-truth`: apply source hierarchy, citations, stale-data checks, fact/assumption labels, and conflict handling.
-- `financials-normalizer` or `excel-data-cleaner`: clean and normalize messy financial source data before the profile uses it.
-- `model-audit-tieout`: audit model values before using them as profile facts.
-
-## Boundary rules
-- If the user asks "is this a good investment?", do not answer from the tearsheet alone; route to the relevant investing skill.
-- If the user asks for a full Credit Markets or distressed memo, route to Credit Markets.
-- If the user asks for a full model, route to model-building skills after profile creation.
-- If the user asks for a meeting brief, use `meeting-prep` after the profile.
-- If source financials are unstructured or inconsistent, normalize before using them.
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/metric-library.md b/plugins/financial-markets/skills/company-tearsheet/references/metric-library.md
deleted file mode 100644
index 8e8302a..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/metric-library.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Metric Library and Sector Overlays
-
-Use this as a starter checklist, not a mandatory field list. Only include metrics that are relevant and source-backed.
-
-## Universal identifiers
-- Legal name, common name, ticker, exchange, CIK/LEI/ISIN/CUSIP if relevant.
-- HQ, founded year, employee count, ownership, parent/subsidiaries, fiscal year-end.
-- Sector, sub-sector, NAICS/SIC/GICS when useful.
-- Management, auditor, rating agencies, debt instruments, and exchange/listing identifiers if relevant.
-
-## Universal financial metrics
-- Revenue / net sales
-- Gross profit and gross margin
-- EBITDA / adjusted EBITDA / operating income
-- EBITDA margin / operating margin
-- Net income / EPS
-- Capex
-- Free cash flow / operating cash flow
-- Cash and equivalents
-- Debt / net debt / lease liabilities if relevant
-- Net leverage / gross leverage
-- Interest expense / coverage
-- Market cap / enterprise value / valuation, if source-backed
-
-## Credit Markets issuer metrics
-- Facility type, amount, maturity, amortization, interest rate/spread
-- Debt stack, liens, collateral, guarantors where disclosed
-- Liquidity, revolver availability, covenant headroom
-- Reported EBITDA, adjusted EBITDA, fixed charge coverage, DSCR
-- Debt service, maturity wall, cash burn, tender/exchange offer terms
-- Trading price, yield, spread, OID, recovery value, rating/outlook
-- Structural priority, secured/unsecured mix, restricted group, issuer/guarantor scope
-
-## Public-market metrics
-- Share price/date, market cap, EV, float, short interest where relevant
-- LTM/NTM revenue, EBITDA, EPS, FCF, margins
-- Guidance, consensus estimates, revisions, surprise history
-- Segment mix, geographic mix, KPI trends
-- Valuation multiples: EV/Sales, EV/EBITDA, P/E, P/FCF, P/B as relevant
-- Recent earnings/catalysts, rating/price target context if source-backed
-
-## Listed fund / manager metrics
-- AUM/AUA, strategy, geography, sector/stage exposure, vehicle type
-- NAV, premium/discount to NAV, market cap, fee-related earnings, management fee rate
-- Flows, organic growth, performance, redemption/liquidity profile where disclosed
-- Portfolio concentration, top holdings, leverage, dividend/distribution yield
-- BDC or closed-end fund metrics: NAV per share, NII, debt/equity, non-accruals, realized/unrealized gains
-
-## Sector overlays
-
-### SaaS / software
-ARR, net revenue retention, gross retention, churn, CAC, LTV, payback, Rule of 40, gross margin, sales efficiency, bookings, billings, RPO, ACV, logo count.
-
-### Marketplace / transaction
-GMV/GTV, take rate, active buyers/sellers, order volume, AOV, contribution margin, cohort retention, liquidity metrics.
-
-### Banks / financial institutions
-Loans, deposits, NIM, NII, fee income, CET1, leverage ratio, ROA, ROE, NPLs, charge-offs, reserve coverage, AUM/AUA.
-
-### Insurance
-Premiums, combined ratio, loss ratio, expense ratio, reserves, investment income, book value, RBC, policy count.
-
-### REITs / real estate
-NOI, same-store NOI, occupancy, rent spreads, AFFO/FFO, cap rate, NAV, debt yield, lease rollover, WALE.
-
-### Energy / E&P
-Production, reserves, realized price, lifting cost, decline rate, capex, free cash flow, hedges, acreage, proved reserves.
-
-### Healthcare / biotech
-Pipeline stage, trial readouts, patient population, TAM, cash runway, R&D spend, regulatory milestones, reimbursement exposure.
-
-### Retail / restaurants / consumer
-Same-store sales, traffic, ticket, store count, unit economics, gross margin, inventory turns, loyalty/users, channel mix.
-
-### Industrial / manufacturing
-Volume, price/mix, backlog, book-to-bill, capacity, utilization, input costs, working capital, capex, supply chain exposure.
-
-### Media / telecom / internet
-Subscribers, ARPU, churn, engagement, ad revenue, MAUs/DAUs, content spend, broadband/net adds, spectrum/debt if relevant.
-
-## Market, Ownership, And Index Metrics
-
-Track market cap, enterprise value, float, ADV, turnover, index membership, benchmark weight, ETF holder base, passive ownership, short interest, borrow where sourced, factor exposure, beta, liquidity score, ownership concentration, governance signals, capital allocation, and key sector KPIs.
-
-For public-equity investor tearsheets, also track top holders, free float, days-to-exit, sell-side coverage count/rating distribution when source-backed, consensus setup, borrow cost or availability when available, crowding read, buyback authorization, dividend/capital return posture, insider ownership/sales where relevant, and market-data as-of timestamp. Label each unavailable field as a source gap.
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/profile-templates.md b/plugins/financial-markets/skills/company-tearsheet/references/profile-templates.md
deleted file mode 100644
index 46e693b..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/profile-templates.md
+++ /dev/null
@@ -1,127 +0,0 @@
-# Profile Templates
-
-## Universal one-page tearsheet
-
-```markdown
-# [Entity] Tearsheet
-**Profile type:** [public_company / equity_issuer_profile / public_sector_peer]
-**As of:** [date/time]
-**Scope:** [requested use case, periods, sources]
-**Source caveat:** [latest / limited / stale / partial / source package only]
-
-## One-line view
-[One concise sentence explaining what the entity is and why it matters for the user's workflow.]
-
-## Business snapshot
-| Field | Detail | Source | Evidence | Confidence |
-|---|---|---|---|---|
-| Business model |  |  |  |  |
-| HQ / footprint |  |  |  |  |
-| Listing / identifiers |  |  |  |  |
-| Segments / products |  |  |  |  |
-| Customers / end markets |  |  |  |  |
-
-## Key metrics
-| Metric | Period | Value | Units | Source | Evidence | Confidence |
-|---|---:|---:|---|---|---|---|
-
-## Recent developments
-- [Sourced bullet]
-- [Sourced bullet]
-- [Sourced bullet]
-
-## Relevance for this workflow
-- [Implication for public equity / earnings / valuation / meeting prep; route credit-first implications to Credit Markets]
-
-## Risks, gaps, and evidence flags
-- [Missing source, stale data, conflict, evidence request, or source-backed risk]
-
-## Recommended next step
-[Downstream skill or missing source needed.]
-```
-
-## Public company / equity profile
-
-Prioritize:
-- ticker, exchange, market cap, enterprise value, share price/date, fiscal year-end.
-- revenue, EBITDA/operating income, EPS/FCF where relevant, margin, growth, net debt/leverage, segment mix.
-- latest reporting event, guidance, consensus context if available, recent stock/credit move only if current.
-- thesis-relevant debates, catalysts, and risks without making a recommendation.
-
-Use when feeding `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `long-short-pitch`, `comps-valuation`, `dcf-model-builder`, or `memo-builder`.
-
-## Standalone HTML tearsheet
-
-When the user requests HTML or selects it during intake, produce a polished standalone HTML tearsheet following `../../../shared/html-artifact-standard.md`. Keep it a compact issuer baseline rather than a dashboard shell or initiation report.
-
-Recommended first-read sequence:
-
-1. Investor read: one factual sentence describing the earnings engine and the most important unresolved diligence question.
-2. Metric strip: four or five sourced metrics that establish scale, profitability, valuation context, and balance-sheet or capital-allocation relevance.
-3. Earnings drivers: a compact table or card row showing the few segments or KPIs that actually drive the long-only review.
-4. Valuation context: trailing or derived multiples, forward consensus, or peers only to the extent sourced.
-5. Catalysts and risks: a short matrix linked to the review question.
-6. Evidence gaps and next route: only gaps that affect further work, plus a concise source ledger.
-
-Use the visible heading `Trailing Valuation Snapshot` or `Valuation Context` when only historical figures or analyst-derived trailing multiples are available. Do not include `Debate` in the heading unless forward estimates, relevant peers, target-price evidence, or explicit market-expectation evidence is sourced.
-
-When a current transaction, rumor, regulatory item, or other live event is material but not the requested focus, feature it in the investor read and catalysts/risks section and identify missing primary evidence in the evidence-gaps block. Do not repeat it through earnings drivers, valuation, and multiple summary panels unless it directly changes those analyses.
-
-Visible evidence labels should be natural investor language:
-
-| Internal support label | Reader-facing HTML label |
-|---|---|
-| `fact_source_reported` | Reported |
-| `fact_provider_standardized` | Provider data |
-| `derived_calculation` | Derived |
-| `issuer_management_claim` | Management statement |
-| `management_adjusted` | Company-defined |
-| `missing_required_source` | Not yet sourced |
-
-## Public issuer / credit profile
-
-Prioritize:
-- issuer, parent/guarantors if disclosed, ticker/CUSIP/ISIN when available, ratings, debt stack, maturity wall, instrument prices/yields/spreads.
-- revenue, EBITDA/operating income, FCF, debt, liquidity, leverage, interest coverage, covenant or indenture terms when public or user-provided.
-- refinancing risk, collateral or structural priority, cash burn, recovery context, trading levels, rating actions, and restructuring milestones.
-- missing credit documents, stale trading levels, and definition conflicts.
-
-Use when feeding Credit Markets, `scenario-sensitivity-generator`, `equity-model-update`, `portfolio-risk-management`, or `memo-builder`.
-
-## Sector peer / competitor profile
-
-Prioritize:
-- public peer identity, business model, segments, geography, KPI set, market cap/EV, current valuation, and latest earnings event.
-- key differences versus the focus company: growth, margin, mix, leverage, capital allocation, disclosure quality, and catalyst exposure.
-- valuation or operating metrics that will feed comps, thesis testing, or peer read-through.
-
-Use when feeding `comps-valuation`, `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `long-short-pitch`, or `memo-builder`.
-
-## Ultra-compact profile block
-
-Use inside a deck, memo, or meeting brief when space is tight:
-
-```markdown
-**[Entity]** is [business model / strategy] serving [customers/end markets] across [geography]. Latest source-backed scale: [metric 1], [metric 2], [metric 3] as of [period]. Current relevance: [why it matters]. Key gaps/flags: [gap or risk].
-```
-
-## Public Equity Investor Profile Additions
-
-Add security and liquidity snapshot, index membership, ETF ownership/flow relevance, factor exposure, short interest, ownership concentration, governance, capital allocation, balance-sheet risk, and next analytical route when available or material to the requested review. The tearsheet remains factual, not a recommendation.
-
-Default security setup / ownership / positioning block:
-
-| Field | Value | As-of | Source | Confidence | Why it matters |
-|---|---|---|---|---|---|
-| Market cap / EV |  |  |  |  | Valuation and investability anchor |
-| Float / free float |  |  |  |  | Capacity, passive flow, squeeze risk |
-| ADV / liquidity / days-to-exit |  |  |  |  | Can a PM build or exit the position? |
-| Index membership / benchmark weight |  |  |  |  | Active-weight and passive-flow relevance |
-| ETF/passive ownership or flow signal |  |  |  |  | Rebalance and flow pressure |
-| Top holders / ownership concentration |  |  |  |  | Governance, sponsorship, overhang |
-| Short interest / borrow / days to cover |  |  |  |  | Short crowding and squeeze risk |
-| Factor exposure / beta |  |  |  |  | Stock-specific versus factor-driven risk |
-| Governance / capital allocation |  |  |  |  | Management quality and minority-holder risk |
-| Sell-side coverage / consensus setup |  |  |  |  | Estimate path and variant wedge |
-
-If a field is unavailable, retain `missing_required_source` with the required provider/export/source in support data. In reader-facing HTML, consolidate non-central unsourced fields into one compact evidence-gaps block rather than displaying a large low-information positioning table.
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/quality-checks.md b/plugins/financial-markets/skills/company-tearsheet/references/quality-checks.md
deleted file mode 100644
index 24b5772..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/quality-checks.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Company Tearsheet Quality Checks
-
-## Entity identity checks
-- Confirm the entity is the correct public company, public issuer, Credit Markets issuer, or security, not a similarly named entity.
-- For public companies, verify ticker/exchange and fiscal year-end.
-- For Credit Markets, verify issuer/parent, instrument identifiers, and rating/security context when material.
-- Do not use this skill for fund/manager diligence; route non-issuer fund or manager profiles outside `company-tearsheet`.
-
-## Source checks
-- Every material metric should have source, period, units, evidence label, and confidence.
-- Do not use stale market data without an as-of date.
-- Do not cite marketing claims as verified third-party facts.
-- Do not rely on web snippets when user-provided or connected sources are available.
-- Flag unaudited, preliminary, pro forma, adjusted, or management-defined numbers.
-
-## Metric checks
-- Revenue, EBITDA, net debt, leverage, margin, AUM/NAV, share price, market cap, EV, and valuation must have period and units.
-- Do not mix fiscal years, calendar years, LTM, quarterly, forecast, and YTD without labels.
-- Do not mix currencies or units without clear conversion disclosure.
-- Derived metrics must show inputs or formula in a note.
-- If source financials are hard to read or inconsistent, route to `financials-normalizer` before finalizing.
-
-## Profile balance checks
-- Keep the tearsheet concise. It should not become a full memo.
-- Separate business description, metrics, developments, implications, and risks.
-- Avoid unsupported adjectives like "best-in-class", "dominant", "high-quality", or "distressed" unless evidenced.
-- Avoid investment recommendations unless explicitly requested and routed to the appropriate analysis skill.
-- For public-equity investor tearsheets, do not omit security setup, ownership, or positioning needs silently. Market cap, float, ADV/liquidity, index membership, ETF/passive ownership, top holders, short interest, borrow/crowding, factor exposure, governance, capital allocation, sell-side coverage, and consensus setup should be sourced when relevant or identified in a compact source/as-of evidence-gaps block.
-- When a current transaction, rumor, regulatory item, or other live event is material but not the requested focus, confirm it is featured in the investor read and catalyst/risk section, with missing primary evidence identified in the evidence-gaps block. Confirm it is not threaded through earnings drivers, valuation, and multiple summary panels unless it directly changes those analyses.
-
-## Valuation framing checks
-- Use the visible heading `Trailing Valuation Snapshot` or `Valuation Context` when valuation support consists of reported historical financials and derived trailing metrics.
-- Do not include `Debate` in the heading unless forward estimates, peer evidence, target-price evidence, or explicit market expectations support a genuine expectations discussion.
-- Do not present simplified enterprise value calculations as a full EV bridge or underwritten valuation case.
-
-## HTML presentation checks
-- For a selected HTML tearsheet, follow `../../../shared/html-artifact-standard.md` and keep the issuer baseline compact rather than forcing the standardized dashboard shell.
-- Use plain investor-facing evidence language; do not expose internal evidence labels such as `fact_source_reported` or `missing_required_source` unless the user requests support detail.
-- Do not fragment tickers, years, dates, numeric ranges, metric names, or product labels into separate citation links.
-- Render and visually inspect local HTML using headless-browser screenshots before delivery, focusing on hierarchy, density, clipping, citation noise, and whitespace.
-
-## Conflict checks
-Flag if:
-- multiple sources show different revenue/EBITDA/debt/AUM/NAV/share count/market cap values.
-- a provider value differs from a filing/source package.
-- a company presentation or management deck differs from filings, releases, transcripts, or provider data.
-- a current source supersedes an older source in the package.
-
-## Final QA checklist
-Before returning:
-1. Entity confirmed.
-2. Source inventory exists or source limitations disclosed.
-3. As-of date shown.
-4. Metrics table includes source, period, units, evidence, confidence.
-5. Stale/conflicting/missing data flagged.
-6. Assumptions separated from facts.
-7. Handoff recommendation is appropriate.
-8. Source materials preserved.
diff --git a/plugins/financial-markets/skills/company-tearsheet/references/source-and-evidence.md b/plugins/financial-markets/skills/company-tearsheet/references/source-and-evidence.md
deleted file mode 100644
index 0d9a263..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/references/source-and-evidence.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Source and Evidence Protocol
-
-## Source hierarchy
-Use the strongest available source that the user is permitted to access.
-
-1. **User-provided source package**: uploaded files, pasted context, emails/Drive/Slack context, internal research, prior models, notes, decks, reports, and workbook extracts.
-2. **Callable connected apps or user-provided approved-provider exports**: market-data, document, research, source-of-truth, and provider sources the user can access. Do not imply direct access when the runtime route is not callable.
-3. **Primary public sources**: SEC/company filings, earnings releases, investor presentations, company websites, press releases, rating agency reports if accessible, court/regulatory filings.
-4. **Trusted provider-standardized sources**: callable provider apps/connectors or user-provided exports from FactSet, S&P Global, CapIQ, LSEG, Morningstar, Moody's, Daloopa, Quartr, Aiera, MSCI, Bloomberg, or another permitted provider. If the requested provider route is not callable, request an export or continue with a clearly labeled fallback.
-5. **Credible secondary sources**: reputable news, industry publications, sell-side or third-party research notes the user provides, reputable databases.
-6. **Web fallback**: public search results when no stronger source is available.
-7. **Assumptions**: user-provided or explicitly disclosed inferred assumptions. Never present assumptions as facts.
-
-If a `financial-source-of-truth` skill is available, follow its more specific source hierarchy and citation protocol.
-
-## Source inventory fields
-Maintain a lightweight `Source_Index` for every tearsheet:
-
-| Field | Description |
-|---|---|
-| `source_id` | Unique identifier such as `S1`, `S2`. |
-| `source_name` | Filing, deck, export, provider, internal doc, or website name. |
-| `source_type` | User file, connected app, primary filing, provider, press release, secondary source, web fallback, assumption. |
-| `provider_or_owner` | Source owner, provider, or internal team. |
-| `as_of_date` | Date the source data represents. |
-| `retrieved_at` | Date/time the assistant accessed it. |
-| `period_covered` | FY/Q/month/LTM/date range. |
-| `source_location` | Page, slide, tab, cell range, URL, file name, message link, or data object. |
-| `freshness_status` | Current, acceptable, stale, preliminary, unknown. |
-| `notes` | Limitations, caveats, conflicts, or access constraints. |
-
-## Stale-data checks
-Use conservative stale-data flags:
-
-| Source / data type | Current if | Stale trigger |
-|---|---:|---:|
-| Market price / market cap / EV | Same day or latest market close | Older than 1 trading day unless historical. |
-| Public financials | Latest filed period or current reported quarter | Superseded by newer earnings release/filing. |
-| Consensus estimates | Latest available set | Older than 7-14 days around earnings; older than 30 days otherwise. |
-| Credit ratings / spreads | Latest available | Older than 1 week for active credits; older than 30 days for static profiles. |
-| Public issuer debt terms / credit metrics | Latest filing, executed document, or latest market data | Superseded by refinancing, amendment, exchange, rating action, or newer filing. |
-| Listed fund / manager AUM, NAV, holdings, or performance | Latest public reporting cycle | Older than latest available reporting cycle or missing as-of date. |
-| Internal research model / estimate | Current model version or stated frozen date | Superseded by newer filing, print, guidance, consensus pull, or model update. |
-
-Always flag data as `unknown` if freshness cannot be determined.
-
-## Evidence labels
-Use exact labels:
-
-- `fact_source_reported`: directly supported by source document or connected system.
-- `fact_provider_standardized`: supplied by trusted provider after standardization.
-- `derived_calculation`: calculated from cited inputs.
-- `issuer_management_claim`: statement from company, issuer, or management that needs evidence support.
-- `management_adjusted`: company-defined adjusted metric or management adjustment.
-- `analyst_adjusted`: analyst normalization, add-back, reclass, or pro forma change.
-- `estimate_consensus`: consensus estimate or provider forecast.
-- `analyst_interpretation`: synthesis based on cited facts, not directly stated by a source.
-- `assumption_user_provided`: assumption supplied by the user.
-- `assumption_inferred`: inferred from incomplete context; disclose and keep low confidence.
-- `missing_required_source`: needed fact or metric not available.
-- `stale_source`: source may be superseded for current decision.
-- `contradicted_source`: source conflicts with another material source.
-- `unknown`: evidence type cannot be determined.
-
-## Confidence labels
-- `high`: primary/connected source, current, clear entity, period, unit, and label.
-- `medium`: trusted provider or clear source with minor mapping ambiguity or age.
-- `low`: stale, preliminary, OCR-heavy, inferred, conflicting, or unclear period/unit/source.
-
-## Citation format
-For chat output, cite sources using the environment's native citation syntax when available. For file or artifact output, use compact source references in tables:
-
-`Metric | Period | Value | Source | Evidence | Confidence`
-
-Where `Source` should be a source ID plus location: `S2 p.14`, `S4 tab Revenue cell D22`, `S1 FY2025 10-K Item 8`, `S5 provider pull 2026-05-07`.
-
-## Conflict handling
-When sources conflict:
-
-1. Preserve both values when material.
-2. Prefer primary source over provider, connected system over copied spreadsheet, final filing over preliminary release, audited over unaudited, latest version over superseded version.
-3. State why a value was selected if one is used.
-4. Add conflict note to `Risks / gaps` if the conflict matters to the profile.
-5. Do not average, smooth, or backsolve conflicting values unless the user asks and the method is disclosed.
-
-## Fact vs assumption standard
-- Facts require support from a source.
-- Calculations require cited inputs and a visible formula or explanation.
-- Claims from company, issuer, or management are still claims, even if cited.
-- Assumptions must be marked as assumptions and should not be blended into fact tables.
-
-## Public Equity Data Freshness
-
-Market cap, price, float, ADV, short interest, ownership, ETF/index membership, consensus, and factor exposure require source labels and as-of dates. If unavailable, use missing evidence and route to the next analytical workflow rather than inventing.
diff --git a/plugins/financial-markets/skills/company-tearsheet/scripts/build_source_checklist.py b/plugins/financial-markets/skills/company-tearsheet/scripts/build_source_checklist.py
deleted file mode 100755
index a0411ce..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/scripts/build_source_checklist.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-"""Create a missing-source checklist for common tearsheet profile types."""
-
-from __future__ import annotations
-
-import argparse
-
-CHECKLISTS = {
-    "public_company": [
-        "Latest annual and interim financial statements or filings",
-        "Latest investor presentation or management overview",
-        "Business segment and geography breakdown",
-        "Current ticker, exchange, share count, and listing identifiers",
-        "Recent press releases or material announcements",
-        "Key operating KPIs by business model",
-    ],
-    "equity_issuer_profile": [
-        "Latest filing, prospectus, indenture, credit agreement, or offering document if available",
-        "Debt stack, ratings, maturities, and spread/price context",
-        "Recent earnings release and transcript if public",
-        "Liquidity and covenant disclosures",
-        "Recovery, collateral, guarantee, or priority information if distressed",
-    ],
-    "public_sector_peer": [
-        "Ticker, exchange, fiscal year-end, and peer rationale",
-        "Latest filing, earnings release, and investor presentation",
-        "Key operating KPIs comparable to the focus company",
-        "Current market cap, EV, share price, and valuation multiples",
-        "Recent catalysts, guidance, and consensus context",
-    ],
-}
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Print a source checklist for a tearsheet profile type"
-    )
-    parser.add_argument("profile_type", choices=sorted(CHECKLISTS), help="Profile type")
-    args = parser.parse_args()
-
-    print(f"# Source checklist: {args.profile_type}")
-    for item in CHECKLISTS[args.profile_type]:
-        print(f"- [ ] {item}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/company-tearsheet/scripts/build_tearsheet_markdown.py b/plugins/financial-markets/skills/company-tearsheet/scripts/build_tearsheet_markdown.py
deleted file mode 100755
index 79cbfb1..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/scripts/build_tearsheet_markdown.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-"""Build a markdown company tearsheet from structured JSON.
-
-This utility is intentionally simple and offline. It helps produce consistent
-one-page profile formatting after the assistant has gathered and labeled facts.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-from typing import Any
-
-
-def fmt(value: Any) -> str:
-    if value is None:
-        return ""
-    return str(value).replace("\n", " ").strip()
-
-
-def table(headers: list[str], rows: list[list[Any]]) -> str:
-    if not rows:
-        return ""
-    out = ["| " + " | ".join(headers) + " |", "|" + "|".join(["---"] * len(headers)) + "|"]
-    for row in rows:
-        padded = list(row)[: len(headers)] + [""] * max(0, len(headers) - len(row))
-        out.append("| " + " | ".join(fmt(cell) for cell in padded) + " |")
-    return "\n".join(out)
-
-
-def build_markdown(data: dict[str, Any]) -> str:
-    entity = fmt(data.get("entity", "Entity"))
-    profile_type = fmt(data.get("profile_type", "profile"))
-    as_of = fmt(data.get("as_of_date", ""))
-    scope = fmt(data.get("scope", ""))
-    caveat = fmt(data.get("source_caveat", ""))
-    sections = data.get("sections", {}) if isinstance(data.get("sections"), dict) else {}
-
-    lines: list[str] = []
-    lines.append(f"# {entity} Tearsheet")
-    lines.append(f"**Profile type:** {profile_type}  ")
-    if as_of:
-        lines.append(f"**As of:** {as_of}  ")
-    if scope:
-        lines.append(f"**Scope:** {scope}  ")
-    if caveat:
-        lines.append(f"**Source caveat:** {caveat}  ")
-
-    one_line = sections.get("one_line_view") or data.get("one_line_view")
-    if one_line:
-        lines.extend(["", "## One-line view", fmt(one_line)])
-
-    snapshot_rows: list[list[Any]] = []
-    snapshot = sections.get("business_snapshot") or data.get("business_snapshot", [])
-    if isinstance(snapshot, list):
-        for item in snapshot:
-            if isinstance(item, dict):
-                snapshot_rows.append(
-                    [
-                        item.get("field", ""),
-                        item.get("detail", ""),
-                        item.get("source", ""),
-                        item.get("evidence", ""),
-                        item.get("confidence", ""),
-                    ]
-                )
-    if snapshot_rows:
-        lines.extend(
-            [
-                "",
-                "## Business snapshot",
-                table(["Field", "Detail", "Source", "Evidence", "Confidence"], snapshot_rows),
-            ]
-        )
-
-    metric_rows: list[list[Any]] = []
-    metrics = data.get("metrics", [])
-    if isinstance(metrics, list):
-        for metric in metrics:
-            if isinstance(metric, dict):
-                metric_rows.append(
-                    [
-                        metric.get("metric", ""),
-                        metric.get("period", ""),
-                        metric.get("value", ""),
-                        metric.get("units", ""),
-                        metric.get("source", ""),
-                        metric.get("evidence", ""),
-                        metric.get("confidence", ""),
-                    ]
-                )
-    if metric_rows:
-        lines.extend(
-            [
-                "",
-                "## Key metrics",
-                table(
-                    ["Metric", "Period", "Value", "Units", "Source", "Evidence", "Confidence"],
-                    metric_rows,
-                ),
-            ]
-        )
-
-    developments = sections.get("recent_developments") or data.get("recent_developments", [])
-    if isinstance(developments, list) and developments:
-        lines.extend(["", "## Recent developments"])
-        for dev in developments:
-            lines.append(f"- {fmt(dev)}")
-
-    relevance = sections.get("workflow_relevance") or data.get("workflow_relevance", [])
-    if isinstance(relevance, list) and relevance:
-        lines.extend(["", "## Relevance for this workflow"])
-        for item in relevance:
-            lines.append(f"- {fmt(item)}")
-    elif relevance:
-        lines.extend(["", "## Relevance for this workflow", fmt(relevance)])
-
-    risks = (
-        sections.get("risks_gaps_flags")
-        or data.get("risks_gaps_flags")
-        or data.get("data_quality_flags", [])
-    )
-    if isinstance(risks, list) and risks:
-        lines.extend(["", "## Risks, gaps, and evidence flags"])
-        for risk in risks:
-            lines.append(f"- {fmt(risk)}")
-    elif risks:
-        lines.extend(["", "## Risks, gaps, and evidence flags", fmt(risks)])
-
-    sources = data.get("sources", [])
-    if isinstance(sources, list) and sources:
-        source_rows: list[list[Any]] = []
-        for source in sources:
-            if isinstance(source, dict):
-                source_rows.append(
-                    [
-                        source.get("source_id", ""),
-                        source.get("source_name", ""),
-                        source.get("source_type", ""),
-                        source.get("as_of_date", ""),
-                        source.get("freshness_status", ""),
-                        source.get("source_location", ""),
-                    ]
-                )
-        lines.extend(
-            [
-                "",
-                "## Source notes",
-                table(["ID", "Source", "Type", "As of", "Freshness", "Location"], source_rows),
-            ]
-        )
-
-    next_step = data.get("recommended_next_step") or sections.get("recommended_next_step")
-    if next_step:
-        lines.extend(["", "## Recommended next step", fmt(next_step)])
-
-    return "\n".join(lines).rstrip() + "\n"
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Build markdown company tearsheet from JSON")
-    parser.add_argument("input_json", type=Path, help="Path to tearsheet JSON")
-    parser.add_argument("output_md", type=Path, help="Output markdown path")
-    args = parser.parse_args()
-
-    data = json.loads(args.input_json.read_text(encoding="utf-8"))
-    if not isinstance(data, dict):
-        raise SystemExit("top-level json must be an object")
-    args.output_md.write_text(build_markdown(data), encoding="utf-8")
-    print(f"wrote {args.output_md}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/company-tearsheet/scripts/validate_tearsheet_json.py b/plugins/financial-markets/skills/company-tearsheet/scripts/validate_tearsheet_json.py
deleted file mode 100755
index c9c0fea..0000000
--- a/plugins/financial-markets/skills/company-tearsheet/scripts/validate_tearsheet_json.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python3
-"""Validate a structured company tearsheet JSON file.
-
-Expected top-level shape:
-{
-  "entity": "ExampleCo",
-  "profile_type": "public_company",
-  "as_of_date": "2026-05-07",
-  "sources": [{"source_id": "S1", "source_name": "...", ...}],
-  "metrics": [{"metric": "Revenue", "period": "FY2025", "value": 123, ...}],
-  "sections": {"one_line_view": "...", "business_snapshot": [...], ...}
-}
-
-This script performs lightweight deterministic checks. It does not verify whether
-source claims are true; the assistant must still apply the skill's evidence rules.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-from typing import Any
-
-ALLOWED_EVIDENCE = {
-    "fact_source_reported",
-    "fact_provider_standardized",
-    "derived_calculation",
-    "issuer_management_claim",
-    "management_adjusted",
-    "analyst_adjusted",
-    "estimate_consensus",
-    "analyst_interpretation",
-    "assumption_user_provided",
-    "assumption_inferred",
-    "stale_source",
-    "contradicted_source",
-    "missing_required_source",
-    "unknown",
-}
-
-ALLOWED_CONFIDENCE = {"high", "medium", "low"}
-ALLOWED_PROFILE_TYPES = {"public_company", "equity_issuer_profile", "public_sector_peer"}
-REQUIRED_TOP_LEVEL = ["entity", "profile_type", "as_of_date"]
-REQUIRED_SOURCE_FIELDS = ["source_id", "source_name", "source_type"]
-REQUIRED_METRIC_FIELDS = ["metric", "period", "value", "units", "source", "evidence", "confidence"]
-
-
-def load_json(path: Path) -> dict[str, Any]:
-    try:
-        data = json.loads(path.read_text(encoding="utf-8"))
-    except json.JSONDecodeError as exc:
-        raise SystemExit(f"invalid json: {exc}") from exc
-    if not isinstance(data, dict):
-        raise SystemExit("top-level json must be an object")
-    return data
-
-
-def validate(data: dict[str, Any]) -> list[str]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    for field in REQUIRED_TOP_LEVEL:
-        if not data.get(field):
-            errors.append(f"missing top-level field: {field}")
-    if data.get("profile_type") and data["profile_type"] not in ALLOWED_PROFILE_TYPES:
-        errors.append(f"profile_type must be one of: {', '.join(sorted(ALLOWED_PROFILE_TYPES))}")
-
-    sources = data.get("sources", [])
-    if not isinstance(sources, list):
-        errors.append("sources must be a list")
-        sources = []
-    source_ids = set()
-    for idx, source in enumerate(sources, start=1):
-        if not isinstance(source, dict):
-            errors.append(f"source {idx} must be an object")
-            continue
-        for field in REQUIRED_SOURCE_FIELDS:
-            if not source.get(field):
-                errors.append(f"source {idx} missing field: {field}")
-        if source.get("source_id"):
-            source_ids.add(str(source["source_id"]))
-        if not source.get("as_of_date"):
-            warnings.append(f"source {idx} missing as_of_date")
-
-    metrics = data.get("metrics", [])
-    if metrics and not isinstance(metrics, list):
-        errors.append("metrics must be a list")
-        metrics = []
-    for idx, metric in enumerate(metrics, start=1):
-        if not isinstance(metric, dict):
-            errors.append(f"metric {idx} must be an object")
-            continue
-        for field in REQUIRED_METRIC_FIELDS:
-            if field not in metric or metric.get(field) in (None, ""):
-                errors.append(f"metric {idx} missing field: {field}")
-        evidence = metric.get("evidence")
-        if evidence and evidence not in ALLOWED_EVIDENCE:
-            errors.append(f"metric {idx} has invalid evidence label: {evidence}")
-        confidence = metric.get("confidence")
-        if confidence and confidence not in ALLOWED_CONFIDENCE:
-            errors.append(f"metric {idx} has invalid confidence label: {confidence}")
-        source_ref = str(metric.get("source", ""))
-        source_id = source_ref.split()[0] if source_ref else ""
-        if source_id and source_ids and source_id not in source_ids:
-            warnings.append(f"metric {idx} source id not found in sources: {source_id}")
-        if not metric.get("period"):
-            errors.append(f"metric {idx} missing period")
-        if metric.get("evidence") == "assumption_inferred" and metric.get("confidence") != "low":
-            warnings.append(f"metric {idx} inferred assumptions should usually be low confidence")
-
-    data_quality_flags = data.get("data_quality_flags", [])
-    if data_quality_flags and not isinstance(data_quality_flags, list):
-        errors.append("data_quality_flags must be a list")
-
-    return [f"ERROR: {e}" for e in errors] + [f"WARNING: {w}" for w in warnings]
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Validate a company tearsheet JSON file")
-    parser.add_argument("input_json", type=Path, help="Path to tearsheet JSON")
-    parser.add_argument("--strict", action="store_true", help="Treat warnings as failures")
-    args = parser.parse_args()
-
-    data = load_json(args.input_json)
-    messages = validate(data)
-    for message in messages:
-        print(message)
-
-    has_error = any(m.startswith("ERROR:") for m in messages)
-    has_warning = any(m.startswith("WARNING:") for m in messages)
-    if has_error or (args.strict and has_warning):
-        return 1
-    print("validation passed")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/comps-valuation/SKILL.md b/plugins/financial-markets/skills/comps-valuation/SKILL.md
deleted file mode 100644
index 934ad6b..0000000
--- a/plugins/financial-markets/skills/comps-valuation/SKILL.md
+++ /dev/null
@@ -1,75 +0,0 @@
----
-name: comps-valuation
-description: Produce Public Equity Investing comparable-company valuation in report or workbook mode. Use for peer selection, multiple analysis, valuation read-throughs, implied prices, comps dashboards, Excel or Sheets comps, refreshable peer tables, model updates, and comps workbook QA. Do not use for DCF-only, credit-security, or generic market commentary requests.
----
-
-# Comps Valuation
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `market_data_estimates`, and `portfolio_models_trackers`.
-
-## Deliverable Intake And Judgment Standard
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML valuation report for report mode or an XLSX workbook when reusable comps calculations and tables are central. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences downstream. For substantive investment judgment, apply `../../shared/final-deliverable-framework.md`.
-
-## Equity Valuation PM Standard
-
-Load `../../shared/equity-valuation-pm-standard.md` and `../../shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, the valuation posture and next underwriting step, and what evidence is missing. State what changes target, rating, sizing, hedge, trim, exit, or watchlist status only when the user requests portfolio action or provides the relevant holding, benchmark, mandate, and liquidity context.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit or public-credit instrument underwriting, or distressed claim valuation.
-
-## Mode Selection
-
-Select the mode from the request and available context; do not ask merely because both modes exist.
-
-- `report` mode: peer framing, multiple interpretation, implied valuation or price read-through, peer-set review, PM-facing comps argument, or a standalone HTML report or explicitly requested standardized dashboard without an editable model.
-- `workbook` mode: Excel, Sheets, CSV/XLSX export, refreshable data, formulas, EV bridges, peer-table templates, workbook/model updates, structured sensitivities, or auditing an existing comps workbook.
-- If an existing workbook is supplied and the requested output changes or validates it, choose `workbook`.
-- Ask one focused question only when either mode is equally plausible and choosing incorrectly would materially change the user's intended artifact or decision workflow. Prefer proceeding with a stated inferred default when reliance is not impaired.
-
-## Common Workflow
-
-1. Establish security, issuer or target, audience mode, valuation date, fiscal basis, currency, and thesis question.
-2. Source and label price, shares, EV bridge, reported/adjusted/consensus denominators, forward estimates, and peer rationale.
-3. Build Core, Secondary, Excluded, and optional Watchlist peer roles.
-4. Test metric comparability, stale data, outliers, valuation range, premium/discount logic, and PM action implications.
-5. State what is priced in, what would change the thesis, and what evidence is missing.
-6. Render or materialize the selected mode.
-
-## Report Mode
-
-Read `references/peer-selection.md`, `references/module-rules.md`, `references/source-and-staleness-rules.md`, `references/valuation-readthrough.md`, `references/output-templates.md`, and `references/p0-integrations.md` only when relevant. For a substantial reusable or HTML comps report, produce a polished standalone HTML comps report following `../../shared/html-artifact-standard.md`; let the valuation question and supported evidence determine the hierarchy. Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render; in that case, load `references/DASHBOARD_PACK.md` and hand over a `public_equity_investing_dashboard.v1` payload.
-
-For a standalone HTML comps report, keep the first read compact:
-
-1. Valuation read: whether the premium or discount is supported and the key unresolved proof point.
-2. Four or five high-signal metrics: current price, primary trading multiple, premium or discount to core peers, implied value range only when supportable, and the key operating proof point.
-3. Peer-set rationale: Core, Secondary, and Excluded peers with concise inclusion or exclusion reasons.
-4. Core trading-comps table.
-5. Premium or discount bridge grounded in growth, margin, business-model fit, and data quality.
-6. Valuation posture, material evidence gaps, and concise source ledger.
-
-Express a valuation posture such as `premium partly supported`, `screening-only`, or `not sufficiently supported for a new-money decision`. Do not issue add, trim, hedge, sizing, or exit instructions unless the user requests portfolio action or supplies relevant holding and mandate context.
-
-When HTML is delivered, keep citations traceable but readable: do not fragment tickers, prices, multiples, percentages, dates, numeric ranges, metric names, or peer labels into separately linked tokens. Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, density, clipping, citation rendering, and whitespace before delivery.
-
-## Workbook Mode
-
-Read `references/workbook/comps-framework.md`, `references/workbook/data-sourcing-and-connectors.md`, `references/workbook/model-workbook-spec.md`, `references/workbook/qa-and-pressure-testing.md`, `references/workbook/review-memo-template.md`, and `references/workbook/dashboard-map.md` only when relevant. Use `scripts/create_comps_template.py`, `scripts/materialize_screening_comps.py`, and `scripts/audit_comps_workbook.py` as appropriate. Preserve an existing workbook before rebuilding it.
-
-## When To Invoke Support
-
-Load `../../shared/support-layer-routing-contract.md` when source/data/QC support is needed. Use `financials-normalizer` for messy issuer or peer inputs, `model-audit-tieout` for standalone workbook audit, and `deck-report-qc` before circulation. Route credit-first conclusions to Credit Markets.
diff --git a/plugins/financial-markets/skills/comps-valuation/agents/openai.yaml b/plugins/financial-markets/skills/comps-valuation/agents/openai.yaml
deleted file mode 100644
index 99fb484..0000000
--- a/plugins/financial-markets/skills/comps-valuation/agents/openai.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-interface:
-  display_name: "Comps Valuation"
-  short_description: "Public-equity comps reports and workbook models"
-  brand_color: "#1F6FEB"
-  default_prompt: "Use $comps-valuation to analyze a public-equity peer set or build a comps valuation workbook."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/comps-valuation/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/comps-valuation/references/DASHBOARD_PACK.md
deleted file mode 100644
index dc7577e..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Comps Analysis Dashboard Pack
-
-Use this pack only when a comps request explicitly selects a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render. For an ordinary standalone HTML comps report, follow the flexible HTML artifact standard and the report-mode guidance in the owning skill instead of this fixed module map.
-
-## Producer Role
-
-`comps-valuation` owns the peer judgment, multiple interpretation, valuation read-through, and premium/discount logic. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `comps_analysis`
-- `layout`: `single_page` for full PM diligence dashboards unless the user explicitly requests tabs
-- `hero.callout`: the single valuation question or peer-set controversy
-- `snapshot`: selected multiple/range, implied valuation skew, peer count, highest-quality comp, largest caveat, and source/as-of posture
-- `sources`: filings and market/consensus provider sources first; label screen-grade, stale, unavailable, or user-provided inputs
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `overview`
-   - `decision_box`: selected comp read, valuation posture, premium/discount view, and what would change the conclusion
-   - `cards`: peer-set thesis, excluded peers, denominator caveats, and source posture
-2. `peer-set`
-   - `table`: peer inclusion/exclusion with business-model fit, geography, size, growth, margin, liquidity, and caveat columns
-3. `valuation`
-   - `table`: trading comps with market data as-of, EV bridge, denominator, multiple, growth/margin context, and citations
-   - `bar_chart`: selected multiple or valuation-range visualization only when the sourced table is complete enough to speed interpretation
-4. `premium-discount`
-   - `cards`: reasons for premium/discount, quality adjustments, and unusable denominators
-   - `scenario_map`: low/base/high selected-multiple cases, implied value ranges, and falsifiers
-5. `refresh-gaps`
-   - `missing_evidence`: missing prices, shares, debt, consensus, stale market data, peer validation gaps, and unsupported multiples
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every market value, multiple, estimate, peer inclusion/exclusion claim, and selected-multiple assumption.
-- If data is unavailable, leave the row visible with `TBD / not sourced` and add a precise `missing_evidence` item.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not invent multiples or consensus fields.
-- Do not turn a weak screen into a decision-grade valuation conclusion.
-- Do not create a workbook or CSV export here; route formula-ready artifacts to `comps-valuation`.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-- Do not fragment tickers, prices, multiples, percentages, dates, numeric ranges, metric names, or peer labels with citations.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm source ledger, peer-set rationale, selected multiple/range, and missing evidence are visible.
-- Confirm all modules are supported by `dashboard-builder` and no chart shell is empty.
-- Confirm citation rendering remains readable and action language is supported by the requested decision context.
-
-## Equity Comps PM Modules
-
-Dashboard payloads should include current price versus implied value, peer-median-to-selected-multiple bridge, premium/discount rationale, what is priced in, estimate path, scenario skew, action rules, source posture, and missing evidence.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/module-rules.md b/plugins/financial-markets/skills/comps-valuation/references/module-rules.md
deleted file mode 100644
index 502f066..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/module-rules.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# Module And Asset-Class Rules
-
-## Corporate
-
-Use EV-based and equity-based multiples as appropriate:
-
-- `EV/LTM Revenue`
-- `EV/NTM Revenue`
-- `EV/LTM EBITDA`
-- `EV/NTM EBITDA`
-- `EV/LTM EBIT`
-- `EV/NTM EBIT`
-- `P/E`
-- `P/FCF`, `FCF yield`, or sector-specific metrics when support exists
-
-Keep reported and adjusted denominators separate. Do not overwrite reported metrics with adjusted ones.
-
-## Banks and lenders
-
-Do not use EV-based multiples by default. Use:
-
-- `P/TBV`
-- `P/B`
-- `P/E`
-- ROE, ROTCE, ROA
-- NIM
-- CET1 or relevant capital ratio
-- deposit mix
-- credit losses, NPLs, reserves, and asset sensitivity where available
-
-Flag whether book value is tangible, common, or total equity.
-
-## Insurance
-
-Use:
-
-- `P/B`
-- `P/E`
-- ROE
-- combined ratio
-- reserve adequacy
-- book-value growth
-
-Label life, P&C, brokerage, and reinsurance differences. Do not force a single multiple across fundamentally different insurance models.
-
-## Asset managers and exchanges
-
-Use:
-
-- `P/E`
-- `EV/EBITDA`
-- AUM, flows, fee rate, and revenue yield where available
-- volume, clearing, transaction, or data revenue metrics for exchanges
-- margin and operating leverage
-
-Separate market beta, flows, and fee-rate changes when explaining premiums or discounts.
-
-## REITs and real assets
-
-Use:
-
-- `P/FFO`
-- `P/AFFO`
-- NAV premium/discount
-- implied cap rate
-- occupancy
-- same-store NOI
-- leverage
-- property-type read-through
-
-For REITs, treat the module as a specialized public-equity comp sheet, not a generic corporate trading comps screen.
-
-### REIT denominator hierarchy
-
-1. Recurring Core / adjusted FFO per share as the primary equity multiple denominator.
-2. Cleaner ex-item Core FFO when promote income, casualty / business-interruption proceeds, lease termination income, large disposition items, or other non-recurring items distort recurring FFO.
-3. Reported NAREIT FFO only when no cleaner recurring FFO metric is available. Label it as reported.
-4. AFFO / CAD as a secondary cross-check when disclosed or cleanly derivable. Do not make AFFO availability determine peer inclusion.
-5. EV/EBITDAre, NAV premium/discount, and implied cap rate as cross-checks when relevant.
-
-### REIT peer preservation
-
-Do not exclude a very close REIT peer only because one secondary field is missing. Include the peer in the core table with `N/A`, `N/M`, or a clearly labeled derived value if:
-
-- it is economically one of the closest public peers;
-- the primary FFO-style denominator is available; and
-- the missing value is not central to the user's requested decision.
-
-Only exclude a close peer if the primary valuation denominator or pricing data cannot be sourced. List the exact missing blocker.
-
-### REIT table
-
-For REIT requests, the core table should usually include:
-
-| Ticker | Peer role | Price / as-of | FFO denominator used | P/FFO | AFFO or CAD denominator | P/AFFO or P/CAD | Leverage | Occupancy basis | Normalized read-through |
-|---|---|---:|---|---:|---|---:|---|---|---|
-
-Prefer a complete table for the closest 4-8 peers over a long table with many missing fields.
-
-## Distressed or stressed companies
-
-Do not let normal trading comps imply false precision. Flag:
-
-- capital-structure impairment;
-- liquidity and going-concern issues;
-- debt trading levels;
-- whether equity value is option value;
-- whether EV is effectively owned by creditors;
-- whether recovery value matters more than trading multiples.
-
-Route to Credit Markets when credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, claim priority, liquidity, or capital-structure impairment is the real task.
-
-## Private-company target
-
-Use public comps only to infer a valuation range. Target financials from CIM, banker deck, management accounts, or management materials are `seller_claim` or `management_claim` unless verified.
-
-Route to `financials-normalizer`, `financial-source-of-truth`, or `three-statement-model-builder` when the target denominator is not reliable.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/output-templates.md b/plugins/financial-markets/skills/comps-valuation/references/output-templates.md
deleted file mode 100644
index b166b25..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/output-templates.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# Output Templates
-
-## Standard comps memo
-
-Use these sections in order:
-
-1. `Executive Summary`
-2. `Peer Set And Rationale`
-3. `Core Comps Table`
-4. `Stats And Outliers`
-5. `Valuation Read-Through`
-6. `QA Flags And Caveats`
-7. `Open Items / Data Requests`
-8. `Comps Output Posture`
-
-## Standalone HTML comps report
-
-When report mode calls for a substantive reusable or HTML artifact, produce a polished standalone HTML comps report following `../../../shared/html-artifact-standard.md`. Keep peer selection, valuation evidence, and the premium or discount question central rather than forcing a standardized dashboard shell.
-
-Recommended first-read sequence:
-
-1. Valuation read: whether the premium or discount is supported and the most important unresolved proof point.
-2. Metric strip: four or five sourced items covering current price, primary multiple, relative premium or discount, supportable implied value range if available, and the key operating proof point.
-3. Peer-set rationale: Core, Secondary, and Excluded peer roles with concise decision-relevant reasons.
-4. Core trading-comps table: prices, EV bridge basis, denominators, multiples, growth/margin context, and as-of/source posture.
-5. Premium or discount bridge: why growth, margins, business model, scale, or data quality support or fail to support the selected relative valuation.
-6. Valuation posture, material evidence gaps, and concise source ledger.
-
-Label forward data as `third-party forward estimates` unless the source and as-of basis support calling it `consensus`; do not allow aggregator-sourced estimates to imply unverified consensus breadth.
-
-Do not turn an ordinary comps report into an action-rules dashboard. Without requested portfolio action or supplied holding and mandate context, use a valuation posture rather than add, trim, hedge, sizing, or exit instructions.
-
-## Core comps table
-
-Default corporate table:
-
-| Company | Ticker | Peer role | Market data as-of | EV | LTM revenue | LTM EBITDA | NTM revenue | NTM EBITDA | EV/LTM rev | EV/NTM rev | EV/LTM EBITDA | EV/NTM EBITDA | Notes |
-|---|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|
-
-Use fewer fields for narrower tasks and add module-specific columns for banks, insurers, REITs, asset managers, exchanges, distressed companies, or private-company targets.
-
-## Screen-grade fallback table
-
-Use this when live market data, current estimates, or source files are unavailable. This is better than returning no output, but it must not imply a valuation conclusion.
-
-Lead with:
-
-`Comps posture: screen-grade; missing market data and/or estimates are explicitly labeled.`
-
-Fallback table:
-
-| Company / candidate | Ticker | Proposed role | Why it belongs / should be tested | Why it may not be clean | Required market / financial fields | Source requirement |
-|---|---|---|---|---|---|---|
-
-Acceptable missing-value labels:
-
-- `TBD`
-- `not sourced`
-- `TBD / not sourced`
-- `not provided`
-- `requires market data`
-- `requires consensus`
-
-Do not use `0.0x`, `NM`, or blank cells to hide missing data. `NM` is only for true negative or non-meaningful denominators after the denominator is known.
-
-Include a `Missing source requirements` section immediately after the fallback table when any metric is unsourced.
-
-## Target valuation add-on
-
-Add these sections when a target is present:
-
-1. `Selected Multiple Set`
-2. `Implied Enterprise Value`
-3. `Bridge To Equity Value`
-4. `Implied Value Per Share`, if applicable
-
-Suggested table:
-
-| Metric | Low | Mid | High | Basis / caveat |
-|---|---:|---:|---:|---|
-| Selected multiple |  |  |  |  |
-| Target denominator |  |  |  |  |
-| Implied EV |  |  |  |  |
-| Net debt / other claims |  |  |  |  |
-| Implied equity value |  |  |  |  |
-| Diluted shares |  |  |  |  |
-| Implied value per share |  |  |  |  |
-
-## QA review
-
-For `qa-review`, return:
-
-1. critical findings first, ordered by severity;
-2. broken definitions or inconsistent numerators and denominators;
-3. weak peer logic or cherry-picking concerns;
-4. missing caveats, stale data, unsupported adjustments, or source conflicts;
-5. a verdict: `usable`, `usable with conditions`, or `not reliable`.
-
-## Data request table
-
-When more data would materially improve the answer, use:
-
-| Priority | Needed item | Why it matters | Affected output | Minimum acceptable substitute |
-|---|---|---|---|---|
-
-Ask for exact missing fields, not a generic data dump.
-
-For fallback comps, default P0 items are current share price/as-of date, share count/dilution, cash/debt/EV bridge, LTM denominators, NTM estimates/as-of date, and source-supported peer rationale.
-
-## Comps output posture
-
-Use one posture label:
-
-- `decision-useful`: peer set, market data, denominators, and EV bridges are current enough and caveated appropriately.
-- `usable-with-caveats`: output is directionally useful but has missing fields, stale data, or peer-set limitations.
-- `screening-only`: useful for early discussion, not enough support for a decision range.
-- `not-reliable`: source conflicts, missing primary data, stale market data, or denominator problems make the output unsafe to rely on.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/p0-integrations.md b/plugins/financial-markets/skills/comps-valuation/references/p0-integrations.md
deleted file mode 100644
index 9b9a848..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/p0-integrations.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# P0 Integrations
-
-## Ownership boundaries
-
-This skill owns public trading comps judgment, peer-set framing, valuation multiple read-throughs, and implied valuation ranges.
-
-Adjacent P0 ownership:
-
-| Area | This skill owns | Adjacent skill owns |
-|---|---|---|
-| Source hierarchy | Use labels and caveats | `financial-source-of-truth` owns source priority, stale-data rules, conflicts, and citation format |
-| Messy exports | Identify unusable data and route | `excel-data-cleaner` owns table cleanup and normalization |
-| Full workbook | Select workbook mode and provide requirements | `comps-valuation` in `workbook` mode owns Excel/Sheets comps models |
-| Workbook audit | Flag issues in memo | `model-audit-tieout` owns formula, link, and model QA |
-| Final materials | Provide comps support | `deck-report-qc` owns deck/report circulation checks |
-| DCF cross-check | Provide market multiple range | `dcf-model-builder` owns DCF mechanics |
-| Credit Markets | Provide leverage, liquidity, or valuation-cushion context only when needed for a public-equity comps view | Credit Markets owns credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis |
-| Model update | Provide peer multiple support | `equity-model-update` owns public-company model refresh from new actuals, guidance, consensus, and KPI disclosures |
-| Investment synthesis | Provide valuation support | `memo-builder` owns full memo narrative and recommendation |
-
-## Downstream handoff contract
-
-When comps support another P0 skill, expose these fields clearly.
-
-### Peer-set handoff
-
-- target / subject;
-- selected peer set;
-- peer role labels;
-- excluded close peers;
-- business-model rationale;
-- geography / size / growth / margin / leverage caveats.
-
-### Valuation handoff
-
-- as-of date;
-- selected multiples;
-- statistic used: median, p25, p75, trimmed mean, or selected range;
-- denominator used for target;
-- implied EV;
-- net debt and other claims;
-- implied equity value;
-- implied value per share, if applicable;
-- source limitations.
-
-### QA handoff
-
-- missing fields;
-- stale fields;
-- source conflicts;
-- adjustment basis;
-- currency / FX issues;
-- outlier treatment;
-- confidence level;
-- output posture label.
-
-## Handoff uses
-
-Use the handoff for:
-
-- `dcf-model-builder` as a market cross-check;
-- Credit Markets as leverage, liquidity, recovery, and refinancing context only when the final view is public equity;
-- `equity-model-update` as public-company model refresh support;
-- `memo-builder` as valuation support;
-- `deck-report-qc` as the tie-out source for final materials.
-
-## Workbook routing
-
-If the user asks for Excel, Google Sheets, refreshable data, formula-driven EV bridge, source tabs, peer-universe management, sensitivity tables, or a file deliverable, use `comps-valuation` in `workbook` mode.
-
-Do not emit ad hoc exported workbook artifacts from `report` mode. If the user needs a downloadable table, CSV/XLSX, workbook shell, or audit-ready file package, preserve the normalized requirements and caveats while switching to `workbook` mode. For a substantial reusable comps report, produce a standalone HTML comps report following the shared HTML artifact standard; hand a normalized payload to `dashboard-builder` only when the user explicitly selects a standardized dashboard or structured payload-driven render.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/peer-selection.md b/plugins/financial-markets/skills/comps-valuation/references/peer-selection.md
deleted file mode 100644
index 285c58f..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/peer-selection.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Peer Selection
-
-## What a good peer set means
-
-A good peer set is:
-
-- explainable in 30 seconds;
-- economically comparable rather than cosmetically similar;
-- liquid enough that market-implied multiples are meaningful;
-- not cherry-picked to force a valuation answer.
-
-## Build peers in this order
-
-1. Start with the narrowest useful taxonomy: sector, industry group, industry, then subindustry.
-2. Add geography and listing filters that reflect how the market prices the business.
-3. Add business-model tags that actually drive multiples, such as subscription, marketplace, regulated, hardware, services, asset-heavy, transaction-driven, or recurring-revenue.
-4. Check size, growth, margin, leverage, cyclicality, liquidity, customer mix, and accounting basis.
-5. Keep 6-12 peers when possible, but prefer a clean narrow set over a broad noisy one.
-6. Allow manual overrides only with a short rationale.
-
-## Peer role labels
-
-Assign each material peer one role:
-
-| Role | Meaning | Treatment |
-|---|---|---|
-| `core_peer` | Closest economic comp | Should influence selected range |
-| `secondary_peer` | Relevant but less direct | Use as context unless core set is too small |
-| `aspirational_peer` | Useful business-model read-through, weaker anchor | Do not let it drive valuation without explanation |
-| `negative_peer` | Shown to explain why it should not anchor valuation | Include in rationale, usually exclude from selected range |
-| `excluded_close_peer` | Economically relevant but missing a required primary field | Name the exact blocker |
-| `not_clean_comp` | Conglomerate, segment-mix, distressed, illiquid, or accounting mismatch | Context only unless justified |
-
-## Inclusion and exclusion rules
-
-- Prefer primary listings.
-- ADRs are acceptable if liquidity is sufficient and share factors are handled correctly.
-- Exclude distressed companies unless the target is also distressed or the distress read-through is central.
-- Exclude banks, insurers, and REITs from corporate peer sets unless the user explicitly wants cross-sector comparisons.
-- Conglomerates can stay only if flagged as `not_clean_comp`.
-- If the target is loss-making, prioritize peers with similar growth and margin profiles and weight revenue or sector-specific KPIs more heavily.
-- Do not silently drop close peers. If a close peer is excluded, list it under `Excluded close peers` with the exact blocker.
-
-## Peer-set review output
-
-For peer-set review tasks, use:
-
-| Company | Proposed role | Keep / move / exclude | Rationale | Missing data / caveat |
-|---|---|---|---|---|
-
-Then conclude with:
-
-- core peer set;
-- secondary context set;
-- excluded close peers;
-- peers to avoid as valuation anchors;
-- the multiples most appropriate for the resulting set.
-
-## Sparse or missing-market-data fallback
-
-When the user asks for a clean comps table but does not provide source data and live market data is unavailable, still produce a peer-selection framework.
-
-Minimum fallback behavior:
-
-- propose 6-12 likely peer candidates when the business model is identifiable;
-- if candidates are uncertain, label them `watchlist_peer` rather than `core_peer`;
-- list obvious excluded or non-clean peers instead of silently dropping them;
-- state the exact fields needed before the peer can anchor valuation;
-- avoid a selected valuation range until market data, estimates, and denominators are sourced.
-
-Use role labels conservatively:
-
-- `core_peer`: only when business-model fit is strong and source data exists or can be clearly requested.
-- `watchlist_peer`: plausible candidate, but source data or fit still needs validation.
-- `negative_peer`: useful to explain why a tempting comp should not drive valuation.
-- `excluded_close_peer`: close peer blocked by missing data, fiscal mismatch, capital structure distortion, or denominator issue.
-
-## Public Equity Peer Filters
-
-After business-model fit, test public-equity investability: liquidity, float, index membership, ETF ownership, short interest/borrow, ADR or share-class issues, consensus coverage, estimate-revision relevance, sector KPI regime, accounting calendar, and source freshness. Debt comps, bond comps, loan comps, CDS, spread/yield relative value, and recovery comps route to Credit Markets unless they are only context for common-equity downside.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/source-and-staleness-rules.md b/plugins/financial-markets/skills/comps-valuation/references/source-and-staleness-rules.md
deleted file mode 100644
index 6ecb255..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/source-and-staleness-rules.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Source And Staleness Rules
-
-## Source hierarchy
-
-Follow `financial-source-of-truth` for source hierarchy, citation format, stale-data checks, and source conflicts. For comps work, prefer:
-
-1. user-provided workbooks, provider exports, filings, investor decks, and source files;
-2. connected financial-data sources or workspace apps;
-3. primary filings, company IR, exchange data, and official announcements;
-4. reputable market-data or estimate providers;
-5. general web/search only as a labeled fallback.
-
-Do not silently blend different source qualities. If a provider value, filing value, and user value disagree, disclose the conflict and choose the value with the clearest provenance for the requested decision.
-
-## Public comps stale-data checks
-
-For each company in the core table, carry or disclose:
-
-- market-data as-of date;
-- financial-statement period end;
-- estimate source and estimate-as-of date when NTM is used;
-- forward-estimate basis: verified consensus or third-party forward estimate;
-- trading and reporting currency;
-- FX date and spot/average convention when currencies differ;
-- EV bridge basis;
-- adjustment basis: reported, adjusted, consensus, company-defined, or inferred.
-
-Never mix today's share price with stale share count, stale cash/debt, stale estimates, or stale denominators without labeling the mismatch in `QA Flags And Caveats`.
-
-## Evidence labels
-
-Use shared evidence labels for major inputs and conclusions:
-
-- `fact_primary`
-- `fact_secondary`
-- `third_party_estimate`
-- `internal_estimate`
-- `management_claim`
-- `seller_claim`
-- `assumption`
-- `inference`
-- `unsupported`
-
-Comps-specific mapping:
-
-- company filing financials = `fact_primary`
-- traded price from a reliable market-data source = `fact_secondary`, with as-of date
-- consensus estimates = `third_party_estimate`
-- forward estimates sourced through a public aggregator or secondary provider without a verified contributor count, estimate-set date, and methodology = `third_party_estimate`; label them in reader-facing output as `third-party forward estimates`, not `consensus`
-- peer-set inclusion rationale = `inference`
-- selected multiple range = `inference`
-- manual peer inclusion/exclusion = `assumption` or `inference`, with rationale
-- target private-company financials from CIM, banker deck, or management materials = `seller_claim` or `management_claim` unless verified
-
-## Canonical input contract
-
-When user files or exports are available, normalize them internally into these fields where practical:
-
-| Table | Required fields | Helpful fields |
-|---|---|---|
-| `entities` | `company_id`, `company_name`, `ticker`, `sector_module`, `reporting_currency`, `is_target` | exchange, country, taxonomy, business model tags |
-| `market_data` | `company_id`, `as_of_date`, `price`, `trading_currency`, `basic_shares` | share factor, ADV, provider market cap |
-| `capital_structure` | `company_id`, `as_of_date`, `cash`, `st_debt`, `lt_debt` | restricted cash, trapped cash, leases, minority interest, preferreds, pensions, investments |
-| `financials` | `company_id`, `ltm_period_end`, `ltm_revenue` | EBITDA, EBIT, net income, CFO, capex, FCF, FFO, AFFO, TBV, book value |
-| `estimates` | `company_id`, `estimate_as_of`, `ntm_method` | NTM revenue, EBITDA, EBIT, net income, EPS, FCF, FFO, AFFO |
-| `dilution` | `company_id`, `instrument_type`, `quantity` | strike, conversion price, treasury stock method notes |
-| `fx_rates` | `date`, `from_ccy`, `to_ccy`, `rate`, `rate_type` | spot, average, period basis |
-| `adjustments` | `company_id`, `metric`, `period_end`, `amount`, `category`, `notes` | source, approval, adjustment basis |
-
-## Data conventions
-
-- Use `company_id` as the stable join key. Do not rely only on ticker strings.
-- Dates must be `YYYY-MM-DD`.
-- Currency codes should be ISO 4217.
-- Monetary values should be absolute, not per-share, unless the field is explicitly per-share.
-- Shares should be actual shares, not thousands.
-- Percent fields should be stored as percent, such as `18.5`, not `0.185`.
-
-## Overrides and assumptions
-
-- Keep sourced values distinguishable from assumptions.
-- Attach a short reason to every manual override or adjustment.
-- Point important inputs back to their source table, filing, provider export, connector path, or stated assumption when possible.
-- Use `consensus` in reader-facing output only when the estimate source, as-of basis, and methodology are clearly supported. Otherwise use `third-party forward estimates` and identify the provider and available timestamp.
-- For downloadable/exported workbook artifacts, use `comps-valuation` in `workbook` mode and preserve overrides in an audit section or notes field there. Report mode should surface the override rationale in the comps read-through, standalone HTML report, or explicitly selected dashboard payload, not emit ad hoc local files.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/valuation-readthrough.md b/plugins/financial-markets/skills/comps-valuation/references/valuation-readthrough.md
deleted file mode 100644
index 5a1846a..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/valuation-readthrough.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# Valuation Read-Through
-
-## Equity value and dilution
-
-Core formulas:
-
-- `market_cap_basic = price * basic_shares`
-- `fd_shares = basic_shares + incr_options_warrants + incr_rsus_psus + incr_convertibles_if_converted`
-- `equity_value_fd = price * fd_shares`
-
-Default for EV-based work: use fully diluted equity value.
-
-Options and warrants:
-
-- If `price <= strike`, incremental shares are `0`.
-- If `price > strike`, use treasury stock method: proceeds repurchase shares at current price, and only the net incremental shares are added.
-- Incremental shares cannot be negative.
-
-RSUs and PSUs: treat as full incremental shares unless a specific net-settlement methodology is provided.
-
-Convertibles: use an if-converted threshold. If treated as converted, add implied shares and exclude convertible principal from debt. Otherwise, keep principal in debt and do not add shares. Disclose the rule.
-
-ADRs/GDRs: adjust either shares with `share_factor` or price with `share_factor`, not both.
-
-## Enterprise value bridge
-
-Use:
-
-`EV = equity_value_fd + debt_like_items + other_claims - cash_like_items - non_operating_assets`
-
-Debt-like items may include short-term debt, long-term debt, lease liabilities, preferred equity, pension deficit, ARO, and similar claims.
-
-Other claims and non-operating assets:
-
-- Add minority interest when denominators are consolidated at 100 percent.
-- Subtract investments when they are non-operating and not already reflected in the denominator.
-- If only book value is available for investments, subtract it and flag it as an approximation.
-
-If provider market cap or EV is available, reconcile to it. Differences above roughly `3%` for market cap or `5%` for EV need explanation.
-
-## Denominator rules
-
-- Use reported LTM and NTM values by default.
-- Use adjusted values only when the user asks or the inputs clearly separate reported and adjusted metrics.
-- Never overwrite reported values with adjusted values.
-- LTM denominators must carry `ltm_period_end`.
-- Warn when the lag between valuation date and LTM period end is greater than `180` days.
-- Treat lag greater than `365` days as critical unless the user accepts stale data.
-- NTM estimates must carry `estimate_as_of`, method, and source.
-- If NTM is missing, compute only LTM multiples.
-- Do not fabricate forward estimates.
-
-## Multiple pairing
-
-- EV must pair with unlevered or enterprise-scope denominators.
-- Market cap, equity value, or price must pair with equity-scope denominators.
-- If using aggregate `P/E`, make sure net income is attributable to common shareholders.
-- If a denominator is negative or zero, show `N/M`.
-- If a denominator is near zero, flag it as unstable.
-
-## FX
-
-If the analysis is in a base currency:
-
-- convert market values and balance sheet items using spot rates;
-- convert income statement and cash flow denominators using average rates;
-- if average is missing, use spot only with an explicit warning.
-
-## Outliers and statistics
-
-Do not treat every outlier as a data error. First ask whether it is a true differentiator, or whether it comes from stale shares, wrong sign, missing minority interest, unit error, or denominator distortion.
-
-Default outlier method: IQR.
-
-- `IQR = Q3 - Q1`
-- lower bound = `Q1 - 1.5 * IQR`
-- upper bound = `Q3 + 1.5 * IQR`
-
-Do not alter underlying data. Keep raw values visible and compute summary stats with and without flagged outliers when useful.
-
-Show valid count, median, mean, trimmed mean, p25, p75, p10, and p90 when space permits.
-
-## Selected multiple range discipline
-
-Do not mechanically use full peer-set min/max as the valuation range.
-
-Default hierarchy:
-
-1. start with core-peer median and interquartile range;
-2. adjust for growth, margin, scale, leverage, cyclicality, liquidity, business mix, and data quality;
-3. use secondary peers as context, not anchors, unless the core set is too small;
-4. disclose whether the selected range is judgmental or mechanically derived.
-
-Use `screening range` when data quality is incomplete, `selected range` when peer set and denominator quality are adequate, and `not supportable` when the range would be mostly guesswork.
-
-## Implied valuation
-
-For target valuation:
-
-1. Choose the multiple set that best matches the target's economics and data quality.
-2. Prefer median over mean when dispersion is wide.
-3. Exclude `N/M` observations from selected statistics.
-4. Apply the selected peer multiple to the target denominator.
-5. Bridge from implied EV to implied equity value.
-6. Convert to per-share value using the correct share basis.
-7. Explain why the selected statistic and peer subset are appropriate.
-
-When forward estimates or a fully diluted EV bridge are unavailable, label any implied value-per-share output as an `illustrative trailing-multiple range`, not a base case, target price, or action threshold.
-
-## PM-Native Multiple Bridge
-
-A comps read-through must bridge from peer median to selected multiple using growth, margin, FCF conversion, ROIC/quality, cyclicality, leverage, liquidity/float, index/ETF ownership, governance, and source confidence. State current price, supportable implied value/share or illustrative trailing-multiple range, upside/downside where supported, what is priced in, rerating/derating mechanism, and valuation posture. Include portfolio action implications only when requested or supported by supplied portfolio context.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook-mode.md b/plugins/financial-markets/skills/comps-valuation/references/workbook-mode.md
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--- a/plugins/financial-markets/skills/comps-valuation/references/workbook-mode.md
+++ /dev/null
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-# Workbook And Export Boundary
-
-`comps-valuation` owns the comps judgment layer in both modes. In `report` mode it may render compact tables in chat for quick reads, and substantial reusable comps work should become a polished standalone HTML comps report following the shared HTML artifact standard. Use `dashboard-builder` only when the user explicitly selects a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render. Use `workbook` mode for CSV, XLSX, Google Sheets, refreshable, formula-driven, or external-circulation workbook artifacts.
-
-Use `comps-valuation` in `workbook` mode when the user asks for:
-
-- Excel or Google Sheets comps
-- CSV/exported/downloadable tables
-- refreshable peer universes
-- formula-driven EV bridges
-- source/provenance tabs
-- valuation range sensitivity tables
-- audit-ready workbook outputs
-- file deliverables for IC, committee, client, board, lender, or senior circulation
-
-If the user provides a spreadsheet or provider export, `comps-valuation` can use it as source context for the comps read-through, standalone HTML report, or explicitly selected dashboard payload. Use `excel-data-cleaner` first when the table is too messy to read reliably.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/comps-framework.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/comps-framework.md
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index 009ff26..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/comps-framework.md
+++ /dev/null
@@ -1,169 +0,0 @@
-# Comparable Company Analysis Framework
-
-Use this reference when selecting peers, choosing multiples, and translating the model into investment judgment.
-
-## What makes a good comparable
-
-A strong peer is similar in the drivers investors actually value. Screen by:
-
-- Business model and revenue model.
-- Product/service mix.
-- End-market and customer base.
-- Geography and regulatory regime.
-- Size and scale economics.
-- Growth profile and margin trajectory.
-- Unit economics or sector KPIs.
-- Capital intensity and working capital profile.
-- Leverage and balance-sheet risk.
-- Cyclicality, commodity exposure, and interest-rate sensitivity.
-- Management quality, governance, liquidity, index inclusion, and ownership structure where relevant.
-
-Avoid the lazy peer set: companies in the same broad industry with materially different economics.
-
-## Peer tiers
-
-Use tiers instead of one undifferentiated list:
-
-- **Target**: The subject company.
-- **Core**: Closest public comparables; should drive valuation range.
-- **Secondary**: Relevant but less directly comparable due to size, geography, margin, growth, business mix, or maturity.
-- **Adjacent**: Useful context, but should not mechanically drive valuation.
-- **Watchlist**: Potential peers requiring more diligence.
-- **Excluded**: Obvious candidates that are not used, with rationale.
-
-A MD/PM-grade model explains both inclusion and exclusion.
-
-## Screen-grade fallback when data is missing
-
-If market data, consensus estimates, EV bridge inputs, workbook dependencies, or uploaded source files are unavailable, do not return an empty failure. Produce a labeled fallback artifact instead.
-
-Lead every fallback output with:
-
-`Comps posture: screen-grade; missing market data and/or estimates are explicitly labeled.`
-
-The fallback artifact should include:
-
-- Target row and proposed peer candidates.
-- Peer tier, peer role, and inclusion/exclusion logic.
-- Missing market-data caveat.
-- Required source fields for price, shares, EV bridge, LTM denominators, NTM estimates, and peer rationale.
-- A statement that no selected valuation range, premium/discount conclusion, or decision-grade multiple is available until sources are populated.
-
-Use `scripts/materialize_screening_comps.py` for this deterministic fallback. It writes `peer_framework.csv`, `missing_data_requests.csv`, `source_requirements.csv`, `screen_grade_comps_support_note.md`, `run_log.json`, and `manifest.json` without requiring workbook dependencies.
-It also writes `screen_grade_comps_framework.xlsx` as the first-read artifact with `Cover`, `Peer Framework`, `Missing Data Requests`, and `Source Requirements` tabs. The CSV, Markdown, JSON, log, and manifest files are hidden support artifacts unless explicitly requested.
-
-## Multiple selection
-
-Choose multiples that match the economic model:
-
-### Enterprise-value multiples
-
-Use when comparing total business value independent of capital structure:
-
-- EV/Revenue: useful for early-stage, high-growth, or low/negative-profitability businesses; must be paired with growth and margin context.
-- EV/EBITDA: common for operating businesses and capital-intensive sectors, but check lease, SBC, capex, and working-capital differences.
-- EV/EBIT: useful when D&A/capex intensity matters and EBITDA overstates cash economics.
-- EV/FCF or FCF yield: useful when FCF conversion is central; check working capital, capex timing, and taxes.
-
-### Equity-value multiples
-
-Use when value is best assessed at the common-equity level:
-
-- P/E: appropriate when earnings are positive and comparable; sensitive to leverage, tax, non-recurring items, and accounting noise.
-- P/B or P/TBV: common for banks, insurers, and asset-heavy financials; interpret with ROE/ROTCE and capital quality.
-- Dividend yield: relevant for yield-oriented assets, utilities, REITs, and mature companies.
-
-### Sector-specific multiples
-
-Use sector metrics when they are the market's true shorthand:
-
-- SaaS/software: EV/Revenue, EV/ARR, Rule of 40, NRR/GRR, FCF margin, growth-adjusted revenue multiple.
-- Internet/marketplaces: EV/Revenue, take rate, GMV, contribution margin, cohort retention, CAC/LTV.
-- Media/telecom: EV/EBITDA, subscriber metrics, ARPU, churn, spectrum or infrastructure economics.
-- Retail/restaurants: EV/EBITDA, same-store sales, unit growth, AUV, store margins, lease-adjusted leverage.
-- Industrials: EV/EBITDA, EV/EBIT, backlog, book-to-bill, capex intensity, cycle exposure.
-- Energy/materials: EV/EBITDA, EV/production, reserves/resources, sustaining capex, commodity sensitivity.
-- Healthcare services: EV/EBITDA, reimbursement risk, same-store/volume growth, payor mix.
-- Biotech/pre-revenue: cash runway, stage-adjusted pipeline value, EV/pipeline asset; revenue/EBITDA metrics may be meaningless.
-- Banks: P/TBV, P/B, ROE/ROTCE, NIM, CET1, credit quality; generally avoid standard EV multiples.
-- Insurers: P/B, P/TBV, ROE, combined ratio, reserve adequacy.
-- Asset managers: P/AUM, EV/fee-related earnings, net flows, fee rate, performance fees.
-- REITs: P/AFFO, EV/EBITDA, cap rates, NOI growth, leverage, occupancy.
-
-## Handling negative or distorted denominators
-
-- Negative EBITDA, EBIT, EPS, or FCF should generally be shown as `NM`.
-- Near-zero denominators can create false outliers; exclude with rationale or use a different metric.
-- For cyclical sectors, use normalized mid-cycle earnings or cycle-adjusted metrics.
-- For one-time disruptions, isolate non-recurring effects and show both reported and adjusted outputs if useful.
-- Do not force a multiple just because a template has the row.
-
-## Premium and discount logic
-
-The selected multiple should reflect the target's relative fundamentals:
-
-Reasons for premium:
-
-- Higher sustainable growth.
-- Better margins or margin expansion runway.
-- Higher ROIC or FCF conversion.
-- More recurring revenue, better retention, stronger pricing power.
-- Stronger balance sheet or lower risk.
-- Scarcity value, strategic asset quality, or superior liquidity.
-
-Reasons for discount:
-
-- Lower growth or weaker visibility.
-- Lower margins, poor FCF conversion, or high capital intensity.
-- Higher leverage, customer concentration, regulatory risk, cyclicality, or commodity exposure.
-- Smaller scale or lower liquidity.
-- Weaker management credibility or poor execution history.
-
-Always connect the premium/discount to evidence in the workbook.
-
-## Statistics and outliers
-
-Calculate statistics, but do not outsource judgment to them:
-
-- Use medians more than means when distributions are skewed.
-- Use 25th/75th percentiles for valuation ranges.
-- Use peer-tier medians and all-peer medians separately.
-- Consider harmonic mean for some ratio central-tendency situations, but explain if used.
-- Exclude outliers only with documented rationale.
-- Keep excluded outliers visible where helpful; do not silently delete inconvenient data.
-
-## Valuation range selection
-
-A strong valuation range should be:
-
-- Narrow enough to be decision-useful.
-- Broad enough to reflect uncertainty.
-- Anchored in Core peer evidence.
-- Adjusted for growth, margins, returns, leverage, business quality, and data confidence.
-- Supported by a sensitivity table.
-- Explained in a narrative that a skeptical IC or board member could challenge.
-
-## Private-company considerations
-
-If valuing a private company:
-
-- Use public comps for market benchmarks but consider illiquidity, size, governance, and information asymmetry.
-- Adjust for control/minority basis depending on use case.
-- Check whether the target's metrics are audited, reviewed, or management-provided.
-- Be explicit about marketability discount, control premium, or transaction-context adjustments if used.
-
-## Common mistakes
-
-- Using broad industry peers without business-model similarity.
-- Mixing reported and adjusted EBITDA.
-- Mixing fiscal-year and calendar-year periods.
-- Ignoring leverage when using equity multiples.
-- Ignoring capex when using EBITDA in capital-intensive businesses.
-- Treating vendor EV as correct without checking share count, cash, debt, converts, preferred, or minority interest.
-- Hiding negative multiples or replacing them with zero.
-- Using stale prices with fresh financials or vice versa without disclosure.
-- Stating a valuation range without explaining why the target deserves it.
-
-## Equity PM Multiple Bridge
-
-The workbook should expose a selected-multiple bridge from peer median to selected value. Required bridge factors: growth, margin, ROIC/quality, FCF conversion, leverage, liquidity/float, cyclicality, index/ETF ownership, short interest/borrow, governance, and data confidence. Route debt comps, bond comps, loan comps, CDS, spread/yield relative value, recovery waterfalls, and credit-security valuation to Credit Markets.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/dashboard-map.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/dashboard-map.md
deleted file mode 100644
index d6be6f5..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/dashboard-map.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Dashboard Map: Comps Model Builder
-
-Use `dashboard-builder` for workbook-backed comps dashboards when valuation ranges, peer selection, outlier handling, or source posture need a reusable investor view.
-
-## Decision Question
-
-What peer-based valuation range is supportable, which peers drive it, and what data quality or comparability issues affect confidence?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `comps_model_builder`
-- `metadata.citation_policy`: `strict`
-- `model_citations_path`: include when workbook cells or audit outputs are cited
-- `sources`: market data, filings, consensus/vendor snapshot, source notes, and workbook audit records
-
-## Recommended Tabs And Modules
-
-1. Valuation Snapshot: `decision_box`, `metric_tiles`.
-2. Peer Universe: `table`, `cards`.
-3. Multiples And Range: `table`, `bar_chart`, `scenario_map`.
-4. Outliers And Denominators: `table`, `missing_evidence`.
-5. Source And QA Posture: `source_list`, `table`.
-
-## Required Evidence
-
-Every peer metric, market price, share count, net debt bridge, and selected multiple should cite source IDs or workbook cell records.
-
-## Do Not
-
-- Do not smooth over peer exclusions without a rejection log.
-- Do not use stale market/consensus data without visible labels.
-- Do not lead with raw workbook-audit JSON or CSV.
-
-
-## Equity Comps Model Dashboard Modules
-
-Dashboards should surface PM decision box, current price versus implied value, selected-multiple bridge, peer quality score, liquidity/float and ETF/index exposure, source freshness, scenario skew, action rules, and missing evidence.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/data-sourcing-and-connectors.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/data-sourcing-and-connectors.md
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--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/data-sourcing-and-connectors.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# Data Sourcing and Connector Playbook
-
-Use this reference when gathering market data, financials, peer information, or source support for a comparable company analysis.
-
-## Source hierarchy
-
-Prioritize sources in this order:
-
-1. **User-provided model and files**: Treat the current workbook and uploaded files as the starting record. Preserve the user's work product and identify where it is incomplete or stale.
-2. **Callable connected routes or exports**: Search Drive, Sheets, Slack, email, Notion, data-room files, internal research, and prior models only when the runtime exposes a scoped route or the user provides an export.
-3. **Licensed market-data routes or exports**: Use sources such as FactSet, Capital IQ, Bloomberg, Refinitiv, PitchBook, Visible Alpha, AlphaSense, Koyfin, or BamSEC only through a callable runtime route or a user-provided export.
-4. **Primary issuer/regulatory sources**: SEC EDGAR, company filings, earnings releases, investor presentations, exchange filings, prospectuses, and annual reports.
-5. **High-quality secondary sources**: Recognized financial data vendors, exchange websites, industry associations, and reputable news sources.
-6. **General search**: Use web search only after trying better sources, or when triangulating gaps. Clearly mark lower-confidence data.
-
-## Connector strategy
-
-### Existing workbook or sheet
-
-Inspect before modifying:
-
-- Sheet names, hidden sheets, named ranges, formulas, external links, comments, and data validation.
-- Last-updated dates, source references, and valuation date.
-- Whether the workbook is a trading comps model, precedent transaction model, hybrid valuation pack, or simple screen.
-- Whether formulas are consistent across peer rows.
-
-Preserve working formulas. Do not rebuild from scratch unless the structure is unusable or the user asks for a clean rebuild.
-
-### Internal documents
-
-Look for:
-
-- Prior comps decks and valuation memos.
-- Industry landscapes, market maps, and peer lists.
-- Investment committee memos, board materials, or diligence notes.
-- Management guidance, KPI definitions, or operating metrics.
-- Analyst notes and prior model assumptions.
-
-Use internal documents for context, peer rationale, KPI definitions, and judgment. Use fresh market data for current prices and market values unless the task is historical.
-
-### Market-data systems
-
-If available, prefer market-data systems for:
-
-- Market price, market cap, shares outstanding, diluted shares, net debt, preferred stock, minority interest, and EV.
-- Consensus estimates for revenue, EBITDA, EBIT, EPS, FCF, KPIs, and fiscal year calendars.
-- Broker consensus and revision history.
-- Screening peer candidates by industry, geography, size, growth, margins, and valuation.
-
-Even with a trusted vendor, check definitions. Vendor EBITDA, adjusted EPS, and FCF definitions can vary materially.
-
-### SEC/company filings
-
-Use primary filings for:
-
-- Reported financials and historical periods.
-- Segment revenue, backlog, customer concentration, risk factors, and accounting policies.
-- Share count, debt, cash, leases, converts, preferred stock, and non-controlling interests.
-- Non-GAAP reconciliations and management adjustments.
-
-For US public companies, SEC company facts and filings can support structured extraction. Still verify XBRL tags and units, because company-specific extensions and context differences can distort comparability.
-
-## Required source log fields
-
-Every sourced metric should be traceable. Use a `Sources` tab or table with:
-
-- Company or ticker.
-- Metric.
-- Period.
-- Source name.
-- Connector path, document name, filing accession, or URL.
-- Retrieval date.
-- Data date or filing date.
-- Source type: reported, adjusted, consensus, management guidance, model estimate, or analyst estimate.
-- Notes on definition and normalization.
-
-## Data confidence labels
-
-Use these labels in the workbook or QA log:
-
-- **High**: Primary filing, company release, or market-data system with definition checked.
-- **Medium**: Reputable secondary source or vendor data with plausible but unverified definition.
-- **Low**: Web search, unsourced model input, stale deck, or unclear definition.
-- **Do not use without review**: Blog/forum/social media data, AI-generated summaries without source support, values with inconsistent units or periods.
-
-## Freshness rules
-
-- Market prices and EV inputs should be as of the valuation date.
-- Consensus estimates should state the retrieval date and consensus period.
-- Historical reported data should tie to filings or earnings releases.
-- If market data and financials are from different dates, disclose the mismatch.
-- If the workbook is being refreshed, do not let stale source dates remain in formulas, comments, or outputs.
-
-## Triangulation rules
-
-When a metric matters to the conclusion, triangulate if possible:
-
-- Compare vendor EV to a manual EV bridge.
-- Compare adjusted EBITDA from vendor to company non-GAAP reconciliation.
-- Compare consensus estimates to investor presentation guidance and recent earnings transcript guidance.
-- Compare peer inclusion against business description, segment mix, and KPI profile, not only industry code.
-
-## Web fallback rules
-
-When using web search:
-
-- Prefer primary company and regulator pages.
-- Use reputable financial data providers only for preliminary values unless definitions are clear.
-- Cite sources in the written summary and record them in the `Sources` tab.
-- Use search to identify filings, transcripts, investor presentations, and peer candidates, not to blindly collect model-critical numbers.
-- Flag all unsourced or lower-confidence numbers in the QA log.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/model-workbook-spec.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/model-workbook-spec.md
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--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/model-workbook-spec.md
+++ /dev/null
@@ -1,253 +0,0 @@
-# Model Workbook Specification
-
-Use this reference when creating, restructuring, or reviewing the Excel/Sheets workbook.
-
-## Workbook principles
-
-- Keep raw data separate from normalized outputs.
-- Use a single `Control` tab for valuation date, currency, source hierarchy, fiscal basis, and scenario selections.
-- Avoid hardcoded values inside formulas. Place assumptions in clearly marked input cells.
-- Every output should trace back to source data, assumptions, and formulas.
-- Use consistent row order across data tabs so peer formulas can be audited quickly.
-- Build for refreshability: formulas should copy across peer rows and update from clearly defined inputs.
-
-## Recommended tabs
-
-### Cover
-
-First visible workbook tab. Required by `../../../../shared/workbook-artifact-standard.md` for generated XLSX artifacts.
-
-Include:
-
-- target company, ticker/security, valuation date, currency, units, workbook mode, and model status;
-- target snapshot and peer-universe counts;
-- selected multiple, low/mid/high implied value per share, and chart-ready valuation range;
-- peer median/quartile tables and fit/outlier indicators where populated;
-- source count, source gaps, stale-market-data flags, and confidence labels;
-- QA status, denominator review flags, leverage/calendar comparability flags, and unresolved checks;
-- workbook map for `Control`, `Universe`, `Market_Data`, `Financials`, `Multiples`, `Benchmarking`, `Valuation`, `Sensitivity`, `Sources`, and `QA_Log`.
-
-Native charts are encouraged when using XlsxWriter. At minimum, include chart-ready cover rows for implied value/share range and primary trading multiple distribution.
-
-### README / Model Guide
-
-Include:
-
-- Model purpose.
-- Target company.
-- Valuation date.
-- Currency and units.
-- Version/date prepared.
-- Major limitations.
-- Update steps.
-
-### Control
-
-Minimum fields:
-
-- Target company/ticker.
-- Valuation date.
-- Reporting currency.
-- Output currency.
-- FX source/date if applicable.
-- Fiscal year-end.
-- LTM period definition.
-- Forward estimate periods.
-- Source priority.
-- Scenario selection.
-- QA status.
-
-### Universe
-
-Recommended columns:
-
-- Ticker.
-- Company name.
-- Exchange/country.
-- Peer tier: Target, Core, Secondary, Adjacent, Watchlist, Excluded.
-- Business description.
-- Segment/revenue mix.
-- Geography.
-- Customer/end-market mix.
-- Size relevance.
-- Growth relevance.
-- Margin relevance.
-- Inclusion rationale.
-- Exclusion rationale.
-- Analyst notes.
-
-### Market_Data
-
-Recommended columns:
-
-- Ticker.
-- Price.
-- Valuation date.
-- Basic shares.
-- Diluted shares.
-- Options/RSUs/converts adjustment.
-- Market capitalization.
-- Total debt.
-- Lease debt treatment.
-- Preferred stock.
-- Minority interest.
-- Cash and short-term investments.
-- Enterprise value.
-- Source.
-- Source confidence.
-
-Enterprise value bridge:
-
-`Equity Value = Share Price * Diluted Shares`
-
-`Enterprise Value = Equity Value + Debt + Preferred Stock + Minority Interest + Other Claims - Cash and Cash Equivalents - Non-operating Investments`
-
-Adapt for sector-specific conventions. For banks and insurers, avoid standard EV metrics unless specifically justified.
-
-### Financials
-
-Recommended columns by period:
-
-- Revenue.
-- Gross profit.
-- EBITDA.
-- EBIT.
-- Net income.
-- EPS.
-- Operating cash flow.
-- Capital expenditure.
-- Free cash flow.
-- Sector KPIs.
-- Source and definition.
-
-Keep reported, adjusted, and normalized figures in separate rows or columns. Do not blend them silently.
-
-### Adjustments
-
-Include:
-
-- Calendarization adjustments.
-- FX translation.
-- Non-recurring items.
-- Stock-based compensation treatment.
-- M&A/pro forma adjustments.
-- Discontinued operations.
-- Lease accounting adjustments.
-- Segment or carve-out adjustments.
-- Consensus definition adjustments.
-
-Every adjustment should include a source, rationale, amount, period, and whether it affects all peers or only one company.
-
-### Multiples
-
-Recommended outputs:
-
-- EV / Revenue: LTM, CY0, CY1, CY2.
-- EV / EBITDA: LTM, CY0, CY1, CY2.
-- EV / EBIT: LTM, CY0, CY1, CY2.
-- P / E: LTM, CY0, CY1, CY2.
-- FCF yield or P / FCF.
-- Sector-specific metrics.
-- Peer statistics: median, mean, harmonic mean where useful, 25th/75th percentile, min/max, core-peer median.
-- Flags for negative, zero, not meaningful, or outlier denominators.
-
-Do not calculate a misleading multiple when the denominator is negative, near zero, not comparable, or not meaningful. Use `NM` and explain.
-
-### Benchmarking
-
-Recommended categories:
-
-- Size: revenue, market cap, EV.
-- Growth: revenue CAGR, EBITDA CAGR, KPI growth.
-- Profitability: gross margin, EBITDA margin, EBIT margin, FCF margin, ROIC.
-- Capital intensity: capex/revenue, working capital, leverage.
-- Quality: recurring revenue, retention, pricing power, customer concentration, cyclicality, regulation, asset intensity.
-- Valuation premium/discount rationale.
-
-### Valuation
-
-Include:
-
-- Selected peer set and selected multiple range.
-- Target metric used for valuation.
-- Implied enterprise value.
-- Net debt and other claims.
-- Implied equity value.
-- Diluted shares.
-- Implied value per share.
-- Scenario outputs.
-- Conclusion narrative.
-
-### Sensitivity
-
-Include at least one two-way sensitivity table:
-
-- Selected multiple range vs target financial metric.
-- Selected multiple range vs net debt or diluted share count.
-- Growth/margin scenario vs selected multiple where appropriate.
-
-### Sources
-
-Each key source should include:
-
-- Company/ticker.
-- Metric.
-- Period.
-- Source name.
-- Document or connector path.
-- URL/accession if applicable.
-- Retrieval date.
-- Data date.
-- Confidence level.
-- Notes.
-
-### QA_Log
-
-Include:
-
-- Check name.
-- Status: Pass, Fail, Warning, Not run.
-- Finding.
-- Fix/action.
-- Owner or next step.
-- Date completed.
-
-## Excel formula rules
-
-- Use formulas for calculated fields whenever possible.
-- Keep assumption cells separate and clearly marked.
-- Use `IFERROR` sparingly; do not hide real issues. Prefer explicit denominator checks.
-- Use `NM` or blank with flags for not-meaningful multiples.
-- For peer statistics, calculate both all-peer and core-peer statistics.
-- Use consistent rows and columns across tabs to allow formula copy-down.
-- Avoid volatile formulas unless necessary.
-- Avoid external links in final deliverables unless explicitly requested.
-
-## Formatting conventions
-
-Use a consistent convention, adapted to the user's template if one exists:
-
-- Inputs: clearly shaded or marked.
-- Formulas: standard model style.
-- Linked cells: visually distinct from local formulas.
-- Outputs: clearly highlighted and easy to find.
-- Hardcodes in output tabs: avoided unless labeled as assumptions.
-- Negative values and `NM`: visible and not hidden by formatting.
-
-## Formula QA checks
-
-Check for:
-
-- Broken formulas: `#REF!`, `#VALUE!`, `#DIV/0!`, `#N/A`.
-- Inconsistent formulas across peer rows.
-- Hidden hardcodes in formulas or output ranges.
-- Circular references.
-- External links.
-- Wrong signs for cash, debt, minority interest, and non-operating assets.
-- Currency/unit mismatches.
-- LTM and NTM mismatches.
-- Outliers included without rationale.
-
-## Public Equity PM Workbook Additions
-
-Include `Multiple_Bridge` and `PM_Action_Box` tabs or equivalent sections. The Cover should show current price, implied value/share range, upside/downside to spot, selected multiple rationale, peer-set confidence, what is priced in, PM action implication, and unresolved data gaps.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/qa-and-pressure-testing.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/qa-and-pressure-testing.md
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--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/qa-and-pressure-testing.md
+++ /dev/null
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-# QA and Pressure Testing Rubric
-
-Use this reference for MD/PM-grade review of a comps model or analysis.
-
-## Overall standard
-
-A strong comps model must be accurate, transparent, dynamic, and defensible. It should let a senior decision-maker understand:
-
-- What peer evidence matters.
-- Which data is trusted and why.
-- How the model handles exceptions.
-- Why the selected valuation range is appropriate.
-- What would change the conclusion.
-
-## Red-team questions
-
-### Investment logic
-
-- What is the actual decision this model supports?
-- Does the selected peer set answer that decision, or just fill a template?
-- What are the top three drivers of the target's multiple?
-- Does the target deserve a premium, discount, or in-line valuation?
-- Is the selected range driven by Core peers or diluted by weak comparables?
-
-### Peer universe
-
-- Are Core peers similar in business model, not just industry classification?
-- Are obvious peers missing?
-- Are any included peers too large, too small, too geographically different, or too diversified?
-- Are excluded peers documented with rationale?
-- Are private or acquired companies being mixed with trading comps without context?
-
-### Data and sources
-
-- Are market prices current as of the valuation date?
-- Are source dates documented?
-- Are reported, adjusted, consensus, and internally estimated figures separated?
-- Are data definitions consistent across companies?
-- Are units, currencies, and fiscal periods consistent?
-- Are key figures triangulated against primary sources or trusted vendor systems?
-
-### Normalization
-
-- Are LTM and forward periods correctly defined?
-- Is calendarization needed and documented?
-- Are non-recurring items treated consistently?
-- Are lease accounting, SBC, M&A, discontinued operations, and FX considered where material?
-- Are negative or immaterial denominators handled as not meaningful rather than forced into ratios?
-
-### Formula integrity
-
-- Are formulas consistent across peer rows?
-- Are calculated outputs formula-driven rather than hardcoded?
-- Are there broken links, external references, or circular formulas?
-- Are signs correct for cash, debt, minority interest, preferred stock, and other claims?
-- Are formulas using the intended period and metric?
-- Are hidden rows/columns, filters, or grouped sections concealing issues?
-
-### Valuation conclusion
-
-- Is the selected multiple range supported by Core peer statistics and qualitative judgment?
-- Does the range reflect growth, margin, quality, leverage, and risk differences?
-- Are sensitivities robust enough for senior review?
-- Does the written conclusion include confidence level and open diligence items?
-- Could a skeptical PM or board member understand and challenge the reasoning?
-
-## Quality gates
-
-Use these gates before final delivery:
-
-1. **Structure gate**: Workbook includes clear tabs for inputs, data, normalization, multiples, valuation, sources, and QA.
-2. **Source gate**: Every key metric has a source, retrieval date, and confidence label.
-3. **Formula gate**: No unexplained broken formulas, hardcoded outputs, or inconsistent peer-row formulas.
-4. **Comparability gate**: Peer tiers and inclusion/exclusion rationale are documented.
-5. **Normalization gate**: LTM/NTM, currency, fiscal-year, and adjustment logic are explicit.
-6. **Statistics gate**: Median, percentile, and peer-tier views are shown; outliers are explained.
-7. **Judgment gate**: Selected valuation range is not a blind mean; it reflects business quality and risk.
-8. **Communication gate**: Executive summary is decision-oriented and highlights uncertainty.
-
-## Red flags requiring escalation
-
-- User asks for a fairness, tax, legal, solvency, or formal valuation opinion without appropriate caveats.
-- Public-company data conflicts materially across sources and cannot be reconciled.
-- Target has no meaningful public comps and the conclusion relies on weak analogies.
-- A model has formula errors in final output cells.
-- Stale market data is used for a current valuation.
-- A selected valuation range is materially inconsistent with Core peer evidence and no rationale is provided.
-- The target has negative or volatile financials and the model still uses simple EV/EBITDA or P/E without adjustment.
-
-## Review memo template
-
-Use this structure for a pressure-test memo:
-
-### Bottom line
-
-State whether the model is ready for senior review, needs targeted fixes, or requires a rebuild.
-
-### Highest-priority issues
-
-List the 3-7 issues that most affect valuation conclusion or credibility.
-
-### Peer universe judgment
-
-Assess Core peer quality, missing peers, and exclusions.
-
-### Data and formula integrity
-
-Summarize source freshness, data confidence, formula issues, hardcodes, and link risks.
-
-### Valuation judgment
-
-Assess selected range, premium/discount logic, outlier treatment, and sensitivity quality.
-
-### Recommended fixes
-
-Give specific actions in priority order.
-
-### Residual risk
-
-State what uncertainty remains after fixes.
diff --git a/plugins/financial-markets/skills/comps-valuation/references/workbook/review-memo-template.md b/plugins/financial-markets/skills/comps-valuation/references/workbook/review-memo-template.md
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index db197ba..0000000
--- a/plugins/financial-markets/skills/comps-valuation/references/workbook/review-memo-template.md
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-# Output and Review Memo Templates
-
-Use these templates when delivering written summaries, workbook notes, or pressure-test outputs.
-
-## Build or update summary
-
-### Executive conclusion
-
-- Target:
-- Valuation date:
-- Selected valuation method:
-- Selected multiple range:
-- Implied EV / equity value / per-share range:
-- Confidence level:
-- Bottom-line judgment:
-
-### Peer universe
-
-- Core peers:
-- Secondary peers:
-- Adjacent/context peers:
-- Excluded obvious peers and rationale:
-- Main comparability caveats:
-
-### Key assumptions and data sources
-
-- Source hierarchy used:
-- Market data date:
-- Financial/consensus data date:
-- Currency and units:
-- LTM/NTM definitions:
-- Major normalizations:
-- Source gaps:
-
-### Valuation outputs
-
-- Peer median and percentile range:
-- Selected range rationale:
-- Implied valuation range:
-- Sensitivities:
-- Premium/discount rationale:
-
-### Risks and diligence gaps
-
-- Data risks:
-- Peer risks:
-- Business-model risks:
-- Accounting/normalization risks:
-- Market/regime risks:
-
-### QA status
-
-- Checks completed:
-- Issues fixed:
-- Unresolved warnings:
-- Recommended next steps:
-- Deterministic artifact status, if scripts were used:
-- Run log:
-- Output manifest:
-
-## Pressure-test summary
-
-### Bottom line
-
-State whether the analysis is: ready for senior review, directionally usable with fixes, or not defensible.
-
-### Critical issues
-
-Rank issues by impact on valuation and credibility.
-
-### Peer-set critique
-
-Challenge weak peers, missing peers, and unjustified exclusions.
-
-### Data/model critique
-
-Discuss stale data, inconsistent definitions, hardcodes, formula issues, and missing source support.
-
-### Judgment critique
-
-Assess whether the selected multiple range is supported by fundamentals, not just peer averages.
-
-### Fix plan
-
-List concrete changes by priority:
-
-1. Must fix before use.
-2. Should fix for better quality.
-3. Nice to have.
-
-## Language examples
-
-Use precise senior-review language:
-
-- "The model is directionally useful, but not yet IC-ready because the Core peer set includes two companies with materially different revenue models and the selected EV/revenue range does not adjust for the target's lower FCF conversion."
-- "The target merits a discount to the Core median despite similar growth because retention, gross margin, and capital intensity are weaker than the peer group."
-- "The valuation range is too wide to be decision-useful; tighten it around Core peer percentiles and support any premium with specific fundamentals."
-- "EV/EBITDA is not the right primary multiple here because EBITDA is negative and the market is valuing the group on growth-adjusted revenue and FCF conversion."
diff --git a/plugins/financial-markets/skills/comps-valuation/scripts/audit_comps_workbook.py b/plugins/financial-markets/skills/comps-valuation/scripts/audit_comps_workbook.py
deleted file mode 100755
index 4950a31..0000000
--- a/plugins/financial-markets/skills/comps-valuation/scripts/audit_comps_workbook.py
+++ /dev/null
@@ -1,491 +0,0 @@
-#!/usr/bin/env python3
-"""Audit a comparable company analysis workbook for structural and formula issues.
-
-Usage:
-    python audit_comps_workbook.py model.xlsx --json-out audit.json --markdown-out audit.md
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-from collections import Counter, defaultdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-EXPECTED_SHEET_GROUPS = {
-    "control": ["control", "input", "assumption"],
-    "universe": ["universe", "peer", "comp"],
-    "market_data": ["market", "price", "ev", "capitalization"],
-    "financials": ["financial", "statement", "estimate", "consensus"],
-    "adjustments": ["adjust", "normal", "calendar"],
-    "multiples": ["multiple", "valuation metric"],
-    "benchmarking": ["benchmark", "kpi", "operating"],
-    "valuation": ["valuation", "output", "summary"],
-    "sources": ["source", "citation", "footnote"],
-    "qa": ["qa", "check", "audit"],
-}
-
-ERROR_PATTERNS = ("#REF!", "#DIV/0!", "#VALUE!", "#NAME?", "#NUM!", "#N/A")
-SOURCE_TERMS = (
-    "source",
-    "retrieval",
-    "as of",
-    "data date",
-    "valuation date",
-    "filing",
-    "accession",
-    "url",
-)
-
-
-def normalize(name: str) -> str:
-    return re.sub(r"[^a-z0-9]+", " ", name.lower()).strip()
-
-
-def sheet_matches(sheet_name: str, terms: list[str]) -> bool:
-    normalized = normalize(sheet_name)
-    return any(term in normalized for term in terms)
-
-
-def find_sheet_groups(sheet_names: list[str]) -> dict[str, list[str]]:
-    matches: dict[str, list[str]] = {}
-    for group, terms in EXPECTED_SHEET_GROUPS.items():
-        matches[group] = [name for name in sheet_names if sheet_matches(name, terms)]
-    return matches
-
-
-def used_cells(ws) -> list[Any]:
-    return [cell for row in ws.iter_rows() for cell in row if cell.value not in (None, "")]
-
-
-def audit_workbook(path: Path) -> dict[str, Any]:
-    try:
-        from openpyxl import load_workbook
-    except ImportError as exc:  # pragma: no cover
-        raise SystemExit("openpyxl is required. Install with: pip install openpyxl") from exc
-
-    wb = load_workbook(path, data_only=False, read_only=False)
-    sheet_names = wb.sheetnames
-    groups = find_sheet_groups(sheet_names)
-
-    findings: list[dict[str, str]] = []
-    metrics: dict[str, Any] = {
-        "workbook": str(path),
-        "audited_at": datetime.utcnow().isoformat(timespec="seconds") + "Z",
-        "sheet_count": len(sheet_names),
-        "sheets": sheet_names,
-        "matched_sheet_groups": groups,
-    }
-
-    missing_groups = [group for group, names in groups.items() if not names]
-    if missing_groups:
-        findings.append(
-            {
-                "severity": "warning",
-                "category": "structure",
-                "finding": "Missing or unclear expected sheet groups: " + ", ".join(missing_groups),
-                "recommendation": "Add or clearly label tabs for these functions, or document where they are handled.",
-            }
-        )
-
-    formula_counts = Counter()
-    hardcode_counts = Counter()
-    error_cells: list[str] = []
-    external_formula_cells: list[str] = []
-    formula_by_sheet: dict[str, int] = {}
-    numeric_by_sheet = Counter()
-    comments_by_sheet: dict[str, int] = {}
-    source_term_hits: list[str] = []
-
-    ticker_values: list[str] = []
-    ticker_headers_seen = False
-
-    for ws in wb.worksheets:
-        cells = used_cells(ws)
-        formula_count = 0
-        numeric_count = 0
-        comments_count = 0
-        for cell in cells:
-            value = cell.value
-            coord = f"{ws.title}!{cell.coordinate}"
-            if cell.comment:
-                comments_count += 1
-            if isinstance(value, str):
-                lower = value.lower()
-                if any(term in lower for term in SOURCE_TERMS):
-                    source_term_hits.append(coord)
-                if value.startswith("="):
-                    formula_count += 1
-                    formula_counts[ws.title] += 1
-                    if any(pattern in value.upper() for pattern in ERROR_PATTERNS):
-                        error_cells.append(coord)
-                    if "[" in value and "]" in value:
-                        external_formula_cells.append(coord)
-                elif any(pattern in value.upper() for pattern in ERROR_PATTERNS):
-                    error_cells.append(coord)
-            elif isinstance(value, (int, float)):
-                numeric_count += 1
-                numeric_by_sheet[ws.title] += 1
-                if (
-                    any(
-                        key in normalize(ws.title)
-                        for key in ("valuation", "output", "summary", "multiple")
-                    )
-                    and cell.row > 3
-                ):
-                    hardcode_counts[ws.title] += 1
-            if isinstance(value, str) and normalize(value) in ("ticker", "tickers"):
-                ticker_headers_seen = True
-                # collect values under this header in the same column
-                for r in range(cell.row + 1, min(ws.max_row, cell.row + 60) + 1):
-                    maybe = ws.cell(r, cell.column).value
-                    if isinstance(maybe, str) and maybe.strip() and not maybe.startswith("="):
-                        ticker_values.append(maybe.strip().upper())
-        formula_by_sheet[ws.title] = formula_count
-        numeric_by_sheet[ws.title] = numeric_count
-        comments_by_sheet[ws.title] = comments_count
-
-        if any(key in normalize(ws.title) for key in ("valuation", "output", "summary")):
-            if formula_count == 0 and numeric_count > 5:
-                findings.append(
-                    {
-                        "severity": "warning",
-                        "category": "formula_integrity",
-                        "finding": f"{ws.title} has numeric values but no formulas detected.",
-                        "recommendation": "Review whether valuation outputs are hardcoded instead of linked to assumptions and data tabs.",
-                    }
-                )
-
-    metrics["formula_counts_by_sheet"] = formula_by_sheet
-    metrics["numeric_counts_by_sheet"] = dict(numeric_by_sheet)
-    metrics["comments_by_sheet"] = comments_by_sheet
-    metrics["external_formula_cells"] = external_formula_cells[:100]
-    metrics["error_cells"] = error_cells[:100]
-    metrics["hardcode_counts_on_output_like_sheets"] = dict(hardcode_counts)
-
-    if error_cells:
-        findings.append(
-            {
-                "severity": "critical",
-                "category": "formula_integrity",
-                "finding": f"Detected formula or cell error patterns in {len(error_cells)} cells.",
-                "recommendation": "Inspect and fix error cells before senior review. Do not rely on affected outputs.",
-            }
-        )
-
-    if external_formula_cells:
-        findings.append(
-            {
-                "severity": "warning",
-                "category": "formula_integrity",
-                "finding": f"Detected external workbook references in {len(external_formula_cells)} formula cells.",
-                "recommendation": "Break, document, or preserve external links intentionally. Final deliverables should not depend on hidden external files unless requested.",
-            }
-        )
-
-    if hardcode_counts:
-        findings.append(
-            {
-                "severity": "warning",
-                "category": "formula_integrity",
-                "finding": "Output-like sheets contain numeric hardcodes: "
-                + ", ".join(f"{k}={v}" for k, v in hardcode_counts.items()),
-                "recommendation": "Confirm these are labeled assumptions. Replace hidden hardcoded outputs with formulas where possible.",
-            }
-        )
-
-    if not source_term_hits:
-        findings.append(
-            {
-                "severity": "warning",
-                "category": "sources",
-                "finding": "No obvious source/date terminology detected in workbook cells.",
-                "recommendation": "Add a Sources tab with source name, retrieval date, data date, metric, period, and confidence label.",
-            }
-        )
-
-    if ticker_headers_seen:
-        dupes = [ticker for ticker, count in Counter(ticker_values).items() if count > 1]
-        if dupes:
-            findings.append(
-                {
-                    "severity": "warning",
-                    "category": "peer_universe",
-                    "finding": "Duplicate ticker values detected: " + ", ".join(dupes[:20]),
-                    "recommendation": "Check for duplicate peers, repeated target rows, or accidental copy/paste errors.",
-                }
-            )
-    else:
-        findings.append(
-            {
-                "severity": "info",
-                "category": "peer_universe",
-                "finding": "No explicit ticker header detected.",
-                "recommendation": "Ensure the peer universe has a clear ticker/company identifier column.",
-            }
-        )
-
-    # Check whether source and QA tabs have meaningful comments/logs.
-    comments_total = sum(comments_by_sheet.values())
-    if comments_total == 0:
-        findings.append(
-            {
-                "severity": "info",
-                "category": "documentation",
-                "finding": "No cell comments detected.",
-                "recommendation": "Consider adding comments to major assumptions and sourced inputs for auditability.",
-            }
-        )
-
-    # Scoring: start at 100 and subtract by severity.
-    score = 100
-    penalty = {"critical": 25, "warning": 10, "info": 3}
-    for item in findings:
-        score -= penalty.get(item["severity"], 0)
-    metrics["quality_score_directional"] = max(score, 0)
-
-    return {"metrics": metrics, "findings": findings}
-
-
-def to_markdown(report: dict[str, Any]) -> str:
-    metrics = report["metrics"]
-    findings = report["findings"]
-    by_severity = defaultdict(list)
-    for item in findings:
-        by_severity[item["severity"]].append(item)
-
-    lines = [
-        "# Comps Workbook Audit",
-        "",
-        f"Workbook: `{metrics['workbook']}`",
-        f"Audited at: {metrics['audited_at']}",
-        f"Directional quality score: {metrics['quality_score_directional']}/100",
-        "",
-        "## Sheet coverage",
-        "",
-    ]
-    for group, sheets in metrics["matched_sheet_groups"].items():
-        status = ", ".join(sheets) if sheets else "missing/unclear"
-        lines.append(f"- {group}: {status}")
-    lines.extend(["", "## Findings", ""])
-    if not findings:
-        lines.append(
-            "No issues detected by automated checks. Manual MD/PM review is still required."
-        )
-    else:
-        for severity in ("critical", "warning", "info"):
-            if severity in by_severity:
-                lines.append(f"### {severity.title()}")
-                lines.append("")
-                for item in by_severity[severity]:
-                    lines.append(
-                        f"- **{item['category']}**: {item['finding']} Recommendation: {item['recommendation']}"
-                    )
-                lines.append("")
-    lines.extend(
-        [
-            "## Notes",
-            "",
-            "This automated audit checks structure, formulas, external links, obvious hardcodes, source terminology, comments, and duplicate tickers. It does not replace manual peer selection, normalization, or valuation judgment review.",
-        ]
-    )
-    return "\n".join(lines)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Audit a comps workbook for structural and formula issues."
-    )
-    parser.add_argument("workbook", help="Path to .xlsx workbook.")
-    parser.add_argument("--json-out", help="Optional path for JSON audit output.")
-    parser.add_argument("--markdown-out", help="Optional path for Markdown audit output.")
-    parser.add_argument(
-        "--run-log", help="Optional path for run_log.json. Defaults beside the first file output."
-    )
-    return parser.parse_args()
-
-
-def default_run_log_path(args: argparse.Namespace) -> Path | None:
-    if args.run_log:
-        return Path(args.run_log).resolve()
-    if args.json_out:
-        return Path(args.json_out).resolve().parent / "run_log.json"
-    if args.markdown_out:
-        return Path(args.markdown_out).resolve().parent / "run_log.json"
-    return None
-
-
-def output_paths(args: argparse.Namespace, run_log_path: Path | None) -> dict[str, str]:
-    paths: dict[str, str] = {}
-    if args.json_out:
-        paths["json_report"] = str(Path(args.json_out).resolve())
-    if args.markdown_out:
-        paths["markdown_report"] = str(Path(args.markdown_out).resolve())
-    if run_log_path:
-        paths["run_log"] = str(run_log_path)
-        paths["manifest"] = str(run_log_path.parent / "manifest.json")
-    return paths
-
-
-def output_manifest(paths: dict[str, str]) -> list[dict[str, Any]]:
-    return [
-        {
-            "key": key,
-            "path": artifact_path,
-            "required": key in {"run_log", "manifest"},
-            "written": key in {"run_log", "manifest"} or Path(artifact_path).exists(),
-            "description": "Comps workbook audit artifact.",
-            "artifact_role": "machine_support"
-            if key in {"run_log", "manifest"}
-            else "support_artifact",
-            "hidden_unless_requested": True,
-            "user_visible_default": False,
-            "support_reason": "Audit JSON/Markdown/logs are support artifacts; do not present them as the primary comps deliverable.",
-        }
-        for key, artifact_path in paths.items()
-    ]
-
-
-def write_run_log(
-    run_log_path: Path | None,
-    *,
-    status: str,
-    workbook: Path,
-    outputs: dict[str, str],
-    warnings: list[str],
-    hard_failures: list[str],
-    dependency_status: dict[str, str],
-    audit_report: dict[str, Any] | None = None,
-) -> None:
-    if run_log_path is None:
-        return
-    run_log_path.parent.mkdir(parents=True, exist_ok=True)
-    manifest = output_manifest(outputs)
-    payload = {
-        "status": status,
-        "model_status": "not-decision-ready" if hard_failures else "senior-review-ready",
-        "artifact_level": "audit_export",
-        "workbook_mode": "existing_workbook_audit",
-        "artifact_mode": "support_only",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "workbook": str(workbook),
-        "source_basis": [],
-        "warnings": warnings,
-        "hard_failures": hard_failures,
-        "dependency_status": dependency_status,
-        "outputs": outputs,
-        "primary_human_deliverable": None,
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": {
-            "lead_with": "source_workbook_plus_audit_summary",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-        "output_manifest": manifest,
-        "audit_summary": {
-            "finding_count": len(audit_report.get("findings", [])) if audit_report else 0,
-            "quality_score_directional": audit_report.get("metrics", {}).get(
-                "quality_score_directional"
-            )
-            if audit_report
-            else None,
-        },
-    }
-    run_log_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
-    (run_log_path.parent / "manifest.json").write_text(
-        json.dumps(
-            {
-                "primary_human_deliverable": None,
-                "artifact_mode": "support_only",
-                "support_artifacts_user_visible_default": False,
-                "final_response_guidance": payload["final_response_guidance"],
-                "outputs": manifest,
-            },
-            indent=2,
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-
-def main() -> int:
-    args = parse_args()
-    path = Path(args.workbook).resolve()
-    run_log_path = default_run_log_path(args)
-    outputs = output_paths(args, run_log_path)
-    if not path.exists():
-        failure = f"Workbook not found: {path}"
-        write_run_log(
-            run_log_path,
-            status="failed",
-            workbook=path,
-            outputs=outputs,
-            warnings=[],
-            hard_failures=[failure],
-            dependency_status={"openpyxl": "not_checked"},
-        )
-        print(failure)
-        return 1
-    try:
-        report = audit_workbook(path)
-        if args.json_out:
-            out = Path(args.json_out)
-            out.parent.mkdir(parents=True, exist_ok=True)
-            out.write_text(json.dumps(report, indent=2), encoding="utf-8")
-        if args.markdown_out:
-            out = Path(args.markdown_out)
-            out.parent.mkdir(parents=True, exist_ok=True)
-            out.write_text(to_markdown(report), encoding="utf-8")
-    except SystemExit as exc:
-        failure = str(exc)
-        write_run_log(
-            run_log_path,
-            status="failed",
-            workbook=path,
-            outputs=outputs,
-            warnings=[],
-            hard_failures=[failure],
-            dependency_status={
-                "openpyxl": "missing" if "openpyxl is required" in failure else "unknown"
-            },
-        )
-        print(f"ERROR: {failure}")
-        return 1
-    except Exception as exc:
-        failure = str(exc)
-        write_run_log(
-            run_log_path,
-            status="failed",
-            workbook=path,
-            outputs=outputs,
-            warnings=[],
-            hard_failures=[failure],
-            dependency_status={
-                "openpyxl": "missing" if "openpyxl is required" in failure else "unknown"
-            },
-        )
-        print(f"ERROR: {failure}")
-        return 1
-    warnings = [
-        item["finding"]
-        for item in report.get("findings", [])
-        if item.get("severity") in {"warning", "critical"}
-    ]
-    write_run_log(
-        run_log_path,
-        status="completed",
-        workbook=path,
-        outputs=outputs,
-        warnings=warnings,
-        hard_failures=[],
-        dependency_status={"openpyxl": "available"},
-        audit_report=report,
-    )
-    print(to_markdown(report))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/comps-valuation/scripts/create_comps_template.py b/plugins/financial-markets/skills/comps-valuation/scripts/create_comps_template.py
deleted file mode 100755
index 900d24b..0000000
--- a/plugins/financial-markets/skills/comps-valuation/scripts/create_comps_template.py
+++ /dev/null
@@ -1,1140 +0,0 @@
-#!/usr/bin/env python3
-"""Create a comparable company analysis workbook template.
-
-Usage:
-    python create_comps_template.py --output comps_template.xlsx --target "ExampleCo" --ticker EXM
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-from datetime import date
-from pathlib import Path
-
-PEER_ROWS = 20
-
-
-def clean(value: object) -> str:
-    return "" if value is None else str(value).strip()
-
-
-def pick(row: dict[str, str], *keys: str) -> str:
-    for key in keys:
-        value = clean(row.get(key))
-        if value:
-            return value
-    return ""
-
-
-def load_input_rows(path: Path | None) -> list[dict[str, str]]:
-    if path is None:
-        return []
-    with path.open("r", encoding="utf-8-sig", newline="") as handle:
-        return [
-            {clean(k).lower().replace(" ", "_"): clean(v) for k, v in row.items()}
-            for row in csv.DictReader(handle)
-        ][:PEER_ROWS]
-
-
-def numeric_or_string(ws, row: int, col: int, value: str, fmt) -> None:
-    value = clean(value)
-    if not value:
-        ws.write_blank(row, col, None, fmt)
-        return
-    try:
-        ws.write_number(row, col, float(value.replace(",", "")), fmt)
-    except ValueError:
-        ws.write(row, col, value, fmt)
-
-
-def population_status(rows: list[dict[str, str]]) -> dict[str, object]:
-    if not rows:
-        return {
-            "workbook_mode": "template_shell",
-            "model_status": "template-ready",
-            "status_label": "Template shell",
-            "warning": "Template shell only; populate with sourced market data, financials, and peer rationale before investment use.",
-            "missing_required_fields": [],
-            "populated_peer_count": 0,
-        }
-    required = [
-        "ticker",
-        "company",
-        "peer_tier",
-        "price",
-        "basic_shares",
-        "revenue_ltm",
-        "source_name",
-        "market_data_date",
-    ]
-    missing: list[str] = []
-    for index, row in enumerate(rows, start=1):
-        for field in required:
-            if not pick(row, field):
-                missing.append(f"row_{index}:{field}")
-    return {
-        "workbook_mode": "populated_supplied_data",
-        "model_status": "screen-grade" if missing else "senior-review-ready",
-        "status_label": "Populated supplied data",
-        "warning": "Supplied rows populated; verify live market data, estimate dates, source definitions, peer rationale, and formula tie-out before investment use.",
-        "missing_required_fields": missing,
-        "populated_peer_count": len(rows),
-    }
-
-
-def write_title(ws, title: str) -> None:
-    ws.write("A1", title)
-
-
-def add_table(ws, row: int, col: int, headers: list[str], rows: int, header_fmt, body_fmt) -> None:
-    for idx, header in enumerate(headers):
-        ws.write(row, col + idx, header, header_fmt)
-    for r in range(row + 1, row + rows + 1):
-        for c in range(col, col + len(headers)):
-            ws.write_blank(r, c, None, body_fmt)
-    ws.autofilter(row, col, row + rows, col + len(headers) - 1)
-    ws.freeze_panes(row + 1, 0)
-
-
-def create_workbook(
-    output: Path,
-    target: str,
-    ticker: str,
-    currency: str,
-    valuation_date: str,
-    input_rows: list[dict[str, str]] | None = None,
-) -> dict[str, object]:
-    try:
-        import xlsxwriter
-    except ImportError as exc:  # pragma: no cover
-        raise RuntimeError("XlsxWriter is required. Install with: pip install XlsxWriter") from exc
-
-    wb = xlsxwriter.Workbook(str(output))
-    title_fmt = wb.add_format({"bold": True, "font_size": 16})
-    header_fmt = wb.add_format(
-        {
-            "bold": True,
-            "font_color": "white",
-            "bg_color": "#1F4E78",
-            "border": 1,
-            "text_wrap": True,
-            "align": "center",
-        }
-    )
-    subheader_fmt = wb.add_format({"bold": True, "bg_color": "#D9EAF7", "border": 1})
-    body_fmt = wb.add_format({"border": 1, "text_wrap": True, "valign": "top"})
-    input_fmt = wb.add_format(
-        {"bg_color": "#FFF2CC", "border": 1, "text_wrap": True, "valign": "top"}
-    )
-    formula_fmt = wb.add_format(
-        {"bg_color": "#DDEBF7", "border": 1, "text_wrap": True, "valign": "top"}
-    )
-    output_fmt = wb.add_format(
-        {"bg_color": "#E2F0D9", "border": 1, "text_wrap": True, "valign": "top"}
-    )
-
-    input_rows = input_rows or []
-    status = population_status(input_rows)
-    target_input = input_rows[0] if input_rows else {}
-    current_price = pick(target_input, "price", "share_price", "current_price")
-
-    # Cover
-    cover_ws = wb.add_worksheet("Cover")
-    cover_ws.hide_gridlines(2)
-    cover_ws.write("A1", "Comparable Company Analysis Dashboard Cover", title_fmt)
-    cover_ws.write(
-        "A2",
-        "First-tab review page: populate the workbook, then use this cover to read the target, peer set, valuation range, source posture, and QA status.",
-        body_fmt,
-    )
-    cover_ws.write_row(3, 0, ["Section", "Metric", "Value", "Notes"], header_fmt)
-    cover_rows = [
-        ("Header", "Target company", target, "Subject company"),
-        ("Header", "Ticker", ticker, "Subject ticker/security"),
-        (
-            "Header",
-            "Valuation date",
-            valuation_date,
-            "Market data and sources should tie to this date",
-        ),
-        (
-            "Header",
-            "Currency / units",
-            f"{currency} / USD millions except per-share data",
-            "Update if non-USD or mixed units are used",
-        ),
-        (
-            "Status",
-            "Model status",
-            status["status_label"],
-            "Decision grade requires sourced market data, financials, definitions, and QA",
-        ),
-        (
-            "Status",
-            "Workbook mode",
-            status["workbook_mode"],
-            "Formula-ready workbook populated only from supplied rows when provided",
-        ),
-        (
-            "Peer universe",
-            "Total tickers loaded",
-            "=COUNTA(Universe!A2:A21)",
-            "Includes target and any watchlist/excluded names",
-        ),
-        (
-            "Peer universe",
-            "Core peers",
-            '=COUNTIF(Universe!C2:C21,"Core")',
-            "Use only true comparables for selected statistics",
-        ),
-        (
-            "Valuation output",
-            "Selected multiple",
-            "=Valuation!B3",
-            "Document rationale against peer statistics",
-        ),
-        (
-            "Valuation output",
-            "Low implied value / share",
-            "=Valuation!H13",
-            "Chart-ready valuation range",
-        ),
-        (
-            "Valuation output",
-            "Mid implied value / share",
-            "=Valuation!H14",
-            "Primary reference case",
-        ),
-        (
-            "Valuation output",
-            "High implied value / share",
-            "=Valuation!H15",
-            "Upside valuation case",
-        ),
-        (
-            "PM judgment",
-            "Current price",
-            "=PM_Action_Box!B3",
-            "Anchor valuation to spot and market-data as-of date",
-        ),
-        (
-            "PM judgment",
-            "Implied upside/downside",
-            "=PM_Action_Box!B5",
-            "Do not leave valuation disconnected from stock price",
-        ),
-        (
-            "PM judgment",
-            "What is priced in",
-            "=PM_Action_Box!B6",
-            "State market-implied expectation before action",
-        ),
-        (
-            "PM judgment",
-            "Variant estimate path",
-            "=PM_Action_Box!B7",
-            "Identify the estimate line that must differ from consensus",
-        ),
-        ("PM judgment", "PM action box", "=PM_Action_Box!B8", "Add/trim/exit/watchlist rule"),
-        (
-            "Source posture",
-            "Source entries",
-            "=COUNTA(Sources!A2:A41)",
-            "Every market/financial input should have source support",
-        ),
-        (
-            "Source posture",
-            "Source caveat",
-            "Supplied rows; verify" if input_rows else "Needs support",
-            "Populate Sources with retrieval date, data date, and confidence",
-        ),
-        (
-            "QA posture",
-            "Open QA checks",
-            '=COUNTIF(QA_Log!B2:B31,"Not run")',
-            "Resolve before senior review",
-        ),
-        (
-            "QA posture",
-            "Denominator review",
-            "Required",
-            "Flag negative EBITDA/EPS, non-comparable calendars, leverage, and one-time adjustments",
-        ),
-        (
-            "Workbook map",
-            "Control",
-            "Inputs and model status",
-            "Set valuation date, currency, fiscal basis, and status",
-        ),
-        (
-            "Workbook map",
-            "Universe / Market_Data / Financials",
-            "Peer definitions and source inputs",
-            "Do not hide excluded or weak-fit companies",
-        ),
-        (
-            "Workbook map",
-            "Multiples / Benchmarking",
-            "Trading multiple and fit analysis",
-            "Use outlier flags before calculating conclusions",
-        ),
-        (
-            "Workbook map",
-            "Multiple_Bridge",
-            "Peer median to selected multiple",
-            "Bridge growth, margin, quality, leverage, liquidity, and source confidence",
-        ),
-        (
-            "Workbook map",
-            "Valuation / Sensitivity",
-            "Conclusion and range",
-            "Tie final selected range to peer evidence",
-        ),
-        (
-            "Workbook map",
-            "PM_Action_Box",
-            "PM decision and action rules",
-            "Anchor value to current price and missing evidence",
-        ),
-        (
-            "Workbook map",
-            "Sources / QA_Log",
-            "Evidence and review trail",
-            "Source gaps stay visible",
-        ),
-    ]
-    for r, (section, metric, value, notes) in enumerate(cover_rows, 4):
-        cover_ws.write(r, 0, section, body_fmt)
-        cover_ws.write(r, 1, metric, body_fmt)
-        if isinstance(value, str) and value.startswith("="):
-            cover_ws.write_formula(r, 2, value, output_fmt)
-        else:
-            cover_ws.write(r, 2, value, output_fmt)
-        cover_ws.write(r, 3, notes, body_fmt)
-    cover_ws.write("F3", "Chart 1: implied value / share range", subheader_fmt)
-    cover_ws.write("F19", "Chart 2: peer EV / CY1 revenue once populated", subheader_fmt)
-    cover_ws.set_column("A:A", 22)
-    cover_ws.set_column("B:B", 28)
-    cover_ws.set_column("C:C", 28)
-    cover_ws.set_column("D:D", 72)
-    cover_ws.set_column("F:M", 16)
-
-    # README
-    ws = wb.add_worksheet("README")
-    ws.hide_gridlines(2)
-    ws.write("A1", "Comparable Company Analysis Model Guide", title_fmt)
-    rows = [
-        (
-            "Purpose",
-            "Institutional trading comps workbook shell for valuation and peer benchmarking.",
-        ),
-        ("Target", target),
-        ("Ticker", ticker),
-        ("Valuation date", valuation_date),
-        ("Currency", currency),
-        (
-            "Cover workflow",
-            "Use the Cover tab as the first senior-review page. It should show the target snapshot, peer set, valuation range, source posture, and QA status after the workbook is populated.",
-        ),
-        (
-            "Update instructions",
-            "Populate Control, Universe, Market_Data, Financials, Adjustments, then review Cover, Multiples, Benchmarking, Valuation, Sensitivity, Sources, and QA_Log.",
-        ),
-        (
-            "Model limitations",
-            "Template only. Replace placeholder peers and assumptions with sourced data.",
-        ),
-    ]
-    for r, (label, value) in enumerate(rows, 2):
-        ws.write(r, 0, label, subheader_fmt)
-        ws.write(r, 1, value, body_fmt)
-    ws.set_column("A:A", 24)
-    ws.set_column("B:B", 120)
-
-    # Control
-    ws = wb.add_worksheet("Control")
-    ws.hide_gridlines(2)
-    ws.write("A1", "Control Panel", title_fmt)
-    controls = [
-        ("Target company", target),
-        ("Target ticker", ticker),
-        ("Valuation date", valuation_date),
-        ("Reporting currency", currency),
-        ("Units", "USD millions except per-share data"),
-        ("Fiscal basis", "Calendarized"),
-        ("Primary use case", "Price target / valuation range"),
-        ("Selected peer tier", "Core"),
-        ("Model status", status["status_label"]),
-    ]
-    for r, (label, value) in enumerate(controls, 2):
-        ws.write(r, 0, label, subheader_fmt)
-        ws.write(r, 1, value, input_fmt)
-        ws.write_comment(r, 1, "Input cell: update or link to a clearly sourced assumption.")
-    ws.set_column("A:A", 28)
-    ws.set_column("B:B", 44)
-
-    # Universe
-    ws = wb.add_worksheet("Universe")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Peer Tier",
-        "Country",
-        "Business Description",
-        "Revenue Mix / Segment Notes",
-        "Geography / End Market",
-        "Size Fit",
-        "Growth Fit",
-        "Margin Fit",
-        "Liquidity / Float",
-        "Index Membership",
-        "ETF Ownership / Flow",
-        "Short Interest / Borrow",
-        "ADR / Share-Class Issue",
-        "Consensus Coverage",
-        "Estimate Revision Relevance",
-        "Sector KPI Regime",
-        "Inclusion Rationale",
-        "Exclusion Rationale",
-        "Data Confidence",
-        "Analyst Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    if input_rows:
-        for r, row in enumerate(input_rows, 1):
-            ws.write_row(
-                r,
-                0,
-                [
-                    pick(row, "ticker") or ticker,
-                    pick(row, "company") or target,
-                    pick(row, "peer_tier") or ("Target" if r == 1 else "Core"),
-                    pick(row, "country"),
-                    pick(row, "business_description", "business_model"),
-                    pick(row, "revenue_mix", "segment_notes"),
-                    pick(row, "geography", "end_market"),
-                    pick(row, "size_fit"),
-                    pick(row, "growth_fit"),
-                    pick(row, "margin_fit"),
-                    pick(row, "liquidity_float", "float", "adv"),
-                    pick(row, "index_membership"),
-                    pick(row, "etf_ownership_flow", "etf_ownership", "passive_ownership"),
-                    pick(row, "short_interest_borrow", "short_interest", "borrow"),
-                    pick(row, "adr_share_class_issue"),
-                    pick(row, "consensus_coverage"),
-                    pick(row, "estimate_revision_relevance"),
-                    pick(row, "sector_kpi_regime"),
-                    pick(row, "inclusion_rationale", "peer_rationale"),
-                    pick(row, "exclusion_rationale"),
-                    pick(row, "confidence", "data_confidence"),
-                    pick(row, "analyst_notes", "notes"),
-                ],
-                input_fmt,
-            )
-    else:
-        ws.write_row(
-            1,
-            0,
-            [
-                ticker,
-                target,
-                "Target",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "",
-                "Subject company",
-                "",
-                "",
-                "",
-            ],
-            input_fmt,
-        )
-    ws.set_column("A:A", 12)
-    ws.set_column("B:B", 26)
-    ws.set_column("C:C", 16)
-    ws.set_column("E:N", 28)
-
-    # Market_Data
-    ws = wb.add_worksheet("Market_Data")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Peer Tier",
-        "Price",
-        "Basic Shares",
-        "Dilution Adj.",
-        "Diluted Shares",
-        "Equity Value",
-        "Debt",
-        "Preferred",
-        "Minority Interest",
-        "Cash & Equiv.",
-        "Other Non-op Assets",
-        "Enterprise Value",
-        "Market Data Date",
-        "Source",
-        "Confidence",
-        "Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    for r in range(1, PEER_ROWS + 1):
-        excel_row = r + 1
-        ws.write_formula(r, 0, f"=Universe!A{excel_row}", formula_fmt)
-        ws.write_formula(r, 1, f"=Universe!B{excel_row}", formula_fmt)
-        ws.write_formula(r, 2, f"=Universe!C{excel_row}", formula_fmt)
-        ws.write_formula(r, 6, f'=IFERROR(E{excel_row}+F{excel_row},"")', formula_fmt)
-        ws.write_formula(r, 7, f'=IFERROR(D{excel_row}*G{excel_row},"")', formula_fmt)
-        ws.write_formula(
-            r,
-            13,
-            f'=IFERROR(H{excel_row}+I{excel_row}+J{excel_row}+K{excel_row}-L{excel_row}-M{excel_row},"")',
-            formula_fmt,
-        )
-    for r, row in enumerate(input_rows, 1):
-        for col, key_set in {
-            3: ("price", "share_price", "current_price"),
-            4: ("basic_shares", "shares", "shares_outstanding"),
-            5: ("dilution_adj", "dilution_adjustment"),
-            8: ("debt", "total_debt"),
-            9: ("preferred", "preferred_equity"),
-            10: ("minority_interest",),
-            11: ("cash", "cash_equiv", "cash_and_equivalents"),
-            12: ("other_non_op_assets", "non_operating_assets"),
-            14: ("market_data_date", "as_of_date"),
-            15: ("source_name", "source"),
-            16: ("confidence", "data_confidence"),
-            17: ("notes",),
-        }.items():
-            numeric_or_string(ws, r, col, pick(row, *key_set), input_fmt)
-    ws.set_column("A:R", 16)
-    ws.set_column("B:B", 26)
-    ws.set_column("R:R", 34)
-
-    # Financials
-    ws = wb.add_worksheet("Financials")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Peer Tier",
-        "LTM Revenue",
-        "CY1 Revenue",
-        "CY2 Revenue",
-        "LTM EBITDA",
-        "CY1 EBITDA",
-        "CY2 EBITDA",
-        "LTM EBIT",
-        "CY1 EBIT",
-        "CY2 EBIT",
-        "LTM Net Income",
-        "CY1 EPS",
-        "CY2 EPS",
-        "LTM FCF",
-        "CY1 FCF",
-        "Revenue Growth CY1",
-        "Revenue Growth CY2",
-        "EBITDA Margin LTM",
-        "EBITDA Margin CY1",
-        "FCF Margin CY1",
-        "Source",
-        "Confidence",
-        "Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    for r in range(1, PEER_ROWS + 1):
-        excel_row = r + 1
-        ws.write_formula(r, 0, f"=Universe!A{excel_row}", formula_fmt)
-        ws.write_formula(r, 1, f"=Universe!B{excel_row}", formula_fmt)
-        ws.write_formula(r, 2, f"=Universe!C{excel_row}", formula_fmt)
-        ws.write_formula(r, 17, f'=IFERROR(E{excel_row}/D{excel_row}-1,"NM")', formula_fmt)
-        ws.write_formula(r, 18, f'=IFERROR(F{excel_row}/E{excel_row}-1,"NM")', formula_fmt)
-        ws.write_formula(r, 19, f'=IFERROR(G{excel_row}/D{excel_row},"NM")', formula_fmt)
-        ws.write_formula(r, 20, f'=IFERROR(H{excel_row}/E{excel_row},"NM")', formula_fmt)
-        ws.write_formula(r, 21, f'=IFERROR(Q{excel_row}/E{excel_row},"NM")', formula_fmt)
-    for r, row in enumerate(input_rows, 1):
-        for col, key_set in {
-            3: ("revenue_ltm", "ltm_revenue"),
-            4: ("revenue_cy1", "cy1_revenue", "ntm_revenue"),
-            5: ("revenue_cy2", "cy2_revenue"),
-            6: ("ebitda_ltm", "ltm_ebitda"),
-            7: ("ebitda_cy1", "cy1_ebitda", "ntm_ebitda"),
-            8: ("ebitda_cy2", "cy2_ebitda"),
-            9: ("ebit_ltm", "ltm_ebit"),
-            10: ("ebit_cy1", "cy1_ebit"),
-            11: ("ebit_cy2", "cy2_ebit"),
-            12: ("net_income_ltm", "ltm_net_income"),
-            13: ("eps_cy1", "cy1_eps", "ntm_eps"),
-            14: ("eps_cy2", "cy2_eps"),
-            15: ("fcf_ltm", "ltm_fcf"),
-            16: ("fcf_cy1", "cy1_fcf", "ntm_fcf"),
-            22: ("source_name", "source"),
-            23: ("confidence", "data_confidence"),
-            24: ("notes",),
-        }.items():
-            numeric_or_string(ws, r, col, pick(row, *key_set), input_fmt)
-    ws.set_column("A:Y", 16)
-    ws.set_column("B:B", 26)
-    ws.set_column("Y:Y", 34)
-
-    # Adjustments
-    ws = wb.add_worksheet("Adjustments")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Period",
-        "Adjustment Type",
-        "Reported Metric",
-        "Reported Value",
-        "Adjustment",
-        "Normalized Value",
-        "Rationale",
-        "Source",
-        "Confidence",
-        "Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    ws.set_column("A:L", 18)
-    ws.set_column("I:I", 40)
-    ws.set_column("L:L", 34)
-
-    # Multiples
-    ws = wb.add_worksheet("Multiples")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Peer Tier",
-        "EV",
-        "EV / LTM Rev",
-        "EV / CY1 Rev",
-        "EV / CY2 Rev",
-        "EV / LTM EBITDA",
-        "EV / CY1 EBITDA",
-        "EV / CY2 EBITDA",
-        "EV / LTM EBIT",
-        "EV / CY1 EBIT",
-        "P / CY1 EPS",
-        "P / CY2 EPS",
-        "FCF Yield CY1",
-        "Rule of 40 / Sector KPI",
-        "Outlier Flag",
-        "Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    for r in range(1, PEER_ROWS + 1):
-        excel_row = r + 1
-        ws.write_formula(r, 0, f"=Universe!A{excel_row}", formula_fmt)
-        ws.write_formula(r, 1, f"=Universe!B{excel_row}", formula_fmt)
-        ws.write_formula(r, 2, f"=Universe!C{excel_row}", formula_fmt)
-        ws.write_formula(r, 3, f"=Market_Data!N{excel_row}", formula_fmt)
-        formulas = [
-            f'=IFERROR(IF(Financials!D{excel_row}>0,D{excel_row}/Financials!D{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!E{excel_row}>0,D{excel_row}/Financials!E{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!F{excel_row}>0,D{excel_row}/Financials!F{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!G{excel_row}>0,D{excel_row}/Financials!G{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!H{excel_row}>0,D{excel_row}/Financials!H{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!I{excel_row}>0,D{excel_row}/Financials!I{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!J{excel_row}>0,D{excel_row}/Financials!J{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!K{excel_row}>0,D{excel_row}/Financials!K{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!N{excel_row}>0,Market_Data!D{excel_row}/Financials!N{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!O{excel_row}>0,Market_Data!D{excel_row}/Financials!O{excel_row},"NM"),"NM")',
-            f'=IFERROR(IF(Financials!Q{excel_row}>0,Financials!Q{excel_row}/Market_Data!H{excel_row},"NM"),"NM")',
-            f'=IFERROR(Financials!R{excel_row}+Financials!V{excel_row},"")',
-        ]
-        for c, formula in enumerate(formulas, 4):
-            ws.write_formula(r, c, formula, formula_fmt)
-    stats_row = PEER_ROWS + 3
-    ws.write(stats_row, 0, "Peer Statistics", subheader_fmt)
-    labels = ["All Peer Median", "Core Peer Median", "25th Percentile", "75th Percentile"]
-    for idx, label in enumerate(labels, stats_row + 1):
-        ws.write(idx, 0, label, subheader_fmt)
-    ws.set_column("A:R", 16)
-    ws.set_column("B:B", 26)
-    ws.set_column("R:R", 34)
-
-    # Benchmarking
-    ws = wb.add_worksheet("Benchmarking")
-    ws.hide_gridlines(2)
-    headers = [
-        "Ticker",
-        "Company",
-        "Peer Tier",
-        "Revenue Growth",
-        "EBITDA Margin",
-        "FCF Margin",
-        "Leverage",
-        "ROIC / Quality KPI",
-        "Business Quality Notes",
-        "Premium / Discount Rationale",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS, header_fmt, body_fmt)
-    for r in range(1, PEER_ROWS + 1):
-        excel_row = r + 1
-        ws.write_formula(r, 0, f"=Universe!A{excel_row}", formula_fmt)
-        ws.write_formula(r, 1, f"=Universe!B{excel_row}", formula_fmt)
-        ws.write_formula(r, 2, f"=Universe!C{excel_row}", formula_fmt)
-        ws.write_formula(r, 3, f"=Financials!R{excel_row}", formula_fmt)
-        ws.write_formula(r, 4, f"=Financials!U{excel_row}", formula_fmt)
-        ws.write_formula(r, 5, f"=Financials!V{excel_row}", formula_fmt)
-    ws.set_column("A:J", 18)
-    ws.set_column("I:J", 42)
-
-    # Multiple bridge
-    ws = wb.add_worksheet("Multiple_Bridge")
-    ws.hide_gridlines(2)
-    ws.write("A1", "Peer Median To Selected Multiple Bridge", title_fmt)
-    bridge_headers = [
-        "Bridge Item",
-        "Direction",
-        "Adjustment",
-        "Rationale",
-        "Source",
-        "PM Judgment",
-    ]
-    for c, h in enumerate(bridge_headers):
-        ws.write(2, c, h, header_fmt)
-    bridge_rows = [
-        (
-            "Peer median starting point",
-            "Base",
-            "",
-            "Use clean core peer median before judgment",
-            "Multiples / Benchmarking",
-            "Do not anchor to min/max without reason",
-        ),
-        (
-            "Growth premium / discount",
-            "Input",
-            "",
-            "Relative revenue/EPS/FCF growth versus peers",
-            "Financials / consensus",
-            "Only pay for durable growth",
-        ),
-        (
-            "Margin and FCF quality",
-            "Input",
-            "",
-            "Quality of earnings and cash conversion",
-            "Financials / adjustments",
-            "EBITDA without cash conversion deserves a lower multiple",
-        ),
-        (
-            "ROIC / business quality",
-            "Input",
-            "",
-            "ROIC, moat, recurrence, cyclicality, and reinvestment runway",
-            "Benchmarking",
-            "Quality premium needs proof",
-        ),
-        (
-            "Leverage and balance sheet",
-            "Input",
-            "",
-            "Net debt, refinancing risk, dilution risk as common-equity factors",
-            "Market_Data / filings",
-            "Route debt-security valuation to Credit Markets",
-        ),
-        (
-            "Liquidity / float",
-            "Input",
-            "",
-            "Capacity, float, ADV, and trading liquidity",
-            "Market data",
-            "Illiquidity can limit PM action",
-        ),
-        (
-            "Index / ETF ownership",
-            "Input",
-            "",
-            "Passive ownership, rebalance flow, and factor exposure",
-            "Index / ETF source",
-            "Flow can affect timing, not intrinsic value alone",
-        ),
-        (
-            "Data confidence",
-            "Input",
-            "",
-            "Freshness and source support",
-            "Sources",
-            "Weak data caps decision grade",
-        ),
-        (
-            "Selected multiple",
-            "Output",
-            "",
-            "Final selected multiple after bridge",
-            "PM judgment",
-            "Must tie to current price and action rule",
-        ),
-    ]
-    for r, row in enumerate(bridge_rows, 3):
-        for c, value in enumerate(row):
-            ws.write(r, c, value, input_fmt if c in {1, 2, 3, 4, 5} else body_fmt)
-    ws.set_column("A:F", 28)
-
-    # Valuation
-    ws = wb.add_worksheet("Valuation")
-    ws.hide_gridlines(2)
-    ws.write("A1", "Valuation Conclusion", title_fmt)
-    vals = [
-        ("Selected multiple", "EV / CY1 Revenue"),
-        ("Low multiple", ""),
-        ("Mid multiple", ""),
-        ("High multiple", ""),
-        ("Target financial metric", ""),
-        ("Net debt / (cash)", ""),
-        ("Diluted shares", ""),
-    ]
-    for r, (label, value) in enumerate(vals, 2):
-        ws.write(r, 0, label, subheader_fmt)
-        ws.write(r, 1, value, input_fmt)
-        ws.write_comment(
-            r,
-            1,
-            "Valuation input: support with peer evidence, source data, or documented judgment.",
-        )
-    headers = [
-        "Case",
-        "Selected Multiple",
-        "Target Metric",
-        "Implied EV",
-        "Net Debt",
-        "Implied Equity Value",
-        "Diluted Shares",
-        "Implied Value / Share",
-    ]
-    for c, h in enumerate(headers):
-        ws.write(11, c, h, header_fmt)
-    cases = [("Low", "=B4"), ("Mid", "=B5"), ("High", "=B6")]
-    for i, (case, mult_formula) in enumerate(cases, 12):
-        excel_row = i + 1
-        ws.write(i, 0, case, output_fmt)
-        ws.write_formula(i, 1, mult_formula, formula_fmt)
-        ws.write_formula(i, 2, "=B7", formula_fmt)
-        ws.write_formula(i, 3, f'=IFERROR(B{excel_row}*C{excel_row},"")', formula_fmt)
-        ws.write_formula(i, 4, "=B8", formula_fmt)
-        ws.write_formula(i, 5, f'=IFERROR(D{excel_row}-E{excel_row},"")', formula_fmt)
-        ws.write_formula(i, 6, "=B9", formula_fmt)
-        ws.write_formula(i, 7, f'=IFERROR(F{excel_row}/G{excel_row},"")', formula_fmt)
-    ws.write("A18", "Executive conclusion", subheader_fmt)
-    ws.write(
-        "A19", "State selected range, key rationale, confidence level, and major risks.", body_fmt
-    )
-    ws.set_column("A:H", 22)
-
-    # PM action box
-    ws = wb.add_worksheet("PM_Action_Box")
-    ws.hide_gridlines(2)
-    ws.write("A1", "PM Action Box", title_fmt)
-    action_rows = [
-        ("Current price", current_price, "Market data as-of and source required"),
-        ("Implied value / share", "=Valuation!H14", "Base valuation output"),
-        ("Upside/downside to spot", "", "Compare implied value range to current stock price"),
-        ("What is priced in", "", "Market-implied expectation before recommending action"),
-        (
-            "Variant estimate path",
-            "",
-            "Revenue, margin, EPS, FCF, or KPI line that must differ from consensus",
-        ),
-        (
-            "PM action implication",
-            "",
-            "add / press / hold / trim / exit / hedge / watchlist / wait for proof",
-        ),
-        ("Add / trim / exit rule", "", "Observable threshold tied to model line and source"),
-        ("Missing evidence", "", "Unresolved source, consensus, market-data, or diligence gaps"),
-    ]
-    ws.write_row(2, 0, ["Metric", "Value", "Notes"], header_fmt)
-    for r, row in enumerate(action_rows, 3):
-        ws.write(r, 0, row[0], subheader_fmt)
-        if isinstance(row[1], str) and row[1].startswith("="):
-            ws.write_formula(r, 1, row[1], output_fmt)
-        else:
-            ws.write(r, 1, row[1], input_fmt)
-        ws.write(r, 2, row[2], body_fmt)
-    ws.set_column("A:A", 28)
-    ws.set_column("B:B", 32)
-    ws.set_column("C:C", 80)
-
-    # Sensitivity
-    ws = wb.add_worksheet("Sensitivity")
-    ws.hide_gridlines(2)
-    ws.write("A1", "Sensitivity Analysis", title_fmt)
-    ws.write("A3", "Multiple vs Target Metric", subheader_fmt)
-    metrics = ["Metric -10%", "Metric -5%", "Base Metric", "Metric +5%", "Metric +10%"]
-    multiples = ["Low - 0.5x", "Low", "Mid", "High", "High + 0.5x"]
-    for c, label in enumerate(metrics, 1):
-        ws.write(3, c, label, header_fmt)
-    for r, label in enumerate(multiples, 4):
-        ws.write(r, 0, label, header_fmt)
-        for c in range(1, 6):
-            ws.write_formula(r, c, "=NA()", formula_fmt)
-    ws.set_column("A:F", 18)
-
-    # Sources
-    ws = wb.add_worksheet("Sources")
-    ws.hide_gridlines(2)
-    headers = [
-        "Company/Ticker",
-        "Metric",
-        "Period",
-        "Source Name",
-        "Connector Path / URL / Accession",
-        "Retrieval Date",
-        "Data Date",
-        "Source Type",
-        "Confidence",
-        "Definition Notes",
-    ]
-    add_table(ws, 0, 0, headers, PEER_ROWS * 2, header_fmt, body_fmt)
-    for r, row in enumerate(input_rows, 1):
-        ws.write_row(
-            r,
-            0,
-            [
-                pick(row, "ticker"),
-                "market and financial inputs",
-                pick(row, "period", "market_data_date", "as_of_date"),
-                pick(row, "source_name", "source"),
-                pick(row, "source_url", "url", "connector_path"),
-                pick(row, "retrieval_date", "retrieved_at"),
-                pick(row, "market_data_date", "as_of_date", "source_date"),
-                pick(row, "source_type"),
-                pick(row, "confidence", "data_confidence"),
-                pick(row, "definition_notes", "notes"),
-            ],
-            input_fmt,
-        )
-    ws.set_column("A:J", 20)
-    ws.set_column("E:E", 48)
-    ws.set_column("J:J", 42)
-
-    # QA_Log
-    ws = wb.add_worksheet("QA_Log")
-    ws.hide_gridlines(2)
-    headers = ["Check", "Status", "Finding", "Fix / Action", "Owner", "Date", "Notes"]
-    add_table(ws, 0, 0, headers, 30, header_fmt, body_fmt)
-    checks = [
-        "Workbook structure",
-        "Source log complete",
-        "Formula consistency",
-        "Broken formula/errors",
-        "External links",
-        "Peer rationale",
-        "Denominator treatment",
-        "Market data freshness",
-        "Normalization documented",
-        "Valuation range rationale",
-    ]
-    for r, check in enumerate(checks, 1):
-        ws.write(r, 0, check, body_fmt)
-        ws.write(r, 1, "Review required" if input_rows else "Not run", input_fmt)
-    ws.set_column("A:G", 24)
-    ws.set_column("C:D", 44)
-
-    value_chart = wb.add_chart({"type": "column"})
-    value_chart.add_series(
-        {
-            "name": "Implied value / share",
-            "categories": "=Valuation!$A$13:$A$15",
-            "values": "=Valuation!$H$13:$H$15",
-            "fill": {"color": "#5B9BD5"},
-        }
-    )
-    value_chart.set_title({"name": "Implied Value / Share"})
-    value_chart.set_y_axis({"name": f"{currency} / share"})
-    value_chart.set_legend({"none": True})
-    cover_ws.insert_chart("F4", value_chart, {"x_scale": 1.15, "y_scale": 1.05})
-
-    peer_chart = wb.add_chart({"type": "column"})
-    peer_chart.add_series(
-        {
-            "name": "EV / CY1 Revenue",
-            "categories": "=Multiples!$A$2:$A$21",
-            "values": "=Multiples!$F$2:$F$21",
-            "fill": {"color": "#70AD47"},
-        }
-    )
-    peer_chart.set_title({"name": "Peer EV / CY1 Revenue"})
-    peer_chart.set_y_axis({"name": "x"})
-    peer_chart.set_legend({"none": True})
-    cover_ws.insert_chart("F20", peer_chart, {"x_scale": 1.15, "y_scale": 1.05})
-
-    wb.close()
-    return status
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Create a comparable company analysis workbook template."
-    )
-    parser.add_argument(
-        "--output", default="comps_analysis_template.xlsx", help="Output .xlsx path."
-    )
-    parser.add_argument("--target", default="TargetCo", help="Target company name.")
-    parser.add_argument("--ticker", default="TGT", help="Target company ticker.")
-    parser.add_argument("--currency", default="USD", help="Model currency.")
-    parser.add_argument(
-        "--valuation-date", default=date.today().isoformat(), help="Valuation date YYYY-MM-DD."
-    )
-    parser.add_argument(
-        "--input-csv",
-        type=Path,
-        help="Optional supplied target/peer data CSV to populate the workbook.",
-    )
-    return parser.parse_args()
-
-
-def artifact_manifest(
-    output: Path, *, status: str, workbook_written: bool
-) -> list[dict[str, object]]:
-    output_dir = output.parent
-    return [
-        {
-            "key": "workbook",
-            "path": str(output),
-            "required": True,
-            "written": workbook_written,
-            "description": "Comparable-company analysis workbook template.",
-            "artifact_role": "primary_human_deliverable"
-            if workbook_written
-            else "support_artifact",
-            "hidden_unless_requested": not workbook_written,
-            "user_visible_default": workbook_written,
-            "support_reason": ""
-            if workbook_written
-            else "Workbook was not written because generation failed.",
-        },
-        {
-            "key": "run_log",
-            "path": str(output_dir / "run_log.json"),
-            "required": True,
-            "written": True,
-            "description": "Status, warnings, hard failures, dependency status, and output paths.",
-            "artifact_role": "machine_support",
-            "hidden_unless_requested": True,
-            "user_visible_default": False,
-            "support_reason": "Run log is automation/audit support for the workbook.",
-        },
-        {
-            "key": "manifest",
-            "path": str(output_dir / "manifest.json"),
-            "required": True,
-            "written": True,
-            "description": "Machine-readable output manifest.",
-            "artifact_role": "machine_support",
-            "hidden_unless_requested": True,
-            "user_visible_default": False,
-            "support_reason": "Manifest is routing support, not the comps deliverable.",
-        },
-    ]
-
-
-def write_run_artifacts(output: Path, payload: dict[str, object]) -> None:
-    output_dir = output.parent
-    output_dir.mkdir(parents=True, exist_ok=True)
-    (output_dir / "run_log.json").write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
-    manifest = {
-        "primary_human_deliverable": payload.get("primary_human_deliverable"),
-        "artifact_mode": payload.get("artifact_mode", "workbook"),
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": payload.get(
-            "final_response_guidance",
-            {
-                "lead_with": "primary_human_deliverable",
-                "mention_support_artifacts": "only_briefly_unless_requested",
-            },
-        ),
-        "outputs": payload["output_manifest"],
-    }
-    (output_dir / "manifest.json").write_text(
-        json.dumps(manifest, indent=2) + "\n", encoding="utf-8"
-    )
-
-
-def main() -> int:
-    args = parse_args()
-    output = Path(args.output).resolve()
-    output.parent.mkdir(parents=True, exist_ok=True)
-    try:
-        input_rows = load_input_rows(args.input_csv)
-        status_info = create_workbook(
-            output, args.target, args.ticker, args.currency, args.valuation_date, input_rows
-        )
-    except RuntimeError as exc:
-        run_log = {
-            "status": "failed",
-            "model_status": "not-decision-ready",
-            "artifact_level": "template_workbook",
-            "workbook_mode": "template_shell",
-            "artifact_mode": "blocked",
-            "source_basis": [],
-            "warnings": [],
-            "hard_failures": [str(exc)],
-            "dependency_status": {"XlsxWriter": "missing"},
-            "outputs": {
-                "workbook": str(output),
-                "run_log": str(output.parent / "run_log.json"),
-                "manifest": str(output.parent / "manifest.json"),
-            },
-            "primary_human_deliverable": None,
-            "support_artifacts_user_visible_default": False,
-            "final_response_guidance": {
-                "lead_with": "blocked_status",
-                "mention_support_artifacts": "include run log for repair",
-            },
-            "output_manifest": artifact_manifest(output, status="failed", workbook_written=False),
-        }
-        write_run_artifacts(output, run_log)
-        print(f"ERROR: {exc}")
-        return 1
-
-    run_log = {
-        "status": "completed",
-        "model_status": status_info["model_status"],
-        "artifact_level": "template_workbook",
-        "workbook_mode": status_info["workbook_mode"],
-        "artifact_mode": "workbook",
-        "source_basis": [],
-        "warnings": [status_info["warning"]],
-        "hard_failures": [],
-        "populated_peer_count": status_info["populated_peer_count"],
-        "missing_required_fields": status_info["missing_required_fields"],
-        "dependency_status": {"XlsxWriter": "available"},
-        "outputs": {
-            "workbook": str(output),
-            "run_log": str(output.parent / "run_log.json"),
-            "manifest": str(output.parent / "manifest.json"),
-        },
-        "primary_human_deliverable": str(output),
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": {
-            "lead_with": "primary_human_deliverable",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-        "output_manifest": artifact_manifest(output, status="completed", workbook_written=True),
-    }
-    write_run_artifacts(output, run_log)
-    print(f"Created comps workbook template: {output}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/comps-valuation/scripts/materialize_screening_comps.py b/plugins/financial-markets/skills/comps-valuation/scripts/materialize_screening_comps.py
deleted file mode 100644
index f3e74bd..0000000
--- a/plugins/financial-markets/skills/comps-valuation/scripts/materialize_screening_comps.py
+++ /dev/null
@@ -1,561 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize a screen-grade comps framework when market data is incomplete.
-
-This helper is a deterministic fallback, not a populated market-data model. It
-writes a peer framework, source requirements, missing-data requests, markdown
-summary, run log, and output manifest so comps workflows never fail with an
-empty artifact when live data or workbook dependencies are unavailable.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import sys
-from datetime import date, datetime, timezone
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-SKILL_ROOT = SCRIPT_DIR.parent
-PLUGIN_ROOT = SKILL_ROOT.parents[1]
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from shared.artifact_packager import dict_rows_to_sheet, write_cover_first_workbook  # noqa: E402
-
-DEFAULT_REQUIRED_METRICS = [
-    "current price",
-    "basic and diluted shares",
-    "cash and debt",
-    "minority interest / preferred / leases where relevant",
-    "LTM revenue",
-    "LTM EBITDA or sector denominator",
-    "NTM revenue estimate",
-    "NTM EBITDA or EPS estimate where meaningful",
-    "market data as-of date",
-    "estimate source and estimate as-of date",
-]
-
-SOURCE_REQUIREMENTS = [
-    (
-        "Market data",
-        "Price, market cap, shares, EV bridge",
-        "fact_secondary",
-        "Market-data provider or exchange data with as-of date",
-    ),
-    (
-        "Company filings",
-        "Cash, debt, minority interest, preferreds, share count support",
-        "fact_primary",
-        "Latest 10-K/10-Q/20-F/6-K or equivalent",
-    ),
-    (
-        "Consensus estimates",
-        "NTM revenue, EBITDA, EBIT, EPS, FCF",
-        "third_party_estimate",
-        "Consensus provider export with estimate-as-of date",
-    ),
-    (
-        "Peer rationale",
-        "Business model, geography, growth/margin fit, inclusion/exclusion logic",
-        "inference",
-        "Analyst judgment tied to source evidence",
-    ),
-    (
-        "Adjustments",
-        "One-time items, lease/SBC/capex normalization, calendarization",
-        "assumption",
-        "Documented adjustment source and rationale",
-    ),
-]
-
-
-def normalize_header(header: str) -> str:
-    return header.strip().lower().replace(" ", "_").replace("-", "_")
-
-
-def load_peer_rows(peer_csv: Path | None, warnings: list[str]) -> list[dict[str, str]]:
-    if peer_csv is None:
-        return []
-    if not peer_csv.exists():
-        warnings.append(
-            f"Peer CSV not found: {peer_csv}; using placeholder/watchlist peer framework."
-        )
-        return []
-    with peer_csv.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        rows = []
-        for row in reader:
-            normalized = {
-                normalize_header(key or ""): str(value or "").strip() for key, value in row.items()
-            }
-            company = (
-                normalized.get("company")
-                or normalized.get("company_name")
-                or normalized.get("name")
-            )
-            ticker = normalized.get("ticker") or normalized.get("symbol")
-            if company or ticker:
-                rows.append(
-                    {
-                        "company": company or "TBD",
-                        "ticker": ticker or "TBD",
-                        "peer_tier": normalized.get("peer_tier")
-                        or normalized.get("tier")
-                        or "Watchlist",
-                        "business_model_note": normalized.get("business_model_note")
-                        or normalized.get("business_model")
-                        or "",
-                        "inclusion_rationale": normalized.get("inclusion_rationale")
-                        or normalized.get("rationale")
-                        or "",
-                        "exclusion_rationale": normalized.get("exclusion_rationale") or "",
-                    }
-                )
-    return rows
-
-
-def peer_list_rows(peer_list: str) -> list[dict[str, str]]:
-    rows = []
-    for item in [part.strip() for part in peer_list.split(",") if part.strip()]:
-        if ":" in item:
-            ticker, company = [part.strip() for part in item.split(":", 1)]
-        else:
-            ticker, company = item, item
-        rows.append(
-            {
-                "company": company,
-                "ticker": ticker.upper(),
-                "peer_tier": "Watchlist",
-                "business_model_note": "User-provided peer candidate; validate business-model fit.",
-                "inclusion_rationale": "Requires source-supported peer rationale before use in selected range.",
-                "exclusion_rationale": "",
-            }
-        )
-    return rows
-
-
-def build_peer_framework(args: argparse.Namespace) -> list[dict[str, str]]:
-    warnings: list[str] = getattr(args, "warnings", [])
-    rows = [
-        {
-            "company": args.target,
-            "ticker": args.ticker,
-            "peer_tier": "Target",
-            "peer_role": "target",
-            "business_model_note": args.business_model or "Target business model not provided.",
-            "inclusion_rationale": "Subject company.",
-            "exclusion_rationale": "",
-            "market_data_status": "missing"
-            if args.market_data_status == "missing"
-            else args.market_data_status,
-            "required_metrics": "; ".join(DEFAULT_REQUIRED_METRICS),
-            "source_requirement": "Target market data, filings, financials, estimates, and EV bridge required.",
-            "notes": "Do not use this row for valuation until populated with sourced values.",
-        }
-    ]
-
-    input_rows = load_peer_rows(args.peer_csv, warnings) + peer_list_rows(args.peer_list or "")
-    if not input_rows:
-        input_rows = [
-            {
-                "company": f"Peer candidate {idx}",
-                "ticker": "TBD",
-                "peer_tier": "Watchlist",
-                "business_model_note": "Placeholder peer candidate.",
-                "inclusion_rationale": "Identify actual public peer and validate fit.",
-                "exclusion_rationale": "",
-            }
-            for idx in range(1, args.placeholder_peer_count + 1)
-        ]
-
-    for row in input_rows:
-        tier = row.get("peer_tier") or "Watchlist"
-        rows.append(
-            {
-                "company": row.get("company", "TBD"),
-                "ticker": row.get("ticker", "TBD"),
-                "peer_tier": tier,
-                "peer_role": "core_peer" if tier.lower() == "core" else "watchlist_peer",
-                "business_model_note": row.get("business_model_note", ""),
-                "inclusion_rationale": row.get("inclusion_rationale")
-                or "Validate similarity in model, growth, margin, geography, and capitalization.",
-                "exclusion_rationale": row.get("exclusion_rationale", ""),
-                "market_data_status": args.market_data_status,
-                "required_metrics": "; ".join(DEFAULT_REQUIRED_METRICS),
-                "source_requirement": "Populate market data, filings, consensus, EV bridge, and source dates before decision use.",
-                "notes": "Screen-grade candidate; not a valuation anchor until sourced.",
-            }
-        )
-    return rows
-
-
-def build_missing_data_rows(args: argparse.Namespace) -> list[dict[str, str]]:
-    return [
-        {
-            "priority": "P0",
-            "needed_item": "Current share price and market-data as-of date",
-            "why_it_matters": "Enterprise and equity value become stale immediately.",
-            "affected_output": "EV, P/E, all trading multiples",
-            "minimum_substitute": "User-provided price with date",
-        },
-        {
-            "priority": "P0",
-            "needed_item": "Basic/diluted share count and dilution treatment",
-            "why_it_matters": "Equity value and per-share outputs can be wrong without share basis.",
-            "affected_output": "Market cap, equity value, P/E, per-share value",
-            "minimum_substitute": "Latest filing share count plus stated dilution assumption",
-        },
-        {
-            "priority": "P0",
-            "needed_item": "Cash, debt, preferreds, minority interest, leases, and other EV bridge items",
-            "why_it_matters": "EV can be materially misstated.",
-            "affected_output": "EV/revenue, EV/EBITDA, EV/FCF",
-            "minimum_substitute": "Latest filing balance sheet with bridge caveat",
-        },
-        {
-            "priority": "P0",
-            "needed_item": "LTM financials and period end",
-            "why_it_matters": "Denominator quality drives comparability.",
-            "affected_output": "LTM multiples and margin context",
-            "minimum_substitute": "Latest reported fiscal period with LTM bridge",
-        },
-        {
-            "priority": "P0",
-            "needed_item": "Consensus NTM estimates and estimate-as-of date",
-            "why_it_matters": "Forward multiples are not usable without estimate source and date.",
-            "affected_output": "NTM multiples and selected range",
-            "minimum_substitute": "User-provided estimate clearly labeled as assumption",
-        },
-        {
-            "priority": "P1",
-            "needed_item": "Peer inclusion/exclusion rationale",
-            "why_it_matters": "Prevents cherry-picked peer set.",
-            "affected_output": "Selected multiple range and premium/discount logic",
-            "minimum_substitute": f"{args.sector or 'Sector'} peer screen with explicit caveats",
-        },
-        {
-            "priority": "P1",
-            "needed_item": "Accounting adjustments and non-recurring items",
-            "why_it_matters": "Reported and adjusted denominators may not be comparable.",
-            "affected_output": "EBITDA, EPS, FCF, sector KPIs",
-            "minimum_substitute": "Reported-only view labeled as unadjusted",
-        },
-    ]
-
-
-def write_csv(path: Path, rows: list[dict[str, str]]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    fields = list(rows[0].keys()) if rows else []
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fields)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def write_screen_grade_workbook(
-    output_dir: Path,
-    args: argparse.Namespace,
-    peer_rows: list[dict[str, str]],
-    missing_rows: list[dict[str, str]],
-    source_rows: list[dict[str, str]],
-) -> Path:
-    workbook = output_dir / "screen_grade_comps_framework.xlsx"
-    peer_count = max(len(peer_rows) - 1, 0)
-    cover_rows = [
-        ["Screen-Grade Comps Framework"],
-        ["Primary human deliverable", "Yes"],
-        ["Target", args.target],
-        ["Ticker", args.ticker],
-        [
-            "Sector / business model",
-            f"{args.sector or 'not provided'} / {args.business_model or 'not provided'}",
-        ],
-        ["Valuation date", args.valuation_date],
-        ["Model status", "screen-grade"],
-        ["Market data status", args.market_data_status],
-        ["Peer candidates included", peer_count],
-        [
-            "PM decision impact",
-            "Framework is useful for peer triage, but not valuation, target, rating, sizing, or circulation until source fields are populated.",
-        ],
-        [
-            "Next workflow",
-            "Populate market data and estimates, then run comps-valuation or create a populated comps-valuation workbook.",
-        ],
-        [
-            "Support artifacts",
-            "CSV, Markdown, JSON, and logs are hidden support files unless requested.",
-        ],
-    ]
-    tables = {
-        "Peer Framework": dict_rows_to_sheet(peer_rows),
-        "Missing Data Requests": dict_rows_to_sheet(missing_rows),
-        "Source Requirements": dict_rows_to_sheet(source_rows),
-    }
-    return write_cover_first_workbook(workbook, cover_rows, tables)
-
-
-def markdown_summary(
-    args: argparse.Namespace, peer_rows: list[dict[str, str]], output_dir: Path
-) -> str:
-    peer_count = max(len(peer_rows) - 1, 0)
-    lines = [
-        "# Screen-Grade Comparable Companies Framework",
-        "",
-        "Comps posture: screen-grade; missing market data and/or estimates are explicitly labeled.",
-        "",
-        "Market data, estimates, EV bridge, and source dates must be populated before decision use.",
-        "",
-        "## Scope",
-        "",
-        f"- Target: {args.target} ({args.ticker})",
-        f"- Sector / business model: {args.sector or 'not provided'} / {args.business_model or 'not provided'}",
-        f"- Valuation date: {args.valuation_date}",
-        f"- Peer candidates included: {peer_count}",
-        "",
-        "## What This Fallback Provides",
-        "",
-        "- Peer set framework with inclusion/exclusion logic fields.",
-        "- Missing-market-data caveats and explicit source requirements.",
-        "- Data request list for decision-grade comps work.",
-        "- No implied valuation conclusion and no live market-data claim.",
-        "",
-        "## Initial Peer Framework",
-        "",
-        "| Company | Ticker | Tier | Role | Market data status | Inclusion logic |",
-        "|---|---|---|---|---|---|",
-    ]
-    for row in peer_rows[:12]:
-        lines.append(
-            f"| {row['company']} | {row['ticker']} | {row['peer_tier']} | {row['peer_role']} | "
-            f"{row['market_data_status']} | {row['inclusion_rationale']} |"
-        )
-    lines.extend(
-        [
-            "",
-            "## Source Requirements",
-            "",
-            "| Source area | Required evidence | Evidence label | Minimum acceptable source |",
-            "|---|---|---|---|",
-        ]
-    )
-    for area, evidence, label, minimum in SOURCE_REQUIREMENTS:
-        lines.append(f"| {area} | {evidence} | {label} | {minimum} |")
-    lines.extend(
-        [
-            "",
-            "## Output Files",
-            "",
-            f"- `peer_framework.csv`: {output_dir / 'peer_framework.csv'}",
-            f"- `missing_data_requests.csv`: {output_dir / 'missing_data_requests.csv'}",
-            f"- `source_requirements.csv`: {output_dir / 'source_requirements.csv'}",
-            f"- `screen_grade_comps_support_note.md`: {output_dir / 'screen_grade_comps_support_note.md'}",
-            "",
-            "## Next Step",
-            "",
-            "Populate the required source fields or hand the framework to `comps-valuation` for a screen-grade read-through with explicit caveats.",
-        ]
-    )
-    return "\n".join(lines) + "\n"
-
-
-def manifest_rows(output_dir: Path, *, workbook_primary: bool = True) -> list[dict[str, Any]]:
-    outputs = [
-        (
-            "workbook",
-            "screen_grade_comps_framework.xlsx",
-            True,
-            "Cover-first screen-grade comps framework workbook.",
-            "primary_human_deliverable" if workbook_primary else "support_artifact",
-            not workbook_primary,
-        ),
-        (
-            "peer_framework",
-            "peer_framework.csv",
-            True,
-            "Screen-grade peer framework and rationale fields.",
-            "support_artifact",
-            True,
-        ),
-        (
-            "missing_data_requests",
-            "missing_data_requests.csv",
-            True,
-            "Exact missing items needed for decision-grade comps.",
-            "support_artifact",
-            True,
-        ),
-        (
-            "source_requirements",
-            "source_requirements.csv",
-            True,
-            "Source hierarchy and evidence labels.",
-            "support_artifact",
-            True,
-        ),
-        (
-            "support_note",
-            "screen_grade_comps_support_note.md",
-            True,
-            "Human-readable fallback comps support note.",
-            "narrative_support",
-            True,
-        ),
-        (
-            "run_log",
-            "run_log.json",
-            True,
-            "Status, warnings, hard failures, source basis, and output paths.",
-            "machine_support",
-            True,
-        ),
-        (
-            "manifest",
-            "manifest.json",
-            True,
-            "Machine-readable output manifest.",
-            "machine_support",
-            True,
-        ),
-    ]
-    return [
-        {
-            "key": key,
-            "path": str(output_dir / filename),
-            "required": required,
-            "written": (output_dir / filename).exists(),
-            "artifact_role": artifact_role,
-            "hidden_unless_requested": hidden,
-            "user_visible_default": not hidden,
-            "description": description,
-            "support_reason": ""
-            if not hidden
-            else "Support artifact for audit/import/provenance; the XLSX workbook is the first-read deliverable.",
-        }
-        for key, filename, required, description, artifact_role, hidden in outputs
-    ]
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Materialize a screen-grade comps fallback framework."
-    )
-    parser.add_argument(
-        "--output-dir", type=Path, default=Path("/tmp/public_equity_investing_comps_fallback")
-    )
-    parser.add_argument("--target", default="TargetCo")
-    parser.add_argument("--ticker", default="TGT")
-    parser.add_argument("--sector", default="")
-    parser.add_argument("--business-model", default="")
-    parser.add_argument("--valuation-date", default=date.today().isoformat())
-    parser.add_argument(
-        "--market-data-status",
-        choices=["missing", "stale", "user-provided", "connector-provided"],
-        default="missing",
-    )
-    parser.add_argument(
-        "--peer-csv", type=Path, help="Optional CSV with company/ticker/peer_tier/rationale fields."
-    )
-    parser.add_argument(
-        "--peer-list", default="", help="Comma-separated peers; use TICKER:Company when helpful."
-    )
-    parser.add_argument("--placeholder-peer-count", type=int, default=8)
-    return parser.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    output_dir = args.output_dir.resolve()
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    warnings: list[str] = [
-        "Screen-grade fallback only; no live market data, EV bridge, estimates, or valuation conclusion is generated.",
-        "Populate source requirements before using as a decision-grade comparable-company model.",
-    ]
-    args.warnings = warnings
-    hard_failures: list[str] = []
-    try:
-        peer_rows = build_peer_framework(args)
-        missing_rows = build_missing_data_rows(args)
-        source_rows = [
-            {
-                "source_area": area,
-                "required_evidence": evidence,
-                "evidence_label": label,
-                "minimum_acceptable_source": minimum,
-            }
-            for area, evidence, label, minimum in SOURCE_REQUIREMENTS
-        ]
-
-        write_csv(output_dir / "peer_framework.csv", peer_rows)
-        write_csv(output_dir / "missing_data_requests.csv", missing_rows)
-        write_csv(output_dir / "source_requirements.csv", source_rows)
-        (output_dir / "screen_grade_comps_support_note.md").write_text(
-            markdown_summary(args, peer_rows, output_dir), encoding="utf-8"
-        )
-        write_screen_grade_workbook(output_dir, args, peer_rows, missing_rows, source_rows)
-    except Exception as exc:
-        hard_failures.append(str(exc))
-
-    outputs = {
-        "workbook": str(output_dir / "screen_grade_comps_framework.xlsx"),
-        "peer_framework": str(output_dir / "peer_framework.csv"),
-        "missing_data_requests": str(output_dir / "missing_data_requests.csv"),
-        "source_requirements": str(output_dir / "source_requirements.csv"),
-        "support_note": str(output_dir / "screen_grade_comps_support_note.md"),
-        "run_log": str(output_dir / "run_log.json"),
-        "manifest": str(output_dir / "manifest.json"),
-    }
-    payload = {
-        "status": "failed" if hard_failures else "completed",
-        "model_status": "screen-grade" if not hard_failures else "not-decision-ready",
-        "artifact_level": "screen_grade_fallback",
-        "workbook_mode": "screen_grade_workbook",
-        "artifact_mode": "workbook" if not hard_failures else "blocked",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "source_basis": ["user_input", "explicit_assumption"],
-        "warnings": warnings,
-        "hard_failures": hard_failures,
-        "dependency_status": {
-            "python_stdlib": "available",
-            "XlsxWriter": "not_required",
-            "openpyxl": "not_required",
-        },
-        "outputs": outputs,
-        "primary_human_deliverable": None if hard_failures else outputs["workbook"],
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": {
-            "lead_with": "blocked_status" if hard_failures else "primary_human_deliverable",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-        "output_manifest": [],
-    }
-    payload["output_manifest"] = manifest_rows(output_dir, workbook_primary=not hard_failures)
-    (output_dir / "run_log.json").write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
-    (output_dir / "manifest.json").write_text(
-        json.dumps(
-            {
-                "primary_human_deliverable": payload["primary_human_deliverable"],
-                "artifact_mode": payload["artifact_mode"],
-                "support_artifacts_user_visible_default": False,
-                "final_response_guidance": payload["final_response_guidance"],
-                "outputs": payload["output_manifest"],
-            },
-            indent=2,
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-    if hard_failures:
-        print("ERROR: " + "; ".join(hard_failures))
-        return 1
-    print(f"Wrote screen-grade comps fallback to {output_dir}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/comps-valuation/scripts/requirements.txt b/plugins/financial-markets/skills/comps-valuation/scripts/requirements.txt
deleted file mode 100644
index 026889f..0000000
--- a/plugins/financial-markets/skills/comps-valuation/scripts/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-openpyxl>=3.0.10
-XlsxWriter>=3.0.0
diff --git a/plugins/financial-markets/skills/dcf-model-builder/SKILL.md b/plugins/financial-markets/skills/dcf-model-builder/SKILL.md
deleted file mode 100644
index 9925fe7..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/SKILL.md
+++ /dev/null
@@ -1,91 +0,0 @@
----
-name: dcf-model-builder
-description: Use when building public-equity DCF valuation workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits; use model-audit-tieout.
----
-
-# DCF Model Builder
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is an XLSX DCF valuation workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-Own DCF-specific valuation builds: FCFF/FCFE method choice, WACC or cost of equity, terminal value, EV-to-equity bridge, per-share value, valuation sensitivities, QA checks, and a formula-first XLSX workbook.
-
-## Hard Contract
-
-- Default model-build artifact level is `banker_formula_workbook`; use the formula workbook path for normal public-equity DCF build, refresh, valuation, and model-package requests.
-- Formula mode is executed through `scripts/build_banker_formula_workbook.py` and must be labeled `banker_formula_workbook` only when its run log, workbook inspection, and `model_citations.json` prove the formula builder ran successfully.
-- Deterministic scripts remain available for controlled computed values, smoke tests, or explicit lightweight support exports. They produce `output/model.xlsx`, `output/plan.json`, `output/run_log.json`, optional support note `output/support_note.md`, and `output/manifest.json`. Legacy `output/report.md` is written only with `--write-report-md`.
-- Formula scripts produce `banker_formula_workbook.xlsx`, `banker_formula_workbook_run_log.json`, `model_citations.json`, and `manifest.json` in the selected output directory. The workbook is the hero deliverable; citation JSON, run logs, and manifests are support artifacts.
-- Formula and deterministic workbooks must start with a `Cover` tab that functions as an investment landing page: issuer, current price versus implied value, valuation range, case outputs, DCF bridge, WACC/terminal assumptions, sensitivity drivers, source posture, warnings/hard failures, and workbook map.
-- `run_log.json` and `manifest.json` are required reliability artifacts; hard failures force `not-decision-ready`.
-- Never delete, overwrite, or mutate source data unless explicitly requested. Write new outputs under `output/` or a clearly named copy.
-- Keep material inputs source-labeled: `reported`, `company_guidance`, `consensus`, `management_case`, `user_provided`, `connected_app`, `web_research`, `analyst_estimate`, `placeholder`, or `derived`.
-- If placeholders, stale data, unsupported WACC, missing share count/net debt, or weak analyst estimates drive value, mark the output no higher than `screen-grade`.
-
-## Routing
-
-Use this skill when the deliverable is a DCF build, refresh, rerun, valuation sensitivity, reverse DCF, or DCF model package. Route away when the primary ask is workbook audit/debug (`model-audit-tieout`), source hierarchy (`financial-source-of-truth`), raw financial normalization (`financials-normalizer`), raw spreadsheet cleanup (`excel-data-cleaner`), expanded stress architecture (`scenario-sensitivity-generator`), trading comps (`comps-valuation`), or memo/deck polish.
-
-For complex tasks, split the work into source-of-truth, accounting normalization, operating forecast, cost of capital, terminal value/ROIC, scenario valuation, and audit workstreams. If sub-agents are unavailable, emulate those workstreams explicitly before running the pipeline.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when source/data/QC/style support is needed. Use `financial-source-of-truth` before relying on load-bearing market data, filings, guidance, consensus, share count, net debt, WACC, or terminal assumptions. Use `financials-normalizer` before plan creation when historical financials, KPI schedules, segment data, guidance, consensus/provider exports, share count, net debt, or capital allocation inputs are messy. Use `excel-data-cleaner` before importing malformed tables. Use `model-audit-tieout` for formula integrity, external links, recalc/cache posture, and final tie-out; use `deck-report-qc` only for circulation packs. Support artifacts stay secondary to the formula workbook or HTML report.
-
-## Input Handling
-
-- Preferred input is a `plan.json` matching the schema.
-- No context: run `assets/plan_template.json` only as illustrative and label placeholders.
-- Named company with no data: gather connected/public data when allowed; otherwise ask targeted questions or build a caveated screen-grade plan.
-- Partial context: preserve user assumptions, fill only non-economic structure when obvious, and label gaps.
-- Full plan: validate first; do not silently change conclusion-driving assumptions.
-- When the investment question turns on an operating KPI such as bookings, GBV, take rate, volumes, users, units, or retention, load `references/industry-playbooks.md` and build from the sector-relevant driver where the workbook supports it. If formula mode cannot represent that driver schedule, disclose the proxy prominently on the Cover or Executive Summary and in Source Notes, and keep the model no higher than `screen-grade`; do not silently substitute a generic revenue CAGR.
-
-## Required Workflow
-
-1. Create or identify a valid `plan.json`.
-2. Validate with `python3 scripts/validate_plan.py path/to/plan.json`.
-3. Default path: execute `python3 scripts/build_banker_formula_workbook.py path/to/plan.json --output-dir output` and inspect `banker_formula_workbook_run_log.json`.
-4. Use `python3 scripts/run_pipeline.py path/to/plan.json` only for controlled computed values, smoke tests, explicit lightweight support exports, or when formula mode fails and the fallback is clearly labeled `deterministic_export`.
-5. Review the relevant run log for hard failures, warnings, checks, source basis, workbook inspection, and P0/model handoff.
-6. Render and visually inspect the generated workbook before delivery. Confirm that template example data is absent from a named-company model, actual/estimate periods align across tabs, core outputs and sources are legible, and checks disclose any unsupported driver or roll-forward.
-7. Deliver the workbook as the hero artifact and link the normalized plan, run log, manifest, citation ledger, and optional support note as audit files.
-
-Smoke test:
-
-```bash
-python3 scripts/validate_plan.py assets/plan_template.json
-python3 scripts/build_banker_formula_workbook.py assets/plan_template.json --output-dir /private/tmp/public-equity-investing-dcf-formula
-python3 scripts/run_pipeline.py assets/plan_template.json
-```
-
-## Formula Mode Guardrail
-
-Load `references/banker-formula-workbook-contract.md` before using formula mode. If the formula builder fails, the template is missing, required tabs/formulas/styles are not present, external links appear, or `model_citations.json` is absent, do not describe any fallback artifact as a banker formula workbook.
-
-## Status Labels
-
-Use exactly: `decision-grade`, `senior-review-ready`, `screen-grade`, `not-decision-ready`, `blocked`. Use `blocked` only before a run log exists; after execution, hard failures mean `not-decision-ready`.
-
-## Deferred Reference Router
-
-Load `references/reference-router.md` only when deeper schema, math, QA, integration, sector, or senior valuation judgment guidance is needed. The router preserves the detailed reference map without loading every reference during invocation.
-
-## Equity Valuation PM Standard
-
-Load `shared/equity-valuation-pm-standard.md` and `shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, what changes target, rating, sizing, hedge, trim, exit, or watchlist status, and what evidence is missing.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instrument underwriting, or distressed claim valuation.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/agents/openai.yaml b/plugins/financial-markets/skills/dcf-model-builder/agents/openai.yaml
deleted file mode 100644
index 70eb9ab..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "DCF Model Builder"
-  short_description: "Builds formula-first public-equity DCF workbooks with source labels, FCFF/FCFE logic, WACC, terminal value, EV/equity bridge, sensitivities, valuation checks, and QA logs."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/dcf-model-builder/assets/plan_template.json b/plugins/financial-markets/skills/dcf-model-builder/assets/plan_template.json
deleted file mode 100644
index b2de008..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/assets/plan_template.json
+++ /dev/null
@@ -1,262 +0,0 @@
-{
-  "meta": {
-    "company": "Northstar Industrial Software, Inc.",
-    "ticker": "NSIS",
-    "industry": "industrial software / vertical SaaS",
-    "currency": "USD",
-    "units": "$mm except per-share data",
-    "valuation_date": "2026-05-11",
-    "as_of_date": "2026-03-31",
-    "accounting_basis": "US GAAP",
-    "valuation_purpose": "public_equity_screen",
-    "model_type": "fcff"
-  },
-  "source_basis": [
-    {
-      "id": "src_hist_001",
-      "topic": "historicals",
-      "label": "reported",
-      "source_name": "FY2025 Form 10-K financial statements (illustrative template source)",
-      "source_type": "company_filing",
-      "as_of_date": "2025-12-31",
-      "confidence": "high",
-      "notes": "Illustrative source entry used for smoke testing; replace with real filing reference for live work."
-    },
-    {
-      "id": "src_forecast_001",
-      "topic": "forecast",
-      "label": "analyst_estimate",
-      "source_name": "Analyst operating case based on reported history and management-style KPIs",
-      "source_type": "analyst_model",
-      "as_of_date": "2026-05-11",
-      "confidence": "medium",
-      "notes": "Replace with management plan, consensus, or sourced operating drivers for decision-grade use."
-    },
-    {
-      "id": "src_wacc_001",
-      "topic": "wacc",
-      "label": "analyst_estimate",
-      "source_name": "Illustrative market-data build for cost of capital",
-      "source_type": "analyst_model",
-      "as_of_date": "2026-05-11",
-      "confidence": "medium",
-      "notes": "Use current risk-free rate, peer beta, ERP, debt yields, and target capital structure for live work."
-    },
-    {
-      "id": "src_terminal_001",
-      "topic": "terminal_value",
-      "label": "analyst_estimate",
-      "source_name": "Analyst terminal-value framework",
-      "source_type": "analyst_model",
-      "as_of_date": "2026-05-11",
-      "confidence": "medium",
-      "notes": "Terminal growth and exit-multiple cross-check are illustrative."
-    },
-    {
-      "id": "src_bridge_001",
-      "topic": "net_debt",
-      "label": "reported",
-      "source_name": "Latest balance sheet and debt schedule (illustrative template source)",
-      "source_type": "company_filing",
-      "as_of_date": "2026-03-31",
-      "confidence": "high",
-      "notes": "Replace with latest cash, debt, leases, pensions, minorities, and non-operating assets."
-    },
-    {
-      "id": "src_shares_001",
-      "topic": "share_count",
-      "label": "reported",
-      "source_name": "Latest diluted share count disclosure (illustrative template source)",
-      "source_type": "company_filing",
-      "as_of_date": "2026-03-31",
-      "confidence": "high",
-      "notes": "Replace with current diluted shares and option/convertible treatment."
-    }
-  ],
-  "timeline": {
-    "start_year": 2025,
-    "horizon_years": 5,
-    "periodicity": "annual"
-  },
-  "historicals": {
-    "latest_year": 2025,
-    "revenue": 1200.0,
-    "ebitda": 300.0,
-    "ebit": 220.0,
-    "cash_taxes": 52.8,
-    "da": 80.0,
-    "capex": 95.0,
-    "change_nwc": 18.0,
-    "net_working_capital": 140.0,
-    "unlevered_fcf": 134.2,
-    "source_id": "src_hist_001"
-  },
-  "forecast": {
-    "cash_flow_basis": "fcff",
-    "mid_year_convention": true,
-    "source_id": "src_forecast_001",
-    "notes": "Forecasts are driver-based using revenue growth, EBIT margin, cash tax, D&A intensity, capex intensity, and NWC intensity."
-  },
-  "wacc": {
-    "risk_free_rate": 0.043,
-    "beta": 1.12,
-    "equity_risk_premium": 0.055,
-    "size_premium": 0.01,
-    "company_specific_premium": 0.0,
-    "country_risk_premium": 0.0,
-    "pre_tax_cost_of_debt": 0.0625,
-    "marginal_tax_rate": 0.24,
-    "target_debt_pct": 0.2,
-    "target_equity_pct": 0.8,
-    "source_id": "src_wacc_001",
-    "preferred_pct": 0.0,
-    "pre_tax_cost_of_preferred": 0.0
-  },
-  "terminal_value": {
-    "method": "perpetual_growth",
-    "perpetual_growth_rate": 0.025,
-    "exit_ebitda_multiple": 13.0,
-    "source_id": "src_terminal_001"
-  },
-  "ev_to_equity_bridge": {
-    "cash": 180.0,
-    "debt": 650.0,
-    "leases": 45.0,
-    "minorities": 0.0,
-    "associates": 0.0,
-    "pensions": 15.0,
-    "preferred_stock": 0.0,
-    "non_operating_assets": 25.0,
-    "options": 0.0,
-    "other_debt_like_items": 0.0,
-    "diluted_shares": 92.5,
-    "net_debt_source_id": "src_bridge_001",
-    "share_count_source_id": "src_shares_001"
-  },
-  "scenarios": {
-    "base": {
-      "description": "Base case assumes durable vertical software growth, modest operating leverage, and steady reinvestment.",
-      "revenue_growth": [
-        0.09,
-        0.08,
-        0.07,
-        0.06,
-        0.05
-      ],
-      "ebit_margin": [
-        0.195,
-        0.205,
-        0.215,
-        0.225,
-        0.235
-      ],
-      "tax_rate": 0.24,
-      "da_percent_revenue": 0.06,
-      "capex_percent_revenue": 0.07,
-      "nwc_percent_revenue": [
-        0.12,
-        0.12,
-        0.115,
-        0.115,
-        0.11
-      ],
-      "terminal_growth_rate": 0.025,
-      "wacc_adjustment": 0.0
-    },
-    "downside": {
-      "description": "Downside case reflects slower new bookings, weaker operating leverage, higher risk premium, and lower terminal growth.",
-      "revenue_growth": [
-        0.03,
-        0.02,
-        0.02,
-        0.025,
-        0.03
-      ],
-      "ebit_margin": [
-        0.175,
-        0.175,
-        0.18,
-        0.185,
-        0.19
-      ],
-      "tax_rate": 0.24,
-      "da_percent_revenue": 0.06,
-      "capex_percent_revenue": 0.075,
-      "nwc_percent_revenue": [
-        0.125,
-        0.125,
-        0.12,
-        0.12,
-        0.115
-      ],
-      "terminal_growth_rate": 0.015,
-      "wacc_adjustment": 0.005
-    },
-    "upside": {
-      "description": "Upside case reflects stronger retention, better pricing, margin expansion, and lower perceived risk.",
-      "revenue_growth": [
-        0.12,
-        0.11,
-        0.1,
-        0.08,
-        0.07
-      ],
-      "ebit_margin": [
-        0.205,
-        0.22,
-        0.235,
-        0.25,
-        0.26
-      ],
-      "tax_rate": 0.24,
-      "da_percent_revenue": 0.058,
-      "capex_percent_revenue": 0.068,
-      "nwc_percent_revenue": [
-        0.12,
-        0.115,
-        0.11,
-        0.105,
-        0.1
-      ],
-      "terminal_growth_rate": 0.03,
-      "wacc_adjustment": -0.0025
-    }
-  },
-  "sensitivities": {
-    "wacc_delta": [
-      -0.01,
-      -0.005,
-      0.0,
-      0.005,
-      0.01
-    ],
-    "terminal_growth_delta": [
-      -0.01,
-      -0.005,
-      0.0,
-      0.005,
-      0.01
-    ],
-    "exit_multiple_delta": [
-      -2.0,
-      -1.0,
-      0.0,
-      1.0,
-      2.0
-    ],
-    "revenue_growth_delta": [
-      -0.02,
-      -0.01,
-      0.0,
-      0.01,
-      0.02
-    ],
-    "ebit_margin_delta": [
-      -0.03,
-      -0.015,
-      0.0,
-      0.015,
-      0.03
-    ]
-  }
-}
diff --git a/plugins/financial-markets/skills/dcf-model-builder/assets/templates/banker_formula_workbook_template.xlsx b/plugins/financial-markets/skills/dcf-model-builder/assets/templates/banker_formula_workbook_template.xlsx
deleted file mode 100644
index b31a82f..0000000
Binary files a/plugins/financial-markets/skills/dcf-model-builder/assets/templates/banker_formula_workbook_template.xlsx and /dev/null differ
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/banker-formula-workbook-contract.md b/plugins/financial-markets/skills/dcf-model-builder/references/banker-formula-workbook-contract.md
deleted file mode 100644
index 103bab8..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/banker-formula-workbook-contract.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Banker Formula Workbook Contract
-
-Formula mode is the default user-facing model-build path. It materializes `assets/templates/banker_formula_workbook_template.xlsx`, preserves the template's formulas, tabs, styles, and checks, then writes plan-derived inputs, source notes, scenario controls, and Public Equity Investing posture warnings.
-
-Run with:
-
-```bash
-python3 scripts/build_banker_formula_workbook.py path/to/plan.json --output-dir output
-```
-
-Formula-mode outputs:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-The workbook is the human deliverable. The run log, manifest, and citation ledger are support/audit artifacts. The JSON artifacts are not the user-facing deliverable unless the user asks for the audit files directly. `model_citations.json` must validate against the Public Equity Investing model-citation schema before dashboard handoff.
-
-## Truthfulness Gate
-
-Only call an artifact `banker_formula_workbook` when all of the following are true:
-
-- the formula builder ran;
-- `banker_formula_workbook.xlsx` exists;
-- required sheets are present;
-- formula count clears the model-specific inspection threshold;
-- required formula-bearing sheets are populated;
-- `Cover` is the first visible sheet;
-- named ranges or `model_citations.json` provide a stable source-to-cell anchor map;
-- styles are preserved;
-- no external workbook links are present;
-- the formula run log says `workbook_mode: "banker_formula_workbook"`;
-- `model_citations.json` exists with sheet/cell-level provenance for key outputs.
-
-If any gate fails, do not label the deterministic export or partial output as a banker formula workbook. Fall back honestly to `deterministic_export`, or mark the formula-mode run `not-decision-ready`.
-
-## Required Workbook Shape
-
-Required sheets:
-
-- `Cover`
-- `Executive Summary`
-- `Control Panel`
-- `Historical Financials`
-- `Revenue Build`
-- `Margin Cost Build`
-- `Working Capital`
-- `Capex D&A`
-- `Tax Schedule`
-- `Unlevered FCF`
-- `WACC`
-- `Terminal Value`
-- `DCF Valuation`
-- `Sensitivities`
-- `Checks`
-- `Source Notes`
-
-The `Cover` sheet must remain first and must carry public-equity context: issuer/ticker, valuation date, source posture, placeholder warnings, model mode, market-price inputs when available, selected valuation outputs, and workbook map.
-
-For a named-company model, template example values must never survive as apparent company history. Populate sourced actual periods only; render unavailable historical periods blank or clearly unavailable. Period headers must reconcile across Control Panel, historical, operating-build, FCF, and valuation sheets before the workbook can be used.
-
-The formula workbook must surface economic integrity checks as well as mechanical formula checks. At minimum, do not reject a sourced net-cash position merely because net debt is negative, and treat a modeled negative PP&E roll-forward as `not-decision-ready` until reinvestment assumptions are repaired. Where PP&E cannot be supported from source data, withhold the roll-forward and disclose the gap rather than projecting a fabricated asset balance.
-
-## Source-To-Cell Provenance
-
-`model_citations.json` is the handoff from workbook generation to dashboards. Each record should include workbook path, sheet, cell or range, value when known, formula where available, scenario/case, line item, section, source ID, evidence label, source label, source date/freshness, and optional URL.
-
-Dashboards may cite model cells such as `DCF Valuation!B18` instead of generic "model output." Keep the citation ledger as a support artifact; hero the workbook or rendered dashboard for the end user.
-
-## Limitations
-
-Formula mode preserves and populates the bundled DCF template. It does not synthesize arbitrary new tabs, evaluate Excel formulas in Python, or guarantee every user-specific model architecture. Use formula mode by default for user-facing model builds; use deterministic mode only for controlled computed values, smoke tests, explicit lightweight support exports, or honest fallback when formula mode is unavailable.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/cash-flow-methods.md b/plugins/financial-markets/skills/dcf-model-builder/references/cash-flow-methods.md
deleted file mode 100644
index 3d29e76..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/cash-flow-methods.md
+++ /dev/null
@@ -1,147 +0,0 @@
-# Cash Flow Methods and Model Selection
-
-## Purpose
-
-Use this reference to select the right valuation method and build the cash flow schedule correctly.
-
-## FCFF DCF
-
-Use FCFF for most non-financial operating companies when valuing enterprise value.
-
-Core formula:
-
-`ufcf = ebit * (1 - cash tax rate) + d&a - capex - change in net working capital - other required reinvestment`
-
-FCFF should exclude financing effects:
-
-- no interest expense in unlevered FCF
-- no debt repayment in unlevered FCF
-- no dividends in unlevered FCF
-- use WACC as the discount rate
-- bridge from enterprise value to equity value after calculating EV
-
-Common FCFF errors:
-
-- using net income instead of NOPAT without adjusting for interest
-- double-counting tax shield through both WACC and cash flows
-- subtracting debt paydown inside FCFF and again in the EV-to-equity bridge
-- mixing levered and unlevered cash flows
-- terminal value based on EBITDA while FCF method uses inconsistent assumptions
-
-## FCFE DCF
-
-Use FCFE when valuing equity cash flows directly, especially when leverage is stable or intentionally modeled.
-
-General formula:
-
-`fcfe = net income + d&a - capex - change in net working capital + net debt issuance - preferred dividends and other equity-claim cash flows`
-
-Discount FCFE using cost of equity, not WACC.
-
-Common FCFE errors:
-
-- discounting FCFE at WACC
-- ignoring debt maturity or refinancing needs
-- assuming net borrowing is permanent without support
-- treating levered FCF as comparable to enterprise-value DCF output
-
-## Dividend Discount Model
-
-Use DDM when dividends are the meaningful distributable cash flow, especially for regulated financial institutions or stable dividend-paying companies.
-
-Build around:
-
-- earnings
-- dividend payout ratio
-- regulatory capital or capital adequacy constraints
-- sustainable ROE
-- growth
-- cost of equity
-
-Common DDM errors:
-
-- treating dividends as discretionary when capital rules constrain them
-- using payout ratios that do not support growth
-- ignoring buybacks when material
-- using DDM for companies whose dividends are not tied to cash generation
-
-## Financial institutions
-
-For banks, insurers, asset managers, and similar businesses, do not force a standard enterprise-value FCFF DCF unless the user explicitly asks and the limitations are stated.
-
-Prefer:
-
-- DDM
-- FCFE
-- excess return model
-- price-to-book / ROE cross-check
-- embedded value or other sector method where applicable
-
-Key drivers:
-
-- net interest margin
-- loan / asset growth
-- credit losses
-- regulatory capital
-- ROE
-- payout capacity
-- book value growth
-- cost of equity
-
-## Private companies
-
-For private companies, clearly separate:
-
-- company-specific assumptions
-- market-derived assumptions
-- placeholders
-- diligence questions
-
-Additional considerations:
-
-- illiquidity or size premium, if justified
-- management add-backs and normalization
-- customer concentration
-- owner compensation adjustments
-- debt-like items
-- working capital peg or transaction adjustments
-- option pool, preferred equity, or liquidation preferences where relevant
-
-## Distressed or turnaround companies
-
-A DCF for a distressed company must include liquidity and downside realism.
-
-Consider:
-
-- short-term cash runway
-- debt maturities
-- covenant issues
-- restructuring costs
-- asset sales
-- refinancing risk
-- terminal value after stabilization, not before
-- going-concern uncertainty
-
-Flag valuations that assume a clean recovery without modeling the cash cost and timing of getting there.
-
-## Project or asset DCF
-
-For infrastructure, real estate, energy, mining, or project finance, match the forecast to asset life or contract life.
-
-Key drivers:
-
-- project life
-- production or utilization
-- pricing / tariffs
-- operating costs
-- maintenance capex
-- abandonment or decommissioning costs
-- tax depreciation
-- debt sculpting
-- residual value
-
-Do not use a perpetuity terminal value for finite-life assets unless there is a defensible renewal or residual-value assumption.
-
-## Credit Markets Boundary
-
-For Public Equity Investing, liquidity stress and debt maturity risk are included only to judge common-equity impairment, survivability, and dilution/refinancing risk. If the user asks for recovery value, claim priority, covenant remedies, debt trading value, restructuring waterfall, or public/private credit underwriting, route to Credit Markets.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/dashboard-map.md b/plugins/financial-markets/skills/dcf-model-builder/references/dashboard-map.md
deleted file mode 100644
index c4f321f..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/dashboard-map.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Dashboard Map: DCF Model Builder
-
-Use `dashboard-builder` for model-backed valuation dashboards when the DCF workbook output needs a browsable investor view.
-
-## Decision Question
-
-What valuation range is supportable, what drives it, and how does it compare with current market price or the decision threshold?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `dcf_model_builder`
-- `metadata.citation_policy`: `strict`
-- `model_citations_path`: `model_citations.json` from formula mode when workbook cells are cited
-- `sources`: filings, release/transcript, consensus/market data, WACC inputs, and model citation records
-
-## Recommended Tabs And Modules
-
-1. Valuation Read: `decision_box`, `metric_tiles`.
-2. Scenario Range: `scenario_map`, `table`.
-3. Value Drivers: `bar_chart`, `table`, `cards`.
-4. WACC And Terminal Value: `table`, `scenario_map`.
-5. Source And Model QA: `source_list`, `missing_evidence`, `table`.
-
-## Required Evidence
-
-Dashboard values sourced from workbook formulas should cite model-output records such as `model-output:dcf-value-per-share`, which resolve to workbook/sheet/cell/range and formula details.
-
-## Do Not
-
-- Do not cite generic model output when `model_citations.json` exists.
-- Do not claim formula-workbook status if the formula builder failed inspection.
-- Do not expose raw citation JSON as the user-facing artifact.
-
-
-## Equity Valuation PM Modules
-
-Dashboards for DCF work should surface a PM decision box, current price versus implied value/share, what is priced in, variant estimate path, DCF bridge, reverse DCF, scenario skew, downside mechanism, action rules, source posture, and missing evidence. Sector context belongs inside this dashboard rather than a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/dcf-architecture.md b/plugins/financial-markets/skills/dcf-model-builder/references/dcf-architecture.md
deleted file mode 100644
index b6cb576..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/dcf-architecture.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# DCF Architecture and Excel Build Standards
-
-## Purpose
-
-Use this reference when creating a new DCF workbook, adding a DCF module to an existing model, or improving the structure of a partial DCF.
-
-Current shipped behavior has two explicit paths: `scripts/build_banker_formula_workbook.py` is the default user-facing path and materializes the bundled live-formula template as `banker_formula_workbook`; the local deterministic pipeline produces a values-based `deterministic_export` support workbook for controlled computed values, smoke tests, explicit lightweight exports, or honest fallback. Do not claim the deterministic pipeline creates a fully linked formula workbook.
-
-## Architecture principles
-
-- Make the model easy to understand before making it complex.
-- Keep assumptions visible and editable.
-- For formula-workbook builds, keep calculations formula-driven and consistent across periods. In the shipped deterministic export, preserve formula intent through `formula_basis`, checks, and source labels rather than live Excel formulas.
-- Keep outputs separated from inputs and calculations.
-- Build to the decision, not to a generic template.
-- Preserve a sound existing workbook structure where possible.
-- Rebuild only when the existing structure prevents reliable valuation.
-
-## Recommended workbook tabs for manual/formula workbooks
-
-Use these tabs for a full standalone DCF, adapting to the complexity of the assignment.
-
-| Tab | Purpose |
-| --- | --- |
-| `README` or `Model Guide` | Explain purpose, scope, data sources, limitations, and how to use the model. |
-| `Control Panel` | Valuation date, currency, units, selected case, discounting convention, output toggles. |
-| `Sources` | Source documents, market data, filing references, user assumptions, and unresolved data needs. |
-| `Historical` | Historical financials and KPIs, clearly separated from forecasts. |
-| `Adjustments` | Normalization adjustments, non-recurring items, accounting changes, restatements. |
-| `Assumptions` | Forecast drivers by case. |
-| `Forecast` | Revenue, margins, operating expenses, EBIT, taxes, D&A, capex, working capital. |
-| `FCF` | FCFF, FCFE, or other cash flow build depending on method. |
-| `WACC` | Cost of equity, cost of debt, tax shield, capital structure, discount rate. |
-| `Terminal Value` | Perpetuity growth, exit multiple, terminal assumptions, and implied metrics. |
-| `Valuation Bridge` | PV of FCF, PV of terminal value, EV, bridge to equity value, per-share value. |
-| `Sensitivity` | Data tables and driver sensitivities. |
-| `Scenarios` | Base, downside, upside, and custom case logic. |
-| `Checks` | Formula, tie-out, scenario, sensitivity, and valuation-control checks. |
-| `Dashboard` | Executive summary, valuation range, key drivers, charts, and caveats. |
-
-For an existing 3-statement model, a DCF module may need only `DCF`, `WACC`, `Sensitivity`, and `Checks` tabs if forecast schedules already exist.
-
-## Existing workbook integration
-
-When the user already has a DCF or forecast model:
-
-1. Map the existing forecast engine.
-2. Identify the true source of forecast values.
-3. Link the DCF to the existing case or scenario control.
-4. Preserve existing user inputs that are coherent and labeled.
-5. Replace buried hardcodes with linked assumptions when appropriate.
-6. Add missing checks and sensitivities.
-7. Do not create duplicate valuation engines unless the user asks for a clean rebuilt version.
-8. If the existing structure is unreliable, explain why and build a cleaner module.
-
-## Input and formula conventions for manual/future formula workbooks
-
-Use the user's existing convention if it is clear and consistent. Otherwise use these conventions:
-
-- Inputs / assumptions: visually distinct from formulas.
-- Formulas: copied consistently across forecast periods.
-- Links to other tabs: clearly traceable.
-- Hardcodes inside formulas: avoid unless standard constants or explicitly justified.
-- Placeholders: visibly marked and included in a missing-data list.
-- Source references: add comments or source notes where practical.
-- Time periods: use one consistent date spine.
-- Units: repeat units on every major tab and output section.
-- Cases: scenario switches must change calculation cells, not merely dashboard labels.
-
-## Formula design standards for manual/future formula workbooks
-
-- Use simple formulas over clever formulas.
-- Avoid deeply nested logic when helper rows improve auditability.
-- Avoid volatile functions unless needed.
-- Avoid `INDIRECT` and `OFFSET` where direct links or structured ranges are safer.
-- Use named ranges only when they improve readability and are maintained correctly.
-- Avoid entire-column references in large models when performance matters.
-- Build checks close to the logic they test and summarize them on `Checks` or `Dashboard`.
-
-## Time horizon and granularity
-
-Choose granularity based on the business and decision.
-
-- Annual: typical mature public-company DCF, high-level valuation, board estimate.
-- Quarterly: public-company updates, businesses with near-term inflection, covenant or liquidity relevance.
-- Monthly: distressed, startup runway, project finance, working-capital-heavy businesses, seasonal businesses, or transaction models needing detailed cash timing.
-
-Choose horizon based on when the company reaches a defensible steady state.
-
-- Mature company: often 5 years.
-- High-growth company: often 7 to 10 years if fade to steady state needs time.
-- Turnaround: long enough to show stabilization and normalized cash flow.
-- Asset / project model: match project life, concession term, or asset economics.
-
-## Dashboard standards
-
-The dashboard should not be decorative. It should answer the senior decision-maker's questions:
-
-- What is the valuation range?
-- What drives the value?
-- What is the most important assumption?
-- How much of value comes from the terminal value?
-- Does downside still support the decision?
-- What assumptions are unsupported or require diligence?
-- What cross-checks support or contradict the DCF?
-
-Recommended dashboard elements:
-
-- valuation range chart
-- base / downside / upside valuation table
-- WACC x terminal growth sensitivity
-- value bridge from enterprise value to equity value
-- key operating forecast metrics
-- FCF conversion trend
-- terminal value share of enterprise value
-- key caveats and open questions
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/industry-playbooks.md b/plugins/financial-markets/skills/dcf-model-builder/references/industry-playbooks.md
deleted file mode 100644
index 90b635a..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/industry-playbooks.md
+++ /dev/null
@@ -1,196 +0,0 @@
-# Industry and Situation Playbooks
-
-## Purpose
-
-Use this reference to adapt the DCF to the company rather than forcing a generic template.
-
-## SaaS and subscription software
-
-Key drivers:
-
-- ARR or revenue base
-- new bookings
-- retention / churn
-- net revenue retention
-- ARPU
-- customer acquisition cost
-- sales efficiency
-- gross margin
-- R&D and S&M leverage
-- stock-based compensation treatment
-
-DCF judgment:
-
-- growth must tie to retention and new business.
-- terminal margins should reflect mature software economics, not early-stage burn.
-- reinvestment should support growth; do not assume rapid growth with no sales or R&D spend.
-- stock-based compensation treatment should be explicit.
-
-## Marketplace / platform
-
-Key drivers:
-
-- GMV or transaction volume
-- take rate
-- active users / buyers / sellers
-- frequency
-- incentives and contra-revenue
-- fulfillment or payment costs
-
-DCF judgment:
-
-- take-rate expansion should be justified by platform power.
-- incentive reduction may hurt growth.
-- terminal margins should reflect competitive dynamics and required platform investment.
-
-## Ecommerce / retail / restaurants
-
-Key drivers:
-
-- stores or users
-- traffic
-- conversion
-- basket size
-- same-store sales
-- gross margin
-- fulfillment / occupancy / labor costs
-- inventory and payables
-
-DCF judgment:
-
-- working capital and inventory matter.
-- margin assumptions must reflect promotions, shipping, shrink, labor, and rent.
-- capex should include new locations, maintenance, and technology investment.
-
-## Manufacturing / industrials
-
-Key drivers:
-
-- volume
-- price
-- utilization
-- backlog
-- raw materials
-- labor
-- plant capacity
-- capex
-- working capital
-
-DCF judgment:
-
-- revenue growth may require capacity expansion.
-- margins should reflect utilization and input costs.
-- terminal capex should not fall below maintenance needs.
-- cyclicality should be considered in terminal assumptions.
-
-## Healthcare services
-
-Key drivers:
-
-- patient volume
-- reimbursement rate
-- payer mix
-- labor costs
-- utilization
-- regulatory changes
-- capex and facility needs
-
-DCF judgment:
-
-- reimbursement and regulation can dominate value.
-- labor inflation and capacity constraints should be explicit.
-- terminal assumptions should reflect mature reimbursement environment.
-
-## Biotech / life sciences
-
-Key drivers:
-
-- probability of technical and regulatory success
-- development milestones
-- market size
-- launch timing
-- pricing
-- patent life
-- R&D spend
-
-DCF judgment:
-
-- use risk-adjusted DCF when outcomes are binary.
-- do not apply a simple mature-company DCF to pre-revenue assets without probability weighting.
-- terminal value may be inappropriate if patent life or product life is finite.
-
-## Energy / mining / commodity businesses
-
-Key drivers:
-
-- production volume
-- reserves or resource life
-- commodity price
-- operating cost
-- capex
-- reclamation / abandonment costs
-- taxes and royalties
-
-DCF judgment:
-
-- use asset life and reserve assumptions.
-- commodity price decks require source labeling.
-- terminal value may be inappropriate for finite-life assets.
-- sensitivity to price, volume, and capex is critical.
-
-## Real estate / infrastructure / project finance
-
-Key drivers:
-
-- asset life
-- rent / tariff / contracted revenue
-- utilization
-- operating cost
-- maintenance capex
-- debt amortization
-- residual value
-
-DCF judgment:
-
-- forecast term should match contract or asset life.
-- debt sculpting and distributions may matter.
-- use project-level cash flows and appropriate discount rate.
-
-## Financial institutions
-
-Key drivers:
-
-- asset growth
-- net interest margin
-- fee income
-- credit losses
-- operating efficiency
-- regulatory capital
-- ROE
-- payout ratio
-
-DCF judgment:
-
-- avoid standard FCFF enterprise-value DCF unless explicitly justified.
-- use DDM, FCFE, or excess-return logic.
-- cost of equity is often more relevant than WACC.
-- book value and ROE cross-checks matter.
-
-## Distressed / turnaround
-
-Key drivers:
-
-- liquidity
-- maturities
-- covenant headroom
-- restructuring costs
-- asset sales
-- stabilization timeline
-- refinancing feasibility
-
-DCF judgment:
-
-- model the path to survival before valuing the steady state.
-- downside case should test liquidity.
-- terminal value should not assume recovery without funding.
-- valuation bridge should capture debt-like claims and restructuring obligations.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/integrity-controls.md b/plugins/financial-markets/skills/dcf-model-builder/references/integrity-controls.md
deleted file mode 100644
index 0b861da..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/integrity-controls.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# DCF Integrity Controls and QA
-
-## Purpose
-
-Use this reference to verify that a DCF is mechanically reliable before presenting results.
-
-## Minimum checks
-
-Every DCF must include visible checks for:
-
-- no active Excel errors in output-impacting cells
-- valuation method and discount rate match cash flow type
-- FCF links to forecast schedules
-- terminal value uses the correct forecast year
-- discounting convention is consistent
-- WACC inputs flow to valuation formulas
-- terminal value inputs flow to valuation formulas
-- EV-to-equity bridge ties
-- share count and per-share value tie
-- selected scenario feeds the forecast and valuation
-- sensitivity tables link to live outputs
-- missing inputs and placeholders are flagged
-
-## FCFF-specific checks
-
-- EBIT or NOPAT base excludes financing effects.
-- Tax calculation is on operating profit, not post-interest profit.
-- D&A is added back only if it was included in EBIT or EBITDA logic correctly.
-- Capex is subtracted with correct sign.
-- Change in net working capital sign is correct.
-- Debt repayment is not subtracted from unlevered FCF.
-- WACC is used as discount rate.
-- Enterprise value is bridged to equity value after DCF output.
-
-## FCFE-specific checks
-
-- Net income is the starting point or levered cash flow logic is otherwise clear.
-- Net borrowing is included or excluded deliberately.
-- Debt maturity and refinancing assumptions are coherent.
-- Cost of equity is used as discount rate.
-- Output is equity value, not enterprise value.
-
-## Terminal value checks
-
-- Terminal growth is less than discount rate.
-- Terminal growth is reasonable for the currency and business maturity.
-- Terminal year is normalized.
-- Terminal margin is sustainable.
-- Terminal capex and working capital are not artificially low.
-- Exit multiple applies to the correct metric.
-- Implied perpetuity growth from exit multiple is reasonable when cross-checked.
-- Terminal value is discounted using the correct period.
-
-## WACC checks
-
-- Risk-free rate currency matches cash flow currency.
-- ERP and beta assumptions are documented or marked as placeholders.
-- Cost of debt reflects the right risk and timing.
-- Tax shield is realistic.
-- Capital structure is target or justified.
-- WACC is not reverse-engineered to force a desired valuation.
-
-## Equity bridge checks
-
-- Cash treatment is clear: excess cash vs operating cash.
-- Debt and debt-like items are captured.
-- Preferred equity and minority interest are not ignored.
-- Lease treatment is consistent with EBITDA and EV conventions.
-- Diluted shares are used when appropriate.
-- Per-share value divides by the correct share count.
-
-## Formula QA
-
-Check for:
-
-- formula errors such as `#REF!`, `#VALUE!`, `#DIV/0!`, `#NAME?`, `#NUM!`, `#N/A`, `#SPILL!`
-- hardcoded assumptions inside formula regions
-- formula inconsistencies across forecast periods
-- overwritten formulas
-- broken named ranges
-- external links that are stale or broken
-- circular references that are not intentional
-- manual calculation mode causing stale outputs
-- hidden tabs or rows affecting valuation
-
-## Severity framework
-
-| Severity | Meaning | Examples |
-| --- | --- | --- |
-| Blocker | Valuation cannot be trusted. | FCFF includes debt repayment; terminal value not discounted; WACC disconnected; equity bridge missing debt. |
-| High | Material issue likely to mislead decisions. | Unsupported WACC; stale share count; sensitivities linked to wrong case; terminal assumptions unrealistic. |
-| Medium | Issue could affect outputs or confidence. | Missing source note; weak downside case; limited working capital support; missing cross-check. |
-| Low | Hygiene or maintainability issue. | Formatting inconsistency; unclear label; overly complex formula. |
-| Question | Could be valid but requires confirmation. | Tax rate, capital structure, terminal multiple, or add-back treatment may be intentional. |
-
-## QA workflow after building or fixing
-
-1. Recalculate the workbook.
-2. Confirm no output-impacting formula errors.
-3. Test scenario switch behavior.
-4. Test at least one sensitivity table.
-5. Trace FCFF / FCFE to forecast schedules.
-6. Trace WACC to discounting formulas.
-7. Trace terminal value to final or normalized forecast year.
-8. Trace enterprise value to equity value and per-share value.
-9. Run directional pressure tests.
-10. Summarize limitations and open items.
-
-## Decision-ready sign-off standard
-
-A DCF is decision-ready only when:
-
-- the valuation method matches the company and purpose
-- key assumptions are visible and supported or flagged
-- formulas are reliable and auditable
-- controls pass
-- sensitivities cover the true value drivers
-- downside case is meaningful
-- terminal value is defensible
-- valuation bridge is complete
-- limitations are disclosed
-
-If any of these fail, say the model is not decision-ready and identify the remediation order.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/model-math.md b/plugins/financial-markets/skills/dcf-model-builder/references/model-math.md
deleted file mode 100644
index 2a4e427..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/model-math.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# DCF Model Math
-
-## FCFF forecast
-For each forecast period:
-
-```text
-Revenue_t = Revenue_(t-1) * (1 + Growth_t)
-EBIT_t = Revenue_t * EBIT Margin_t
-Cash Taxes_t = max(EBIT_t * Tax Rate_t, 0)
-NOPAT_t = EBIT_t - Cash Taxes_t
-D&A_t = Revenue_t * D&A % Revenue_t
-Capex_t = Revenue_t * Capex % Revenue_t
-NWC_t = Revenue_t * NWC % Revenue_t
-Change in NWC_t = NWC_t - NWC_(t-1)
-Unlevered FCF_t = NOPAT_t + D&A_t - Capex_t - Change in NWC_t
-EBITDA_t = EBIT_t + D&A_t
-```
-
-## FCFE support
-When `forecast.cash_flow_basis = fcfe`, the pipeline values equity cash flows directly using cost of equity. Required scenario support is `net_income_margin` or sufficient assumptions to derive net income, and `net_borrowing` when leverage changes.
-
-```text
-Net Income_t = Revenue_t * Net Income Margin_t
-FCFE_t = Net Income_t + D&A_t - Capex_t - Change in NWC_t + Net Borrowing_t
-```
-
-The FCFE path is useful for stable-leverage companies and some financial-style analyses. Banks and insurers usually need dividend discount, excess capital, or residual income models; route those to a sector overlay or specialized valuation workflow.
-
-## Cost of equity and WACC
-
-```text
-Cost of Equity = Risk-Free Rate + Beta * Equity Risk Premium + Size Premium + Country Risk Premium + Company-Specific Premium
-After-Tax Cost of Debt = Pre-Tax Cost of Debt * (1 - Marginal Tax Rate)
-WACC = Target Equity % * Cost of Equity + Target Debt % * After-Tax Cost of Debt + Preferred % * After-Tax Cost of Preferred
-```
-
-For FCFF, discount at WACC. For FCFE, discount at cost of equity.
-
-## Present value
-The pipeline discounts explicit FCF using mid-year convention when `forecast.mid_year_convention = true`:
-
-```text
-PV FCF_t = FCF_t / (1 + Discount Rate)^(t - 0.5)
-```
-
-Terminal value is discounted at the end of the final forecast year:
-
-```text
-PV Terminal Value = Terminal Value / (1 + Discount Rate)^n
-```
-
-## Terminal value
-Perpetual growth:
-
-```text
-Terminal FCF = Final Forecast FCF * (1 + g)
-Terminal Value = Terminal FCF / (Discount Rate - g)
-```
-
-Exit multiple:
-
-```text
-Terminal Value = Final Forecast EBITDA * Exit EBITDA Multiple
-```
-
-Always cross-check terminal value with:
-- terminal value as percent of enterprise value;
-- implied terminal EBITDA multiple;
-- implied terminal FCF yield;
-- terminal growth vs discount rate;
-- terminal margin vs history and peers;
-- reinvestment and ROIC coherence where data is available.
-
-## Enterprise-to-equity bridge
-For FCFF:
-
-```text
-Equity Value = Enterprise Value
-             + Cash
-             + Non-Operating Assets
-             + Associates
-             - Debt
-             - Leases
-             - Minorities
-             - Pensions
-             - Preferred Stock
-             - Options / Net Settlement
-             - Other Debt-Like Items
-```
-
-For FCFE, the present value is equity value directly. The pipeline still calculates an implied enterprise value by adding debt-like items and subtracting cash/non-operating assets for comparability.
-
-## Per-share value
-
-```text
-Value per Share = Equity Value / Diluted Shares
-```
-
-Use diluted shares, options, warrants, RSUs, convertibles, and buyback treatment carefully. Do not ignore dilution for high-SBC or option-heavy companies.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/output-and-review.md b/plugins/financial-markets/skills/dcf-model-builder/references/output-and-review.md
deleted file mode 100644
index 426f128..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/output-and-review.md
+++ /dev/null
@@ -1,161 +0,0 @@
-# Output and Review Standards
-
-## Purpose
-
-Use this reference when presenting the completed DCF, reviewing an existing DCF, or summarizing pressure-test results.
-
-## Final model deliverables
-
-When building or completing a DCF workbook, produce or preserve:
-
-- clean input sections
-- cash flow schedule
-- WACC / discount rate schedule
-- terminal value schedule
-- enterprise-to-equity bridge
-- per-share or ownership value output
-- sensitivity tables
-- scenario outputs
-- visible checks
-- executive dashboard or summary
-- notes on source status and open items
-
-## Written summary format
-
-Use this structure unless the user asks for something else.
-
-### 1. Header
-
-State:
-
-- company / asset
-- valuation date
-- currency and units
-- method used
-- model mode: built from scratch, completed existing DCF, repaired, updated, pressure-tested, or reviewed
-- overall result: decision-ready, usable with caveats, needs fixes, or not decision-ready
-
-### 2. Executive conclusion
-
-Write a short senior-level paragraph covering:
-
-- valuation range
-- base case conclusion
-- most important value drivers
-- biggest risks or assumptions requiring diligence
-- whether the model is ready to rely on
-
-### 3. Key valuation outputs
-
-Include:
-
-- enterprise value
-- equity value
-- value per share or ownership stake value, if relevant
-- PV of forecast FCF
-- PV of terminal value
-- terminal value as percentage of EV
-- WACC / discount rate
-- terminal growth or exit multiple
-- base / downside / upside outputs
-
-### 4. Assumption summary
-
-Summarize:
-
-- revenue growth assumptions
-- margin assumptions
-- tax assumptions
-- working capital assumptions
-- capex and D&A assumptions
-- WACC assumptions
-- terminal value assumptions
-- bridge items and share count assumptions
-
-Separate:
-
-- sourced facts
-- user-provided assumptions
-- modeler assumptions
-- placeholders / open items
-
-### 5. Sensitivity and scenario summary
-
-State:
-
-- most important sensitivity
-- downside valuation range
-- upside valuation range
-- whether sensitivities are linked and working
-- where the valuation conclusion is fragile
-
-### 6. Model checks
-
-Report pass / fail / not tested for:
-
-- formula errors
-- cash flow method consistency
-- WACC linkage
-- terminal value linkage
-- EV-to-equity bridge
-- scenario switch
-- sensitivity linkage
-- placeholder inputs
-- external links / macros / opaque dependencies
-
-### 7. Issues and remediation
-
-When reviewing or repairing a model, use this table:
-
-| # | Severity | Type | Sheet / Range | Issue | Evidence | Valuation impact | Recommended fix |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-
-Sort by severity and valuation impact.
-
-### 8. Open questions
-
-List the specific questions that must be answered before relying on the model.
-
-Examples:
-
-- What is the correct diluted share count at the valuation date?
-- Are leases treated as debt-like items in the EV bridge?
-- Should management add-backs be normalized or excluded?
-- What peer set supports the selected beta or exit multiple?
-- What terminal margin is defensible given competition?
-
-### 9. Sign-off statement
-
-Use one of these styles:
-
-- **This DCF is not decision-ready until the blocker and high-severity issues above are fixed and retested.**
-- **This DCF appears usable with caveats in the tested scope, subject to the limitations and open items above.**
-- **No material issues were found in the tested scope, but this is not a guarantee outside the reviewed workbook, linked logic, and tested scenarios.**
-- **The model is directionally useful for discussion, but the valuation conclusion should not be relied on until the unsupported assumptions are sourced and sensitivities are retested.**
-
-## Tone standard
-
-Write like a senior analyst briefing a Managing Director or Portfolio Manager.
-
-- Be direct.
-- Quantify where possible.
-- Separate fact from judgment.
-- Avoid false precision.
-- Explain what matters most.
-- Identify what could break the conclusion.
-- Do not bury caveats in footnotes.
-
-## Examples of strong comments
-
-- "The base-case valuation is most sensitive to terminal margin and WACC; the downside case still assumes margin expansion, so it is not a true stress case."
-- "The model uses FCFF but subtracts debt repayment inside the FCF schedule, which mixes enterprise and equity cash flow logic and overstates the conservatism of the valuation bridge."
-- "Terminal value represents 78% of enterprise value. That is not automatically wrong for this growth profile, but the terminal margin and reinvestment assumptions need support before the range can be relied on."
-- "The selected WACC appears reverse-engineered relative to the company's risk profile. The workbook needs a transparent cost-of-capital build or a clearly stated assumption range."
-
-## Examples of weak comments to avoid
-
-- "Looks good."
-- "DCF seems reasonable."
-- "Maybe check WACC."
-- "Terminal value might be high."
-- "The valuation is $52.17 per share" without a range, support, or sensitivity context.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/output-spec.md b/plugins/financial-markets/skills/dcf-model-builder/references/output-spec.md
deleted file mode 100644
index e433d1b..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/output-spec.md
+++ /dev/null
@@ -1,151 +0,0 @@
-# Output Specification
-
-## Default generated files
-
-The default model-build path is the banker formula workbook path. It writes:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-The deterministic support pipeline remains available for explicit lightweight runs, controlled computed values, smoke tests, and honest fallback exports. It writes:
-- `output/model.xlsx`
-- `output/plan.json`
-- `output/run_log.json`
-- `output/manifest.json`
-
-It may also write `output/support_note.md` as an optional support note or renderer input. Legacy `output/report.md` is written only with `--write-report-md`. Do not use the Markdown note as the lead user-facing deliverable when the workbook is the requested artifact.
-
-## Workbook mode
-Two workbook modes are supported:
-
-- `banker_formula_workbook`: default live formula workbook produced only by `scripts/build_banker_formula_workbook.py`.
-- `deterministic_export`: values-based support workbook produced by `scripts/run_pipeline.py` for controlled computed values, smoke tests, explicit lightweight exports, or clearly labeled fallback.
-
-Do not call the deterministic workbook a fully linked banker model. Formula mode may be claimed only when `banker_formula_workbook_run_log.json` confirms `workbook_mode: "banker_formula_workbook"`, required sheets/formulas/styles/no-external-link checks pass, and `model_citations.json` exists.
-
-## Formula-mode files
-
-Formula mode is the default for user-facing model builds and writes:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-`model_citations.json` contains source-to-cell provenance for key valuation outputs. Each record should include workbook path, sheet, cell/range, formula, value when known, scenario, section, line item, source ID, evidence label, source label, source date/freshness, and optional URL.
-
-If formula mode fails its truthfulness gate, do not call the fallback a banker workbook. Either fix formula mode, mark the run `not-decision-ready`, or use `deterministic_export` with the limitation stated loudly.
-
-## Deterministic support XLSX sheets
-The bundled workbook uses value-based long-format sheets:
-
-### `Cover`
-First visible workbook tab. Required by `shared/workbook-artifact-standard.md`.
-
-The cover is a PM/analyst dashboard, not a raw data table. It must summarize company, model type, valuation date, currency/units, model status, workbook mode, hard failures, warnings, selected valuation range, scenario value/share outputs, base-case DCF bridge, WACC/terminal assumptions, sensitivity drivers, source posture, and a workbook map.
-
-If the stdlib writer cannot create native charts, the cover must still include chart-ready scenario, bridge, and sensitivity rows.
-
-### `Summary`
-One row per scenario with EV, equity value, value per share, WACC/cost of equity, terminal value, PV of FCF, PV of terminal value, and terminal value percent of EV.
-
-### `Model`
-Long-format valuation rows. Required columns:
-- `section`
-- `line_item`
-- `scenario`
-- `period`
-- `value`
-- `units`
-- `evidence_label`
-- `source_id`
-- `notes`
-
-Required sections/rows include:
-- `IS`: revenue, EBITDA, EBIT, taxes
-- `FCF`: NOPAT, D&A, capex, change in NWC, unlevered FCF or FCFE
-- `WACC`: cost of equity, cost of debt, tax rate, target capital structure, WACC
-- `TV`: terminal value, PV of terminal value, TV percent of EV
-- `VALUATION`: PV of FCF, EV, equity value, diluted shares, value per share
-- `CHECKS`: DCF QA checks
-- `ASSUMPTIONS`: source-labeled assumptions
-
-### `Sensitivities`
-Long-format sensitivity outputs. Required columns:
-- `sensitivity`
-- `x_axis`
-- `x_value`
-- `y_axis`
-- `y_value`
-- `metric`
-- `value`
-- `units`
-
-The pipeline emits WACC/terminal growth, WACC/exit multiple, and revenue-growth/EBIT-margin sensitivity rows.
-
-### `Checks`
-Machine-computed hard failures, warnings, and informational checks.
-
-### `Assumptions`
-Material source basis and key assumption values.
-
-### `Run Log`
-Top-level run status, model status, generated paths, and caveats.
-
-## `run_log.json` schema
-The runner writes this shape:
-
-```json
-{
-  "status": "completed",
-  "model_status": "screen-grade",
-  "workbook_mode": "deterministic_export",
-  "artifact_level": "deterministic_export",
-  "source_basis": [],
-  "hard_failures": [],
-  "warnings": [],
-  "assumptions": {},
-  "checks": {},
-  "outputs": {},
-  "output_manifest": [],
-  "p0_handoff": {}
-}
-```
-
-`status` is `completed` when the helper ran without hard failures and `failed` when validation or execution blocks decision use. `output_manifest` mirrors `output/manifest.json` and records required artifacts, paths, and whether each artifact was written.
-
-Allowed `model_status` values:
-- `decision-grade`
-- `senior-review-ready`
-- `screen-grade`
-- `not-decision-ready`
-- `blocked`
-
-Use `blocked` only for pre-run messaging when no run log can be produced. If `hard_failures` is non-empty, `model_status` must be `not-decision-ready`.
-
-## P0 handoff fields
-`p0_handoff` must include:
-- `selected_valuation_range`
-- `scenarios`: base/downside/upside EV, equity value, value per share
-- `wacc_and_terminal_assumptions`
-- `key_value_drivers`
-- `major_caveats`
-- `model_status`
-- `paths`: workbook, plan, run log, manifest, and optional support note
-
-Formula-mode handoffs must also expose `model_citations` or `model_citations_path` when a dashboard cites workbook cells.
-
-## Support Note Template
-When `output/support_note.md` is enabled, it is a support note for review, renderer input, or audit. The workbook `Cover` remains the first user-facing landing page. The support note should include:
-1. Model status and artifact level.
-2. Valuation range and scenario table.
-3. Base-case DCF bridge.
-4. WACC and terminal assumptions.
-5. Sensitivity summary and key drivers.
-6. QA checks: hard failures and warnings.
-7. Source-basis notes and caveats.
-8. Generated artifact paths, led by the workbook.
-
-The support note must not claim decision-grade status when placeholders, stale data, unsupported WACC, missing source basis, or hard failures are present.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/p0-integrations.md b/plugins/financial-markets/skills/dcf-model-builder/references/p0-integrations.md
deleted file mode 100644
index 0b62939..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/p0-integrations.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# P0 Integrations
-
-## Shared core
-- `financial-source-of-truth`: use before modeling when source freshness, source hierarchy, citations, or fact/assumption separation matters. Feed its outputs into `source_basis`.
-- `financials-normalizer`: use before modeling when historicals come from filings, PDFs, XLS/CSV, VDR exports, management statements, or non-standard accounting presentations. Feed normalized financials into `historicals` and driver assumptions.
-- `excel-data-cleaner`: use when raw Excel/CSV data has messy headers, dates, units, merged cells, or QA flags before it becomes `plan.json` input.
-- `model-audit-tieout`: use after model generation for formula, hardcode, source, sign, link, and tie-out checks. This DCF skill includes machine checks but not a full external workbook audit.
-- `scenario-sensitivity-generator`: use when the user needs bespoke downside cases, break-even cases, Monte Carlo-style stress, commodity decks, rate cases, or event probabilities beyond bundled sensitivities.
-- `memo-builder`: use `p0_handoff`, the workbook `Cover`, and optional `support_note.md` to produce public-equity investment, client, PM, or committee memos.
-- `deck-report-qc` and `style-guide-adapter`: use after exporting exhibits into decks or reports.
-
-## Public Equity Investing
-- `equity-model-update`: use after earnings, filings, guidance changes, or transcript updates before running the DCF.
-- `comps-valuation`: use for peer multiple support, market cross-checks, implied valuation, and football-field style support.
-- Credit Markets: use when credit instruments, creditworthiness, covenant packages, restructuring, recovery waterfalls, spread/yield relative value, or debt-security valuation drive the case; keep only common-equity read-through locally.
-- `long-short-pitch`: use when converting the DCF into a tradable long/short recommendation with catalysts, risk, and variant view.
-- `thesis-tracker`: use the DCF drivers to create confirm/disconfirm signals and update cadence.
-- `earnings-preview` and `earnings-deep-dive`: use to update assumptions around prints.
-- `sector-context-overlay`: use for banks, insurance, REITs, E&P, biotech/pharma, exchanges/market infrastructure, marketplaces, SaaS, and other supported areas where generic FCFF is insufficient.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/plan-schema.md b/plugins/financial-markets/skills/dcf-model-builder/references/plan-schema.md
deleted file mode 100644
index 60e2c1b..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/plan-schema.md
+++ /dev/null
@@ -1,194 +0,0 @@
-# DCF Plan Schema
-
-## Purpose
-`plan.json` is the complete shared input contract for the default banker formula workbook path and the deterministic support pipeline. It must contain enough source-labeled assumptions to produce a base, downside, upside, sensitivities, checks, and P0 handoff without relying on hidden defaults.
-
-## Required top-level fields
-- `meta`
-- `source_basis`
-- `timeline`
-- `historicals`
-- `forecast`
-- `wacc`
-- `terminal_value`
-- `ev_to_equity_bridge`
-- `scenarios`
-- `sensitivities`
-
-## `meta`
-Required:
-- `company`
-- `industry`
-- `currency`
-- `units`
-- `valuation_date` as `YYYY-MM-DD`
-- `as_of_date` as `YYYY-MM-DD`
-- `accounting_basis`
-- `valuation_purpose`
-- `model_type`: `fcff` or `fcfe`
-
-## `source_basis`
-Array of evidence entries. Required topics:
-- `historicals`
-- `forecast`
-- `wacc`
-- `terminal_value`
-- `share_count`
-- `net_debt`
-
-Required fields per entry:
-- `id`: unique string referenced elsewhere in the plan
-- `topic`: one of the required topics or another useful topic such as `market_data`, `management_guidance`, `consensus`, `leases`, `pensions`, `tax`, `working_capital`
-- `label`: evidence label
-- `source_name`
-- `source_type`
-- `as_of_date`
-- `confidence`: `high`, `medium`, or `low`
-- `notes`
-
-Allowed evidence labels:
-- `reported`
-- `company_guidance`
-- `consensus`
-- `management_case`
-- `user_provided`
-- `connected_app`
-- `web_research`
-- `analyst_estimate`
-- `placeholder`
-- `derived`
-
-Decision-grade work should use mostly `reported`, `company_guidance`, `consensus`, `management_case`, `user_provided`, `connected_app`, `web_research`, and `derived` labels, with citations or source details. Heavy `analyst_estimate` support is screen-grade. Any `placeholder` affecting value is screen-grade at best.
-
-## `timeline`
-Required:
-- `start_year`: last actual year or stub base year
-- `horizon_years`: integer from 1 to 15
-- `periodicity`: `annual` or `quarterly`
-
-The current bundled pipeline is annual-oriented. Quarterly inputs can validate only if all scenario vectors match the horizon periods; otherwise convert to annual before running.
-
-## `historicals`
-Required for FCFF:
-- `latest_year`
-- `revenue`
-- `ebitda`
-- `ebit`
-- `cash_taxes`
-- `da`
-- `capex`
-- `change_nwc`
-- `net_working_capital`
-- `unlevered_fcf`
-- `source_id`
-
-Optional, but required to display a PP&E roll-forward:
-- `ppe`: source-backed ending PP&E balance for `latest_year`
-
-Required for FCFE also include:
-- `net_income`
-- `levered_fcf` if available
-
-## `forecast`
-Required:
-- `cash_flow_basis`: `fcff` or `fcfe`
-- `mid_year_convention`: boolean
-- `source_id`
-
-FCFF scenarios must include revenue growth, EBIT margin, tax rate, D&A percent of revenue, capex percent of revenue, and NWC percent of revenue.
-
-FCFE scenarios must additionally include either `net_income_margin` or sufficient income-statement assumptions, plus `net_borrowing` if the capital structure is expected to change.
-
-## `wacc`
-Required:
-- `risk_free_rate`
-- `beta`
-- `equity_risk_premium`
-- `size_premium`
-- `pre_tax_cost_of_debt`
-- `marginal_tax_rate`
-- `target_debt_pct`
-- `target_equity_pct`
-- `source_id`
-
-Optional:
-- `company_specific_premium`
-- `country_risk_premium`
-- `preferred_pct`
-- `pre_tax_cost_of_preferred`
-
-Weights should sum to approximately 100%. WACC must be positive and greater than perpetual terminal growth for perpetual-growth terminal value.
-
-## `terminal_value`
-Required:
-- `method`: `perpetual_growth` or `exit_multiple`
-- `source_id`
-
-If `method = perpetual_growth`, include `perpetual_growth_rate`.
-If `method = exit_multiple`, include `exit_ebitda_multiple`.
-Best practice is to include both `perpetual_growth_rate` and `exit_ebitda_multiple` so the model can cross-check implied values.
-
-## `ev_to_equity_bridge`
-Required when per-share value is requested:
-- `cash`
-- `debt`
-- `leases`
-- `minorities`
-- `associates`
-- `pensions`
-- `preferred_stock`
-- `non_operating_assets`
-- `options`
-- `other_debt_like_items`
-- `diluted_shares`
-- `net_debt_source_id`
-- `share_count_source_id`
-
-Positive bridge convention:
-- Add cash, non-operating assets, associates.
-- Subtract debt, leases, minorities, pensions, preferred stock, options net settlement, and other debt-like items.
-
-## `scenarios`
-Must include `base`, `downside`, and `upside`.
-
-Required FCFF scenario fields:
-- `description`
-- `revenue_growth`: scalar or list with `horizon_years` values
-- `ebit_margin`: scalar or list with `horizon_years` values
-- `tax_rate`: scalar or list
-- `da_percent_revenue`: scalar or list
-- `capex_percent_revenue`: scalar or list
-- `nwc_percent_revenue`: scalar or list
-- `terminal_growth_rate` when perpetual growth is used
-- `wacc_adjustment`: numeric, may be zero
-
-Optional fields:
-- `exit_ebitda_multiple`
-- `net_borrowing`
-- `net_income_margin`
-- `commentary`
-
-## `sensitivities`
-Required arrays:
-- `wacc_delta`
-- `terminal_growth_delta`
-- `exit_multiple_delta`
-- `revenue_growth_delta`
-- `ebit_margin_delta`
-
-Each array should contain negative, zero, and positive cases. Directionality checks use these outputs.
-
-## Screen-grade vs decision-grade
-Screen-grade inputs may use rough analyst estimates, placeholders, stale market data, or high-level assumptions. They can support triage but not capital allocation.
-
-Decision-grade inputs require:
-- current valuation date and market data;
-- source-labeled historicals from filings or company materials;
-- explicit bridge to latest cash, debt, leases, pensions, minorities, options, and shares;
-- defensible WACC support;
-- terminal value cross-checks;
-- scenario and sensitivity support;
-- no unresolved hard failures;
-- warnings that are immaterial or explicitly accepted by the user.
-
-For businesses whose valuation question is driven by operating KPIs, include the relevant source-backed driver schedule or disclose why the current workbook uses revenue as a proxy. Marketplace/platform examples include GBV or transaction volume, take rate, bookings/nights, active users, and frequency.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/qa-checks.md b/plugins/financial-markets/skills/dcf-model-builder/references/qa-checks.md
deleted file mode 100644
index 50755ee..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/qa-checks.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# QA Checks
-
-## Hard failures
-Hard failures force `model_status = not-decision-ready`.
-
-- Forecast cash flows are missing, non-numeric, or all unavailable.
-- Terminal value method is missing or invalid.
-- Perpetual growth terminal value has discount rate less than or equal to terminal growth.
-- WACC/cost of equity is less than or equal to zero or cannot be supported by inputs.
-- Discounting math fails, produces non-finite values, or produces negative enterprise value without explicit distressed context.
-- EV-to-equity bridge is missing when per-share value is requested.
-- Diluted shares are missing or less than or equal to zero when value per share is calculated.
-- Sensitivity directionality fails: value should increase as WACC decreases, terminal growth increases, exit multiple increases, revenue growth increases, or EBIT margin increases.
-- Source basis is missing for material valuation topics: historicals, forecast, WACC, terminal value, share count, net debt.
-- Required base/downside/upside scenarios are missing.
-- Actual/estimate period labels disagree across the workbook or sourced actuals are mixed with retained template example history.
-- A source-backed PP&E roll-forward becomes negative under the selected forecast assumptions.
-
-## Warnings
-Warnings reduce confidence but may still allow a screen-grade or senior-review-ready output.
-
-- Terminal value is more than 75% of enterprise value; greater than 85% is a major warning.
-- Terminal growth exceeds 4% or appears inconsistent with currency, maturity, or macro context.
-- WACC uses weak `analyst_estimate` or `placeholder` evidence labels.
-- Forecast margin expansion exceeds 500 bps without strong source support.
-- Capex intensity falls while growth accelerates, without an explicit asset-light rationale.
-- NWC release is large and recurring without support.
-- Source dates are mixed, stale, or not contemporaneous across market data, financials, share count, and debt.
-- SBC, leases, pensions, NOLs, minorities, convertibles, or non-operating assets are material but not modeled.
-- Exit multiple and perpetuity-growth methods imply materially different terminal values without explanation.
-- Scenario spread is too narrow to be decision-useful.
-- Placeholders remain active in a named-company model.
-- An operating-driver question is modeled through a revenue proxy because the workbook does not support the relevant KPI build.
-- Opening PP&E is unavailable, so capex and D&A sustainability cannot be tested through an asset roll-forward.
-
-## Senior-review red flags
-- Good-company/bad-stock issue not addressed through market-implied expectations.
-- Terminal margin equals a cyclical peak or management target with no fade.
-- Growth and reinvestment are inconsistent with ROIC economics.
-- Forecast beats consensus but the thesis does not explain why.
-- The DCF value is materially different from trading comps, precedents, or LBO support without explanation.
-- Dilution and stock-based compensation are ignored for a high-SBC company.
-- Cash taxes are assumed equal to book taxes despite material NOLs, credits, or foreign tax structure.
-- Lease-heavy business uses EBITDA and EV bridge inconsistently.
-- Acquisition-driven growth is treated as organic.
-- Model output is presented as a point estimate rather than a range.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/reference-router.md b/plugins/financial-markets/skills/dcf-model-builder/references/reference-router.md
deleted file mode 100644
index 688ffc2..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/reference-router.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# DCF Reference Router
-
-Load the narrowest reference needed for the current task. Keep the invoke path in `SKILL.md` compact; use these deferred docs for model nuance, implementation details, and senior review standards.
-
-## Default Formula Workbook Contract
-
-- `plan-schema.md`: input schema, required fields, source-basis expectations, and decision-grade requirements.
-- `output-spec.md`: generated artifacts, workbook sheets, `run_log.json`, `manifest.json`, report template, and P0 handoff shape.
-- `qa-checks.md`: hard failures, warnings, senior-review red flags, and status downgrade logic.
-- `banker-formula-workbook-contract.md`: default formula workbook mode, required sheets, inspection gate, truthfulness rules, and `model_citations.json` source-to-cell handoff.
-
-## Valuation Mechanics
-
-- `model-math.md`: DCF formulas, valuation bridge mechanics, and economic coherence checks.
-- `cash-flow-methods.md`: FCFF, FCFE, DDM, financial-institution, and special-situation method selection.
-- `wacc-terminal-value.md`: cost of capital, terminal growth, exit multiple, and terminal-value reasonableness.
-- `sensitivity-and-scenarios.md`: base/downside/upside design, reverse DCF, and pressure-test grids.
-
-## Senior Judgment And Architecture
-
-- `dcf-architecture.md`: preserved workbook architecture, tabs, formulas, and formatting conventions.
-- `valuation-judgment.md`: forecast challenge, valuation reasonableness, ROIC/reinvestment tests, and senior review prompts.
-- `integrity-controls.md`: workbook QA, formula controls, checks, sign-off standards, and artifact integrity.
-- `industry-playbooks.md`: sector-specific DCF adaptations and model-driver choices.
-- `output-and-review.md`: final review, communication standards, caveats, and decision-readiness narrative.
-
-## Integrations
-
-- `p0-integrations.md`: coordination with launch-priority Public Equity Investing and shared-core skills.
-
-## Loading Rule
-
-Do not load all references by default. Start with the formula workbook contract docs for user-facing model builds, deterministic support docs for implementation or QA issues, valuation mechanics docs for assumption/math issues, and senior judgment docs only when the user needs full diligence-quality reasoning.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/sensitivity-and-scenarios.md b/plugins/financial-markets/skills/dcf-model-builder/references/sensitivity-and-scenarios.md
deleted file mode 100644
index 3243aa6..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/sensitivity-and-scenarios.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Sensitivities, Scenarios, and Pressure Tests
-
-## Purpose
-
-Use this reference to make DCF outputs decision-ready rather than static.
-
-## Scenario design
-
-Build at least base, downside, and upside cases unless the user asks for a narrower model.
-
-A scenario should change the economic drivers, not just the labels.
-
-Base case:
-
-- reasonable forecast supported by history, management inputs, and modeler judgment
-- not automatically management's most optimistic plan
-
-Downside case:
-
-- tests the key risks in the investment thesis
-- includes adverse revenue, margin, working capital, capex, and discount-rate assumptions where relevant
-- should not preserve all upside initiatives while slightly reducing growth
-
-Upside case:
-
-- reflects credible outperformance
-- should not assume impossible market share, margin, or capital efficiency
-
-## Required sensitivities
-
-For most DCFs, include:
-
-- WACC x terminal growth
-- WACC x exit multiple, if exit multiple method is used
-- revenue growth x EBITDA margin or EBIT margin
-- FCF conversion or capex sensitivity when capital intensity matters
-- downside / base / upside valuation range
-
-For public companies, consider:
-
-- value per share sensitivity
-- premium / discount to current market price, if current market price is available and verified
-
-For transaction work, consider:
-
-- purchase price sensitivity
-- exit value sensitivity
-- leverage or financing sensitivity if linked to valuation use case
-
-## Pressure tests
-
-Run relevant pressure tests before sign-off.
-
-### Directional test
-
-Change a key driver and confirm valuation moves in the expected direction.
-
-Examples:
-
-- higher WACC lowers value
-- higher terminal growth raises value
-- lower margin lowers value
-- higher capex lowers FCFF
-- longer working capital days lowers near-term FCF
-
-### Scenario switch test
-
-Confirm that selecting base, downside, or upside changes the actual forecast engine and DCF output.
-
-Flag as High or Blocker if scenario labels change but the calculations do not.
-
-### Sensitivity linkage test
-
-For the shipped deterministic export, confirm sensitivity rows were regenerated from the active plan, scenario outputs, and valuation engine rather than pasted from stale files. For user-provided workbooks or future formula-workbook builders, confirm data tables or sensitivity formulas point to live valuation outputs and active assumption cells.
-
-Flag as High if sensitivity outputs are pasted values, stale, or linked to the wrong case.
-
-### Terminal value concentration test
-
-Calculate terminal value as a percentage of enterprise value.
-
-High terminal value share is not automatically wrong, but it means the conclusion depends heavily on terminal assumptions. Flag when the terminal value share is high and the terminal assumptions are weakly supported.
-
-### Reverse DCF test
-
-When helpful, ask what growth, margin, WACC, or terminal assumptions are required to justify a target valuation or market price.
-
-Use this to distinguish:
-
-- cheap because assumptions are conservative
-- expensive because expectations are high
-- apparently cheap because the model contains unsupported assumptions
-
-### Downside survivability test
-
-For levered, distressed, or cash-burning companies, test whether the company remains liquid or financeable in downside assumptions.
-
-Flag if the valuation assumes survival but the model does not show the funding path.
-
-## Sensitivity presentation standards
-
-- Label rows and columns clearly.
-- State units and assumptions.
-- Use regenerated deterministic outputs in the shipped pipeline. Use live formulas or data tables only when reviewing an existing workbook or when a future formula-workbook builder is present.
-- Highlight base case within sensitivity tables when possible.
-- Avoid sensitivities around immaterial drivers.
-- Prioritize the two or three assumptions that determine the valuation conclusion.
-
-## Common weak sensitivity designs
-
-Flag these issues:
-
-- sensitivities only around terminal growth and WACC when the main uncertainty is revenue or margins
-- cases that are too close together to matter
-- upside and downside cases that are symmetrical despite asymmetric risks
-- static sensitivity tables not linked to the model
-- sensitivities that use stale terminal values or wrong forecast cases
-- too many sensitivities that obscure the few important drivers
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/valuation-judgment.md b/plugins/financial-markets/skills/dcf-model-builder/references/valuation-judgment.md
deleted file mode 100644
index 4489be4..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/valuation-judgment.md
+++ /dev/null
@@ -1,166 +0,0 @@
-# Valuation Judgment Guide
-
-## Purpose
-
-Use this reference to apply senior investor judgment rather than merely building mathematically correct schedules.
-
-A DCF is an argument about future cash generation, risk, reinvestment, and durability. The formulas matter, but the assumptions determine whether the model is useful.
-
-## Senior judgment standard
-
-Before accepting a DCF conclusion, challenge:
-
-- Is the forecast economically plausible?
-- Does the model explain how growth is achieved?
-- Does margin expansion require credible scale, mix, pricing, or cost structure logic?
-- Does the company reinvest enough to support the forecast?
-- Does working capital absorb cash as the business grows?
-- Does ROIC improve for a reason, or just because the model says so?
-- Does terminal value assume a business that is better than the forecast supports?
-- Are downside cases meaningfully adverse?
-- Are cross-checks consistent with the DCF conclusion?
-
-## Forecast challenge framework
-
-### Revenue
-
-Do not accept revenue growth as a blind CAGR when a driver model is possible.
-
-Prefer drivers such as:
-
-- customers x ARPU
-- volume x price
-- stores x sales per store
-- units x ASP
-- bookings x conversion x retention
-- assets x utilization x yield
-- market size x share
-- contracted revenue x renewals x new business
-
-Challenge revenue assumptions for:
-
-- market size constraints
-- share gains with no competitive explanation
-- price increases unsupported by pricing power
-- volume growth beyond capacity
-- churn, retention, or renewal assumptions that are too optimistic
-- growth that ignores cyclicality or normalization
-
-### Margins
-
-Margin assumptions should reflect business mix, scale, input costs, labor, pricing, automation, utilization, and competitive pressure.
-
-Challenge:
-
-- gross margin expansion without mix or cost logic
-- EBITDA margin above peers without explanation
-- sudden step-ups that do not tie to a cost program
-- cost cuts that do not affect growth capacity
-- terminal margins that exceed sustainable competitive economics
-
-### Taxes
-
-Tax assumptions should match the method and fact pattern.
-
-Challenge:
-
-- FCFF taxes calculated after interest expense
-- cash taxes assumed equal to GAAP taxes when NOLs, credits, or timing differences matter
-- losses that never create NOLs or valuation allowance implications
-- tax rates that ignore jurisdiction mix
-- terminal tax rate that is inconsistent with normalized profitability
-
-### Working capital
-
-Working capital should reflect business mechanics, not just a percentage plugged into FCF.
-
-Challenge:
-
-- growth without receivables or inventory investment
-- payables stretch treated as permanent free financing
-- cash conversion cycle improving indefinitely
-- negative working capital businesses treated like traditional inventory businesses
-- step changes in working capital with no operating explanation
-
-### Capex and D&A
-
-Capex and D&A must be economically consistent.
-
-Challenge:
-
-- capex below maintenance needs in terminal years
-- high growth with no growth capex
-- D&A exceeding or falling below capex in ways that imply unexplained asset aging
-- terminal FCF boosted by starving capex
-- asset-light businesses modeled as capital intensive without reason, or vice versa
-
-### ROIC and reinvestment
-
-Use ROIC as a reality check.
-
-Challenge:
-
-- ROIC rising far above WACC without a moat or fade logic
-- growth continuing with declining reinvestment
-- margin and capital intensity assumptions that create unrealistic value creation
-- terminal ROIC that implies permanent excess returns without competitive defense
-
-## Terminal value challenge
-
-Terminal value often drives most of a DCF. Treat it as a primary valuation assumption, not a back-end formula.
-
-Challenge:
-
-- terminal growth above sustainable long-term economic growth
-- terminal margins above what the business can defend
-- terminal year not normalized
-- exit multiple inconsistent with terminal growth, margins, and ROIC
-- terminal value share so high that near-term forecast discipline barely matters
-- using an exit multiple without explaining what buyers would pay and why
-
-## Downside case quality
-
-A downside case should test the thesis, not simply make the base case slightly less optimistic.
-
-A credible downside should consider:
-
-- slower growth
-- weaker pricing
-- margin compression
-- customer churn or volume decline
-- working capital drag
-- higher capex or reinvestment
-- higher cost of capital
-- lower exit multiple or terminal growth
-- delayed operating improvements
-
-Flag a downside case as weak if it still assumes successful execution of all major initiatives.
-
-## Investment committee lens
-
-When summarizing the DCF, answer:
-
-- What has to be true for the valuation to be right?
-- Which assumptions are most debatable?
-- What is the downside if management misses plan?
-- What is the valuation floor from more conservative assumptions?
-- What non-DCF evidence supports or contradicts the conclusion?
-- What diligence should be done before relying on the model?
-
-## Red flags
-
-Treat these as issues requiring disclosure or revision:
-
-- unsupported terminal value assumptions
-- WACC selected to make the valuation work
-- management case used as base case without challenge
-- no downside case
-- sensitivities that do not cover the real value drivers
-- equity bridge missing debt-like items
-- share count not diluted or not current
-- terminal-year FCF boosted by unsustainably low capex or working capital
-- valuation conclusion presented without range or caveats
-
-## Public Equity PM Valuation Overlay
-
-A DCF is useful for this plugin only if it translates into common-equity judgment. Require current price versus DCF-implied value, reverse DCF / market-implied expectations, consensus bridge, upside/downside to spot, target-price implication, dilution/SBC treatment, and falsifiers. Distressed recovery, restructuring waterfall, debt-security valuation, or credit-spread relative value routes to Credit Markets; retain only the common-equity downside implication here.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/references/wacc-terminal-value.md b/plugins/financial-markets/skills/dcf-model-builder/references/wacc-terminal-value.md
deleted file mode 100644
index b48b1f0..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/references/wacc-terminal-value.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# WACC, Terminal Value, and Valuation Bridge Standards
-
-## Purpose
-
-Use this reference when building or reviewing discount rates, terminal value, and enterprise-to-equity value bridges.
-
-## WACC build
-
-For FCFF DCFs, WACC should reflect the risk of the operating cash flows and the currency of the cash flows.
-
-Required inputs:
-
-- valuation date
-- currency
-- risk-free rate
-- equity risk premium
-- beta or business risk assumption
-- cost of equity
-- pre-tax cost of debt
-- marginal tax rate or cash tax rate used for shield
-- after-tax cost of debt
-- target debt / equity or debt / total capital
-- WACC
-
-Challenge each input:
-
-- Risk-free rate: match duration and currency.
-- ERP: should be appropriate to market and valuation date.
-- Beta: should reflect business risk, leverage, and peer set if used.
-- Cost of debt: should reflect current borrowing risk, not stale coupon alone.
-- Tax shield: should be available and realistic.
-- Capital structure: should reflect target or sustainable structure, not an accidental current mix.
-
-## Cost of equity
-
-Basic CAPM structure:
-
-`cost of equity = risk-free rate + beta * equity risk premium + relevant premiums`
-
-Use relevant premiums cautiously:
-
-- size premium
-- country risk premium
-- company-specific risk premium
-- illiquidity premium
-
-Do not stack premiums without explaining why each is necessary and not double-counted.
-
-## Cost of debt
-
-Cost of debt should generally reflect current market borrowing cost or credit risk, not merely book interest expense.
-
-Adjust for:
-
-- floating vs fixed debt
-- current credit spreads
-- refinancing risk
-- tax deductibility
-- distressed or covenant-limited borrowing
-
-## Terminal value by perpetuity growth
-
-Formula:
-
-`terminal value = final-year fcf * (1 + terminal growth) / (discount rate - terminal growth)`
-
-Required checks:
-
-- terminal growth must be below discount rate
-- terminal growth should be sustainable in the forecast currency
-- final-year FCF should be normalized
-- terminal reinvestment must support terminal growth
-- terminal margin and ROIC must be economically coherent
-
-Common errors:
-
-- terminal growth too high
-- using a non-normalized final year
-- applying growth to the wrong year
-- not discounting terminal value correctly
-- using FCFF terminal value with cost of equity
-
-## Terminal value by exit multiple
-
-Formula:
-
-`terminal value = terminal metric * selected exit multiple`
-
-Common metrics:
-
-- EBITDA
-- EBIT
-- revenue for some high-growth businesses, with caution
-- book value or assets for financials, with caution
-
-Required checks:
-
-- metric definition matches the multiple
-- terminal metric is normalized
-- selected multiple is supported or clearly marked as an assumption
-- implied perpetuity growth is reasonable
-- implied ROIC and margin profile are reasonable
-
-Do not use an exit multiple as a shortcut to avoid thinking about sustainable cash flow.
-
-## Discounting convention
-
-State and apply the convention consistently:
-
-- year-end discounting
-- mid-year convention
-- stub-period discounting for partial forecast years
-
-Common errors:
-
-- applying mid-year convention to FCF but year-end to terminal value without explanation
-- discounting terminal value using the wrong period
-- ignoring stub periods when valuation date is not fiscal year-end
-
-## Enterprise value to equity value bridge
-
-The valuation bridge must be explicit.
-
-Start with enterprise value and adjust:
-
-- plus cash and cash equivalents, if excess or non-operating treatment is appropriate
-- plus non-operating investments
-- plus assets held for sale or unconsolidated investments when appropriate
-- less debt
-- less leases if treated as debt-like
-- less preferred equity
-- less minority interest
-- less pension deficits or other debt-like liabilities
-- less restructuring obligations or other claims where appropriate
-- plus / less tax assets or liabilities when separately valued
-
-Then divide by:
-
-- diluted shares outstanding for public companies
-- fully diluted ownership or capitalization for private companies where relevant
-
-Common bridge errors:
-
-- double-counting cash
-- omitting debt-like liabilities
-- using basic instead of diluted share count
-- ignoring preferred equity or liquidation preferences
-- using stale share count or market data
-- mixing enterprise-value and equity-value multiples
-
-## Valuation range
-
-Always present a range. The midpoint may be useful, but the range is more honest.
-
-A good valuation range should be based on the assumptions most likely to change value:
-
-- WACC
-- terminal growth
-- exit multiple
-- revenue growth
-- margin
-- reinvestment / FCF conversion
-- share count or bridge items when material
-
-## Equity-Centered Credit Inputs
-
-Cost of debt, leverage, liquidity, and refinancing risk may affect WACC, FCFE, or the EV-to-equity bridge. Do not turn this into a credit-security valuation. Use Credit Markets for spread/yield relative value, covenant-package analysis, recovery waterfall, distressed claim valuation, bond comps, loan comps, CDS, or debt-security valuation.
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook.py
deleted file mode 100755
index 21de868..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env python3
-"""Public entrypoint for the bundled DCF formula workbook materializer."""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from runtime_loader import load_runtime_module
-
-_runtime = load_runtime_module("build_banker_formula_workbook_runtime")
-
-__all__ = [name for name in dir(_runtime) if not name.startswith("_")]
-globals().update({name: getattr(_runtime, name) for name in __all__})
-
-
-if __name__ == "__main__":
-    raise SystemExit(_runtime.main())
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook_runtime b/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook_runtime
deleted file mode 100644
index e9183d8..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/build_banker_formula_workbook_runtime
+++ /dev/null
@@ -1,1120 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize the bundled DCF banker formula workbook template from plan.json.
-
-This is a template materializer: it preserves the shipped workbook's formulas,
-styles, tabs, and checks, then writes plan-derived inputs, source notes, and
-posture warnings into the control areas.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-import zipfile
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Tuple
-from xml.etree import ElementTree as ET
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-SKILL_ROOT = SCRIPT_DIR.parent
-PLUGIN_ROOT = SKILL_ROOT.parents[1]
-sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from skill_core import load_json, validate_plan_structure  # noqa: E402
-from shared.model_citations import validate_model_citations  # noqa: E402
-from shared.workbook_inspection import inspect_formula_workbook  # noqa: E402
-
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-ET.register_namespace("", NS_MAIN)
-ET.register_namespace("r", NS_REL)
-
-DEFAULT_TEMPLATE = SKILL_ROOT / "assets" / "templates" / "banker_formula_workbook_template.xlsx"
-OUTPUT_WORKBOOK = "banker_formula_workbook.xlsx"
-OUTPUT_RUN_LOG = "banker_formula_workbook_run_log.json"
-OUTPUT_MANIFEST = "manifest.json"
-OUTPUT_MODEL_CITATIONS = "model_citations.json"
-PARTIAL_CONTEXT_WARNING = "Screen-grade only; open assumptions or evidence gaps remain."
-TEMPLATE_FORECAST_PERIODS = 6
-FORECAST_COLS = ["B", "C", "D", "E", "F", "G"]
-MODEL_FORECAST_COLS = ["E", "F", "G", "H", "I", "J"]
-
-REQUIRED_SHEETS = [
-    "Cover",
-    "Executive Summary",
-    "Control Panel",
-    "Historical Financials",
-    "Revenue Build",
-    "Margin Cost Build",
-    "Working Capital",
-    "Capex D&A",
-    "Tax Schedule",
-    "Unlevered FCF",
-    "WACC",
-    "Terminal Value",
-    "DCF Valuation",
-    "Sensitivities",
-    "Checks",
-    "Source Notes",
-]
-
-
-class FormulaValue:
-    """Formula payload for an updated workbook cell."""
-
-    def __init__(self, expression: str) -> None:
-        self.expression = expression
-
-
-def formula(expression: str) -> FormulaValue:
-    return FormulaValue(expression)
-
-
-def _file_record(path: Path, role: str, description: str) -> Dict[str, Any]:
-    return {
-        "path": str(path),
-        "role": role,
-        "description": description,
-        "exists": True if path.name == OUTPUT_MANIFEST else path.exists(),
-    }
-
-
-def write_output_manifest(output_dir: Path, run_log: Dict[str, Any]) -> Dict[str, Any]:
-    manifest_path = output_dir / OUTPUT_MANIFEST
-    workbook_path = output_dir / OUTPUT_WORKBOOK
-    run_log_path = output_dir / OUTPUT_RUN_LOG
-    model_citations_path = output_dir / OUTPUT_MODEL_CITATIONS
-    hard_failures = run_log.get("hard_failures", [])
-    failed = bool(hard_failures)
-    status = run_log.get("status") or ("failed" if failed else "completed")
-    readiness_reason = (
-        "Formula workbook was written, but readiness gates failed. Treat it as a diagnostic artifact until repaired."
-        if failed
-        else ""
-    )
-    manifest = {
-        "manifest_version": "1.0",
-        "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
-        "skill": "dcf-model-builder",
-        "status": status,
-        "artifact_mode": "banker_formula_workbook",
-        "model_status": run_log.get("model_status"),
-        "output_dir": str(output_dir),
-        "primary_human_deliverable": str(workbook_path),
-        "first_read": {"path": str(workbook_path), "role": "primary_human_deliverable", "why": "Open the formula workbook first; support files are audit/provenance material."},
-        "human_deliverables": [_file_record(workbook_path, "human_deliverable", "banker formula workbook")],
-        "companion_deliverables": [],
-        "support_artifacts": [
-            {"path": str(run_log_path), "role": "support_artifact", "artifact_type": "json", "description": "Formula workbook run log.", "user_visible_default": False, "support_reason": "Run log is audit support for workbook generation."},
-            {"path": str(model_citations_path), "role": "support_artifact", "artifact_type": "json", "description": "Source-to-cell provenance ledger.", "user_visible_default": False, "support_reason": "Citation JSON is renderer/provenance support, not the main deliverable."},
-        ],
-        "support_artifacts_user_visible_default": False,
-        "blocked_or_partial_status": {
-            "status": "not-decision-ready" if failed else "complete",
-            "reason": readiness_reason,
-            "missing_inputs": run_log.get("missing_inputs", []),
-            "hard_failures": hard_failures,
-        },
-        "final_response_guidance": {
-            "lead_with": "workbook_diagnostic_status" if failed else "primary_human_deliverable",
-            "mention_support_artifacts": "include run log and model citations for repair" if failed else "only_briefly_unless_requested",
-        },
-        "agent_artifacts": [
-            _file_record(run_log_path, "agent_artifact", "formula workbook run log"),
-            _file_record(model_citations_path, "agent_artifact", "source-to-cell provenance ledger"),
-            _file_record(manifest_path, "agent_artifact", "agent-facing output manifest"),
-        ],
-        "hard_failure_count": len(hard_failures),
-        "warning_count": len(run_log.get("warnings", [])),
-        "discipline_note": (
-            "Do not use this workbook for target, rating, sizing, or circulation until formula inspection failures are repaired."
-            if failed
-            else "Use the workbook as the main deliverable; manifest/run_log files are agent-facing support artifacts."
-        ),
-    }
-    manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
-    return manifest
-
-
-def qname(tag: str) -> str:
-    return f"{{{NS_MAIN}}}{tag}"
-
-
-def col_to_num(col: str) -> int:
-    value = 0
-    for char in col:
-        value = value * 26 + ord(char.upper()) - 64
-    return value
-
-
-def cell_ref_parts(ref: str) -> Tuple[str, int]:
-    import re
-
-    match = re.fullmatch(r"([A-Z]+)([0-9]+)", ref)
-    if not match:
-        raise ValueError(f"Invalid cell reference: {ref}")
-    return match.group(1), int(match.group(2))
-
-
-def cell_sort_key(ref: str) -> Tuple[int, int]:
-    col, row = cell_ref_parts(ref)
-    return row, col_to_num(col)
-
-
-def is_number(value: Any) -> bool:
-    return isinstance(value, (int, float)) and not isinstance(value, bool) and math.isfinite(float(value))
-
-
-def source_for_topic(plan: Dict[str, Any], topic: str) -> Optional[Dict[str, Any]]:
-    for source in plan.get("source_basis", []):
-        if isinstance(source, dict) and source.get("topic") == topic:
-            return source
-    return None
-
-
-def source_for_id(plan: Dict[str, Any], source_id: Optional[str]) -> Optional[Dict[str, Any]]:
-    if not source_id:
-        return None
-    for source in plan.get("source_basis", []):
-        if isinstance(source, dict) and str(source.get("id")) == str(source_id):
-            return source
-    return None
-
-
-def source_details(plan: Dict[str, Any], *, source_id: Optional[str] = None, topic: Optional[str] = None) -> Dict[str, str]:
-    source = source_for_id(plan, source_id) or (source_for_topic(plan, topic) if topic else None)
-    if not source:
-        return {
-            "source_id": str(source_id or topic or "model-output"),
-            "evidence_label": "derived",
-            "source_label": "Model output",
-            "source_date": "",
-            "freshness": "derived",
-            "url": "",
-        }
-    return {
-        "source_id": str(source.get("id") or source_id or topic or "source"),
-        "evidence_label": str(source.get("label") or source.get("evidence_label") or ""),
-        "source_label": str(source.get("source_name") or source.get("label") or source.get("title") or source.get("id") or ""),
-        "source_date": str(source.get("as_of_date") or source.get("date") or source.get("accessed_at") or ""),
-        "freshness": str(source.get("confidence") or source.get("freshness") or ""),
-        "url": str(source.get("url") or source.get("source_url") or ""),
-    }
-
-
-def citation_record(
-    plan: Dict[str, Any],
-    workbook_path: Path,
-    *,
-    record_id: str,
-    title: str,
-    sheet: str,
-    cell: str,
-    line_item: str,
-    section: str,
-    formula: str,
-    value: Any = "",
-    topic: Optional[str] = None,
-    source_id: Optional[str] = None,
-    scenario: str = "base",
-) -> Dict[str, Any]:
-    source = source_details(plan, source_id=source_id, topic=topic)
-    short_label = f"Model: {sheet}!{cell}"
-    return {
-        "id": record_id,
-        "title": title,
-        "short_label": short_label,
-        "type": "model_cell",
-        "quality": "model_output",
-        "citation_id": record_id,
-        "workbook_path": str(workbook_path),
-        "sheet": sheet,
-        "cell_or_range": cell,
-        "cell": cell,
-        "range": cell,
-        "metric_name": title,
-        "value": "" if value is None else str(value),
-        "formula": formula,
-        "source_ids": [source["source_id"]],
-        "assumption_flag": source["evidence_label"] in {"assumption", "internal_estimate", "estimate"},
-        "tie_out_status": "model_generated",
-        "scenario": scenario,
-        "statement": "DCF",
-        "section": section,
-        "line_item": line_item,
-        "source_id": source["source_id"],
-        "evidence_label": source["evidence_label"],
-        "source_label": source["source_label"],
-        "source_date": source["source_date"],
-        "freshness": source["freshness"],
-        "url": source["url"],
-        "aliases": [line_item, title, short_label],
-        "notes": "Formula workbook cell provenance generated by Public Equity Investing DCF formula mode.",
-    }
-
-
-def build_model_citations(plan: Dict[str, Any], workbook_path: Path) -> List[Dict[str, Any]]:
-    bridge = plan.get("ev_to_equity_bridge", {})
-    wacc = plan.get("wacc", {})
-    terminal = plan.get("terminal_value", {})
-    forecast = plan.get("forecast", {})
-    return [
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-wacc-base",
-            title="Base WACC",
-            sheet="WACC",
-            cell="B17",
-            line_item="wacc",
-            section="Cost of Capital",
-            formula="=B8*B15+B13*B14",
-            topic="wacc",
-            source_id=wacc.get("source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-terminal-value",
-            title="Terminal Value",
-            sheet="Terminal Value",
-            cell="B12",
-            line_item="terminal_value",
-            section="Terminal Value",
-            formula='=IF(B4="Gordon Growth",B10,B11)',
-            topic="terminal_value",
-            source_id=terminal.get("source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-enterprise-value",
-            title="Enterprise Value",
-            sheet="DCF Valuation",
-            cell="B6",
-            line_item="enterprise_value",
-            section="Valuation",
-            formula="=B4+B5",
-            topic="forecast",
-            source_id=forecast.get("source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-equity-value",
-            title="Equity Value",
-            sheet="DCF Valuation",
-            cell="B16",
-            line_item="equity_value",
-            section="EV to Equity Bridge",
-            formula="=B6-B11-B12-B13+B14+B15",
-            topic="net_debt",
-            source_id=bridge.get("net_debt_source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-diluted-shares",
-            title="Diluted Shares",
-            sheet="DCF Valuation",
-            cell="B17",
-            line_item="diluted_shares",
-            section="EV to Equity Bridge",
-            formula="='Control Panel'!$B$86",
-            value=bridge.get("diluted_shares"),
-            topic="share_count",
-            source_id=bridge.get("share_count_source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-value-per-share",
-            title="DCF Value Per Share",
-            sheet="DCF Valuation",
-            cell="B18",
-            line_item="value_per_share",
-            section="Valuation",
-            formula="=B16/B17",
-            topic="share_count",
-            source_id=bridge.get("share_count_source_id"),
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:dcf-historical-revenue",
-            title="Historical Revenue",
-            sheet="Historical Financials",
-            cell="D6",
-            line_item="revenue",
-            section="Historical Financials",
-            formula="input from plan.json",
-            value=plan.get("historicals", {}).get("revenue"),
-            topic="historicals",
-            source_id=plan.get("historicals", {}).get("source_id"),
-        ),
-    ]
-
-
-def source_row(source: Optional[Dict[str, Any]], fallback_note: str) -> Tuple[str, str, str, str, str]:
-    if not source:
-        return ("Missing source", "placeholder", "", "Low", fallback_note)
-    return (
-        str(source.get("source_name", source.get("id", "source_basis"))),
-        str(source.get("label", "placeholder")),
-        str(source.get("as_of_date", "")),
-        str(source.get("confidence", "low")).title(),
-        str(source.get("notes", fallback_note)),
-    )
-
-
-def screen_grade_required(plan: Dict[str, Any]) -> bool:
-    weak_labels = {"placeholder", "analyst_estimate"}
-    for source in plan.get("source_basis", []):
-        label = str(source.get("label", "")).lower()
-        confidence = str(source.get("confidence", "")).lower()
-        if label in weak_labels or confidence == "low":
-            return True
-    return False
-
-
-def forecast_period_labels(plan: Dict[str, Any], warnings: List[str]) -> List[str]:
-    start_year = int(plan.get("historicals", {}).get("latest_year", plan.get("timeline", {}).get("start_year", 2025))) + 1
-    horizon = int(plan.get("timeline", {}).get("horizon_years", TEMPLATE_FORECAST_PERIODS))
-    if horizon != TEMPLATE_FORECAST_PERIODS:
-        warnings.append(
-            f"Formula template has {TEMPLATE_FORECAST_PERIODS} forecast columns; plan has {horizon}. "
-            "Values were extended or truncated for template compatibility."
-        )
-    return [f"{start_year + i}E" for i in range(TEMPLATE_FORECAST_PERIODS)]
-
-
-def merged_updates(*parts: Dict[str, Any]) -> Dict[str, Any]:
-    updates: Dict[str, Any] = {}
-    for part in parts:
-        updates.update(part)
-    return updates
-
-
-def build_period_header_updates(plan: Dict[str, Any], warnings: List[str]) -> Dict[str, Dict[str, Any]]:
-    hist_year = int(plan.get("historicals", {}).get("latest_year", plan.get("timeline", {}).get("start_year", 2025)))
-    forecast_labels = forecast_period_labels(plan, warnings)
-    headers = {"B5": "N/A", "C5": "N/A", "D5": f"{hist_year}A"}
-    update_series(headers, 5, forecast_labels, MODEL_FORECAST_COLS)
-    return {
-        sheet: dict(headers)
-        for sheet in [
-            "Historical Financials",
-            "Revenue Build",
-            "Margin Cost Build",
-            "Working Capital",
-            "Capex D&A",
-            "Tax Schedule",
-            "Unlevered FCF",
-        ]
-    }
-
-
-def extend_values(value: Any, length: int, field_name: str, warnings: List[str], default: Optional[float] = None) -> List[Optional[float]]:
-    if isinstance(value, list):
-        values = [float(v) if is_number(v) else default for v in value]
-    elif is_number(value):
-        values = [float(value)] * length
-    else:
-        values = [default] * length
-
-    if not values:
-        values = [default] * length
-    if len(values) < length:
-        warnings.append(f"{field_name} supplied {len(values)} values; final value repeated to fill template forecast columns.")
-        fill = values[-1]
-        values = values + [fill] * (length - len(values))
-    if len(values) > length:
-        warnings.append(f"{field_name} supplied {len(values)} values; truncated to template forecast columns.")
-        values = values[:length]
-    return values
-
-
-def scenario_values(plan: Dict[str, Any], scenario_name: str, field: str, warnings: List[str], default: Optional[float] = None) -> List[Optional[float]]:
-    scenario = plan.get("scenarios", {}).get(scenario_name, {})
-    return extend_values(scenario.get(field), TEMPLATE_FORECAST_PERIODS, f"scenarios.{scenario_name}.{field}", warnings, default)
-
-
-def update_series(updates: Dict[str, Any], row: int, values: List[Any], cols: List[str] = FORECAST_COLS) -> None:
-    for col, value in zip(cols, values):
-        if value is not None:
-            updates[f"{col}{row}"] = value
-
-
-def ebitda_margin_values(plan: Dict[str, Any], scenario_name: str, warnings: List[str]) -> List[Optional[float]]:
-    ebit = scenario_values(plan, scenario_name, "ebit_margin", warnings, 0.0)
-    da = scenario_values(plan, scenario_name, "da_percent_revenue", warnings, 0.0)
-    return [(e or 0.0) + (d or 0.0) for e, d in zip(ebit, da)]
-
-
-def wacc_value(plan: Dict[str, Any]) -> float:
-    wacc = plan.get("wacc", {})
-    tax = float(wacc.get("marginal_tax_rate", 0.0) or 0.0)
-    cost_equity = (
-        float(wacc.get("risk_free_rate", 0.0) or 0.0)
-        + float(wacc.get("beta", 0.0) or 0.0) * float(wacc.get("equity_risk_premium", 0.0) or 0.0)
-        + float(wacc.get("size_premium", 0.0) or 0.0)
-        + float(wacc.get("company_specific_premium", 0.0) or 0.0)
-        + float(wacc.get("country_risk_premium", 0.0) or 0.0)
-    )
-    after_tax_debt = float(wacc.get("pre_tax_cost_of_debt", 0.0) or 0.0) * (1.0 - tax)
-    debt_pct = float(wacc.get("target_debt_pct", 0.0) or 0.0)
-    equity_pct = float(wacc.get("target_equity_pct", 1.0) or 1.0)
-    preferred_pct = float(wacc.get("preferred_pct", 0.0) or 0.0)
-    preferred_cost = float(wacc.get("pre_tax_cost_of_preferred", 0.0) or 0.0)
-    base = cost_equity * equity_pct + after_tax_debt * debt_pct + preferred_cost * preferred_pct
-    return base + float(plan.get("scenarios", {}).get("base", {}).get("wacc_adjustment", 0.0) or 0.0)
-
-
-def net_debt_value(plan: Dict[str, Any]) -> float:
-    bridge = plan.get("ev_to_equity_bridge", {})
-    debt_like = (
-        float(bridge.get("debt", 0.0) or 0.0)
-        + float(bridge.get("leases", 0.0) or 0.0)
-        + float(bridge.get("pensions", 0.0) or 0.0)
-        + float(bridge.get("other_debt_like_items", 0.0) or 0.0)
-    )
-    return debt_like - float(bridge.get("cash", 0.0) or 0.0)
-
-
-def current_share_price_value(plan: Dict[str, Any]) -> Optional[float]:
-    bridge = plan.get("ev_to_equity_bridge", {})
-    current_share_price = (
-        bridge.get("current_share_price")
-        or plan.get("market", {}).get("current_share_price")
-        or plan.get("meta", {}).get("current_share_price")
-    )
-    return float(current_share_price) if is_number(current_share_price) else None
-
-
-def data_quality_hard_failures(plan: Dict[str, Any], warnings: List[str]) -> List[str]:
-    failures: List[str] = []
-    hist = plan.get("historicals", {})
-    opening_ppe = hist.get("ppe")
-    if is_number(opening_ppe):
-        revenue = float(hist.get("revenue", 0.0) or 0.0)
-        revenues: List[float] = []
-        for growth in scenario_values(plan, "base", "revenue_growth", warnings, 0.0):
-            revenue *= 1.0 + float(growth or 0.0)
-            revenues.append(revenue)
-        capex_rates = scenario_values(plan, "base", "capex_percent_revenue", warnings, 0.0)
-        da_rates = scenario_values(plan, "base", "da_percent_revenue", warnings, 0.0)
-        ppe = float(opening_ppe)
-        if ppe < 0.0:
-            failures.append("negative_historical_ppe")
-            warnings.append("Source-backed opening PP&E is negative; reconcile the historical balance before investment use.")
-            return failures
-        for revenue_value, capex_rate, da_rate in zip(revenues, capex_rates, da_rates):
-            ppe += revenue_value * (float(capex_rate or 0.0) - float(da_rate or 0.0))
-            if ppe < -0.1:
-                failures.append("negative_forecast_ppe_rollforward")
-                warnings.append("Base-case capex and D&A assumptions drive ending PP&E below zero; reconcile reinvestment assumptions before investment use.")
-                break
-    return failures
-
-
-def build_control_panel_updates(
-    plan: Dict[str, Any], missing_inputs: List[str], warnings: List[str], model_status: str
-) -> Dict[str, Any]:
-    meta = plan.get("meta", {})
-    bridge = plan.get("ev_to_equity_bridge", {})
-    wacc = plan.get("wacc", {})
-    terminal = plan.get("terminal_value", {})
-    periods = forecast_period_labels(plan, warnings)
-    screen_grade = screen_grade_required(plan)
-
-    current_share_price = current_share_price_value(plan)
-    if current_share_price is None:
-        missing_inputs.append("current_share_price")
-
-    updates: Dict[str, Any] = {
-        "B4": meta.get("company", "Company"),
-        "B5": meta.get("currency", "USD"),
-        "B6": meta.get("units", "$mm except per-share data"),
-        "B7": meta.get("valuation_date", meta.get("as_of_date", "")),
-        "B8": model_status,
-        "B9": "banker_formula_workbook",
-        "B10": "Base",
-        "B11": "Yes" if plan.get("forecast", {}).get("mid_year_convention") else "No",
-        "B12": current_share_price if current_share_price is not None else 0.0,
-        "B13": PARTIAL_CONTEXT_WARNING if screen_grade else "Source-backed DCF; review checks before circulation.",
-        "B14": "Gordon Growth" if terminal.get("method") == "perpetual_growth" else "Exit Multiple",
-        "B15": 0.0,
-        "B69": wacc.get("risk_free_rate", 0.0),
-        "B70": wacc.get("beta", 0.0),
-        "B71": wacc.get("equity_risk_premium", 0.0),
-        "B72": wacc.get("size_premium", 0.0),
-        "B73": wacc.get("pre_tax_cost_of_debt", 0.0),
-        "B74": wacc.get("target_debt_pct", 0.0),
-        "B76": "No",
-        "B77": wacc_value(plan),
-        "B79": "No external links",
-        "B83": plan.get("scenarios", {}).get("base", {}).get("terminal_growth_rate", terminal.get("perpetual_growth_rate", 0.0)),
-        "B84": terminal.get("exit_ebitda_multiple", 0.0),
-        "B85": net_debt_value(plan),
-        "B86": bridge.get("diluted_shares", 0.0),
-        "B87": bridge.get("minorities", 0.0),
-        "B88": bridge.get("preferred_stock", 0.0),
-        "B89": float(bridge.get("non_operating_assets", 0.0) or 0.0) + float(bridge.get("associates", 0.0) or 0.0),
-        "B90": -float(bridge.get("options", 0.0) or 0.0),
-    }
-    update_series(updates, 18, periods)
-
-    row_map = {"base": 21, "downside": 22, "upside": 23}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "revenue_growth", warnings, 0.0))
-    row_map = {"base": 26, "downside": 27, "upside": 28}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, ebitda_margin_values(plan, scenario_name, warnings))
-    row_map = {"base": 31, "downside": 32, "upside": 33}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "ebit_margin", warnings, 0.0))
-    row_map = {"base": 36, "downside": 37, "upside": 38}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "da_percent_revenue", warnings, 0.0))
-    row_map = {"base": 41, "downside": 42, "upside": 43}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "capex_percent_revenue", warnings, 0.0))
-    row_map = {"base": 46, "downside": 47, "upside": 48}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "nwc_percent_revenue", warnings, 0.0))
-    row_map = {"base": 51, "downside": 52, "upside": 53}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "tax_rate", warnings, 0.0))
-    row_map = {"base": 56, "downside": 57, "upside": 58}
-    for scenario_name, row in row_map.items():
-        update_series(updates, row, scenario_values(plan, scenario_name, "tax_rate", warnings, 0.0))
-
-    # The DCF template uses component working-capital drivers; the plan schema
-    # currently provides total operating NWC % revenue. Map that total to AR and
-    # zero the remaining components so model NWC still equals the plan driver.
-    base_nwc = scenario_values(plan, "base", "nwc_percent_revenue", warnings, 0.0)
-    update_series(updates, 61, base_nwc)
-    for row in [62, 63, 64, 65]:
-        update_series(updates, row, [0.0] * TEMPLATE_FORECAST_PERIODS)
-    missing_inputs.append("working_capital_component_split")
-
-    if "gross_profit_margin" not in plan.get("historicals", {}) and "gross_margin" not in plan.get("historicals", {}):
-        missing_inputs.append("gross_profit_margin_or_gross_profit_history")
-    if current_share_price is None:
-        warnings.append("Current share price was not provided; formula workbook uses 0.0 placeholder for upside/downside until supplied.")
-    warnings.append("Total operating NWC % revenue was mapped to the AR bucket because no AR/inventory/AP/OCA/OCL split is present in plan.json.")
-    return updates
-
-
-def build_cover_updates(plan: Dict[str, Any], model_status: str) -> Dict[str, Any]:
-    meta = plan.get("meta", {})
-    company = str(meta.get("company", "Company"))
-    ticker = str(meta.get("ticker", "")).strip()
-    issuer = f"{company} ({ticker})" if ticker else company
-    return {
-        "A1": f"{issuer} - DCF Valuation Workbook",
-        "A3": "Investment snapshot",
-        "A4": "Company",
-        "B4": issuer,
-        "A5": "Valuation date",
-        "B5": meta.get("valuation_date", meta.get("as_of_date", "")),
-        "A6": "Model status",
-        "B6": model_status,
-        "A7": "Current / implied value per share",
-        "B7": formula('TEXT(\'Executive Summary\'!B10,"$#,##0.00")&" / "&TEXT(\'Executive Summary\'!B9,"$#,##0.00")'),
-        "A8": "Base-case upside / downside",
-        "B8": formula('TEXT(\'Executive Summary\'!B11,"0.0%")'),
-        "A9": "WACC / terminal growth",
-        "B9": formula('TEXT(\'Control Panel\'!B78,"0.0%")&" / "&TEXT(\'Control Panel\'!B83,"0.0%")'),
-        "A31": "Key diligence warnings",
-        "A32": "History coverage",
-        "B32": "Only source-backed historical periods are shown; unavailable history is intentionally blank.",
-        "A33": "Operating drivers",
-        "B33": "Use sector-relevant KPI drivers where material; read Source Notes for disclosed proxies.",
-        "A34": "Model checks",
-        "B34": "Review Checks and Source Notes before using model outputs.",
-        "A35": "Readiness",
-        "B35": model_status,
-    }
-
-
-def build_executive_summary_updates(plan: Dict[str, Any], model_status: str) -> Dict[str, Any]:
-    screen_grade = screen_grade_required(plan)
-    return {
-        "A3": "Posture warning",
-        "B3": (
-            "Not decision-ready; repair hard-failure checks before investment use."
-            if model_status == "not-decision-ready"
-            else PARTIAL_CONTEXT_WARNING if screen_grade else "Source-backed DCF; review checks before circulation."
-        ),
-        "B27": "Add source-backed multi-year history before relying on trend conclusions.",
-    }
-
-
-def build_historical_updates(plan: Dict[str, Any], missing_inputs: List[str], warnings: List[str]) -> Dict[str, Any]:
-    hist = plan.get("historicals", {})
-    source = source_for_topic(plan, "historicals")
-    label = str(source.get("label", hist.get("source_id", "placeholder"))) if source else "placeholder"
-    note = "Latest historical year populated from plan.json; add multi-year sourced history before circulation."
-    latest_year = hist.get("latest_year")
-    updates: Dict[str, Any] = {}
-    for col in ["B", "C"]:
-        for row in [6, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21]:
-            updates[f"{col}{row}"] = ""
-    for ref, expression in {
-        "B9": 'IFERROR(B8/B6,"")',
-        "B11": 'IFERROR(B10/B6,"")',
-        "B14": 'IFERROR(B13/B6,"")',
-        "C7": 'IFERROR(C6/B6-1,"")',
-        "C9": 'IFERROR(C8/C6,"")',
-        "C11": 'IFERROR(C10/C6,"")',
-        "C14": 'IFERROR(C13/C6,"")',
-        "C19": 'IF(AND(ISNUMBER(B18),ISNUMBER(C18)),C18/C6-B18/B6,"")',
-        "D7": 'IFERROR(D6/C6-1,"")',
-        "D19": 'IF(AND(ISNUMBER(C18),ISNUMBER(D18)),D18/D6-C18/C6,"")',
-    }.items():
-        updates[ref] = formula(expression)
-    if is_number(hist.get("revenue")):
-        updates["D6"] = hist["revenue"]
-    gross_profit = hist.get("gross_profit")
-    gross_margin = hist.get("gross_profit_margin", hist.get("gross_margin"))
-    if is_number(gross_profit):
-        updates["D8"] = gross_profit
-    elif is_number(gross_margin) and is_number(hist.get("revenue")):
-        updates["D8"] = float(hist["revenue"]) * float(gross_margin)
-    else:
-        updates["D8"] = ""
-    if is_number(hist.get("ebitda")):
-        updates["D10"] = hist["ebitda"]
-    if is_number(hist.get("da")):
-        updates["D12"] = hist["da"]
-    if is_number(hist.get("ebit")):
-        updates["D13"] = hist["ebit"]
-    if is_number(hist.get("cash_taxes")):
-        updates["D15"] = hist["cash_taxes"]
-    updates["D16"] = hist.get("net_income", "")
-    if is_number(hist.get("capex")):
-        updates["D17"] = hist["capex"]
-    if is_number(hist.get("net_working_capital")):
-        updates["D18"] = hist["net_working_capital"]
-    if is_number(hist.get("unlevered_fcf")):
-        updates["D20"] = hist["unlevered_fcf"]
-    updates["D21"] = hist.get("ppe", "")
-
-    for row in [6, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21]:
-        updates[f"K{row}"] = label
-        updates[f"L{row}"] = note
-    if not is_number(hist.get("net_income")):
-        updates["K16"] = "not provided"
-        updates["L16"] = "Net income was not supplied for this FCFF historical display."
-    if not is_number(hist.get("ppe")):
-        updates["K21"] = "not provided"
-        updates["L21"] = "Opening PP&E was not supplied; forecast roll-forward is withheld."
-    missing_inputs.append("three_year_historical_financials")
-    warnings.append("Only the latest source-backed historical year is available; earlier historical columns were intentionally blanked rather than retaining template values.")
-    return updates
-
-
-def build_revenue_updates(plan: Dict[str, Any]) -> Dict[str, Any]:
-    hist = plan.get("historicals", {})
-    revenue = float(hist.get("revenue", 0.0) or 0.0)
-    updates: Dict[str, Any] = {
-        "B8": "N/A",
-        "C8": "N/A",
-        "D8": "Actual",
-        "B10": "",
-        "C10": "",
-        "B11": "",
-        "C11": "",
-        "D10": revenue,
-        "D11": 0.0,
-    }
-    for ref, expression in {
-        "B12": 'IFERROR(B10/B14,"")',
-        "C9": 'IFERROR(C14/B14-1,"")',
-        "C12": 'IFERROR(C10/C14,"")',
-        "C15": 'IFERROR(C14/B14-1,"")',
-        "D9": 'IFERROR(D14/C14-1,"")',
-        "D15": 'IFERROR(D14/C14-1,"")',
-    }.items():
-        updates[ref] = formula(expression)
-    return updates
-
-
-def build_historical_formula_guard_updates(plan: Dict[str, Any], missing_inputs: List[str], warnings: List[str]) -> Dict[str, Dict[str, Any]]:
-    margin_updates: Dict[str, Any] = {}
-    for row in [10, 14, 16]:
-        margin_updates[f"B{row}"] = formula(f'IFERROR(B{row - 1 if row == 10 else 13 if row == 14 else 15}/B8,"")')
-        margin_updates[f"C{row}"] = formula(f'IFERROR(C{row - 1 if row == 10 else 13 if row == 14 else 15}/C8,"")')
-    margin_updates["C17"] = formula('IF(AND(ISNUMBER(B16),ISNUMBER(C16)),C16-B16,"")')
-    margin_updates["D17"] = formula('IF(AND(ISNUMBER(C16),ISNUMBER(D16)),D16-C16,"")')
-
-    wc_updates: Dict[str, Any] = {}
-    for col in ["B", "C", "D"]:
-        for row in [9, 10, 11, 12, 13, 14, 15]:
-            wc_updates[f"{col}{row}"] = formula('""')
-    for col in ["B", "C"]:
-        for row in [16, 17, 18, 19]:
-            wc_updates[f"{col}{row}"] = formula('""')
-    wc_updates["D17"] = plan.get("historicals", {}).get("change_nwc", "")
-    wc_updates["D18"] = formula('IFERROR(D16/D8,"")')
-
-    capex_updates: Dict[str, Any] = {
-        "B10": formula('IFERROR(B9/B8,"")'),
-        "C10": formula('IFERROR(C9/C8,"")'),
-        "B14": formula('IFERROR(B12/B8,"")'),
-        "C14": formula('IFERROR(C12/C8,"")'),
-        "B15": formula('IFERROR(B12/B11,"")'),
-        "C15": formula('IFERROR(C12/C11,"")'),
-    }
-    if is_number(plan.get("historicals", {}).get("ppe")):
-        for col in ["B", "C"]:
-            for row in [11, 13, 15]:
-                capex_updates[f"{col}{row}"] = "n.m."
-        capex_updates["D11"] = ""
-        capex_updates["D13"] = plan["historicals"]["ppe"]
-        capex_updates["D15"] = ""
-    else:
-        missing_inputs.append("historical_ppe_or_reinvestment_support")
-        warnings.append("PP&E roll-forward is withheld because no source-backed opening PP&E balance was supplied; capex and D&A sustainability requires review.")
-        for col in ["B", "C", "D", *MODEL_FORECAST_COLS]:
-            for row in [11, 13, 15]:
-                capex_updates[f"{col}{row}"] = "n.m."
-
-    tax_updates: Dict[str, Any] = {}
-    for col in ["B", "C"]:
-        tax_updates[f"{col}9"] = formula(f'IFERROR(\'Historical Financials\'!{col}$15/{col}8,"")')
-        tax_updates[f"{col}10"] = formula(f'{col}9')
-
-    fcf_updates: Dict[str, Any] = {
-        "B13": "",
-        "C13": "",
-        "D13": plan.get("historicals", {}).get("change_nwc", ""),
-    }
-    return {
-        "Margin Cost Build": margin_updates,
-        "Working Capital": wc_updates,
-        "Capex D&A": capex_updates,
-        "Tax Schedule": tax_updates,
-        "Unlevered FCF": fcf_updates,
-    }
-
-
-def build_checks_updates() -> Dict[str, Any]:
-    return {
-        "A21": "Ending PP&E non-negative when roll-forward modeled",
-        "B21": formula('IF(COUNT(\'Capex D&A\'!E13:J13)=0,"Not modeled",MIN(\'Capex D&A\'!E13:J13))'),
-        "C21": formula('IF(B21="Not modeled","OPEN",IF(B21>=0,"OK","FAIL"))'),
-        "D21": "A source-backed PP&E roll-forward must not project negative assets",
-        "E21": "Hard failure when modeled",
-    }
-
-
-def build_source_notes_updates(plan: Dict[str, Any]) -> Dict[str, Any]:
-    rows = {
-        5: ("historicals", "Replace with filings, audit reports, or normalized financials."),
-        6: ("forecast", "Replace with sourced operating forecast, management case, or approved analyst case."),
-        7: ("wacc", "Replace with market data as of valuation date."),
-        8: ("terminal_value", "Confirm long-run growth, exit multiple, and terminal ROIC support."),
-        9: ("net_debt", "Replace with latest balance sheet and debt-like item bridge."),
-        10: ("share_count", "Replace with latest diluted share count or treasury-stock-method schedule."),
-    }
-    updates: Dict[str, Any] = {}
-    for row, (topic, fallback_note) in rows.items():
-        source, label, as_of, confidence, note = source_row(source_for_topic(plan, topic), fallback_note)
-        updates[f"B{row}"] = source
-        updates[f"C{row}"] = label
-        updates[f"D{row}"] = as_of
-        updates[f"E{row}"] = confidence
-        updates[f"F{row}"] = note
-        updates[f"G{row}"] = "Model owner"
-        updates[f"H{row}"] = "Open" if label in {"placeholder", "analyst_estimate"} or confidence.lower() == "low" else "Reviewed"
-
-    valuation_date = plan.get("meta", {}).get("valuation_date", "")
-    market = plan.get("market", {})
-    market_source = source_for_id(plan, market.get("source_id")) or source_for_topic(plan, "market_data")
-    price = current_share_price_value(plan)
-    market_fallback = "Current share price was not supplied with an attributable market-data source."
-    if price is not None:
-        market_fallback = f"Current share price input is {price:.2f}; add a source_basis market_data record before investment use."
-    price_source, price_label, price_as_of, price_confidence, price_note = source_row(market_source, market_fallback)
-    if price is None:
-        price_note = market_fallback
-    updates.update(
-        {
-            "B11": price_source,
-            "C11": price_label,
-            "D11": price_as_of or valuation_date,
-            "E11": price_confidence,
-            "F11": price_note,
-            "G11": "Model owner",
-            "H11": "Open" if price is None or price_label in {"placeholder", "analyst_estimate"} or price_confidence.lower() == "low" else "Reviewed",
-            "B12": "Plan schema",
-            "C12": "derived",
-            "D12": valuation_date,
-            "E12": "Medium",
-            "F12": "Forecast begins from total revenue unless a sector-relevant operating-driver schedule is provided; disclose any KPI proxy used for valuation.",
-            "G12": "Model owner",
-            "H12": "Open",
-        }
-    )
-    return updates
-
-
-def xml_sheet_paths(xlsx_path: Path) -> Dict[str, str]:
-    with zipfile.ZipFile(xlsx_path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        rels = ET.fromstring(zf.read("xl/_rels/workbook.xml.rels"))
-        relmap = {rel.attrib["Id"]: rel.attrib["Target"] for rel in rels}
-        sheets_el = workbook.find(qname("sheets"))
-        if sheets_el is None:
-            return {}
-        paths: Dict[str, str] = {}
-        for sheet in sheets_el:
-            rid = sheet.attrib[f"{{{NS_REL}}}id"]
-            target = relmap[rid].lstrip("/")
-            paths[sheet.attrib["name"]] = target if target.startswith("xl/") else f"xl/{target}"
-        return paths
-
-
-def get_or_create_row(sheet_root: ET.Element, row_num: int) -> ET.Element:
-    sheet_data = sheet_root.find(qname("sheetData"))
-    if sheet_data is None:
-        sheet_data = ET.SubElement(sheet_root, qname("sheetData"))
-    for row in sheet_data.findall(qname("row")):
-        if int(row.attrib.get("r", "0")) == row_num:
-            return row
-    row = ET.Element(qname("row"), {"r": str(row_num)})
-    inserted = False
-    for idx, existing in enumerate(list(sheet_data)):
-        if existing.tag == qname("row") and int(existing.attrib.get("r", "0")) > row_num:
-            sheet_data.insert(idx, row)
-            inserted = True
-            break
-    if not inserted:
-        sheet_data.append(row)
-    return row
-
-
-def get_or_create_cell(row: ET.Element, ref: str) -> ET.Element:
-    for cell in row.findall(qname("c")):
-        if cell.attrib.get("r") == ref:
-            return cell
-    cell = ET.Element(qname("c"), {"r": ref})
-    new_col = col_to_num(cell_ref_parts(ref)[0])
-    inserted = False
-    for idx, existing in enumerate(list(row)):
-        if existing.tag == qname("c") and col_to_num(cell_ref_parts(existing.attrib.get("r", "A1"))[0]) > new_col:
-            row.insert(idx, cell)
-            inserted = True
-            break
-    if not inserted:
-        row.append(cell)
-    return cell
-
-
-def set_cell_value(cell: ET.Element, value: Any) -> None:
-    ref = cell.attrib.get("r", "")
-    style = cell.attrib.get("s")
-    cell.attrib.clear()
-    cell.attrib["r"] = ref
-    if style is not None:
-        cell.attrib["s"] = style
-    for child in list(cell):
-        cell.remove(child)
-    if isinstance(value, FormulaValue):
-        f = ET.SubElement(cell, qname("f"))
-        f.text = value.expression
-        return
-    if is_number(value):
-        v = ET.SubElement(cell, qname("v"))
-        v.text = str(float(value))
-    else:
-        cell.attrib["t"] = "inlineStr"
-        is_el = ET.SubElement(cell, qname("is"))
-        t = ET.SubElement(is_el, qname("t"))
-        t.text = "" if value is None else str(value)
-
-
-def patch_sheet_xml(xml_bytes: bytes, updates: Dict[str, Any]) -> bytes:
-    root = ET.fromstring(xml_bytes)
-    for ref, value in sorted(updates.items(), key=lambda item: cell_sort_key(item[0])):
-        _, row_num = cell_ref_parts(ref)
-        row = get_or_create_row(root, row_num)
-        cell = get_or_create_cell(row, ref)
-        cell.attrib["r"] = ref
-        set_cell_value(cell, value)
-    return ET.tostring(root, encoding="utf-8", xml_declaration=True)
-
-
-def materialize_workbook(template: Path, output: Path, updates_by_sheet: Dict[str, Dict[str, Any]]) -> None:
-    sheet_paths = xml_sheet_paths(template)
-    missing = [sheet for sheet in updates_by_sheet if sheet not in sheet_paths]
-    if missing:
-        raise ValueError(f"Template missing sheets required for updates: {missing}")
-    with zipfile.ZipFile(template, "r") as zin, zipfile.ZipFile(output, "w", zipfile.ZIP_DEFLATED) as zout:
-        for item in zin.infolist():
-            data = zin.read(item.filename)
-            for sheet, updates in updates_by_sheet.items():
-                if item.filename == sheet_paths[sheet]:
-                    data = patch_sheet_xml(data, updates)
-                    break
-            zout.writestr(item, data)
-
-
-def refresh_workbook_status(workbook_path: Path, plan: Dict[str, Any], model_status: str) -> None:
-    refreshed_path = workbook_path.with_name(f".{workbook_path.name}.status-refresh.xlsx")
-    materialize_workbook(
-        workbook_path,
-        refreshed_path,
-        {
-            "Cover": {"B6": model_status, "B35": model_status},
-            "Executive Summary": {"B3": build_executive_summary_updates(plan, model_status)["B3"]},
-            "Control Panel": {"B8": model_status},
-        },
-    )
-    refreshed_path.replace(workbook_path)
-
-
-def inspect_workbook(path: Path) -> Dict[str, Any]:
-    return inspect_formula_workbook(
-        path,
-        required_sheets=REQUIRED_SHEETS,
-        workbook_type="dcf",
-    )
-
-def build(plan_path: Path, output_dir: Path, template: Path) -> Dict[str, Any]:
-    plan = load_json(plan_path)
-    validation_errors = validate_plan_structure(plan)
-    if validation_errors:
-        raise ValueError("Plan validation failed before workbook materialization: " + "; ".join(validation_errors))
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-    workbook_path = output_dir / OUTPUT_WORKBOOK
-    run_log_path = output_dir / OUTPUT_RUN_LOG
-    model_citations_path = output_dir / OUTPUT_MODEL_CITATIONS
-    warnings: List[str] = []
-    missing_inputs: List[str] = []
-    hard_failures = data_quality_hard_failures(plan, warnings)
-    initial_model_status = "not-decision-ready" if hard_failures else ("screen-grade" if screen_grade_required(plan) else "senior-review-ready")
-    period_updates = build_period_header_updates(plan, warnings)
-    guarded_historical_updates = build_historical_formula_guard_updates(plan, missing_inputs, warnings)
-    updates_by_sheet = {
-        "Cover": build_cover_updates(plan, initial_model_status),
-        "Executive Summary": build_executive_summary_updates(plan, initial_model_status),
-        "Control Panel": build_control_panel_updates(plan, missing_inputs, warnings, initial_model_status),
-        "Historical Financials": merged_updates(period_updates["Historical Financials"], build_historical_updates(plan, missing_inputs, warnings)),
-        "Revenue Build": merged_updates(period_updates["Revenue Build"], build_revenue_updates(plan)),
-        "Margin Cost Build": merged_updates(period_updates["Margin Cost Build"], guarded_historical_updates["Margin Cost Build"]),
-        "Working Capital": merged_updates(period_updates["Working Capital"], guarded_historical_updates["Working Capital"]),
-        "Capex D&A": merged_updates(period_updates["Capex D&A"], guarded_historical_updates["Capex D&A"]),
-        "Tax Schedule": merged_updates(period_updates["Tax Schedule"], guarded_historical_updates["Tax Schedule"]),
-        "Unlevered FCF": merged_updates(period_updates["Unlevered FCF"], guarded_historical_updates["Unlevered FCF"]),
-        "Checks": build_checks_updates(),
-        "Source Notes": build_source_notes_updates(plan),
-    }
-    materialize_workbook(template, workbook_path, updates_by_sheet)
-    inspection = inspect_workbook(workbook_path)
-    model_citations = build_model_citations(plan, workbook_path)
-    model_citations_path.write_text(
-        json.dumps({"model_citations": model_citations}, indent=2) + "\n",
-        encoding="utf-8",
-    )
-    inspection["anchor_map_path"] = str(model_citations_path)
-    inspection["anchor_map_present"] = len(model_citations) >= 5
-    citation_validation_errors = validate_model_citations({"model_citations": model_citations}, strict=True)
-
-    if not inspection["required_sheets_present"]:
-        hard_failures.append("banker_formula_workbook_missing_required_sheets")
-    if inspection["formula_count"] < inspection["minimum_formula_count"]:
-        hard_failures.append("banker_formula_workbook_formula_count_below_threshold")
-    if not inspection["required_formula_sheets_populated"]:
-        hard_failures.append("banker_formula_workbook_missing_required_formula_sheets")
-    if not inspection["cover_first"]:
-        hard_failures.append("banker_formula_workbook_cover_not_first_visible_sheet")
-    if not inspection.get("has_named_ranges") and not inspection.get("anchor_map_present"):
-        hard_failures.append("banker_formula_workbook_missing_named_ranges_or_anchor_map")
-    if citation_validation_errors:
-        hard_failures.append("banker_formula_workbook_model_citations_invalid")
-    if not inspection["has_styles"]:
-        hard_failures.append("banker_formula_workbook_styles_missing")
-    if not inspection["no_external_links"]:
-        hard_failures.append("banker_formula_workbook_contains_external_links")
-
-    model_status = "not-decision-ready" if hard_failures else ("screen-grade" if screen_grade_required(plan) else "senior-review-ready")
-    if model_status != initial_model_status:
-        refresh_workbook_status(workbook_path, plan, model_status)
-    run_status = "failed" if hard_failures else "completed"
-    run_log = {
-        "status": run_status,
-        "model_status": model_status,
-        "readiness_effect": model_status.replace("-", "_"),
-        "decision_impact": (
-            "Workbook readiness gates failed; the diagnostic workbook must not drive target, rating, sizing, or circulation."
-            if hard_failures
-            else "Formula workbook passed integrity gates; Excel or compatible software should recalculate on open before final circulation."
-        ),
-        "workbook_mode": "banker_formula_workbook",
-        "artifact_level": "banker_formula_workbook_template_materializer",
-        "template_path": str(template),
-        "output_paths": {
-            "banker_formula_workbook": str(workbook_path),
-            "banker_formula_workbook_run_log": str(run_log_path),
-            "model_citations": str(model_citations_path),
-            "manifest": str(output_dir / OUTPUT_MANIFEST),
-        },
-        "partial_context_warning": PARTIAL_CONTEXT_WARNING if screen_grade_required(plan) else None,
-        "missing_inputs": sorted(set(missing_inputs)),
-        "warnings": sorted(set(warnings)),
-        "hard_failures": hard_failures,
-        "workbook_inspection": inspection,
-        "model_citations_count": len(model_citations),
-        "model_citation_validation_errors": citation_validation_errors,
-        "formula_mode_limitations": [
-            "Preserves and populates the bundled DCF formula workbook template.",
-            "Does not synthesize new tabs, formulas, or workbook architecture beyond the shipped template.",
-            "The template has six forecast columns; plan vectors are extended or truncated for formula-mode compatibility.",
-            "Excel or compatible spreadsheet software should recalculate formulas after opening.",
-        ],
-    }
-    run_log_path.write_text(json.dumps(run_log, indent=2), encoding="utf-8")
-    write_output_manifest(output_dir, run_log)
-    return run_log
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Materialize the DCF banker formula workbook template from plan.json.")
-    parser.add_argument("plan_json", type=Path)
-    parser.add_argument("--output-dir", type=Path, default=None, help="Output directory for workbook, run log, and manifest. Defaults to ./output.")
-    parser.add_argument("--template", type=Path, default=DEFAULT_TEMPLATE)
-    args = parser.parse_args()
-    output_dir = args.output_dir if args.output_dir else Path.cwd() / "output"
-    try:
-        run_log = build(args.plan_json, output_dir, args.template)
-    except Exception as exc:
-        print(f"banker_formula_workbook materialization FAILED: {exc}", file=sys.stderr)
-        return 1
-    print(
-        json.dumps(
-            {
-                "model_status": run_log["model_status"],
-                "status": run_log["status"],
-                "workbook_mode": run_log["workbook_mode"],
-                "output_paths": run_log["output_paths"],
-                "hard_failures": run_log["hard_failures"],
-                "warnings": run_log["warnings"],
-                "missing_inputs": run_log["missing_inputs"],
-            },
-            indent=2,
-        )
-    )
-    return 2 if run_log["hard_failures"] else 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/plan_validation.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/plan_validation.py
deleted file mode 100644
index f60870c..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/plan_validation.py
+++ /dev/null
@@ -1,538 +0,0 @@
-"""Structural validation for dcf-model-builder plans.
-
-This module intentionally does not compute valuation outputs. The model engine
-adds final computed WACC/terminal-value validation after this structural pass.
-"""
-
-from __future__ import annotations
-
-import math
-from datetime import datetime
-from typing import Any
-
-ALLOWED_LABELS = {
-    "reported",
-    "company_guidance",
-    "consensus",
-    "management_case",
-    "user_provided",
-    "connected_app",
-    "web_research",
-    "analyst_estimate",
-    "placeholder",
-    "derived",
-}
-
-REQUIRED_SOURCE_TOPICS = {
-    "historicals",
-    "forecast",
-    "wacc",
-    "terminal_value",
-    "share_count",
-    "net_debt",
-}
-
-REQUIRED_TOP_LEVEL = [
-    "meta",
-    "source_basis",
-    "timeline",
-    "historicals",
-    "forecast",
-    "wacc",
-    "terminal_value",
-    "ev_to_equity_bridge",
-    "scenarios",
-    "sensitivities",
-]
-
-SCENARIOS = ["base", "downside", "upside"]
-
-
-def _is_number(value: Any) -> bool:
-    return (
-        isinstance(value, (int, float))
-        and not isinstance(value, bool)
-        and math.isfinite(float(value))
-    )
-
-
-def _date_ok(value: Any) -> bool:
-    if not isinstance(value, str):
-        return False
-    try:
-        datetime.strptime(value, "%Y-%m-%d")
-        return True
-    except ValueError:
-        return False
-
-
-def _date(value: str) -> datetime | None:
-    try:
-        return datetime.strptime(value, "%Y-%m-%d")
-    except Exception:
-        return None
-
-
-def _num(value: Any, default: float = 0.0) -> float:
-    if _is_number(value):
-        return float(value)
-    return float(default)
-
-
-def _get(plan: dict[str, Any], dotted: str, default: Any = None) -> Any:
-    node: Any = plan
-    for part in dotted.split("."):
-        if not isinstance(node, dict) or part not in node:
-            return default
-        node = node[part]
-    return node
-
-
-def _validate_vector(
-    errors: list[str],
-    value: Any,
-    path: str,
-    horizon: int,
-    min_value: float | None = None,
-    max_value: float | None = None,
-    allow_scalar: bool = True,
-) -> None:
-    if isinstance(value, list):
-        if len(value) != horizon:
-            errors.append(f"{path} must contain exactly {horizon} values; found {len(value)}")
-            return
-        values = value
-    elif allow_scalar and _is_number(value):
-        values = [value]
-    else:
-        errors.append(f"{path} must be a number or list of numbers")
-        return
-    for idx, item in enumerate(values):
-        loc = f"{path}[{idx}]" if isinstance(value, list) else path
-        if not _is_number(item):
-            errors.append(f"{loc} must be numeric")
-            continue
-        f = float(item)
-        if min_value is not None and f < min_value:
-            errors.append(f"{loc} must be >= {min_value}")
-        if max_value is not None and f > max_value:
-            errors.append(f"{loc} must be <= {max_value}")
-
-
-def _validate_meta(errors: list[str], plan: dict[str, Any]) -> datetime | None:
-    meta = plan.get("meta", {})
-    for field in [
-        "company",
-        "industry",
-        "currency",
-        "units",
-        "valuation_date",
-        "as_of_date",
-        "accounting_basis",
-        "valuation_purpose",
-        "model_type",
-    ]:
-        if not meta.get(field):
-            errors.append(f"meta.{field} is required")
-    if meta.get("model_type") not in {"fcff", "fcfe"}:
-        errors.append("meta.model_type must be 'fcff' or 'fcfe'")
-    for field in ["valuation_date", "as_of_date"]:
-        if meta.get(field) and not _date_ok(meta[field]):
-            errors.append(f"meta.{field} must be YYYY-MM-DD")
-    return _date(str(meta.get("valuation_date", ""))) or _date(str(meta.get("as_of_date", "")))
-
-
-def _validate_sources(
-    errors: list[str], plan: dict[str, Any], model_cutoff_date: datetime | None
-) -> set[str]:
-    source_basis = plan.get("source_basis")
-    source_ids: set[str] = set()
-    if not isinstance(source_basis, list) or not source_basis:
-        errors.append("source_basis must be a non-empty list")
-        return source_ids
-
-    topics = set()
-    for idx, src in enumerate(source_basis):
-        if not isinstance(src, dict):
-            errors.append(f"source_basis[{idx}] must be an object")
-            continue
-        for field in [
-            "id",
-            "topic",
-            "label",
-            "source_name",
-            "source_type",
-            "as_of_date",
-            "confidence",
-            "notes",
-        ]:
-            if field not in src or src.get(field) in (None, ""):
-                errors.append(f"source_basis[{idx}].{field} is required")
-        sid = str(src.get("id", ""))
-        if sid:
-            if sid in source_ids:
-                errors.append(f"source_basis id '{sid}' is duplicated")
-            source_ids.add(sid)
-        if src.get("topic"):
-            topics.add(str(src.get("topic")))
-        if src.get("label") and src.get("label") not in ALLOWED_LABELS:
-            errors.append(f"source_basis[{idx}].label '{src.get('label')}' is not allowed")
-        if src.get("confidence") and src.get("confidence") not in {
-            "high",
-            "medium",
-            "low",
-        }:
-            errors.append(f"source_basis[{idx}].confidence must be high, medium, or low")
-        if src.get("as_of_date") and not _date_ok(src["as_of_date"]):
-            errors.append(f"source_basis[{idx}].as_of_date must be YYYY-MM-DD")
-        source_as_of_date = _date(str(src.get("as_of_date", "")))
-        if model_cutoff_date and source_as_of_date and source_as_of_date > model_cutoff_date:
-            errors.append(f"source_basis[{idx}].as_of_date cannot be after meta.valuation_date")
-
-    missing_topics = sorted(REQUIRED_SOURCE_TOPICS - topics)
-    if missing_topics:
-        errors.append("source_basis missing required material topics: " + ", ".join(missing_topics))
-    return source_ids
-
-
-def _validate_timeline(errors: list[str], plan: dict[str, Any]) -> int:
-    timeline = plan.get("timeline", {})
-    horizon = timeline.get("horizon_years")
-    if not isinstance(timeline.get("start_year"), int):
-        errors.append("timeline.start_year must be an integer")
-    if not isinstance(horizon, int) or horizon < 1 or horizon > 15:
-        errors.append("timeline.horizon_years must be an integer from 1 to 15")
-        horizon = 0
-    if timeline.get("periodicity") not in {"annual", "quarterly"}:
-        errors.append("timeline.periodicity must be 'annual' or 'quarterly'")
-    return int(horizon or 0)
-
-
-def _validate_historicals(errors: list[str], plan: dict[str, Any]) -> None:
-    historicals = plan.get("historicals", {})
-    hist_required = [
-        "latest_year",
-        "revenue",
-        "ebitda",
-        "ebit",
-        "cash_taxes",
-        "da",
-        "capex",
-        "change_nwc",
-        "net_working_capital",
-        "unlevered_fcf",
-        "source_id",
-    ]
-    for field in hist_required:
-        if field not in historicals:
-            errors.append(f"historicals.{field} is required")
-    numeric_fields = [
-        "revenue",
-        "ebitda",
-        "ebit",
-        "cash_taxes",
-        "da",
-        "capex",
-        "change_nwc",
-        "net_working_capital",
-        "unlevered_fcf",
-    ]
-    for field in numeric_fields:
-        if field in historicals and not _is_number(historicals[field]):
-            errors.append(f"historicals.{field} must be numeric")
-    if (
-        "revenue" in historicals
-        and _is_number(historicals.get("revenue"))
-        and historicals["revenue"] <= 0
-    ):
-        errors.append("historicals.revenue must be greater than zero")
-    if plan.get("meta", {}).get("model_type") == "fcfe" and "net_income" not in historicals:
-        errors.append("historicals.net_income is required for FCFE models")
-
-
-def _validate_source_references(
-    errors: list[str], plan: dict[str, Any], source_ids: set[str]
-) -> None:
-    paths = [
-        "historicals.source_id",
-        "forecast.source_id",
-        "wacc.source_id",
-        "terminal_value.source_id",
-        "ev_to_equity_bridge.net_debt_source_id",
-        "ev_to_equity_bridge.share_count_source_id",
-    ]
-    for path in paths:
-        sid = _get(plan, path)
-        if sid and str(sid) not in source_ids:
-            errors.append(f"{path} references unknown source id '{sid}'")
-
-
-def _validate_forecast(errors: list[str], plan: dict[str, Any]) -> None:
-    forecast = plan.get("forecast", {})
-    meta = plan.get("meta", {})
-    if forecast.get("cash_flow_basis") not in {"fcff", "fcfe"}:
-        errors.append("forecast.cash_flow_basis must be 'fcff' or 'fcfe'")
-    if (
-        forecast.get("cash_flow_basis")
-        and meta.get("model_type")
-        and forecast.get("cash_flow_basis") != meta.get("model_type")
-    ):
-        errors.append("forecast.cash_flow_basis must match meta.model_type")
-    if not isinstance(forecast.get("mid_year_convention"), bool):
-        errors.append("forecast.mid_year_convention must be true or false")
-
-
-def _validate_wacc(errors: list[str], plan: dict[str, Any]) -> None:
-    wacc = plan.get("wacc", {})
-    required = [
-        "risk_free_rate",
-        "beta",
-        "equity_risk_premium",
-        "size_premium",
-        "pre_tax_cost_of_debt",
-        "marginal_tax_rate",
-        "target_debt_pct",
-        "target_equity_pct",
-        "source_id",
-    ]
-    for field in required:
-        if field not in wacc:
-            errors.append(f"wacc.{field} is required")
-    range_checks = {
-        "risk_free_rate": (-0.02, 0.20),
-        "beta": (0.0, 5.0),
-        "equity_risk_premium": (0.0, 0.20),
-        "size_premium": (0.0, 0.15),
-        "company_specific_premium": (-0.05, 0.25),
-        "country_risk_premium": (0.0, 0.30),
-        "pre_tax_cost_of_debt": (0.0, 0.40),
-        "marginal_tax_rate": (0.0, 0.60),
-        "target_debt_pct": (0.0, 1.0),
-        "target_equity_pct": (0.0, 1.0),
-    }
-    for field, (low, high) in range_checks.items():
-        if field not in wacc:
-            continue
-        if not _is_number(wacc[field]):
-            errors.append(f"wacc.{field} must be numeric")
-            continue
-        value = float(wacc[field])
-        if value < low or value > high:
-            errors.append(f"wacc.{field} must be between {low} and {high}")
-    if _is_number(wacc.get("target_debt_pct")) and _is_number(wacc.get("target_equity_pct")):
-        preferred = _num(wacc.get("preferred_pct"), 0.0)
-        total_weight = float(wacc["target_debt_pct"]) + float(wacc["target_equity_pct"]) + preferred
-        if abs(total_weight - 1.0) > 0.02:
-            errors.append(
-                f"wacc capital structure weights must sum to approximately 1.0; found {total_weight:.3f}"
-            )
-
-
-def _validate_terminal_value(errors: list[str], plan: dict[str, Any]) -> str | None:
-    terminal = plan.get("terminal_value", {})
-    method = terminal.get("method")
-    if method not in {"perpetual_growth", "exit_multiple"}:
-        errors.append("terminal_value.method must be 'perpetual_growth' or 'exit_multiple'")
-    if method == "perpetual_growth":
-        if not _is_number(terminal.get("perpetual_growth_rate")):
-            errors.append(
-                "terminal_value.perpetual_growth_rate is required and numeric for perpetual growth"
-            )
-        else:
-            growth = float(terminal["perpetual_growth_rate"])
-            if growth < -0.05 or growth > 0.08:
-                errors.append("terminal_value.perpetual_growth_rate must be between -5% and 8%")
-    if method == "exit_multiple" and not _is_number(terminal.get("exit_ebitda_multiple")):
-        errors.append(
-            "terminal_value.exit_ebitda_multiple is required and numeric for exit multiple"
-        )
-    if (
-        method == "exit_multiple"
-        and _is_number(terminal.get("exit_ebitda_multiple"))
-        and float(terminal["exit_ebitda_multiple"]) <= 0
-    ):
-        errors.append("terminal_value.exit_ebitda_multiple must be positive")
-    if "exit_ebitda_multiple" in terminal and _is_number(terminal.get("exit_ebitda_multiple")):
-        multiple = float(terminal["exit_ebitda_multiple"])
-        if multiple <= 0 or multiple > 100:
-            errors.append(
-                "terminal_value.exit_ebitda_multiple must be positive and less than or equal to 100x"
-            )
-    return method if isinstance(method, str) else None
-
-
-def _validate_bridge(errors: list[str], plan: dict[str, Any]) -> None:
-    bridge = plan.get("ev_to_equity_bridge", {})
-    required = [
-        "cash",
-        "debt",
-        "leases",
-        "minorities",
-        "associates",
-        "pensions",
-        "preferred_stock",
-        "non_operating_assets",
-        "options",
-        "other_debt_like_items",
-        "diluted_shares",
-        "net_debt_source_id",
-        "share_count_source_id",
-    ]
-    for field in required:
-        if field not in bridge:
-            errors.append(f"ev_to_equity_bridge.{field} is required")
-    for field in required:
-        if field.endswith("source_id"):
-            continue
-        if field in bridge and not _is_number(bridge[field]):
-            errors.append(f"ev_to_equity_bridge.{field} must be numeric")
-    if _is_number(bridge.get("diluted_shares")) and float(bridge["diluted_shares"]) <= 0:
-        errors.append("ev_to_equity_bridge.diluted_shares must be greater than zero")
-
-
-def _validate_scenarios(
-    errors: list[str], plan: dict[str, Any], horizon: int, method: str | None
-) -> None:
-    scenarios = plan.get("scenarios", {})
-    forecast = plan.get("forecast", {})
-    if not isinstance(scenarios, dict):
-        errors.append("scenarios must be an object")
-        return
-    for name in SCENARIOS:
-        if name not in scenarios:
-            errors.append(f"scenarios.{name} is required")
-            continue
-        scenario = scenarios[name]
-        if not isinstance(scenario, dict):
-            errors.append(f"scenarios.{name} must be an object")
-            continue
-        if not scenario.get("description"):
-            errors.append(f"scenarios.{name}.description is required")
-        if horizon:
-            _validate_vector(
-                errors,
-                scenario.get("revenue_growth"),
-                f"scenarios.{name}.revenue_growth",
-                horizon,
-                -0.80,
-                2.00,
-            )
-            _validate_vector(
-                errors,
-                scenario.get("ebit_margin"),
-                f"scenarios.{name}.ebit_margin",
-                horizon,
-                -0.50,
-                0.80,
-            )
-            _validate_vector(
-                errors,
-                scenario.get("tax_rate"),
-                f"scenarios.{name}.tax_rate",
-                horizon,
-                0.0,
-                0.60,
-            )
-            _validate_vector(
-                errors,
-                scenario.get("da_percent_revenue"),
-                f"scenarios.{name}.da_percent_revenue",
-                horizon,
-                0.0,
-                1.00,
-            )
-            _validate_vector(
-                errors,
-                scenario.get("capex_percent_revenue"),
-                f"scenarios.{name}.capex_percent_revenue",
-                horizon,
-                0.0,
-                1.50,
-            )
-            _validate_vector(
-                errors,
-                scenario.get("nwc_percent_revenue"),
-                f"scenarios.{name}.nwc_percent_revenue",
-                horizon,
-                -1.0,
-                1.0,
-            )
-            if forecast.get("cash_flow_basis") == "fcfe":
-                _validate_vector(
-                    errors,
-                    scenario.get("net_income_margin"),
-                    f"scenarios.{name}.net_income_margin",
-                    horizon,
-                    -0.50,
-                    0.80,
-                )
-                _validate_vector(
-                    errors,
-                    scenario.get("net_borrowing", 0.0),
-                    f"scenarios.{name}.net_borrowing",
-                    horizon,
-                    -1e9,
-                    1e9,
-                )
-        if not _is_number(scenario.get("wacc_adjustment")):
-            errors.append(f"scenarios.{name}.wacc_adjustment must be numeric")
-        if method == "perpetual_growth":
-            if not _is_number(scenario.get("terminal_growth_rate")):
-                errors.append(f"scenarios.{name}.terminal_growth_rate is required and numeric")
-            else:
-                terminal_growth = float(scenario["terminal_growth_rate"])
-                if terminal_growth < -0.05 or terminal_growth > 0.08:
-                    errors.append(
-                        f"scenarios.{name}.terminal_growth_rate must be between -5% and 8%"
-                    )
-        if scenario.get("exit_ebitda_multiple") is not None and not _is_number(
-            scenario.get("exit_ebitda_multiple")
-        ):
-            errors.append(f"scenarios.{name}.exit_ebitda_multiple must be numeric when supplied")
-
-
-def _validate_sensitivities(errors: list[str], plan: dict[str, Any]) -> None:
-    sensitivities = plan.get("sensitivities", {})
-    fields = [
-        "wacc_delta",
-        "terminal_growth_delta",
-        "exit_multiple_delta",
-        "revenue_growth_delta",
-        "ebit_margin_delta",
-    ]
-    for field in fields:
-        value = sensitivities.get(field)
-        if not isinstance(value, list) or not value:
-            errors.append(f"sensitivities.{field} must be a non-empty list")
-            continue
-        if 0.0 not in [float(v) for v in value if _is_number(v)]:
-            errors.append(f"sensitivities.{field} should include 0.0")
-        for idx, item in enumerate(value):
-            if not _is_number(item):
-                errors.append(f"sensitivities.{field}[{idx}] must be numeric")
-
-
-def validate_plan_structure_without_computed_checks(plan: dict[str, Any]) -> list[str]:
-    """Return actionable structural validation errors. Does not compute valuation outputs."""
-    errors: list[str] = []
-    if not isinstance(plan, dict):
-        return ["plan must be a JSON object"]
-
-    for key in REQUIRED_TOP_LEVEL:
-        if key not in plan:
-            errors.append(f"missing top-level field: {key}")
-    if errors:
-        return errors
-
-    model_cutoff_date = _validate_meta(errors, plan)
-    source_ids = _validate_sources(errors, plan, model_cutoff_date)
-    horizon = _validate_timeline(errors, plan)
-    _validate_historicals(errors, plan)
-    _validate_source_references(errors, plan, source_ids)
-    _validate_forecast(errors, plan)
-    _validate_wacc(errors, plan)
-    method = _validate_terminal_value(errors, plan)
-    _validate_bridge(errors, plan)
-    _validate_scenarios(errors, plan, horizon, method)
-    _validate_sensitivities(errors, plan)
-    return errors
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/run_pipeline.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/run_pipeline.py
deleted file mode 100755
index fbf9180..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/run_pipeline.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python3
-"""Run the deterministic DCF pipeline."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-SKILL_ROOT = SCRIPT_DIR.parent
-PLUGIN_ROOT = SKILL_ROOT.parent.parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from skill_core import (  # noqa: E402
-    build_p0_handoff,
-    build_workbook_sheets,
-    compute_checks,
-    determine_model_status,
-    load_json,
-    normalize_plan,
-    render_report,
-    run_scenario,
-    run_sensitivities,
-    validate_plan_structure,
-    write_json,
-)
-from xlsx_writer import write_xlsx  # noqa: E402
-
-from shared.artifacts import (  # noqa: E402
-    artifact_paths as shared_artifact_paths,
-)
-from shared.artifacts import (
-    build_run_log,
-    write_run_log_bundle,
-)
-from shared.artifacts import (
-    output_manifest as shared_output_manifest,
-)
-from shared.workbook_artifacts import dcf_cover_rows  # noqa: E402
-
-
-def _artifact_paths(output_dir: Path) -> dict[str, str]:
-    return shared_artifact_paths(
-        output_dir,
-        {
-            "workbook": "model.xlsx",
-            "plan": "plan.json",
-            "run_log": "run_log.json",
-            "support_note": "support_note.md",
-            "legacy_report": "report.md",
-            "manifest": "manifest.json",
-        },
-    )
-
-
-def _output_manifest(
-    output_dir: Path,
-    *,
-    support_note_enabled: bool,
-    legacy_report_enabled: bool,
-    written: set[str],
-) -> list[dict[str, Any]]:
-    paths = _artifact_paths(output_dir)
-    optional = set()
-    if not support_note_enabled:
-        optional.add("support_note")
-    if not legacy_report_enabled:
-        optional.add("legacy_report")
-    return shared_output_manifest(
-        paths,
-        optional=optional,
-        written=written,
-        descriptions={
-            "workbook": "Deterministic long-format values workbook.",
-            "plan": "Normalized plan actually used by the pipeline.",
-            "run_log": "Status, warnings, hard failures, checks, and handoff payload.",
-            "support_note": "Markdown support note for review, renderer input, or audit.",
-            "legacy_report": "Legacy report.md copy, written only when explicitly requested.",
-            "manifest": "Machine-readable output manifest.",
-        },
-        artifact_roles={
-            "workbook": "primary_human_deliverable",
-            "support_note": "narrative_support",
-            "legacy_report": "narrative_support",
-            "plan": "machine_support",
-            "run_log": "machine_support",
-            "manifest": "machine_support",
-        },
-        hidden_unless_requested={"support_note", "legacy_report", "plan", "run_log", "manifest"},
-    )
-
-
-def _write_manifest(output_dir: Path, run_log: dict[str, Any]) -> None:
-    write_json(output_dir / "manifest.json", {"outputs": run_log.get("output_manifest", [])})
-
-
-def _blocked_run_log(
-    errors: list[str],
-    output_dir: Path,
-    *,
-    support_note_enabled: bool = True,
-    legacy_report_enabled: bool = False,
-) -> dict[str, Any]:
-    paths = _artifact_paths(output_dir)
-    return build_run_log(
-        hard_failures=errors,
-        warnings=[],
-        outputs=paths,
-        output_manifest_rows=_output_manifest(
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=legacy_report_enabled,
-            written={"run_log", "manifest"},
-        ),
-        checks={"validation_errors": errors},
-        model_status="not-decision-ready",
-        p0_handoff={
-            "selected_valuation_range": {},
-            "scenarios": {},
-            "wacc_and_terminal_assumptions": {},
-            "key_value_drivers": [],
-            "major_caveats": errors,
-            "model_status": "not-decision-ready",
-            "paths": paths,
-        },
-    )
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Run DCF deterministic export pipeline")
-    parser.add_argument("plan_path", help="Path to plan.json")
-    parser.add_argument(
-        "--output-dir",
-        default=str(SKILL_ROOT / "output"),
-        help="Output directory; default skill_root/output",
-    )
-    parser.add_argument(
-        "--print-report", action="store_true", help="Print support note between clear markers"
-    )
-    parser.add_argument(
-        "--no-support-note", action="store_true", help="Do not write output/support_note.md"
-    )
-    parser.add_argument(
-        "--no-report-md", action="store_true", help="Deprecated alias for --no-support-note"
-    )
-    parser.add_argument(
-        "--write-report-md",
-        action="store_true",
-        help="Write legacy output/report.md in addition to support_note.md. Off by default.",
-    )
-    args = parser.parse_args()
-    support_note_enabled = not (args.no_support_note or args.no_report_md)
-
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    try:
-        raw_plan = load_json(args.plan_path)
-    except FileNotFoundError:
-        errors = [f"plan file not found: {args.plan_path}"]
-        run_log = _blocked_run_log(
-            errors,
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=args.write_report_md,
-        )
-        write_run_log_bundle(output_dir, run_log)
-        print("BLOCKED DCF PIPELINE")
-        for e in errors:
-            print(f"- {e}")
-        return 1
-    except json.JSONDecodeError as exc:
-        errors = [f"invalid JSON: line {exc.lineno}, column {exc.colno}: {exc.msg}"]
-        run_log = _blocked_run_log(
-            errors,
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=args.write_report_md,
-        )
-        write_run_log_bundle(output_dir, run_log)
-        print("BLOCKED DCF PIPELINE")
-        for e in errors:
-            print(f"- {e}")
-        return 1
-    except Exception as exc:
-        errors = [f"could not read plan: {exc}"]
-        run_log = _blocked_run_log(
-            errors,
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=args.write_report_md,
-        )
-        write_run_log_bundle(output_dir, run_log)
-        print("BLOCKED DCF PIPELINE")
-        for e in errors:
-            print(f"- {e}")
-        return 1
-
-    validation_errors = validate_plan_structure(raw_plan)
-    if validation_errors:
-        run_log = _blocked_run_log(
-            validation_errors,
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=args.write_report_md,
-        )
-        write_run_log_bundle(output_dir, run_log)
-        print("BLOCKED DCF PIPELINE")
-        for e in validation_errors:
-            print(f"- {e}")
-        return 1
-
-    plan = normalize_plan(raw_plan, SKILL_ROOT)
-    write_json(output_dir / "plan.json", plan)
-
-    scenario_results: dict[str, dict[str, Any]] = {}
-    execution_errors: list[str] = []
-    for scenario_name in ["base", "downside", "upside"]:
-        try:
-            scenario_results[scenario_name] = run_scenario(plan, scenario_name)
-        except Exception as exc:
-            execution_errors.append(f"scenario {scenario_name} failed: {exc}")
-
-    try:
-        sensitivity_result = run_sensitivities(plan)
-    except Exception as exc:
-        sensitivity_result = {
-            "rows": [],
-            "directionality": {
-                "passed": False,
-                "details": [{"check": "sensitivity run", "passed": False, "detail": str(exc)}],
-            },
-        }
-        execution_errors.append(f"sensitivities failed: {exc}")
-
-    checks = compute_checks(plan, scenario_results, sensitivity_result)
-    if execution_errors:
-        checks.setdefault("hard_failures", [])
-        checks["hard_failures"].extend(execution_errors)
-        # Deduplicate while preserving order.
-        checks["hard_failures"] = list(dict.fromkeys(checks["hard_failures"]))
-
-    hard_failures = checks.get("hard_failures", [])
-    warnings = checks.get("warnings", [])
-    model_status = determine_model_status(plan, hard_failures, warnings)
-
-    assumptions = {
-        "company": plan.get("meta", {}).get("company"),
-        "model_type": plan.get("meta", {}).get("model_type"),
-        "valuation_date": plan.get("meta", {}).get("valuation_date"),
-        "horizon_years": plan.get("timeline", {}).get("horizon_years"),
-        "terminal_method": plan.get("terminal_value", {}).get("method"),
-        "cash_flow_basis": plan.get("forecast", {}).get("cash_flow_basis"),
-    }
-
-    p0_handoff = build_p0_handoff(
-        plan, scenario_results, sensitivity_result, model_status, warnings, output_dir
-    )
-    outputs = _artifact_paths(output_dir)
-    written = {"workbook", "plan", "run_log", "manifest"}
-    if support_note_enabled:
-        written.add("support_note")
-    if args.write_report_md:
-        written.add("legacy_report")
-    run_log = build_run_log(
-        hard_failures=hard_failures,
-        warnings=warnings,
-        outputs=outputs,
-        output_manifest_rows=_output_manifest(
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=args.write_report_md,
-            written=written,
-        ),
-        source_basis=plan.get("source_basis", []),
-        assumptions=assumptions,
-        checks=checks,
-        p0_handoff=p0_handoff,
-        model_status=model_status,
-        extra={
-            "primary_human_deliverable": outputs["workbook"],
-            "support_artifacts": [
-                outputs[key] for key in outputs if key != "workbook" and key in written
-            ],
-        },
-    )
-
-    workbook_sheets = {
-        "Cover": dcf_cover_rows(plan, scenario_results, sensitivity_result, checks, run_log),
-        **build_workbook_sheets(plan, scenario_results, sensitivity_result, checks, run_log),
-    }
-    write_xlsx(output_dir / "model.xlsx", workbook_sheets)
-    write_run_log_bundle(output_dir, run_log)
-
-    report = render_report(plan, scenario_results, checks, run_log)
-    if support_note_enabled:
-        (output_dir / "support_note.md").write_text(report, encoding="utf-8")
-    if args.write_report_md:
-        (output_dir / "report.md").write_text(report, encoding="utf-8")
-
-    if args.print_report:
-        print("---DCF_MODEL_SUPPORT_NOTE_START---")
-        print(report.rstrip())
-        print("---DCF_MODEL_SUPPORT_NOTE_END---")
-    else:
-        print(f"DCF pipeline complete. model_status={model_status}. Output: {output_dir}")
-
-    return 0 if not hard_failures else 2
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/runtime_loader.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/runtime_loader.py
deleted file mode 100755
index 17f9476..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/runtime_loader.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-"""Load private runtime modules that are not prompt-facing instructions."""
-
-from __future__ import annotations
-
-import hashlib
-import sys
-from importlib.machinery import SourceFileLoader
-from importlib.util import module_from_spec, spec_from_loader
-from pathlib import Path
-from types import ModuleType
-
-
-def load_runtime_module(module_name: str) -> ModuleType:
-    runtime_path = Path(__file__).resolve().parent / module_name
-    if not runtime_path.exists():
-        raise ImportError(f"runtime module not found: {runtime_path}")
-
-    digest = hashlib.sha1(str(runtime_path).encode("utf-8")).hexdigest()[:12]
-    loader_name = f"_skill_runtime_{module_name}_{digest}"
-    if loader_name in sys.modules:
-        return sys.modules[loader_name]
-
-    loader = SourceFileLoader(loader_name, str(runtime_path))
-    spec = spec_from_loader(loader.name, loader)
-    if spec is None:
-        raise ImportError(f"could not create runtime spec for {runtime_path}")
-    module = module_from_spec(spec)
-    sys.modules[loader.name] = module
-    loader.exec_module(module)
-    return module
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/skill_core.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/skill_core.py
deleted file mode 100755
index 080a108..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/skill_core.py
+++ /dev/null
@@ -1,1592 +0,0 @@
-#!/usr/bin/env python3
-"""Deterministic DCF model engine for dcf-model-builder.
-
-Standard library only. No network calls. No hidden randomness.
-"""
-
-from __future__ import annotations
-
-import copy
-import json
-import math
-import os
-import sys
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from plan_validation import validate_plan_structure_without_computed_checks
-
-ALLOWED_LABELS = {
-    "reported",
-    "company_guidance",
-    "consensus",
-    "management_case",
-    "user_provided",
-    "connected_app",
-    "web_research",
-    "analyst_estimate",
-    "placeholder",
-    "derived",
-}
-
-REQUIRED_SOURCE_TOPICS = {
-    "historicals",
-    "forecast",
-    "wacc",
-    "terminal_value",
-    "share_count",
-    "net_debt",
-}
-
-MODEL_STATUS_VALUES = {
-    "decision-grade",
-    "senior-review-ready",
-    "screen-grade",
-    "not-decision-ready",
-    "blocked",
-}
-
-REQUIRED_TOP_LEVEL = [
-    "meta",
-    "source_basis",
-    "timeline",
-    "historicals",
-    "forecast",
-    "wacc",
-    "terminal_value",
-    "ev_to_equity_bridge",
-    "scenarios",
-    "sensitivities",
-]
-
-SCENARIOS = ["base", "downside", "upside"]
-
-
-def load_json(path: str | os.PathLike[str]) -> dict[str, Any]:
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def write_json(path: str | os.PathLike[str], obj: Any) -> None:
-    Path(path).parent.mkdir(parents=True, exist_ok=True)
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump(obj, f, indent=2, sort_keys=False)
-        f.write("\n")
-
-
-def _is_number(value: Any) -> bool:
-    return (
-        isinstance(value, (int, float))
-        and not isinstance(value, bool)
-        and math.isfinite(float(value))
-    )
-
-
-def _date_ok(value: Any) -> bool:
-    if not isinstance(value, str):
-        return False
-    try:
-        datetime.strptime(value, "%Y-%m-%d")
-        return True
-    except ValueError:
-        return False
-
-
-def _date(value: str) -> datetime | None:
-    try:
-        return datetime.strptime(value, "%Y-%m-%d")
-    except Exception:
-        return None
-
-
-def _num(value: Any, default: float = 0.0) -> float:
-    if _is_number(value):
-        return float(value)
-    return float(default)
-
-
-def _get(plan: dict[str, Any], dotted: str, default: Any = None) -> Any:
-    node: Any = plan
-    for part in dotted.split("."):
-        if not isinstance(node, dict) or part not in node:
-            return default
-        node = node[part]
-    return node
-
-
-def _source_map(plan: dict[str, Any]) -> dict[str, dict[str, Any]]:
-    return {str(s.get("id")): s for s in plan.get("source_basis", []) if isinstance(s, dict)}
-
-
-def _source_label(plan: dict[str, Any], source_id: str | None) -> str:
-    if not source_id:
-        return ""
-    src = _source_map(plan).get(source_id)
-    if not src:
-        return ""
-    return str(src.get("label", ""))
-
-
-def _source_for_topic(plan: dict[str, Any], topic: str) -> dict[str, Any] | None:
-    for src in plan.get("source_basis", []):
-        if isinstance(src, dict) and src.get("topic") == topic:
-            return src
-    return None
-
-
-def _vector(value: Any, horizon: int, field_name: str) -> list[float]:
-    if isinstance(value, list):
-        return [float(v) for v in value]
-    if _is_number(value):
-        return [float(value)] * horizon
-    raise ValueError(f"{field_name} must be a number or list of numbers")
-
-
-def _validate_vector(
-    errors: list[str],
-    value: Any,
-    path: str,
-    horizon: int,
-    min_value: float | None = None,
-    max_value: float | None = None,
-    allow_scalar: bool = True,
-) -> None:
-    if isinstance(value, list):
-        if len(value) != horizon:
-            errors.append(f"{path} must contain exactly {horizon} values; found {len(value)}")
-            return
-        values = value
-    elif allow_scalar and _is_number(value):
-        values = [value]
-    else:
-        errors.append(f"{path} must be a number or list of numbers")
-        return
-    for idx, item in enumerate(values):
-        loc = f"{path}[{idx}]" if isinstance(value, list) else path
-        if not _is_number(item):
-            errors.append(f"{loc} must be numeric")
-            continue
-        f = float(item)
-        if min_value is not None and f < min_value:
-            errors.append(f"{loc} must be >= {min_value}")
-        if max_value is not None and f > max_value:
-            errors.append(f"{loc} must be <= {max_value}")
-
-
-def validate_plan_structure(plan: dict[str, Any]) -> list[str]:
-    """Return actionable validation errors. Does not compute model outputs."""
-    errors = validate_plan_structure_without_computed_checks(plan)
-    if errors:
-        return errors
-
-    try:
-        wacc_info = compute_wacc(plan, "base")
-        discount_rate = (
-            wacc_info["wacc"]
-            if plan["forecast"].get("cash_flow_basis") == "fcff"
-            else wacc_info["cost_of_equity"]
-        )
-        if discount_rate <= 0:
-            errors.append("computed discount rate must be greater than zero")
-        terminal_growth = _num(
-            plan["scenarios"]["base"].get(
-                "terminal_growth_rate",
-                plan["terminal_value"].get("perpetual_growth_rate"),
-            ),
-            0.0,
-        )
-        if (
-            plan["terminal_value"].get("method") == "perpetual_growth"
-            and discount_rate <= terminal_growth
-        ):
-            errors.append("computed discount rate must exceed terminal growth rate")
-    except Exception as exc:
-        errors.append(f"could not compute WACC/terminal validation: {exc}")
-    return errors
-
-
-def normalize_plan(
-    plan: dict[str, Any], skill_root: str | os.PathLike[str] | None = None
-) -> dict[str, Any]:
-    """Return a normalized copy without changing conclusion-driving assumptions."""
-    normalized = copy.deepcopy(plan)
-    notes: list[str] = []
-    wacc = normalized.setdefault("wacc", {})
-    for field in [
-        "company_specific_premium",
-        "country_risk_premium",
-        "preferred_pct",
-        "pre_tax_cost_of_preferred",
-    ]:
-        if field not in wacc:
-            wacc[field] = 0.0
-            notes.append(f"wacc.{field} was absent and was set to 0.0 for calculation transparency")
-    bridge = normalized.setdefault("ev_to_equity_bridge", {})
-    for field in [
-        "cash",
-        "debt",
-        "leases",
-        "minorities",
-        "associates",
-        "pensions",
-        "preferred_stock",
-        "non_operating_assets",
-        "options",
-        "other_debt_like_items",
-    ]:
-        if field not in bridge:
-            bridge[field] = 0.0
-            notes.append(f"ev_to_equity_bridge.{field} was absent and was set to 0.0")
-    normalized["_normalization_notes"] = notes
-    if skill_root is not None:
-        normalized["_skill_root"] = str(skill_root)
-    return normalized
-
-
-def build_timeline(plan: dict[str, Any]) -> list[str]:
-    start = int(plan["timeline"]["start_year"])
-    horizon = int(plan["timeline"]["horizon_years"])
-    periodicity = plan["timeline"].get("periodicity", "annual")
-    if periodicity == "annual":
-        return [str(start + i) for i in range(1, horizon + 1)]
-    # Quarterly support uses Q1... labels from start year.
-    labels: list[str] = []
-    year = start
-    quarter = 1
-    for _ in range(horizon):
-        labels.append(f"{year}Q{quarter}")
-        quarter += 1
-        if quarter > 4:
-            year += 1
-            quarter = 1
-    return labels
-
-
-def compute_wacc(
-    plan: dict[str, Any], scenario_name: str | dict[str, Any] = "base"
-) -> dict[str, float]:
-    wacc = plan["wacc"]
-    if isinstance(scenario_name, dict):
-        scenario = scenario_name
-    else:
-        scenario = plan.get("scenarios", {}).get(str(scenario_name), {})
-    risk_free_rate = float(wacc["risk_free_rate"])
-    beta = float(wacc["beta"])
-    erp = float(wacc["equity_risk_premium"])
-    size = float(wacc.get("size_premium", 0.0))
-    country = float(wacc.get("country_risk_premium", 0.0))
-    company_specific = float(wacc.get("company_specific_premium", 0.0))
-    cost_of_equity = risk_free_rate + beta * erp + size + country + company_specific
-
-    marginal_tax_rate = float(wacc["marginal_tax_rate"])
-    pre_tax_cost_of_debt = float(wacc["pre_tax_cost_of_debt"])
-    after_tax_cost_of_debt = pre_tax_cost_of_debt * (1.0 - marginal_tax_rate)
-    pre_tax_cost_of_preferred = float(wacc.get("pre_tax_cost_of_preferred", 0.0))
-    preferred_pct = float(wacc.get("preferred_pct", 0.0))
-    debt_pct = float(wacc["target_debt_pct"])
-    equity_pct = float(wacc["target_equity_pct"])
-    base_wacc = (
-        equity_pct * cost_of_equity
-        + debt_pct * after_tax_cost_of_debt
-        + preferred_pct * pre_tax_cost_of_preferred
-    )
-    scenario_adjustment = float(scenario.get("wacc_adjustment", 0.0))
-    final_wacc = base_wacc + scenario_adjustment
-
-    return {
-        "risk_free_rate": risk_free_rate,
-        "beta": beta,
-        "equity_risk_premium": erp,
-        "size_premium": size,
-        "country_risk_premium": country,
-        "company_specific_premium": company_specific,
-        "cost_of_equity": cost_of_equity,
-        "pre_tax_cost_of_debt": pre_tax_cost_of_debt,
-        "after_tax_cost_of_debt": after_tax_cost_of_debt,
-        "marginal_tax_rate": marginal_tax_rate,
-        "target_debt_pct": debt_pct,
-        "target_equity_pct": equity_pct,
-        "preferred_pct": preferred_pct,
-        "base_wacc": base_wacc,
-        "scenario_wacc_adjustment": scenario_adjustment,
-        "wacc": final_wacc,
-    }
-
-
-def compute_unlevered_fcf(
-    plan: dict[str, Any], scenario_name: str = "base"
-) -> list[dict[str, float | str]]:
-    scenario = plan["scenarios"][scenario_name]
-    horizon = int(plan["timeline"]["horizon_years"])
-    periods = build_timeline(plan)
-    revenue_growth = _vector(scenario["revenue_growth"], horizon, "revenue_growth")
-    ebit_margin = _vector(scenario["ebit_margin"], horizon, "ebit_margin")
-    tax_rate = _vector(scenario["tax_rate"], horizon, "tax_rate")
-    da_pct = _vector(scenario["da_percent_revenue"], horizon, "da_percent_revenue")
-    capex_pct = _vector(scenario["capex_percent_revenue"], horizon, "capex_percent_revenue")
-    nwc_pct = _vector(scenario["nwc_percent_revenue"], horizon, "nwc_percent_revenue")
-
-    previous_revenue = float(plan["historicals"]["revenue"])
-    previous_nwc = float(
-        plan["historicals"].get("net_working_capital", previous_revenue * nwc_pct[0])
-    )
-    rows: list[dict[str, float | str]] = []
-    for i, period in enumerate(periods):
-        revenue = previous_revenue * (1.0 + revenue_growth[i])
-        ebit = revenue * ebit_margin[i]
-        taxes = max(ebit * tax_rate[i], 0.0)
-        nopat = ebit - taxes
-        da = revenue * da_pct[i]
-        capex = revenue * capex_pct[i]
-        nwc = revenue * nwc_pct[i]
-        change_nwc = nwc - previous_nwc
-        unlevered_fcf = nopat + da - capex - change_nwc
-        ebitda = ebit + da
-        rows.append(
-            {
-                "period": period,
-                "revenue": revenue,
-                "revenue_growth": revenue_growth[i],
-                "ebitda": ebitda,
-                "ebit": ebit,
-                "ebit_margin": ebit_margin[i],
-                "tax_rate": tax_rate[i],
-                "cash_taxes": taxes,
-                "nopat": nopat,
-                "da": da,
-                "capex": capex,
-                "nwc": nwc,
-                "change_nwc": change_nwc,
-                "unlevered_fcf": unlevered_fcf,
-                "cash_flow": unlevered_fcf,
-            }
-        )
-        previous_revenue = revenue
-        previous_nwc = nwc
-    return rows
-
-
-def compute_equity_cash_flows(
-    plan: dict[str, Any], scenario_name: str = "base"
-) -> list[dict[str, float | str]]:
-    scenario = plan["scenarios"][scenario_name]
-    horizon = int(plan["timeline"]["horizon_years"])
-    periods = build_timeline(plan)
-    revenue_growth = _vector(scenario["revenue_growth"], horizon, "revenue_growth")
-    ebit_margin = _vector(scenario["ebit_margin"], horizon, "ebit_margin")
-    net_income_margin = _vector(scenario["net_income_margin"], horizon, "net_income_margin")
-    tax_rate = _vector(scenario["tax_rate"], horizon, "tax_rate")
-    da_pct = _vector(scenario["da_percent_revenue"], horizon, "da_percent_revenue")
-    capex_pct = _vector(scenario["capex_percent_revenue"], horizon, "capex_percent_revenue")
-    nwc_pct = _vector(scenario["nwc_percent_revenue"], horizon, "nwc_percent_revenue")
-    net_borrowing = _vector(scenario.get("net_borrowing", 0.0), horizon, "net_borrowing")
-
-    previous_revenue = float(plan["historicals"]["revenue"])
-    previous_nwc = float(
-        plan["historicals"].get("net_working_capital", previous_revenue * nwc_pct[0])
-    )
-    rows: list[dict[str, float | str]] = []
-    for i, period in enumerate(periods):
-        revenue = previous_revenue * (1.0 + revenue_growth[i])
-        ebit = revenue * ebit_margin[i]
-        net_income = revenue * net_income_margin[i]
-        da = revenue * da_pct[i]
-        capex = revenue * capex_pct[i]
-        nwc = revenue * nwc_pct[i]
-        change_nwc = nwc - previous_nwc
-        fcfe = net_income + da - capex - change_nwc + net_borrowing[i]
-        ebitda = ebit + da
-        rows.append(
-            {
-                "period": period,
-                "revenue": revenue,
-                "revenue_growth": revenue_growth[i],
-                "ebitda": ebitda,
-                "ebit": ebit,
-                "ebit_margin": ebit_margin[i],
-                "net_income": net_income,
-                "net_income_margin": net_income_margin[i],
-                "tax_rate": tax_rate[i],
-                "da": da,
-                "capex": capex,
-                "nwc": nwc,
-                "change_nwc": change_nwc,
-                "net_borrowing": net_borrowing[i],
-                "fcfe": fcfe,
-                "cash_flow": fcfe,
-            }
-        )
-        previous_revenue = revenue
-        previous_nwc = nwc
-    return rows
-
-
-def compute_terminal_value(
-    plan: dict[str, Any],
-    forecast_rows: list[dict[str, Any]],
-    discount_rate: float,
-    scenario_name: str = "base",
-    method_override: str | None = None,
-) -> dict[str, float | str]:
-    if not forecast_rows:
-        raise ValueError("forecast_rows cannot be empty")
-    scenario = plan["scenarios"][scenario_name]
-    terminal = plan["terminal_value"]
-    method = method_override or terminal.get("method", "perpetual_growth")
-    final = forecast_rows[-1]
-    final_fcf = float(final["cash_flow"])
-    final_ebitda = float(final.get("ebitda", 0.0))
-    if method == "perpetual_growth":
-        g = float(scenario.get("terminal_growth_rate", terminal.get("perpetual_growth_rate", 0.0)))
-        if discount_rate <= g:
-            raise ValueError(
-                f"discount rate {discount_rate:.4f} must exceed terminal growth {g:.4f}"
-            )
-        terminal_fcf = final_fcf * (1.0 + g)
-        terminal_value = terminal_fcf / (discount_rate - g)
-        implied_exit_multiple = terminal_value / final_ebitda if final_ebitda else None
-        implied_fcf_yield = terminal_fcf / terminal_value if terminal_value else None
-        return {
-            "method": method,
-            "terminal_growth_rate": g,
-            "terminal_fcf": terminal_fcf,
-            "terminal_value": terminal_value,
-            "exit_ebitda_multiple": float(terminal.get("exit_ebitda_multiple", 0.0)),
-            "implied_exit_ebitda_multiple": implied_exit_multiple
-            if implied_exit_multiple is not None
-            else 0.0,
-            "implied_fcf_yield": implied_fcf_yield if implied_fcf_yield is not None else 0.0,
-        }
-    if method == "exit_multiple":
-        multiple = float(scenario.get("exit_ebitda_multiple", terminal.get("exit_ebitda_multiple")))
-        if multiple <= 0:
-            raise ValueError("exit EBITDA multiple must be positive")
-        terminal_value = final_ebitda * multiple
-        implied_fcf_yield = final_fcf / terminal_value if terminal_value else None
-        implied_growth_rate = None
-        # Rearranged Gordon growth is approximate because final FCF is used rather than next-year FCF.
-        if terminal_value and final_fcf:
-            implied_growth_rate = (discount_rate * terminal_value - final_fcf) / (
-                terminal_value + final_fcf
-            )
-        return {
-            "method": method,
-            "terminal_growth_rate": float(
-                scenario.get("terminal_growth_rate", terminal.get("perpetual_growth_rate", 0.0))
-            ),
-            "terminal_fcf": final_fcf,
-            "terminal_value": terminal_value,
-            "exit_ebitda_multiple": multiple,
-            "implied_exit_ebitda_multiple": multiple,
-            "implied_fcf_yield": implied_fcf_yield if implied_fcf_yield is not None else 0.0,
-            "implied_growth_rate": implied_growth_rate if implied_growth_rate is not None else 0.0,
-        }
-    raise ValueError(f"unsupported terminal value method: {method}")
-
-
-def discount_cash_flows(
-    forecast_rows: list[dict[str, Any]],
-    terminal_value: float,
-    discount_rate: float,
-    mid_year_convention: bool = True,
-) -> dict[str, Any]:
-    if discount_rate <= -1.0:
-        raise ValueError("discount rate must be greater than -100%")
-    pv_rows: list[dict[str, Any]] = []
-    for i, row in enumerate(forecast_rows, start=1):
-        period_power = i - 0.5 if mid_year_convention else i
-        pv = float(row["cash_flow"]) / ((1.0 + discount_rate) ** period_power)
-        pv_rows.append(
-            {
-                "period": row["period"],
-                "cash_flow": row["cash_flow"],
-                "discount_period": period_power,
-                "pv_cash_flow": pv,
-            }
-        )
-    n = len(forecast_rows)
-    pv_terminal = terminal_value / ((1.0 + discount_rate) ** n)
-    pv_fcf = sum(float(r["pv_cash_flow"]) for r in pv_rows)
-    return {
-        "pv_rows": pv_rows,
-        "pv_fcf": pv_fcf,
-        "pv_terminal_value": pv_terminal,
-        "total_pv": pv_fcf + pv_terminal,
-    }
-
-
-def compute_enterprise_value(pv_fcf: float, pv_terminal_value: float) -> float:
-    return float(pv_fcf) + float(pv_terminal_value)
-
-
-def compute_equity_value(plan: dict[str, Any], enterprise_value: float) -> dict[str, float]:
-    bridge = plan["ev_to_equity_bridge"]
-    add_backs = (
-        float(bridge.get("cash", 0.0))
-        + float(bridge.get("non_operating_assets", 0.0))
-        + float(bridge.get("associates", 0.0))
-    )
-    deductions = (
-        float(bridge.get("debt", 0.0))
-        + float(bridge.get("leases", 0.0))
-        + float(bridge.get("minorities", 0.0))
-        + float(bridge.get("pensions", 0.0))
-        + float(bridge.get("preferred_stock", 0.0))
-        + float(bridge.get("options", 0.0))
-        + float(bridge.get("other_debt_like_items", 0.0))
-    )
-    equity_value = enterprise_value + add_backs - deductions
-    return {
-        "add_backs": add_backs,
-        "deductions": deductions,
-        "equity_value": equity_value,
-    }
-
-
-def compute_enterprise_value_from_equity(plan: dict[str, Any], equity_value: float) -> float:
-    bridge = plan["ev_to_equity_bridge"]
-    add_backs = (
-        float(bridge.get("cash", 0.0))
-        + float(bridge.get("non_operating_assets", 0.0))
-        + float(bridge.get("associates", 0.0))
-    )
-    deductions = (
-        float(bridge.get("debt", 0.0))
-        + float(bridge.get("leases", 0.0))
-        + float(bridge.get("minorities", 0.0))
-        + float(bridge.get("pensions", 0.0))
-        + float(bridge.get("preferred_stock", 0.0))
-        + float(bridge.get("options", 0.0))
-        + float(bridge.get("other_debt_like_items", 0.0))
-    )
-    return equity_value - add_backs + deductions
-
-
-def compute_value_per_share(plan: dict[str, Any], equity_value: float) -> float:
-    shares = float(plan["ev_to_equity_bridge"]["diluted_shares"])
-    if shares <= 0:
-        raise ValueError("diluted shares must be greater than zero")
-    return equity_value / shares
-
-
-def run_scenario(
-    plan: dict[str, Any],
-    scenario_name: str = "base",
-    terminal_method_override: str | None = None,
-) -> dict[str, Any]:
-    cash_flow_basis = plan["forecast"].get(
-        "cash_flow_basis", plan["meta"].get("model_type", "fcff")
-    )
-    wacc_info = compute_wacc(plan, scenario_name)
-    discount_rate = (
-        wacc_info["wacc"]
-        if cash_flow_basis == "fcff"
-        else wacc_info["cost_of_equity"]
-        + float(plan["scenarios"][scenario_name].get("wacc_adjustment", 0.0))
-    )
-    forecast_rows = (
-        compute_unlevered_fcf(plan, scenario_name)
-        if cash_flow_basis == "fcff"
-        else compute_equity_cash_flows(plan, scenario_name)
-    )
-    terminal_info = compute_terminal_value(
-        plan,
-        forecast_rows,
-        discount_rate,
-        scenario_name,
-        method_override=terminal_method_override,
-    )
-    pv_info = discount_cash_flows(
-        forecast_rows,
-        float(terminal_info["terminal_value"]),
-        discount_rate,
-        bool(plan["forecast"].get("mid_year_convention", True)),
-    )
-
-    if cash_flow_basis == "fcff":
-        enterprise_value = compute_enterprise_value(
-            float(pv_info["pv_fcf"]), float(pv_info["pv_terminal_value"])
-        )
-        equity_bridge = compute_equity_value(plan, enterprise_value)
-        equity_value = equity_bridge["equity_value"]
-    else:
-        equity_value = compute_enterprise_value(
-            float(pv_info["pv_fcf"]), float(pv_info["pv_terminal_value"])
-        )
-        enterprise_value = compute_enterprise_value_from_equity(plan, equity_value)
-        equity_bridge = {
-            "add_backs": 0.0,
-            "deductions": 0.0,
-            "equity_value": equity_value,
-        }
-
-    value_per_share = compute_value_per_share(plan, equity_value)
-    tv_percent_ev = (
-        float(pv_info["pv_terminal_value"]) / enterprise_value if enterprise_value else 0.0
-    )
-    return {
-        "scenario": scenario_name,
-        "cash_flow_basis": cash_flow_basis,
-        "forecast_rows": forecast_rows,
-        "wacc": wacc_info,
-        "discount_rate": discount_rate,
-        "terminal": terminal_info,
-        "pv": pv_info,
-        "enterprise_value": enterprise_value,
-        "equity_bridge": equity_bridge,
-        "equity_value": equity_value,
-        "value_per_share": value_per_share,
-        "tv_percent_ev": tv_percent_ev,
-    }
-
-
-def _adjust_vector(
-    value: Any,
-    horizon: int,
-    delta: float,
-    floor: float | None = None,
-    cap: float | None = None,
-) -> list[float]:
-    vals = _vector(value, horizon, "adjust_vector")
-    out = []
-    for v in vals:
-        new_v = v + delta
-        if floor is not None:
-            new_v = max(floor, new_v)
-        if cap is not None:
-            new_v = min(cap, new_v)
-        out.append(new_v)
-    return out
-
-
-def _safe_run_value_per_share(
-    plan: dict[str, Any], method: str | None = None
-) -> tuple[float | None, str | None]:
-    try:
-        result = run_scenario(plan, "base", terminal_method_override=method)
-        value = float(result["value_per_share"])
-        if not math.isfinite(value):
-            return None, "non-finite value per share"
-        return value, None
-    except Exception as exc:
-        return None, str(exc)
-
-
-def run_sensitivities(plan: dict[str, Any]) -> dict[str, Any]:
-    rows: list[dict[str, Any]] = []
-    horizon = int(plan["timeline"]["horizon_years"])
-    base_scenario = plan["scenarios"]["base"]
-    base_tg = float(
-        base_scenario.get(
-            "terminal_growth_rate",
-            plan["terminal_value"].get("perpetual_growth_rate", 0.0),
-        )
-    )
-    base_exit = float(
-        base_scenario.get(
-            "exit_ebitda_multiple",
-            plan["terminal_value"].get("exit_ebitda_multiple", 0.0),
-        )
-    )
-    sens = plan.get("sensitivities", {})
-
-    for w_delta in sens.get("wacc_delta", [0.0]):
-        for tg_delta in sens.get("terminal_growth_delta", [0.0]):
-            p = copy.deepcopy(plan)
-            p["terminal_value"]["method"] = "perpetual_growth"
-            p["scenarios"]["base"]["wacc_adjustment"] = float(
-                base_scenario.get("wacc_adjustment", 0.0)
-            ) + float(w_delta)
-            p["scenarios"]["base"]["terminal_growth_rate"] = base_tg + float(tg_delta)
-            value, error = _safe_run_value_per_share(p, method="perpetual_growth")
-            rows.append(
-                {
-                    "sensitivity": "WACC / Terminal Growth",
-                    "x_axis": "wacc_delta",
-                    "x_value": float(w_delta),
-                    "y_axis": "terminal_growth_delta",
-                    "y_value": float(tg_delta),
-                    "metric": "value_per_share",
-                    "value": value,
-                    "units": plan["meta"].get("currency", "") + "/share",
-                    "notes": error or "",
-                }
-            )
-
-    if base_exit > 0:
-        for w_delta in sens.get("wacc_delta", [0.0]):
-            for mult_delta in sens.get("exit_multiple_delta", [0.0]):
-                p = copy.deepcopy(plan)
-                p["terminal_value"]["method"] = "exit_multiple"
-                p["terminal_value"]["exit_ebitda_multiple"] = max(
-                    0.1, base_exit + float(mult_delta)
-                )
-                p["scenarios"]["base"]["exit_ebitda_multiple"] = max(
-                    0.1, base_exit + float(mult_delta)
-                )
-                p["scenarios"]["base"]["wacc_adjustment"] = float(
-                    base_scenario.get("wacc_adjustment", 0.0)
-                ) + float(w_delta)
-                value, error = _safe_run_value_per_share(p, method="exit_multiple")
-                rows.append(
-                    {
-                        "sensitivity": "WACC / Exit Multiple",
-                        "x_axis": "wacc_delta",
-                        "x_value": float(w_delta),
-                        "y_axis": "exit_multiple_delta",
-                        "y_value": float(mult_delta),
-                        "metric": "value_per_share",
-                        "value": value,
-                        "units": plan["meta"].get("currency", "") + "/share",
-                        "notes": error or "",
-                    }
-                )
-
-    for rev_delta in sens.get("revenue_growth_delta", [0.0]):
-        for margin_delta in sens.get("ebit_margin_delta", [0.0]):
-            p = copy.deepcopy(plan)
-            p["scenarios"]["base"]["revenue_growth"] = _adjust_vector(
-                base_scenario["revenue_growth"], horizon, float(rev_delta), floor=-0.9
-            )
-            p["scenarios"]["base"]["ebit_margin"] = _adjust_vector(
-                base_scenario["ebit_margin"],
-                horizon,
-                float(margin_delta),
-                floor=-0.8,
-                cap=0.95,
-            )
-            value, error = _safe_run_value_per_share(p, method=plan["terminal_value"].get("method"))
-            rows.append(
-                {
-                    "sensitivity": "Revenue Growth / EBIT Margin",
-                    "x_axis": "revenue_growth_delta",
-                    "x_value": float(rev_delta),
-                    "y_axis": "ebit_margin_delta",
-                    "y_value": float(margin_delta),
-                    "metric": "value_per_share",
-                    "value": value,
-                    "units": plan["meta"].get("currency", "") + "/share",
-                    "notes": error or "",
-                }
-            )
-
-    directionality = compute_sensitivity_directionality(rows)
-    return {"rows": rows, "directionality": directionality}
-
-
-def _value_at(
-    rows: list[dict[str, Any]], sensitivity: str, x_value: float, y_value: float
-) -> float | None:
-    candidates = [
-        r
-        for r in rows
-        if r.get("sensitivity") == sensitivity
-        and abs(float(r.get("x_value", 999.0)) - x_value) < 1e-9
-        and abs(float(r.get("y_value", 999.0)) - y_value) < 1e-9
-        and r.get("value") is not None
-    ]
-    if not candidates:
-        return None
-    return float(candidates[0]["value"])
-
-
-def compute_sensitivity_directionality(rows: list[dict[str, Any]]) -> dict[str, Any]:
-    checks: dict[str, Any] = {"passed": True, "details": []}
-
-    def add_check(name: str, passed: bool, detail: str) -> None:
-        checks["details"].append({"check": name, "passed": bool(passed), "detail": detail})
-        if not passed:
-            checks["passed"] = False
-
-    def check_axis(
-        sensitivity: str,
-        axis: str,
-        fixed_axis_zero: str,
-        should_increase_with_axis: bool,
-    ) -> None:
-        subset = [
-            r for r in rows if r.get("sensitivity") == sensitivity and r.get("value") is not None
-        ]
-        if not subset:
-            add_check(f"{sensitivity} {axis}", False, "no successful rows")
-            return
-        if axis == "x":
-            fixed_rows = [r for r in subset if abs(float(r.get("y_value", 0.0))) < 1e-9]
-            key = "x_value"
-        else:
-            fixed_rows = [r for r in subset if abs(float(r.get("x_value", 0.0))) < 1e-9]
-            key = "y_value"
-        if len(fixed_rows) < 2:
-            add_check(f"{sensitivity} {axis}", False, "not enough zero-axis rows")
-            return
-        low = min(fixed_rows, key=lambda r: float(r[key]))
-        high = max(fixed_rows, key=lambda r: float(r[key]))
-        low_v = float(low["value"])
-        high_v = float(high["value"])
-        if should_increase_with_axis:
-            passed = high_v >= low_v
-            detail = f"value at high axis {high_v:.2f} vs low axis {low_v:.2f}"
-        else:
-            passed = high_v <= low_v
-            detail = f"value at high axis {high_v:.2f} vs low axis {low_v:.2f}"
-        add_check(f"{sensitivity} {axis} direction", passed, detail)
-
-    check_axis(
-        "WACC / Terminal Growth",
-        "x",
-        "terminal_growth_delta",
-        should_increase_with_axis=False,
-    )
-    check_axis("WACC / Terminal Growth", "y", "wacc_delta", should_increase_with_axis=True)
-    check_axis(
-        "WACC / Exit Multiple",
-        "x",
-        "exit_multiple_delta",
-        should_increase_with_axis=False,
-    )
-    check_axis("WACC / Exit Multiple", "y", "wacc_delta", should_increase_with_axis=True)
-    check_axis(
-        "Revenue Growth / EBIT Margin",
-        "x",
-        "ebit_margin_delta",
-        should_increase_with_axis=True,
-    )
-    check_axis(
-        "Revenue Growth / EBIT Margin",
-        "y",
-        "revenue_growth_delta",
-        should_increase_with_axis=True,
-    )
-    return checks
-
-
-def _source_warnings(plan: dict[str, Any]) -> list[str]:
-    warnings: list[str] = []
-    labels = [
-        str(src.get("label")) for src in plan.get("source_basis", []) if isinstance(src, dict)
-    ]
-    if "placeholder" in labels:
-        warnings.append(
-            "One or more material source entries use placeholder evidence labels; output is screen-grade at best."
-        )
-    weak_topics = [
-        str(src.get("topic"))
-        for src in plan.get("source_basis", [])
-        if isinstance(src, dict)
-        and src.get("label") in {"analyst_estimate", "placeholder"}
-        and src.get("topic") in {"forecast", "wacc", "terminal_value"}
-    ]
-    if weak_topics:
-        warnings.append(
-            "Material valuation inputs rely on analyst estimates/placeholders: "
-            + ", ".join(sorted(set(weak_topics)))
-        )
-    valuation_date = _date(str(plan.get("meta", {}).get("valuation_date", "")))
-    if valuation_date:
-        dated = []
-        for src in plan.get("source_basis", []):
-            if not isinstance(src, dict):
-                continue
-            d = _date(str(src.get("as_of_date", "")))
-            if d:
-                dated.append((src.get("topic"), d))
-                age = abs((valuation_date - d).days)
-                if (
-                    src.get("topic") in {"wacc", "share_count", "net_debt", "market_data"}
-                    and age > 120
-                ):
-                    warnings.append(
-                        f"Source for {src.get('topic')} is {age} days from valuation date; refresh market/bridge data for decision-grade use."
-                    )
-        if dated:
-            min_date = min(d for _, d in dated)
-            max_date = max(d for _, d in dated)
-            spread = (max_date - min_date).days
-            if spread > 365:
-                warnings.append(
-                    f"Source dates span {spread} days; confirm market data, historicals, and share count are contemporaneous."
-                )
-    return warnings
-
-
-def compute_checks(
-    plan: dict[str, Any],
-    scenario_results: dict[str, dict[str, Any]],
-    sensitivity_result: dict[str, Any],
-) -> dict[str, Any]:
-    hard_failures: list[str] = []
-    warnings: list[str] = []
-    informational: list[str] = []
-
-    validation_errors = validate_plan_structure(plan)
-    if validation_errors:
-        hard_failures.extend(validation_errors)
-
-    required_topics = REQUIRED_SOURCE_TOPICS
-    topics = {
-        str(src.get("topic")) for src in plan.get("source_basis", []) if isinstance(src, dict)
-    }
-    missing_topics = sorted(required_topics - topics)
-    if missing_topics:
-        hard_failures.append(
-            "source basis missing for material valuation inputs: " + ", ".join(missing_topics)
-        )
-
-    base = scenario_results.get("base")
-    if not base:
-        hard_failures.append("base scenario did not run")
-    else:
-        forecast_rows = base.get("forecast_rows", [])
-        fcfs = [row.get("cash_flow") for row in forecast_rows]
-        if not fcfs or all(not _is_number(v) for v in fcfs):
-            hard_failures.append("no forecast FCF/FCFE was produced")
-        wacc_value = float(base.get("discount_rate", 0.0))
-        if wacc_value <= 0 or not math.isfinite(wacc_value):
-            hard_failures.append("WACC/cost of equity is non-positive or invalid")
-        terminal = base.get("terminal", {})
-        if not terminal or not _is_number(terminal.get("terminal_value")):
-            hard_failures.append("terminal value missing or invalid")
-        elif terminal.get("method") == "perpetual_growth" and wacc_value <= float(
-            terminal.get("terminal_growth_rate", 0.0)
-        ):
-            hard_failures.append("discount rate must exceed terminal growth")
-        for metric in ["enterprise_value", "equity_value", "value_per_share"]:
-            value = base.get(metric)
-            if not _is_number(value):
-                hard_failures.append(f"discounting math failed for {metric}")
-        bridge = plan.get("ev_to_equity_bridge", {})
-        if (
-            not _is_number(bridge.get("diluted_shares"))
-            or float(bridge.get("diluted_shares", 0.0)) <= 0
-        ):
-            hard_failures.append("EV-to-equity bridge missing valid diluted shares")
-        tv_pct = float(base.get("tv_percent_ev", 0.0))
-        if tv_pct > 0.85:
-            warnings.append(
-                f"Terminal value is {tv_pct:.1%} of enterprise value, which is very high."
-            )
-        elif tv_pct > 0.75:
-            warnings.append(
-                f"Terminal value is {tv_pct:.1%} of enterprise value; senior review should pressure-test terminal assumptions."
-            )
-        terminal_growth = float(terminal.get("terminal_growth_rate", 0.0))
-        if terminal.get("method") == "perpetual_growth" and terminal_growth > 0.04:
-            warnings.append(
-                f"Terminal growth of {terminal_growth:.1%} exceeds a typical mature-company long-run threshold."
-            )
-        hist_margin = float(plan["historicals"].get("ebit", 0.0)) / float(
-            plan["historicals"].get("revenue", 1.0)
-        )
-        final_margin = float(forecast_rows[-1].get("ebit_margin", 0.0)) if forecast_rows else 0.0
-        if final_margin - hist_margin > 0.05:
-            forecast_src = _source_for_topic(plan, "forecast") or {}
-            if forecast_src.get("label") not in {
-                "company_guidance",
-                "management_case",
-                "consensus",
-                "reported",
-                "user_provided",
-            }:
-                warnings.append(
-                    f"Forecast EBIT margin expands by {(final_margin - hist_margin):.1%} versus latest historical margin without strong source support."
-                )
-
-    if not sensitivity_result.get("directionality", {}).get("passed", False):
-        hard_failures.append("sensitivity outputs failed directionality checks")
-
-    warnings.extend(_source_warnings(plan))
-    warnings.extend(plan.get("_normalization_notes", []))
-
-    # Deduplicate preserving order.
-    hard_failures = list(dict.fromkeys(hard_failures))
-    warnings = list(dict.fromkeys(warnings))
-
-    checks = {
-        "hard_failures": hard_failures,
-        "warnings": warnings,
-        "informational": informational,
-        "sensitivity_directionality": sensitivity_result.get("directionality", {}),
-    }
-    if base:
-        checks.update(
-            {
-                "base_tv_percent_ev": base.get("tv_percent_ev"),
-                "base_discount_rate": base.get("discount_rate"),
-                "base_enterprise_value": base.get("enterprise_value"),
-                "base_equity_value": base.get("equity_value"),
-                "base_value_per_share": base.get("value_per_share"),
-            }
-        )
-    return checks
-
-
-def determine_model_status(
-    plan: dict[str, Any], hard_failures: list[str], warnings: list[str]
-) -> str:
-    if hard_failures:
-        return "not-decision-ready"
-    labels = [src.get("label") for src in plan.get("source_basis", []) if isinstance(src, dict)]
-    if "placeholder" in labels:
-        return "screen-grade"
-    if any(label == "analyst_estimate" for label in labels):
-        return "screen-grade"
-    if warnings:
-        return "senior-review-ready"
-    return "decision-grade"
-
-
-def _fmt_pct(x: float | None) -> str:
-    if x is None or not math.isfinite(float(x)):
-        return "n/a"
-    return f"{float(x) * 100:.1f}%"
-
-
-def _fmt_num(x: float | None) -> str:
-    if x is None or not math.isfinite(float(x)):
-        return "n/a"
-    return f"{float(x):,.1f}"
-
-
-def _fmt_price(x: float | None) -> str:
-    if x is None or not math.isfinite(float(x)):
-        return "n/a"
-    return f"{float(x):,.2f}"
-
-
-def build_p0_handoff(
-    plan: dict[str, Any],
-    scenario_results: dict[str, dict[str, Any]],
-    sensitivity_result: dict[str, Any],
-    model_status: str,
-    warnings: list[str],
-    output_dir: str | os.PathLike[str],
-) -> dict[str, Any]:
-    scenario_summary: dict[str, Any] = {}
-    values = []
-    for name in SCENARIOS:
-        r = scenario_results.get(name)
-        if not r:
-            continue
-        scenario_summary[name] = {
-            "enterprise_value": r.get("enterprise_value"),
-            "equity_value": r.get("equity_value"),
-            "value_per_share": r.get("value_per_share"),
-            "discount_rate": r.get("discount_rate"),
-            "terminal_value": r.get("terminal", {}).get("terminal_value"),
-            "terminal_method": r.get("terminal", {}).get("method"),
-            "tv_percent_ev": r.get("tv_percent_ev"),
-        }
-        if _is_number(r.get("value_per_share")):
-            values.append(float(r["value_per_share"]))
-    selected_range = {
-        "low_value_per_share": min(values) if values else None,
-        "high_value_per_share": max(values) if values else None,
-        "basis": "downside/base/upside scenario range",
-    }
-
-    key_drivers = identify_key_value_drivers(sensitivity_result.get("rows", []))
-    base = scenario_results.get("base", {})
-    output = Path(output_dir)
-    return {
-        "selected_valuation_range": selected_range,
-        "scenarios": scenario_summary,
-        "wacc_and_terminal_assumptions": {
-            "base_discount_rate": base.get("discount_rate"),
-            "base_wacc": base.get("wacc", {}).get("wacc"),
-            "base_cost_of_equity": base.get("wacc", {}).get("cost_of_equity"),
-            "terminal_method": base.get("terminal", {}).get("method"),
-            "terminal_growth_rate": base.get("terminal", {}).get("terminal_growth_rate"),
-            "exit_ebitda_multiple": base.get("terminal", {}).get("exit_ebitda_multiple"),
-        },
-        "key_value_drivers": key_drivers,
-        "major_caveats": warnings[:10],
-        "model_status": model_status,
-        "paths": {
-            "workbook": str(output / "model.xlsx"),
-            "plan": str(output / "plan.json"),
-            "run_log": str(output / "run_log.json"),
-            "support_note": str(output / "support_note.md"),
-        },
-    }
-
-
-def identify_key_value_drivers(
-    sensitivity_rows: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
-    drivers: list[dict[str, Any]] = []
-    for sensitivity in sorted({str(r.get("sensitivity")) for r in sensitivity_rows}):
-        vals = [
-            float(r["value"])
-            for r in sensitivity_rows
-            if r.get("sensitivity") == sensitivity and _is_number(r.get("value"))
-        ]
-        if vals:
-            drivers.append(
-                {
-                    "driver": sensitivity,
-                    "value_per_share_range": max(vals) - min(vals),
-                    "low": min(vals),
-                    "high": max(vals),
-                }
-            )
-    drivers.sort(key=lambda d: d["value_per_share_range"], reverse=True)
-    return drivers[:5]
-
-
-def to_model_rows(
-    plan: dict[str, Any],
-    scenario_results: dict[str, dict[str, Any]],
-    sensitivity_rows: list[dict[str, Any]] | None = None,
-    checks: dict[str, Any] | None = None,
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    forecast_source = str(plan.get("forecast", {}).get("source_id", ""))
-    forecast_label = _source_label(plan, forecast_source)
-    wacc_source = str(plan.get("wacc", {}).get("source_id", ""))
-    wacc_label = _source_label(plan, wacc_source)
-    terminal_source = str(plan.get("terminal_value", {}).get("source_id", ""))
-    terminal_label = _source_label(plan, terminal_source)
-    bridge = plan.get("ev_to_equity_bridge", {})
-    bridge_source = str(bridge.get("net_debt_source_id", ""))
-    bridge_label = _source_label(plan, bridge_source)
-    share_source = str(bridge.get("share_count_source_id", ""))
-    share_label = _source_label(plan, share_source)
-    units = plan.get("meta", {}).get("units", "")
-
-    for scenario_name, result in scenario_results.items():
-        for f in result.get("forecast_rows", []):
-            period = f["period"]
-            for section, line_item, key in [
-                ("IS", "Revenue", "revenue"),
-                ("IS", "EBITDA", "ebitda"),
-                ("IS", "EBIT", "ebit"),
-                ("IS", "Cash Taxes", "cash_taxes"),
-                ("FCF", "NOPAT", "nopat"),
-                ("FCF", "D&A", "da"),
-                ("FCF", "Capex", "capex"),
-                ("FCF", "Change in NWC", "change_nwc"),
-                ("FCF", "Unlevered FCF", "unlevered_fcf"),
-                ("FCF", "FCFE", "fcfe"),
-            ]:
-                if key not in f:
-                    continue
-                rows.append(
-                    {
-                        "section": section,
-                        "line_item": line_item,
-                        "scenario": scenario_name,
-                        "period": period,
-                        "value": f[key],
-                        "units": units,
-                        "evidence_label": forecast_label,
-                        "source_id": forecast_source,
-                        "notes": "forecast output",
-                    }
-                )
-        w = result.get("wacc", {})
-        for line_item, key in [
-            ("Cost of Equity", "cost_of_equity"),
-            ("Pre-tax Cost of Debt", "pre_tax_cost_of_debt"),
-            ("After-tax Cost of Debt", "after_tax_cost_of_debt"),
-            ("Marginal Tax Rate", "marginal_tax_rate"),
-            ("Target Debt %", "target_debt_pct"),
-            ("Target Equity %", "target_equity_pct"),
-            ("WACC", "wacc"),
-        ]:
-            rows.append(
-                {
-                    "section": "WACC",
-                    "line_item": line_item,
-                    "scenario": scenario_name,
-                    "period": "valuation_date",
-                    "value": w.get(key),
-                    "units": "%"
-                    if "Rate" in line_item
-                    or "%" in line_item
-                    or line_item
-                    in {
-                        "WACC",
-                        "Cost of Equity",
-                        "Pre-tax Cost of Debt",
-                        "After-tax Cost of Debt",
-                    }
-                    else "x",
-                    "evidence_label": wacc_label,
-                    "source_id": wacc_source,
-                    "notes": "cost of capital output",
-                }
-            )
-        terminal = result.get("terminal", {})
-        for line_item, value in [
-            ("Terminal Value", terminal.get("terminal_value")),
-            ("PV of Terminal Value", result.get("pv", {}).get("pv_terminal_value")),
-            ("TV Percent of EV", result.get("tv_percent_ev")),
-            (
-                "Implied Exit EBITDA Multiple",
-                terminal.get("implied_exit_ebitda_multiple"),
-            ),
-            ("Implied FCF Yield", terminal.get("implied_fcf_yield")),
-        ]:
-            rows.append(
-                {
-                    "section": "TV",
-                    "line_item": line_item,
-                    "scenario": scenario_name,
-                    "period": "terminal",
-                    "value": value,
-                    "units": "%" if "Percent" in line_item or "Yield" in line_item else units,
-                    "evidence_label": terminal_label,
-                    "source_id": terminal_source,
-                    "notes": str(terminal.get("method", "")),
-                }
-            )
-        for line_item, value, label, source_id in [
-            (
-                "PV of FCF",
-                result.get("pv", {}).get("pv_fcf"),
-                forecast_label,
-                forecast_source,
-            ),
-            (
-                "Enterprise Value",
-                result.get("enterprise_value"),
-                bridge_label,
-                bridge_source,
-            ),
-            ("Equity Value", result.get("equity_value"), bridge_label, bridge_source),
-            ("Diluted Shares", bridge.get("diluted_shares"), share_label, share_source),
-            (
-                "Value per Share",
-                result.get("value_per_share"),
-                share_label,
-                share_source,
-            ),
-        ]:
-            rows.append(
-                {
-                    "section": "VALUATION",
-                    "line_item": line_item,
-                    "scenario": scenario_name,
-                    "period": "valuation_date",
-                    "value": value,
-                    "units": plan.get("meta", {}).get("currency", "")
-                    + ("/share" if "Share" in line_item else "mm"),
-                    "evidence_label": label,
-                    "source_id": source_id,
-                    "notes": "valuation output",
-                }
-            )
-
-    if checks:
-        for item in checks.get("hard_failures", []):
-            rows.append(
-                {
-                    "section": "CHECKS",
-                    "line_item": "Hard Failure",
-                    "scenario": "all",
-                    "period": "run",
-                    "value": "",
-                    "units": "",
-                    "evidence_label": "derived",
-                    "source_id": "",
-                    "notes": item,
-                }
-            )
-        for item in checks.get("warnings", []):
-            rows.append(
-                {
-                    "section": "CHECKS",
-                    "line_item": "Warning",
-                    "scenario": "all",
-                    "period": "run",
-                    "value": "",
-                    "units": "",
-                    "evidence_label": "derived",
-                    "source_id": "",
-                    "notes": item,
-                }
-            )
-
-    for src in plan.get("source_basis", []):
-        rows.append(
-            {
-                "section": "ASSUMPTIONS",
-                "line_item": f"Source Basis - {src.get('topic')}",
-                "scenario": "all",
-                "period": str(src.get("as_of_date", "")),
-                "value": "",
-                "units": "",
-                "evidence_label": src.get("label", ""),
-                "source_id": src.get("id", ""),
-                "notes": f"{src.get('source_name', '')}: {src.get('notes', '')}",
-            }
-        )
-    return rows
-
-
-def _rows_to_table(rows: list[dict[str, Any]], columns: list[str] | None = None) -> list[list[Any]]:
-    if not rows:
-        return [columns or []]
-    if columns is None:
-        columns = []
-        for row in rows:
-            for key in row.keys():
-                if key not in columns:
-                    columns.append(key)
-    return [columns] + [[row.get(col, "") for col in columns] for row in rows]
-
-
-def build_workbook_sheets(
-    plan: dict[str, Any],
-    scenario_results: dict[str, dict[str, Any]],
-    sensitivity_result: dict[str, Any],
-    checks: dict[str, Any],
-    run_log: dict[str, Any],
-) -> dict[str, list[list[Any]]]:
-    summary_rows: list[dict[str, Any]] = []
-    for name in SCENARIOS:
-        result = scenario_results.get(name)
-        if not result:
-            continue
-        summary_rows.append(
-            {
-                "scenario": name,
-                "enterprise_value": result.get("enterprise_value"),
-                "equity_value": result.get("equity_value"),
-                "value_per_share": result.get("value_per_share"),
-                "discount_rate": result.get("discount_rate"),
-                "pv_fcf": result.get("pv", {}).get("pv_fcf"),
-                "pv_terminal_value": result.get("pv", {}).get("pv_terminal_value"),
-                "terminal_value": result.get("terminal", {}).get("terminal_value"),
-                "tv_percent_ev": result.get("tv_percent_ev"),
-                "terminal_method": result.get("terminal", {}).get("method"),
-            }
-        )
-
-    model_rows = to_model_rows(plan, scenario_results, sensitivity_result.get("rows", []), checks)
-    sensitivity_rows = sensitivity_result.get("rows", [])
-    check_rows = []
-    for item in checks.get("hard_failures", []):
-        check_rows.append({"severity": "hard_failure", "check": item, "status": "fail"})
-    for item in checks.get("warnings", []):
-        check_rows.append({"severity": "warning", "check": item, "status": "review"})
-    for detail in checks.get("sensitivity_directionality", {}).get("details", []):
-        check_rows.append(
-            {
-                "severity": "check",
-                "check": detail.get("check"),
-                "status": "pass" if detail.get("passed") else "fail",
-                "detail": detail.get("detail"),
-            }
-        )
-
-    assumptions_rows = []
-    for src in plan.get("source_basis", []):
-        assumptions_rows.append(
-            {
-                "id": src.get("id"),
-                "topic": src.get("topic"),
-                "label": src.get("label"),
-                "source_name": src.get("source_name"),
-                "source_type": src.get("source_type"),
-                "as_of_date": src.get("as_of_date"),
-                "confidence": src.get("confidence"),
-                "notes": src.get("notes"),
-            }
-        )
-    assumptions_rows.extend(
-        [
-            {
-                "id": "meta.company",
-                "topic": "meta",
-                "label": "derived",
-                "source_name": plan.get("meta", {}).get("company"),
-                "source_type": "plan",
-                "as_of_date": plan.get("meta", {}).get("as_of_date"),
-                "confidence": "",
-                "notes": "company modeled",
-            },
-            {
-                "id": "meta.model_type",
-                "topic": "meta",
-                "label": "derived",
-                "source_name": plan.get("meta", {}).get("model_type"),
-                "source_type": "plan",
-                "as_of_date": plan.get("meta", {}).get("valuation_date"),
-                "confidence": "",
-                "notes": "cash flow basis",
-            },
-        ]
-    )
-
-    run_rows = [
-        ["field", "value"],
-        ["model_status", run_log.get("model_status")],
-        ["workbook_mode", run_log.get("workbook_mode")],
-        ["hard_failure_count", len(run_log.get("hard_failures", []))],
-        ["warning_count", len(run_log.get("warnings", []))],
-        ["company", plan.get("meta", {}).get("company")],
-        ["valuation_date", plan.get("meta", {}).get("valuation_date")],
-    ]
-    for idx, warning in enumerate(run_log.get("warnings", [])[:20], start=1):
-        run_rows.append([f"warning_{idx}", warning])
-    for idx, failure in enumerate(run_log.get("hard_failures", [])[:20], start=1):
-        run_rows.append([f"hard_failure_{idx}", failure])
-
-    return {
-        "Summary": _rows_to_table(summary_rows),
-        "Model": _rows_to_table(
-            model_rows,
-            [
-                "section",
-                "line_item",
-                "scenario",
-                "period",
-                "value",
-                "units",
-                "evidence_label",
-                "source_id",
-                "notes",
-            ],
-        ),
-        "Sensitivities": _rows_to_table(
-            sensitivity_rows,
-            [
-                "sensitivity",
-                "x_axis",
-                "x_value",
-                "y_axis",
-                "y_value",
-                "metric",
-                "value",
-                "units",
-                "notes",
-            ],
-        ),
-        "Checks": _rows_to_table(check_rows),
-        "Assumptions": _rows_to_table(assumptions_rows),
-        "Run Log": run_rows,
-    }
-
-
-def render_report(
-    plan: dict[str, Any],
-    scenario_results: dict[str, dict[str, Any]],
-    checks: dict[str, Any],
-    run_log: dict[str, Any],
-) -> str:
-    meta = plan.get("meta", {})
-    lines: list[str] = []
-    lines.append(f"# DCF Valuation Report: {meta.get('company', 'Company')}")
-    lines.append("")
-    lines.append(f"**Model status:** `{run_log.get('model_status')}`  ")
-    lines.append(f"**Workbook mode:** `{run_log.get('workbook_mode')}`  ")
-    lines.append(f"**Valuation date:** {meta.get('valuation_date')}  ")
-    lines.append(f"**Currency / units:** {meta.get('currency')} / {meta.get('units')}  ")
-    lines.append("")
-    lines.append("## Valuation range")
-    handoff = run_log.get("p0_handoff", {})
-    value_range = handoff.get("selected_valuation_range", {})
-    lines.append(
-        f"- Scenario value per share range: **{_fmt_price(value_range.get('low_value_per_share'))} to {_fmt_price(value_range.get('high_value_per_share'))}**"
-    )
-    lines.append(f"- Basis: {value_range.get('basis', 'scenario range')}")
-    lines.append("")
-    lines.append("| Scenario | EV | Equity value | Value / share | Discount rate | TV % EV |")
-    lines.append("|---|---:|---:|---:|---:|---:|")
-    for name in SCENARIOS:
-        r = scenario_results.get(name)
-        if not r:
-            continue
-        lines.append(
-            f"| {name.title()} | {_fmt_num(r.get('enterprise_value'))} | {_fmt_num(r.get('equity_value'))} | {_fmt_price(r.get('value_per_share'))} | {_fmt_pct(r.get('discount_rate'))} | {_fmt_pct(r.get('tv_percent_ev'))} |"
-        )
-    lines.append("")
-    base = scenario_results.get("base", {})
-    if base:
-        terminal = base.get("terminal", {})
-        wacc = base.get("wacc", {})
-        lines.append("## Base-case DCF bridge")
-        lines.append(
-            f"- PV of explicit cash flows: **{_fmt_num(base.get('pv', {}).get('pv_fcf'))}**"
-        )
-        lines.append(
-            f"- PV of terminal value: **{_fmt_num(base.get('pv', {}).get('pv_terminal_value'))}**"
-        )
-        lines.append(f"- Enterprise value: **{_fmt_num(base.get('enterprise_value'))}**")
-        lines.append(f"- Equity value: **{_fmt_num(base.get('equity_value'))}**")
-        lines.append(f"- Value per share: **{_fmt_price(base.get('value_per_share'))}**")
-        lines.append("")
-        lines.append("## WACC and terminal value")
-        lines.append(f"- Cost of equity: **{_fmt_pct(wacc.get('cost_of_equity'))}**")
-        lines.append(f"- WACC: **{_fmt_pct(wacc.get('wacc'))}**")
-        lines.append(f"- Terminal method: **{terminal.get('method')}**")
-        lines.append(f"- Terminal growth: **{_fmt_pct(terminal.get('terminal_growth_rate'))}**")
-        lines.append(
-            f"- Implied exit EBITDA multiple: **{_fmt_num(terminal.get('implied_exit_ebitda_multiple'))}x**"
-        )
-        lines.append("")
-    lines.append("## Key value drivers")
-    drivers = handoff.get("key_value_drivers", [])
-    if drivers:
-        lines.append("| Driver | Value/share range | Low | High |")
-        lines.append("|---|---:|---:|---:|")
-        for d in drivers:
-            lines.append(
-                f"| {d.get('driver')} | {_fmt_price(d.get('value_per_share_range'))} | {_fmt_price(d.get('low'))} | {_fmt_price(d.get('high'))} |"
-            )
-    else:
-        lines.append("No sensitivity drivers were available.")
-    lines.append("")
-    lines.append("## QA checks")
-    hard = checks.get("hard_failures", [])
-    warns = checks.get("warnings", [])
-    if hard:
-        lines.append("**Hard failures:**")
-        for item in hard:
-            lines.append(f"- {item}")
-    else:
-        lines.append("No hard failures detected.")
-    if warns:
-        lines.append("")
-        lines.append("**Warnings / senior-review items:**")
-        for item in warns:
-            lines.append(f"- {item}")
-    else:
-        lines.append("")
-        lines.append("No warnings detected.")
-    lines.append("")
-    lines.append("## Source basis")
-    lines.append("| Topic | Label | Source | As of | Confidence |")
-    lines.append("|---|---|---|---|---|")
-    for src in plan.get("source_basis", []):
-        lines.append(
-            f"| {src.get('topic')} | {src.get('label')} | {src.get('source_name')} | {src.get('as_of_date')} | {src.get('confidence')} |"
-        )
-    lines.append("")
-    lines.append("## Generated artifacts")
-    for label, path in handoff.get("paths", {}).items():
-        lines.append(f"- {label}: `{path}`")
-    lines.append("")
-    lines.append(
-        "This deterministic export is value-based. It is not a fully linked banker formula workbook."
-    )
-    return "\n".join(lines).rstrip() + "\n"
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description=(
-            "Library module for dcf-model-builder. "
-            "Use scripts/run_pipeline.py for deterministic CLI execution."
-        )
-    )
-    parser.parse_args()
-    print("This is a library module. Use scripts/run_pipeline.py to build a DCF model export.")
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/validate_plan.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/validate_plan.py
deleted file mode 100755
index d978fe3..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/validate_plan.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python3
-"""Validate a dcf-model-builder plan.json file."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from skill_core import load_json, validate_plan_structure  # noqa: E402
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Validate DCF plan.json")
-    parser.add_argument("plan_path", help="Path to plan.json")
-    args = parser.parse_args()
-
-    try:
-        plan = load_json(args.plan_path)
-    except FileNotFoundError:
-        print(f"ERROR: plan file not found: {args.plan_path}", file=sys.stderr)
-        return 1
-    except json.JSONDecodeError as exc:
-        print(
-            f"ERROR: invalid JSON in {args.plan_path}: line {exc.lineno}, column {exc.colno}: {exc.msg}",
-            file=sys.stderr,
-        )
-        return 1
-    except Exception as exc:
-        print(f"ERROR: could not read plan: {exc}", file=sys.stderr)
-        return 1
-
-    errors = validate_plan_structure(plan)
-    if errors:
-        print("INVALID DCF PLAN")
-        for idx, error in enumerate(errors, start=1):
-            print(f"{idx}. {error}")
-        return 1
-
-    print(f"VALID DCF PLAN: {args.plan_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/dcf-model-builder/scripts/xlsx_writer.py b/plugins/financial-markets/skills/dcf-model-builder/scripts/xlsx_writer.py
deleted file mode 100644
index fb804f7..0000000
--- a/plugins/financial-markets/skills/dcf-model-builder/scripts/xlsx_writer.py
+++ /dev/null
@@ -1,272 +0,0 @@
-"""Minimal XLSX writer for dcf-model-builder deterministic exports."""
-
-from __future__ import annotations
-
-import os
-import zipfile
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-from xml.sax.saxutils import escape
-
-
-def _is_number(value: Any) -> bool:
-    return isinstance(value, (int, float)) and not isinstance(value, bool)
-
-
-def _col_letter(idx: int) -> str:
-    result = ""
-    n = idx
-    while n:
-        n, rem = divmod(n - 1, 26)
-        result = chr(65 + rem) + result
-    return result
-
-
-def _cell_ref(row_idx: int, col_idx: int) -> str:
-    return f"{_col_letter(col_idx)}{row_idx}"
-
-
-def _xml_text(value: Any) -> str:
-    return escape(str(value), {'"': "&quot;"})
-
-
-def _sheet_name(name: str, used: set[str]) -> str:
-    invalid = set("[]:*?/\\")
-    clean = "".join("_" if char in invalid else char for char in name).strip() or "Sheet"
-    clean = clean[:31]
-    original = clean
-    i = 2
-    while clean in used:
-        suffix = f"_{i}"
-        clean = (original[: 31 - len(suffix)] + suffix)[:31]
-        i += 1
-    used.add(clean)
-    return clean
-
-
-def _sheet_xml(rows: list[list[Any]]) -> str:
-    xml_rows: list[str] = []
-    for r_idx, row in enumerate(rows, start=1):
-        cells: list[str] = []
-        for c_idx, value in enumerate(row, start=1):
-            if value is None:
-                continue
-            ref = _cell_ref(r_idx, c_idx)
-            if isinstance(value, bool):
-                cells.append(f'<c r="{ref}" t="b"><v>{1 if value else 0}</v></c>')
-            elif _is_number(value):
-                cells.append(f'<c r="{ref}"><v>{float(value):.12g}</v></c>')
-            else:
-                cells.append(f'<c r="{ref}" t="inlineStr"><is><t>{_xml_text(value)}</t></is></c>')
-        xml_rows.append(f'<row r="{r_idx}">{"".join(cells)}</row>')
-
-    dimension = "A1"
-    if rows:
-        max_cols = max((len(row) for row in rows), default=1)
-        dimension = f"A1:{_cell_ref(len(rows), max_cols)}"
-    cols = "".join(
-        f'<col min="{idx}" max="{idx}" width="18" customWidth="1"/>'
-        for idx in range(1, max((len(row) for row in rows), default=1) + 1)
-    )
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" ',
-            'xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">',
-            f'<dimension ref="{dimension}"/>',
-            '<sheetViews><sheetView showGridLines="0" workbookViewId="0">',
-            '<pane ySplit="1" topLeftCell="A2" activePane="bottomLeft" state="frozen"/>',
-            "</sheetView></sheetViews>",
-            f"<cols>{cols}</cols>",
-            "<sheetData>",
-            "".join(xml_rows),
-            "</sheetData>",
-            "</worksheet>",
-        ]
-    )
-
-
-def _content_types(sheet_count: int) -> str:
-    overrides = [
-        (
-            '<Override PartName="/xl/workbook.xml" '
-            'ContentType="application/vnd.openxmlformats-officedocument.'
-            'spreadsheetml.sheet.main+xml"/>'
-        ),
-        (
-            '<Override PartName="/xl/styles.xml" '
-            'ContentType="application/vnd.openxmlformats-officedocument.'
-            'spreadsheetml.styles+xml"/>'
-        ),
-        (
-            '<Override PartName="/docProps/core.xml" '
-            'ContentType="application/vnd.openxmlformats-package.core-properties+xml"/>'
-        ),
-        (
-            '<Override PartName="/docProps/app.xml" '
-            'ContentType="application/vnd.openxmlformats-officedocument.'
-            'extended-properties+xml"/>'
-        ),
-    ]
-    for idx in range(1, sheet_count + 1):
-        overrides.append(
-            f'<Override PartName="/xl/worksheets/sheet{idx}.xml" '
-            'ContentType="application/vnd.openxmlformats-officedocument.'
-            'spreadsheetml.worksheet+xml"/>'
-        )
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">',
-            '<Default Extension="rels" '
-            'ContentType="application/vnd.openxmlformats-package.relationships+xml"/>',
-            '<Default Extension="xml" ContentType="application/xml"/>',
-            "".join(overrides),
-            "</Types>",
-        ]
-    )
-
-
-def _root_rels() -> str:
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">',
-            '<Relationship Id="rId1" '
-            'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" '
-            'Target="xl/workbook.xml"/>',
-            '<Relationship Id="rId2" '
-            'Type="http://schemas.openxmlformats.org/package/2006/relationships/metadata/core-properties" '
-            'Target="docProps/core.xml"/>',
-            '<Relationship Id="rId3" '
-            'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/extended-properties" '
-            'Target="docProps/app.xml"/>',
-            "</Relationships>",
-        ]
-    )
-
-
-def _workbook_xml(sheet_names: list[str]) -> str:
-    sheets = [
-        f'<sheet name="{_xml_text(name)}" sheetId="{idx}" r:id="rId{idx}"/>'
-        for idx, name in enumerate(sheet_names, start=1)
-    ]
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<workbook xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" ',
-            'xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">',
-            "<bookViews><workbookView/></bookViews>",
-            f"<sheets>{''.join(sheets)}</sheets>",
-            "</workbook>",
-        ]
-    )
-
-
-def _workbook_rels(sheet_count: int) -> str:
-    rels = []
-    for idx in range(1, sheet_count + 1):
-        rels.append(
-            f'<Relationship Id="rId{idx}" '
-            'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" '
-            f'Target="worksheets/sheet{idx}.xml"/>'
-        )
-    rels.append(
-        f'<Relationship Id="rId{sheet_count + 1}" '
-        'Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles" '
-        'Target="styles.xml"/>'
-    )
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">',
-            "".join(rels),
-            "</Relationships>",
-        ]
-    )
-
-
-def _styles_xml() -> str:
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<styleSheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main">',
-            '<fonts count="1"><font><sz val="11"/><color theme="1"/>',
-            '<name val="Calibri"/><family val="2"/></font></fonts>',
-            '<fills count="2"><fill><patternFill patternType="none"/></fill>',
-            '<fill><patternFill patternType="gray125"/></fill></fills>',
-            '<borders count="1"><border><left/><right/><top/><bottom/>',
-            "<diagonal/></border></borders>",
-            '<cellStyleXfs count="1"><xf numFmtId="0" fontId="0" fillId="0" ',
-            'borderId="0"/></cellStyleXfs>',
-            '<cellXfs count="1"><xf numFmtId="0" fontId="0" fillId="0" ',
-            'borderId="0" xfId="0"/></cellXfs>',
-            '<cellStyles count="1"><cellStyle name="Normal" xfId="0" ',
-            'builtinId="0"/></cellStyles>',
-            "</styleSheet>",
-        ]
-    )
-
-
-def _core_xml() -> str:
-    now = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<cp:coreProperties xmlns:cp="http://schemas.openxmlformats.org/package/2006/metadata/core-properties" ',
-            'xmlns:dc="http://purl.org/dc/elements/1.1/" ',
-            'xmlns:dcterms="http://purl.org/dc/terms/" ',
-            'xmlns:dcmitype="http://purl.org/dc/dcmitype/" ',
-            'xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">',
-            "<dc:title>DCF Model Export</dc:title>",
-            "<dc:creator>dcf-model-builder</dc:creator>",
-            f'<dcterms:created xsi:type="dcterms:W3CDTF">{now}</dcterms:created>',
-            f'<dcterms:modified xsi:type="dcterms:W3CDTF">{now}</dcterms:modified>',
-            "</cp:coreProperties>",
-        ]
-    )
-
-
-def _app_xml(sheet_names: list[str]) -> str:
-    names = "".join(f"<vt:lpstr>{_xml_text(name)}</vt:lpstr>" for name in sheet_names)
-    return "".join(
-        [
-            '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>',
-            '<Properties xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties" ',
-            'xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes">',
-            "<Application>dcf-model-builder</Application>",
-            '<HeadingPairs><vt:vector size="2" baseType="variant">',
-            "<vt:variant><vt:lpstr>Worksheets</vt:lpstr></vt:variant>",
-            f"<vt:variant><vt:i4>{len(sheet_names)}</vt:i4></vt:variant>",
-            "</vt:vector></HeadingPairs>",
-            f'<TitlesOfParts><vt:vector size="{len(sheet_names)}" baseType="lpstr">',
-            names,
-            "</vt:vector></TitlesOfParts>",
-            "</Properties>",
-        ]
-    )
-
-
-def write_xlsx(path: str | os.PathLike[str], rows: Any, sheet_name: str = "Model") -> None:
-    """Write a minimal valid XLSX. `rows` may be a table or dict of sheet tables."""
-    sheets_input = rows if isinstance(rows, dict) else {sheet_name: rows}
-    used: set[str] = set()
-    sheet_names = []
-    sheet_tables = []
-    for raw_name, table in sheets_input.items():
-        name = _sheet_name(str(raw_name), used)
-        sheet_names.append(name)
-        sheet_tables.append(table)
-
-    Path(path).parent.mkdir(parents=True, exist_ok=True)
-    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
-        zf.writestr("[Content_Types].xml", _content_types(len(sheet_names)))
-        zf.writestr("_rels/.rels", _root_rels())
-        zf.writestr("xl/workbook.xml", _workbook_xml(sheet_names))
-        zf.writestr("xl/_rels/workbook.xml.rels", _workbook_rels(len(sheet_names)))
-        zf.writestr("xl/styles.xml", _styles_xml())
-        zf.writestr("docProps/core.xml", _core_xml())
-        zf.writestr("docProps/app.xml", _app_xml(sheet_names))
-        for idx, table in enumerate(sheet_tables, start=1):
-            zf.writestr(f"xl/worksheets/sheet{idx}.xml", _sheet_xml(table))
diff --git a/plugins/financial-markets/skills/deck-report-qc/SKILL.md b/plugins/financial-markets/skills/deck-report-qc/SKILL.md
deleted file mode 100644
index 0f4f22e..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/SKILL.md
+++ /dev/null
@@ -1,197 +0,0 @@
----
-name: deck-report-qc
-description: Use when running first-pass QC on Public Equity Investing decks or reports. Do not use as external-circulation certification.
----
-
-# Deck & Report QC
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML senior-review QC report, while direct edits or remediation preserve the source deck or document format. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new standalone reader-facing QC deliverable should it, before source gathering, analysis, or rendering, load `../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved preferences. For a substantive standalone QC review of an existing deck or report with supporting materials, resolve the presentation surface to a polished standalone HTML senior-review QC report unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In interactive runs, ask only remaining material questions such as depth, circulation stage, audience, or review focus; in non-interactive runs, default to the HTML QC report and `Full working analysis` while disclosing those assumptions outside the artifact.
-
-## Purpose
-
-Load `shared/equity-research-support-standard.md` and `shared/support-layer-routing-contract.md` before substantial source, data, QA, or style work.
-
-
-Use this skill as the P0 first-pass quality-control gate for Public Equity Investing deliverables. The default job is to identify issues, prioritize fixes, and produce a senior-review QA pack. Do not imply a deliverable is externally circulable from the heuristic script alone, and do not rewrite, rebuild, or redesign the deliverable unless the user explicitly asks for remediation.
-
-For a substantial standalone HTML QC report, load `../../shared/html-artifact-standard.md`. Let the evidence, circulation question, and highest-impact findings determine the hierarchy rather than forcing the review into a fixed dashboard module inventory.
-
-## Embedded Support Routing
-
-This is an embedded service under the owning workflow unless the user explicitly asks for standalone deck/report QC. Preserve the `owning_workflow` internally, such as `memo-builder`, `long-short-pitch`, `initiating-coverage`, `earnings-preview`, `earnings-deep-dive`, `economic-impact-report`, `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `thesis-tracker`, `meeting-prep`, or `dashboard-builder`.
-
-For substantial embedded work, preserve `decision_impact`, `readiness_effect`, `artifact_role`, and `hidden_unless_requested` in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own the recommendation or rewrite the thesis; state in natural language how QC issues change valuation, EPS, target/rating support, benchmark weight, catalyst read, source support, model confidence, client trust, or circulation readiness. A polished standalone HTML senior-review QC report is the default human deliverable for substantive explicit QC-only work; CSV, JSON, Markdown, issue logs, extraction logs, payloads, and manifests remain secondary/support artifacts unless requested.
-
-## Operating principles
-
-1. Treat every number, unit, footnote, chart, and conclusion as something that must tie to an identified source or model output.
-2. Separate deterministic findings from judgment calls. Mark uncertain items as `needs_review` rather than overclaiming.
-3. Prioritize issues by decision impact. A mismatched EBITDA value, leverage multiple, share price, EPS, revenue/KPI, price target, rating, benchmark weight, catalyst, market-data, or valuation range is more important than minor formatting polish.
-4. Preserve the original artifact. QC should create an issue log and suggested fixes first; edit only when asked.
-5. Apply `financial-source-of-truth` standards for source hierarchy, stale-data checks, citation format, source conflicts, and fact/assumption labels.
-6. Route model-level issues to `model-audit-tieout` and data-shaping issues to `excel-data-cleaner` instead of trying to solve them inside this skill.
-
-## Workflow
-
-### 1. Classify the deliverable
-
-Identify the file type and purpose:
-- investor presentation, research deck, senior-style research deck, valuation deck, sell-side initiation, PM pitch deck, ETF/index diligence note, or circulation draft
-- public-equity IC memo, investment memo, earnings note, macro note, research report, client note, or tearsheet
-- model output deck or report linked to DCF, comps, three-statement, equity-model-update, event-driven, ETF/index, or macro analysis
-- mixed pack with PPTX/PDF/DOCX/XLSX support files
-
-If the user provides multiple files, identify the controlling artifact and the source artifacts. Example: deck is controlling output; model, evidence ledger, filing, release, transcript, and source tables are supporting materials.
-
-### 2. Extract first-pass text, numbers, and sources
-
-For PPTX, DOCX, XLSX, CSV, TXT, or markdown files, run the bundled first-pass scan script when available:
-
-```bash
-python scripts/inspect_deck_report.py <file1> <file2> --outdir qc_out
-```
-
-Use the script output as a first-pass map only. It is not a substitute for visual review, chart inspection, model tie-out, source-of-truth review, or PDF rendering.
-
-For PDFs, screenshots, image-heavy slides, or scanned materials, use PDF/rendering tools to inspect pages visually before finalizing QC. If charts are embedded as images, state that the underlying chart data could not be extracted unless the model/source file is provided.
-
-For each `critical` or `high` finding that relies on visible content, render and inspect the cited source pages or slides. Keep a plain-language record of what pages, workbook tabs, and support files were inspected, and of what could not be independently verified.
-
-### 3. Build the QC map
-
-Create or infer:
-- page/slide/section list
-- main title and thesis by page
-- all repeated metrics and key claims
-- source footnotes and citation coverage
-- chart titles, axes, units, legends, and cited data source
-- model-output tables and valuation/returns ranges
-- section-level narrative conclusions
-
-Consult `references/qc-playbook.md` for QC categories and `references/extraction-and-tieout.md` for extraction and tie-out guidance.
-
-### 4. Run issue checks
-
-Check at minimum:
-- repeated numbers: same metric, company, period, and unit should match unless there is a disclosed reason
-- units: millions/billions, dollars/local currency, percentages/bps, turns, multiples, per-share, nominal/real, annualized/LTM/NTM should be explicit and consistent
-- source footnotes: each data-heavy page should identify source, as-of date, period, and whether data is company-reported, regulator-filed, market/vendor, broker/consensus, management-provided, model-derived, or internal estimate
-- charts: chart title, axis units, legends, series labels, chart numbers, and narrative takeaway should agree
-- narrative consistency: executive summary, page titles, subtitles, bullets, charts, and conclusion should not contradict each other
-- formatting: titles, subtitles, page numbers, fonts, alignment, table formatting, footnote style, decimal precision, capitalization, and repeated labels should be consistent
-- caveats: preliminary, unaudited, company-provided, non-filed, promotional, model-derived, and assumption-led items should be labeled
-- compliance hygiene: do not add legal disclaimers unless requested, but flag missing caveats/disclosures where the analysis relies on uncertain or restricted inputs
-
-Consult `references/issue-taxonomy.md` for severity and issue-type definitions.
-
-### 5. Assign evidence confidence
-
-Classify each consequential issue using one of these reader-facing confidence descriptions:
-- `confirmed internal mismatch`: proved by contradictory values, labels, calculations, or statements within the supplied artifacts
-- `externally verified error`: proved against a controlling primary or trusted dated external source
-- `needs review`: suspected issue or unresolved conflict that requires a source, model, data export, or user confirmation
-
-Do not state that an identifier, market fact, source claim, or company fact is confirmed wrong merely because supplied materials conflict or appear unlikely. Without a controlling source, state the internal conflict and route it for confirmation.
-
-### 6. Decide the review posture
-
-Assign one of these postures:
-- `first-pass-clear`: no heuristic blockers were identified, but visual/source/model review may still be required
-- `senior-review-ready`: mostly ready, with limited open questions or judgement calls
-- `needs-targeted-fixes`: specific corrections are required before circulation
-- `not-circulable`: material numerical, source, chart, or narrative issues remain
-- `blocked`: necessary source/model files are missing
-
-Medium source gaps, repeated-number mismatches, and unit/period ambiguity usually mean `needs-targeted-fixes`, not `senior-review-ready`.
-
-### 7. Produce the QC output
-
-Default output should be a senior-review QC readout. For standalone QC-only work, produce a polished standalone HTML senior-review QC report following `../../shared/html-artifact-standard.md`. Use chat only when the user explicitly requests a lightweight response. When the script is used, the reader-facing artifact should be `public_equity_investing_deck_qc_report.html`; CSV, JSON, manifests, payloads, and support notes are audit/import support unless the user asks for them.
-
-Use `dashboard-builder`, `references/DASHBOARD_PACK.md`, and `references/dashboard-map.md` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, issue cockpit, remediation tracker, or structured payload-driven render. On that optional path, `deck-report-qc` owns issue identification, severity, tie-out judgment, circulation posture, and remediation sequence; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-
-The QC readout should include:
-1. Executive QC verdict
-2. Circulation posture
-3. Review scope and evidence limitations: what was inspected, tied out, externally verified, and not independently verified
-4. Decision-critical tie-out or recommendation support, when applicable
-5. Top issues by severity and remediation sequence
-6. Issue log table with confidence descriptions
-7. Repeated metric / number tie-out table
-8. Source and footnote coverage table
-9. Chart and narrative tie-out findings
-10. Formatting/presentation polish findings
-11. Open questions / missing support files
-
-Use `references/output-templates.md` for default templates.
-
-For standalone HTML, keep the first screen focused on the verdict, circulation posture, evidence scope, and the few findings that change senior reliance. When a valuation, target-price, recommendation, rating, benchmark-weight, or other decision-critical tie-out exists, place the `Decision-Critical Tie-Out` section before `Top Issues`, `Must Fix Before Circulation`, or the full issue log; do not make the reader pass through the broader findings register before seeing the central control failure. Place comprehensive registers, source-coverage tables, and presentation-polish findings lower in the report.
-
-Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, at both desktop and narrow/mobile widths. Tables may scroll horizontally inside a clearly bounded table wrapper on narrow screens, but they must not widen the entire page. In mobile QA, verify that the document viewport itself has no horizontal overflow, for example `document.documentElement.scrollWidth <= document.documentElement.clientWidth`; use constrained grid/section children and `max-width: 100%; overflow-x: auto` table wrappers where needed. Iterate on hierarchy, table density, clipping, contrast, and whitespace before delivery.
-
-When `scripts/inspect_deck_report.py` was used before a substantive standalone HTML review, write a small JSON review record identifying `completed_reviews` and remaining `missing_inputs`, then finalize the existing output path after HTML visual inspection:
-
-```bash
-python scripts/inspect_deck_report.py --finalize \
-  --outdir <final-output-dir> \
-  --scan-dir <first-pass-output-dir> \
-  --primary-report <final-output-dir>/public_equity_investing_deck_qc_report.html \
-  --review-record <final-output-dir>/qc_review_record.json
-```
-
-Finalization must make the polished HTML the sole primary human deliverable, reconcile manifest status to the work actually performed, and remove the provisional dashboard contract for ordinary standalone HTML reviews. Add `--keep-dashboard-contract` only when the user explicitly selected the standardized-dashboard path.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source and number tie-out, chart/visual review, narrative consistency, formatting/circulation posture, and issue log. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-When embedded in a broader workflow, "lead" means lead for QC only; the owning workflow remains the investment-artifact owner.
-
-
-## Severity rules
-
-Use these severities:
-- `critical`: could change investment decision, valuation, rating, price target, sizing, market read, or client trust
-- `high`: material inconsistency or missing support that must be fixed before circulation
-- `medium`: localized inconsistency, unclear caveat, formatting issue, or missing source detail that should be fixed
-- `low`: polish item that does not affect substance
-- `needs_review`: possible issue that requires visual, model, source, or user confirmation
-
-Never hide uncertainty. If a number may be wrong but cannot be proven wrong from available files, label it `needs_review` and ask for the model/source support.
-
-## P0 skill routing
-
-Use `references/p0-integrations.md` when deciding whether an issue belongs in this skill or should be routed to another P0 skill.
-
-Common routes:
-- source hierarchy, stale data, citation standard, source conflict, fact/assumption labeling -> `financial-source-of-truth`
-- workbook formula, model logic, sensitivity, scenario, source tie-out -> `model-audit-tieout`
-- messy tabular data, duplicated rows, bad date/number formats -> `excel-data-cleaner`
-- valuation model construction or repair -> `dcf-model-builder`, `comps-valuation`, or `three-statement-model-builder`
-- public issuer, equity event, or thesis support -> `event-driven-analyzer`, `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, or `long-short-pitch`; credit-first packs, public-credit memos, bond/loan/CDS decks, covenant/recovery packs, and debt-security materials route to Credit Markets
-- final IC synthesis -> `memo-builder`
-
-## Final checks before responding
-
-Before final output, verify:
-- every critical/high issue has location, evidence, why it matters, and suggested fix
-- every consequential issue identifies whether it is a `confirmed internal mismatch`, an `externally verified error`, or `needs review`
-- every repeated metric table distinguishes exact mismatch from possible period/unit mismatch
-- source gaps are not presented as factual errors unless a controlling source proves the issue
-- formatting findings are separated from investment-substance findings
-- the final posture matches the severity of remaining issues
-- the response does not imply the deck/report is fully verified if charts, screenshots, PDFs, or source models were not inspectable
-- the HTML report and any support manifest or review note agree on what was visually inspected, tied out, and still unverified
-- standalone HTML keeps any decision-critical tie-out above the broader issue register and does not introduce page-level horizontal overflow at narrow/mobile widths
diff --git a/plugins/financial-markets/skills/deck-report-qc/agents/openai.yaml b/plugins/financial-markets/skills/deck-report-qc/agents/openai.yaml
deleted file mode 100644
index 535b98a..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Deck & Report QC"
-  short_description: "First-pass QC for public-equity-investing decks, reports, memos, and model outputs before senior review."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/deck-report-qc/references/DASHBOARD_PACK.md
deleted file mode 100644
index 36239a2..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Deck Report QC Dashboard Pack
-
-Use this pack only when the user explicitly selects a standardized dashboard, reusable dashboard template, issue cockpit, remediation tracker, or structured payload-driven render for a Public Equity Investing deck/report QC review. For an ordinary substantive standalone QC review, produce a polished standalone HTML senior-review QC report following the flexible HTML artifact standard and the owning skill's evidence-scope guidance instead of this fixed module map.
-
-## Producer Role
-
-`deck-report-qc` owns issue identification, severity, source/number tie-out, chart/narrative checks, circulation posture, and remediation sequence. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `deck_report_qc`
-- `layout`: `single_page` for reusable QC packs unless the user explicitly requests tabs
-- `hero.callout`: whether the deliverable is clear, needs targeted fixes, blocked, or not circulable
-- `snapshot`: QC posture, issue count, critical/high count, source coverage, repeated-number status, chart tie-out status, missing support files
-- `sources`: controlling artifact, support model/files, evidence ledger, filings/releases/transcripts, and extracted first-pass scan outputs
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `qc-verdict`
-   - `decision_box`: circulation posture, top blocker, recommended remediation order, and review confidence
-   - `metric_tiles`: issue counts, critical/high count, source coverage, repeated-number status, chart status, missing support
-2. `issue-log`
-   - `table`: page/slide/section, severity, issue type, finding, evidence, suggested fix, and owner/status if known
-3. `number-source-tieout`
-   - `table`: repeated metrics, units, periods, source/model tie-out, mismatches, and confidence
-   - `cards`: source-footnote coverage and caveat/disclosure gaps
-4. `chart-narrative`
-   - `table`: chart titles, axes, series, narrative takeaway, source support, and inconsistency notes
-5. `remediation`
-   - `timeline`: fix sequence, dependencies, missing support files, and review checkpoints
-   - `missing_evidence`: unavailable model/source files, inaccessible charts, scanned pages, weak source metadata, and unresolved judgment calls
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite the controlling artifact and every support file or source used for a QC finding.
-- Label deterministic findings versus judgment calls and use `needs_review` when the evidence is incomplete.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not certify external circulation from heuristic scans alone.
-- Do not edit the original deck/report unless the user explicitly asks for remediation.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm severity ordering, repeated-number tie-out, source coverage, chart/narrative findings, and remediation sequence are visible.
-- Confirm uncertainty is labeled rather than hidden.
-
-
-public-equity research support layer requirement: QC dashboards should surface equity decision impact first: EPS/revenue/KPI support, valuation and price-target support, rating/stance support, benchmark/ETF/index exposure, catalyst claims, source conflicts, market-data freshness, and any Credit Markets handoff.
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/dashboard-map.md b/plugins/financial-markets/skills/deck-report-qc/references/dashboard-map.md
deleted file mode 100644
index b2b92f1..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/dashboard-map.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Dashboard Map: Deck And Report QC
-
-Use `dashboard-builder` for Public Equity Investing QC only when the user explicitly selects a standardized dashboard, reusable dashboard template, issue cockpit, remediation tracker, or structured payload-driven render. An ordinary substantive standalone QC review uses a polished standalone HTML senior-review QC report rather than this fixed dashboard map.
-
-## Decision Question
-
-Is the deck, report, note, or memo ready for PM/senior review, and what issues block confidence?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `deck_report_qc`
-- `metadata.citation_policy`: `strict`
-- `sources`: include reviewed artifact, model files, source pack, filing/release/transcript references, and any style guide
-
-## Recommended Tabs And Modules
-
-1. QC Verdict: `decision_box`, `metric_tiles`, `missing_evidence`.
-2. Issue Register: `table`, `cards`.
-3. Repeated Number Tie-Out: `table`, `bar_chart`.
-4. Source And Footnote Coverage: `table`, `source_list`.
-5. Chart And Narrative Tie-Out: `table`, `cards`.
-6. Remediation Path: `timeline`, `question_list`.
-
-## Required Evidence
-
-Each issue row needs location, finding, evidence, why it matters, suggested fix, confidence, owner route, and status. Unsupported claims should route to `financial-source-of-truth`; model breaks should route to `model-audit-tieout`.
-
-## Do Not
-
-- Do not call a document externally ready based only on text extraction.
-- Do not hide visual/PDF/chart review gaps.
-- Do not make issue CSV or scan JSON the lead artifact unless requested.
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/extraction-and-tieout.md b/plugins/financial-markets/skills/deck-report-qc/references/extraction-and-tieout.md
deleted file mode 100644
index 183210e..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/extraction-and-tieout.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# Extraction and Tie-out Guidance
-
-## First-pass extraction
-
-Use `scripts/inspect_deck_report.py` for first-pass extraction from PPTX, DOCX, XLSX, CSV, TXT, and markdown files. The script creates:
-- `segments.csv`: extracted text by slide/page/section/sheet/line range
-- `numbers.csv`: detected numerical mentions with rough metric keys and units
-- `qc_issue_log.csv`: heuristic issues requiring review
-- `qc_support_note.md`: first-pass scan support note
-- `scan.json`: structured output for follow-up analysis
-
-The script is intentionally conservative. Treat its findings as leads, not final conclusions.
-
-For a substantive standalone review that produces a refined HTML report after the scan, write a JSON completion record and run the script's `--finalize` path. The record should include `completed_reviews`, `missing_inputs`, and optionally `status` and `reason`. Finalization points the manifest to the polished HTML, retains extraction files as support, removes the provisional report and dashboard contract for ordinary HTML reports, and records only genuinely open verification items. Use `--keep-dashboard-contract` only for an explicitly selected standardized-dashboard output.
-
-## Visual review requirement
-
-Always inspect the original deliverable when available. Deterministic text extraction can miss:
-- numbers embedded in images or screenshots
-- chart labels rendered graphically
-- cut-off text, overlap, font issues, and alignment problems
-- small footnotes and low-contrast text
-- PDF-only rendering issues
-- data tables converted to images
-
-For PDF, scanned, or image-heavy materials, render pages and visually inspect the relevant pages. For PPTX, use presentation rendering where available to verify layout and chart appearance.
-
-For a consequential visual finding, record the pages or slides actually inspected. A final QC report should distinguish:
-- source pages/slides visually inspected
-- workbook tabs or cells tied out
-- external sources independently checked
-- facts or claims not independently verified
-
-When the final output is a local standalone HTML report, render and inspect screenshots through a local headless browser rather than the in-app Browser plugin. Check the opening viewport and material downstream sections for hierarchy, table density, clipping, contrast, and legibility before delivery. Inspect at a narrow/mobile viewport as well: wide tables may scroll within their own wrappers, but the document itself must not horizontally overflow; verify `document.documentElement.scrollWidth <= document.documentElement.clientWidth`.
-
-## Tie-out hierarchy
-
-Tie out deck/report values in this order:
-1. controlling model or source workbook for output values
-2. primary filings, audited financials, transcript, release, credit agreement, indenture, court/regulatory filing, company supplemental, or management reporting package
-3. trusted data provider, consensus source, broker research, rating agency, or market-data export
-4. company, management, broker, or user-provided materials, clearly labeled
-5. internal estimate or analyst assumption, clearly labeled
-
-If sources conflict, use `financial-source-of-truth` conflict handling. Do not silently average or choose the value that looks best.
-
-## Evidence confidence
-
-Use reader-facing confidence labels proportionate to the work performed:
-- `confirmed internal mismatch`: the supplied materials prove their own values, labels, calculations, or statements conflict.
-- `externally verified error`: a controlling primary or trusted dated external source proves the claim or value wrong.
-- `needs review`: support is absent, conflicting, insufficiently dated, or not controlling.
-
-For example, a deck identifier that conflicts with a supplied source note is a confirmed internal conflict. It becomes an externally verified identifier error only after confirmation against an appropriate controlling source.
-
-## Repeated metric matching
-
-A repeated metric should match when the following are the same:
-- entity or asset
-- metric definition
-- period or as-of date
-- currency and scale
-- scenario
-- source/model version
-
-Do not flag as a confirmed mismatch when period, definition, or scenario differs. Instead flag `needs_review` and state the likely reason.
-
-Examples:
-- `FY25E EBITDA $120m` vs `2025E EBITDA $120m`: likely same metric
-- `Adjusted EBITDA $120m` vs `Lender EBITDA $135m`: not necessarily a mismatch; check definitions
-- `Net leverage 4.2x` vs `First-lien net leverage 3.1x`: not necessarily a mismatch
-- `10 bps` vs `0.10%`: same magnitude but different unit expression
-- `margin up 100 bps` vs `margin up 1.0%`: label as percentage-point convention issue, not necessarily numerical error
-
-## Chart review workflow
-
-For each chart:
-1. Record chart title, period, units, series names, and source.
-2. Compare the visual direction to the title and takeaway bullet.
-3. Compare key labeled values to any table/model/source.
-4. Check whether the axis baseline or scaling could mislead.
-5. Confirm source/date and whether chart is based on actuals, estimates, company guidance, consensus case, credit/event case, or internal model.
-6. Flag ambiguity if chart data is image-only and cannot be extracted.
-
-## Source footnote coverage test
-
-For each page/section, classify source coverage:
-- `complete`: source, period/as-of date, and caveats are present
-- `partial`: source is present but period/as-of/caveat is incomplete
-- `missing`: no meaningful source for material data
-- `not_applicable`: conceptual page with no material facts or numbers
-
-Do not require sources for pure table-of-contents, process, or placeholder pages unless they contain factual or numerical claims.
-
-## Completion record
-
-If support artifacts, manifests, or review notes describe review status, update them to reflect the work actually performed. Do not deliver an HTML report that relies on rendered source-page inspection or workbook tie-out while a companion status note says those steps were not performed.
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/issue-taxonomy.md b/plugins/financial-markets/skills/deck-report-qc/references/issue-taxonomy.md
deleted file mode 100644
index 3d652d2..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/issue-taxonomy.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Issue Taxonomy
-
-Use this taxonomy to classify deck and report QC findings.
-
-## Severity levels
-
-| Severity | Definition | Examples |
-|---|---|---|
-| critical | likely to change decision, valuation, market view, recommendation, or trust in the analysis | wrong EPS/EBITDA in valuation summary; EV/EBITDA conflicts with model; price target or rating support conflicts with source; source contradicts thesis |
-| high | material issue that must be fixed before circulation | repeated metric mismatch; missing source on key market data; chart contradicts title; unit ambiguity in valuation table |
-| medium | localized issue that should be fixed but probably does not change the decision | stale as-of date, inconsistent decimals, unclear footnote, minor chart label mismatch |
-| low | polish or formatting issue | inconsistent capitalization, alignment, extra spaces, minor style inconsistency |
-| needs_review | possible issue that cannot be confirmed from available files | chart appears image-only; number may use different period; source/model missing |
-
-## Issue types
-
-### number_mismatch
-Same metric appears with conflicting values.
-Required fields: location, metric, values, likely controlling value, suggested fix.
-
-### unit_or_period_ambiguity
-Metric lacks clear unit, currency, scale, period, or scenario.
-Required fields: location, ambiguous term, why it matters, suggested fix.
-
-### source_gap
-Material claim, table, or chart lacks source, as-of date, period, or caveat.
-Required fields: location, claim/metric, missing source element, suggested fix.
-
-### source_conflict
-Two sources disagree or the deck/report value conflicts with controlling source.
-Required fields: locations/sources, conflicting values, source hierarchy, recommended resolution.
-
-### chart_narrative_mismatch
-Chart visual/data does not support title, subtitle, or bullet takeaway.
-Required fields: location, chart statement, conflicting evidence, suggested fix.
-
-### chart_format_or_label_issue
-Axis, legend, series, label, scale, or chart format is confusing or inconsistent.
-Required fields: location, issue, possible interpretation risk, suggested fix.
-
-### narrative_contradiction
-Executive summary, section page, conclusion, risk page, or recommendation conflicts with another part of the document.
-Required fields: conflicting locations, statements, recommended rewrite direction.
-
-### caveat_or_disclosure_gap
-Claim relies on estimates, company/management materials, unaudited figures, non-GAAP data, assumptions, or preliminary numbers without labeling.
-Required fields: location, claim, needed caveat.
-
-### formatting_consistency
-Style, alignment, labels, spacing, capitalization, page numbering, or table formatting is inconsistent.
-Required fields: location, issue, fix.
-
-### readability_accessibility
-Content is hard to read or navigate because of small font, low contrast, missing title, overloaded page, color-only encoding, or image text.
-Required fields: location, issue, audience impact, fix.
-
-### missing_support_file
-A required model/source/deck/report supporting artifact is missing.
-Required fields: missing file, why needed, what can/cannot be verified without it.
-
-## Recommended remediation order
-
-1. Fix critical and high number/source/model issues.
-2. Fix chart-narrative mismatches and misleading titles.
-3. Fix unit, period, and caveat ambiguity.
-4. Fix source footnotes and as-of dates.
-5. Fix formatting and readability issues.
-6. Re-run the QC pass after changes.
-
-## Confidence labels
-
-Use these labels in the issue log:
-- `confirmed`: issue is directly supported by available files
-- `likely`: issue appears likely but needs one supporting file or visual confirmation
-- `possible`: heuristic or judgment issue; needs human review
-- `blocked`: cannot assess without missing source/model/visual artifact
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/output-templates.md b/plugins/financial-markets/skills/deck-report-qc/references/output-templates.md
deleted file mode 100644
index 5f96a58..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/output-templates.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# Output Templates
-
-## Default QC report
-
-```markdown
-# Deck / Report QC
-
-## Executive QC verdict
-- First-pass posture: [first-pass-clear | senior-review-ready | needs-targeted-fixes | not-circulable | blocked]
-- Highest severity: [critical | high | medium | low | needs_review]
-- Bottom line: [1-3 sentences]
-
-## Review scope and evidence limitations
-| Evidence area | Reviewed / available | Status | Limitation or next confirmation |
-|---|---|---|---|
-| Controlling artifact | PDF pages/slides inspected: [...] | visually inspected | [...] |
-| Supporting model/workbook | Tabs/outputs tied out: [...] | tied out / partial | [...] |
-| External source verification | Sources checked: [...] | verified / not performed | [...] |
-| Not independently verified | Market data, company facts, estimates, source claims: [...] | open | [...] |
-
-## Decision-critical tie-out
-| Decision input | Deck/report value | Controlling support value | Confidence | Decision impact / correction |
-|---|---|---|---|---|
-| DCF value/share | [...] | [...] | confirmed internal mismatch | [...] |
-
-## Top issues to fix first
-| Priority | Severity | Confidence | Location | Issue | Why it matters | Suggested fix |
-|---:|---|---|---|---|---|---|
-| 1 | high | confirmed internal mismatch | Slide 7 | FY25E EBITDA differs from valuation summary | impacts multiple and valuation range | tie to model tab Outputs cell X |
-
-## Issue log
-| ID | Severity | Type | Confidence | Location | Finding | Evidence | Suggested fix | Owner/route |
-|---|---|---|---|---|---|---|---|---|
-
-## Recommended remediation sequence
-1. [fix]
-2. [fix]
-3. [fix]
-
-## Repeated metric / number tie-out
-| Metric | Locations | Values found | Unit/period/scenario | Status | Comment |
-|---|---|---|---|---|---|
-
-## Source and footnote coverage
-| Location | Material data/claim | Source present? | As-of/period present? | Caveat needed? | Status |
-|---|---|---:|---:|---:|---|
-
-## Chart and narrative tie-out
-| Location | Chart / table | Narrative claim | Tie-out status | Issue / fix |
-|---|---|---|---|---|
-
-## Formatting and readability
-| Location | Issue | Suggested fix |
-|---|---|---|
-
-## Missing files / open questions
-- [source/model/file needed and why]
-```
-
-## Confidence descriptions
-
-Use these labels in reader-facing QC output:
-- `confirmed internal mismatch`: contradiction or calculation failure proved within the supplied deck, report, workbook, or source pack.
-- `externally verified error`: incorrect value or claim proved against a controlling primary or trusted dated external source.
-- `needs review`: potential issue or unresolved conflict that cannot be proven from available support.
-
-Do not describe an external market fact, company identifier, or third-party claim as wrong when the supplied files only demonstrate an internal discrepancy.
-
-## Standalone HTML senior-review report
-
-For substantial standalone QC work, render a polished HTML report following `../../../shared/html-artifact-standard.md`. Keep the opening view focused on the verdict, circulation posture, review scope, and decision-critical tie-out. When a decision-critical tie-out exists, it must appear before a narrative `Top Issues`, `Must Fix Before Circulation`, or full issue-log section; metric tiles may summarize it, but do not substitute for the tie-out table or bridge. Put the complete issue register and source/presentation detail below that first-read layer. Use color to signal severity sparingly and avoid turning the review into a generic dashboard shell.
-
-At narrow/mobile widths, tables may use a horizontally scrollable wrapper with a brief cue where useful, but the overall HTML page must remain constrained to the viewport. During local headless-browser QA, confirm `document.documentElement.scrollWidth <= document.documentElement.clientWidth` at a representative mobile width. Apply `min-width: 0` to grid or section children and `max-width: 100%; overflow-x: auto` to table wrappers when wide tables are retained.
-
-## Fast senior-readout format
-
-Use this when the user asks for a quick review or red flags only:
-
-```markdown
-## QC readout
-Posture: [posture]
-
-Must fix before circulation:
-1. [issue - location - why it matters]
-2. [issue - location - why it matters]
-
-Should fix:
-1. [issue]
-2. [issue]
-
-Looks clean:
-- [area reviewed]
-
-Blocked / not verified:
-- [missing support]
-```
-
-## Issue log CSV schema
-
-Use these columns if producing a CSV issue log:
-
-```text
-issue_id,severity,issue_type,confidence,source_file,location,metric_or_claim,finding,evidence,why_it_matters,suggested_fix,owner_route,status
-```
-
-## Circulation posture language
-
-Use concise language:
-- `first-pass-clear`: no heuristic blockers identified; still requires visual/source/model review before external circulation.
-- `senior-review-ready`: suitable for MD/PM/partner review with limited marked questions.
-- `needs-targeted-fixes`: do not circulate externally until listed fixes are made.
-- `not-circulable`: material inconsistencies remain; artifact could mislead decision-makers.
-- `blocked`: cannot assess because required source/model files are missing.
-
-Medium source gaps, repeated-number mismatches, and unit/period ambiguity should generally be mapped to `needs-targeted-fixes`.
-
-## Remediation routing language
-
-Examples:
-- `route to model-audit-tieout`: formula/model output issue, not a deck-only issue.
-- `route to financial-source-of-truth`: source conflict or stale-data rule needed.
-- `route to excel-data-cleaner`: source table is too messy to tie out reliably.
-- `route to dcf-model-builder`: valuation model needs rebuild or scenario correction.
-- `route to memo-builder`: after fixes, synthesize issue implications for IC.
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/p0-integrations.md b/plugins/financial-markets/skills/deck-report-qc/references/p0-integrations.md
deleted file mode 100644
index eb1d98a..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/p0-integrations.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# P0 Integrations
-
-Use this map to fit `deck-report-qc` into the Public Equity Investing P0 stack.
-
-## financial-source-of-truth
-
-Use for:
-- source hierarchy and controlling-source decisions
-- stale-data checks and as-of-date standards
-- citation format
-- source conflict handling
-- fact / assumption / issuer claim / management claim / third-party estimate labeling
-
-Deck/report QC should identify source issues, then apply the source-of-truth standard to resolve them.
-
-## model-audit-tieout
-
-Use for:
-- model formula errors
-- workbook structure problems
-- hardcodes, broken links, inconsistent formula families
-- sensitivity/scenario logic
-- source-to-model tie-outs
-- formula-driven numbers that differ from deck/report outputs
-
-Deck/report QC should not rebuild models. It should route model problems to model audit.
-
-## excel-data-cleaner
-
-Use for:
-- badly formatted source tables
-- inconsistent date/number formats
-- duplicate records
-- merged-cell tabular data
-- CSV/XLSX cleanup before tie-out
-
-## three-statement-model-builder
-
-Use when a deck/report issue reveals missing or broken operating model logic, including financial-statement linkage, working capital, debt, capex, taxes, or cash flow.
-
-## dcf-model-builder
-
-Use when valuation deck/report issues involve DCF forecast drivers, WACC, terminal value, EV-to-equity bridge, sensitivity tables, or intrinsic value range.
-
-## comps-valuation
-
-Use when issues involve peer selection, calendarization, metric normalization, EV/equity bridge, multiple calculation, outlier treatment, or implied valuation range.
-
-## equity-model-update
-
-Use when issues involve model changes from filings, earnings releases, transcripts, guidance, consensus, KPI disclosures, share count, net debt, or market-data updates.
-
-## Credit Markets
-
-Use when issues involve a public-credit memo, bond/loan/CDS analysis, covenant disclosure, spread/yield relative value, recovery, restructuring, or distressed-security value. In this plugin, retain only common-equity read-through such as maturity wall, liquidity, refinancing risk, or CDS/spread signal.
-
-## event-driven-analyzer
-
-Use when issues involve M&A, spin-offs, activism, litigation, regulatory approvals, tenders, CVRs, strategic reviews, restructurings, index events, or other special situations.
-
-## earnings-preview and earnings-deep-dive
-
-Use when issues involve reported vs expected results, consensus, guidance deltas, transcript quotes, KPI tables, peer read-throughs, or earnings reaction history.
-
-## economic-impact-report
-
-Use when issues involve catalyst implications, macro shock pathways, cross-asset spillovers, winners/losers, or monitoring signals.
-
-## memo-builder
-
-Use after QC fixes when the user wants final IC/committee synthesis, decision framing, open questions, risks, and recommendation language.
-
-## long-short-pitch and thesis-tracker
-
-Use when issues involve investment thesis support, variant perception, catalyst path, risk/reward, thesis drift, invalidation tests, or ongoing monitoring signals.
diff --git a/plugins/financial-markets/skills/deck-report-qc/references/qc-playbook.md b/plugins/financial-markets/skills/deck-report-qc/references/qc-playbook.md
deleted file mode 100644
index fb66bd2..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/references/qc-playbook.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# QC Playbook
-
-Use this playbook to review public-equity-investing decks, reports, memos, tearsheets, senior review materials, research notes, and committee packs.
-
-## 1. Number consistency
-
-Review all recurring numbers across the deliverable:
-- revenue, EBITDA, EBIT, EBITDA margin, gross margin, ARR, NRR, FCF, capex, working capital, cash, debt, net debt, enterprise value, equity value, share price, fully diluted shares, EPS, book value
-- growth rates, CAGR, bps movement, multiple, leverage, P/E, EV/EBITDA, P/B, NPV, NAV, price target, benchmark weight, ETF/index exposure, probability-weighted value
-- public-equity market data: price, market cap, EV, multiples, estimates, consensus, short interest, volume, rate, FX, commodity, index level, spread
-- Credit Markets handoff / equity-risk signal data: maturity wall, CDS/spread signal, ratings pressure, refinancing risk, liquidity runway, tender/exchange terms, event spread, probability-weighted value. Route credit-security analysis to Credit Markets.
-
-Flag issues when:
-- the same metric / company / period / unit appears with different values
-- a summary page conflicts with a detailed page
-- a chart value conflicts with a table or bullet
-- a model-output number lacks a model/source reference
-- the sign convention changes without explanation
-- the denominator changes without explanation, e.g. margin on revenue vs gross revenue, leverage on reported EBITDA vs adjusted EBITDA
-
-## 2. Unit and period discipline
-
-Every data-heavy page should make unit and period clear:
-- currency: USD, EUR, GBP, local currency, constant currency, nominal/real
-- scale: dollars, thousands, millions, billions
-- rate unit: percent, basis points, yield, spread
-- multiple unit: x, turns, EV/EBITDA, P/E, P/B, debt/EBITDA
-- time basis: fiscal year, calendar year, quarter, LTM, NTM, run-rate, annualized, as-of date
-- scenario: base, downside, upside, company guidance, Street/consensus case, internal case, Credit Markets handoff case, event case
-
-Common errors:
-- `$m` on one page and `$mm` on another
-- bps change described as a percent change
-- FY and CY estimates mixed in the same table
-- reported EBITDA, adjusted EBITDA, Street EBITDA, and internal-case EBITDA used interchangeably
-- market data stale relative to the date of the report
-- margin deltas shown as percentages instead of percentage points
-
-## 3. Source and footnote hygiene
-
-A page with material facts or market data should identify:
-- source name
-- source date or as-of date
-- period covered
-- whether the data is company-reported, regulator-filed, broker/consensus, market-data provider, management-provided, company-provided non-filed, model-derived, or internal estimate
-- important caveats such as unaudited, preliminary, pro forma, non-GAAP, adjusted, constant-currency, annualized, or management estimate
-
-Flag pages where:
-- data-heavy content has no source footnote
-- source footnote says only `company filings` without period/form if the point is specific
-- market-sensitive data lacks an as-of date
-- company/management materials are used as fact without verification language
-- consensus or broker data is cited without date/provider
-- source footnotes do not match the figures shown
-
-## 4. Chart-to-narrative consistency
-
-For every chart, inspect:
-- chart title and subtitle
-- x-axis and y-axis labels
-- units, scale, and period
-- legend and series names
-- source and as-of date
-- visible values and labels
-- narrative bullet immediately above/below the chart
-
-Flag if:
-- chart title overstates the chart conclusion
-- bullet says improvement but chart shows decline or mixed performance
-- axis scale truncation exaggerates movement without disclosure
-- chart uses bps but bullet uses percent, or vice versa
-- chart data labels do not match the source table/model
-- chart period does not match text period
-- chart source is missing, stale, or inconsistent with surrounding sources
-
-## 5. Narrative and thesis consistency
-
-Compare:
-- executive summary vs section pages
-- page titles vs chart findings
-- investment merits vs risks
-- valuation conclusion vs model output
-- recommendation vs issue log/open evidence gaps
-- equity thesis vs downside, solvency, or liquidity-risk evidence
-- macro conclusion vs monitoring signals
-
-Flag contradictions, unsupported superlatives, or claims that require qualification. Examples:
-- `clear market leader` without market share support
-- `resilient through-cycle margins` when margins declined in downturn years
-- `balance sheet risk is manageable` without liquidity, maturity, or refinancing support
-- `attractive valuation` when implied valuation is above peers without explanation
-
-## 6. Formatting and presentation polish
-
-Check:
-- page numbers, headers, footers, section dividers
-- consistent title capitalization and punctuation
-- font size, font family, line spacing, alignment, and indentation
-- table headers, units rows, decimal precision, subtotal/total formatting
-- colors, legends, callout boxes, and negative-number formatting
-- footnote size, order, and source style
-- orphan bullets, cut-off labels, overlapping shapes, low-resolution images
-
-Formatting issues should not be inflated into investment-substance issues unless they obscure the analysis or create confusion.
-
-## 7. Accessibility and readability checks
-
-For decks circulated broadly, also flag:
-- missing or duplicate slide titles
-- unreadable small fonts
-- low contrast or color-only encoding
-- charts without clear labels
-- complex tables with no obvious row/column headers
-- images used as the sole carrier of important text
-
-Do not over-focus on accessibility for internal working drafts unless the user asks. However, flag readability problems that impede senior review.
diff --git a/plugins/financial-markets/skills/deck-report-qc/scripts/inspect_deck_report.py b/plugins/financial-markets/skills/deck-report-qc/scripts/inspect_deck_report.py
deleted file mode 100755
index 2dbcf3f..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/scripts/inspect_deck_report.py
+++ /dev/null
@@ -1,1042 +0,0 @@
-#!/usr/bin/env python3
-"""
-First-pass deck/report QC extractor for Public Equity Investing deliverables.
-
-Supported inputs: .pptx, .docx, .xlsx, .xlsm, .csv, .txt, .md
-Outputs: public_equity_investing_deck_qc_report.html plus support CSV/JSON/support-note artifacts
-
-This script is intentionally conservative. It identifies leads for review; it does
-not replace visual review, model tie-out, or source-of-truth analysis. After that
-review is completed, use --finalize to reconcile the final HTML and manifest.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import re
-import sys
-import xml.etree.ElementTree as ET
-import zipfile
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Iterable
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-PLUGIN_ROOT = Path(__file__).resolve().parents[3]
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from qc_analysis import analyze_segments, posture_from_issues
-
-from shared.artifact_packager import (  # noqa: E402
-    artifact_item,
-    logs_dir,
-    support_dir,
-    write_artifact_manifest,
-    write_dashboard_contract,
-    write_report_html,
-)
-
-TEXT_EXTS = {".txt", ".md", ".markdown"}
-ZIP_EXTS = {".pptx", ".docx", ".xlsx", ".xlsm"}
-
-COMMON_METRICS = [
-    "revenue",
-    "sales",
-    "arr",
-    "nrr",
-    "gross margin",
-    "ebitda margin",
-    "ebitda",
-    "adjusted ebitda",
-    "street ebitda",
-    "internal-case ebitda",
-    "ebit",
-    "eps",
-    "free cash flow",
-    "fcf",
-    "capex",
-    "working capital",
-    "nwc",
-    "cash",
-    "debt",
-    "net debt",
-    "enterprise value",
-    "equity value",
-    "market cap",
-    "share price",
-    "ev",
-    "valuation",
-    "multiple",
-    "ev/ebitda",
-    "p/e",
-    "irr",
-    "moic",
-    "npv",
-    "nav",
-    "ltv",
-    "dscr",
-    "debt yield",
-    "leverage",
-    "net leverage",
-    "gross leverage",
-    "liquidity",
-    "refinancing",
-    "rating",
-    "credit spread signal",
-    "rate",
-    "inflation",
-    "cagr",
-    "growth",
-    "margin",
-    "bps",
-    "price target",
-    "wacc",
-    "terminal growth",
-    "terminal value",
-    "multiple",
-]
-COMMON_METRICS.sort(key=len, reverse=True)
-
-PERIOD_RE = re.compile(
-    r"\b(fy\s?\d{2,4}e?|cy\s?\d{2,4}e?|20\d{2}e?|19\d{2}|q[1-4]\s?['`]?\d{2}|q[1-4]\s?20\d{2}|ltm|ntm|ytd|run[- ]rate|base case|downside|upside|management case|company guidance|street case|consensus case|credit handoff case|event case|internal case)\b",
-    re.I,
-)
-NUMBER_RE = re.compile(
-    r"(?<![A-Za-z0-9])(?P<full>(?P<prefix>[$€£])?\s*\(?-?\d+(?:,\d{3})*(?:\.\d+)?\)?\s*(?P<suffix>%|bps?|x|turns?|mm|m|bn|b|k|thousand|million|billion)?)",
-    re.I,
-)
-
-REPORT_FILENAME = "public_equity_investing_deck_qc_report.html"
-CONTRACT_FILENAME = "deck_report_qc_dashboard_contract.json"
-
-
-def local_name(tag: str) -> str:
-    return tag.rsplit("}", 1)[-1]
-
-
-def safe_text(parts: Iterable[str]) -> str:
-    text = " ".join(p.strip() for p in parts if p and p.strip())
-    text = re.sub(r"\s+", " ", text).strip()
-    return text
-
-
-def xml_text(xml_bytes: bytes) -> str:
-    try:
-        root = ET.fromstring(xml_bytes)
-    except Exception:
-        return ""
-    vals = []
-    for elem in root.iter():
-        if local_name(elem.tag) in {"t", "v"} and elem.text:
-            vals.append(elem.text)
-    return safe_text(vals)
-
-
-def paragraphs_from_xml(xml_bytes: bytes) -> list[str]:
-    try:
-        root = ET.fromstring(xml_bytes)
-    except Exception:
-        return []
-    paras: list[str] = []
-    for elem in root.iter():
-        if local_name(elem.tag) == "p":
-            vals = []
-            for child in elem.iter():
-                if local_name(child.tag) == "t" and child.text:
-                    vals.append(child.text)
-            txt = safe_text(vals)
-            if txt:
-                paras.append(txt)
-    return paras
-
-
-def numeric_sort_key(name: str) -> tuple[int, str]:
-    nums = re.findall(r"\d+", name)
-    return (int(nums[-1]) if nums else 0, name)
-
-
-def extract_pptx(path: Path) -> list[dict[str, str]]:
-    segments: list[dict[str, str]] = []
-    with zipfile.ZipFile(path) as zf:
-        slide_names = sorted(
-            [n for n in zf.namelist() if re.match(r"ppt/slides/slide\d+\.xml$", n)],
-            key=numeric_sort_key,
-        )
-        for idx, name in enumerate(slide_names, 1):
-            text = xml_text(zf.read(name))
-            title = text.split(". ", 1)[0][:120] if text else ""
-            segments.append(
-                {
-                    "source_file": str(path),
-                    "file_type": "pptx",
-                    "location_type": "slide",
-                    "location": f"Slide {idx}",
-                    "title": title,
-                    "text": text,
-                }
-            )
-        note_names = sorted(
-            [n for n in zf.namelist() if re.match(r"ppt/notesSlides/notesSlide\d+\.xml$", n)],
-            key=numeric_sort_key,
-        )
-        for idx, name in enumerate(note_names, 1):
-            text = xml_text(zf.read(name))
-            if text:
-                segments.append(
-                    {
-                        "source_file": str(path),
-                        "file_type": "pptx",
-                        "location_type": "notes",
-                        "location": f"Notes {idx}",
-                        "title": "speaker notes",
-                        "text": text,
-                    }
-                )
-        chart_names = sorted(
-            [n for n in zf.namelist() if re.match(r"ppt/charts/chart\d+\.xml$", n)],
-            key=numeric_sort_key,
-        )
-        for idx, name in enumerate(chart_names, 1):
-            text = xml_text(zf.read(name))
-            if text:
-                segments.append(
-                    {
-                        "source_file": str(path),
-                        "file_type": "pptx",
-                        "location_type": "chart_xml",
-                        "location": f"Chart {idx}",
-                        "title": "embedded chart data",
-                        "text": text,
-                    }
-                )
-    return segments
-
-
-def extract_docx(path: Path) -> list[dict[str, str]]:
-    segments: list[dict[str, str]] = []
-    with zipfile.ZipFile(path) as zf:
-        for name, label in [
-            ("word/document.xml", "paragraph"),
-            ("word/footnotes.xml", "footnote"),
-            ("word/endnotes.xml", "endnote"),
-            ("word/comments.xml", "comment"),
-        ]:
-            if name not in zf.namelist():
-                continue
-            paras = paragraphs_from_xml(zf.read(name))
-            for idx, para in enumerate(paras, 1):
-                segments.append(
-                    {
-                        "source_file": str(path),
-                        "file_type": "docx",
-                        "location_type": label,
-                        "location": f"{label.title()} {idx}",
-                        "title": para[:100],
-                        "text": para,
-                    }
-                )
-    return segments
-
-
-def read_shared_strings(zf: zipfile.ZipFile) -> list[str]:
-    if "xl/sharedStrings.xml" not in zf.namelist():
-        return []
-    try:
-        root = ET.fromstring(zf.read("xl/sharedStrings.xml"))
-    except Exception:
-        return []
-    strings = []
-    for si in root.iter():
-        if local_name(si.tag) == "si":
-            vals = []
-            for child in si.iter():
-                if local_name(child.tag) == "t" and child.text:
-                    vals.append(child.text)
-            strings.append("".join(vals))
-    return strings
-
-
-def extract_xlsx(path: Path) -> list[dict[str, str]]:
-    segments: list[dict[str, str]] = []
-    with zipfile.ZipFile(path) as zf:
-        shared = read_shared_strings(zf)
-        sheet_names = sorted(
-            [n for n in zf.namelist() if re.match(r"xl/worksheets/sheet\d+\.xml$", n)],
-            key=numeric_sort_key,
-        )
-        for idx, name in enumerate(sheet_names, 1):
-            try:
-                root = ET.fromstring(zf.read(name))
-            except Exception:
-                continue
-            rows = []
-            for row in root.iter():
-                if local_name(row.tag) != "row":
-                    continue
-                vals = []
-                for c in row:
-                    if local_name(c.tag) != "c":
-                        continue
-                    ref = c.attrib.get("r", "")
-                    ctype = c.attrib.get("t")
-                    val_text = ""
-                    formula = ""
-                    for child in c:
-                        lname = local_name(child.tag)
-                        if lname == "f" and child.text:
-                            formula = "=" + child.text
-                        elif lname == "v" and child.text:
-                            raw = child.text
-                            if ctype == "s":
-                                try:
-                                    val_text = shared[int(raw)]
-                                except Exception:
-                                    val_text = raw
-                            else:
-                                val_text = raw
-                        elif lname == "is":
-                            val_text = safe_text(
-                                [
-                                    t.text
-                                    for t in child.iter()
-                                    if local_name(t.tag) == "t" and t.text
-                                ]
-                            )
-                    if formula:
-                        vals.append(f"{ref}:{formula}")
-                    elif val_text:
-                        vals.append(f"{ref}:{val_text}")
-                if vals:
-                    rows.append(" | ".join(vals))
-            if rows:
-                text = "\n".join(rows)
-                segments.append(
-                    {
-                        "source_file": str(path),
-                        "file_type": "xlsx",
-                        "location_type": "sheet",
-                        "location": f"Sheet {idx}",
-                        "title": f"Sheet {idx}",
-                        "text": text[:200000],
-                    }
-                )
-    return segments
-
-
-def extract_csv(path: Path) -> list[dict[str, str]]:
-    raw = path.read_text(errors="replace")
-    lines = raw.splitlines()
-    segments = []
-    block_size = 50
-    for start in range(0, len(lines), block_size):
-        block = lines[start : start + block_size]
-        if not block:
-            continue
-        segments.append(
-            {
-                "source_file": str(path),
-                "file_type": "csv",
-                "location_type": "row_block",
-                "location": f"Rows {start + 1}-{start + len(block)}",
-                "title": block[0][:100] if block else "",
-                "text": "\n".join(block),
-            }
-        )
-    return segments
-
-
-def extract_text_file(path: Path) -> list[dict[str, str]]:
-    raw = path.read_text(errors="replace")
-    lines = raw.splitlines()
-    segments = []
-    block_size = 40
-    for start in range(0, len(lines), block_size):
-        block = lines[start : start + block_size]
-        if not block:
-            continue
-        segments.append(
-            {
-                "source_file": str(path),
-                "file_type": path.suffix.lower().lstrip("."),
-                "location_type": "line_block",
-                "location": f"Lines {start + 1}-{start + len(block)}",
-                "title": block[0][:100] if block else "",
-                "text": "\n".join(block),
-            }
-        )
-    return segments
-
-
-def extract_file(path: Path) -> list[dict[str, str]]:
-    ext = path.suffix.lower()
-    if ext == ".pptx":
-        return extract_pptx(path)
-    if ext == ".docx":
-        return extract_docx(path)
-    if ext in {".xlsx", ".xlsm"}:
-        return extract_xlsx(path)
-    if ext == ".csv":
-        return extract_csv(path)
-    if ext in TEXT_EXTS:
-        return extract_text_file(path)
-    return [
-        {
-            "source_file": str(path),
-            "file_type": ext.lstrip("."),
-            "location_type": "unsupported",
-            "location": "File",
-            "title": "unsupported file type",
-            "text": "",
-        }
-    ]
-
-
-def normalize_number(raw: str) -> tuple[float | None, str]:
-    s = raw.strip().replace(" ", "")
-    prefix = "currency" if any(sym in s for sym in "$€£") else ""
-    suffix_match = re.search(r"(%|bps?|x|turns?|mm|m|bn|b|k|thousand|million|billion)$", s, re.I)
-    suffix = suffix_match.group(1).lower() if suffix_match else ""
-    neg = s.startswith("-") or ("(" in s and ")" in s)
-    num_s = re.sub(r"[^0-9.\-]", "", s)
-    try:
-        val = float(num_s)
-    except Exception:
-        return None, "unknown"
-    if neg and val > 0:
-        val *= -1
-    unit_class = "number"
-    multiplier = 1.0
-    if suffix == "%":
-        unit_class = "percent"
-    elif suffix in {"bp", "bps"}:
-        unit_class = "bps"
-    elif suffix in {"x", "turn", "turns"}:
-        unit_class = "multiple"
-    elif suffix in {"k", "thousand"}:
-        unit_class = "currency_or_count"
-        multiplier = 1_000.0
-    elif suffix in {"m", "mm", "million"}:
-        unit_class = "currency_or_count"
-        multiplier = 1_000_000.0
-    elif suffix in {"b", "bn", "billion"}:
-        unit_class = "currency_or_count"
-        multiplier = 1_000_000_000.0
-    elif prefix:
-        unit_class = "currency"
-    return val * multiplier, unit_class
-
-
-def metric_key(context: str) -> str:
-    c = context.lower()
-    marker = c.find("__num__")
-    if marker < 0:
-        marker = len(c) // 2
-
-    metric = ""
-    best_dist = 10**9
-    for m in COMMON_METRICS:
-        for hit in re.finditer(r"\b" + re.escape(m) + r"\b", c):
-            dist = min(abs(hit.start() - marker), abs(hit.end() - marker))
-            if dist < best_dist:
-                best_dist = dist
-                metric = m
-
-    period = ""
-    best_period_dist = 10**9
-    for hit in PERIOD_RE.finditer(context):
-        dist = min(abs(hit.start() - marker), abs(hit.end() - marker))
-        if dist < best_period_dist:
-            best_period_dist = dist
-            period = hit.group(0).lower().replace(" ", "")
-
-    if metric:
-        return "|".join([p for p in [metric, period] if p])
-    # Fallback to words immediately before the number.
-    before = re.sub(r"[^A-Za-z0-9%/ -]", " ", context[: max(context.find("__NUM__"), 0)])
-    words = [w.lower() for w in before.split()[-5:] if len(w) > 2]
-    return " ".join(words[-4:]) if words else "unknown"
-
-
-def extract_numbers(segments: list[dict[str, str]]) -> list[dict[str, str]]:
-    rows: list[dict[str, str]] = []
-    for seg in segments:
-        text = seg.get("text", "") or ""
-        for match in NUMBER_RE.finditer(text):
-            raw = match.group("full").strip()
-            start, end = match.span()
-            # Skip numbers that are clearly components of common date strings such as 1/15/2026 or 2026-01-15.
-            if (start > 0 and text[start - 1] in "/-") or (
-                end < len(text) and text[end : end + 1] in "/-"
-            ):
-                continue
-            # Skip obvious year-only cases unless context suggests a metric.
-            val, unit_class = normalize_number(raw)
-            if val is None:
-                continue
-            context = text[max(0, start - 100) : min(len(text), end + 100)].replace("\n", " ")
-            marked_context = (
-                context[: start - max(0, start - 100)]
-                + "__NUM__"
-                + context[start - max(0, start - 100) :]
-            )
-            key = metric_key(marked_context)
-            if (
-                unit_class == "number"
-                and 1900 <= abs(val) <= 2100
-                and not re.search(
-                    r"\b(fy|cy|q[1-4]|year|revenue|ebitda|margin|growth|debt|valuation|source|as of)\b",
-                    context,
-                    re.I,
-                )
-            ):
-                continue
-            rows.append(
-                {
-                    "source_file": seg["source_file"],
-                    "file_type": seg["file_type"],
-                    "location": seg["location"],
-                    "location_type": seg["location_type"],
-                    "metric_key": key,
-                    "raw_value": raw,
-                    "normalized_value": f"{val:.8g}",
-                    "unit_class": unit_class,
-                    "context": re.sub(r"\s+", " ", context).strip(),
-                }
-            )
-    return rows
-
-
-def write_csv(path: Path, rows: list[dict[str, str]], fieldnames: list[str]) -> None:
-    with path.open("w", newline="", encoding="utf-8") as f:
-        writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
-        writer.writeheader()
-        for row in rows:
-            writer.writerow(row)
-
-
-def write_report_text(
-    input_files: list[Path],
-    segments: list[dict[str, str]],
-    numbers: list[dict[str, str]],
-    issues: list[dict[str, str]],
-) -> str:
-    severity_rank = {"critical": 0, "high": 1, "medium": 2, "needs_review": 3, "low": 4}
-    top = sorted(issues, key=lambda i: severity_rank.get(i["severity"], 99))[:20]
-    lines = []
-    lines.append("# First-pass Deck / Report QC Scan")
-    lines.append("")
-    lines.append(f"Generated: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC")
-    lines.append(f"Input files: {', '.join(str(p) for p in input_files)}")
-    lines.append("")
-    lines.append("## Summary")
-    lines.append(f"- Segments extracted: {len(segments)}")
-    lines.append(f"- Numerical mentions detected: {len(numbers)}")
-    lines.append(f"- Potential issues flagged: {len(issues)}")
-    lines.append(f"- First-pass scan posture: {posture_from_issues(issues)}")
-    lines.append("")
-    lines.append(
-        "This scan is a first pass. It is not a final external-circulation certification. Confirm chart, formatting, source, and model issues through visual review and source/model tie-out."
-    )
-    lines.append("")
-    lines.append("## Top flagged issues")
-    if not top:
-        lines.append(
-            "No heuristic issues flagged. Still perform visual review and source/model tie-out before circulation."
-        )
-    else:
-        lines.append("| ID | Severity | Type | Location | Finding | Suggested fix |")
-        lines.append("|---|---|---|---|---|---|")
-        for i in top:
-            lines.append(
-                f"| {i['issue_id']} | {i['severity']} | {i['issue_type']} | {i['location']} | {i['finding']} | {i['suggested_fix']} |"
-            )
-    lines.append("")
-    lines.append("## Files generated")
-    lines.append("- segments.csv")
-    lines.append("- numbers.csv")
-    lines.append("- issues.csv")
-    lines.append("- scan.json")
-    return "\n".join(lines)
-
-
-def load_review_record(path: Path) -> dict[str, object]:
-    try:
-        record = json.loads(path.read_text(encoding="utf-8"))
-    except FileNotFoundError as exc:
-        raise ValueError(f"missing review record: {path}") from exc
-    except json.JSONDecodeError as exc:
-        raise ValueError(f"invalid review record JSON: {path}: {exc}") from exc
-    if not isinstance(record, dict):
-        raise ValueError("review record must be a JSON object")
-    for key in ("completed_reviews", "missing_inputs"):
-        values = record.get(key, [])
-        if not isinstance(values, list) or not all(isinstance(value, str) for value in values):
-            raise ValueError(f"review record field {key!r} must be a list of strings")
-    return record
-
-
-def finalized_support_artifacts(
-    scan_dir: Path, review_record_path: Path, contract_path: Path | None
-) -> list[dict[str, object]]:
-    candidates = [
-        (
-            scan_dir / "support" / "segments.csv",
-            "csv",
-            "Extracted text segment inventory.",
-            True,
-            "CSV is extraction support.",
-        ),
-        (
-            scan_dir / "support" / "numbers.csv",
-            "csv",
-            "Extracted number inventory.",
-            True,
-            "CSV supports number tie-out.",
-        ),
-        (
-            scan_dir / "support" / "qc_issue_log.csv",
-            "csv",
-            "Raw first-pass QC issue leads.",
-            True,
-            "CSV is first-pass issue extraction support.",
-        ),
-        (
-            scan_dir / "support" / "repeated_number_tieout.csv",
-            "csv",
-            "Repeated metric tie-out leads.",
-            True,
-            "CSV supports model/source reconciliation.",
-        ),
-        (
-            scan_dir / "support" / "source_coverage.csv",
-            "csv",
-            "Source coverage issue leads.",
-            True,
-            "CSV supports source review.",
-        ),
-        (
-            scan_dir / "support" / "chart_narrative_tieout.csv",
-            "csv",
-            "Chart and narrative tie-out leads.",
-            True,
-            "CSV supports visual/narrative review.",
-        ),
-        (
-            scan_dir / "support" / "qc_support_note.md",
-            "markdown",
-            "First-pass extraction support note.",
-            True,
-            "Markdown is extraction support, not the final QC report.",
-        ),
-        (
-            scan_dir / "logs" / "scan.json",
-            "json",
-            "Machine-readable extraction scan.",
-            True,
-            "JSON is internal extraction support.",
-        ),
-        (
-            review_record_path,
-            "json",
-            "Completed-review and remaining-verification record.",
-            False,
-            "Review record reconciles final artifact status and open verification items.",
-        ),
-    ]
-    if contract_path is not None:
-        candidates.append(
-            (
-                contract_path,
-                "json",
-                "Explicit standardized-dashboard contract.",
-                False,
-                "Contract JSON is renderer plumbing for the requested dashboard route.",
-            )
-        )
-    return [
-        artifact_item(path, "support_artifact", kind, description, False, analysis, reason)
-        for path, kind, description, analysis, reason in candidates
-        if path.exists()
-    ]
-
-
-def update_dashboard_contract(
-    contract_path: Path, primary_report: Path, status: dict[str, object]
-) -> None:
-    if not contract_path.exists():
-        return
-    try:
-        contract = json.loads(contract_path.read_text(encoding="utf-8"))
-    except json.JSONDecodeError:
-        return
-    if not isinstance(contract, dict):
-        return
-    deliverable = contract.get("deliverable")
-    if isinstance(deliverable, dict):
-        deliverable["primary_artifact"] = str(primary_report)
-        actions = deliverable.get("hero_actions", [])
-        if isinstance(actions, list):
-            for action in actions:
-                if isinstance(action, dict) and action.get("label") == "Open QC report":
-                    action["path"] = str(primary_report)
-    hero = contract.get("hero")
-    if isinstance(hero, dict):
-        hero["callout"] = str(primary_report)
-    contract["blocked_output_context"] = {
-        "blocked": status.get("status") == "blocked",
-        "reason": status.get("reason", ""),
-        "completed_reviews": status.get("completed_reviews", []),
-        "missing_inputs": status.get("missing_inputs", []),
-    }
-    contract_path.write_text(json.dumps(contract, indent=2) + "\n", encoding="utf-8")
-
-
-def finalize_review(args: argparse.Namespace) -> int:
-    if not args.primary_report or not args.review_record:
-        print(
-            "error: --finalize requires --primary-report and --review-record",
-            file=sys.stderr,
-        )
-        return 2
-    outdir = Path(args.outdir).resolve()
-    scan_dir = Path(args.scan_dir or args.outdir).resolve()
-    primary_report = Path(args.primary_report).resolve()
-    review_record_path = Path(args.review_record).resolve()
-    if not primary_report.exists():
-        print(f"error: missing primary report: {primary_report}", file=sys.stderr)
-        return 2
-    if primary_report.suffix.lower() not in {".html", ".htm"}:
-        print("error: finalized primary report must be HTML", file=sys.stderr)
-        return 2
-    try:
-        record = load_review_record(review_record_path)
-    except ValueError as exc:
-        print(f"error: {exc}", file=sys.stderr)
-        return 2
-
-    completed_reviews = list(record.get("completed_reviews", []))
-    missing_inputs = list(record.get("missing_inputs", []))
-    status = str(record.get("status") or ("partial" if missing_inputs else "complete"))
-    reason = str(
-        record.get("reason")
-        or (
-            "Internal QC review completed; external/source confirmation remains open."
-            if missing_inputs
-            else "QC review steps recorded as complete."
-        )
-    )
-    final_status: dict[str, object] = {
-        "status": status,
-        "reason": reason,
-        "completed_reviews": completed_reviews,
-        "missing_inputs": missing_inputs,
-    }
-
-    contract_path = scan_dir / "logs" / CONTRACT_FILENAME
-    retained_contract: Path | None = None
-    if args.keep_dashboard_contract:
-        update_dashboard_contract(contract_path, primary_report, final_status)
-        retained_contract = contract_path if contract_path.exists() else None
-    else:
-        contract_path.unlink(missing_ok=True)
-
-    provisional_report = scan_dir / REPORT_FILENAME
-    if provisional_report != primary_report:
-        provisional_report.unlink(missing_ok=True)
-    provisional_manifest = scan_dir / "manifest.json"
-    if provisional_manifest != outdir / "manifest.json":
-        provisional_manifest.unlink(missing_ok=True)
-
-    write_artifact_manifest(
-        outdir,
-        "deck-report-qc",
-        "html_report",
-        primary_report,
-        support_artifacts=finalized_support_artifacts(
-            scan_dir, review_record_path, retained_contract
-        ),
-        blocked_or_partial_status=final_status,
-        extra={"review_completion_record": str(review_record_path)},
-    )
-    print(f"finalized QC review manifest at {outdir / 'manifest.json'}")
-    print(f"primary_report={primary_report} status={status}")
-    return 0
-
-
-def main(argv: list[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(
-        description="First-pass QC extractor for Public Equity Investing decks and reports"
-    )
-    parser.add_argument(
-        "files", nargs="*", help="input files: pptx, docx, xlsx, xlsm, csv, txt, md"
-    )
-    parser.add_argument("--outdir", default="qc_out", help="output directory")
-    parser.add_argument(
-        "--finalize",
-        action="store_true",
-        help="finalize a completed substantive review around its polished HTML report",
-    )
-    parser.add_argument(
-        "--scan-dir",
-        help="directory containing first-pass extraction support; defaults to --outdir",
-    )
-    parser.add_argument("--primary-report", help="final polished HTML report for --finalize")
-    parser.add_argument("--review-record", help="JSON completion record for --finalize")
-    parser.add_argument(
-        "--keep-dashboard-contract",
-        action="store_true",
-        help="retain/update the dashboard contract only for an explicitly selected dashboard route",
-    )
-    args = parser.parse_args(argv)
-
-    if args.finalize:
-        return finalize_review(args)
-    if not args.files:
-        parser.error("at least one input file is required unless --finalize is used")
-
-    input_files = [Path(f).resolve() for f in args.files]
-    outdir = Path(args.outdir).resolve()
-    outdir.mkdir(parents=True, exist_ok=True)
-
-    segments: list[dict[str, str]] = []
-    for path in input_files:
-        if not path.exists():
-            print(f"error: missing file: {path}", file=sys.stderr)
-            return 2
-        if path.suffix.lower() in ZIP_EXTS and not zipfile.is_zipfile(path):
-            print(
-                f"warning: {path} does not look like a valid zip-based Office file",
-                file=sys.stderr,
-            )
-        try:
-            segments.extend(extract_file(path))
-        except Exception as exc:
-            segments.append(
-                {
-                    "source_file": str(path),
-                    "file_type": path.suffix.lower().lstrip("."),
-                    "location_type": "error",
-                    "location": "File",
-                    "title": "extraction error",
-                    "text": f"extraction failed: {exc}",
-                }
-            )
-
-    numbers = extract_numbers(segments)
-    issues = analyze_segments(segments, numbers)
-
-    csv_dir = support_dir(outdir)
-    log_dir = logs_dir(outdir)
-    write_csv(
-        csv_dir / "segments.csv",
-        segments,
-        ["source_file", "file_type", "location_type", "location", "title", "text"],
-    )
-    write_csv(
-        csv_dir / "numbers.csv",
-        numbers,
-        [
-            "source_file",
-            "file_type",
-            "location",
-            "location_type",
-            "metric_key",
-            "raw_value",
-            "normalized_value",
-            "unit_class",
-            "context",
-        ],
-    )
-    issue_fields = [
-        "issue_id",
-        "severity",
-        "issue_type",
-        "confidence",
-        "source_file",
-        "location",
-        "metric_or_claim",
-        "finding",
-        "evidence",
-        "why_it_matters",
-        "suggested_fix",
-        "owner_route",
-        "status",
-    ]
-    write_csv(csv_dir / "qc_issue_log.csv", issues, issue_fields)
-    repeated_number_rows = [
-        issue for issue in issues if issue.get("issue_type") == "number_mismatch"
-    ]
-    source_coverage_rows = [issue for issue in issues if issue.get("issue_type") == "source_gap"]
-    chart_narrative_rows = [
-        issue
-        for issue in issues
-        if issue.get("issue_type")
-        in {"narrative_contradiction", "chart_tieout", "unit_or_period_ambiguity"}
-    ]
-    summary_fields = [
-        "issue_id",
-        "severity",
-        "source_file",
-        "location",
-        "metric_or_claim",
-        "finding",
-        "evidence",
-        "suggested_fix",
-        "status",
-    ]
-    write_csv(csv_dir / "repeated_number_tieout.csv", repeated_number_rows, summary_fields)
-    write_csv(csv_dir / "source_coverage.csv", source_coverage_rows, summary_fields)
-    write_csv(csv_dir / "chart_narrative_tieout.csv", chart_narrative_rows, summary_fields)
-    (log_dir / "scan.json").write_text(
-        json.dumps({"segments": segments, "numbers": numbers, "issues": issues}, indent=2),
-        encoding="utf-8",
-    )
-    report_text = write_report_text(input_files, segments, numbers, issues)
-    support_note_path = csv_dir / "qc_support_note.md"
-    support_note_path.write_text(report_text, encoding="utf-8")
-    report_path = write_report_html(
-        outdir / REPORT_FILENAME,
-        "Public Equity Investing Deck / Report QC",
-        report_text,
-    )
-    contract_path = log_dir / CONTRACT_FILENAME
-    write_dashboard_contract(
-        contract_path,
-        "deck-report-qc",
-        "Public Equity Investing Deck / Report QC",
-        "Public Equity Investing deliverable",
-        "report_only",
-        report_path,
-        executive_summary="First-pass QC report with issue severity, repeated-number tie-out, source coverage, and chart/narrative review leads.",
-        hero_actions=[
-            {"label": "Open QC report", "path": str(report_path)},
-            {"label": "Review issue log", "path": str(csv_dir / "qc_issue_log.csv")},
-        ],
-        supporting_outputs=[
-            {"path": str(csv_dir / "qc_issue_log.csv"), "label": "QC issue log", "role": "support"}
-        ],
-        report_body=[{"heading": "QC Summary", "body": report_text}],
-        readiness_posture=posture_from_issues(issues),
-        blocked_output_context={
-            "blocked": False,
-            "reason": "Text extraction is first-pass and does not replace visual, model, or source review.",
-            "missing_inputs": [
-                "Visual page review",
-                "Model tie-out",
-                "Source-of-truth confirmation",
-            ],
-        },
-    )
-    write_artifact_manifest(
-        outdir,
-        "deck-report-qc",
-        "html_report",
-        report_path,
-        support_artifacts=[
-            artifact_item(
-                csv_dir / "segments.csv",
-                "support_artifact",
-                "csv",
-                "Extracted text segment inventory.",
-                False,
-                True,
-                "CSV is extraction support.",
-            ),
-            artifact_item(
-                csv_dir / "numbers.csv",
-                "support_artifact",
-                "csv",
-                "Extracted number inventory.",
-                False,
-                True,
-                "CSV supports number tie-out.",
-            ),
-            artifact_item(
-                csv_dir / "qc_issue_log.csv",
-                "support_artifact",
-                "csv",
-                "Raw QC issue log.",
-                False,
-                True,
-                "CSV backs the HTML QC report.",
-            ),
-            artifact_item(
-                csv_dir / "repeated_number_tieout.csv",
-                "support_artifact",
-                "csv",
-                "Repeated metric tie-out leads.",
-                False,
-                True,
-                "CSV supports model/source reconciliation.",
-            ),
-            artifact_item(
-                csv_dir / "source_coverage.csv",
-                "support_artifact",
-                "csv",
-                "Source coverage issue leads.",
-                False,
-                True,
-                "CSV supports source review.",
-            ),
-            artifact_item(
-                csv_dir / "chart_narrative_tieout.csv",
-                "support_artifact",
-                "csv",
-                "Chart and narrative tie-out leads.",
-                False,
-                True,
-                "CSV supports visual/narrative review.",
-            ),
-            artifact_item(
-                support_note_path,
-                "support_artifact",
-                "markdown",
-                "Narrative support note backing the HTML report.",
-                False,
-                True,
-                "Markdown is support material, not the lead artifact.",
-            ),
-            artifact_item(
-                log_dir / "scan.json",
-                "support_artifact",
-                "json",
-                "Machine-readable extraction scan.",
-                False,
-                True,
-                "JSON is internal extraction support.",
-            ),
-            artifact_item(
-                contract_path,
-                "support_artifact",
-                "json",
-                "Dashboard/report contract.",
-                False,
-                False,
-                "Contract JSON is renderer plumbing.",
-            ),
-        ],
-        blocked_or_partial_status={
-            "status": "partial",
-            "reason": "First-pass extraction does not replace visual, model, or source review.",
-            "missing_inputs": [
-                "Visual page review",
-                "Model tie-out",
-                "Source-of-truth confirmation",
-            ],
-        },
-    )
-
-    print(f"wrote QC scan to {outdir}")
-    print(
-        f"segments={len(segments)} numbers={len(numbers)} issues={len(issues)} posture={posture_from_issues(issues)}"
-    )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/deck-report-qc/scripts/qc_analysis.py b/plugins/financial-markets/skills/deck-report-qc/scripts/qc_analysis.py
deleted file mode 100644
index 48de8aa..0000000
--- a/plugins/financial-markets/skills/deck-report-qc/scripts/qc_analysis.py
+++ /dev/null
@@ -1,279 +0,0 @@
-"""Issue-detection logic for the Public Equity Investing deck/report QC helper."""
-
-from __future__ import annotations
-
-import math
-import re
-from collections import defaultdict
-
-SOURCE_TERMS = re.compile(
-    r"\b(source|sources|note|notes|footnote|as of|as-of|company filing|"
-    r"filings|10-k|10-q|8-k|transcript|press release|factset|bloomberg|"
-    r"capital iq|s&p|lseg|refinitiv|morningstar|moody|fitch|management|"
-    r"issuer|company presentation|investor presentation|model|internal "
-    r"estimate|consensus)\b",
-    re.I,
-)
-DATE_TERMS = re.compile(
-    r"\b(as of|as-of|through|dated|date|period ended|fye|fiscal year|"
-    r"quarter ended|q[1-4]|fy\d{2,4}|cy\d{2,4}|ltm|ntm|ytd|"
-    r"\d{1,2}/\d{1,2}/\d{2,4}|\d{4}-\d{2}-\d{2}|jan\.?|feb\.?|"
-    r"mar\.?|apr\.?|may|jun\.?|jul\.?|aug\.?|sep\.?|sept\.?|oct\.?|"
-    r"nov\.?|dec\.?)\b",
-    re.I,
-)
-MARKET_TERMS = re.compile(
-    r"\b(share price|stock price|market cap|enterprise value|ev|equity "
-    r"value|yield|spread|bps|rate|fx|commodity|index|multiple|consensus|"
-    r"estimate|short interest|volume|price target|valuation|trading|"
-    r"credit spread)\b",
-    re.I,
-)
-CLAIM_WORDS = re.compile(
-    r"\b(best[- ]in[- ]class|market leader|clear leader|dominant|unique|"
-    r"proprietary|significant|compelling|attractive|resilient|defensible|"
-    r"downside protected|conservative|undervalued|overvalued|mispriced|"
-    r"strong visibility|high quality|mission critical)\b",
-    re.I,
-)
-
-
-def add_issue(
-    issues: list[dict[str, str]],
-    severity: str,
-    issue_type: str,
-    confidence: str,
-    source_file: str,
-    location: str,
-    metric_or_claim: str,
-    finding: str,
-    evidence: str,
-    why: str,
-    fix: str,
-    route: str = "deck-report-qc",
-) -> None:
-    issues.append(
-        {
-            "issue_id": f"QC-{len(issues) + 1:03d}",
-            "severity": severity,
-            "issue_type": issue_type,
-            "confidence": confidence,
-            "source_file": source_file,
-            "location": location,
-            "metric_or_claim": metric_or_claim,
-            "finding": finding,
-            "evidence": evidence,
-            "why_it_matters": why,
-            "suggested_fix": fix,
-            "owner_route": route,
-            "status": "open",
-        }
-    )
-
-
-def analyze_segment_sources(
-    issues: list[dict[str, str]],
-    seg: dict[str, str],
-    seg_nums: list[dict[str, str]],
-) -> None:
-    text = seg.get("text", "") or ""
-    number_count = len(seg_nums)
-    source_present = bool(SOURCE_TERMS.search(text))
-    date_present = bool(DATE_TERMS.search(text))
-    market_present = bool(MARKET_TERMS.search(text))
-    if number_count >= 5 and not source_present:
-        add_issue(
-            issues,
-            "medium",
-            "source_gap",
-            "possible",
-            seg["source_file"],
-            seg["location"],
-            "data-heavy page/section",
-            "material numerical content appears without an obvious source footnote",
-            f"{number_count} numerical mentions detected; no source terms detected",
-            "unsupported numbers reduce senior-review confidence and make tie-out difficult",
-            "add source, period/as-of date, and relevant caveats",
-            "financial-source-of-truth",
-        )
-    elif number_count >= 5 and source_present and market_present and not date_present:
-        add_issue(
-            issues,
-            "medium",
-            "source_gap",
-            "possible",
-            seg["source_file"],
-            seg["location"],
-            "market-sensitive data",
-            "market-sensitive numerical content has a source but no obvious as-of date",
-            "source terms and market terms detected, but no date/as-of term detected",
-            "market data can become stale quickly",
-            "add as-of date or period for market/consensus data",
-            "financial-source-of-truth",
-        )
-
-
-def suffix_set(seg_nums: list[dict[str, str]]) -> set[str]:
-    suffixes: set[str] = set()
-    for number_row in seg_nums:
-        raw_value = number_row["raw_value"].lower()
-        if re.search(r"\b(mm|m|million)\b|\d(mm|m)$", raw_value):
-            suffixes.add("millions")
-        if re.search(r"\b(bn|b|billion)\b|\d(bn|b)$", raw_value):
-            suffixes.add("billions")
-        if "%" in raw_value:
-            suffixes.add("percent")
-        if "bp" in raw_value:
-            suffixes.add("bps")
-    return suffixes
-
-
-def analyze_segment_units(
-    issues: list[dict[str, str]],
-    seg: dict[str, str],
-    seg_nums: list[dict[str, str]],
-) -> None:
-    suffixes = suffix_set(seg_nums)
-    if {"millions", "billions"}.issubset(suffixes):
-        add_issue(
-            issues,
-            "needs_review",
-            "unit_or_period_ambiguity",
-            "possible",
-            seg["source_file"],
-            seg["location"],
-            "mixed scale",
-            "both million and billion scale markers appear in the same page/section",
-            ", ".join(sorted(suffixes)),
-            "mixed scale may be correct, but should be explicit in table headers and labels",
-            "confirm units are intentional and label scale clearly",
-        )
-    if {"percent", "bps"}.issubset(suffixes):
-        add_issue(
-            issues,
-            "needs_review",
-            "unit_or_period_ambiguity",
-            "possible",
-            seg["source_file"],
-            seg["location"],
-            "percent vs bps",
-            "both percent and bps markers appear in the same page/section",
-            ", ".join(sorted(suffixes)),
-            "percentage levels and percentage-point changes are often confused",
-            "confirm bps/percentage-point convention in labels and bullets",
-        )
-
-
-def analyze_segment_claims(
-    issues: list[dict[str, str]],
-    seg: dict[str, str],
-    seg_nums: list[dict[str, str]],
-) -> None:
-    text = seg.get("text", "") or ""
-    source_present = bool(SOURCE_TERMS.search(text))
-    claim_match = CLAIM_WORDS.search(text)
-    if claim_match and len(seg_nums) < 2 and not source_present:
-        claim = claim_match.group(0)
-        add_issue(
-            issues,
-            "needs_review",
-            "narrative_contradiction",
-            "possible",
-            seg["source_file"],
-            seg["location"],
-            claim,
-            "strong qualitative claim appears with limited visible support",
-            claim,
-            "senior reviewers will expect evidence for strong claims",
-            "add support, soften language, or route to evidence ledger",
-            "financial-source-of-truth",
-        )
-
-
-def analyze_repeated_metrics(
-    issues: list[dict[str, str]],
-    numbers: list[dict[str, str]],
-) -> None:
-    groups: dict[str, list[dict[str, str]]] = defaultdict(list)
-    for number_row in numbers:
-        if number_row["metric_key"] and number_row["metric_key"] != "unknown":
-            groups[number_row["metric_key"]].append(number_row)
-
-    for key, rows in groups.items():
-        if len(rows) < 2:
-            continue
-        locs = sorted({row["location"] for row in rows})
-        values_by_unit: dict[str, list[float]] = defaultdict(list)
-        for row in rows:
-            try:
-                values_by_unit[row["unit_class"]].append(float(row["normalized_value"]))
-            except Exception:
-                pass
-        for unit_class, values in values_by_unit.items():
-            uniq = sorted({round(v, 4) for v in values if math.isfinite(v)})
-            if len(uniq) < 2:
-                continue
-            lo, hi = min(uniq), max(uniq)
-            denom = max(abs(hi), abs(lo), 1.0)
-            diff_pct = abs(hi - lo) / denom
-            threshold = 0.005 if unit_class in {"percent", "bps", "multiple"} else 0.01
-            if diff_pct >= threshold:
-                examples = [
-                    f"{row['location']}: {row['raw_value']} ({row['context'][:120]})"
-                    for row in rows[:8]
-                ]
-                add_issue(
-                    issues,
-                    "needs_review",
-                    "number_mismatch",
-                    "possible",
-                    rows[0]["source_file"],
-                    "; ".join(locs[:6]),
-                    key,
-                    "same detected metric key appears with different values across locations",
-                    " | ".join(examples),
-                    "repeated metrics should tie unless period, unit, scenario, or definition differs",
-                    "verify period/unit/scenario; update summary and detail pages to controlling value",
-                    "model-audit-tieout",
-                )
-
-
-def analyze_segments(
-    segments: list[dict[str, str]],
-    numbers: list[dict[str, str]],
-) -> list[dict[str, str]]:
-    issues: list[dict[str, str]] = []
-    for seg in segments:
-        text = seg.get("text", "") or ""
-        if not text.strip():
-            continue
-        seg_nums = [
-            number_row
-            for number_row in numbers
-            if number_row["source_file"] == seg["source_file"]
-            and number_row["location"] == seg["location"]
-        ]
-        analyze_segment_sources(issues, seg, seg_nums)
-        analyze_segment_units(issues, seg, seg_nums)
-        analyze_segment_claims(issues, seg, seg_nums)
-
-    analyze_repeated_metrics(issues, numbers)
-    return issues
-
-
-def posture_from_issues(issues: list[dict[str, str]]) -> str:
-    severities = [issue["severity"] for issue in issues]
-    targeted_issue_types = {"source_gap", "number_mismatch", "unit_or_period_ambiguity"}
-    if "critical" in severities:
-        return "not-circulable"
-    if "high" in severities:
-        return "needs-targeted-fixes"
-    if "medium" in severities:
-        return "needs-targeted-fixes"
-    if any(issue["issue_type"] in targeted_issue_types for issue in issues):
-        return "needs-targeted-fixes"
-    if "needs_review" in severities:
-        return "needs-targeted-fixes"
-    if issues:
-        return "senior-review-ready"
-    return "first-pass-clear"
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/SKILL.md b/plugins/financial-markets/skills/earnings-deep-dive/SKILL.md
deleted file mode 100644
index f1e00a0..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/SKILL.md
+++ /dev/null
@@ -1,120 +0,0 @@
----
-name: earnings-deep-dive
-description: Use when analyzing public-company earnings after results, guidance, transcript, or call commentary. Do not use for pre-print previews.
----
-
-# Earnings Deep Dive
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML post-earnings report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For an explicit `deep dive`, `full report`, or reusable/source-heavy post-print package, resolve presentation to a polished standalone HTML post-earnings report unless the user requests another format, an explicitly quick/no-file answer, or workbook/model-update output. In interactive runs, ask only remaining material questions such as depth, audience/use, or focus. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-Produce a decision-grade, audit-ready post-print package after results are available.
-
-Default to the full post-print package. A new standalone reader-facing post-print output should be a polished standalone HTML post-earnings report following `../../shared/html-artifact-standard.md`; use chat only when the user explicitly requests a lightweight response. Use `dashboard-builder` only for the optional standardized-dashboard route below. Use deterministic file mode only when the user supplies `plan.json`, normalized CSVs, model-update inputs, or explicitly asks for files.
-
-## Route
-
-- `full deep dive`: default analytical route for post-earnings deep dives, earnings-print analysis, and investor-facing post-print questions. An explicit deep dive, full report, or reusable/source-heavy package defaults to polished standalone HTML.
-- `one-page tear sheet`: use only when the user explicitly asks for a summary, one-pager, quick read, brief, or TL;DR.
-- `audit-ready model update`: use only when the user supplies or references a model/workbook, driver registry, output registry, normalized CSVs, model-update inputs, or explicit data to update a model.
-- `quote and debate map`: standalone only when the user asks only for transcript quotes/debate; otherwise include it inside the full deep dive.
-- `standardized dashboard`: only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, tabbed dashboard, or structured payload-driven render, keep this skill as the analysis owner and hand the resulting `public_equity_investing_dashboard.v1` payload to `dashboard-builder`. Use `references/DASHBOARD_PACK.md` for module mapping.
-- `deterministic file mode`: validate inputs, run shipped scripts, fail QA on unresolved user-facing placeholders, and disclose packet versus workbook-apply path.
-
-Load `references/REFERENCE_ROUTER.md` first, then only the route-specific reference needed for the selected artifact.
-
-## Non-Negotiables
-
-- Never invent numbers, quotes, guidance, definitions, accounting facts, estimate timestamps, source tags, or catalyst dates.
-- Use official filings/releases before decks and transcripts; transcripts support narrative and call-only guidance, not primary GAAP numbers.
-- Every reported number and every quote needs a source tag; analyst-derived numbers need formula/assumption and confidence.
-- Full deep dives must include transcript evidence and a debate-map treatment when transcript evidence is available. If no transcript is available, show a concise visible limitation labelled `transcript not provided` or `transcript source not found` and list the exact missing artifact; do not render an empty Q&A table.
-- For transcript Q&A, capture questioner name, firm if available, answering executive, topic, section, source tag, why it matters, bull/bear implication, and falsifier/next check.
-- Keep GAAP/non-GAAP, reported/constant-currency, company-guided/analyst-derived, units/scale, and unavailable-data labels explicit.
-- Always run an EPS quality screen: ask whether headline EPS could misstate recurring operating performance. Include a full EPS quality / ex-gain bridge when GAAP EPS surprise is distorted by below-the-line, tax, mark-to-market, equity-investment, FX, restructuring, litigation, asset-sale, impairment, share-count, or other non-recurring items; otherwise state that no material EPS-quality trigger was identified from available sources.
-- Full deep dives must include quarterly key metrics and growth trajectory using the issuer's actual business drivers, not only generic revenue/EPS/margin. Use sector-context-overlay when the company-specific KPI set is not obvious.
-- For HTML reports or standardized dashboard handoffs, include earnings visualizations when source-backed data exists: quarterly revenue, gross profit, net income, and the best source-backed profitability margin history; estimated EPS versus actual EPS for the past five quarters on a consistent basis; and equity-price history annotated with material market events. Omit any chart whose required series is missing, stale, or not comparable, and surface that gap clearly.
-- Treat margin selection as an analytical decision, not a template default. Default to net margin only when net income is a fair recurring-profitability proxy. Prefer operating margin when net income is distorted by below-the-line, tax, mark-to-market, equity-investment, FX, restructuring, litigation, asset-sale, impairment, or other non-recurring items. Prefer adjusted operating margin, EBITDA margin, contribution margin, or FCF margin when that is the issuer's source-backed investor KPI. For dashboard payloads, set `financial_trend_chart.data.margin_metric`, `margin_label`, and `margin_rationale` whenever the line is not plain net margin.
-- Rank highlight/snapshot metrics by investor salience. A growth rate, acceleration, surprise %, guide delta, backlog growth, margin inflection, or clean/normalized metric should be the tile value when it better explains the stock-moving point than the absolute reported amount; put the absolute amount in the detail.
-- Full deep dives must include read-throughs when the print, filing, transcript, or management interviews mention customers, suppliers, peers, competitors, platforms, channels, commodities, regions, or adjacent industries.
-- Full deep dives must include major news coverage and market events when recent or upcoming events change the interpretation of the print, guidance, estimate revisions, multiple, risk, positioning, or read-throughs. Scan the last quarter, last twelve months, and forward-looking anticipated events; cite every event and label uncertain windows.
-- Capture catalysts learned from the release, filing, transcript, Q&A, and management interviews. Separate dated catalysts from inferred monitoring windows.
-- Use precise absence labels: `not guided`, `not disclosed`, `not provided`, `source not provided`, or `MISSING: <dependency>` only where appropriate.
-- Generated Markdown support notes must not contain unresolved bracket tokens, `TODO`, or authoring placeholders.
-
-## Chat Contract
-
-Default sections for full deep dive: setup/source posture, dense executive summary, PM bottom line, granular beat/miss or guide-versus-bar, EPS quality screen, quarterly key metrics, growth trajectory, guidance delta/deep dive, what changed, revision/stock setup, load-bearing drivers, transcript quote/Q&A and debate map, read-throughs, major news and market events, model/thesis impact, catalysts/watch list/falsifiers, source limitations, and open questions. For investor-facing prompts add thesis change, likely estimate revision, stock/valuation skew, and next catalyst.
-
-Use the evidence pack that supports the selected artifact without shrinking the user-facing analysis:
-
-- Full deep dive: release/filing, deck/prepared remarks, transcript, estimates, and prior-quarter/prior-guide sources where available.
-- Explicit summary or one-page tear sheet: release or 8-K, deck if available, estimate source, and transcript only for high-signal quotes.
-- Audit-ready model update: release/filing/deck, estimate set, prior guide, and model/workbook or normalized driver inputs supplied or referenced by the user.
-- Standalone quote/debate: transcript plus release/deck to cross-check numeric claims.
-
-## HTML Guidance
-
-For a substantive HTML deep dive, load `../../shared/html-artifact-standard.md` and let the company-specific investment debate determine the layout.
-
-- Title the artifact as a post-earnings deep dive identifying the company, ticker, and reported period.
-- Start with a direct verdict answering the investor's question, 4-6 high-signal metric tiles, one compact decision box, and a quality-of-print bridge separating headline results from recurring operating evidence.
-- Use only distinct decision-relevant tiles. Prefer 4-5 tiles when additional metrics repeat the same analytical point; combine related buyback, leverage, interest-expense, or cash-flow signals into one capital-allocation-quality tile when that improves readability.
-- Put supporting analysis below that first read: beat/miss and guidance, EPS quality, company-specific growth drivers, capital allocation, valuation/stock setup, catalysts, falsifiers, source limitations, and evidence ledger as relevant.
-- Give each first-read element a distinct job: the verdict answers the investment question and explains why; the decision box states thesis change, estimate direction, valuation/stock skew, action discipline, and next proof point; metric tiles show evidence rather than restating the verdict; the quality-of-print bridge reconciles headline results to recurring equity value. Do not repeat the same conclusion across these elements.
-- Describe evidence posture in reader-facing terms by naming sources obtained and important confirmations still missing, for example `Company release reviewed; filing and transcript confirmation pending`. Avoid internal-sounding quality labels such as `research-grade` in the visible artifact.
-- Include transcript Q&A, read-throughs, market-events tables, scenario sections, and charts only when substantive and evidence-supported. A missing transcript should be a concise limitation callout, not an empty table.
-- Do not render blank scenario cards, placeholder modules, or visible source cells marked unsourced when the claim has a cited source.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: release and filing numbers, transcript/Q&A, estimates and guidance, model/thesis impact, and source QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Deterministic Contract
-
-Use only when requested or file/model inputs are supplied:
-
-- `scripts/validate_plan.py`
-- `scripts/validate_normalized_inputs.py`
-- `scripts/run_plan.py`
-- `scripts/apply_model_updates.py`
-- `scripts/model_diff.py`
-- `scripts/verify_tearsheet.py`
-
-If workbook apply fails or is unsafe, deliver a driver update packet and explain the limitation. The bundled plan defaults to packet/dry-run mode and writes outside the skill tree.
-
-## Standardized Dashboard Handoff
-
-Use `dashboard-builder` only when the user explicitly selects the standardized dashboard, reusable dashboard-template, or structured payload-driven rendering path. This skill still owns the analysis and maps it into `references/DASHBOARD_PACK.md`; prefer `layout: "single_page"` with sticky contents for full PM diligence dashboards unless the user explicitly asks for tabs. Ordinary standalone HTML deep dives use the flexible HTML guidance above rather than a fixed module inventory.
-
-## Public Equity PM Judgment Layer
-
-For substantial post-print work, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Default PM question: did the quarter change the thesis, estimates, valuation support, or sizing?
-
-Required PM judgment:
-- Lead with thesis change, estimate revision direction, valuation support, and position action.
-- Bridge headline versus clean result, guide delta, quality of beat/miss, transcript evidence, management credibility, and next falsifier.
-- Separate reported facts, management claims, consensus, market data, model output, assumptions, and PM judgment.
-- For sell-side mode, add rating/target implications and risk-to-rating. For hedge fund mode, add add/trim/cover triggers.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/agents/openai.yaml b/plugins/financial-markets/skills/earnings-deep-dive/agents/openai.yaml
deleted file mode 100644
index 0782851..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Earnings Deep Dive & Summary"
-  short_description: "Full post-earnings deep dives with beat/miss, EPS quality, transcript Q&A, debate map, model impact, and falsifiers."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/artifact_index.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/artifact_index.csv
deleted file mode 100644
index a914560..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/artifact_index.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-ArtifactID,ArtifactType,FilePath,ReceivedTimestamp,AsOfTimestamp,SourceSystem,AuthoritativeFlag,Notes
-PR,PressRelease,MISSING,MISSING,MISSING,MISSING,N,
-10Q,10-Q/10-K,MISSING,MISSING,MISSING,SEC,Y,
-8K,8-K,MISSING,MISSING,MISSING,SEC,Y,
-DECK,EarningsDeck,MISSING,MISSING,MISSING,Company,N,
-TRANSCRIPT,Transcript,MISSING,MISSING,MISSING,MISSING,N,
-ESTIMATES,EstimateSet,MISSING,MISSING,MISSING,MISSING,N,
-MODEL,PriorModelWorkbook,MISSING,MISSING,MISSING,Internal,N,
-DRIVER_REGISTRY,DriverRegistry,MISSING,MISSING,MISSING,Internal,N,
-OUTPUT_REGISTRY,OutputRegistry,MISSING,MISSING,MISSING,Internal,N,
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/changelog.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/changelog.csv
deleted file mode 100644
index 2ab74e1..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/changelog.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-Timestamp,ModelVersionID,ChangedBy,Section,ItemChanged,OldValue,NewValue,Why,SourceTag
-MISSING,MISSING,MISSING,Drivers,REV_GROWTH,MISSING,MISSING,MISSING,MISSING
-MISSING,MISSING,MISSING,Drivers,GM_PCT,MISSING,MISSING,MISSING,MISSING
-MISSING,MISSING,MISSING,Outputs,EPS_FY1,MISSING,MISSING,MISSING,MISSING
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/deep_dive.md b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/deep_dive.md
deleted file mode 100644
index aa70d01..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/deep_dive.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Earnings Deep Dive — {TICKER} {FISCAL_PERIOD}
-
-## 1. Headline results vs expectations
-- Revenue: {reported / not disclosed}; expectation: {consensus / internal / not provided}; source: {SourceTag}
-- EPS: {reported / not disclosed}; expectation: {consensus / internal / not provided}; source: {SourceTag}
-- Margin: {reported / not disclosed}; expectation: {consensus / internal / not provided}; source: {SourceTag}
-
-## 2. EPS quality screen
-- {No material EPS-quality trigger identified / full ex-gain bridge required / source not provided}
-- Bridge if triggered: GAAP diluted EPS -> less non-operating or non-recurring items -> tax/share-count normalization if supported -> estimated operating or recurring EPS -> comparable consensus basis.
-
-## 3. Segment performance
-- {Segment read-through, or `not disclosed` with limitation}
-
-## 4. Margin and opex drivers
-- {Driver summary, or `not disclosed` with limitation}
-
-## 5. Cash flow and balance sheet
-- {CFO / capex / FCF / balance-sheet read-through, or `not disclosed` with limitation}
-
-## 6. Guidance and outlook
-- {Company-guided values, qualitative guidance, or `not guided`}
-
-## 7. Transcript themes + Q&A
-- {Complete quotes and themes, or `transcript not available from source set`}
-
-## 8. Model changes + diff summary
-- {Reported actual / company-guided / analyst-derived / not provided bridge}
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_registry.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_registry.csv
deleted file mode 100644
index 6a49e0d..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_registry.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-DriverID,DriverName,Definition,Units,Cadence,Owner,MappingType,Worksheet,Cell,NamedRange,SourceOfTruth,Notes
-REV_GROWTH,Revenue growth %,MISSING,%,Quarterly,MISSING,NamedRange,MISSING,,REV_GROWTH,Model,MISSING
-GM_PCT,Gross margin %,MISSING,%,Quarterly,MISSING,NamedRange,MISSING,,GM_PCT,Model,MISSING
-OPEX_SBC,Stock-based comp,MISSING,USDm,Quarterly,MISSING,NamedRange,MISSING,,OPEX_SBC,Model,MISSING
-CAPEX,Capex,MISSING,USDm,Quarterly,MISSING,NamedRange,MISSING,,CAPEX,Model,MISSING
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_updates.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_updates.csv
deleted file mode 100644
index 408865d..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/driver_updates.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-DriverID,Period,NewValue,Units,Why,SourceTag,OldValue,Confidence
-REV_GROWTH,MISSING,MISSING,%,MISSING,MISSING,,Med
-GM_PCT,MISSING,MISSING,%,MISSING,MISSING,,Med
-OPEX_SBC,MISSING,MISSING,USDm,MISSING,MISSING,,Low
-CAPEX,MISSING,MISSING,USDm,MISSING,MISSING,,Low
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/executive_overview.md b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/executive_overview.md
deleted file mode 100644
index 71e3d00..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/executive_overview.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Executive Overview — {TICKER} {FISCAL_PERIOD}
-
-## What matters (ranked)
-1. {Most important thesis / estimate revision point}
-2. {Second point}
-3. {Third point}
-4. {Optional additional point}
-5. {Optional additional point}
-6. {Optional additional point}
-7. {Optional additional point}
-8. {Optional additional point}
-9. {Optional additional point}
-10. {Optional additional point}
-
-## Stock move frame (vs expectations)
-{Beat/miss, guide-versus-bar, or `expectation set not provided`}
-
-## Bull / Base / Bear narrative delta
-{What changed, what would falsify each side, and key source limits}
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_estimates.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_estimates.csv
deleted file mode 100644
index abd8c85..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_estimates.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-MetricName,Period,EstimateType,Value,Units,AsOf,Source,Notes
-Revenue,MISSING,Consensus,MISSING,USDm,MISSING,MISSING,
-Revenue,MISSING,Internal,MISSING,USDm,MISSING,MISSING,
-GAAP EPS (Diluted),MISSING,Consensus,MISSING,USD,MISSING,MISSING,
-GAAP EPS (Diluted),MISSING,Internal,MISSING,USD,MISSING,MISSING,
-Operating Income,MISSING,Consensus,MISSING,USDm,MISSING,MISSING,
-Operating Income,MISSING,Internal,MISSING,USDm,MISSING,MISSING,
-Adjusted EBITDA,MISSING,Consensus,MISSING,USDm,MISSING,MISSING,
-Adjusted EBITDA,MISSING,Internal,MISSING,USDm,MISSING,MISSING
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_guidance.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_guidance.csv
deleted file mode 100644
index 15863a3..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_guidance.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-MetricName,Period,Low,High,Units,GAAP_Flag,Assumptions,SourceTag,Notes
-Revenue,MISSING,MISSING,MISSING,USDm,GAAP,MISSING,MISSING,
-GAAP EPS (Diluted),MISSING,MISSING,MISSING,USD,GAAP,MISSING,MISSING,
-Adjusted EPS,MISSING,MISSING,MISSING,USD,Non-GAAP,MISSING,MISSING,MISSING
-Operating Income,MISSING,MISSING,MISSING,USDm,GAAP,MISSING,MISSING,
-Adjusted EBITDA,MISSING,MISSING,MISSING,USDm,Non-GAAP,MISSING,MISSING,MISSING
-Key KPI (example),MISSING,MISSING,MISSING,MISSING,NA,MISSING,MISSING,
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_metrics.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_metrics.csv
deleted file mode 100644
index 96d93c1..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_metrics.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-MetricName,Period,Value,Units,GAAP_Flag,Segment,IsTearSheet,DisplayOrder,SourceTag,ComparableGAAPMetricName,ReconciliationSourceTag,Notes
-Revenue,MISSING,MISSING,USDm,GAAP,Total,Y,1,MISSING,,,
-GAAP EPS (Diluted),MISSING,MISSING,USD,GAAP,Total,Y,2,MISSING,,,
-Operating Income,MISSING,MISSING,USDm,GAAP,Total,Y,3,MISSING,,,
-Adjusted EBITDA,MISSING,MISSING,USDm,Non-GAAP,Total,Y,4,MISSING,Operating Income,MISSING,MISSING
-Key KPI (example),MISSING,MISSING,MISSING,NA,Total,Y,5,MISSING,,,
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_quotes.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_quotes.csv
deleted file mode 100644
index fb807e3..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/normalized_quotes.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-Section,Speaker,Questioner,TopicTag,QuoteText,SourceTag
-Prepared,MISSING,,Strategy,MISSING,MISSING
-Q&A,MISSING,"Analyst (Firm)",Guidance,MISSING,MISSING
-Q&A,MISSING,"Analyst (Firm)",Demand,MISSING,MISSING
-Q&A,MISSING,"Analyst (Firm)",Margins,MISSING,MISSING
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/output_registry.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/output_registry.csv
deleted file mode 100644
index ee1aef4..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/output_registry.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-OutputID,OutputName,Units,MappingType,Worksheet,Cell,NamedRange,Notes
-REV_NTM,Next-twelve-month revenue,USDm,NamedRange,MISSING,,REV_NTM,MISSING
-EPS_FY1,FY1 EPS,USD,NamedRange,MISSING,,EPS_FY1,MISSING
-FCF_FY1,FY1 FCF,USDm,NamedRange,MISSING,,FCF_FY1,MISSING
-TARGET_PRICE,Target price,USD,NamedRange,MISSING,,TARGET_PRICE,MISSING
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/plan.template.json b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/plan.template.json
deleted file mode 100644
index e808c33..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/plan.template.json
+++ /dev/null
@@ -1,53 +0,0 @@
-{
-  "event": {
-    "ticker": "MISSING",
-    "company_name": "MISSING",
-    "fiscal_period": "MISSING",
-    "event_date": "MISSING",
-    "timezone": "America/New_York",
-    "base_currency": "USD",
-    "base_scale": "m"
-  },
-  "inputs": {
-    "artifact_index_csv": "output/audit/ArtifactIndex.csv",
-    "artifacts": {
-      "press_release": "MISSING",
-      "sec_filing_10q_or_10k": "MISSING",
-      "sec_filing_8k": "MISSING",
-      "deck": "MISSING",
-      "transcript": "MISSING"
-    },
-    "normalized": {
-      "metrics_csv": "normalized/metrics.csv",
-      "estimates_csv": "normalized/estimates.csv",
-      "guidance_csv": "normalized/guidance.csv",
-      "quotes_csv": "normalized/quotes.csv",
-      "driver_updates_csv": "normalized/driver_updates.csv"
-    },
-    "model": {
-      "prior_model_xlsx": "MISSING",
-      "driver_registry_csv": "MISSING",
-      "output_registry_csv": "MISSING"
-    }
-  },
-  "outputs": {
-    "output_dir": "/tmp/public_equity_investing_earnings_deep_dive_output",
-    "render": {
-      "executive_overview": true,
-      "tear_sheet": true,
-      "deep_dive": true
-    },
-    "model_update": {
-      "enabled": false,
-      "mode": "packet",
-      "version_tag_timezone": "America/New_York",
-      "write_diff": false
-    }
-  },
-  "controls": {
-    "allow_call_only_guidance": true,
-    "require_gaap_for_non_gaap": true,
-    "require_source_tags": true,
-    "dry_run": true
-  }
-}
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/tear_sheet.md b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/tear_sheet.md
deleted file mode 100644
index 3b02760..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/tear_sheet.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# {TICKER} — {FISCAL_PERIOD} Earnings Tear Sheet
-
-**Event:** {EVENT_DATETIME} ({TIMEZONE})  
-**Estimate set (as-of):** {ESTIMATE_ASOF}  
-**Sources:** {ARTIFACT_OR_SOURCE_SUMMARY}  
-
-## Bottom line
-{State whether the quarter changes the thesis, estimate revision path, or stock-reaction setup.}
-
-## Beat/Miss vs expectations
-| Metric | Reported | Consensus | Δ | Surprise % | Internal | Δ | SourceTag |
-|---|---:|---:|---:|---:|---:|---:|---|
-| Revenue | {reported / not disclosed} | {consensus / not provided} | {Δ / not provided} | {surprise / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-| GAAP EPS (Diluted) | {reported / not disclosed} | {consensus / not provided} | {Δ / not provided} | {surprise / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-| Op Inc / EBITDA (specify) | {reported / not disclosed} | {consensus / not provided} | {Δ / not provided} | {surprise / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-| Key KPI (specify) | {reported / not disclosed} | {consensus / not provided} | {Δ / not provided} | {surprise / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-
-## EPS quality screen
-| Check | Status | Read-through | SourceTag |
-|---|---|---|---|
-| GAAP EPS versus operating / adjusted EPS | {expanded bridge required / no material trigger / source not provided} | {recurring-earnings implication} | {SourceTag} |
-| Below-the-line, tax, mark-to-market, share-count, or non-recurring items | {identified / not identified / source not provided} | {what to exclude, isolate, or monitor} | {SourceTag} |
-
-## Guidance delta
-| Metric | Period | Guide (Low) | Guide (High) | Mid | Consensus | Δ(mid) | Internal | Δ(mid) | SourceTag |
-|---|---|---:|---:|---:|---:|---:|---:|---:|---|
-| Revenue | {period} | {low / not guided} | {high / not guided} | {mid / not guided} | {consensus / not provided} | {Δ / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-| GAAP EPS (Diluted) | {period} | {low / not guided} | {high / not guided} | {mid / not guided} | {consensus / not provided} | {Δ / not provided} | {internal / not provided} | {Δ / not provided} | {SourceTag} |
-
-## Drivers (2–3 load-bearing; add more only if needed)
-- {Driver 1 — causal, source-supported}
-- {Driver 2}
-- {Driver 3}
-
-## Key quotes (4–8 complete quotes)
-> **[{TopicTag}] {Speaker} ({Section})**: “{Quote}”  
-> Why it matters: {Model/thesis implication}. Source: {SourceTag}
-
-## Model impact
-| Metric | Period | Source type | Value / range | Formula or assumption | Confidence | Source |
-|---|---|---|---:|---|---|---|
-| Revenue | {period} | {reported actual / company-guided / analyst-derived / not provided} | {value} | {formula or assumption} | {confidence} | {SourceTag} |
-| EBITDA / operating-profit proxy | {period} | {company-guided / analyst-derived / not guided} | {value} | {formula or assumption} | {confidence} | {SourceTag} |
-| FCF | {period} | {company-guided / analyst-derived / not guided} | {value} | {formula or assumption} | {confidence} | {SourceTag} |
-
-## Watch list (next quarter)
-1. {Watch item}
-2. {Watch item}
-3. {Watch item}
-4. {Watch item}
-5. {Watch item}
-
-## Source notes / limits
-- {Label web fallback, third-party transcript, missing consensus timestamp, or analyst-derived proxy assumptions.}
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/what_changed_diff.csv b/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/what_changed_diff.csv
deleted file mode 100644
index 48c996c..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/assets/templates/what_changed_diff.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-Rank,Category,Item,Old,New,Delta,Units,PrimaryImpact,SourceTag,Notes
-1,Driver,REV_GROWTH,MISSING,MISSING,MISSING,%,N12m Revenue,MISSING,
-2,Driver,GM_PCT,MISSING,MISSING,MISSING,%,N12m EPS,MISSING,
-3,Output,EPS_FY1,MISSING,MISSING,MISSING,USD,FY1 EPS,MISSING,
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/CHAT_REPORT_CONTRACT.md b/plugins/financial-markets/skills/earnings-deep-dive/references/CHAT_REPORT_CONTRACT.md
deleted file mode 100644
index fa6bdc8..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/CHAT_REPORT_CONTRACT.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Chat Report Contract
-
-Use this reference after `earnings-deep-dive` is selected for the analysis and evidence contract in the already resolved presentation surface. A chat-native response is permitted only when the user explicitly selected a lightweight chat response; otherwise create the polished standalone HTML report before responding and use chat only as its concise cover note. For explicit standardized dashboards, additionally use `DASHBOARD_PACK.md` and `dashboard-builder`; do not make Markdown the final deliverable for substantive reusable artifacts.
-
-## Non-Negotiables
-
-- Never invent numbers, quotes, guidance, definitions, accounting facts, or estimate timestamps.
-- Every reported number and every quote must carry a source tag. Analyst-derived numbers must show formula or assumption.
-- Source precedence: `10-Q/10-K` -> `8-K / Exhibit 99.1` -> `Earnings PR` -> `Deck / Prepared Remarks` -> `Transcript`.
-- Treat transcripts as narrative support, not the primary numeric source; cross-check transcript numbers against authoritative tables when possible.
-- For non-GAAP metrics, show the closest GAAP comparable and reconciliation source.
-- Keep GAAP/non-GAAP, reported/constant-currency, basic/diluted, percent/bps, and company-guided/analyst-derived distinctions explicit.
-- Run an EPS quality screen before interpreting EPS surprise. Show a full ex-gain / recurring EPS bridge when GAAP EPS is distorted by below-the-line, tax, mark-to-market, equity-investment, FX, restructuring, litigation, asset-sale, impairment, share-count, or other non-recurring items.
-- Use precise absence labels: `not guided`, `not disclosed`, `not provided`, `source not provided`, or `MISSING: <dependency>`.
-- Anchor falsifiers to the next real catalyst window. Do not invent arbitrary dates.
-- Do not edit workbooks, send messages, publish, or take external actions unless the user explicitly asks and approves.
-
-## Inputs
-
-Minimum:
-
-- company or ticker;
-- fiscal quarter, period, or enough context to identify the latest reported quarter.
-
-Helpful:
-
-- earnings PR, SEC filing, deck/prepared remarks, transcript;
-- consensus or internal estimates with `AsOf`;
-- prior-quarter tear sheet, prior guide, or prior model;
-- driver registry, output registry, or model-update plan.
-
-If critical information is missing, ask up to 5 targeted questions only when the missing data blocks the core task. Otherwise proceed with available connected/public sources and label source limitations.
-
-## Source Pack
-
-Use the evidence pack that supports the selected artifact without shrinking the user-facing analysis.
-
-- Full deep dive: release or filing, deck/prepared remarks, transcript, estimates, prior-quarter or prior-guide source where available.
-- Explicit summary or one-page tear sheet: release or 8-K, deck if available, estimate source, transcript only for 2-4 high-signal quotes.
-- Audit-ready model update: release or 8-K, deck, prior guidance, estimate set, and user-supplied or referenced model inputs.
-- Standalone quote/debate map: transcript plus release/deck for numeric cross-checks.
-
-If user artifacts are absent, prefer official IR and SEC materials, then reputable transcript/consensus providers, then clearly labeled web fallback. Freeze and disclose the estimate set `AsOf`; if unavailable, say `estimate timestamp not provided`.
-
-## Default Output Contract
-
-Unless the user explicitly asks for a summary/one-pager/brief/quick read or a narrower artifact, default to a full deep dive in the selected presentation surface. Explicit full or reusable/source-heavy post-earnings reports default to polished standalone HTML under the skill's intake rule. Every full deep dive should include:
-
-- setup: ticker, quarter, event timing if known, artifacts used, estimate set as-of or limitation;
-- bottom line on whether the quarter changes the investment case;
-- beat/miss or guide-versus-bar analysis when estimates are available;
-- EPS quality screen, expanded into an ex-gain / recurring EPS bridge when headline EPS is materially distorted or the estimate basis is mismatched;
-- prior-versus-new guidance or narrative change when relevant;
-- 2 to 3 load-bearing drivers;
-- transcript quote/Q&A map, when transcript evidence is available, with questioner name, firm if available, answering executive, topic, source tag, why it matters, bull/bear implication, and next-check/falsifier;
-- debate map that separates bull case, bear case, what changed, and what would falsify each side;
-- model or thesis impact, including stock-reaction or valuation relevance when decision-useful;
-- watch list or falsifiers with measurable items where possible;
-- source limitations, open questions, and confidence notes.
-
-If the transcript is unavailable, include a concise visible evidence limitation labelled `transcript not provided` or `transcript source not found` and name the missing artifact. Do not create an empty Q&A table or shorten the rest of the report solely because the transcript is missing.
-
-Use a one-page tear sheet only when the user explicitly asks for a summary, one-pager, quick read, brief, or TL;DR.
-
-Use audit-ready model update mode only when the user supplies or references a model/workbook, driver registry, output registry, normalized CSVs, model-update inputs, or explicit data to update the model. A normal full deep dive may include model/thesis impact, but should not imply a deterministic model update happened without model inputs.
-
-For investor-facing prompts, include a compact decision box:
-
-- `Thesis change`: strengthened, weakened, unchanged, or mixed;
-- `Likely estimate revision`: revenue, EPS/margin, FCF, and timing;
-- `Stock / valuation skew`: upside/downside, multiple support, or risk premium;
-- `Next catalyst`: next earnings, guidance update, investor day, KPI event, or not found.
-
-## Final QA
-
-Before finishing, verify:
-
-- every number and quote has a source tag;
-- estimate set `AsOf` is shown or explicitly unavailable;
-- EPS surprise has been screened for non-recurring, below-the-line, tax, mark-to-market, share-count, or estimate-basis distortion;
-- guidance period, midpoint, and GAAP/non-GAAP basis are clear;
-- unavailable values use precise labels, not avoidable `MISSING`;
-- company guidance is separate from analyst-derived assumptions;
-- surprise percentages are not computed against zero or near-zero estimates;
-- transcript points, when available, identify questioner name, firm if available, answering executive, speaker, and section;
-- a full deep dive includes substantiated transcript/Q&A evidence or a precise transcript-absence callout, plus a debate-map treatment;
-- falsifiers/watch-list items are tied to a real catalyst or reporting cadence;
-- output length matches the requested mode.
-
-## PM Bottom Line Contract
-
-Every substantial post-print report should say whether the quarter changed the business thesis, stock thesis, estimate path, valuation support, or sizing. Include quality-of-beat/miss, revision bridge, next catalyst, source caveats, and action discipline.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/earnings-deep-dive/references/DASHBOARD_PACK.md
deleted file mode 100644
index a503240..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,105 +0,0 @@
-# Earnings Deep Dive Dashboard Pack
-
-Use this only when a post-earnings request explicitly selects a standardized dashboard, reusable dashboard template, PM cockpit, tabbed dashboard, or structured payload-driven render. For an ordinary standalone HTML deep dive or full report, use the flexible HTML standard instead of this fixed module map.
-
-## Producer Role
-
-`earnings-deep-dive` owns the post-print analysis. `dashboard-builder` owns the shared HTML shell, module rendering, and responsive QA.
-
-## Recommended Payload
-
-- `mode`: `earnings_deep_dive`
-- `layout`: `single_page` for full PM diligence dashboards unless the user explicitly requests tabs
-- `hero.callout`: whether the quarter changed the investment case
-- `snapshot`: 4-8 PM-selective highlights such as revenue surprise %, clean EPS beat, segment growth surprise, guide change, backlog, margin inflection, capex guide, stock/valuation skew, or next catalyst. Do not use a `Quarter` tile when the quarter is in the hero. When a growth rate or surprise is the investor signal, make it the tile value and put the absolute amount in the detail.
-- `financial_trend_chart`: include when the last several reported quarters have source-backed revenue, gross profit, net income, and a comparable profitability margin. Use net margin only when it is a good recurring-profitability proxy; otherwise set `margin_metric`, `margin_label`, and `margin_rationale` for operating margin, adjusted operating margin, EBITDA margin, FCF margin, or the issuer's source-backed KPI.
-- `eps_actual_vs_estimate_chart`: include when the past five quarters have estimated EPS and actual EPS on the same GAAP/adjusted/clean basis. Use fewer quarters only when the module title states the available window.
-- `equity_price_event_chart`: include only when equity-price history is source-backed and substantive: daily tapes need at least 10 distinct points over at least 7 calendar days, hourly tapes need at least 24 time-stamped points over at least 6 hours, and minute/intraday tapes need at least 60 time-stamped points over at least 45 minutes. If the available post-print tape is only a few closes or article-reported prices, omit the chart and use `market_events` plus `missing_evidence`.
-- `market_events`: source-backed recent news, market events, regulatory/macro items, peer moves, management interviews, and upcoming catalysts that changed or could change the post-print read.
-- `sources`: release/filing first, deck/prepared remarks next, transcript for narrative, consensus/market data with `as_of`
-
-## Tabs And Modules
-
-1. `verdict`
-   - `executive_summary`: dense PM paragraph with clean print, quality of beat, guide, call-only datapoints, contradictions, watch items, and net read
-   - `decision_box`: PM bottom line, thesis change, estimate revision, stock skew, next catalyst
-   - `cards`: what changed, what matters, and what remains unresolved
-2. `beat-miss`
-   - `table`: granular reported vs consensus/internal estimates, actual, variance, surprise %, source, basis, and PM read. Include segment/margin/constant-currency metrics where relevant, not only revenue and EPS.
-3. `eps-quality`
-   - `table`: GAAP/adjusted/operating/recurring EPS screen, below-the-line items, tax/share-count/one-time items, and clean EPS beat/miss where derivable
-4. `quarterly-metrics`
-   - `financial_trend_chart`: revenue, gross profit, net income, and the selected profitability margin by quarter when complete comparable data exists. Use operating margin or another explicitly labeled metric when net margin is distorted by below-the-line gains/losses, tax, FX, equity-investment marks, restructuring, impairments, litigation, asset sales, or other one-time items.
-   - `eps_actual_vs_estimate_chart`: estimated EPS versus actual EPS for the past five quarters when source-backed estimate history exists
-   - `key_metrics`: company-specific quarterly key metrics with absolute values, growth, consensus/guide where available, trajectory, and PM read
-   - `growth_trajectory`: acceleration/deceleration across the issuer's load-bearing metrics
-   - `bar_chart`: compact chart for segment growth, revenue mix, margin trajectory, or another small comparable metric set when the visual speeds interpretation
-5. `guidance`
-   - `table`: deep guidance bridge with prior view, new frame, vs prior, consensus if sourced, read, and source. Include qualitative guidance when the company does not provide revenue/EPS guide.
-   - `cards`: guide credibility, cadence, and contradictions
-6. `drivers`
-   - `cards`: 2-3 load-bearing drivers and segment/KPI read-throughs
-7. `transcript-map` (only when transcript evidence is available)
-   - `transcript_qa`: analyst, firm, topic, management response, short direct quote where available, quality of response, and PM implication
-   - `cards`: high-signal call points, contradictions, and quote context within citation limits
-8. `read-throughs` (only when substantive read-through evidence is available)
-   - `read_throughs`: names, relationship to issuer, what management said, read-through, implication, and confidence
-9. `market-events` (only when substantive cited events are available)
-   - `equity_price_event_chart`: equity-price history annotated with material events only when the price tape passes the daily/hourly/minute minimums above
-   - `market_events`: last-quarter, last-twelve-month, and forward-looking company/sector/macro/regulatory events with source, impact, investor read, and confidence
-   - `cards`: contradictions between the event tape and management's post-print narrative when relevant
-10. `model-impact`
-   - `table`: revenue, margin, EPS, FCF, valuation, and thesis impact
-11. `watch-list`
-   - `timeline`: next catalysts and falsifier cadence
-   - `missing_evidence`: source gaps and model inputs still needed
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every material number, reported metric, estimate, guide field, quote, event date, market move, KPI, and assumption inline where it appears.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-- Keep the top-level source ledger mandatory; label missing transcript, consensus, price, peer, model, and market data in `missing_evidence`.
-- Keep raw JSON, Markdown notes, CSV exports, and run logs as support/audit material unless the user explicitly asks for them.
-
-## Do Not
-
-- Do not use transcript claims as primary numeric evidence when release/filing tables are available.
-- Do not hide EPS-quality issues behind a headline beat/miss.
-- Do not chart net margin as the default line when the EPS-quality screen shows a material distortion. Pick the margin that best reflects recurring operating performance and state the rationale.
-- Do not populate highlights mechanically from reported absolute values when a surprise %, growth rate, guide change, margin inflection, or normalized number better captures what matters.
-- Do not summarize recent news mechanically. Include only events that affect the quarter interpretation, guidance credibility, estimate revision, multiple, risk, read-throughs, or next-catalyst path.
-- Do not include uncited event claims. Prefer filings/releases/transcripts for company events; use reputable news/market sources for external events and label source posture.
-- Do not populate chart shells with blanks. If revenue/gross profit/net income/selected margin, EPS estimate/actual history, or price/event data is missing, omit the relevant chart and add a precise `missing_evidence` item.
-- Do not render empty scenario cards, transcript tables, event tables, or other blank modules. Use a concise `missing_evidence` item when the absence matters.
-- Do not display `Not sourced` for a market event whose citation resolves to the source ledger; carry its cited source through to the visible event row.
-- Do not waste dashboard space. Favor dense tables, compact chart/table pairings, and short PM reads over airy narrative blocks.
-- Do not overfit KPI modules to one company. Use sector and business-model context to select the issuer's own drivers.
-- Do not invent transcript questions, speakers, firms, or quotes.
-- Do not use a company logo as a hard dependency; prefer ticker identity tile.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the verdict, beat/miss, EPS quality, guide bridge, source ledger, and missing evidence are visible; confirm transcript maps, read-throughs, and market events only when populated and source-supported.
-- Confirm no chart shell renders without complete, comparable source-backed data.
-- Confirm no blank scenario card or empty optional module renders and cited market events display their source.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/EXAMPLES.md b/plugins/financial-markets/skills/earnings-deep-dive/references/EXAMPLES.md
deleted file mode 100644
index 9f066c0..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/EXAMPLES.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Worked Examples (Templates filled with placeholders)
-
-## Table of contents
-1. [Example plan.json](#example-planjson)
-2. [Example normalized rows](#example-normalized-rows)
-3. [Example tear sheet](#example-tear-sheet)
-4. [Example changelog + diff](#example-changelog--diff)
-
----
-
-## Example plan.json
-```json
-{
-  "event": {
-    "ticker": "MISSING",
-    "company_name": "MISSING",
-    "fiscal_period": "FY2026Q2",
-    "event_date": "MISSING",
-    "timezone": "America/New_York",
-    "base_currency": "USD",
-    "base_scale": "m"
-  },
-  "inputs": {
-    "artifact_index_csv": "output/audit/ArtifactIndex.csv",
-    "artifacts": {
-      "press_release": "01_Raw/earnings_pr.pdf",
-      "sec_filing_10q_or_10k": "01_Raw/10q.pdf",
-      "sec_filing_8k": "01_Raw/8k.pdf",
-      "deck": "01_Raw/deck.pdf",
-      "transcript": "01_Raw/transcript.txt"
-    },
-    "normalized": {
-      "metrics_csv": "normalized/metrics.csv",
-      "estimates_csv": "normalized/estimates.csv",
-      "guidance_csv": "normalized/guidance.csv",
-      "quotes_csv": "normalized/quotes.csv",
-      "driver_updates_csv": "normalized/driver_updates.csv"
-    },
-    "model": {
-      "prior_model_xlsx": "02_Model/PriorModel.xlsx",
-      "driver_registry_csv": "02_Model/driver_registry.csv",
-      "output_registry_csv": "02_Model/output_registry.csv"
-    }
-  },
-  "outputs": {
-    "output_dir": "output",
-    "render": {
-      "executive_overview": true,
-      "tear_sheet": true,
-      "deep_dive": true
-    },
-    "model_update": {
-      "enabled": true,
-      "mode": "apply",
-      "write_diff": true
-    }
-  },
-  "controls": {
-    "allow_call_only_guidance": true,
-    "require_gaap_for_non_gaap": true,
-    "require_source_tags": true,
-    "dry_run": false
-  }
-}
-```
-
-## Example normalized rows
-
-### metrics.csv
-```csv
-MetricName,Period,Value,Units,GAAP_Flag,Segment,IsTearSheet,DisplayOrder,SourceTag,ComparableGAAPMetricName,ReconciliationSourceTag,Notes
-Revenue,FY2026Q2,MISSING,USDm,GAAP,Total,Y,1,"10-Q | p.MISSING, Consolidated Statements of Operations | Revenue",,,
-GAAP EPS (Diluted),FY2026Q2,MISSING,USD,GAAP,Total,Y,2,"10-Q | p.MISSING, Consolidated Statements of Operations | EPS",,,
-Adjusted EBITDA,FY2026Q2,MISSING,USDm,Non-GAAP,Total,Y,3,"Earnings PR (Exhibit 99.1) | p.MISSING, Table MISSING | Adj EBITDA","Operating income","Earnings PR (Exhibit 99.1) | p.MISSING, Non-GAAP reconciliation | Adj EBITDA",MISSING
-```
-
-### quotes.csv
-```csv
-Section,Speaker,Questioner,TopicTag,QuoteText,SourceTag
-Q&A,CFO,"Analyst (Firm)",Guidance,"MISSING",MISSING 
-```
-
-## Example tear sheet
-See template: `assets/templates/tear_sheet.md`.
-
-## Example changelog + diff
-See templates:
-- `assets/templates/changelog.csv`
-- `assets/templates/what_changed_diff.csv`
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/INDEX.md b/plugins/financial-markets/skills/earnings-deep-dive/references/INDEX.md
deleted file mode 100644
index fd3c265..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/INDEX.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Reference Index (Earnings Deep Dive & Summary)
-
-- [PLAYBOOK.md](./PLAYBOOK.md)
-- [REFERENCE_ROUTER.md](./REFERENCE_ROUTER.md)
-- [OUTPUT_MODES.md](./OUTPUT_MODES.md)
-- [SCHEMAS.md](./SCHEMAS.md)
-- [SOURCE_TAGGING.md](./SOURCE_TAGGING.md)
-- [METRIC_CATALOG.md](./METRIC_CATALOG.md)
-- [QUALITY_CHECKS.md](./QUALITY_CHECKS.md)
-- [MODEL_DIFF_GUIDE.md](./MODEL_DIFF_GUIDE.md)
-- [SCRIPT_QUICKSTART.md](./SCRIPT_QUICKSTART.md)
-- [EXAMPLES.md](./EXAMPLES.md)
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/METRIC_CATALOG.md b/plugins/financial-markets/skills/earnings-deep-dive/references/METRIC_CATALOG.md
deleted file mode 100644
index 939032f..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/METRIC_CATALOG.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# KPI + Metric Catalog (with sector adaptations)
-
-## Table of contents
-1. [Universal headline set](#universal-headline-set)
-2. [Revenue drivers](#revenue-drivers)
-3. [Margin drivers](#margin-drivers)
-4. [Opex drivers](#opex-drivers)
-5. [Working capital + cash](#working-capital--cash)
-6. [Sector adaptations](#sector-adaptations)
-
----
-
-## Universal headline set
-Include (GAAP unless specified):
-- Revenue
-- Gross margin (or gross profit + revenue)
-- Operating income/margin
-- Net income
-- Diluted shares, GAAP EPS, adjusted/operating EPS where provided, and EPS basis
-- CFO, Capex, FCF
-- Key balance sheet items (cash, debt, inventory, A/R, A/P) if disclosed
-
-## Revenue drivers
-Use what exists in sources; do not fabricate.
-
-Common decomposition patterns:
-- **Price × Volume** (units + ASP)
-- **Traffic × Ticket** (retail)
-- **Seats × ARPU** (subscription)
-- **Subscribers × ARPU** (consumer subs)
-- **Backlog × conversion** (industrial)
-
-Common supporting KPIs:
-- Orders / bookings / billings
-- Backlog and backlog growth
-- RPO / cRPO (SaaS)
-- Net adds, churn, retention (SaaS)
-- Same-store sales / comps (retail)
-
-## Margin drivers
-Possible bridge drivers (use only if supported by evidence):
-- Mix (segment/product/customer)
-- Pricing/promo
-- Input costs (commodities, freight)
-- Utilization/throughput/efficiency
-- FX
-- One-time items (restructuring, inventory charges)
-
-## Opex drivers
-- Headcount and headcount growth
-- SBC (stock-based compensation)
-- Marketing efficiency (CAC, CAC payback, S&M as % of revenue)
-- R&D intensity
-- G&A run-rate vs one-time
-
-## EPS quality and below-the-line
-
-Use for post-print EPS interpretation. Do not capitalize a reported EPS beat as recurring unless the bridge supports it.
-
-Check:
-- GAAP EPS versus adjusted or operating EPS, including definition and reconciliation source.
-- Operating income, segment profit, FCF, and revenue surprise versus EPS surprise.
-- Below-the-line items: interest, other income/expense, equity-investment marks, FX, pension, litigation, asset sales, impairments, restructuring, and tax rate.
-- Diluted share-count changes, buybacks, converts, or SBC dilution.
-- Consensus basis: GAAP, adjusted, operating, continuing operations, or provider-standardized.
-
-Output only the full bridge when distortion is material or uncertain. Otherwise include a concise note that no material EPS-quality trigger was identified from available sources.
-
-## Working capital + cash
-Track both $ and days (if inputs exist):
-- DSO, DPO, DIO
-- Deferred revenue (SaaS)
-- Inventory turns
-- Capex plans (maintenance vs growth if disclosed)
-- FCF conversion (FCF / net income)
-
-## Sector adaptations
-
-### SaaS / subscription
-Primary KPIs:
-- ARR, net new ARR
-- RPO/cRPO
-- Net revenue retention (NRR), gross retention (GRR)
-- Churn (logo/revenue)
-- Seats, customers, ARPU
-- Billings/bookings
-- FCF margin and rule-of-40 components
-
-### Semis / hardware
-Primary KPIs:
-- Units shipped, ASP
-- Backlog, book-to-bill
-- Channel inventory, days of inventory
-- Utilization, capacity, wafer starts
-- Gross margin guide drivers
-
-### Banks / financials
-Primary KPIs:
-- Net interest income (NII), net interest margin (NIM)
-- Loan growth, deposit growth, deposit betas
-- Provision for credit losses (PCL)
-- Net charge-offs, delinquencies
-- CET1 / capital ratios
-- Efficiency ratio
-
-### Retail / consumer
-Primary KPIs:
-- Comparable sales (traffic × ticket)
-- Gross margin drivers: promo, shrink, freight
-- Inventory and markdown cadence
-- Loyalty metrics (if provided)
-
-### Industrial / services
-Primary KPIs:
-- Orders, backlog, book-to-bill
-- Utilization, labor rates
-- Mix by end market
-- Pricing realization
-
-### Internet / advertising
-Primary KPIs:
-- DAU/MAU (by region if provided)
-- ARPU (by region)
-- Ad impressions, pricing (CPM), engagement metrics (time spent)
-- Headcount and opex leverage
-
-### Payments / fintech
-Primary KPIs:
-- TPV / GPV, transactions
-- Take rate / net revenue yield
-- Active accounts / merchants
-- Loss rates, credit metrics (if applicable)
-
-### Insurance
-Primary KPIs:
-- Premiums written/earned
-- Loss ratio, expense ratio, combined ratio
-- PIF / policies in force
-- Catastrophe loss disclosures
-
-### Energy / upstream
-Primary KPIs:
-- Production volume (oil/gas/NGL)
-- Realized prices and hedges
-- LOE per unit, finding & development cost
-- Capex plans and return of capital
-
-### Airlines / travel
-Primary KPIs:
-- RASM / PRASM, CASM
-- Load factor, capacity (ASMs), demand (RPMs)
-- Unit revenue and fuel cost drivers
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/MODEL_DIFF_GUIDE.md b/plugins/financial-markets/skills/earnings-deep-dive/references/MODEL_DIFF_GUIDE.md
deleted file mode 100644
index 791b1f1..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/MODEL_DIFF_GUIDE.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Model Diff Methodology (Signal, not cell noise)
-
-## Table of contents
-1. [Goals](#goals)
-2. [What to diff](#what-to-diff)
-3. [How to rank changes](#how-to-rank-changes)
-4. [Workbook diff strategy](#workbook-diff-strategy)
-5. [Using the script](#using-the-script)
-
----
-
-## Goals
-The diff exists to answer:
-- **What changed in the model?** (inputs and key outputs)
-- **Why did it change?** (source-tagged rationale)
-- **Which changes matter most for near-term valuation KPIs?**
-
-## What to diff
-1) **Driver inputs** (from `driver_registry.csv`)
-2) **Key outputs** (from `output_registry.csv`)
-3) Optional: scenario switches and key assumptions (WACC, terminal growth, etc.) if your model has them as drivers.
-
-Do NOT diff:
-- Formatting, column widths, styles
-- Intermediate calculation blocks unless they are decision-critical
-
-## How to rank changes
-Rank by estimated impact priority. In absence of a full valuation engine:
-1. Next-quarter revenue/EPS/FCF changes
-2. FY revenue/EPS/FCF changes
-3. Margin and opex run-rate assumptions
-4. Working capital and capex assumptions
-
-## Workbook diff strategy
-- Prefer **named ranges** for stability.
-- If using cells, diff only the explicit cell map in the registry (not the whole sheet).
-- Treat blanks carefully:
-  - blank vs 0 can be meaningful; retain both
-  - if a cell contains a formula, diff the evaluated value and optionally the formula string (advanced)
-
-## Using the script
-Run:
-
-```bash
-python scripts/model_diff.py \
-  --old <prior_model.xlsx> \
-  --new <updated_model.xlsx> \
-  --driver-registry <driver_registry.csv> \
-  --output-registry <output_registry.csv> \
-  --out output/audit/WhatChanged_Diff.csv
-```
-
-The script outputs:
-- `Category` = Driver or Output
-- Old/New/Delta
-- Rank (simple heuristic)
-
-Then add `SourceTag` and `Why` where missing.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/OUTPUT_MODES.md b/plugins/financial-markets/skills/earnings-deep-dive/references/OUTPUT_MODES.md
deleted file mode 100644
index 19b34f5..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/OUTPUT_MODES.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Output Modes
-
-## Deterministic File Mode
-
-Use when the user supplies local `plan.json`, normalized CSVs, or asks to run scripts.
-
-- Run `validate_plan.py` and `validate_normalized_inputs.py` before `run_plan.py`.
-- Treat generated Markdown as a support note or renderer input unless the user explicitly requests Markdown. Unresolved bracket tokens, `TODO`, or authoring placeholders are hard failures.
-- `MISSING` is allowed in input templates/staging files; generated reports should prefer `not guided`, `not disclosed`, `not provided`, or `source not provided`.
-- Disclose whether model update ran as `packet` or workbook `apply`. Fall back to packet if apply is unsafe.
-
-## One-Page Tear Sheet
-
-Use only when the user explicitly asks for a summary, one-pager, quick read, brief, or TL;DR. Target 700-1,100 words unless asked otherwise. Order: setup, PM bottom line, beat/miss, guidance delta, 2-3 drivers, high-signal quotes, model/thesis impact, watch list/source limits. Include revision/stock skew for investment-decision prompts.
-
-## Full Deep Dive
-
-This is the default analytical post-print deliverable. Include setup/source posture, executive summary, what changed, revision/stock setup, beat/miss, EPS quality screen, guidance delta, drivers, transcript evidence and debate map when available, model impact, watch list, falsifiers/next checks, and missing data/open questions. Move raw source detail to a compact appendix. For an explicit deep dive, full report, or reusable/source-heavy artifact, render a polished standalone HTML report following `../../../shared/html-artifact-standard.md` rather than treating Markdown as the final file.
-
-```markdown
-## Setup
-Ticker/quarter/event; sources used; estimate set as-of; next catalyst.
-
-## Executive summary
-- ...
-
-## What changed / PM bottom line
-| Item | Prior/bar | New | Implication |
-|---|---|---|---|
-
-## Revision / stock setup
-| Lens | Read-through |
-|---|---|
-| Thesis change | |
-| Likely estimate revision | |
-| Stock / valuation skew | |
-
-## Beat / miss vs expectations
-| Metric | Reported | Consensus | Delta | Surprise % | Internal | Delta | Source |
-|---|---:|---:|---:|---:|---:|---:|---|
-
-## EPS quality screen
-| Check | Status | Read-through | Source |
-|---|---|---|---|
-| GAAP EPS vs operating / adjusted EPS | expanded bridge required / no material trigger / source not provided | | |
-| Below-the-line, tax, mark-to-market, share-count, or non-recurring items | | | |
-
-Expand this into a full ex-gain / recurring EPS bridge when triggered. If no trigger is identified, state the conclusion and keep moving.
-
-## Guidance delta
-| Metric | Period | Low | High | Mid | Consensus | Delta(mid) | Internal | Delta(mid) | Source |
-|---|---|---:|---:|---:|---:|---:|---:|---:|---|
-
-## Transcript quote / Q&A map
-| Topic | Questioner | Firm | Answering executive | Section | Quote / paraphrase | Why it matters | Bull / bear implication | Falsifier / next check | Source |
-|---|---|---|---|---|---|---|---|---|---|
-
-If no transcript is available, replace this table with a concise evidence-gap callout labelled `transcript not provided` or `transcript source not found`; do not invent questions, speakers, firms, or quotes.
-
-## Debate map
-Bull case, bear case, what changed this quarter, and falsifiers tied to the next catalyst.
-
-## Drivers, model impact, watch list, falsifiers, missing data
-```
-
-## Audit-Ready Model Update
-
-Use only when the user supplies or references a model/workbook, driver registry, output registry, normalized CSVs, model-update inputs, or explicit data to update a model. Separate reported actual, company-guided, analyst-derived, and not-provided lines. Show formula/assumption and confidence for analyst-derived proxies. Prioritize next-quarter/full-year revenue, margin/profit, EPS, and FCF. Separate GAAP EPS, adjusted/operating EPS, and recurring EPS drivers before updating forward EPS estimates. If EBITDA is derived from operating-profit guidance, show the operating-profit anchor first.
-
-## HTML Full Deep Dive
-
-Use flexible standalone HTML for explicit full post-earnings deep dives and reusable/source-heavy post-print reports. The opening view should contain the verdict, 4-6 decision-relevant metrics, one decision box, and the bridge from reported print to recurring investment evidence. Add transcript, scenario, read-through, market-event, or chart sections only when their contents are substantive and source-supported; omit empty modules.
-
-## Standardized Dashboard
-
-Use `DASHBOARD_PACK.md` and `dashboard-builder` only when the user explicitly requests the standardized dashboard, a reusable dashboard template, or a structured payload-driven render.
-
-## Quote and Debate Map
-
-Standalone mode is only for quote/debate-only requests. In a full deep dive, include this as a required section. Use 6-8 complete, non-fragmentary quotes or concise compliant excerpts when transcript access allows. For each: topic, questioner name, firm if available, answering executive, section, quote or paraphrase, source tag, why it matters, bull/bear implication, and falsifier/next check. Tie falsifiers to the next real catalyst or reporting cadence.
-
-## Common Failure Modes
-
-- Mixed GAAP/non-GAAP, reported/CC, units, periods, or estimate definitions.
-- Treating a one-time or below-the-line EPS beat as recurring operating earnings.
-- Transcript cited for a filed GAAP number.
-- Surprise percent against near-zero expectations.
-- Numeric range invented from qualitative guidance.
-- `MISSING` used in final copy where a precise absence label is better.
-- No source tag on a number or quote.
-- Investment prompt missing thesis/revision/stock skew.
-- Quotes clipped so tightly they lose meaning.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/PLAYBOOK.md b/plugins/financial-markets/skills/earnings-deep-dive/references/PLAYBOOK.md
deleted file mode 100644
index cd41546..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/PLAYBOOK.md
+++ /dev/null
@@ -1,100 +0,0 @@
-# Earnings Deep Dive Playbook
-
-Use for full mechanics after `SKILL.md` routes the request.
-
-## Source Precedence
-
-- GAAP financials: filed 10-Q/10-K, filed 8-K/exhibits, earnings release tables, deck/prepared remarks, transcript only as narrative cross-check.
-- Guidance: filed PR/exhibit first; call-only guidance is allowed only when labeled `CALL-ONLY`.
-- Reissues: use final version, note timestamp, and preserve source trail.
-- No attachments: use connected/institutional sources, then company IR/SEC, then reputable transcript/consensus providers, then labeled web fallback.
-
-## Normalization
-
-- Pick base currency/scale and convert at ingestion; never mix `$m` and `$bn`.
-- Keep reported and constant-currency separate.
-- EPS must specify GAAP/non-GAAP, basic/diluted, and share-count source; adjusted EPS needs reconciliation source plus GAAP comparable.
-- Every post-print runs an EPS quality screen. Do not let a headline GAAP EPS beat drive the conclusion until below-the-line, tax, mark-to-market, share-count, and non-recurring items are checked.
-- KPI definition changes require a schema-change note and driver-registry update.
-
-## Expectations
-
-State the benchmark: consensus for stock-impact, internal estimates for decision benchmark, whisper only if labeled separately. Every output should show estimate set ID/vendor, as-of timestamp, metric definition, unit, and GAAP/non-GAAP basis.
-
-Core math:
-
-- Beat/miss: `reported - expected`.
-- Surprise %: `(reported - expected) / expected`; suppress when expected is zero/near-zero.
-- Margin/rate deltas: bps or percentage points.
-- Guidance midpoint: `(low + high) / 2`; delta to consensus/internal when available.
-
-## EPS Quality Screen
-
-Run this check in every full deep dive. Keep it short unless triggered.
-
-Include a full EPS quality / ex-gain bridge when any trigger is present:
-
-- GAAP EPS surprise is much larger than revenue, operating income, segment profit, FCF, or adjusted EPS surprise.
-- EPS is affected by equity-investment gains/losses, mark-to-market items, tax benefits/charges, FX, asset sales, impairments, litigation, restructuring, pension items, or other below-the-line/non-recurring items.
-- Consensus basis is unclear or mismatched: GAAP EPS versus adjusted EPS versus operating EPS.
-- Management emphasizes adjusted EPS, operating income, FCF, or segment profit instead of GAAP EPS.
-- The stock reaction appears to capitalize a headline EPS beat that may not be recurring.
-- The issuer has material investment holdings, fair-value accounting, insurance reserves, or financial-asset marks.
-
-Bridge format when triggered:
-
-| Step | EPS / amount | Source | Treatment | Read-through |
-|---|---:|---|---|---|
-| Reported GAAP diluted EPS |  |  | Starting point |  |
-| Less: identified non-operating / non-recurring items |  |  | Remove or isolate |  |
-| Tax/share-count normalization if material |  |  | Adjust only with support |  |
-| Estimated operating / recurring EPS |  |  | Analyst-derived if not company-reported |  |
-| Consensus basis used for beat/miss |  |  | Must match definition |  |
-
-If no trigger is found, write a one-line conclusion: `No material EPS-quality trigger identified from available sources; GAAP and operating signals appear directionally aligned, subject to filing tie-out.`
-
-## Analysis Modules
-
-- Standardized P&L: revenue, gross profit/margin, opex, operating income/margin, below-the-line, net income, diluted shares, EPS, non-GAAP bridge.
-- Drivers: isolate 2-3 items explaining the print; for each show what moved, why it moved, and how it changes forward expectations.
-- Segment/mix: recreate disclosed segment tables; quantify mix only when segment data supports it.
-- Volume/price/mix: decompose only with required source inputs; otherwise keep causal narrative qualitative and label missing inputs.
-- Margin/opex: separate mix, price, input costs, utilization, FX, one-time items, cash opex, SBC, and run-rate versus temporary items.
-- Cash quality: CFO, capex, FCF, working capital, accruals, reserves, pull-forward/deferral language when evidenced.
-
-## Guidance and Outlook
-
-Capture metric, period, low/high/mid, units, GAAP/non-GAAP, assumptions, and source tag. Qualitative guidance remains exact wording; do not invent numeric ranges. Multiple currencies or reported/CC guidance stay separate.
-
-## Transcript
-
-Scan guidance caveats, demand indicators, margin/opex commentary, risks/constraints, and Q&A pushback. Full deep dives must include a transcript/Q&A and debate-map section. Quotes or paraphrases need section, speaker, questioner name, firm if available, answering executive, topic, source tag, why it matters, bull/bear implication, falsifier/next check, and driver/thesis impact. If the transcript is unavailable, keep the section and label the missing artifact rather than omitting it.
-
-## Model Update Logic
-
-1. Update actual quarter actuals/KPIs from authoritative sources.
-2. Update forward drivers only when guidance or analyst-derived assumptions support the change.
-3. Map every update to `DriverID`, period, old/new value, source tag, why, and confidence.
-4. Use audit-ready model update mode only when the user supplies or references a model/workbook, driver registry, output registry, normalized CSVs, model-update inputs, or explicit data to update the model.
-5. Use packet mode unless the user supplies a prior workbook/registry and asks for apply.
-6. Diff the story: key drivers, output deltas, guide/estimate bridge, and open items; suppress formatting noise.
-
-## Sector Adaptation
-
-Use only KPIs that matter this quarter:
-
-- SaaS/subscription: ARR, RPO/cRPO, NRR/GRR, churn, seats/customers, ARPU, billings, FCF margin.
-- Semis/hardware: units, ASP, backlog, book-to-bill, channel inventory, utilization, capacity, gross margin guide.
-- Banks/financials: NII, NIM, loan/deposit growth, beta, PCL, charge-offs, CET1, efficiency ratio.
-- Consumer/retail: comps, traffic, ticket, pricing/promo, shrink, inventory, markdowns.
-- Industrials/services: orders, backlog, book-to-bill, utilization, labor, pricing, end-market mix.
-- Internet/ads/marketplaces: DAU/MAU, ARPU, impressions, pricing, engagement, take rate, merchant/account growth.
-- Insurance/energy/travel: use loss/combined ratios; production/realized price/LOE/capex; RASM/CASM/load factor/fuel, respectively.
-
-## QA
-
-Before delivery, confirm source tags, unit/scale consistency, estimate definitions, GAAP/non-GAAP labels, EPS quality screen completion, precise absence labels, quote completeness, no unresolved placeholders, and visible source limitations.
-
-## PM Post-Print Judgment
-
-Start from thesis change, estimate revision direction, valuation support, and position action. Separate headline results, clean results, guidance delta, call-only commentary, management credibility, Street revision implications, and next falsifier.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/QUALITY_CHECKS.md b/plugins/financial-markets/skills/earnings-deep-dive/references/QUALITY_CHECKS.md
deleted file mode 100644
index e46cf5c..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/QUALITY_CHECKS.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# Quality Checks
-
-## Hard Fails
-
-- Any user-facing number or quote lacks a source tag.
-- GAAP/non-GAAP, reported/CC, unit/scale, period, or estimate definition is mixed without labeling.
-- Non-GAAP metric lacks closest GAAP comparable or reconciliation source when available.
-- EPS surprise is interpreted without screening for below-the-line, tax, mark-to-market, share-count, non-recurring, or estimate-basis distortion.
-- Transcript used as primary source for a filed numeric claim.
-- Qualitative guidance converted into a number without a supplied mapping.
-- Workbook apply attempted without explicit user request and required workbook/registries.
-- Final user-facing report/dashboard, or any generated support note, includes unresolved `{PLACEHOLDER}`, `[TOKEN]`, `TODO`, or authoring `MISSING: add ...`.
-
-## Chat Checklist
-
-- Source posture and limitations are visible near the top.
-- Estimate set/vendor/as-of are disclosed.
-- Beat/miss and guidance deltas use compatible definitions.
-- EPS quality screen is present; if a trigger exists, the full ex-gain / recurring EPS bridge is shown with source tags and confidence.
-- The memo identifies what changed, why it matters, and how it changes forward estimates/thesis.
-- Quotes are complete enough to be useful and tied to a driver/debate.
-- Falsifiers tie to a real next catalyst or reporting cadence.
-- Investor-facing prompts include thesis change, likely estimate revision, and stock/valuation skew.
-
-## Deterministic Checklist
-
-- `validate_plan.py` passes before running.
-- `validate_normalized_inputs.py` passes or warnings are disclosed.
-- Generated outputs write outside the packaged skill tree.
-- `verify_tearsheet.py` passes for generated report text or support notes before they are rendered or handed off.
-- Packet/apply mode and any workbook limitations are disclosed.
-
-## PM Judgment QA
-
-Fail if the output only summarizes earnings, misses clean-versus-headline quality, lacks transcript/source caveats, omits model/estimate implications, or avoids the position action question.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/REFERENCE_ROUTER.md b/plugins/financial-markets/skills/earnings-deep-dive/references/REFERENCE_ROUTER.md
deleted file mode 100644
index 49afd45..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/REFERENCE_ROUTER.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Reference Router
-
-Load only what the request requires.
-
-| Need | Load |
-|---|---|
-| Chat-native report contract and source pack | `CHAT_REPORT_CONTRACT.md` |
-| Output shape/template | `OUTPUT_MODES.md` |
-| Full operating mechanics | `PLAYBOOK.md` |
-| Scripted plan/CSV/model contracts | `SCHEMAS.md`, `SCRIPT_QUICKSTART.md` |
-| Source tags and absence labels | `SOURCE_TAGGING.md`, `QUALITY_CHECKS.md` |
-| Metrics and sector KPIs | `METRIC_CATALOG.md` |
-| EPS quality / ex-gain bridge | `PLAYBOOK.md`, `METRIC_CATALOG.md`, `QUALITY_CHECKS.md` |
-| Model diff/update detail | `MODEL_DIFF_GUIDE.md` |
-| Examples | `EXAMPLES.md` |
-| Polished HTML full deep dive | `../../../shared/html-artifact-standard.md`, `CHAT_REPORT_CONTRACT.md`, `OUTPUT_MODES.md`, then `QUALITY_CHECKS.md` |
-| Explicit standardized dashboard | `DASHBOARD_PACK.md`, then `QUALITY_CHECKS.md` |
-
-Fast route:
-
-- Default post-print requests route to full deep dive and usually need `CHAT_REPORT_CONTRACT.md`, `PLAYBOOK.md`, `OUTPUT_MODES.md`, and `METRIC_CATALOG.md`.
-- One-page tear sheet is only for explicit summary/one-pager/quick/brief/TL;DR requests and usually needs `CHAT_REPORT_CONTRACT.md`, `OUTPUT_MODES.md`, and `QUALITY_CHECKS.md`.
-- Audit-ready model update is only for user-supplied or referenced model/model-update inputs and usually needs `MODEL_DIFF_GUIDE.md`, `SCHEMAS.md`, and `SCRIPT_QUICKSTART.md`.
-- Deterministic mode always needs `SCHEMAS.md`, `SCRIPT_QUICKSTART.md`, and QA checks.
-- An explicit deep dive, full report, or reusable/source-heavy package defaults to polished standalone HTML.
-- Standardized dashboard output keeps `earnings-deep-dive` as the analysis owner and uses `dashboard-builder` only when explicitly selected.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/SCHEMAS.md b/plugins/financial-markets/skills/earnings-deep-dive/references/SCHEMAS.md
deleted file mode 100644
index fc5a442..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/SCHEMAS.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# Schemas
-
-Plan and normalized CSV contracts for deterministic mode. Templates live in `assets/templates/`.
-
-## Plan
-
-Required top-level keys: `event`, `inputs`, `outputs`. Recommended controls: `allow_call_only_guidance`, `require_gaap_for_non_gaap`, `require_source_tags`, `dry_run`.
-
-Event fields: `ticker`, `company_name`, `fiscal_period`, `event_date`, `timezone`, `base_currency`, `base_scale`.
-
-Inputs:
-
-- `artifact_index_csv`
-- `artifacts.press_release`, `sec_filing_10q_or_10k` or equivalent filing key, `sec_filing_8k`, `deck`, `transcript`
-- `normalized.metrics_csv`, `estimates_csv`, `guidance_csv`, `quotes_csv`, `driver_updates_csv`
-- `model.prior_model_xlsx`, `driver_registry_csv`, `output_registry_csv`
-
-Outputs:
-
-- `output_dir`
-- `render.executive_overview`, `tear_sheet`, `deep_dive`
-- `model_update.enabled`, `mode` (`packet` or `apply`), `write_diff`
-
-The bundled template defaults to packet/dry-run and writes to `/tmp`. Enable workbook `apply` only when the user supplies a real workbook/registries and explicitly asks for it.
-
-## Normalized Inputs
-
-| CSV | Required columns |
-|---|---|
-| `metrics.csv` | `MetricName`, `Period`, `Value`, `Units`, `GAAP_Flag`, `Segment`, `IsTearSheet`, `DisplayOrder`, `SourceTag` |
-| `estimates.csv` | `MetricName`, `Period`, `EstimateType` (`Consensus`/`Internal`/`Whisper`), `Value`, `Units`, `AsOf`, `Source` |
-| `guidance.csv` | `MetricName`, `Period`, `Low`, `High`, `Units`, `GAAP_Flag`, `SourceTag` |
-| `quotes.csv` | `Section`, `Speaker`, `Questioner`, `TopicTag`, `QuoteText`, `SourceTag` |
-| `driver_updates.csv` | `DriverID`, `Period`, `NewValue`, `Units`, `Why`, `SourceTag` |
-
-Recommended: metric group, comparable GAAP metric and reconciliation source for non-GAAP, assumptions, old value, confidence, and notes. For EPS-related rows, add basis/notes that identify GAAP EPS, adjusted EPS, operating EPS, below-the-line items, tax effects, share-count changes, and any non-recurring items used in the EPS quality screen.
-
-## Model Registry
-
-`driver_registry.csv`: `DriverID`, `DriverName`, `Units`, `Cadence`, `MappingType` (`NamedRange`/`Cell`), `Worksheet`, `Cell` or `NamedRange`, plus definition/owner/source notes when available.
-
-`output_registry.csv`: `OutputID`, `OutputName`, `Units`, `MappingType`, `Worksheet`, `Cell` or `NamedRange`.
-
-## Audit Outputs
-
-`ChangeLog.csv`: timestamp, model version, changed by, section, item, old/new value, why, source tag.
-
-`WhatChanged_Diff.csv`: output/driver ID, old/new value, delta, units, source, why, confidence.
-
-For structured templates, `MISSING` is allowed. For user-facing analysis, use precise labels such as `not guided`, `not disclosed`, `not provided`, or `source not provided`.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/SCRIPT_QUICKSTART.md b/plugins/financial-markets/skills/earnings-deep-dive/references/SCRIPT_QUICKSTART.md
deleted file mode 100644
index 827c306..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/SCRIPT_QUICKSTART.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Deterministic Script Quickstart
-
-These scripts support deterministic parts of the earnings deep-dive workflow: validation, beat/miss and guidance math, tear-sheet generation, model-update packets, and model diffs.
-
-From the skill root, copy the templates into a temporary workspace. The bundled template defaults to packet/dry-run mode and writes outside the skill tree so sample execution does not dirty the repository.
-
-```bash
-TMPDIR="$(mktemp -d)"
-mkdir -p "$TMPDIR/normalized"
-cp assets/templates/plan.template.json "$TMPDIR/plan.json"
-cp assets/templates/normalized_*.csv "$TMPDIR/normalized/"
-cp assets/templates/driver_updates.csv "$TMPDIR/normalized/driver_updates.csv"
-
-python scripts/validate_plan.py "$TMPDIR/plan.json"
-python scripts/validate_normalized_inputs.py "$TMPDIR/plan.json"
-python scripts/run_plan.py "$TMPDIR/plan.json"
-python scripts/verify_tearsheet.py /tmp/public_equity_investing_earnings_deep_dive_output/TearSheet.md
-```
-
-Switch `outputs.model_update.enabled` to `true` and `mode` to `apply` only when the user has supplied a real prior model and explicitly wants workbook changes.
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/references/SOURCE_TAGGING.md b/plugins/financial-markets/skills/earnings-deep-dive/references/SOURCE_TAGGING.md
deleted file mode 100644
index 3d1fb61..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/references/SOURCE_TAGGING.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# Source Tagging (Mandatory)
-
-## Table of contents
-1. [Rules](#rules)
-2. [Standard format](#standard-format)
-3. [Examples by artifact type](#examples-by-artifact-type)
-4. [Common failure modes](#common-failure-modes)
-
----
-
-## Rules
-1) Every number and quote must have a Source Tag.
-2) A Source Tag must be **specific enough that a human can find it in under 30 seconds**.
-3) Use the most authoritative source available (filings > PR tables > deck > transcript).
-4) If the only source is the transcript, label `CALL-ONLY` and cross-check any numeric claim against filings if possible.
-5) If the value is not present, use the most precise label: `not guided`, `not disclosed`, `not provided`, or `MISSING: <dependency>`.
-
-## Standard format
-Use a single string in this form:
-
-```
-<ArtifactLabel> | <LocationPointer> | <OptionalNotes>
-```
-
-Where:
-- **ArtifactLabel** = one of: `10-Q`, `10-K`, `8-K`, `Earnings PR (Exhibit 99.1)`, `Deck`, `Prepared Remarks`, `Transcript Prepared`, `Transcript Q&A`, `Company IR`, `SEC`, `Consensus/Data Provider`, `Web Fallback`, `Model`
-- **LocationPointer** should be one of:
-  - `p.<page>` + `Table <name/number>`
-  - `p.<page>` + `Section <heading>`
-  - `Slide <#>`
-  - `line <#>` (for transcript) + speaker context
-
-## Examples by artifact type
-
-### SEC filings / exhibits
-- `10-Q | p.12, Consolidated Statements of Operations | GAAP revenue`
-- `8-K | Exhibit 99.1 p.3, Table 1 | Non-GAAP reconciliation`
-
-### Earnings press release (non-filed copy)
-- `Earnings PR | p.2, Highlights bullets | Guidance range statement`
-
-### Earnings deck
-- `Deck | Slide 7, Segment performance table | Segment revenue`
-
-### Transcript (Prepared)
-- `Transcript Prepared | CEO, line 145–162 | Demand commentary`
-
-### Transcript (Q&A)
-- `Transcript Q&A | CFO → Analyst (Firm), line 980–1006 | GM guide cadence`
-
-### Model references (for internal traceability)
-- `Model v2026.02.21_1430ET | Drivers tab, NamedRange: GM_Assumption | Update applied`
-
-### Public-source fallback
-- `Consensus/Data Provider | retrieved 2026-04-07 | revenue estimate proxy`
-- `Web Fallback | provider page, retrieved 2026-04-07 | third-party transcript; official transcript not found`
-
-## Common failure modes
-- **Too vague:** `10-Q` (missing page/table)
-- **Wrong precedence:** citing transcript for a GAAP number that is in the 10-Q
-- **No speaker context:** transcript quote without speaker + section
-- **No reconciliation:** non-GAAP metric with no GAAP comparable + reconciliation tag
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/__init__.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/__init__.py
deleted file mode 100644
index 2b2da76..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""Package marker for earnings-deep-dive script modules."""
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description="Package marker. Use run_plan.py, validate_plan.py, or verify_tearsheet.py for CLI workflows."
-    )
-    parser.parse_args()
-    print("This is a package marker. Use a concrete earnings-deep-dive script for execution.")
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/apply_model_updates.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/apply_model_updates.py
deleted file mode 100644
index 0098059..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/apply_model_updates.py
+++ /dev/null
@@ -1,264 +0,0 @@
-#!/usr/bin/env python3
-"""Apply driver updates to an Excel operating model via a driver registry.
-
-This script is intentionally limited to **explicit** mapped inputs:
-- NamedRange (single-cell) OR
-- Worksheet + Cell
-
-It does NOT attempt to recalc formulas (Excel should recalc on open).
-
-Outputs:
-- Updated model copy with version tag in filename
-- output/audit/ChangeLog.csv (and optional ChangeLog worksheet)
-"""
-
-from __future__ import annotations
-
-import shutil
-import sys
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print("Usage: python scripts/apply_model_updates.py plan.json")
-    print("Apply explicit driver updates to an Excel model via a driver registry.")
-    raise SystemExit(0)
-
-import openpyxl
-import pandas as pd
-
-try:
-    from .utils.excel_utils import set_value
-    from .utils.io_utils import ensure_dir, read_json, write_text
-    from .utils.validation_utils import as_float_or_none, is_missing
-except ImportError:
-    from utils.excel_utils import set_value
-    from utils.io_utils import ensure_dir, read_json
-    from utils.validation_utils import as_float_or_none, is_missing
-
-
-def _read_csv(path: str) -> pd.DataFrame:
-    return pd.read_csv(path, dtype=str, keep_default_na=False)
-
-
-def _now_iso(tz: str | None = None) -> str:
-    # Use local time; timezone label is informational.
-    return datetime.now().isoformat(timespec="seconds")
-
-
-def _make_version_tag(plan: dict[str, Any]) -> str:
-    ev = plan.get("event", {})
-    ticker = ev.get("ticker", "MISSING")
-    period = ev.get("fiscal_period", "MISSING")
-    tz = (
-        plan.get("outputs", {})
-        .get("model_update", {})
-        .get("version_tag_timezone", ev.get("timezone", ""))
-    )
-    ts = datetime.now().strftime("%Y.%m.%d_%H%M")
-    tz_lbl = tz.split("/")[-1] if tz else "LOCAL"
-    return f"{ticker}_Model_v{ts}{tz_lbl}_{period}_PostEarnings"
-
-
-def _safe_name(x: str) -> str:
-    return "".join([c for c in x if c.isalnum() or c in "._-"])
-
-
-def _append_changelog_sheet(wb: openpyxl.Workbook, changelog_df: pd.DataFrame) -> None:
-    ws_name = "ChangeLog"
-    if ws_name in wb.sheetnames:
-        ws = wb[ws_name]
-    else:
-        ws = wb.create_sheet(ws_name)
-        ws.append(list(changelog_df.columns))
-
-    # Find first empty row
-    row_idx = ws.max_row + 1
-    for _, row in changelog_df.iterrows():
-        ws.append([row.get(c) for c in changelog_df.columns])
-
-
-def apply_model_updates(plan: dict[str, Any]) -> tuple[Path, Path]:
-    out_dir = Path(plan.get("outputs", {}).get("output_dir", "output"))
-    ensure_dir(str(out_dir / "model"))
-    ensure_dir(str(out_dir / "audit"))
-
-    model = plan.get("inputs", {}).get("model", {})
-    prior_model = model.get("prior_model_xlsx")
-    driver_reg_path = model.get("driver_registry_csv")
-
-    norm = plan.get("inputs", {}).get("normalized", {})
-    driver_updates_path = norm.get("driver_updates_csv")
-
-    if is_missing(prior_model) or is_missing(driver_reg_path) or is_missing(driver_updates_path):
-        raise ValueError(
-            "Model update requires prior_model_xlsx, driver_registry_csv, and driver_updates_csv"
-        )
-
-    prior_model_path = Path(prior_model)
-    if not prior_model_path.exists():
-        raise FileNotFoundError(f"Prior model not found: {prior_model}")
-
-    version_tag = _make_version_tag(plan)
-    ext = prior_model_path.suffix
-    new_name = _safe_name(version_tag) + ext
-    new_model_path = out_dir / "model" / new_name
-
-    dry_run = bool(plan.get("controls", {}).get("dry_run", False))
-    if not dry_run:
-        shutil.copy2(prior_model_path, new_model_path)
-
-    driver_reg = _read_csv(driver_reg_path)
-    updates = _read_csv(driver_updates_path)
-
-    # Index registry by DriverID
-    reg = {str(r["DriverID"]): r for _, r in driver_reg.iterrows()}
-
-    changelog_rows = []
-    changed_by = plan.get("controls", {}).get("prepared_by", "AI")
-    model_version_id = version_tag
-
-    keep_vba = ext.lower() in {".xlsm", ".xltm"}
-    wb = openpyxl.load_workbook(
-        new_model_path if not dry_run else prior_model_path, keep_vba=keep_vba
-    )
-
-    try:
-        for _, u in updates.iterrows():
-            did = str(u.get("DriverID") or "").strip()
-            if not did:
-                continue
-            if did not in reg:
-                # Unknown driver: skip but record
-                changelog_rows.append(
-                    {
-                        "Timestamp": _now_iso(),
-                        "ModelVersionID": model_version_id,
-                        "ChangedBy": changed_by,
-                        "Section": "Drivers",
-                        "ItemChanged": did,
-                        "OldValue": "MISSING",
-                        "NewValue": u.get("NewValue"),
-                        "Why": u.get("Why"),
-                        "SourceTag": u.get("SourceTag"),
-                    }
-                )
-                continue
-
-            new_val_raw = u.get("NewValue")
-            if is_missing(new_val_raw):
-                continue
-
-            r = reg[did]
-            mapping_type = str(r.get("MappingType") or "").strip() or "NamedRange"
-            sheet = str(r.get("Worksheet") or "").strip() or None
-            cell = str(r.get("Cell") or "").strip() or None
-            named_range = str(r.get("NamedRange") or "").strip() or None
-
-            # Read old value (from mapped cell)
-            try:
-                if mapping_type == "NamedRange" and named_range:
-                    # resolve and read via openpyxl
-                    dn = wb.defined_names.get(named_range)
-                    old_val = None
-                    if dn:
-                        dests = list(dn.destinations)
-                        if len(dests) == 1:
-                            sname, addr = dests[0]
-                            old_val = wb[sname][addr].value
-                elif mapping_type == "Cell" and sheet and cell:
-                    old_val = wb[sheet][cell].value
-                else:
-                    old_val = None
-            except Exception:
-                old_val = None
-
-            # Parse numeric if possible
-            new_val_num = as_float_or_none(new_val_raw)
-            new_val = new_val_num if new_val_num is not None else new_val_raw
-
-            # Write
-            try:
-                ws_name, ws_cell = set_value(wb, mapping_type, sheet, cell, named_range, new_val)
-            except Exception as e:
-                changelog_rows.append(
-                    {
-                        "Timestamp": _now_iso(),
-                        "ModelVersionID": model_version_id,
-                        "ChangedBy": changed_by,
-                        "Section": "Drivers",
-                        "ItemChanged": did,
-                        "OldValue": str(old_val) if old_val is not None else "MISSING",
-                        "NewValue": str(new_val_raw),
-                        "Why": u.get("Why"),
-                        "SourceTag": u.get("SourceTag"),
-                    }
-                )
-                continue
-
-            changelog_rows.append(
-                {
-                    "Timestamp": _now_iso(),
-                    "ModelVersionID": model_version_id,
-                    "ChangedBy": changed_by,
-                    "Section": "Drivers",
-                    "ItemChanged": did,
-                    "OldValue": str(old_val) if old_val is not None else "MISSING",
-                    "NewValue": str(new_val_raw),
-                    "Why": u.get("Why"),
-                    "SourceTag": u.get("SourceTag"),
-                }
-            )
-
-        changelog_df = pd.DataFrame(
-            changelog_rows,
-            columns=[
-                "Timestamp",
-                "ModelVersionID",
-                "ChangedBy",
-                "Section",
-                "ItemChanged",
-                "OldValue",
-                "NewValue",
-                "Why",
-                "SourceTag",
-            ],
-        )
-
-        changelog_path = out_dir / "audit" / "ChangeLog.csv"
-        changelog_df.to_csv(changelog_path, index=False)
-
-        # Optional: add/append ChangeLog tab
-        try:
-            if len(changelog_df) > 0:
-                _append_changelog_sheet(wb, changelog_df)
-        except Exception:
-            pass
-
-        if not dry_run:
-            wb.save(new_model_path)
-
-        return new_model_path, changelog_path
-    finally:
-        wb.close()
-
-
-def main() -> int:
-    if len(sys.argv) != 2:
-        print("Usage: python scripts/apply_model_updates.py plan.json")
-        return 1
-
-    plan = read_json(sys.argv[1])
-    new_model, changelog = apply_model_updates(plan)
-    print(f"Wrote updated model: {new_model}")
-    print(f"Wrote changelog: {changelog}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/model_diff.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/model_diff.py
deleted file mode 100644
index 17972e9..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/model_diff.py
+++ /dev/null
@@ -1,270 +0,0 @@
-#!/usr/bin/env python3
-"""Produce a signal-oriented model diff using DriverRegistry + OutputRegistry.
-
-This diff intentionally ignores "cell noise" and compares only mapped drivers and key outputs.
-
-Notes:
-- openpyxl does not recalc formulas. Output values may reflect cached Excel calculation state.
-- If cached values are missing, the diff will show formulas instead.
-"""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print(
-        "Usage: python scripts/model_diff.py plan.json output/audit/WhatChanged_Diff.csv [new_model_path]"
-    )
-    print("Produce a signal-oriented model diff from driver and output registries.")
-    raise SystemExit(0)
-
-import openpyxl
-import pandas as pd
-
-try:
-    from .utils.excel_utils import _resolve_named_range
-    from .utils.io_utils import ensure_dir, read_json
-    from .utils.validation_utils import as_float_or_none, is_missing
-except ImportError:
-    from utils.excel_utils import _resolve_named_range
-    from utils.io_utils import ensure_dir, read_json
-    from utils.validation_utils import as_float_or_none, is_missing
-
-
-def _read_csv(path: str) -> pd.DataFrame:
-    return pd.read_csv(path, dtype=str, keep_default_na=False)
-
-
-def _get_cell(
-    wb: openpyxl.Workbook,
-    mapping_type: str,
-    sheet: str | None,
-    cell: str | None,
-    named_range: str | None,
-) -> tuple[str, str]:
-    if mapping_type == "NamedRange":
-        if not named_range:
-            raise ValueError("NamedRange mapping requires NamedRange")
-        loc = _resolve_named_range(wb, named_range)
-        return loc.sheet, loc.cell
-    if mapping_type == "Cell":
-        if not sheet or not cell:
-            raise ValueError("Cell mapping requires Worksheet + Cell")
-        return sheet, cell
-    raise ValueError(f"Unknown MappingType: {mapping_type}")
-
-
-def _read_value_or_formula(
-    path: Path, mapping_type: str, sheet: str | None, cell: str | None, named_range: str | None
-) -> Any:
-    # cached values
-    wb_val = openpyxl.load_workbook(path, data_only=True)
-    wb_for = openpyxl.load_workbook(path, data_only=False)
-    try:
-        s, c = _get_cell(wb_val, mapping_type, sheet, cell, named_range)
-        v = wb_val[s][c].value
-        if v is not None:
-            return v
-        # fallback to formula text
-        sf, cf = _get_cell(wb_for, mapping_type, sheet, cell, named_range)
-        f = wb_for[sf][cf].value
-        return f if f is not None else None
-    finally:
-        wb_val.close()
-        wb_for.close()
-
-
-def write_diff(
-    plan: dict[str, Any], out_csv_path: Path, new_model_path: Path | None = None
-) -> Path:
-    out_dir = Path(plan.get("outputs", {}).get("output_dir", "output"))
-    ensure_dir(str(out_dir / "audit"))
-
-    model = plan.get("inputs", {}).get("model", {})
-    old_path = Path(model.get("prior_model_xlsx"))
-    driver_reg_path = model.get("driver_registry_csv")
-    out_reg_path = model.get("output_registry_csv")
-
-    if new_model_path is None:
-        # pick most recent model in output/model
-        model_dir = out_dir / "model"
-        if model_dir.exists():
-            candidates = sorted(
-                model_dir.glob("*.xls*"), key=lambda p: p.stat().st_mtime, reverse=True
-            )
-            if candidates:
-                new_model_path = candidates[0]
-    if new_model_path is None:
-        raise ValueError("Could not determine new model path; pass new_model_path explicitly")
-
-    if not old_path.exists():
-        raise FileNotFoundError(f"Old model not found: {old_path}")
-    if not new_model_path.exists():
-        raise FileNotFoundError(f"New model not found: {new_model_path}")
-    if is_missing(driver_reg_path):
-        raise ValueError("inputs.model.driver_registry_csv is required for diff")
-
-    driver_reg = _read_csv(driver_reg_path)
-    out_reg = (
-        _read_csv(out_reg_path)
-        if out_reg_path and str(out_reg_path).upper() != "MISSING" and Path(out_reg_path).exists()
-        else pd.DataFrame()
-    )
-
-    # Driver updates source tags (best-effort)
-    norm = plan.get("inputs", {}).get("normalized", {})
-    driver_updates_path = norm.get("driver_updates_csv")
-    source_by_driver = {}
-    if driver_updates_path and Path(driver_updates_path).exists():
-        du = _read_csv(driver_updates_path)
-        for _, r in du.iterrows():
-            did = str(r.get("DriverID") or "").strip()
-            if did:
-                source_by_driver[did] = r.get("SourceTag")
-
-    items: list[dict[str, Any]] = []
-
-    # Drivers
-    for _, r in driver_reg.iterrows():
-        did = str(r.get("DriverID") or "").strip()
-        if not did:
-            continue
-        mapping_type = str(r.get("MappingType") or "NamedRange").strip() or "NamedRange"
-        ws = str(r.get("Worksheet") or "").strip() or None
-        cell = str(r.get("Cell") or "").strip() or None
-        nr = str(r.get("NamedRange") or "").strip() or None
-        units = str(r.get("Units") or "").strip()
-
-        old = _read_value_or_formula(old_path, mapping_type, ws, cell, nr)
-        new = _read_value_or_formula(new_model_path, mapping_type, ws, cell, nr)
-
-        old_f = as_float_or_none(old)
-        new_f = as_float_or_none(new)
-        delta = None
-        if old_f is not None and new_f is not None:
-            delta = new_f - old_f
-
-        items.append(
-            {
-                "Category": "Driver",
-                "Item": did,
-                "Old": old,
-                "New": new,
-                "Delta": delta,
-                "Units": units,
-                "PrimaryImpact": "MISSING",
-                "SourceTag": source_by_driver.get(did, "MISSING"),
-                "Notes": f"{mapping_type}:{nr or ''}{ws or ''}{cell or ''}",
-            }
-        )
-
-    # Outputs
-    if not out_reg.empty:
-        for _, r in out_reg.iterrows():
-            oid = str(r.get("OutputID") or "").strip()
-            if not oid:
-                continue
-            mapping_type = str(r.get("MappingType") or "NamedRange").strip() or "NamedRange"
-            ws = str(r.get("Worksheet") or "").strip() or None
-            cell = str(r.get("Cell") or "").strip() or None
-            nr = str(r.get("NamedRange") or "").strip() or None
-            units = str(r.get("Units") or "").strip()
-
-            old = _read_value_or_formula(old_path, mapping_type, ws, cell, nr)
-            new = _read_value_or_formula(new_model_path, mapping_type, ws, cell, nr)
-
-            old_f = as_float_or_none(old)
-            new_f = as_float_or_none(new)
-            delta = None
-            if old_f is not None and new_f is not None:
-                delta = new_f - old_f
-
-            items.append(
-                {
-                    "Category": "Output",
-                    "Item": oid,
-                    "Old": old,
-                    "New": new,
-                    "Delta": delta,
-                    "Units": units,
-                    "PrimaryImpact": oid,
-                    "SourceTag": "MISSING",
-                    "Notes": "Output value may be cached; recalc in Excel",
-                }
-            )
-
-    # Rank by absolute delta (numeric), then category, then item
-    def _abs_delta(x):
-        d = x.get("Delta")
-        if d is None:
-            return -1
-        try:
-            return abs(float(d))
-        except Exception:
-            return -1
-
-    items_sorted = sorted(
-        items, key=lambda x: (_abs_delta(x), x["Category"], x["Item"]), reverse=True
-    )
-
-    out_rows = []
-    for idx, it in enumerate(items_sorted, start=1):
-        out_rows.append(
-            {
-                "Rank": idx,
-                "Category": it["Category"],
-                "Item": it["Item"],
-                "Old": it["Old"],
-                "New": it["New"],
-                "Delta": it["Delta"],
-                "Units": it["Units"],
-                "PrimaryImpact": it["PrimaryImpact"],
-                "SourceTag": it["SourceTag"],
-                "Notes": it["Notes"],
-            }
-        )
-
-    df_out = pd.DataFrame(
-        out_rows,
-        columns=[
-            "Rank",
-            "Category",
-            "Item",
-            "Old",
-            "New",
-            "Delta",
-            "Units",
-            "PrimaryImpact",
-            "SourceTag",
-            "Notes",
-        ],
-    )
-    out_csv_path.parent.mkdir(parents=True, exist_ok=True)
-    df_out.to_csv(out_csv_path, index=False)
-    return out_csv_path
-
-
-def main() -> int:
-    if len(sys.argv) < 3:
-        print(
-            "Usage: python scripts/model_diff.py plan.json output/audit/WhatChanged_Diff.csv [new_model_path]"
-        )
-        return 1
-
-    plan = read_json(sys.argv[1])
-    out_path = Path(sys.argv[2])
-    new_model = Path(sys.argv[3]) if len(sys.argv) >= 4 else None
-    write_diff(plan, out_path, new_model)
-    print(f"Wrote diff: {out_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/requirements.txt b/plugins/financial-markets/skills/earnings-deep-dive/scripts/requirements.txt
deleted file mode 100644
index 8deba87..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-pandas>=1.5
-openpyxl>=3.1
-python-dateutil>=2.8
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/run_plan.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/run_plan.py
deleted file mode 100644
index 785ea5d..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/run_plan.py
+++ /dev/null
@@ -1,776 +0,0 @@
-#!/usr/bin/env python3
-"""Run the Earnings Deep Dive & Summary pipeline.
-
-This script intentionally focuses on deterministic steps:
-- Validate inputs
-- Compute beat/miss and guidance deltas
-- Render markdown outputs (ExecutiveOverview, TearSheet, DeepDive)
-- Optionally update a model workbook via driver registry and create a changelog + diff
-
-It does **not** invent narrative. If drivers bullets, watch list, or narrative
-are not provided, generated reports use precise limitation labels and fail QA
-if authoring placeholders remain.
-"""
-
-from __future__ import annotations
-
-import re
-import subprocess
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print("Usage: python scripts/run_plan.py plan.json")
-    print("Run the deterministic earnings deep-dive pipeline.")
-    raise SystemExit(0)
-
-import pandas as pd
-
-try:
-    from .utils.io_utils import ensure_dir, read_json, write_text
-    from .utils.math_utils import compute_delta
-    from .utils.validation_utils import as_float_or_none, is_missing
-    from .validate_plan import validate_plan
-except ImportError:
-    from utils.io_utils import ensure_dir, read_json, write_text
-    from utils.math_utils import compute_delta
-    from utils.validation_utils import as_float_or_none, is_missing
-    from validate_plan import validate_plan
-
-
-def _read_csv(path: str) -> pd.DataFrame:
-    return pd.read_csv(path, dtype=str, keep_default_na=False)
-
-
-def _format_number(val: float | None, units: str) -> str:
-    if val is None:
-        return "not provided"
-    # Heuristic formatting
-    if units.strip() in {"USD", "EPS"}:
-        return f"{val:.2f}"
-    if units.endswith("%"):
-        return f"{val:.2f}%"
-    # default
-    if abs(val) >= 1000:
-        return f"{val:,.0f}"
-    return f"{val:,.2f}"
-
-
-def _format_pct(val: float | None) -> str:
-    if val is None:
-        return "not provided"
-    return f"{val * 100:.1f}%"
-
-
-def _display(value: Any, fallback: str = "not provided") -> str:
-    return fallback if is_missing(value) else str(value)
-
-
-PLACEHOLDER_RE = re.compile(
-    r"(\{[A-Z0-9_]+\}|\bTODO\b|MISSING:\s*(add|explain|map|summarize|write|list|populate|decompose))",
-    re.IGNORECASE,
-)
-
-
-def unresolved_placeholder_errors(outputs: dict[str, str]) -> list[str]:
-    errors: list[str] = []
-    for name, text in outputs.items():
-        matches = sorted({m.group(0) for m in PLACEHOLDER_RE.finditer(text)})
-        if matches:
-            errors.append(f"{name}: unresolved authoring placeholder(s): {', '.join(matches[:6])}")
-    return errors
-
-
-@dataclass
-class BeatMissRow:
-    metric: str
-    period: str
-    units: str
-    reported: float | None
-    consensus: float | None
-    internal: float | None
-    delta_cons: float | None
-    surprise_cons: float | None
-    delta_int: float | None
-    surprise_int: float | None
-    source_tag: str
-    gaap_flag: str = ""
-    comparable_gaap_metric: str = ""
-    reconciliation_source_tag: str = ""
-    notes: str = ""
-
-
-@dataclass
-class GuidanceRow:
-    metric: str
-    period: str
-    units: str
-    low: float | None
-    high: float | None
-    midpoint: float | None
-    consensus: float | None
-    internal: float | None
-    delta_cons_mid: float | None
-    delta_int_mid: float | None
-    source_tag: str
-
-
-def _pick_first(df: pd.DataFrame, metric: str, period: str, est_type: str) -> float | None:
-    sub = df[
-        (df["MetricName"] == metric) & (df["Period"] == period) & (df["EstimateType"] == est_type)
-    ]
-    if len(sub) == 0:
-        return None
-    return as_float_or_none(sub.iloc[0]["Value"])
-
-
-def compute_beat_miss(metrics_df: pd.DataFrame, estimates_df: pd.DataFrame) -> list[BeatMissRow]:
-    rows: list[BeatMissRow] = []
-    m = metrics_df.copy()
-    m["IsTearSheet"] = m["IsTearSheet"].astype(str)
-    m = m[m["IsTearSheet"].str.upper().isin(["Y", "YES", "TRUE", "1"])].copy()
-
-    def _order(x: str) -> int:
-        try:
-            return int(float(x))
-        except Exception:
-            return 999
-
-    m["_order"] = m["DisplayOrder"].apply(_order)
-    m = m.sort_values("_order")
-
-    for _, r in m.iterrows():
-        metric = r["MetricName"]
-        period = r["Period"]
-        units = r["Units"]
-        reported = as_float_or_none(r["Value"])
-        source_tag = _display(r.get("SourceTag"), "source not provided")
-
-        cons = _pick_first(estimates_df, metric, period, "Consensus")
-        internal = _pick_first(estimates_df, metric, period, "Internal")
-
-        dcons, scons = compute_delta(reported, cons)
-        dint, sint = compute_delta(reported, internal)
-
-        rows.append(
-            BeatMissRow(
-                metric=metric,
-                period=period,
-                units=units,
-                reported=reported,
-                consensus=cons,
-                internal=internal,
-                delta_cons=dcons,
-                surprise_cons=scons,
-                delta_int=dint,
-                surprise_int=sint,
-                source_tag=source_tag,
-                gaap_flag=str(r.get("GAAP_Flag") or ""),
-                comparable_gaap_metric=str(r.get("ComparableGAAPMetricName") or ""),
-                reconciliation_source_tag=str(r.get("ReconciliationSourceTag") or ""),
-                notes=str(r.get("Notes") or ""),
-            )
-        )
-
-    return rows
-
-
-def compute_guidance(guidance_df: pd.DataFrame, estimates_df: pd.DataFrame) -> list[GuidanceRow]:
-    rows: list[GuidanceRow] = []
-    for _, r in guidance_df.iterrows():
-        metric = r["MetricName"]
-        period = r["Period"]
-        units = r["Units"]
-        low = as_float_or_none(r["Low"])
-        high = as_float_or_none(r["High"])
-        source_tag = _display(r.get("SourceTag"), "source not provided")
-
-        midpoint: float | None = None
-        if low is not None and high is not None:
-            midpoint = (low + high) / 2.0
-
-        cons = _pick_first(estimates_df, metric, period, "Consensus")
-        internal = _pick_first(estimates_df, metric, period, "Internal")
-
-        dcons, _ = compute_delta(midpoint, cons)
-        dint, _ = compute_delta(midpoint, internal)
-
-        rows.append(
-            GuidanceRow(
-                metric=metric,
-                period=period,
-                units=units,
-                low=low,
-                high=high,
-                midpoint=midpoint,
-                consensus=cons,
-                internal=internal,
-                delta_cons_mid=dcons,
-                delta_int_mid=dint,
-                source_tag=source_tag,
-            )
-        )
-    return rows
-
-
-def _md_table_beatmiss(rows: list[BeatMissRow]) -> str:
-    lines: list[str] = []
-    lines.append(
-        "| Metric | Reported | Consensus | Δ | Surprise % | Internal | Δ | Surprise % | SourceTag |"
-    )
-    lines.append("|---|---:|---:|---:|---:|---:|---:|---:|---|")
-    for r in rows:
-        lines.append(
-            "| {m} | {rep} | {c} | {dc} | {sc} | {i} | {di} | {si} | {src} |".format(
-                m=r.metric,
-                rep=_format_number(r.reported, r.units),
-                c=_format_number(r.consensus, r.units),
-                dc=_format_number(r.delta_cons, r.units),
-                sc=_format_pct(r.surprise_cons),
-                i=_format_number(r.internal, r.units),
-                di=_format_number(r.delta_int, r.units),
-                si=_format_pct(r.surprise_int),
-                src=r.source_tag,
-            )
-        )
-    return "\n".join(lines)
-
-
-def _md_table_guidance(rows: list[GuidanceRow]) -> str:
-    lines: list[str] = []
-    lines.append(
-        "| Metric | Period | Low | High | Mid | Consensus | Δ(mid) | Internal | Δ(mid) | SourceTag |"
-    )
-    lines.append("|---|---|---:|---:|---:|---:|---:|---:|---:|---|")
-    for r in rows:
-        lines.append(
-            "| {m} | {p} | {lo} | {hi} | {mid} | {c} | {dc} | {i} | {di} | {src} |".format(
-                m=r.metric,
-                p=r.period,
-                lo=_format_number(r.low, r.units),
-                hi=_format_number(r.high, r.units),
-                mid=_format_number(r.midpoint, r.units),
-                c=_format_number(r.consensus, r.units),
-                dc=_format_number(r.delta_cons_mid, r.units),
-                i=_format_number(r.internal, r.units),
-                di=_format_number(r.delta_int_mid, r.units),
-                src=r.source_tag,
-            )
-        )
-    return "\n".join(lines)
-
-
-EPS_DISTORTION_TERMS = (
-    "asset sale",
-    "below-the-line",
-    "equity investment",
-    "fair value",
-    "fx",
-    "gain",
-    "impairment",
-    "litigation",
-    "loss",
-    "mark-to-market",
-    "non-operating",
-    "non-recurring",
-    "one-time",
-    "pension",
-    "restructuring",
-    "share count",
-    "tax",
-)
-
-
-def _metric_has(row: BeatMissRow, *needles: str) -> bool:
-    metric = row.metric.lower()
-    return all(needle.lower() in metric for needle in needles)
-
-
-def _first_row(rows: list[BeatMissRow], *needles: str, period: str = "") -> BeatMissRow | None:
-    for row in rows:
-        if period and row.period != period:
-            continue
-        if _metric_has(row, *needles):
-            return row
-    return None
-
-
-def _distortion_text(row: BeatMissRow) -> str:
-    text = " ".join(
-        [
-            row.metric,
-            row.gaap_flag,
-            row.comparable_gaap_metric,
-            row.reconciliation_source_tag,
-            row.notes,
-        ]
-    ).lower()
-    found = [term for term in EPS_DISTORTION_TERMS if term in text]
-    return ", ".join(found)
-
-
-def _surprise_gap_flag(eps_row: BeatMissRow, peer_row: BeatMissRow | None, label: str) -> str:
-    if eps_row.surprise_cons is None or peer_row is None or peer_row.surprise_cons is None:
-        return ""
-    eps_surprise = abs(eps_row.surprise_cons)
-    peer_surprise = abs(peer_row.surprise_cons)
-    if eps_surprise >= 0.15 and eps_surprise > max(peer_surprise * 2.0, peer_surprise + 0.10):
-        return f"GAAP EPS surprise materially outpaced {label} surprise"
-    return ""
-
-
-def render_eps_quality_screen(rows: list[BeatMissRow]) -> str:
-    gaap_eps_rows = [
-        row
-        for row in rows
-        if "eps" in row.metric.lower()
-        and ("gaap" in row.metric.lower() or row.gaap_flag.lower() == "gaap")
-    ]
-    if not gaap_eps_rows:
-        return (
-            "EPS quality screen not run: GAAP diluted EPS row was not provided in "
-            "normalized metrics. Add GAAP EPS, adjusted/operating EPS if available, "
-            "and below-the-line notes before using EPS surprise as a recurring "
-            "earnings signal."
-        )
-
-    lines: list[str] = []
-    lines.append("| Check | Status | Read-through | Source |")
-    lines.append("|---|---|---|---|")
-
-    for eps_row in gaap_eps_rows:
-        revenue_row = _first_row(rows, "revenue", period=eps_row.period)
-        operating_row = _first_row(rows, "operating", period=eps_row.period)
-        adjusted_eps_row = _first_row(rows, "adjusted", "eps", period=eps_row.period)
-        flags = [
-            flag
-            for flag in [
-                _distortion_text(eps_row),
-                _surprise_gap_flag(eps_row, revenue_row, "revenue"),
-                _surprise_gap_flag(eps_row, operating_row, "operating income"),
-            ]
-            if flag
-        ]
-
-        if flags:
-            status = "expanded bridge required"
-            read_through = (
-                "Do not capitalize the full GAAP EPS beat as recurring until "
-                + "; ".join(flags)
-                + " is isolated."
-            )
-        else:
-            status = "no material trigger in normalized inputs"
-            read_through = (
-                "GAAP EPS still needs filing/release tie-out, but no explicit "
-                "distortion flag was supplied."
-            )
-
-        lines.append(
-            f"| GAAP EPS surprise quality ({eps_row.period}) | {status} | "
-            f"{read_through} | {eps_row.source_tag} |"
-        )
-
-        if adjusted_eps_row:
-            lines.append(
-                "| Adjusted / operating EPS comparison | provided | "
-                f"Compare {_format_number(eps_row.reported, eps_row.units)} GAAP EPS "
-                f"with {_format_number(adjusted_eps_row.reported, adjusted_eps_row.units)} "
-                "adjusted/operating EPS before updating forward EPS. | "
-                f"{adjusted_eps_row.source_tag} |"
-            )
-        else:
-            lines.append(
-                "| Adjusted / operating EPS comparison | source not provided | "
-                "Request company reconciliation or calculate a clearly labeled "
-                "analyst-derived recurring EPS bridge if the EPS beat matters. | "
-                "source not provided |"
-            )
-
-    return "\n".join(lines)
-
-
-def _read_optional_lines(path: Path, max_items: int) -> list[str]:
-    if not path.exists():
-        return []
-    txt = path.read_text(encoding="utf-8").splitlines()
-    items: list[str] = []
-    for line in txt:
-        line = line.strip()
-        if not line:
-            continue
-        items.append(line)
-        if len(items) >= max_items:
-            break
-    return items
-
-
-def render_tearsheet(
-    plan: dict[str, Any],
-    beatmiss_rows: list[BeatMissRow],
-    guidance_rows: list[GuidanceRow],
-    quotes_df: pd.DataFrame,
-    driver_updates_df: pd.DataFrame,
-    diff_path: Path | None,
-    estimate_asof: str,
-    artifact_summary: str,
-) -> str:
-    ev = plan["event"]
-    ticker = _display(ev.get("ticker"))
-    period = _display(ev.get("fiscal_period"))
-
-    est_asof = estimate_asof
-
-    norm_dir = Path(
-        plan.get("inputs", {}).get("normalized", {}).get("metrics_csv", "normalized/metrics.csv")
-    ).parent
-    drivers_bullets = _read_optional_lines(norm_dir / "drivers_bullets.md", 8)
-    if not drivers_bullets:
-        drivers_bullets = [
-            "Driver bullets not provided in normalized inputs; add source-supported driver bullets for committee use."
-        ]
-
-    watch_list = _read_optional_lines(norm_dir / "watch_list.md", 5)
-    if not watch_list:
-        watch_list = [
-            "Watch list not provided in normalized inputs; add measurable next-quarter watch items."
-        ]
-
-    # Quotes (max 8)
-    quotes: list[dict[str, str]] = []
-    if not quotes_df.empty:
-        for _, r in quotes_df.iterrows():
-            if len(quotes) >= 8:
-                break
-            qt = r.get("QuoteText")
-            if is_missing(qt):
-                continue
-            quotes.append(
-                {
-                    "Section": _display(r.get("Section")),
-                    "Speaker": _display(r.get("Speaker")),
-                    "Questioner": r.get("Questioner") or "",
-                    "TopicTag": _display(r.get("TopicTag")),
-                    "QuoteText": qt,
-                    "SourceTag": _display(r.get("SourceTag"), "source not provided"),
-                }
-            )
-
-    # Model impact from driver_updates
-    model_impact_lines: list[str] = []
-    if not driver_updates_df.empty:
-        for _, r in driver_updates_df.iterrows():
-            did = _display(r.get("DriverID"))
-            newv = _display(r.get("NewValue"))
-            units = r.get("Units") or ""
-            why = _display(r.get("Why"))
-            src = _display(r.get("SourceTag"), "source not provided")
-            oldv = _display(r.get("OldValue"))
-            model_impact_lines.append(
-                f"- {did}: {oldv} → {newv} ({units}) | Why: {why} | Source: {src}"
-            )
-            if len(model_impact_lines) >= 8:
-                break
-    else:
-        model_impact_lines.append(
-            "- Driver updates not provided; create a driver update packet before using this as a model-impact artifact."
-        )
-
-    diff_note = ""
-    if diff_path and diff_path.exists():
-        diff_note = f"\n\n**Diff:** see `{diff_path.as_posix()}`"
-
-    lines: list[str] = []
-    lines.append(f"# {ticker} — {period} Earnings Tear Sheet")
-    lines.append("")
-    lines.append(f"**Event:** {_display(ev.get('event_date'))} ({_display(ev.get('timezone'))})  ")
-    lines.append(f"**Estimate set (as-of):** {est_asof}  ")
-    lines.append(f"**Artifacts:** {artifact_summary}  ")
-    lines.append(f"**Base units:** {_display(ev.get('base_currency'))}{ev.get('base_scale', '')}  ")
-    lines.append("")
-
-    lines.append("## Beat/Miss vs expectations")
-    lines.append(_md_table_beatmiss(beatmiss_rows))
-    lines.append("")
-
-    lines.append("## EPS quality screen")
-    lines.append(render_eps_quality_screen(beatmiss_rows))
-    lines.append("")
-
-    lines.append("## Guidance delta")
-    lines.append(_md_table_guidance(guidance_rows))
-    lines.append("")
-
-    lines.append("## Drivers (5–8)")
-    for b in drivers_bullets:
-        if b.startswith("-"):
-            lines.append(b)
-        else:
-            lines.append(f"- {b}")
-    lines.append("")
-
-    lines.append("## Key quotes (max 4–8)")
-    if quotes:
-        for q in quotes:
-            qhdr = f"**[{q['TopicTag']}] {q['Speaker']} ({q['Section']})**"
-            if q["Section"] == "Q&A" and q["Questioner"]:
-                qhdr = f"**[{q['TopicTag']}] {q['Speaker']} (Q&A; Q: {q['Questioner']})**"
-            lines.append(f"> {qhdr}: “{q['QuoteText']}”  ")
-            lines.append(f"> Source: {q['SourceTag']}")
-            lines.append("")
-    else:
-        lines.append(
-            "> Quote pack not provided in normalized inputs; add high-signal quotes with speaker and SourceTag for quote mode."
-        )
-        lines.append("")
-
-    lines.append("## Model impact")
-    lines.append("Top driver changes applied (old → new):")
-    lines += model_impact_lines
-    lines.append("")
-    lines.append("Key output deltas (old → new):")
-    lines.append(
-        "- Output deltas not computed in this run; recalc model or provide output registry to summarize top deltas."
-    )
-    lines.append(diff_note)
-    lines.append("")
-
-    lines.append("## Watch list (next quarter)")
-    for i, w in enumerate(watch_list, start=1):
-        w = w.lstrip("0123456789. ")
-        lines.append(f"{i}. {w}")
-
-    return "\n".join(lines)
-
-
-def render_executive_overview(
-    plan: dict[str, Any], beatmiss_rows: list[BeatMissRow], guidance_rows: list[GuidanceRow]
-) -> str:
-    ev = plan["event"]
-    ticker = _display(ev.get("ticker"))
-    period = _display(ev.get("fiscal_period"))
-
-    # Heuristic ranking: guidance deltas first, then headline surprises.
-    bullets: list[str] = []
-
-    # Guidance bullets (top 5)
-    for r in guidance_rows[:5]:
-        if r.midpoint is None:
-            continue
-        bullets.append(
-            f"Guidance ({r.metric}, {r.period}) midpoint vs consensus: {_format_number(r.delta_cons_mid, r.units)} ({r.units}); Source: {r.source_tag}"
-        )
-
-    # Beat/miss bullets (top 5)
-    for r in beatmiss_rows[:5]:
-        if r.reported is None:
-            continue
-        bullets.append(
-            f"Reported {r.metric} vs consensus: {_format_number(r.delta_cons, r.units)} ({_format_pct(r.surprise_cons)}); Source: {r.source_tag}"
-        )
-
-    # Pad to 10
-    while len(bullets) < 10:
-        bullets.append(
-            "Additional source-supported what-mattered point not provided in normalized inputs."
-        )
-
-    lines: list[str] = []
-    lines.append(f"# Executive Overview — {ticker} {period}")
-    lines.append("")
-    lines.append("## What matters (ranked)")
-    for i, b in enumerate(bullets[:15], start=1):
-        lines.append(f"{i}. {b}")
-    lines.append("")
-    lines.append("## Stock move frame (vs expectations)")
-    lines.append(
-        "Stock move frame not provided in normalized inputs; frame the move against the estimate set before committee use."
-    )
-    lines.append("")
-    lines.append("## EPS quality screen")
-    lines.append(render_eps_quality_screen(beatmiss_rows))
-    lines.append("")
-    lines.append("## Bull / Base / Bear narrative delta")
-    lines.append("Bull/base/bear narrative delta not provided in normalized inputs.")
-    return "\n".join(lines)
-
-
-def render_deep_dive(
-    plan: dict[str, Any],
-    beatmiss_rows: list[BeatMissRow],
-    guidance_rows: list[GuidanceRow],
-    quotes_df: pd.DataFrame,
-) -> str:
-    ev = plan["event"]
-    ticker = _display(ev.get("ticker"))
-    period = _display(ev.get("fiscal_period"))
-
-    lines: list[str] = []
-    lines.append(f"# Earnings Deep Dive — {ticker} {period}")
-    lines.append("")
-    lines.append("## 1. Headline results vs expectations")
-    lines.append(_md_table_beatmiss(beatmiss_rows))
-    lines.append("")
-    lines.append("## 2. EPS quality screen")
-    lines.append(render_eps_quality_screen(beatmiss_rows))
-    lines.append("")
-    lines.append("## 3. Guidance and outlook")
-    lines.append(_md_table_guidance(guidance_rows))
-    lines.append("")
-    lines.append("## 4. Results drivers (write-up)")
-    lines.append(
-        "Results-driver narrative not provided in normalized inputs; add source-tagged driver decomposition before using as a full deep dive."
-    )
-    lines.append("")
-    lines.append("## 5. Transcript themes + Q&A (high signal)")
-    if not quotes_df.empty:
-        for _, r in quotes_df.head(12).iterrows():
-            qt = r.get("QuoteText")
-            if is_missing(qt):
-                continue
-            lines.append(
-                f"- [{_display(r.get('TopicTag'))}] {_display(r.get('Speaker'))} ({_display(r.get('Section'))}): “{qt}” (Source: {_display(r.get('SourceTag'), 'source not provided')})"
-            )
-    else:
-        lines.append("Quote themes not provided in normalized inputs.")
-    lines.append("")
-    lines.append("## 6. Model changes")
-    lines.append(
-        "Model-change narrative not provided in normalized inputs; map surprises and guidance into driver updates before using as a model-update artifact."
-    )
-    return "\n".join(lines)
-
-
-def main() -> int:
-    if len(sys.argv) != 2:
-        print("Usage: python scripts/run_plan.py plan.json")
-        return 1
-
-    plan_path = sys.argv[1]
-    plan = read_json(plan_path)
-
-    # Plan validation (structure)
-    v = validate_plan(plan)
-    if v["errors"]:
-        for e in v["errors"]:
-            print(f"ERROR: {e}")
-        return 1
-
-    out_dir = Path(plan.get("outputs", {}).get("output_dir", "output"))
-    ensure_dir(str(out_dir))
-    ensure_dir(str(out_dir / "audit"))
-    ensure_dir(str(out_dir / "model"))
-
-    # Validate normalized inputs (structural + SourceTag hygiene)
-    val_script = Path(__file__).resolve().parent / "validate_normalized_inputs.py"
-    proc = subprocess.run([sys.executable, str(val_script), plan_path])
-    if proc.returncode != 0:
-        return proc.returncode
-
-    # Read normalized tables
-    norm = plan.get("inputs", {}).get("normalized", {})
-    metrics_df = _read_csv(norm["metrics_csv"])
-    estimates_df = _read_csv(norm["estimates_csv"])
-    guidance_df = _read_csv(norm["guidance_csv"])
-    quotes_df = _read_csv(norm["quotes_csv"])
-    driver_updates_df = _read_csv(norm["driver_updates_csv"])
-
-    # Estimate set AsOf (best-effort)
-    estimate_asof = "not provided"
-    if "AsOf" in estimates_df.columns:
-        vals = [
-            v
-            for v in estimates_df["AsOf"].tolist()
-            if str(v).strip() and str(v).strip().upper() != "MISSING"
-        ]
-        if vals:
-            estimate_asof = str(vals[0]).strip()
-
-    # Artifact summary (for tear sheet header)
-    artifacts = plan.get("inputs", {}).get("artifacts", {})
-    parts = []
-    if isinstance(artifacts, dict):
-        for k, v in artifacts.items():
-            if isinstance(v, str) and v.strip().upper() != "MISSING":
-                parts.append(f"{k}={Path(v).name}")
-    artifact_summary = ", ".join(parts) if parts else "not provided"
-
-    beatmiss_rows = compute_beat_miss(metrics_df, estimates_df)
-    guidance_rows = compute_guidance(guidance_df, estimates_df)
-
-    # Optional model update
-    diff_path: Path | None = None
-    if plan.get("outputs", {}).get("model_update", {}).get("enabled"):
-        mode = plan.get("outputs", {}).get("model_update", {}).get("mode", "apply")
-        if mode == "apply":
-            try:
-                try:
-                    from .apply_model_updates import apply_model_updates
-                except ImportError:
-                    from apply_model_updates import apply_model_updates
-
-                updated_model_path, changelog_path = apply_model_updates(plan)
-                if plan.get("outputs", {}).get("model_update", {}).get("write_diff"):
-                    try:
-                        try:
-                            from .model_diff import write_diff
-                        except ImportError:
-                            from model_diff import write_diff
-
-                        diff_path = out_dir / "audit" / "WhatChanged_Diff.csv"
-                        write_diff(plan, diff_path, updated_model_path)
-                    except Exception as e:
-                        print(f"WARN: diff generation failed: {e}")
-            except Exception as e:
-                print(f"WARN: model update failed: {e}")
-        else:
-            # packet mode
-            packet_path = out_dir / "audit" / "ModelUpdatePacket.csv"
-            driver_updates_df.to_csv(packet_path, index=False)
-
-    # Render markdown outputs
-    tearsheet_md = render_tearsheet(
-        plan,
-        beatmiss_rows,
-        guidance_rows,
-        quotes_df,
-        driver_updates_df,
-        diff_path,
-        estimate_asof,
-        artifact_summary,
-    )
-    exec_md = render_executive_overview(plan, beatmiss_rows, guidance_rows)
-    deep_md = render_deep_dive(plan, beatmiss_rows, guidance_rows, quotes_df)
-
-    write_text(exec_md, str(out_dir / "ExecutiveOverview.md"))
-    write_text(tearsheet_md, str(out_dir / "TearSheet.md"))
-    write_text(deep_md, str(out_dir / "DeepDive.md"))
-
-    output_errors = unresolved_placeholder_errors(
-        {
-            "ExecutiveOverview.md": exec_md,
-            "TearSheet.md": tearsheet_md,
-            "DeepDive.md": deep_md,
-        }
-    )
-    if output_errors:
-        qa_path = out_dir / "audit" / "PlaceholderQA.md"
-        write_text(
-            "# Placeholder QA\n\n" + "\n".join(f"- {e}" for e in output_errors) + "\n", str(qa_path)
-        )
-        for e in output_errors:
-            print(f"ERROR: {e}")
-        return 1
-
-    print(f"Wrote outputs to: {out_dir}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/__init__.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/excel_utils.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/excel_utils.py
deleted file mode 100644
index 459eacf..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/excel_utils.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-import openpyxl
-
-
-@dataclass
-class WorkbookLocation:
-    sheet: str
-    cell: str
-
-
-def _resolve_named_range(wb: openpyxl.Workbook, name: str) -> WorkbookLocation:
-    dn = wb.defined_names.get(name)
-    if dn is None:
-        raise KeyError(f"NamedRange not found: {name}")
-    dests = list(dn.destinations)
-    if len(dests) != 1:
-        raise ValueError(
-            f"NamedRange '{name}' resolves to {len(dests)} destinations; only single-cell ranges are supported"
-        )
-    sheet, cell = dests[0]
-    return WorkbookLocation(sheet=sheet, cell=cell)
-
-
-def get_value(
-    wb_path: str,
-    mapping_type: str,
-    sheet: str | None,
-    cell: str | None,
-    named_range: str | None,
-    data_only: bool = True,
-):
-    wb = openpyxl.load_workbook(wb_path, data_only=data_only)
-    try:
-        if mapping_type == "NamedRange":
-            if not named_range:
-                raise ValueError("NamedRange mapping requires named_range")
-            loc = _resolve_named_range(wb, named_range)
-            ws = wb[loc.sheet]
-            return ws[loc.cell].value
-        if mapping_type == "Cell":
-            if not sheet or not cell:
-                raise ValueError("Cell mapping requires sheet and cell")
-            ws = wb[sheet]
-            return ws[cell].value
-        raise ValueError(f"Unknown MappingType: {mapping_type}")
-    finally:
-        wb.close()
-
-
-def set_value(
-    wb: openpyxl.Workbook,
-    mapping_type: str,
-    sheet: str | None,
-    cell: str | None,
-    named_range: str | None,
-    new_value,
-):
-    if mapping_type == "NamedRange":
-        if not named_range:
-            raise ValueError("NamedRange mapping requires named_range")
-        loc = _resolve_named_range(wb, named_range)
-        ws = wb[loc.sheet]
-        ws[loc.cell].value = new_value
-        return (loc.sheet, loc.cell)
-    if mapping_type == "Cell":
-        if not sheet or not cell:
-            raise ValueError("Cell mapping requires sheet and cell")
-        ws = wb[sheet]
-        ws[cell].value = new_value
-        return (sheet, cell)
-    raise ValueError(f"Unknown MappingType: {mapping_type}")
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/io_utils.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/io_utils.py
deleted file mode 100644
index 92a5e4b..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/io_utils.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import json
-from pathlib import Path
-from typing import Any
-
-
-def read_json(path: str) -> dict[str, Any]:
-    p = Path(path)
-    if not p.exists():
-        raise FileNotFoundError(f"JSON file not found: {path}")
-    return json.loads(p.read_text(encoding="utf-8"))
-
-
-def write_json(obj: Any, path: str) -> None:
-    p = Path(path)
-    p.parent.mkdir(parents=True, exist_ok=True)
-    p.write_text(json.dumps(obj, indent=2, ensure_ascii=False), encoding="utf-8")
-
-
-def ensure_dir(path: str) -> Path:
-    p = Path(path)
-    p.mkdir(parents=True, exist_ok=True)
-    return p
-
-
-def write_text(text: str, path: str) -> None:
-    p = Path(path)
-    p.parent.mkdir(parents=True, exist_ok=True)
-    p.write_text(text, encoding="utf-8")
-
-
-def file_exists_or_missing(path: str) -> bool:
-    if path is None:
-        return False
-    if isinstance(path, str) and path.strip().upper() == "MISSING":
-        return False
-    return Path(path).exists()
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/math_utils.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/math_utils.py
deleted file mode 100644
index 264cea3..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/math_utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from __future__ import annotations
-
-
-def compute_delta(
-    reported: float | None, expected: float | None
-) -> tuple[float | None, float | None]:
-    """Return (delta, surprise_pct). Surprise pct is None if expected is 0/None."""
-    if reported is None or expected is None:
-        return None, None
-    delta = reported - expected
-    if expected == 0:
-        return delta, None
-    return delta, delta / expected
-
-
-def margin_delta_bps(
-    reported: float | None, expected: float | None, unit_hint: str
-) -> float | None:
-    """Compute margin delta in bps.
-
-    unit_hint:
-      - 'percent' when inputs are in % points (e.g., 35.2)
-      - 'decimal' when inputs are 0-1 (e.g., 0.352)
-    """
-    if reported is None or expected is None:
-        return None
-    if unit_hint == "decimal":
-        return (reported - expected) * 10000.0
-    # default percent
-    return (reported - expected) * 100.0
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/validation_utils.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/validation_utils.py
deleted file mode 100644
index 6a0feda..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/utils/validation_utils.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Sequence
-
-
-@dataclass
-class ValidationIssue:
-    level: str  # "ERROR" or "WARN"
-    message: str
-
-
-def is_missing(val: Any) -> bool:
-    if val is None:
-        return True
-    if isinstance(val, float):
-        # NaN
-        return val != val
-    if isinstance(val, str) and val.strip() == "":
-        return True
-    if isinstance(val, str) and val.strip().upper() == "MISSING":
-        return True
-    return False
-
-
-def as_float_or_none(val: Any) -> float | None:
-    """Parse a numeric value; return None if MISSING or unparsable."""
-    if is_missing(val):
-        return None
-    try:
-        return float(str(val).replace(",", ""))
-    except Exception:
-        return None
-
-
-def require_columns(df, required: Sequence[str]) -> list[str]:
-    missing = [c for c in required if c not in df.columns]
-    return missing
-
-
-def enum_check(value: Any, allowed: Sequence[str]) -> bool:
-    if is_missing(value):
-        return False
-    return str(value).strip() in set(allowed)
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_normalized_inputs.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_normalized_inputs.py
deleted file mode 100644
index 5f19fb6..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_normalized_inputs.py
+++ /dev/null
@@ -1,332 +0,0 @@
-#!/usr/bin/env python3
-"""Validate normalized CSV inputs (metrics/estimates/guidance/quotes/driver updates).
-
-Rules enforced:
-- Required columns exist
-- Non-empty rows for required deliverables
-- For any non-MISSING metric value, SourceTag must be present and not MISSING
-- Non-GAAP rows must include comparable GAAP metric + reconciliation source
-
-Exit codes:
-  0 = valid (may still have warnings)
-  1 = errors found
-"""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print("Usage: python scripts/validate_normalized_inputs.py plan.json")
-    print("Validate normalized CSV inputs for the earnings deep-dive pipeline.")
-    raise SystemExit(0)
-
-import pandas as pd
-
-try:
-    from .utils.io_utils import ensure_dir, read_json, write_text
-    from .utils.validation_utils import as_float_or_none, is_missing, require_columns
-except ImportError:
-    from utils.io_utils import ensure_dir, read_json, write_text
-    from utils.validation_utils import as_float_or_none, is_missing, require_columns
-
-
-REQ_METRICS = [
-    "MetricName",
-    "Period",
-    "Value",
-    "Units",
-    "GAAP_Flag",
-    "Segment",
-    "IsTearSheet",
-    "DisplayOrder",
-    "SourceTag",
-]
-
-REQ_ESTIMATES = [
-    "MetricName",
-    "Period",
-    "EstimateType",
-    "Value",
-    "Units",
-    "AsOf",
-    "Source",
-]
-
-REQ_GUIDANCE = [
-    "MetricName",
-    "Period",
-    "Low",
-    "High",
-    "Units",
-    "GAAP_Flag",
-    "SourceTag",
-]
-
-REQ_QUOTES = [
-    "Section",
-    "Speaker",
-    "Questioner",
-    "TopicTag",
-    "QuoteText",
-    "SourceTag",
-]
-
-REQ_DRIVER_UPDATES = [
-    "DriverID",
-    "Period",
-    "NewValue",
-    "Units",
-    "Why",
-    "SourceTag",
-]
-
-
-def _read_csv(path: str) -> pd.DataFrame:
-    return pd.read_csv(path, dtype=str, keep_default_na=False)
-
-
-def _validate_metrics(df: pd.DataFrame, require_source_tags: bool) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    missing_cols = require_columns(df, REQ_METRICS)
-    if missing_cols:
-        errors.append(f"metrics.csv missing columns: {missing_cols}")
-        return errors, warnings
-
-    # Basic row checks
-    if len(df) == 0:
-        errors.append("metrics.csv has no rows")
-        return errors, warnings
-
-    for i, row in df.iterrows():
-        metric = row.get("MetricName")
-        value = row.get("Value")
-        src = row.get("SourceTag")
-        gaap_flag = str(row.get("GAAP_Flag") or "").strip()
-
-        if is_missing(metric):
-            errors.append(f"metrics.csv row {i + 2}: MetricName is required")
-
-        if not is_missing(value):
-            # Only enforce SourceTag when the metric is present
-            if require_source_tags and is_missing(src):
-                errors.append(
-                    f"metrics.csv row {i + 2} ({metric}): Value present but SourceTag is MISSING"
-                )
-
-        if gaap_flag == "Non-GAAP":
-            comp = row.get("ComparableGAAPMetricName")
-            recon = row.get("ReconciliationSourceTag")
-            if is_missing(comp) or is_missing(recon):
-                errors.append(
-                    f"metrics.csv row {i + 2} ({metric}): Non-GAAP requires ComparableGAAPMetricName and ReconciliationSourceTag"
-                )
-
-    return errors, warnings
-
-
-def _validate_estimates(df: pd.DataFrame) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    missing_cols = require_columns(df, REQ_ESTIMATES)
-    if missing_cols:
-        errors.append(f"estimates.csv missing columns: {missing_cols}")
-        return errors, warnings
-
-    allowed = {"Consensus", "Internal", "Whisper"}
-    for i, row in df.iterrows():
-        et = str(row.get("EstimateType") or "").strip()
-        if et and et not in allowed:
-            errors.append(
-                f"estimates.csv row {i + 2}: EstimateType must be one of {sorted(allowed)}"
-            )
-    return errors, warnings
-
-
-def _validate_guidance(df: pd.DataFrame, require_source_tags: bool) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    missing_cols = require_columns(df, REQ_GUIDANCE)
-    if missing_cols:
-        errors.append(f"guidance.csv missing columns: {missing_cols}")
-        return errors, warnings
-
-    for i, row in df.iterrows():
-        metric = row.get("MetricName")
-        low = as_float_or_none(row.get("Low"))
-        high = as_float_or_none(row.get("High"))
-        src = row.get("SourceTag")
-
-        if require_source_tags and (low is not None or high is not None) and is_missing(src):
-            errors.append(
-                f"guidance.csv row {i + 2} ({metric}): guidance present but SourceTag is MISSING"
-            )
-
-        if low is not None and high is not None and low > high:
-            errors.append(f"guidance.csv row {i + 2} ({metric}): Low > High")
-
-        if (low is None) ^ (high is None):
-            warnings.append(
-                f"guidance.csv row {i + 2} ({metric}): only one of Low/High is populated"
-            )
-
-    return errors, warnings
-
-
-def _validate_quotes(df: pd.DataFrame, require_source_tags: bool) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    missing_cols = require_columns(df, REQ_QUOTES)
-    if missing_cols:
-        errors.append(f"quotes.csv missing columns: {missing_cols}")
-        return errors, warnings
-
-    allowed = {"Prepared", "Q&A"}
-    for i, row in df.iterrows():
-        section = str(row.get("Section") or "").strip()
-        if section and section not in allowed:
-            errors.append(f"quotes.csv row {i + 2}: Section must be Prepared or Q&A")
-
-        qt = row.get("QuoteText")
-        if not is_missing(qt) and require_source_tags and is_missing(row.get("SourceTag")):
-            errors.append(f"quotes.csv row {i + 2}: QuoteText present but SourceTag is MISSING")
-
-    return errors, warnings
-
-
-def _validate_driver_updates(
-    df: pd.DataFrame, require_source_tags: bool
-) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    missing_cols = require_columns(df, REQ_DRIVER_UPDATES)
-    if missing_cols:
-        errors.append(f"driver_updates.csv missing columns: {missing_cols}")
-        return errors, warnings
-
-    for i, row in df.iterrows():
-        dv = row.get("NewValue")
-        if not is_missing(dv) and as_float_or_none(dv) is None:
-            warnings.append(
-                f"driver_updates.csv row {i + 2}: NewValue is non-numeric (will be written as text)"
-            )
-
-        if require_source_tags and not is_missing(dv) and is_missing(row.get("SourceTag")):
-            errors.append(
-                f"driver_updates.csv row {i + 2}: NewValue present but SourceTag is MISSING"
-            )
-
-    return errors, warnings
-
-
-def main() -> int:
-    if len(sys.argv) != 2:
-        print("Usage: python scripts/validate_normalized_inputs.py plan.json")
-        return 1
-
-    plan_path = sys.argv[1]
-    plan = read_json(plan_path)
-
-    out_dir = plan.get("outputs", {}).get("output_dir", "output")
-    audit_dir = ensure_dir(str(Path(out_dir) / "audit"))
-
-    require_source_tags = bool(plan.get("controls", {}).get("require_source_tags", True))
-
-    norm = plan.get("inputs", {}).get("normalized", {})
-    paths = {
-        "metrics": norm.get("metrics_csv"),
-        "estimates": norm.get("estimates_csv"),
-        "guidance": norm.get("guidance_csv"),
-        "quotes": norm.get("quotes_csv"),
-        "driver_updates": norm.get("driver_updates_csv"),
-    }
-
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    dfs: dict[str, pd.DataFrame] = {}
-    for k, p in paths.items():
-        if not p:
-            errors.append(f"Plan missing normalized path for {k}")
-            continue
-        if not Path(p).exists():
-            errors.append(f"Missing file: {p} (copy from assets/templates)")
-            continue
-        try:
-            dfs[k] = _read_csv(p)
-        except Exception as e:
-            errors.append(f"Failed to read {p}: {e}")
-
-    if "metrics" in dfs:
-        e, w = _validate_metrics(dfs["metrics"], require_source_tags)
-        errors += e
-        warnings += w
-    if "estimates" in dfs:
-        e, w = _validate_estimates(dfs["estimates"])
-        errors += e
-        warnings += w
-    if "guidance" in dfs:
-        e, w = _validate_guidance(dfs["guidance"], require_source_tags)
-        errors += e
-        warnings += w
-    if "quotes" in dfs:
-        e, w = _validate_quotes(dfs["quotes"], require_source_tags)
-        errors += e
-        warnings += w
-    if "driver_updates" in dfs:
-        e, w = _validate_driver_updates(dfs["driver_updates"], require_source_tags)
-        errors += e
-        warnings += w
-
-    # Render report
-    lines: list[str] = []
-    lines.append("# Normalized Inputs Validation Report\n")
-    lines.append(f"Plan: `{plan_path}`\n")
-
-    if warnings:
-        lines.append("## Warnings")
-        for w in warnings:
-            lines.append(f"- {w}")
-        lines.append("")
-
-    if errors:
-        lines.append("## Errors")
-        for e in errors:
-            lines.append(f"- {e}")
-        lines.append("")
-
-    if not errors:
-        lines.append("✅ Inputs are valid (warnings may exist).")
-
-    report_path = audit_dir / "ValidationReport_NormalizedInputs.md"
-    write_text("\n".join(lines), str(report_path))
-
-    # Console
-    if warnings:
-        print("WARNINGS:")
-        for w in warnings:
-            print(f"  - {w}")
-    if errors:
-        print("ERRORS:")
-        for e in errors:
-            print(f"  - {e}")
-        print(f"\nSee {report_path}")
-        return 1
-
-    print(f"Inputs valid. See {report_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_plan.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_plan.py
deleted file mode 100644
index 60795eb..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/validate_plan.py
+++ /dev/null
@@ -1,180 +0,0 @@
-#!/usr/bin/env python3
-"""Validate plan.json for the earnings deep dive pipeline.
-
-This validator is intentionally strict on *structure* and *mode gating*,
-while allowing values to be 'MISSING' to preserve no-hallucination discipline.
-
-Exit codes:
-  0 = valid (may still have warnings)
-  1 = errors found
-"""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print("Usage: python scripts/validate_plan.py plan.json")
-    print("Validate an earnings deep-dive plan.json.")
-    raise SystemExit(0)
-
-try:
-    from .utils.io_utils import read_json, write_text
-    from .utils.validation_utils import is_missing
-except ImportError:
-    from utils.io_utils import read_json, write_text
-    from utils.validation_utils import is_missing
-
-
-def _p(x: Any) -> str:
-    return str(x)
-
-
-def validate_plan(plan: dict[str, Any]) -> dict[str, list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    # Required top-level keys
-    for key in ["event", "inputs", "outputs"]:
-        if key not in plan:
-            errors.append(f"Missing top-level key: '{key}'")
-
-    if errors:
-        return {"errors": errors, "warnings": warnings}
-
-    event = plan.get("event", {})
-    inputs = plan.get("inputs", {})
-    outputs = plan.get("outputs", {})
-    controls = plan.get("controls", {})
-
-    # Event fields (presence)
-    for key in ["ticker", "fiscal_period", "event_date", "timezone", "base_currency", "base_scale"]:
-        if key not in event:
-            errors.append(f"event.{key} is required")
-
-    # Inputs: artifact index
-    if "artifact_index_csv" not in inputs:
-        warnings.append(
-            "inputs.artifact_index_csv missing; recommend recording ArtifactIndex.csv for audit"
-        )
-
-    # Inputs: normalized
-    normalized = inputs.get("normalized", {})
-    for k in ["metrics_csv", "estimates_csv", "guidance_csv", "quotes_csv", "driver_updates_csv"]:
-        if k not in normalized:
-            errors.append(f"inputs.normalized.{k} is required (path)")
-
-    # Outputs
-    if "output_dir" not in outputs:
-        errors.append("outputs.output_dir is required")
-
-    render = outputs.get("render", {})
-    if not isinstance(render, dict):
-        errors.append("outputs.render must be an object")
-
-    model_update = outputs.get("model_update", {})
-    if model_update.get("enabled"):
-        mode = model_update.get("mode", "apply")
-        model = inputs.get("model", {})
-        prior = model.get("prior_model_xlsx")
-        driver_reg = model.get("driver_registry_csv")
-
-        if mode not in ("apply", "packet"):
-            errors.append("outputs.model_update.mode must be 'apply' or 'packet'")
-
-        if mode == "apply":
-            if is_missing(prior):
-                errors.append(
-                    "Model update mode=apply requires inputs.model.prior_model_xlsx (or set mode=packet)"
-                )
-            if is_missing(driver_reg):
-                errors.append(
-                    "Model update mode=apply requires inputs.model.driver_registry_csv (or set mode=packet)"
-                )
-
-        # Output registry needed only if diff enabled
-        if model_update.get("write_diff"):
-            out_reg = model.get("output_registry_csv")
-            if is_missing(out_reg):
-                warnings.append(
-                    "Diff requested but inputs.model.output_registry_csv is MISSING; diff will include drivers only"
-                )
-
-    # Controls sanity
-    if controls.get("require_source_tags") is False:
-        warnings.append(
-            "controls.require_source_tags=false conflicts with skill MUST rules; expect refusal or forced true"
-        )
-
-    return {"errors": errors, "warnings": warnings}
-
-
-def main() -> int:
-    if len(sys.argv) != 2:
-        print("Usage: python scripts/validate_plan.py plan.json")
-        return 1
-
-    plan_path = sys.argv[1]
-    try:
-        plan = read_json(plan_path)
-    except Exception as e:
-        print(f"ERROR: failed to read plan: {e}")
-        return 1
-
-    result = validate_plan(plan)
-    errors = result["errors"]
-    warnings = result["warnings"]
-
-    report_lines: list[str] = []
-    report_lines.append("# Plan Validation Report\n")
-    report_lines.append(f"Plan: `{plan_path}`\n")
-
-    if warnings:
-        report_lines.append("## Warnings\n")
-        for w in warnings:
-            report_lines.append(f"- {w}")
-        report_lines.append("")
-
-    if errors:
-        report_lines.append("## Errors\n")
-        for e in errors:
-            report_lines.append(f"- {e}")
-        report_lines.append("")
-
-    report_text = "\n".join(report_lines)
-
-    # Write to output folder if available
-    out_dir = None
-    try:
-        out_dir = plan.get("outputs", {}).get("output_dir")
-    except Exception:
-        out_dir = None
-
-    if out_dir:
-        report_path = Path(out_dir) / "audit" / "ValidationReport_Plan.md"
-        write_text(report_text, str(report_path))
-
-    # Console summary
-    if warnings:
-        print("WARNINGS:")
-        for w in warnings:
-            print(f"  - {w}")
-    if errors:
-        print("ERRORS:")
-        for e in errors:
-            print(f"  - {e}")
-        print("\nPlan is NOT valid.")
-        return 1
-
-    print("Plan is valid (warnings may exist).")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-deep-dive/scripts/verify_tearsheet.py b/plugins/financial-markets/skills/earnings-deep-dive/scripts/verify_tearsheet.py
deleted file mode 100644
index 5537fdc..0000000
--- a/plugins/financial-markets/skills/earnings-deep-dive/scripts/verify_tearsheet.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python3
-"""Lightweight tear sheet QA.
-
-Checks:
-- Required section headers exist
-- No unrendered {PLACEHOLDER} tokens remain
-- Beat/Miss and Guidance tables exist and include a SourceTag column
-- Table rows do not have empty SourceTag cells
-- Warn if tear sheet is likely >1 page (heuristic by line count)
-
-Exit codes:
-  0 = pass (warnings may exist)
-  1 = fail
-"""
-
-from __future__ import annotations
-
-import re
-import sys
-from pathlib import Path
-
-
-def _find_table_block(lines: list[str], header_pattern: str) -> list[str]:
-    # Find the first markdown table after a header
-    for i, line in enumerate(lines):
-        if re.match(header_pattern, line.strip()):
-            # scan forward for the first table header line
-            for j in range(i + 1, min(i + 50, len(lines))):
-                if lines[j].strip().startswith("|") and "SourceTag" in lines[j]:
-                    # take until blank line
-                    block = []
-                    for k in range(j, len(lines)):
-                        if not lines[k].strip():
-                            break
-                        block.append(lines[k])
-                    return block
-    return []
-
-
-def main() -> int:
-    if any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-        print("Usage: python scripts/verify_tearsheet.py output/TearSheet.md")
-        return 0
-    if len(sys.argv) != 2:
-        print("Usage: python scripts/verify_tearsheet.py output/TearSheet.md")
-        return 1
-
-    path = Path(sys.argv[1])
-    if not path.exists():
-        print(f"ERROR: Tear sheet not found: {path}")
-        return 1
-
-    txt = path.read_text(encoding="utf-8")
-    lines = txt.splitlines()
-
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    # Required sections
-    required_headers = [
-        "## Beat/Miss vs expectations",
-        "## Guidance delta",
-        "## Drivers",
-        "## Key quotes",
-        "## Model impact",
-        "## Watch list",
-    ]
-    for h in required_headers:
-        if h not in txt:
-            errors.append(f"Missing required section header: {h}")
-
-    # Unrendered placeholders
-    if re.search(r"\{[A-Z0-9_]+\}", txt):
-        errors.append("Found unrendered {PLACEHOLDER} token(s)")
-    if re.search(r"\bTODO\b", txt):
-        errors.append("Found TODO placeholder token(s)")
-    unresolved_missing = re.findall(
-        r"MISSING:\s*(add|explain|map|summarize|write|list|populate|decompose)[^\n]*",
-        txt,
-        flags=re.IGNORECASE,
-    )
-    if unresolved_missing:
-        errors.append("Found unresolved authoring placeholder(s) beginning with MISSING:")
-
-    # Tables
-    beat_tbl = _find_table_block(lines, r"^## Beat/Miss vs expectations$")
-    guid_tbl = _find_table_block(lines, r"^## Guidance delta$")
-
-    if not beat_tbl:
-        errors.append("Beat/Miss table not found or missing SourceTag column")
-    if not guid_tbl:
-        errors.append("Guidance table not found or missing SourceTag column")
-
-    def _check_src_cells(tbl: list[str], label: str):
-        # Skip first two lines (header + separator)
-        for row in tbl[2:]:
-            if not row.strip().startswith("|"):
-                continue
-            cells = [c.strip() for c in row.strip().strip("|").split("|")]
-            if len(cells) < 1:
-                continue
-            src = cells[-1]
-            if src == "":
-                errors.append(f"{label}: empty SourceTag cell")
-
-    if beat_tbl:
-        _check_src_cells(beat_tbl, "Beat/Miss")
-    if guid_tbl:
-        _check_src_cells(guid_tbl, "Guidance")
-
-    # 1-page heuristic
-    if len(lines) > 140:
-        warnings.append(
-            f"Tear sheet is {len(lines)} lines; may exceed one page depending on formatting"
-        )
-
-    if warnings:
-        print("WARNINGS:")
-        for w in warnings:
-            print(f"  - {w}")
-
-    if errors:
-        print("ERRORS:")
-        for e in errors:
-            print(f"  - {e}")
-        return 1
-
-    print("Tear sheet QA passed (warnings may exist).")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/earnings-preview/SKILL.md b/plugins/financial-markets/skills/earnings-preview/SKILL.md
deleted file mode 100644
index 6f1325f..0000000
--- a/plugins/financial-markets/skills/earnings-preview/SKILL.md
+++ /dev/null
@@ -1,112 +0,0 @@
----
-name: earnings-preview
-description: Use when preparing full pre-earnings preview reports with executive summary, expectation bar, guidance credibility, KPI dashboard, scenarios, and call questions. Do not use after results or for short summaries unless the user explicitly asks for a summary/short version.
----
-
-# Earnings Preview
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML pre-earnings report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For an explicit pre-earnings preview, full preview report, or reusable/source-heavy pre-print package, the default resolves the presentation surface to a polished standalone HTML pre-earnings report unless the user requests an alternate surface, a quick/no-file answer, or workbook/model output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML pre-earnings report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-Produce a full, decision-grade pre-print report by default: executive summary first, then what the market expects, what can move the stock, what must be answered on the call, and what evidence is missing.
-
-## Route
-
-- `full preview report`: default route for any pre-earnings preview request. For an explicit pre-earnings preview, full preview report, or reusable/source-heavy pre-print package, produce a polished standalone HTML pre-earnings report following `../../shared/html-artifact-standard.md`; let the named investor question determine the hierarchy. Always surface the expectation bar, stock-reaction drivers, key evidence, call watch items, source posture, and missing evidence. Add KPI trajectories, guidance credibility, peer/sector read-throughs, macro context, market events, reaction/options context, bull/base/bear cases, and extended call questions only when relevant and source-supported.
-- `explicit short summary`: use only when the user explicitly asks for `summary`, `short`, `quick read`, `one-pager`, `top things to watch`, or similar compression. Do not maintain this as a separate tear-sheet artifact; pare back the full preview report while preserving freeze time, source posture, key bar numbers, top debates, call questions, and missing evidence.
-- `standardized dashboard`: use only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, tabbed dashboard, or structured payload-driven render. Keep this skill as the analysis owner and hand the resulting `public_equity_investing_dashboard.v1` payload to `dashboard-builder`. Use `references/DASHBOARD_PACK.md` for module mapping.
-- `deterministic export pack`: only when requested or plan-driven; validate local inputs, run packaged scripts, and write support artifacts such as `preview_note.md`, `exports.xlsx`, `exports/*.csv`, `qa_report.*`, and `run_manifest.json`. The workbook or rendered dashboard/report is the hero artifact; CSV/JSON/Markdown sidecars are support/audit files unless explicitly requested.
-
-Load `references/REFERENCE_ROUTER.md` first, then only the smallest reference needed for the chosen route.
-
-## Non-Negotiables
-
-- State freeze time and source timestamps for consensus, whisper, options, price/reaction, and other time-sensitive inputs.
-- Never fabricate numbers, dates, guidance, option data, peer read-throughs, or whisper color.
-- Separate company/SEC/IR facts, consensus, whisper, analyst inference, user assumptions, and web fallback.
-- Keep GAAP/non-GAAP labels, units, scale, period mapping, and KPI definitions explicit.
-- Identify EPS-quality landmines before the print: tax rate, share count, equity-investment marks, FX, asset sales, impairments, restructuring, litigation, non-operating income/expense, and any mismatch between GAAP EPS, adjusted EPS, and consensus basis.
-- Include the last reported baseline and the consensus/guide bar where sourced. Include quarterly key metrics and growth trajectory when they sharpen the investor question or the stock-reaction setup rather than as mandatory display inventory.
-- For dashboard handoffs, include the earnings visualization pack only when source-backed data exists: quarterly revenue, gross profit, net income, and the best source-backed profitability margin history; estimated EPS versus actual EPS for the past five quarters; and equity-price history annotated with material market events. Omit any chart whose required series is missing, stale, or not comparable, and surface that gap in `missing_evidence`.
-- Treat the margin line in financial trend charts as part of the pre-print risk setup. Default to net margin only when net income is a fair recurring-profitability proxy. Prefer operating margin or another issuer-specific source-backed margin when net income has been distorted by tax, equity-investment marks, FX, asset sales, impairments, restructuring, litigation, or other non-operating items. State the selected `margin_metric`, `margin_label`, and `margin_rationale` in dashboard payloads when using a line other than net margin.
-- Rank dashboard highlights by investor salience rather than mechanical size. If a growth rate, acceleration, surprise %, guide delta, backlog, or normalized metric is what matters for the stock, use that as the highlighted value and put the absolute value in the supporting detail.
-- Include major news coverage and market events when they can affect the earnings setup, guide credibility, estimate bar, multiple, positioning, or call questions. Scan the last quarter, the last twelve months, and forward-looking anticipated events; cite every event and label uncertain windows.
-- Use primary company/SEC/IR and connected sources before general web; label fallback sources.
-- Do not imply MNPI or confidential whisper data; weak whisper support becomes qualitative setup language.
-- Without sourced implied move, relevant positioning/context, and adequate consensus or whisper evidence for the stated question, provide an earnings setup and reaction framework rather than a trade-ready position instruction. Use `Wait for proof`, `Monitor`, or similarly evidence-calibrated language where appropriate.
-- Treat an options-implied move as an earnings reaction bar only when the contract tenor reasonably isolates the earnings event. If the available expiry includes substantial pre-event trading time or another material catalyst window, label the metric as `expiry-tenor volatility context`, explain the limitation, and do not feature it as a first-read earnings-move tile unless the limitation is immediately prominent.
-- Do not create optional exports or support files unless requested or plan-driven. The workflow-resolved standalone HTML pre-earnings report is the planned hero artifact for an explicit full preview request.
-- Do not shorten the default preview just because some inputs are missing; keep full analysis of the investment question and label unavailable optional modules or inputs explicitly.
-
-## Workflow
-
-1. Classify route. Default to `full preview report` unless the user explicitly asks for a summary/short format.
-2. Anchor the preview quarter; map `t`, `t-1`, `t-4`, and `t-8`.
-3. Freeze sources/timestamps and normalize units, GAAP/non-GAAP basis, KPI definitions, and source posture.
-4. Identify 3-6 stock-moving KPIs and build the expectation bar: consensus, guide, whisper if sourced, prior setup, key-metric baseline, growth trajectory, EPS-quality watch items, and base framing.
-5. Add guidance credibility, peer read-throughs, reaction/options, macro/sector context, and major news/market events when relevant and sourced. Separate company-specific events from peer, macro, regulatory, legal, FX/rates, commodity, industry, and upcoming catalyst events.
-6. Build supportable bull/base/bear framing and write only decision-relevant call questions with listen-fors and falsifiers. Convert material news/event items into call questions when they create a contradiction or open diligence item.
-7. Run QA for executive-summary coverage, freeze time, period consistency, source labels, units/scale, chart/display unit agreement, readable citation placement, cited event claims, missing evidence, and unresolved placeholders.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source and consensus freeze, KPI/guide bar, peer/macro/reaction context, scenarios, call questions, and QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Deterministic Contract
-
-Scripts are local materializers; they do not fetch data or replace analyst judgment.
-
-- Required inputs: `company_master.csv`, `fiscal_period_index.csv`, `reported_financials.csv`, `kpi_timeseries.csv`, `consensus_estimates.csv`.
-- Required for full scripted pack: `guidance_history.csv` and canonical event file `event_calendar.csv` can be empty but must exist with headers.
-- Optional inputs: `whisper_estimates.csv`, `price_returns.csv`, `options_snapshot.csv`, `qual_notes.csv`, `scenario_assumptions.csv`.
-- Schema source: `references/SCHEMAS.md` plus `assets/plan_schema.json`.
-- Generated Markdown support notes must not contain unresolved bracket tokens, `TODO`, or unfinished placeholders.
-- `exports.xlsx` starts with `Cover`, a dashboard sheet summarizing company/ticker, preview period, freeze time, workbook mode, warnings, consensus bar, bull/base/bear revenue and EPS, KPI dashboard row counts, call watch item, input file count, and workbook map.
-
-## Handoffs
-
-Use `financial-source-of-truth` for evidence conflicts, `financials-normalizer` for messy tables, `scenario-sensitivity-generator` for deeper cases, `dashboard-builder` for responsive pre-earnings dashboard rendering, `earnings-deep-dive` after the print, `equity-model-update` for model refreshes, `long-short-pitch` for trade expression, and `memo-builder` for formal memos.
-
-## HTML Guidance
-
-For a substantive HTML full preview report, load `../../shared/html-artifact-standard.md` and apply these preview-specific requirements:
-
-- Lead with the expectation bar and the stock-reaction debate. If the user names a specific issue, such as AI memory demand, put that issue and its reaction drivers ahead of broad KPI archives or general diligence sections.
-- Use four to six first-read tiles only when each has a distinct decision job, such as event timing, guide/consensus bar, operating proof point, market setup, an event-isolating implied move when available, or most important evidence gap.
-- Make visible what is known, what the market likely requires, what could surprise, and what evidence is missing before taking incremental event risk.
-- Include detailed KPI dashboards, peer/macro context, historical reactions, options/implied-move analysis, scenario maps, and extended call-question lists only when relevant and source-backed. Do not force a fixed dashboard module inventory into a flexible report.
-- Match chart axes and labels to the units stated in the adjacent headings or tables. Omit a misleading chart rather than mixing units or scales.
-- Keep citations traceable but readable; do not fragment dates, times, ticker symbols, product names, or product specifications into separately linked characters or tokens.
-- Visually inspect the HTML according to the shared artifact standard before delivery and iterate on hierarchy, legibility, clipping, crowding, citation noise, and whether the requested investment question is immediately visible.
-
-## Public Equity PM Judgment Layer
-
-For substantial previews, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Default PM question: is the bar beatable, does it matter for the stock, and what would change sizing into or after the print?
-
-Required PM judgment:
-- State the expectation bar, consensus dispersion, guidance setup, implied move or reaction risk when available, and the estimate-revision path.
-- Separate company fundamentals from stock setup: what is priced in, what the market may be ignoring, and what would make a bad print buyable or a good print fadeable.
-- Include call-question falsifiers, listen-for items, and position actions: `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `watchlist`, or `wait for proof`.
-- For ETF/index diligence, include constituent weight, passive ownership/flow relevance, liquidity, benchmark exposure, and rebalance/event risk when relevant.
diff --git a/plugins/financial-markets/skills/earnings-preview/agents/openai.yaml b/plugins/financial-markets/skills/earnings-preview/agents/openai.yaml
deleted file mode 100644
index 380d0d6..0000000
--- a/plugins/financial-markets/skills/earnings-preview/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Earnings-Preview"
-  short_description: "Full pre-earnings preview reports with expectation bar, EPS-quality watch, scenarios, and call questions."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/earnings-preview/assets/plan_schema.json b/plugins/financial-markets/skills/earnings-preview/assets/plan_schema.json
deleted file mode 100644
index e375070..0000000
--- a/plugins/financial-markets/skills/earnings-preview/assets/plan_schema.json
+++ /dev/null
@@ -1,63 +0,0 @@
-{
-  "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "title": "Earnings Preview Pack plan.json",
-  "type": "object",
-  "required": [
-    "ticker",
-    "fiscal_period_id",
-    "sector_pack",
-    "input_dir",
-    "output_dir",
-    "freeze_time"
-  ],
-  "properties": {
-    "ticker": {"type": "string"},
-    "company_name": {"type": "string"},
-    "fiscal_period_id": {"type": "string", "description": "Format FYYYYYQ[1-4]"},
-    "sector_pack": {"type": "string"},
-    "input_dir": {"type": "string"},
-    "output_dir": {"type": "string"},
-    "freeze_time": {"type": "string", "description": "ISO8601, ideally UTC"},
-    "consensus_statistic": {"type": "string", "enum": ["median", "mean", "last"], "default": "median"},
-
-    "kpi_overrides": {
-      "type": "object",
-      "properties": {
-        "include": {"type": "array", "items": {"type": "string"}},
-        "exclude": {"type": "array", "items": {"type": "string"}}
-      },
-      "additionalProperties": false
-    },
-
-    "inputs": {
-      "type": "object",
-      "properties": {
-        "company_master": {"type": "string"},
-        "fiscal_period_index": {"type": "string"},
-        "event_calendar": {"type": "string"},
-        "reported_financials": {"type": "string"},
-        "kpi_timeseries": {"type": "string"},
-        "guidance_history": {"type": "string"},
-        "consensus_estimates": {"type": "string"},
-        "whisper_estimates": {"type": "string"},
-        "price_returns": {"type": "string"},
-        "options_snapshot": {"type": "string"},
-        "qual_notes": {"type": "string"},
-        "scenario_assumptions": {"type": "string"}
-      },
-      "additionalProperties": true
-    },
-
-    "outputs": {
-      "type": "object",
-      "properties": {
-        "render_note": {"type": "boolean", "default": true},
-        "export_xlsx": {"type": "boolean", "default": true},
-        "export_csv": {"type": "boolean", "default": true},
-        "note_template": {"type": "string"}
-      },
-      "additionalProperties": true
-    }
-  },
-  "additionalProperties": true
-}
diff --git a/plugins/financial-markets/skills/earnings-preview/assets/sector_kpi_packs.yaml b/plugins/financial-markets/skills/earnings-preview/assets/sector_kpi_packs.yaml
deleted file mode 100644
index 2bf3523..0000000
--- a/plugins/financial-markets/skills/earnings-preview/assets/sector_kpi_packs.yaml
+++ /dev/null
@@ -1,137 +0,0 @@
-# Default KPI packs by sector. Metric IDs should match the canonical list used in input files.
-# You can override or extend KPIs per run in plan.json via kpi_overrides.include/exclude.
-
-packs:
-  saas:
-    display_name: "SaaS / Subscription"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - arr
-      - nrr
-      - grr
-      - net_new_arr
-      - gross_margin
-      - operating_margin
-      - fcf_margin
-    secondary_kpis:
-      - bookings
-      - billings
-      - rpo
-      - sbc_pct_revenue
-      - headcount
-      - cac
-      - cac_payback_months
-    driver_model: "saas_simple"
-
-  internet_consumer:
-    display_name: "Internet Consumer / Engagement"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - dau
-      - mau
-      - dau_mau
-      - arpu
-      - cpm
-    secondary_kpis:
-      - time_spent_minutes
-      - ads_load
-      - conversion_rate
-      - aov
-    driver_model: "consumer_ads_simple"
-
-  payments_fintech:
-    display_name: "Payments / Fintech"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - tpv
-      - take_rate
-      - active_accounts
-      - loss_rate
-    secondary_kpis:
-      - net_revenue
-      - gross_profit
-      - chargeback_rate
-    driver_model: "payments_simple"
-
-  semiconductors:
-    display_name: "Semiconductors / Hardware"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - gross_margin
-      - units
-      - asp
-      - backlog
-      - inventory_days
-    secondary_kpis:
-      - book_to_bill
-      - lead_time_weeks
-      - utilization_pct
-    driver_model: "units_asp_simple"
-
-  consumer_retail:
-    display_name: "Consumer / Retail"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - comp_sales
-      - gross_margin
-      - inventory
-      - inventory_turns
-    secondary_kpis:
-      - traffic
-      - aur
-      - markdown_rate
-      - shrink_rate
-    driver_model: "retail_simple"
-
-  industrials:
-    display_name: "Industrials"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - orders
-      - backlog
-      - book_to_bill
-      - operating_margin
-      - fcf
-    secondary_kpis:
-      - price_mix
-      - volume
-      - utilization_pct
-    driver_model: "industrials_simple"
-
-  banks:
-    display_name: "Banks"
-    primary_kpis:
-      - eps_diluted
-      - nii
-      - nim
-      - deposits
-      - loans
-      - net_charge_offs
-      - cet1
-    secondary_kpis:
-      - fee_income
-      - efficiency_ratio
-      - npl
-    driver_model: "banks_simple"
-
-  energy:
-    display_name: "Energy"
-    primary_kpis:
-      - revenue
-      - eps_diluted
-      - production_volume
-      - realized_price
-      - loe
-      - capex
-      - fcf
-      - net_debt
-    secondary_kpis:
-      - hedge_realized_price
-      - decline_rate
-    driver_model: "energy_simple"
diff --git a/plugins/financial-markets/skills/earnings-preview/assets/templates/driver_model_templates.yaml b/plugins/financial-markets/skills/earnings-preview/assets/templates/driver_model_templates.yaml
deleted file mode 100644
index 3dddc21..0000000
--- a/plugins/financial-markets/skills/earnings-preview/assets/templates/driver_model_templates.yaml
+++ /dev/null
@@ -1,162 +0,0 @@
-# Simple driver model templates used for scenario translation and sensitivity tables.
-# These are intentionally lightweight and heuristic; replace with your house model as needed.
-
-models:
-  saas_simple:
-    description: "SaaS: revenue/ARR driven; retention and net adds are key debates."
-    required_kpis:
-      - revenue
-      - arr
-      - nrr
-      - gross_margin
-      - operating_margin
-    sensitivity_rules:
-      # Heuristic: 1% ARR difference -> ~1% revenue difference (quarterly near-term)
-      - target_metric_id: revenue
-        driver_metric_id: arr
-        driver_type: pct
-        beta: 1.0
-        units: "pct_to_pct"
-      # Heuristic: 100 bps NRR -> +0.5% revenue over near term (tunable)
-      - target_metric_id: revenue
-        driver_metric_id: nrr
-        driver_type: bps
-        beta: 0.005
-        units: "pct_per_bps"
-
-  consumer_ads_simple:
-    description: "Ads-driven consumer internet: engagement and pricing (CPM) drive revenue."
-    required_kpis:
-      - revenue
-      - dau
-      - mau
-      - arpu
-      - cpm
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: dau
-        driver_type: pct
-        beta: 0.7
-        units: "pct_to_pct"
-      - target_metric_id: revenue
-        driver_metric_id: cpm
-        driver_type: pct
-        beta: 0.5
-        units: "pct_to_pct"
-
-  payments_simple:
-    description: "Payments: TPV and take rate drive net revenue."
-    required_kpis:
-      - revenue
-      - tpv
-      - take_rate
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: tpv
-        driver_type: pct
-        beta: 0.8
-        units: "pct_to_pct"
-      - target_metric_id: revenue
-        driver_metric_id: take_rate
-        driver_type: bps
-        beta: 0.01
-        units: "pct_per_bps"
-
-  units_asp_simple:
-    description: "Semis/hardware: units and ASP drive revenue; GM drives EPS."
-    required_kpis:
-      - revenue
-      - units
-      - asp
-      - gross_margin
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: units
-        driver_type: pct
-        beta: 0.6
-        units: "pct_to_pct"
-      - target_metric_id: revenue
-        driver_metric_id: asp
-        driver_type: pct
-        beta: 0.6
-        units: "pct_to_pct"
-
-  retail_simple:
-    description: "Retail: comps drive revenue; GM and markdowns drive EPS."
-    required_kpis:
-      - revenue
-      - comp_sales
-      - gross_margin
-      - inventory
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: comp_sales
-        driver_type: pct
-        beta: 0.9
-        units: "pct_to_pct"
-      - target_metric_id: eps_diluted
-        driver_metric_id: gross_margin
-        driver_type: bps
-        beta: 0.02
-        units: "eps_per_bps"
-
-  industrials_simple:
-    description: "Industrials: orders/backlog and margins drive the quarter."
-    required_kpis:
-      - revenue
-      - orders
-      - backlog
-      - operating_margin
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: orders
-        driver_type: pct
-        beta: 0.5
-        units: "pct_to_pct"
-      - target_metric_id: operating_margin
-        driver_metric_id: price_mix
-        driver_type: pct
-        beta: 0.1
-        units: "pp_per_pct"
-
-  banks_simple:
-    description: "Banks: NIM and balances drive NII; credit drives EPS."
-    required_kpis:
-      - eps_diluted
-      - nii
-      - nim
-      - deposits
-      - loans
-      - net_charge_offs
-    sensitivity_rules:
-      - target_metric_id: nii
-        driver_metric_id: nim
-        driver_type: bps
-        beta: 0.015
-        units: "pct_per_bps"
-      - target_metric_id: eps_diluted
-        driver_metric_id: net_charge_offs
-        driver_type: pct
-        beta: -0.3
-        units: "pct_to_pct"
-
-  energy_simple:
-    description: "Energy: volume and realized price drive revenue/FCF; costs and capex drive leverage."
-    required_kpis:
-      - revenue
-      - production_volume
-      - realized_price
-      - loe
-      - capex
-      - fcf
-    sensitivity_rules:
-      - target_metric_id: revenue
-        driver_metric_id: production_volume
-        driver_type: pct
-        beta: 0.6
-        units: "pct_to_pct"
-      - target_metric_id: revenue
-        driver_metric_id: realized_price
-        driver_type: pct
-        beta: 0.6
-        units: "pct_to_pct"
diff --git a/plugins/financial-markets/skills/earnings-preview/assets/templates/preview_note_template.md b/plugins/financial-markets/skills/earnings-preview/assets/templates/preview_note_template.md
deleted file mode 100644
index b6729f2..0000000
--- a/plugins/financial-markets/skills/earnings-preview/assets/templates/preview_note_template.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# [COMPANY_NAME] ([TICKER]) Earnings Preview Pack - [FISCAL_PERIOD_ID]
-Freeze time: [FREEZE_TS] | Consensus snapshot: [CONSENSUS_SNAPSHOT_TS]
-Preview quarter mapping: t = [PREVIEW_PERIOD] | t-1 = [PRIOR_PERIOD] | t-4 = [YEAR_AGO_PERIOD]
-
-## Page 1 - Setup snapshot
-
-### 1) Thesis / setup (one paragraph)
-[THESIS_1P]
-
-### 2) Last quarter recap
-| Item | Takeaway |
-|---|---|
-| Beat / miss | [LAST_Q_BEAT_MISS] |
-| Narrative | [LAST_Q_NARRATIVE] |
-| Stock reaction | [LAST_Q_REACTION] |
-| Debated KPI / issue | [LAST_Q_DEBATED_KPI] |
-| Still relevant now | [LAST_Q_STILL_RELEVANT] |
-
-### 3) Outstanding guidance
-| Metric | Guidance value or wording | Source quarter | Type | Note |
-|---|---|---|---|---|
-| Revenue | [GUIDE_REV] | [GUIDE_REV_SOURCE_Q] | [GUIDE_REV_TYPE] | [GUIDE_REV_NOTE] |
-| EPS | [GUIDE_EPS] | [GUIDE_EPS_SOURCE_Q] | [GUIDE_EPS_TYPE] | [GUIDE_EPS_NOTE] |
-| KPI / margin | [GUIDE_KPI] | [GUIDE_KPI_SOURCE_Q] | [GUIDE_KPI_TYPE] | [GUIDE_KPI_NOTE] |
-
-### 4) Guidance credibility + whisper framing
-- Guidance track record: [GUIDANCE_CREDIBILITY]
-- Whisper framing: [WHISPER_FRAMING]
-- Provenance / confidence: [WHISPER_CONFIDENCE]
-
-### 5) What matters this quarter (top 3-5 KPIs)
-- [WHAT_MATTERS_1]
-- [WHAT_MATTERS_2]
-- [WHAT_MATTERS_3]
-
-### 6) Expectation bar: Street vs whisper vs our base
-
-| Metric | Street (cons) | Whisper | Delta (whisper-cons) | Notes |
-|---|---:|---:|---:|---|
-| Revenue | [CONS_REV] | [WHISPER_REV] | [REV_DELTA] | [REV_NOTE] |
-| EPS (dil.) | [CONS_EPS] | [WHISPER_EPS] | [EPS_DELTA] | [EPS_NOTE] |
-| KPI 1 | [CONS_KPI1] | [WHISPER_KPI1] | [KPI1_DELTA] | [KPI1_NOTE] |
-| KPI 2 | [CONS_KPI2] | [WHISPER_KPI2] | [KPI2_DELTA] | [KPI2_NOTE] |
-
-### 7) EPS-quality watch
-[EPS_QUALITY_WATCH]
-
-### 8) Options / positioning (optional)
-- Implied move: [IMPLIED_MOVE_PCT]% (method: ATM straddle/IV)
-- Skew/term notes: [VOL_NOTES]
-
----
-
-## Page 2 - KPI dashboard + scenarios
-
-### 5) KPI dashboard (auto-flag what matters)
-[KPI_DASHBOARD_TABLE]
-
-Legend: growth is QoQ/YoY for level metrics; bps deltas for rate metrics. Two-year stack used where seasonal.
-
-### 6) Peer read-throughs
-| Category | Name | Signal | Why it matters | Read-through | Confidence |
-|---|---|---|---|---|---|
-| [CATEGORY] | [NAME] | [SIGNAL] | [WHY] | [READ_THROUGH] | [CONFIDENCE] |
-
-### 7) Scenarios (bull/base/bear)
-
-| Case | Key driver(s) | Revenue | EPS | Expected reaction | Kill criteria |
-|---|---|---:|---:|---|---|
-| Bull | [BULL_DRIVERS] | [BULL_REV] | [BULL_EPS] | [BULL_RXN] | [BULL_KILL] |
-| Base | [BASE_DRIVERS] | [BASE_REV] | [BASE_EPS] | [BASE_RXN] | [BASE_KILL] |
-| Bear | [BEAR_DRIVERS] | [BEAR_REV] | [BEAR_EPS] | [BEAR_RXN] | [BEAR_KILL] |
-
----
-
-## Page 3 - Debate map + call questions
-
-### 8) Debate map (cruxes)
-**Bull case**
-- [BULL_1]
-- [BULL_2]
-
-**Bear case**
-- [BEAR_1]
-- [BEAR_2]
-
-**Where we think the bar is**
-- [BAR_1]
-
-### 9) Must-answer questions for the call
-[DYNAMIC_QUESTION_LIST]
-
-### 10) Historical reaction framework
-| Quarter | Beat / miss context | Next-day move | Reaction driver | Continued or reversed |
-|---|---|---:|---|---|
-| [QTR] | [CONTEXT] | [MOVE] | [DRIVER] | [DRIFT] |
-
-Current implied move vs history: [IMPLIED_VS_HISTORY]
-
-### 11) Macro / sector backdrop
-- Positive: [MACRO_POSITIVE]
-- Negative: [MACRO_NEGATIVE]
-- Uncertain: [MACRO_UNCERTAIN]
-
-### 12) Potential surprises / catalysts
-| Item | Likely direction | Evidence strength | Signal quality |
-|---|---|---|---|
-| [CATALYST] | [DIRECTION] | [EVIDENCE] | [SIGNAL_QUALITY] |
-
-### 13) Final pre-earnings checklist
-- Top expectation-bar numbers: [TOP_BAR_NUMBERS]
-- Top 3 metrics to watch: [TOP_3_METRICS]
-- Bull catalyst: [BULL_CATALYST]
-- Bear risk: [BEAR_RISK]
-- Key read-through implication: [KEY_READ_THROUGH]
-- Historical average move vs current implied move: [MOVE_COMPARISON]
-- Single most important call watch: [CALL_WATCH]
-
-### 14) Watch-fors / landmines
-- [WATCHFOR_1]
-- [WATCHFOR_2]
-- [WATCHFOR_3]
-
----
-
-## Page 4 (optional) - Appendix
-
-### A1) Prior quarter: surprise and stock reaction
-[PRIOR_Q_TABLE]
-
-### A2) Notes and provenance
-[PROVENANCE_NOTES]
diff --git a/plugins/financial-markets/skills/earnings-preview/assets/templates/scenario_assumptions_template.csv b/plugins/financial-markets/skills/earnings-preview/assets/templates/scenario_assumptions_template.csv
deleted file mode 100644
index 2a324ce..0000000
--- a/plugins/financial-markets/skills/earnings-preview/assets/templates/scenario_assumptions_template.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-scenario_name,metric_id,delta_type,delta_value,notes
-base,revenue,pct,0.0,Base case anchored to consensus
-bull,revenue,pct,0.02,Bull: +2% rev vs consensus
-bear,revenue,pct,-0.03,Bear: -3% rev vs consensus
-bull,gross_margin,bps,50,Bull: +50 bps GM vs consensus
-bear,gross_margin,bps,-75,Bear: -75 bps GM vs consensus
-bull,eps_diluted,pct,0.05,Bull: +5% EPS vs consensus (or compute via margin)
-bear,eps_diluted,pct,-0.10,Bear: -10% EPS vs consensus (or compute via margin)
diff --git a/plugins/financial-markets/skills/earnings-preview/references/CALL_QUESTION_BANK.md b/plugins/financial-markets/skills/earnings-preview/references/CALL_QUESTION_BANK.md
deleted file mode 100644
index 606fdb3..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/CALL_QUESTION_BANK.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Call Question Bank
-
-Select 5-12 questions tied to the quarter's stock debate. Each question needs why it matters, thesis-validating answer, thesis-breaking answer, and listen-fors.
-
-## Universal Buckets
-
-- Demand: order trends, usage, retention, backlog, traffic, conversion, pipeline quality.
-- Pricing: price realization, discounting, mix, promo intensity, contract duration.
-- Margins: gross margin drivers, utilization, input costs, cloud/infra, labor, freight, one-time items.
-- Guidance: assumptions, conservatism, macro sensitivity, FX, seasonality, visibility.
-- GTM/customer: enterprise/SMB mix, channel health, sales productivity, churn, cohort behavior.
-- Competition: win rates, share shifts, pricing pressure, substitute products.
-- Ops/capital allocation: inventory, working capital, capex, buybacks, M&A, debt, restructuring.
-
-## Sector Add-ons
-
-- SaaS: ARR/RPO conversion, NRR/GRR, churn, seat expansion, sales cycles, AI/cloud cost impact.
-- Internet/ads/marketplaces: engagement, monetization, take rate, advertiser demand, seller/merchant health.
-- Consumer/retail: traffic vs ticket, promo/markdowns, inventory, shrink, loyalty, channel mix.
-- Semis/hardware: backlog, lead times, channel inventory, utilization, capacity, ASP, mix, customer digestion.
-- Banks/financials: deposit beta, NIM path, credit migration, charge-offs, capital return, regulatory constraints.
-- Industrials: orders, book-to-bill, backlog conversion, price/cost, labor, end-market mix.
-- Energy: production, realized price, capex discipline, hedges, decline rates, return of capital.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/earnings-preview/references/DASHBOARD_PACK.md
deleted file mode 100644
index ff7145f..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Earnings Preview Dashboard Pack
-
-Use this pack only for an explicitly selected standardized dashboard, reusable dashboard template, PM cockpit, tabbed dashboard, or structured payload-driven render for a pre-earnings request. For an ordinary standalone HTML full preview report, follow the flexible HTML artifact standard and the owning skill's preview-specific guidance instead of this fixed module map.
-
-## Producer Role
-
-`earnings-preview` owns the analysis. `dashboard-builder` owns the shared HTML shell, module rendering, and responsive QA.
-
-## Recommended Payload
-
-- `mode`: `earnings_preview`
-- `layout`: `single_page` for full PM diligence dashboards unless the user explicitly requests tabs
-- `hero.callout`: the single stock-moving debate into the print
-- `snapshot`: event timing, latest price/valuation setup if sourced, top consensus/guide bar, most important KPI bar, biggest guide-cadence risk. Do not duplicate the quarter if it appears in the hero. When a growth rate, acceleration, or surprise is the stock-moving setup, make it the tile value and put the absolute amount in the detail.
-- `financial_trend_chart`: include when source-backed revenue, gross profit, net income, and a comparable profitability margin history exists for the last several reported quarters. Use net margin only when it is a good recurring-profitability proxy; otherwise set `margin_metric`, `margin_label`, and `margin_rationale` for operating margin, adjusted operating margin, EBITDA margin, FCF margin, or the issuer's source-backed KPI.
-- `eps_actual_vs_estimate_chart`: include when the past five quarters have estimated EPS and actual EPS on the same GAAP/adjusted/clean basis.
-- `equity_price_event_chart`: include only when sourced equity-price history is substantive enough to visualize: daily tapes need at least 10 distinct points over at least 7 calendar days, hourly tapes need at least 24 time-stamped points over at least 6 hours, and minute/intraday tapes need at least 60 time-stamped points over at least 45 minutes. If only a few closes or article-reported prices are available, omit the chart and use `market_events` plus `missing_evidence`.
-- `market_events`: source-backed major news, market events, regulatory/macro items, peer read-throughs, and upcoming catalysts from the last quarter, last twelve months, and forward-looking window when relevant to the print.
-- `sources`: company IR/filings first, consensus/market sources second, web fallback clearly labeled
-
-## Tabs And Modules
-
-1. `overview`
-   - `decision_box`: stance into the print, bar location, top stock-moving cruxes, one thing that changes conviction
-   - `cards`: top debates or key diligence questions
-2. `expectation-bar`
-   - `table`: consensus, guide, whisper if sourced, source timestamp, confidence, and PM read
-   - `missing_evidence`: terminal-only consensus, options, or whisper fields
-3. `kpi-board`
-   - `financial_trend_chart`: revenue, gross profit, net income, and the selected profitability margin by quarter when complete comparable data exists. Prefer operating margin or another explicitly labeled metric when historical net margin is distorted by non-operating, tax, equity-investment, FX, restructuring, litigation, impairment, asset-sale, or other one-time items.
-   - `eps_actual_vs_estimate_chart`: estimated EPS versus actual EPS for the past five quarters when estimate history exists
-   - `key_metrics`: quarterly key-metric baseline, consensus/guide bar, required growth/acceleration, and PM read using the issuer's business model
-   - `growth_trajectory`: acceleration/deceleration map for the metrics most likely to drive the print
-   - `bar_chart`: compact chart for KPI bars, revenue mix, segment growth, or guide delta when the visual helps a PM scan faster
-4. `guidance`
-   - `cards`: guide bridge, cadence, credibility, and key model pressure points
-5. `macro-reads`
-   - `cards` or `table`: peer, macro, sector, channel, and alternative-data read-throughs with confidence
-6. `market-events`
-   - `equity_price_event_chart`: recent equity-price history annotated with material company/sector/macro/regulatory events only when the price tape passes the daily/hourly/minute minimums above
-   - `market_events`: recent company/sector/macro/regulatory news and upcoming events with date/window, source, potential impact, PM read, and confidence
-   - `missing_evidence`: any terminal-only news, channel-check, options, regulatory, or peer-event fields still needed
-7. `scenarios`
-   - `scenario_map`: bull/base/bear drivers, ranges where sourced, and falsifiers
-8. `call-prep`
-   - `question_list`: must-answer questions, why each matters, listen-fors, validates/breaks thesis
-9. `refresh-gaps`
-   - `missing_evidence`: exact data refresh list before trading the print
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every material number, estimate, guide/consensus field, event date, market move, KPI, and assumption inline where it appears.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-- Keep the top-level source ledger mandatory; label missing consensus, options, whisper, peer, and market data in `missing_evidence`.
-- Keep raw JSON, Markdown notes, CSV exports, and run logs as support/audit material unless the user explicitly asks for them.
-
-## Do Not
-
-- Do not let the dashboard replace the full pre-print analytical structure.
-- Do not hide missing options-implied move, consensus timestamp, or whisper support.
-- Do not imply a trade-ready pre-print position from company guidance and public operating evidence alone when implied move, relevant market context, or required expectation evidence is missing.
-- Do not label a multi-day or broad-expiry options measure as an earnings implied move when it contains substantial pre-event trading time or another material catalyst window; label it `expiry-tenor volatility context` and make the limitation prominent.
-- Do not chart net margin as the default line when the historical EPS-quality screen shows a material distortion. Pick the margin that best frames recurring operating performance into the print and state the rationale.
-- Do not treat news coverage as a generic clipping service. Only include events that can plausibly affect estimates, guide credibility, multiple, risk, positioning, or call questions.
-- Do not include uncited market events. Use primary sources where possible, then reputable news/market-data fallback with explicit `as_of`.
-- Do not populate chart shells with blanks. If financial trend, selected margin, EPS estimate/actual, or price/event series are missing or not comparable, omit the chart and add a precise `missing_evidence` item.
-- Do not waste space. Pre-print dashboards should be dense, with compact tables/charts and short PM reads rather than loose narrative sections.
-- Do not use generic KPI rows when sector or business-model metrics are available. Use `sector-context-overlay` to pick the right KPIs when needed.
-- Do not use a company logo as a hard dependency; prefer ticker identity tile.
-- Do not render a chart whose unit or scale conflicts with adjacent headings or tables.
-- Do not render citation links that fragment readable dates, times, ticker symbols, product names, or product specifications.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the expectation bar, KPI dashboard, market events, scenarios, call-prep questions, source ledger, and missing evidence are visible.
-- Confirm no chart shell renders without complete, comparable source-backed data.
-- Confirm chart units match neighboring labels and tables, citations remain readable, and the recommendation language matches the evidence available before the print.
-- Confirm any hero-tile implied move reasonably isolates earnings; otherwise use the `expiry-tenor volatility context` label or move it out of the first-read tiles.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/EXECUTION_PLAYBOOK.md b/plugins/financial-markets/skills/earnings-preview/references/EXECUTION_PLAYBOOK.md
deleted file mode 100644
index f230e5e..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/EXECUTION_PLAYBOOK.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Earnings Preview Execution Playbook
-
-Use this reference for detailed mechanics after `SKILL.md` has routed the request.
-
-## Quarter Anchoring
-
-- Identify latest reported quarter and upcoming preview quarter.
-- Map `t`, `t-1`, `t-4`, and `t-8` to exact fiscal periods.
-- Use the same `t` across consensus, guidance, KPI history, options, reaction analysis, and scenarios.
-- If the preview quarter is ambiguous, ask for the period before presenting precise tables.
-
-## Core Formulas
-
-- Level metric QoQ: `(x_t / x_t-1) - 1`.
-- Level metric YoY: `(x_t / x_t-4) - 1`.
-- Level metric two-year stack: `(x_t / x_t-8) - 1`.
-- Rate metric changes: use percentage-point or bps deltas, not percent growth.
-- Surprise: `reported - consensus`; for level metrics, `(reported / consensus) - 1`.
-- Guidance midpoint: `(low + high) / 2`.
-- Options implied move from straddle: `straddle price / spot`.
-- Options implied move from IV: `iv * sqrt(dte / 365)`.
-
-Suppress percentage changes when the denominator is zero, negative, structurally unstable, or definitionally mismatched.
-
-## Whisper Framing
-
-- External whisper: show value/range, provenance, timestamp, and confidence.
-- No external whisper but guidance history exists: infer only a labeled analyst-derived implied whisper from guide midpoint, historical beat pattern, and conservatism trend.
-- Weak support: use soft setup language, not a hard estimate.
-- Never blend whisper and consensus without explaining the bridge.
-
-## Memo Modules
-
-Use the full module set by default. If an input is unavailable, keep the section and state the missing evidence rather than silently shrinking the report. Only compress the module set when the user explicitly asks for a summary, short version, one-pager, quick read, or top-things-to-watch note.
-
-- Executive summary: stance into the print, bar location, key cruxes, and conviction-changing evidence.
-- Last quarter recap: beat/miss, narrative, stock reaction, debated KPI, still-relevant issue.
-- Outstanding guidance: metric, value/wording, source quarter, type, note.
-- Guidance credibility: beat rate, beat size, conservatism trend, and data limits.
-- Expectation bar: Street, whisper, prior guide, base framing, and bar location.
-- KPI dashboard: 3 to 6 KPIs that actually set the quarter.
-- Peer read-throughs: competitor, supplier, customer, and bellwether signals.
-- Bull/base/bear: explicit drivers, expected print, reaction, and falsifiers.
-- Call questions: why it matters, validate, break, and listen-fors.
-- Historical reaction: next-day move, driver, and continued/reversed.
-- Macro/sector backdrop: short positive/negative/uncertain bullets only.
-- Final checklist: top bar numbers, top 3 metrics, bull catalyst, bear risk, read-through, move comparison, and single call watch.
-
-## Deterministic Script Mode
-
-The shipped scripts are local materializers. They read CSV inputs, validate schema, render a markdown preview note, export CSV/XLSX support tables, and write a run manifest. They do not fetch data, infer current earnings dates, or replace analyst judgment.
-
-Canonical event file name: `event_calendar.csv`.
-
-Required scripted inputs:
-- `company_master.csv`
-- `fiscal_period_index.csv`
-- `reported_financials.csv`
-- `kpi_timeseries.csv`
-- `consensus_estimates.csv`
-
-Optional scripted inputs:
-- `guidance_history.csv`
-- `event_calendar.csv`
-- `whisper_estimates.csv`
-- `price_returns.csv`
-- `options_snapshot.csv`
-- `qual_notes.csv`
-- `scenario_assumptions.csv`
-
-If deterministic outputs contain unresolved bracket tokens, `TODO`, or unfinished placeholders, treat the run as failed.
-
-## PM Setup Rubric
-
-Answer whether the bar is beatable, whether it matters, and what would change sizing into or after the print. Cover expectation bar, consensus dispersion, guidance bridge, implied move, buyside whisper if sourced, positioning, estimate-revision risk, catalyst asymmetry, and call-question falsifiers.
-
-Consensus, options, ownership, short interest, and positioning require as-of dates and source labels. If unavailable, mark them as missing evidence rather than inferring.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/METRICS_LIBRARY.md b/plugins/financial-markets/skills/earnings-preview/references/METRICS_LIBRARY.md
deleted file mode 100644
index 043cec1..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/METRICS_LIBRARY.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Metrics Library
-
-Use this for formulas and framing rules; keep output labels explicit.
-
-## Notation and Guardrails
-
-- `t` = preview quarter; `t-1` = prior quarter; `t-4` = year-ago quarter; `t-8` = two years ago.
-- Every numeric series needs unit, scale, period, source, and GAAP/non-GAAP or KPI definition where relevant.
-- Do not compute percent change when the denominator is zero/negative/unstable; show absolute delta and flag limitation.
-- If KPI definitions change, mark YoY/stack as not comparable unless a restated bridge exists.
-
-## Core Formulas
-
-- Level QoQ: `x_t / x_t-1 - 1`; rate QoQ: percentage-point delta.
-- Level YoY: `x_t / x_t-4 - 1`; rate YoY: percentage-point delta.
-- Two-year stack: `x_t / x_t-8 - 1`; optional CAGR: `(x_t / x_t-8)^(1/2) - 1`.
-- Margin: numerator/denominator must be named; gross margin = `gross_profit / revenue`, op margin = `operating_income / revenue`.
-- Surprise absolute: `reported - expected`; surprise percent for level metrics: `reported / expected - 1`.
-- Margin/rate surprise: show bps or percentage-point delta, not level percent surprise.
-- Guide midpoint: `(low + high) / 2`; guide delta: `reported_or_consensus - guide_mid`.
-- Options implied move: preferred `ATM straddle / spot`; fallback `IV * sqrt(DTE / 365)` and label as approximation.
-
-## EPS Quality Watch
-
-Before the print, flag whether headline EPS could be a poor proxy for recurring operating performance.
-
-Watch items:
-- Consensus basis: GAAP EPS, adjusted EPS, operating EPS, continuing operations, or provider-standardized.
-- Tax rate, below-the-line income/expense, interest income, FX, equity-investment marks, asset sales, impairments, litigation, restructuring, and pension items.
-- Diluted share-count movement from buybacks, converts, SBC, or dilution.
-- Whether the real bar is revenue, operating income, FCF, segment profit, or EPS.
-
-If these items are likely material, add a call question and tell the user that post-print EPS should be bridged before updating forward estimates.
-
-## Whisper Rules
-
-Whisper is not consensus. Store and discuss separately with `value/range`, `as_of`, `provenance`, and confidence.
-
-- Strong support: compare whisper to consensus and guide with source timestamp.
-- Weak support: communicate as soft setup/range, not a hard number.
-- No external whisper: only derive an `implied whisper` from guidance history or beat-pattern math when sourced; otherwise say not provided.
-- Never blend consensus and whisper without explaining the bridge.
-
-## Scenario Rules
-
-- Base defaults to consensus or guide-consistent case.
-- Bull/bear move only the KPIs that matter to the thesis; include driver, value/range, source/assumption, and falsifier.
-- If historical forecast error exists, use it for widths; otherwise label generic placeholders such as +/-2% revenue, +/-50-100 bps margin, or +/-5-10% EPS as assumptions.
-
-## Flags
-
-Flag items likely to matter:
-
-- Consensus/guide/whisper deltas above sector materiality.
-- Trend break versus trailing four-quarter slope or historical forecast error.
-- YoY deceleration >300 bps for growth metrics or margin compression >100 bps, unless sector-specific threshold overrides.
-- Guidance midpoint materially below/above consensus.
-
-## Common KPI Families
-
-- SaaS/subscription: ARR, net new ARR, RPO/cRPO, billings, NRR/GRR, churn, customers/seats, ARPU/ARPA, FCF margin.
-- Internet/marketplace/ads: users, engagement, impressions, pricing, take rate, GOV/GMV/GPV, active merchants/accounts.
-- Consumer/retail: comps, traffic, ticket, pricing/promo, inventory, shrink, markdown cadence, gross margin.
-- Semis/hardware: units, ASP, backlog, book-to-bill, channel inventory, utilization, capacity, gross margin guide.
-- Banks/financials: NII, NIM, deposits, deposit beta, loan growth, PCL, charge-offs, CET1, efficiency ratio.
-- Industrials/services: orders, backlog, book-to-bill, utilization, labor rates, pricing, end-market mix.
-- Energy: production, realized price, hedges, LOE/unit, capex, FCF, return of capital.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/OUTPUT_SPEC.md b/plugins/financial-markets/skills/earnings-preview/references/OUTPUT_SPEC.md
deleted file mode 100644
index e97b550..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/OUTPUT_SPEC.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Output Spec
-
-Use this only after `SKILL.md` routes the request.
-
-## Human-Readable Outputs
-
-### Full preview report
-
-Target: default full deliverable for pre-earnings preview requests. For an explicit pre-earnings preview, full preview report, or reusable/source-heavy pre-print package, render a polished standalone HTML report following `../../../shared/html-artifact-standard.md` and the owning skill's HTML guidance. Use `dashboard-builder` only when the standardized dashboard path is explicitly selected. Chat can remain the surface only when the user explicitly requests a lightweight response. Missing data should create labeled gaps and data requests rather than unsupported conclusions or decorative empty modules.
-
-Required analytical objects:
-
-1. Executive summary: stance into the print, expectation-bar location, stock-reaction debate, and the one thing that would change conviction.
-2. Setup/source freeze: ticker/company, quarter, event timing, freeze time, source timestamps, and relevant source gaps.
-3. Expectation bar: guide, consensus/whisper where sourced, key operating proof points, what the stock likely requires, and why.
-4. Reaction framework: what is already priced in, what could drive an upside or downside reaction, and what evidence is needed before taking incremental event risk.
-5. Call watch items and missing evidence: focused questions/listen-fors/falsifiers plus the data refresh required before the print.
-
-Conditional analytical objects: last-quarter recap, guidance-credibility bridge, EPS-quality watch, KPI dashboard, peer/sector or macro read-throughs, market events, reaction/options context, historical move analysis, bull/base/bear scenarios, and expanded call preparation. Include them only where they answer the question or provide source-backed decision support. Present an options-derived metric as an earnings implied move only when its tenor reasonably isolates the event; otherwise label it `expiry-tenor volatility context` and disclose the extra time or catalysts included.
-
-Final checklist: top bar numbers, most decision-relevant operating proofs, upside and downside reaction drivers, key read-through where sourced, historical average move versus an event-isolating implied move only when both are sourced, and the single most important call watch.
-
-### Explicit short summary
-
-Use only when the user explicitly asks for a shorter output such as `summary`, `short`, `quick read`, `one-pager`, or `top things to watch`.
-
-This is a compression of the full preview report, not a separately maintained tear sheet. Preserve:
-
-- Freeze time, source posture, and missing-data caveats.
-- 3-5 key bar numbers/KPIs, if sourced.
-- 2-3 stock-moving debates.
-- 5-8 must-answer call questions with listen-fors.
-- Explicit data requests or source limits.
-
-## Table Specs
-
-KPI dashboard columns: `metric`, `t_consensus`, `t_whisper`, `t-1_actual`, `t-4_actual`, `qoq`, `yoy`, `2yr_stack`, `guide_mid`, `vs_guide`, `flag`, `commentary`.
-
-Consensus/whisper columns: `metric`, `period`, `consensus_value`, `whisper_value_or_range`, `delta_abs`, `delta_pct`, `confidence`, `source_timestamp`, `provenance/notes`.
-
-EPS-quality watch columns: `item`, `why_it_could_distort_eps`, `pre_print_risk`, `post_print_evidence_needed`, `model_line_affected`.
-
-Peer read-through columns: `category`, `name`, `signal`, `why_it_matters`, `read_through`, `confidence`.
-
-Historical reaction columns: `quarter`, `beat_miss_context`, `next_day_move`, `driver`, `continued_or_reversed`.
-
-Call-question fields: `question`, `why_it_matters`, `validates_thesis`, `breaks_thesis`, `listen_fors`.
-
-## Deterministic Workbook
-
-Current `scripts/run_plan.py` tabs when inputs exist:
-
-- `Cover`
-- `01_PeriodIndex`
-- `02_Financials_Q`
-- `03_KPIs_Q`
-- `04_Guidance_History`
-- `05_Consensus_Snapshot`
-- `06_Whisper`
-- `07_KPI_Dashboard`
-- `08_Cons_vs_Whisper`
-
-`Cover` is the first visible dashboard tab. It summarizes company/ticker, preview period, freeze time, workbook mode, warnings, consensus bar, scenario framing, KPI dashboard counts, source/input posture, and workbook map.
-
-Sidecars: `qa_report.json`, `qa_report.md`, `run_manifest.json`. These are support/audit artifacts unless explicitly requested. Do not advertise future tabs until the exporter writes them. Workbook conventions: stable column order, no merged cells, explicit `unit` and `scale`.
-
-## Public Equity PM Output Additions
-
-Include audience mode, expectation bar, what is priced in, estimate-revision risk, positioning/reaction setup, call-question falsifiers, and position action triggers. ETF/index diligence should add constituent weight, passive-flow relevance, liquidity, benchmark exposure, and rebalance/event risk when relevant.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/QA_RULES.md b/plugins/financial-markets/skills/earnings-preview/references/QA_RULES.md
deleted file mode 100644
index 96726e8..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/QA_RULES.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# QA Rules
-
-Use before returning a human-readable report or packaging deterministic outputs.
-
-## Hard Fails
-
-- Fabricated number/date/source, unlabeled web fallback, or unlabeled analyst assumption.
-- Missing freeze time for time-sensitive market, consensus, whisper, option, or price data.
-- Mixed GAAP/non-GAAP, period, unit, scale, or KPI definition without labeling.
-- A chart axis or displayed scale that conflicts with the stated unit in adjacent tables, headings, or narrative.
-- An options-derived metric labeled as an earnings implied move when the cited expiry includes substantial pre-event trading time or another material catalyst window without a prominent `expiry-tenor volatility context` qualification.
-- Whisper presented as consensus or implied MNPI/confidential information.
-- Generated user-facing report/dashboard text, or any generated support note, contains unresolved `[TOKEN]`, `{PLACEHOLDER}`, `TODO`, or unfinished authoring text.
-- Scripted pack writes outputs into the packaged skill tree instead of the requested/temp output directory.
-
-## Human-Readable Report QA
-
-Confirm:
-
-- Preview quarter and `t/t-1/t-4/t-8` mapping are explicit.
-- Every bar number has source/as-of, unit, scale, and definition.
-- Guidance is separated from consensus/whisper and labeled by period/scope/basis.
-- EPS-quality watch is included when EPS matters: consensus basis, tax/share-count, below-the-line, mark-to-market, or one-time item risk.
-- Peer, macro, reaction, and options sections appear only when sourced or are marked missing.
-- Options-derived volatility is labeled as an earnings implied move only when the tenor reasonably isolates the event; broader expiries are labeled `expiry-tenor volatility context` and are not treated as a clean earnings hurdle.
-- Bull/base/bear cases state drivers, assumptions, and falsifiers.
-- Call questions include why they matter and listen-fors.
-- Missing evidence is visible rather than hidden.
-- HTML hierarchy makes the expectation bar and stock-reaction debate immediately visible for a full preview report.
-- Citations are traceable without fragmenting readable dates, times, ticker symbols, product names, or product specifications.
-
-## Deterministic QA
-
-Run plan validation before `run_plan.py`. Check required files, required columns, output manifest, workbook tabs listed in `OUTPUT_SPEC.md`, `qa_report.*`, and generated report/support-note placeholder scans.
-
-Accept `PASS_WITH_WARNINGS` only when warnings are limitations, not broken calculations or unresolved placeholders.
-
-## PM Judgment QA
-
-Fail the output if it lacks freeze time, expectation bar, stock-moving debate, source labels for consensus/options/positioning, or falsifier/listen-for questions. Penalize generic earnings summaries that do not say what would change sizing or watchlist status.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/REFERENCE_ROUTER.md b/plugins/financial-markets/skills/earnings-preview/references/REFERENCE_ROUTER.md
deleted file mode 100644
index f499928..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/REFERENCE_ROUTER.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Reference Router
-
-Load only what the request requires.
-
-| Need | Load |
-|---|---|
-| Default full preview report or explicit summary compression | `OUTPUT_SPEC.md`, then `QA_RULES.md` |
-| Scripted pack or local CSV inputs | `SCHEMAS.md`, `quickstart.md`, `assets/plan_schema.json` |
-| Formulas, whisper rules, reaction/options logic | `METRICS_LIBRARY.md` |
-| Sector KPI selection | `SECTOR_KPI_PACKS.md`, then `assets/sector_kpi_packs.yaml` |
-| Call questions | `CALL_QUESTION_BANK.md` |
-| Standalone HTML full preview report | `OUTPUT_SPEC.md`, then `QA_RULES.md`; apply `../../../shared/html-artifact-standard.md` |
-| Explicit standardized dashboard / payload-driven render | `OUTPUT_SPEC.md`, `DASHBOARD_PACK.md`, then `QA_RULES.md` |
-
-Fast route:
-
-- Full preview is the default and usually needs `OUTPUT_SPEC.md`, `METRICS_LIBRARY.md`, and `QA_RULES.md`.
-- Summary/short output is explicit-only and should compress the full preview structure rather than using a separate tear-sheet contract.
-- Deterministic export always needs `SCHEMAS.md`, `quickstart.md`, and the JSON schema.
-- Standalone HTML full preview reports follow the flexible artifact standard and keep the expectation bar and reaction debate as the primary visual objects.
-- Standardized dashboard output keeps `earnings-preview` as the analysis owner and uses `dashboard-builder` only for its selected schema-driven rendering path.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/SCHEMAS.md b/plugins/financial-markets/skills/earnings-preview/references/SCHEMAS.md
deleted file mode 100644
index 861ec08..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/SCHEMAS.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Schemas
-
-Minimum deterministic CSV contract for `scripts/`. Quick chat previews can proceed from ticker/company plus connected or public sources; substantial reusable previews should route through the dashboard/report framework.
-
-## Conventions
-
-- CSV preferred; XLSX is acceptable when scripts support it.
-- Column names are normalized case-insensitively to snake_case.
-- Use ISO dates/timestamps, explicit `currency_code`, `unit`, `scale`, `ticker`, `fiscal_period_id`, and `metric_id`.
-- Canonical event file is `event_calendar.csv`.
-
-## Required Scripted Files
-
-- `company_master.csv`
-- `fiscal_period_index.csv`
-- `reported_financials.csv`
-- `kpi_timeseries.csv`
-- `consensus_estimates.csv`
-- `guidance_history.csv` (may be empty with headers)
-- `event_calendar.csv` (earnings timing at minimum)
-
-Optional: `whisper_estimates.csv`, `price_returns.csv`, `options_snapshot.csv`, `qual_notes.csv`, `scenario_assumptions.csv`.
-
-## Required Columns
-
-| File | Required columns |
-|---|---|
-| `company_master.csv` | `ticker`, `company_name`, `sector` or `sector_pack`, `currency_code`, `fiscal_year_end_month` |
-| `fiscal_period_index.csv` | `ticker`, `fiscal_period_id`, `fiscal_year`, `fiscal_quarter`, `period_start_date`, `period_end_date` |
-| `event_calendar.csv` | `ticker`, `event_type`, `event_datetime_utc`, `event_name`; recommended `fiscal_period_id`, `source`, `notes` |
-| `reported_financials.csv` | `ticker`, `fiscal_period_id`, `metric_id`, `value`, `unit`, `scale`, `as_reported_date` |
-| `kpi_timeseries.csv` | `ticker`, `fiscal_period_id`, `metric_id`, `value`, `unit`, `scale`, `as_reported_date` |
-| `guidance_history.csv` | `ticker`, `guidance_date`, `fiscal_period_id`, `metric_id`, `low`, `high`, `unit`, `scale` |
-| `consensus_estimates.csv` | `ticker`, `snapshot_datetime`, `fiscal_period_id`, `metric_id`, `statistic`, `estimate_value`, `unit`, `scale` |
-| `whisper_estimates.csv` | `ticker`, `asof_datetime`, `fiscal_period_id`, `metric_id`, `whisper_value`, `unit`, `scale` |
-| `price_returns.csv` | `ticker`, `date`, `close` |
-| `options_snapshot.csv` | `ticker`, `snapshot_datetime`, `spot_price`, and either straddle price or IV/DTE fields |
-| `qual_notes.csv` | `ticker`, `fiscal_period_id`, `tag`, `note`, `source`, `note_datetime` |
-| `scenario_assumptions.csv` | `ticker`, `fiscal_period_id`, `scenario`, `metric_id`, `value`, `unit`, `scale`, `source_or_assumption` |
-
-Recommended optional columns preserve nuance: GAAP/non-GAAP basis, segment, vendor, high/low, number of estimates, provenance, confidence, notes, restatement or KPI-definition version, exchange/country, shares, net debt.
-
-## Plan Keys
-
-`plan.json` is validated by `assets/plan_schema.json`. Required top-level keys are `ticker`, `fiscal_period_id`, `sector_pack`, `freeze_time`, `input_dir`, and `output_dir`. The deterministic runner reads from local files only and writes outside the packaged skill when using the sample plan.
-
-## QA
-
-Inputs should carry source/as-of metadata sufficient to reproduce the pack. If a required optional source is missing for a requested section, render a precise limitation (`not provided`, `not guided`, `source not provided`) rather than a placeholder.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/SECTOR_KPI_PACKS.md b/plugins/financial-markets/skills/earnings-preview/references/SECTOR_KPI_PACKS.md
deleted file mode 100644
index ba5563d..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/SECTOR_KPI_PACKS.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Sector KPI Packs
-
-`sector_pack` selects default KPIs from `assets/sector_kpi_packs.yaml`; scripts also load driver templates from `assets/templates/driver_model_templates.yaml`.
-
-## Selection Rules
-
-- Prefer the company's actual disclosure model over a generic sector label.
-- Use the pack as a default, then add/remove KPIs based on what can move the stock this quarter.
-- Preserve company-specific metric definitions and mark definition changes or restatements.
-- Do not force unsupported KPIs into a preview; label missing but important KPIs as data requests.
-
-## Default Pack Families
-
-- `saas`: revenue, ARR/RPO/billings, NRR/GRR/churn, customers/seats, ARPU/ARPA, gross/operating/FCF margin.
-- `internet_marketplace`: GMV/GOV/GPV, active users/merchants, take rate, transactions, engagement, ad pricing/load, contribution margin.
-- `consumer_retail`: revenue, comps, traffic, ticket, units, gross margin, inventory, markdowns, shrink, store/channel mix.
-- `semis_hardware`: revenue, units, ASP, backlog, book-to-bill, utilization, inventory/channel inventory, gross margin, capex.
-- `banks_financials`: NII, NIM, loan/deposit growth, deposit beta, PCL, charge-offs, delinquencies, CET1, efficiency ratio.
-- `industrials_services`: orders, backlog, book-to-bill, utilization, pricing, mix, labor, operating margin, FCF.
-- `energy`: production, realized prices, hedges, LOE/unit, capex, FCF, leverage, return of capital.
-
-## Safe Edits
-
-When editing YAML packs, keep metric IDs aligned with input files, add source/definition notes in the analysis, and update tests or sample inputs if a scripted output depends on the new metric.
diff --git a/plugins/financial-markets/skills/earnings-preview/references/quickstart.md b/plugins/financial-markets/skills/earnings-preview/references/quickstart.md
deleted file mode 100644
index c67bd5e..0000000
--- a/plugins/financial-markets/skills/earnings-preview/references/quickstart.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Earnings Preview Pack Quickstart
-
-The deterministic sample run builds a pre-earnings preview note, model-driver tables, QA outputs, and a run manifest.
-
-From the skill root after installing dependencies, copy the bundled sample plan to a temporary workspace and run from there. This keeps examples from dirtying the packaged skill folder.
-
-```bash
-TMPDIR="$(mktemp -d)"
-cp assets/sample_run/plan.json "$TMPDIR/plan.json"
-python scripts/validate_plan.py "$TMPDIR/plan.json"
-python scripts/run_plan.py "$TMPDIR/plan.json"
-```
-
-Outputs land in `/tmp/public_equity_investing_earnings_preview_sample_output/ACME/FY2026Q1/` unless you edit `output_dir` in the copied plan.
diff --git a/plugins/financial-markets/skills/earnings-preview/requirements.txt b/plugins/financial-markets/skills/earnings-preview/requirements.txt
deleted file mode 100644
index 25e91be..0000000
--- a/plugins/financial-markets/skills/earnings-preview/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-# Minimal runtime deps for the deterministic scripts.
-# No network access is required for execution.
-
-pandas>=2.0
-numpy>=1.24
-openpyxl>=3.1
-PyYAML>=6.0
-python-dateutil>=2.8
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/__init__.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/__init__.py
deleted file mode 100644
index 656b8ac..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Internal library for earnings-preview-pack scripts."""
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/calc.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/calc.py
deleted file mode 100644
index d090f19..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/calc.py
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""Core calculations for the preview pack.
-
-All functions are deterministic and should be unit-tested.
-"""
-
-from __future__ import annotations
-
-import math
-import re
-
-import pandas as pd
-
-PERIOD_RE = re.compile(r"^FY(?P<year>[0-9]{4})Q(?P<q>[1-4])$")
-
-
-def parse_fiscal_period_id(fiscal_period_id: str) -> tuple[int, int]:
-    m = PERIOD_RE.match(str(fiscal_period_id).strip())
-    if not m:
-        raise ValueError(f"Invalid fiscal_period_id format: {fiscal_period_id} (expected FY2026Q1)")
-    return int(m.group("year")), int(m.group("q"))
-
-
-def shift_period(fiscal_period_id: str, delta_quarters: int) -> str:
-    """Shift a FYxxxxQx by delta_quarters (negative for past)."""
-    y, q = parse_fiscal_period_id(fiscal_period_id)
-    idx = (y * 4 + (q - 1)) + delta_quarters
-    new_y = idx // 4
-    new_q = idx % 4 + 1
-    return f"FY{new_y:04d}Q{new_q}"
-
-
-def is_rate_metric(metric_id: str, unit: str) -> bool:
-    """Heuristic: treat ratios/percents and *_margin/*_rate as rates."""
-    u = (unit or "").lower()
-    mid = (metric_id or "").lower()
-    if u in {"ratio", "pct", "percent", "bps"}:
-        return True
-    if mid.endswith(("_margin", "_rate")) or mid in {"nrr", "grr", "nim"}:
-        return True
-    return False
-
-
-def safe_pct_change(curr: float | None, prev: float | None) -> float | None:
-    if curr is None or prev is None:
-        return None
-    try:
-        if pd.isna(curr) or pd.isna(prev):
-            return None
-    except Exception:
-        pass
-    if prev <= 0:
-        return None
-    return curr / prev - 1.0
-
-
-def safe_abs_change(curr: float | None, prev: float | None) -> float | None:
-    if curr is None or prev is None:
-        return None
-    try:
-        if pd.isna(curr) or pd.isna(prev):
-            return None
-    except Exception:
-        pass
-    return curr - prev
-
-
-def safe_bps_change(curr_ratio: float | None, prev_ratio: float | None) -> float | None:
-    """Return change in basis points (assumes ratios, e.g., 0.742)."""
-    d = safe_abs_change(curr_ratio, prev_ratio)
-    if d is None:
-        return None
-    return d * 10_000.0
-
-
-def two_year_stack(curr: float | None, two_year_ago: float | None) -> float | None:
-    return safe_pct_change(curr, two_year_ago)
-
-
-def trend_slope(values: list[float]) -> float | None:
-    """Simple slope estimate over equally spaced periods.
-
-    Returns slope per period (not percent). Use only for directional 'trend' flags.
-    """
-    vals = [v for v in values if v is not None and not (isinstance(v, float) and math.isnan(v))]
-    if len(vals) < 3:
-        return None
-    n = len(vals)
-    xs = list(range(n))
-    x_mean = sum(xs) / n
-    y_mean = sum(vals) / n
-    num = sum((x - x_mean) * (y - y_mean) for x, y in zip(xs, vals))
-    den = sum((x - x_mean) ** 2 for x in xs)
-    if den == 0:
-        return None
-    return num / den
-
-
-def auto_flag_delta(curr_est: float | None, cons_est: float | None, metric_is_rate: bool) -> bool:
-    """Heuristic flag when whisper differs from consensus meaningfully."""
-    if curr_est is None or cons_est is None:
-        return False
-    try:
-        if pd.isna(curr_est) or pd.isna(cons_est):
-            return False
-    except Exception:
-        pass
-
-    if metric_is_rate:
-        # Ratio: flag if >=25 bps difference (tunable)
-        return abs(curr_est - cons_est) >= 0.0025
-    # Level: flag if >=1% difference (tunable)
-    if cons_est == 0:
-        return False
-    return abs(curr_est / cons_est - 1.0) >= 0.01
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/changelog.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/changelog.py
deleted file mode 100644
index 01127b8..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/changelog.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-"""Changelog generation.
-
-If a previous run_manifest.json exists in the output folder, diff it against the new manifest.
-This is intentionally simple and only diffs file hashes and top-level plan settings.
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-from .io_utils import read_json
-
-
-def diff_manifests(prev: dict, curr: dict) -> list[str]:
-    lines: list[str] = []
-
-    # Top-level fields
-    for key in ["freeze_time", "consensus_statistic", "sector_pack", "fiscal_period_id"]:
-        pv = prev.get(key)
-        cv = curr.get(key)
-        if pv != cv:
-            lines.append(f"- {key}: {pv} -> {cv}")
-
-    # Input file hashes
-    prev_files = (prev.get("inputs", {}) or {}).get("files", {}) or {}
-    curr_files = (curr.get("inputs", {}) or {}).get("files", {}) or {}
-    for name in sorted(set(prev_files) | set(curr_files)):
-        ph = (prev_files.get(name) or {}).get("sha256")
-        ch = (curr_files.get(name) or {}).get("sha256")
-        if ph != ch:
-            lines.append(f"- input {name}: {ph} -> {ch}")
-
-    if not lines:
-        return ["- No changes vs prior manifest."]
-    return lines
-
-
-def maybe_write_changelog(output_run_dir: Path, new_manifest: dict) -> None:
-    prev_path = output_run_dir / "run_manifest.previous.json"
-    if not prev_path.exists():
-        return
-    prev = read_json(prev_path)
-    if not isinstance(prev, dict):
-        return
-    lines = diff_manifests(prev, new_manifest)
-    out_path = output_run_dir / "changelog.md"
-    out_path.write_text("# Changelog\n\n" + "\n".join(lines) + "\n", encoding="utf-8")
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/io_utils.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/io_utils.py
deleted file mode 100644
index 86f4a46..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/io_utils.py
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-"""I/O utilities (CSV/XLSX) + small formatting helpers.
-
-Design goals:
-- Deterministic: no network, no terminal dependencies.
-- Tolerant: case-insensitive columns, stable output ordering.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import json
-import re
-from pathlib import Path
-from typing import Iterable
-
-import pandas as pd
-
-SNAKE_RE_1 = re.compile(r"(.)([A-Z][a-z]+)")
-SNAKE_RE_2 = re.compile(r"([a-z0-9])([A-Z])")
-
-
-def to_snake_case(name: str) -> str:
-    """Convert column names to snake_case."""
-    name = name.strip().replace(" ", "_").replace("-", "_")
-    name = SNAKE_RE_1.sub(r"\1_\2", name)
-    name = SNAKE_RE_2.sub(r"\1_\2", name)
-    name = re.sub(r"__+", "_", name)
-    return name.lower()
-
-
-def normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
-    df = df.copy()
-    df.columns = [to_snake_case(str(c)) for c in df.columns]
-    return df
-
-
-def read_table(path: Path) -> pd.DataFrame:
-    """Read a CSV or XLSX into a DataFrame."""
-    path = Path(path)
-    if not path.exists():
-        raise FileNotFoundError(f"Input file not found: {path}")
-
-    if path.suffix.lower() in {".csv", ".txt"}:
-        df = pd.read_csv(path)
-    elif path.suffix.lower() in {".xlsx", ".xls"}:
-        df = pd.read_excel(path)
-    else:
-        raise ValueError(f"Unsupported file type: {path.suffix}")
-
-    return normalize_columns(df)
-
-
-def require_columns(df: pd.DataFrame, required: Iterable[str], table_name: str) -> list[str]:
-    """Return a list of missing columns (do not raise)."""
-    required = [to_snake_case(c) for c in required]
-    missing = [c for c in required if c not in df.columns]
-    return missing
-
-
-def coerce_numeric(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    df = df.copy()
-    for c in cols:
-        c = to_snake_case(c)
-        if c in df.columns:
-            df[c] = pd.to_numeric(df[c], errors="coerce")
-    return df
-
-
-def sha256_file(path: Path) -> str:
-    h = hashlib.sha256()
-    with open(path, "rb") as f:
-        for chunk in iter(lambda: f.read(8192), b""):
-            h.update(chunk)
-    return h.hexdigest()
-
-
-def write_json(path: Path, obj: object) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump(obj, f, indent=2, sort_keys=True)
-
-
-def read_json(path: Path) -> object:
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def scale_suffix(scale: float) -> str:
-    try:
-        s = float(scale)
-    except Exception:
-        return ""
-    if s == 1_000:
-        return "k"
-    if s == 1_000_000:
-        return "m"
-    if s == 1_000_000_000:
-        return "b"
-    return ""
-
-
-def fmt_number(value: float | None, unit: str = "", scale: float = 1.0, decimals: int = 1) -> str:
-    """Human-readable number for markdown note.
-
-    - Currency scaled values use k/m/b suffix.
-    - Ratios are rendered as percent.
-    """
-    if value is None:
-        return ""
-    try:
-        if pd.isna(value):
-            return ""
-    except Exception:
-        pass
-
-    unit_l = (unit or "").lower()
-
-    # Ratios and percents
-    if unit_l in {"ratio", "pct", "percent"}:
-        # assume ratio in [0,1] for margins/retention; render as %
-        return f"{value * 100:.{decimals}f}%"
-
-    # Basis points (already in bps)
-    if unit_l == "bps":
-        return f"{value:.0f}bps"
-
-    # Currency / counts
-    suf = scale_suffix(scale)
-    try:
-        v = float(value)
-    except Exception:
-        return str(value)
-
-    if suf:
-        return f"{v:,.{decimals}f}{suf}"
-
-    # Non-scaled
-    if abs(v) >= 1000:
-        return f"{v:,.{decimals}f}"
-    return f"{v:.{decimals}f}"
-
-
-def df_to_markdown_table(df: pd.DataFrame, max_rows: int = 50) -> str:
-    """Render a small DataFrame as a markdown table (no external deps)."""
-    if df is None or df.empty:
-        return "(no data)"
-
-    df = df.copy()
-    if len(df) > max_rows:
-        df = df.head(max_rows)
-
-    # Convert everything to string
-    cols = list(df.columns)
-    rows = []
-    for _, r in df.iterrows():
-        rows.append(["" if pd.isna(r[c]) else str(r[c]) for c in cols])
-
-    # Column widths
-    widths = [len(str(c)) for c in cols]
-    for row in rows:
-        widths = [max(w, len(str(cell))) for w, cell in zip(widths, row)]
-
-    def fmt_row(vals: list[str]) -> str:
-        return "| " + " | ".join(str(v).ljust(w) for v, w in zip(vals, widths)) + " |"
-
-    header = fmt_row([str(c) for c in cols])
-    sep = "| " + " | ".join("-" * w for w in widths) + " |"
-    body = "\n".join(fmt_row(r) for r in rows)
-    return "\n".join([header, sep, body])
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/kpi_packs.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/kpi_packs.py
deleted file mode 100644
index 12157de..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/kpi_packs.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env python3
-"""Load KPI packs and driver models from YAML assets."""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import yaml
-
-
-def load_sector_packs(path: Path) -> dict[str, dict]:
-    data = yaml.safe_load(Path(path).read_text(encoding="utf-8"))
-    packs = data.get("packs", {}) if isinstance(data, dict) else {}
-    return packs
-
-
-def get_kpis_for_pack(packs: dict[str, dict], sector_pack: str) -> list[str]:
-    pack = packs.get(sector_pack)
-    if not pack:
-        return []
-    kpis = []
-    for key in ["primary_kpis", "secondary_kpis"]:
-        vals = pack.get(key, [])
-        if isinstance(vals, list):
-            kpis.extend([str(x) for x in vals])
-    # De-duplicate preserving order
-    seen = set()
-    out = []
-    for k in kpis:
-        if k not in seen:
-            out.append(k)
-            seen.add(k)
-    return out
-
-
-def load_driver_models(path: Path) -> dict[str, dict]:
-    data = yaml.safe_load(Path(path).read_text(encoding="utf-8"))
-    models = data.get("models", {}) if isinstance(data, dict) else {}
-    return models
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/qa.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/qa.py
deleted file mode 100644
index 9c4143f..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/qa.py
+++ /dev/null
@@ -1,187 +0,0 @@
-#!/usr/bin/env python3
-"""QA and validation rules.
-
-Two layers:
-- Plan validation: required keys, obvious type errors.
-- Data validation: required files, required columns, basic sanity checks.
-
-This module is deliberately conservative: "errors" are hard failures, "warnings" require review.
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import pandas as pd
-
-from .io_utils import read_table, require_columns
-
-REQUIRED_PLAN_KEYS = [
-    "ticker",
-    "fiscal_period_id",
-    "sector_pack",
-    "freeze_time",
-    "input_dir",
-    "output_dir",
-]
-
-# Minimal required input files for deterministic outputs
-DEFAULT_INPUT_FILES = {
-    "company_master": "company_master.csv",
-    "fiscal_period_index": "fiscal_period_index.csv",
-    "reported_financials": "reported_financials.csv",
-    "kpi_timeseries": "kpi_timeseries.csv",
-    "consensus_estimates": "consensus_estimates.csv",
-}
-
-OPTIONAL_INPUT_FILES = {
-    "guidance_history": "guidance_history.csv",
-    "event_calendar": "event_calendar.csv",
-    "price_returns": "price_returns.csv",
-    "whisper_estimates": "whisper_estimates.csv",
-    "qual_notes": "qual_notes.csv",
-    "scenario_assumptions": "scenario_assumptions.csv",
-    "options_snapshot": "options_snapshot.csv",
-}
-
-REQUIRED_COLUMNS = {
-    "company_master": ["ticker", "company_name", "sector_pack", "currency_code"],
-    "fiscal_period_index": ["fiscal_period_id", "fiscal_year", "fiscal_quarter"],
-    "reported_financials": ["ticker", "fiscal_period_id", "metric_id", "value", "unit", "scale"],
-    "kpi_timeseries": ["ticker", "fiscal_period_id", "metric_id", "value", "unit", "scale"],
-    "consensus_estimates": [
-        "ticker",
-        "fiscal_period_id",
-        "metric_id",
-        "estimate_value",
-        "statistic",
-        "snapshot_datetime",
-    ],
-    "guidance_history": ["ticker", "fiscal_period_id", "metric_id", "guide_date"],
-    "event_calendar": ["ticker", "event_datetime_utc", "event_type"],
-    "price_returns": ["ticker", "date"],
-    "whisper_estimates": ["ticker", "fiscal_period_id", "metric_id", "snapshot_datetime"],
-    "qual_notes": ["ticker", "fiscal_period_id", "note"],
-    "scenario_assumptions": ["scenario_name", "metric_id", "delta_type", "delta_value"],
-    "options_snapshot": ["ticker", "asof_datetime"],
-}
-
-
-def resolve_input_paths(plan: dict, skill_root: Path) -> dict[str, Path]:
-    """Return a dict of input_name -> Path.
-
-    Rules:
-    - If plan.inputs.<name> exists, use it (relative to skill root unless absolute)
-    - Else default to plan.input_dir/<default_filename>
-    """
-    input_dir = Path(plan.get("input_dir", ""))
-    if not input_dir.is_absolute():
-        input_dir = (skill_root / input_dir).resolve()
-
-    overrides = plan.get("inputs", {}) or {}
-
-    paths: dict[str, Path] = {}
-    for name, filename in {**DEFAULT_INPUT_FILES, **OPTIONAL_INPUT_FILES}.items():
-        if isinstance(overrides, dict) and overrides.get(name):
-            p = Path(str(overrides[name]))
-            if not p.is_absolute():
-                p = (skill_root / p).resolve()
-        else:
-            p = (input_dir / filename).resolve()
-        paths[name] = p
-    return paths
-
-
-def validate_plan(plan: dict) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    for k in REQUIRED_PLAN_KEYS:
-        if k not in plan or plan.get(k) in (None, ""):
-            errors.append(f"Missing required plan field: {k}")
-
-    # Light checks
-    if (
-        "ticker" in plan
-        and isinstance(plan.get("ticker"), str)
-        and plan.get("ticker") != plan.get("ticker").upper()
-    ):
-        warnings.append("Ticker is not uppercase; this is allowed but can cause joins to fail.")
-
-    # Freeze time should look like ISO
-    ft = str(plan.get("freeze_time", ""))
-    if ft and "T" not in ft:
-        warnings.append("freeze_time does not look like ISO8601 (missing 'T').")
-
-    return errors, warnings
-
-
-def validate_inputs(
-    plan: dict, skill_root: Path
-) -> tuple[list[str], list[str], dict[str, pd.DataFrame]]:
-    """Validate input files and required columns. Returns (errors, warnings, loaded_dfs)."""
-    errors: list[str] = []
-    warnings: list[str] = []
-    dfs: dict[str, pd.DataFrame] = {}
-
-    paths = resolve_input_paths(plan, skill_root)
-
-    # Required files must exist
-    for name in DEFAULT_INPUT_FILES.keys():
-        p = paths[name]
-        if not p.exists():
-            errors.append(f"Missing required input file: {name} ({p})")
-
-    # Optional files
-    for name in OPTIONAL_INPUT_FILES.keys():
-        p = paths[name]
-        if not p.exists():
-            warnings.append(f"Optional input file not found: {name} ({p})")
-
-    # If required missing, stop early
-    if errors:
-        return errors, warnings, dfs
-
-    # Load and validate columns
-    for name, p in paths.items():
-        if not p.exists():
-            continue
-        try:
-            df = read_table(p)
-        except Exception as e:
-            if name in DEFAULT_INPUT_FILES:
-                errors.append(f"Failed reading required file {name}: {p} ({e})")
-            else:
-                warnings.append(f"Failed reading optional file {name}: {p} ({e})")
-            continue
-
-        req_cols = REQUIRED_COLUMNS.get(name)
-        if req_cols:
-            missing = require_columns(df, req_cols, name)
-            if missing:
-                msg = f"{name} missing required columns: {missing}"
-                if name in DEFAULT_INPUT_FILES:
-                    errors.append(msg)
-                else:
-                    warnings.append(msg)
-        dfs[name] = df
-
-    # Period existence checks
-    fiscal_period_id = plan.get("fiscal_period_id")
-    if fiscal_period_id and "fiscal_period_index" in dfs:
-        fpi = dfs["fiscal_period_index"]
-        if fiscal_period_id not in set(fpi.get("fiscal_period_id", [])):
-            errors.append(f"fiscal_period_id {fiscal_period_id} not found in fiscal_period_index")
-
-    # Consensus availability
-    if fiscal_period_id and "consensus_estimates" in dfs:
-        ce = dfs["consensus_estimates"]
-        mask = (ce.get("ticker") == plan.get("ticker")) & (
-            ce.get("fiscal_period_id") == fiscal_period_id
-        )
-        if mask.sum() == 0:
-            errors.append(
-                f"No consensus_estimates rows found for {plan.get('ticker')} {fiscal_period_id}"
-            )
-
-    return errors, warnings, dfs
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/lib/render.py b/plugins/financial-markets/skills/earnings-preview/scripts/lib/render.py
deleted file mode 100644
index f5d0ca5..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/lib/render.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-"""Template rendering.
-
-Templates use bracket tokens like `[TICKER]`.
-This keeps things simple and avoids external dependencies.
-"""
-
-from __future__ import annotations
-
-import re
-from pathlib import Path
-
-TOKEN_RE = re.compile(r"\[(?P<token>[A-Z0-9_]+)\]")
-
-
-def render_text(template_text: str, mapping: dict[str, str]) -> str:
-    def repl(m: re.Match) -> str:
-        tok = m.group("token")
-        return str(mapping.get(tok, m.group(0)))
-
-    return TOKEN_RE.sub(repl, template_text)
-
-
-def render_file(template_path: Path, mapping: dict[str, str]) -> str:
-    text = Path(template_path).read_text(encoding="utf-8")
-    return render_text(text, mapping)
-
-
-def write_rendered(template_path: Path, mapping: dict[str, str], out_path: Path) -> None:
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(render_file(template_path, mapping), encoding="utf-8")
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/make_plan.py b/plugins/financial-markets/skills/earnings-preview/scripts/make_plan.py
deleted file mode 100644
index c4bda7d..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/make_plan.py
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/usr/bin/env python3
-"""Generate a plan.json for the Earnings Preview Pack pipeline.
-
-This is a convenience helper. You can always edit the generated plan.json by hand.
-
-Example:
-  python scripts/make_plan.py --input_dir ./input --ticker ACME --fiscal_period_id FY2026Q1 --sector_pack saas --out_dir ./run
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-
-def now_utc_iso() -> str:
-    return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
-
-
-def main() -> None:
-    p = argparse.ArgumentParser()
-    p.add_argument("--input_dir", required=True, help="Folder containing input CSV/XLSX files")
-    p.add_argument("--ticker", required=True, help="Ticker (uppercase recommended)")
-    p.add_argument("--fiscal_period_id", required=True, help="Format FY2026Q1")
-    p.add_argument(
-        "--sector_pack", required=True, help="One of the packs in assets/sector_kpi_packs.yaml"
-    )
-    p.add_argument("--out_dir", required=True, help="Folder where plan.json will be written")
-    p.add_argument("--company_name", default="", help="Optional company name")
-    p.add_argument(
-        "--freeze_time", default="", help="Optional ISO8601 timestamp; default is now (UTC)"
-    )
-    p.add_argument(
-        "--consensus_statistic",
-        default="median",
-        choices=["median", "mean", "last"],
-        help="Consensus statistic",
-    )
-    p.add_argument("--scenario_file", default="", help="Optional scenario assumptions CSV")
-
-    args = p.parse_args()
-
-    from lib.io_utils import write_json
-
-    out_dir = Path(args.out_dir).resolve()
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    plan = {
-        "ticker": args.ticker.strip(),
-        "company_name": args.company_name.strip(),
-        "fiscal_period_id": args.fiscal_period_id.strip(),
-        "sector_pack": args.sector_pack.strip(),
-        "freeze_time": args.freeze_time.strip() or now_utc_iso(),
-        "consensus_statistic": args.consensus_statistic,
-        "input_dir": str(Path(args.input_dir)),
-        "output_dir": str(out_dir / "output"),
-        "kpi_overrides": {"include": [], "exclude": []},
-        "templates": {
-            "preview_note": "assets/templates/preview_note_template.md",
-        },
-        "scenarios": {"file": args.scenario_file.strip()} if args.scenario_file.strip() else {},
-        "options": {"enabled": False},
-    }
-
-    plan_path = out_dir / "plan.json"
-    write_json(plan_path, plan)
-    print(f"Wrote plan: {plan_path}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/run_plan.py b/plugins/financial-markets/skills/earnings-preview/scripts/run_plan.py
deleted file mode 100644
index 56f4ba0..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/run_plan.py
+++ /dev/null
@@ -1,921 +0,0 @@
-#!/usr/bin/env python3
-"""Execute an Earnings Preview Pack plan.
-
-This script is deterministic: it reads local inputs, computes tables, fills templates, and writes outputs.
-It does NOT fetch data from the internet.
-
-Example:
-  python scripts/run_plan.py path/to/plan.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import math
-import re
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-if __name__ == "__main__" and any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-    print("Usage: python scripts/run_plan.py plan.json")
-    print("Execute an earnings preview pack plan.")
-    raise SystemExit(0)
-
-import pandas as pd
-from lib.calc import (
-    auto_flag_delta,
-    is_rate_metric,
-    safe_bps_change,
-    safe_pct_change,
-    shift_period,
-    trend_slope,
-    two_year_stack,
-)
-from lib.changelog import maybe_write_changelog
-from lib.io_utils import df_to_markdown_table, fmt_number, read_json, sha256_file, write_json
-from lib.kpi_packs import get_kpis_for_pack, load_sector_packs
-from lib.qa import resolve_input_paths, validate_inputs, validate_plan
-from lib.render import TOKEN_RE, render_text
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-
-
-def utcnow_iso() -> str:
-    return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
-
-
-def render_with_defaults(template_path: Path, mapping: dict[str, str], default: str) -> str:
-    """Render bracket-token templates and fill any unsupported tokens explicitly."""
-    text = template_path.read_text(encoding="utf-8")
-    complete = dict(mapping)
-    for token in set(TOKEN_RE.findall(text)):
-        if token not in complete or complete[token] in (None, ""):
-            complete[token] = default
-    return render_text(text, complete)
-
-
-def unresolved_output_errors(output_name: str, text: str) -> list[str]:
-    errors: list[str] = []
-    if re.search(r"\[[A-Za-z0-9_]+\]", text):
-        errors.append(f"{output_name}: unresolved bracket token remains")
-    if re.search(r"\bTODO\b", text):
-        errors.append(f"{output_name}: TODO placeholder remains")
-    if re.search(r"\{\{[^}]+\}\}|\{[A-Z0-9_]+\}", text):
-        errors.append(f"{output_name}: unresolved brace placeholder remains")
-    return errors
-
-
-def get_value(
-    df: pd.DataFrame | None, ticker: str, fiscal_period_id: str, metric_id: str, value_col: str
-) -> float | None:
-    if df is None or df.empty:
-        return None
-    m = (
-        (df.get("ticker") == ticker)
-        & (df.get("fiscal_period_id") == fiscal_period_id)
-        & (df.get("metric_id") == metric_id)
-    )
-    if m.sum() == 0:
-        return None
-    v = df.loc[m, value_col].iloc[0]
-    try:
-        return float(v)
-    except Exception:
-        return None
-
-
-def get_unit_scale(
-    df: pd.DataFrame | None, ticker: str, fiscal_period_id: str, metric_id: str, value_col: str
-) -> tuple[str, float]:
-    if df is None or df.empty:
-        return ("", 1.0)
-    m = (
-        (df.get("ticker") == ticker)
-        & (df.get("fiscal_period_id") == fiscal_period_id)
-        & (df.get("metric_id") == metric_id)
-    )
-    if m.sum() == 0:
-        return ("", 1.0)
-    row = df.loc[m].iloc[0]
-    unit = str(row.get("unit", ""))
-    try:
-        scale = float(row.get("scale", 1.0))
-    except Exception:
-        scale = 1.0
-    return unit, scale
-
-
-def fmt_metric(
-    df: pd.DataFrame | None, ticker: str, fiscal_period_id: str, metric_id: str, value_col: str
-) -> str:
-    v = get_value(df, ticker, fiscal_period_id, metric_id, value_col)
-    unit, scale = get_unit_scale(df, ticker, fiscal_period_id, metric_id, value_col)
-    rate = is_rate_metric(metric_id, unit)
-    decimals = 2 if rate else 1
-    return fmt_number(v, unit, scale, decimals)
-
-
-def pick_company_name(plan: dict, company_master: pd.DataFrame | None, ticker: str) -> str:
-    if plan.get("company_name"):
-        return str(plan.get("company_name")).strip()
-    if company_master is None or company_master.empty:
-        return ticker
-    m = company_master.get("ticker") == ticker
-    if m.sum() == 0:
-        return ticker
-    return str(company_master.loc[m, "company_name"].iloc[0])
-
-
-def build_kpi_dashboard(
-    ticker: str,
-    fiscal_period_id: str,
-    actuals: pd.DataFrame,
-    consensus: pd.DataFrame,
-    whisper: pd.DataFrame | None,
-    kpi_list: list[str],
-) -> pd.DataFrame:
-    """Pretty (string) dashboard table for the note."""
-
-    t = fiscal_period_id
-    t_1 = shift_period(t, -1)
-    t_4 = shift_period(t, -4)
-    t_8 = shift_period(t, -8)
-
-    rows = []
-    for metric_id in kpi_list:
-        # Prefer unit/scale from consensus (for the upcoming quarter), else fall back to last actual.
-        cons_unit, cons_scale = get_unit_scale(consensus, ticker, t, metric_id, "estimate_value")
-        act_unit, act_scale = get_unit_scale(actuals, ticker, t_1, metric_id, "value")
-        unit = cons_unit or act_unit
-        scale = cons_scale if cons_unit else act_scale
-        metric_rate = is_rate_metric(metric_id, unit)
-
-        last_act = get_value(actuals, ticker, t_1, metric_id, "value")
-        yoy_base = get_value(actuals, ticker, t_4, metric_id, "value")
-        stack_base = get_value(actuals, ticker, t_8, metric_id, "value")
-
-        cons_est = get_value(consensus, ticker, t, metric_id, "estimate_value")
-        whi_est = (
-            get_value(whisper, ticker, t, metric_id, "whisper_value")
-            if whisper is not None
-            else None
-        )
-
-        # Growth/deltas
-        if metric_rate:
-            qoq = safe_bps_change(cons_est, last_act)
-            yoy = safe_bps_change(cons_est, yoy_base)
-        else:
-            qoq = safe_pct_change(cons_est, last_act)
-            yoy = safe_pct_change(cons_est, yoy_base)
-
-        stack2 = two_year_stack(cons_est, stack_base) if (not metric_rate) else None
-
-        # Trend: last 4 actual points (t-1 ... t-4)
-        hist_vals = [
-            get_value(actuals, ticker, shift_period(t, -i), metric_id, "value") for i in range(1, 5)
-        ]
-        tr = trend_slope([v for v in hist_vals if v is not None])
-
-        flag = auto_flag_delta(whi_est, cons_est, metric_rate)
-
-        rows.append(
-            {
-                "flag": "!" if flag else "",
-                "metric_id": metric_id,
-                "t_cons": fmt_number(cons_est, unit, scale, decimals=2 if metric_rate else 1),
-                "t_1_act": fmt_number(last_act, unit, scale, decimals=2 if metric_rate else 1),
-                "qoq": (
-                    fmt_number(qoq, "bps", 1, 0)
-                    if metric_rate
-                    else ("" if qoq is None else f"{qoq * 100:.1f}%")
-                ),
-                "yoy": (
-                    fmt_number(yoy, "bps", 1, 0)
-                    if metric_rate
-                    else ("" if yoy is None else f"{yoy * 100:.1f}%")
-                ),
-                "stack_2yr": ("" if stack2 is None else f"{stack2 * 100:.1f}%"),
-                "whisper": fmt_number(whi_est, unit, scale, decimals=2 if metric_rate else 1),
-                "trend": "" if tr is None else ("up" if tr > 0 else "down"),
-                "commentary": "",
-            }
-        )
-
-    df = pd.DataFrame(rows)
-    return df[
-        [
-            "flag",
-            "metric_id",
-            "t_cons",
-            "t_1_act",
-            "qoq",
-            "yoy",
-            "stack_2yr",
-            "whisper",
-            "trend",
-            "commentary",
-        ]
-    ]
-
-
-def build_cons_vs_whisper_table(
-    ticker: str,
-    fiscal_period_id: str,
-    consensus: pd.DataFrame,
-    whisper: pd.DataFrame | None,
-    metrics: list[str],
-) -> pd.DataFrame:
-    t = fiscal_period_id
-    rows = []
-    for metric_id in metrics:
-        cons_val = get_value(consensus, ticker, t, metric_id, "estimate_value")
-        cons_unit, cons_scale = get_unit_scale(consensus, ticker, t, metric_id, "estimate_value")
-        metric_rate = is_rate_metric(metric_id, cons_unit)
-
-        whi_val = (
-            get_value(whisper, ticker, t, metric_id, "whisper_value")
-            if whisper is not None
-            else None
-        )
-        conf = None
-        prov = ""
-        if whisper is not None:
-            m = (
-                (whisper.get("ticker") == ticker)
-                & (whisper.get("fiscal_period_id") == t)
-                & (whisper.get("metric_id") == metric_id)
-            )
-            if m.sum() > 0:
-                r = whisper.loc[m].iloc[0]
-                conf = r.get("confidence_score")
-                prov = str(r.get("provenance", ""))
-
-        if metric_rate:
-            delta = safe_bps_change(whi_val, cons_val)
-            delta_str = fmt_number(delta, "bps", 1, 0)
-        else:
-            delta = safe_pct_change(whi_val, cons_val)
-            delta_str = "" if delta is None else f"{delta * 100:.1f}%"
-
-        rows.append(
-            {
-                "metric_id": metric_id,
-                "consensus": fmt_number(
-                    cons_val, cons_unit, cons_scale, decimals=2 if metric_rate else 1
-                ),
-                "whisper": fmt_number(
-                    whi_val, cons_unit, cons_scale, decimals=2 if metric_rate else 1
-                ),
-                "delta": delta_str,
-                "confidence": ""
-                if conf is None or (isinstance(conf, float) and math.isnan(conf))
-                else str(conf),
-                "provenance": prov,
-            }
-        )
-    return pd.DataFrame(rows)
-
-
-def load_scenarios(path: Path) -> dict[str, dict[str, dict[str, float]]]:
-    if not path.exists():
-        return {}
-    df = pd.read_csv(path)
-    df.columns = [str(c).strip().lower() for c in df.columns]
-    needed = {"scenario_name", "metric_id", "delta_type", "delta_value"}
-    if not needed.issubset(set(df.columns)):
-        return {}
-    df["delta_value"] = pd.to_numeric(df["delta_value"], errors="coerce")
-    out: dict[str, dict[str, dict[str, float]]] = {}
-    for _, r in df.iterrows():
-        scen = str(r["scenario_name"]).strip().lower()
-        mid = str(r["metric_id"]).strip()
-        dt = str(r["delta_type"]).strip().lower()
-        dv = r["delta_value"]
-        if scen not in out:
-            out[scen] = {}
-        out[scen][mid] = {"delta_type": dt, "delta_value": float(dv) if not pd.isna(dv) else 0.0}
-    return out
-
-
-def apply_delta(base_value: float | None, delta_type: str, delta_value: float) -> float | None:
-    if base_value is None:
-        return None
-    if delta_type == "pct":
-        return base_value * (1.0 + delta_value)
-    if delta_type == "abs":
-        return base_value + delta_value
-    if delta_type == "bps":
-        return base_value + (delta_value / 10_000.0)
-    return base_value
-
-
-def build_questions(sector_pack: str) -> tuple[str, list[str]]:
-    """Return (markdown question list, watch-fors)."""
-
-    universal = [
-        (
-            "Demand",
-            [
-                "What is changing in demand vs last quarter (by segment/channel), and what is the leading indicator?",
-                "Where are you seeing pipeline conversion improve/worsen, and what is the time-to-close trend?",
-            ],
-        ),
-        (
-            "Pricing",
-            [
-                "What is price vs volume/mix this quarter? Any change in discounting or promotions?",
-                "Are you seeing willingness-to-pay change (new logo pricing, renewals, upsells)?",
-            ],
-        ),
-        (
-            "Margins",
-            [
-                "What are the gross margin drivers (mix, input costs, cloud costs, utilization)?",
-                "What is the incremental margin on revenue upside/downside into next quarter?",
-            ],
-        ),
-        (
-            "Guidance",
-            [
-                "How should we translate this quarter into next-quarter and FY guidance (assumptions and conservatism)?",
-                "What changed since last quarter in the guidance algorithm (macro, FX, backlog, pipeline)?",
-            ],
-        ),
-        (
-            "Competition",
-            [
-                "What are you seeing competitively (win/loss, pricing pressure, feature parity)?",
-            ],
-        ),
-        (
-            "Capital allocation",
-            [
-                "Any changes in buybacks, capex, hiring pace, or balance sheet priorities?",
-            ],
-        ),
-    ]
-
-    watch = [
-        "Vague 'macro' explanations without segment detail",
-        "Guidance framed as a range but with asymmetric downside risk",
-        "Non-GAAP addbacks expanding without clear explanation",
-    ]
-
-    addl: list[str] = []
-    sp = sector_pack.lower()
-    if sp == "saas":
-        addl = [
-            "NRR and churn: what is driving change (usage, seats, pricing, downgrades)?",
-            "Bookings/billings: what is the pipeline build and conversion into next quarter?",
-            "AI monetization: attach rate, pricing, cannibalization, and gross margin impact.",
-        ]
-        watch.extend(
-            [
-                "NRR stability vs seasonal/one-time factors",
-                "RPO growth slowing (demand pullback signal)",
-            ]
-        )
-    elif sp == "semiconductors":
-        addl = [
-            "Channel inventory: weeks on hand and evidence of double-ordering/destocking.",
-            "Backlog quality: firm vs cancellable, and conversion timing.",
-            "Units vs ASP: what is mix vs true demand?",
-        ]
-        watch.extend(["Lead times collapsing", "Backlog shrinking faster than shipments"])
-    elif sp == "consumer_retail":
-        addl = [
-            "Comps breakdown: traffic vs ticket vs price; any trade-down?",
-            "Markdown intensity and inventory health.",
-            "Shrink/labor: what changed and what is the path forward?",
-        ]
-        watch.extend(
-            ["Promo cadence increasing (margin risk)", "Inventory build without sales acceleration"]
-        )
-
-    lines = []
-    qnum = 1
-    for cat, qs in universal:
-        lines.append(f"**{cat}**")
-        for q in qs:
-            lines.append(f"{qnum}) {q}")
-            qnum += 1
-        lines.append("")
-
-    if addl:
-        lines.append(f"**Sector add-ons ({sector_pack})**")
-        for q in addl:
-            lines.append(f"{qnum}) {q}")
-            qnum += 1
-        lines.append("")
-
-    return "\n".join(lines).strip(), watch
-
-
-def write_exports_xlsx(path: Path, tables: dict[str, pd.DataFrame | None]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    with pd.ExcelWriter(path, engine="openpyxl") as writer:
-        for sheet, df in tables.items():
-            if df is None:
-                continue
-            sheet_name = sheet[:31]
-            df.to_excel(writer, sheet_name=sheet_name, index=False)
-
-
-def write_exports_csv(dir_path: Path, tables: dict[str, pd.DataFrame | None]) -> None:
-    dir_path.mkdir(parents=True, exist_ok=True)
-    for name, df in tables.items():
-        if df is None:
-            continue
-        (dir_path / f"{name}.csv").write_text(df.to_csv(index=False), encoding="utf-8")
-
-
-def build_prior_quarter_table(ticker: str, fiscal_period_id: str, actuals: pd.DataFrame) -> str:
-    """Small context table: last 4 quarters revenue and EPS actual."""
-    periods = [shift_period(fiscal_period_id, -i) for i in range(1, 5)]
-    rows = []
-    for p in periods:
-        rows.append(
-            {
-                "period": p,
-                "revenue": fmt_metric(actuals, ticker, p, "revenue", "value"),
-                "eps_diluted": fmt_metric(actuals, ticker, p, "eps_diluted", "value"),
-            }
-        )
-    return df_to_markdown_table(pd.DataFrame(rows))
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser()
-    ap.add_argument("plan_path", help="Path to plan.json")
-    args = ap.parse_args()
-
-    plan_path = Path(args.plan_path).resolve()
-    plan = read_json(plan_path)
-    if not isinstance(plan, dict):
-        raise SystemExit("plan.json must be a JSON object")
-
-    # Validate plan and inputs
-    errors, warnings = validate_plan(plan)
-    e2, w2, dfs = validate_inputs(plan, SKILL_ROOT)
-    errors.extend(e2)
-    warnings.extend(w2)
-    if errors:
-        raise SystemExit(
-            "Validation failed. Run scripts/validate_plan.py and fix errors before executing."
-        )
-
-    ticker = str(plan.get("ticker")).strip()
-    fiscal_period_id = str(plan.get("fiscal_period_id")).strip()
-    sector_pack = str(plan.get("sector_pack")).strip()
-
-    output_base = Path(str(plan.get("output_dir")))
-    if not output_base.is_absolute():
-        output_base = (SKILL_ROOT / output_base).resolve()
-    output_run_dir = (output_base / ticker / fiscal_period_id).resolve()
-    output_run_dir.mkdir(parents=True, exist_ok=True)
-
-    # Preserve previous manifest for diff
-    prev_manifest_path = output_run_dir / "run_manifest.json"
-    if prev_manifest_path.exists():
-        (output_run_dir / "run_manifest.previous.json").write_text(
-            prev_manifest_path.read_text(encoding="utf-8"), encoding="utf-8"
-        )
-
-    # Load inputs
-    paths = resolve_input_paths(plan, SKILL_ROOT)
-
-    reported_financials = dfs.get("reported_financials")
-    kpi_timeseries = dfs.get("kpi_timeseries")
-    consensus = dfs.get("consensus_estimates")
-
-    # Coerce numeric
-    for df, col in [
-        (reported_financials, "value"),
-        (kpi_timeseries, "value"),
-        (consensus, "estimate_value"),
-    ]:
-        if df is not None and col in df.columns:
-            df[col] = pd.to_numeric(df[col], errors="coerce")
-
-    whisper = dfs.get("whisper_estimates")
-    if whisper is not None and "whisper_value" in whisper.columns:
-        whisper["whisper_value"] = pd.to_numeric(whisper["whisper_value"], errors="coerce")
-
-    # Combine actuals
-    actuals = pd.concat([reported_financials, kpi_timeseries], ignore_index=True, sort=False)
-
-    # KPI list from sector pack + overrides
-    packs = load_sector_packs(SKILL_ROOT / "assets/sector_kpi_packs.yaml")
-    default_kpis = get_kpis_for_pack(packs, sector_pack)
-    overrides = plan.get("kpi_overrides", {}) or {}
-    inc = [str(x) for x in (overrides.get("include") or [])]
-    exc = {str(x) for x in (overrides.get("exclude") or [])}
-
-    kpi_list: list[str] = []
-    for k in default_kpis + inc:
-        if k and k not in exc and k not in kpi_list:
-            kpi_list.append(k)
-
-    # KPI dashboard + key tables
-    kpi_dash = build_kpi_dashboard(ticker, fiscal_period_id, actuals, consensus, whisper, kpi_list)
-
-    # Expectation bar: revenue, eps, + first two non-(rev,eps) KPIs
-    key_metrics = ["revenue", "eps_diluted"]
-    extras = [m for m in kpi_list if m not in key_metrics]
-    key_metrics.extend(extras[:2])
-    cons_vs_whisper = build_cons_vs_whisper_table(
-        ticker, fiscal_period_id, consensus, whisper, key_metrics
-    )
-
-    # Scenario framing
-    scen_file = (plan.get("scenarios") or {}).get("file")
-    scenarios = load_scenarios((SKILL_ROOT / scen_file).resolve()) if scen_file else {}
-
-    base_rev = get_value(consensus, ticker, fiscal_period_id, "revenue", "estimate_value")
-    base_eps = get_value(consensus, ticker, fiscal_period_id, "eps_diluted", "estimate_value")
-
-    def scen_val(s: str, metric_id: str, base: float | None) -> float | None:
-        d = (scenarios.get(s, {}) or {}).get(metric_id)
-        if not d:
-            return base
-        return apply_delta(base, d.get("delta_type", ""), float(d.get("delta_value", 0.0)))
-
-    bull_rev = scen_val("bull", "revenue", base_rev)
-    bear_rev = scen_val("bear", "revenue", base_rev)
-    bull_eps = scen_val("bull", "eps_diluted", base_eps)
-    bear_eps = scen_val("bear", "eps_diluted", base_eps)
-
-    # Questions + watch-fors
-    q_list_md, watch_fors = build_questions(sector_pack)
-    call_questions = [line for line in q_list_md.splitlines() if re.match(r"^\d+\)", line.strip())]
-
-    # Company name and timestamps
-    company_master = dfs.get("company_master")
-    company_name = pick_company_name(plan, company_master, ticker)
-    freeze_ts = str(plan.get("freeze_time"))
-
-    # Consensus snapshot timestamp (best effort)
-    cons_ts = ""
-    try:
-        cr = consensus[
-            (consensus.get("ticker") == ticker)
-            & (consensus.get("fiscal_period_id") == fiscal_period_id)
-        ]
-        if len(cr) > 0:
-            cons_ts = str(cr.get("snapshot_datetime").iloc[0])
-    except Exception:
-        cons_ts = ""
-
-    # Fill KPI placeholders
-    kpi1 = extras[0] if len(extras) > 0 else ""
-    kpi2 = extras[1] if len(extras) > 1 else ""
-
-    def cons_row(metric_id: str) -> dict:
-        if cons_vs_whisper is None or cons_vs_whisper.empty:
-            return {}
-        m = cons_vs_whisper["metric_id"] == metric_id
-        if m.sum() == 0:
-            return {}
-        r = cons_vs_whisper.loc[m].iloc[0].to_dict()
-        return {k: "" if v is None else str(v) for k, v in r.items()}
-
-    r_rev = cons_row("revenue")
-    r_eps = cons_row("eps_diluted")
-    r_k1 = cons_row(kpi1) if kpi1 else {}
-    r_k2 = cons_row(kpi2) if kpi2 else {}
-
-    # Prior quarter table
-    prior_tbl = build_prior_quarter_table(ticker, fiscal_period_id, actuals)
-
-    # Provenance notes
-    prov_lines = [
-        f"Inputs: {len([p for p in paths.values() if p.exists()])} files on disk.",
-        f"Consensus snapshot: {cons_ts or freeze_ts}.",
-        "See run_manifest.json for exact file paths and SHA256 hashes.",
-    ]
-
-    template_path = Path(
-        str(
-            (plan.get("templates") or {}).get(
-                "preview_note", "assets/templates/preview_note_template.md"
-            )
-        )
-    )
-    if not template_path.is_absolute():
-        template_path = (SKILL_ROOT / template_path).resolve()
-
-    top_metrics = [m for m in [kpi1, kpi2, extras[2] if len(extras) > 2 else ""] if m]
-    top_metric_text = (
-        ", ".join(top_metrics) if top_metrics else "revenue, EPS, and the most debated company KPI"
-    )
-    bar_numbers = f"Revenue {fmt_metric(consensus, ticker, fiscal_period_id, 'revenue', 'estimate_value')}; EPS {fmt_metric(consensus, ticker, fiscal_period_id, 'eps_diluted', 'estimate_value')}"
-    source_limited = "not modeled in deterministic sample inputs"
-
-    mapping = {
-        "COMPANY_NAME": company_name,
-        "TICKER": ticker,
-        "FISCAL_PERIOD_ID": fiscal_period_id,
-        "PREVIEW_PERIOD": fiscal_period_id,
-        "PRIOR_PERIOD": shift_period(fiscal_period_id, -1),
-        "YEAR_AGO_PERIOD": shift_period(fiscal_period_id, -4),
-        "FREEZE_TS": freeze_ts,
-        "CONSENSUS_SNAPSHOT_TS": cons_ts or freeze_ts,
-        "THESIS_1P": f"{company_name} is framed against a consensus bar of {bar_numbers}. The deterministic pack is screen-grade: it uses local sample inputs, not live market data, so peer read-through, options, and stock-reaction sections are explicitly marked where unsupported. The core debate should focus on {top_metric_text}.",
-        "WHAT_MATTERS_1": extras[0] if len(extras) > 0 else "revenue",
-        "WHAT_MATTERS_2": extras[1] if len(extras) > 1 else "EPS",
-        "WHAT_MATTERS_3": extras[2] if len(extras) > 2 else "guidance quality",
-        "CONS_REV": fmt_metric(consensus, ticker, fiscal_period_id, "revenue", "estimate_value"),
-        "CONS_EPS": fmt_metric(
-            consensus, ticker, fiscal_period_id, "eps_diluted", "estimate_value"
-        ),
-        "WHISPER_REV": r_rev.get("whisper", ""),
-        "WHISPER_EPS": r_eps.get("whisper", ""),
-        "REV_DELTA": r_rev.get("delta", ""),
-        "EPS_DELTA": r_eps.get("delta", ""),
-        "REV_NOTE": "",
-        "EPS_NOTE": "Confirm whether consensus is GAAP, adjusted, operating, or provider-standardized EPS.",
-        "EPS_QUALITY_WATCH": (
-            "| Item | Why it could distort EPS | Post-print evidence needed | Model line affected |\n"
-            "|---|---|---|---|\n"
-            "| EPS basis | GAAP, adjusted, operating, and provider-standardized EPS can "
-            "produce different surprise math. | Company reconciliation, filing EPS "
-            "table, and consensus definition/as-of. | EPS / net income |\n"
-            "| Tax / below-the-line / share count | Non-operating gains, tax items, FX, "
-            "interest, marks, or dilution can flatter or depress headline EPS. | "
-            "Source-tagged bridge from GAAP EPS to recurring or operating EPS if "
-            "material. | EPS / net income / share count |"
-        ),
-        "CONS_KPI1": r_k1.get("consensus", ""),
-        "WHISPER_KPI1": r_k1.get("whisper", ""),
-        "KPI1_DELTA": r_k1.get("delta", ""),
-        "KPI1_NOTE": "",
-        "CONS_KPI2": r_k2.get("consensus", ""),
-        "WHISPER_KPI2": r_k2.get("whisper", ""),
-        "KPI2_DELTA": r_k2.get("delta", ""),
-        "KPI2_NOTE": "",
-        "IMPLIED_MOVE_PCT": "N/A",
-        "VOL_NOTES": "N/A",
-        "KPI_DASHBOARD_TABLE": df_to_markdown_table(kpi_dash),
-        "BULL_DRIVERS": "upside to consensus on revenue/EPS and clean KPI momentum",
-        "BASE_DRIVERS": "consensus case with stable KPI trend and no definition break",
-        "BEAR_DRIVERS": "miss versus consensus or KPI deceleration versus recent trend",
-        "BULL_REV": fmt_number(bull_rev, "USD", 1_000_000, 1) if bull_rev is not None else "",
-        "BASE_REV": fmt_number(base_rev, "USD", 1_000_000, 1) if base_rev is not None else "",
-        "BEAR_REV": fmt_number(bear_rev, "USD", 1_000_000, 1) if bear_rev is not None else "",
-        "BULL_EPS": fmt_number(bull_eps, "USD", 1, 2) if bull_eps is not None else "",
-        "BASE_EPS": fmt_number(base_eps, "USD", 1, 2) if base_eps is not None else "",
-        "BEAR_EPS": fmt_number(bear_eps, "USD", 1, 2) if bear_eps is not None else "",
-        "BULL_RXN": "positive revision skew if beat is broad-based",
-        "BASE_RXN": "stock reaction depends on quality of guidance and KPI commentary",
-        "BEAR_RXN": "negative revision skew if miss is tied to demand or margin quality",
-        "BULL_KILL": "bull case weakens if KPI trend fails to confirm revenue/EPS upside",
-        "BASE_KILL": "base case weakens if guidance quality or KPI definition changes",
-        "BEAR_KILL": "bear case weakens if upside is broad-based and guidance is credible",
-        "BULL_1": "Consensus may understate the operating leverage if revenue/KPI momentum holds.",
-        "BULL_2": "A clean guide and stable KPI definitions would raise confidence in upside quality.",
-        "BEAR_1": "Revenue/EPS upside without KPI support may be low-quality or one-time.",
-        "BEAR_2": "Guidance conservatism or metric definition changes could blur the true bar.",
-        "BAR_1": f"The bar is primarily {bar_numbers}; unsupported sections should be treated as data requests, not conclusions.",
-        "DYNAMIC_QUESTION_LIST": q_list_md,
-        "WATCHFOR_1": watch_fors[0] if len(watch_fors) > 0 else "",
-        "WATCHFOR_2": watch_fors[1] if len(watch_fors) > 1 else "",
-        "WATCHFOR_3": watch_fors[2] if len(watch_fors) > 2 else "",
-        "PRIOR_Q_TABLE": prior_tbl,
-        "PROVENANCE_NOTES": "\n".join([f"- {x}" for x in prov_lines]),
-        "LAST_Q_BEAT_MISS": "See prior-quarter table; deterministic sample does not include explicit surprise history.",
-        "LAST_Q_NARRATIVE": "Not modeled in deterministic sample inputs.",
-        "LAST_Q_REACTION": "Not modeled in deterministic sample inputs.",
-        "LAST_Q_DEBATED_KPI": top_metrics[0]
-        if top_metrics
-        else "not modeled in deterministic sample inputs",
-        "LAST_Q_STILL_RELEVANT": "Confirm KPI durability and guidance quality.",
-        "GUIDE_REV": "Not provided in deterministic sample inputs",
-        "GUIDE_REV_SOURCE_Q": source_limited,
-        "GUIDE_REV_TYPE": source_limited,
-        "GUIDE_REV_NOTE": source_limited,
-        "GUIDE_EPS": "Not provided in deterministic sample inputs",
-        "GUIDE_EPS_SOURCE_Q": source_limited,
-        "GUIDE_EPS_TYPE": source_limited,
-        "GUIDE_EPS_NOTE": source_limited,
-        "GUIDE_KPI": "Not provided in deterministic sample inputs",
-        "GUIDE_KPI_SOURCE_Q": source_limited,
-        "GUIDE_KPI_TYPE": source_limited,
-        "GUIDE_KPI_NOTE": source_limited,
-        "GUIDANCE_CREDIBILITY": "Guidance history was not analyzed in this deterministic sample run.",
-        "WHISPER_FRAMING": "Whisper is shown only where local whisper inputs exist; otherwise treat as unavailable.",
-        "WHISPER_CONFIDENCE": "source-dependent",
-        "CATEGORY": "peer",
-        "NAME": source_limited,
-        "SIGNAL": source_limited,
-        "WHY": source_limited,
-        "READ_THROUGH": source_limited,
-        "CONFIDENCE": "low",
-        "QTR": source_limited,
-        "CONTEXT": source_limited,
-        "MOVE": source_limited,
-        "DRIVER": source_limited,
-        "DRIFT": source_limited,
-        "IMPLIED_VS_HISTORY": source_limited,
-        "MACRO_POSITIVE": source_limited,
-        "MACRO_NEGATIVE": source_limited,
-        "MACRO_UNCERTAIN": source_limited,
-        "CATALYST": source_limited,
-        "DIRECTION": source_limited,
-        "EVIDENCE": source_limited,
-        "SIGNAL_QUALITY": source_limited,
-        "TOP_BAR_NUMBERS": bar_numbers,
-        "TOP_3_METRICS": top_metric_text,
-        "BULL_CATALYST": "broad-based beat plus credible guide",
-        "BEAR_RISK": "KPI/margin miss or low-quality guide",
-        "KEY_READ_THROUGH": source_limited,
-        "MOVE_COMPARISON": source_limited,
-        "CALL_WATCH": call_questions[0]
-        if call_questions
-        else "guidance assumptions and KPI durability",
-    }
-
-    rendered_note = render_with_defaults(template_path, mapping, source_limited)
-    (output_run_dir / "preview_note.md").write_text(rendered_note, encoding="utf-8")
-
-    # Exports
-    cover = pd.DataFrame(
-        [
-            {
-                "section": "Header",
-                "metric": "Company / ticker",
-                "value": f"{company_name} / {ticker}",
-                "notes": "Earnings preview export pack landing page.",
-            },
-            {
-                "section": "Header",
-                "metric": "Preview period",
-                "value": fiscal_period_id,
-                "notes": f"Sector pack: {sector_pack}",
-            },
-            {
-                "section": "Header",
-                "metric": "Freeze time / consensus snapshot",
-                "value": f"{freeze_ts} / {cons_ts or freeze_ts}",
-                "notes": "Refresh inputs before relying on the workbook for PM/IC use.",
-            },
-            {
-                "section": "Status",
-                "metric": "Workbook mode",
-                "value": "earnings_preview_export_pack",
-                "notes": "Deterministic local-input export pack, not a live-market data pull.",
-            },
-            {
-                "section": "Status",
-                "metric": "Warnings",
-                "value": len(warnings),
-                "notes": "; ".join(str(w) for w in warnings[:3]) if warnings else "None flagged",
-            },
-            {
-                "section": "Executive read-through",
-                "metric": "Consensus bar",
-                "value": bar_numbers,
-                "notes": f"Core debate: {top_metric_text}",
-            },
-            {
-                "section": "Scenario framing",
-                "metric": "Revenue bull / base / bear",
-                "value": f"{mapping['BULL_REV']} / {mapping['BASE_REV']} / {mapping['BEAR_REV']}",
-                "notes": "Scenario assumptions are local-plan driven.",
-            },
-            {
-                "section": "Scenario framing",
-                "metric": "EPS bull / base / bear",
-                "value": f"{mapping['BULL_EPS']} / {mapping['BASE_EPS']} / {mapping['BEAR_EPS']}",
-                "notes": "Confirm GAAP/adjusted/operating EPS definition before comparing surprises.",
-            },
-            {
-                "section": "KPI dashboard",
-                "metric": "KPI rows",
-                "value": len(kpi_dash),
-                "notes": "See 07_KPI_Dashboard for chart-ready detail.",
-            },
-            {
-                "section": "KPI dashboard",
-                "metric": "Consensus vs whisper rows",
-                "value": len(cons_vs_whisper),
-                "notes": "See 08_Cons_vs_Whisper for deltas and confidence.",
-            },
-            {
-                "section": "Call watch",
-                "metric": "Primary call question",
-                "value": call_questions[0]
-                if call_questions
-                else "guidance assumptions and KPI durability",
-                "notes": "Use preview_note.md for the full written setup.",
-            },
-            {
-                "section": "Source posture",
-                "metric": "Input files found",
-                "value": len([p for p in paths.values() if p.exists()]),
-                "notes": "See run_manifest.json for paths, mtimes, and hashes.",
-            },
-            {
-                "section": "Workbook map",
-                "metric": "01-06 source tables",
-                "value": "Period, financials, KPIs, guidance, consensus, whisper",
-                "notes": "Raw/support exports; do not treat missing sections as analyzed.",
-            },
-            {
-                "section": "Workbook map",
-                "metric": "07-08 dashboard tables",
-                "value": "KPI dashboard and consensus-vs-whisper",
-                "notes": "Use these as chart-ready dashboard data.",
-            },
-        ]
-    )
-    tables: dict[str, pd.DataFrame | None] = {
-        "Cover": cover,
-        "01_PeriodIndex": dfs.get("fiscal_period_index"),
-        "02_Financials_Q": dfs.get("reported_financials"),
-        "03_KPIs_Q": dfs.get("kpi_timeseries"),
-        "04_Guidance_History": dfs.get("guidance_history"),
-        "05_Consensus_Snapshot": dfs.get("consensus_estimates"),
-        "06_Whisper": dfs.get("whisper_estimates"),
-        "07_KPI_Dashboard": kpi_dash,
-        "08_Cons_vs_Whisper": cons_vs_whisper,
-    }
-
-    write_exports_xlsx(output_run_dir / "exports.xlsx", tables)
-    write_exports_csv(output_run_dir / "exports", tables)
-
-    # QA report
-    output_errors = []
-    output_errors.extend(unresolved_output_errors("preview_note.md", rendered_note))
-
-    qa = {
-        "status": "FAIL" if output_errors else ("PASS_WITH_WARNINGS" if warnings else "PASS"),
-        "errors": output_errors,
-        "warnings": warnings,
-        "generated_at": utcnow_iso(),
-    }
-    write_json(output_run_dir / "qa_report.json", qa)
-    (output_run_dir / "qa_report.md").write_text(
-        "# QA report\n\n"
-        + (
-            "## Errors\n" + "\n".join([f"- {e}" for e in output_errors]) + "\n\n"
-            if output_errors
-            else ""
-        )
-        + (
-            "## Warnings\n" + "\n".join([f"- {w}" for w in warnings])
-            if warnings
-            else "- No warnings"
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-    # Run manifest
-    inputs_manifest = {}
-    for name, p in paths.items():
-        if p.exists():
-            inputs_manifest[name] = {
-                "path": str(p),
-                "sha256": sha256_file(p),
-                "mtime_utc": datetime.fromtimestamp(p.stat().st_mtime, tz=timezone.utc)
-                .replace(microsecond=0)
-                .isoformat()
-                .replace("+00:00", "Z"),
-            }
-
-    manifest = {
-        "run_id": utcnow_iso(),
-        "ticker": ticker,
-        "company_name": company_name,
-        "fiscal_period_id": fiscal_period_id,
-        "sector_pack": sector_pack,
-        "freeze_time": freeze_ts,
-        "consensus_statistic": plan.get("consensus_statistic"),
-        "plan_path": str(plan_path),
-        "outputs": {
-            "output_run_dir": str(output_run_dir),
-            "preview_note": str(output_run_dir / "preview_note.md"),
-            "exports_xlsx": str(output_run_dir / "exports.xlsx"),
-            "exports_csv_dir": str(output_run_dir / "exports"),
-        },
-        "inputs": {"files": inputs_manifest},
-        "warnings": warnings,
-    }
-    write_json(output_run_dir / "run_manifest.json", manifest)
-
-    # Changelog (if previous manifest exists)
-    maybe_write_changelog(output_run_dir, manifest)
-
-    print(f"Wrote outputs to: {output_run_dir}")
-    if output_errors:
-        raise SystemExit("Output QA failed. See qa_report.md for unresolved placeholders.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/earnings-preview/scripts/validate_plan.py b/plugins/financial-markets/skills/earnings-preview/scripts/validate_plan.py
deleted file mode 100644
index a189e8a..0000000
--- a/plugins/financial-markets/skills/earnings-preview/scripts/validate_plan.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python3
-"""Validate a plan.json and its referenced inputs.
-
-Exit codes:
-- 0: PASS (warnings may exist)
-- 1: FAIL
-
-Writes next to the plan file:
-- validation_report.json
-- validation_report.md
-
-Example:
-  python scripts/validate_plan.py path/to/plan.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser()
-    ap.add_argument("plan_path", help="Path to plan.json")
-    args = ap.parse_args()
-
-    from lib.io_utils import read_json, write_json
-    from lib.qa import validate_inputs, validate_plan
-
-    plan_path = Path(args.plan_path).resolve()
-
-    plan = read_json(plan_path)
-    if not isinstance(plan, dict):
-        raise SystemExit("plan.json must be a JSON object")
-
-    errors, warnings = validate_plan(plan)
-    e2, w2, _dfs = validate_inputs(plan, SKILL_ROOT)
-    errors.extend(e2)
-    warnings.extend(w2)
-
-    status = "PASS" if not errors else "FAIL"
-
-    report = {
-        "status": status,
-        "errors": errors,
-        "warnings": warnings,
-        "plan_path": str(plan_path),
-    }
-
-    out_dir = plan_path.parent
-    write_json(out_dir / "validation_report.json", report)
-
-    md_lines = [f"# Validation report ({status})", "", f"Plan: {plan_path}", ""]
-    if errors:
-        md_lines.append("## Errors (must fix)")
-        md_lines.extend([f"- {e}" for e in errors])
-        md_lines.append("")
-    if warnings:
-        md_lines.append("## Warnings (review)")
-        md_lines.extend([f"- {w}" for w in warnings])
-        md_lines.append("")
-
-    (out_dir / "validation_report.md").write_text("\n".join(md_lines) + "\n", encoding="utf-8")
-
-    print(status)
-    if errors:
-        for e in errors:
-            print(f"ERROR: {e}")
-        raise SystemExit(1)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/economic-impact-report/SKILL.md b/plugins/financial-markets/skills/economic-impact-report/SKILL.md
deleted file mode 100644
index fdc3a0d..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/SKILL.md
+++ /dev/null
@@ -1,186 +0,0 @@
----
-name: economic-impact-report
-description: Use when translating a specific event, policy change, macro shock, or industry development into public-equity issuer, sector, earnings, valuation, positioning, and portfolio implications. Do not use for standalone macro strategy, rates, FX, credit, futures, or generic market commentary.
----
-
-# Economic Impact Report
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML economic-impact report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive reusable economic-impact report or explicit HTML public-equity shock analysis, the default resolves the presentation surface to a polished standalone HTML economic-impact report unless the user requests an alternate surface, a quick/no-file answer, or a standardized dashboard. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML economic-impact report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Goal
-
-Produce a decision-grade public-equity economic impact report. This is not a news summary and not a generic cross-asset macro note; it should tell a senior public-equity investor what changed, why it matters for listed equities, what reprices first, what is still uncertain, and which issuers, sectors, estimates, valuation debates, positioning risks, and portfolio actions matter most.
-
-Default to a full decision-grade economic impact report unless the user explicitly asks for a summary, quick read, one-pager, brief, TL;DR, or another compressed format. For a new standalone reader-facing report, produce a polished standalone HTML economic-impact report following `../../shared/html-artifact-standard.md`; chat-only is appropriate only when the user explicitly requests a lightweight response.
-
-## Use When
-
-Use this skill when the user asks for:
-
-- economic or market impact of a specific event
-- first-, second-, third-, or fourth-order implications
-- transmission map from shock to consequence
-- winners and losers with mechanisms
-- public-equity implications from rates, FX, options, futures, commodities, credit, policy, geopolitics, labor, supply-chain, demand, or regulatory shocks
-- scenario analysis, catalysts, monitoring signals, or "what would change the view"
-- a senior public-equity investor memo on policy, macro, regulatory, geopolitical, supply, credit-signal, commodity, industry, or earnings shocks
-
-Do not use for:
-
-- pure article summaries with no impact question
-- generic macro explainers with no event or catalyst
-- narrow workflows that need another local skill first, such as DCF, earnings deep dive, catalyst calendar, or spreadsheet cleanup
-- one-line market color where a causal map is overkill
-- standalone macro, rates, FX, futures, options, commodities, or credit strategy without a listed-equity issuer/sector/portfolio decision; route out
-- standalone macro strategy, rates strategy, FX strategy, futures curve work, options-volatility trade construction, credit-security relative value, or debt-security selection; route those to the appropriate plugin, with Credit Markets owning credit instruments
-
-## Non-Negotiables
-
-- Bottom line first.
-- Separate fact, inference, assumption, and scenario.
-- Separate direct effects from higher-order effects.
-- Separate immediate listed-equity implications, medium-term earnings/estimate implications, valuation/multiple implications, positioning/flow implications, and structural consequences.
-- Every major claim must tie to a transmission channel.
-- Every named issuer, sector, country, commodity, rate, currency, credit signal, or asset-class input must connect to a public-equity mechanism.
-- Quantify whenever a reasonable range is possible.
-- Distinguish what is genuinely new from stale reporting or prior expectations.
-- Distinguish what is likely priced from what may still be mispriced.
-- Address the strongest counterargument.
-- End with scenarios, monitoring signals, falsifiers, and the public-equity action posture: `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `watchlist`, `pass`, `wait for proof`, or `re-underwrite`.
-- If current facts matter, verify dates, timestamps, and latest state before analyzing. Distinguish reported events from confirmed facts, and distinguish spot, futures, closing, and intraday commodity or market observations.
-
-## Causal Spine
-
-Use this full sequence:
-
-1. Event and baseline.
-2. What is new.
-3. Transmission channels.
-4. First variables likely to reprice.
-5. Direct winners/losers.
-6. Higher-order consequences.
-7. Scenarios, catalysts, public-equity expression, and portfolio action.
-
-If you cannot name the channel, do more source work before writing. Load `references/impact-framework.md` for timing, directness, confidence, domain, and quantification rules.
-
-## Workflow
-
-1. Define the event precisely, state its evidence status and market-data cut-off, and name the public-equity decision it could change.
-2. Identify what changed versus consensus/baseline and what listed equities already appear to discount.
-3. Draft the transmission map before broad research, ending every channel in issuer, sector, earnings, valuation, positioning, or portfolio implications.
-4. Write the key hypotheses, confirmers, and falsifiers.
-5. Gather evidence, prioritizing primary sources and recency.
-6. Rank impacts by sign, magnitude, timing, confidence, and directness. Group representative issuer or sector candidates only when they share the same transmission channel, first affected line item, and directional read-through; split rows when the economics differ.
-7. Identify what is priced and what may still be mispriced in the affected equities, peer group, sector, index, or factor exposure. Ground priced-in conclusions primarily in affected issuer/sector price action, revisions or valuation movement, or the relevant transmission market; use broad-index performance as context only.
-8. Build base/upside/downside scenarios.
-9. Write the strongest counterargument.
-10. End with monitoring, falsifiers, and what would change portfolio action.
-
-Use `references/workflow.md` for full-form reports.
-
-## No Portfolio / Watchlist Fallback
-
-If the user does not provide a portfolio, position list, thesis, benchmark, or watchlist, do not stall and do not invent holdings. Build a general public-equity exposure map:
-
-- industries and sub-industries most exposed;
-- countries, currencies, and geographic revenue/cost exposures;
-- public companies and relevant private companies with public-equity read-throughs;
-- commodities, rates, FX, credit-spread, volatility, or demand variables that transmit into equity estimates or multiples;
-- likely beneficiaries, losers, second-order peers, suppliers, customers, and crowded expressions;
-- a research queue using `watchlist`, `wait for proof`, `pass`, or `re-underwrite` rather than portfolio-specific add/trim/exit language.
-
-Portfolio-specific action should be labeled `not available without holdings / active weights / mandate`. The report must still terminate in issuer impact, sector/peer impact, earnings or estimate path, valuation/multiple impact, positioning/flow impact, and next Public Equity Investing workflow.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: event verification, issuer/sector exposure, earnings and valuation read-through, positioning/flow impact, scenarios, and counterarguments. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-## Output Modes
-
-- **Full economic-impact report:** default analytical route for broad or substantial reusable public-equity impact requests, especially those with many issuers, scenarios, sources, or monitoring items. For a substantive reusable economic-impact report or explicit HTML public-equity shock analysis, produce a polished standalone HTML economic-impact report following `../../shared/html-artifact-standard.md`; let the event and equity transmission channels determine the report structure.
-- **Standardized dashboard:** use only when the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render. For that path, use `references/DASHBOARD_PACK.md`: `economic-impact-report` owns the public-equity transmission logic, scenario analysis, issuer/sector exposure map, earnings and valuation read-through, positioning implications, and portfolio watch items; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-- **Explicit quick PM read:** use only when the user explicitly asks for quick, short, summary, one-page, brief, or TL;DR. Include event status, public-equity bottom line, primary transmission channel, most affected listed exposures, what is priced, what needs proof, and next monitor.
-
-
-## Output Contract
-
-Unless the user explicitly asks for a shorter or narrower format, produce the full report. The first 150 to 250 words should stand alone as an executive summary, but the rest of the report should still include the full causal map and investment implications.
-
-Minimum sections:
-
-- `Executive Call`
-- `Event Status And Market Baseline`
-- `Source And Freshness`
-- `What Is New Vs. Expected`
-- `Event-To-Equity Transmission Map`
-- `Public Equity Framing`
-- `Ranked Equity Impact Map` / `Issuer And Sector Impact` with issuer and sector exposure candidates
-- `What Is Priced In Vs. What Requires Proof`
-- `Earnings, Valuation, And Positioning Read-Through`
-- `Scenario Matrix`
-- `Monitoring Triggers And Research Queue`
-- `Bottom-Line Judgment`
-
-Use `references/report-template.md` for the full structure, `references/domain-checklists.md` for domain-specific checks, and `references/quality-bar.md` before final delivery.
-
-For Markdown draft/support outputs explicitly requested by the user or used as renderer input, run `scripts/check_economic_impact_report.py <support_note.md>` when practical. The checker validates required sections, source/freshness posture, and unresolved placeholders; it is a QA guardrail, not a replacement for source work. Markdown files should not be the lead user-facing artifact when an HTML report is expected.
-
-Use draft mode while iterating and delivery mode before circulating or sending a final file:
-
-```bash
-python scripts/check_economic_impact_report.py --mode draft path/to/support_note.md
-python scripts/check_economic_impact_report.py --mode delivery path/to/support_note.md
-```
-
-`--mode delivery` is the default and must fail if the report is missing a dedicated `Source And Freshness` section, lacks a data cut-off/as-of date, lacks sources used, omits the stale/missing-data assessment, or discloses stale, missing, unknown, unsupported, or weak evidence for load-bearing claims. `--mode draft` surfaces the same source/freshness issues as warnings so the report can remain a work in progress.
-
-## HTML Guidance
-
-For a substantive standalone HTML economic-impact report, load `../../shared/html-artifact-standard.md` and use these workflow-specific requirements:
-
-- Front-load an investor conclusion and an `Event Status And Market Baseline` block stating what is verified, what is reported or assumed, the as-of timestamp, relevant market benchmark, and material gaps. When material intraday observations inform the view, state an exact research cut-off time and time zone. For commodity shocks, distinguish benchmark, spot versus futures, contract/month when relevant, intraday versus close, and source timestamp.
-- Make the `Event-To-Equity Transmission Map` the primary analytical object: event or shock -> channel -> first variable -> issuer/sector driver -> financial line item -> public-equity action or monitoring implication.
-- Follow it with a `Ranked Equity Impact Map` that separates direct beneficiaries/losers from conditional and higher-order expressions. Combine exposures in one row only when their transmission channel, first affected line item, and directional read-through match; otherwise split them even if they are thematically adjacent. When company-specific work has not been completed, label named issuers as representative exposure candidates or a research queue, not investment conclusions.
-- Include `What Is Priced In Vs. What Requires Proof` before the detailed scenario and monitoring material. Ground priced-in claims primarily in evidence from affected equities or sectors, estimate revisions, valuation movement, or transmission-market indicators such as commodity curves, product spreads, or freight; broad-index performance is supporting context, not primary proof. Keep immediate market impact separate from estimate revisions, multiple effects, positioning/flow risk, and structural implications.
-- Use visible fact/inference/scenario distinctions where a reader could otherwise confuse a reported event, an assumed shock, a market observation, and PM judgment.
-- Cite load-bearing figures, dated facts, and consequential disputed claims near use; use compact section-level source notes when several adjacent claims share sources. Do not repeat citation chips through every cell, monitoring row, or conclusion when they compete with the analysis.
-- If no portfolio was supplied, keep the visible action posture to `watchlist`, `wait for proof`, `pass`, or `re-underwrite` and state what evidence would convert the screen into portfolio action.
-- Visually inspect the HTML according to the shared artifact standard before delivery and iterate on hierarchy, legibility, clipping, table density, citation noise, and whether the transmission decision is immediately visible.
-
-## Edge Cases
-
-- If the event is underspecified but analyzable, state your interpretation and proceed.
-- If the user provides only an article/headline, extract the surprise, baseline, and causal map.
-- If a domain is not materially affected, say `not material`.
-- If current facts or market moves are uncertain, verify before claiming a view.
-- If the event looks dramatic but economically minor, say so clearly.
-- If the effect is mostly expected or priced, say so clearly.
-- If the cleanest expression is not equities, say so, but do not turn this skill into the non-equity trade-construction owner.
-
-## Reference Map
-
-- `references/impact-framework.md`: timing, directness, confidence, transmission channels, quantification, and domain checks.
-- `references/workflow.md`: full workflow and failure modes.
-- `references/report-template.md`: full report template.
-- `references/domain-checklists.md`: public companies, industries, countries, rates, options, futures, and cross-asset checks.
-- `references/quality-bar.md`: final quality bar.
diff --git a/plugins/financial-markets/skills/economic-impact-report/agents/openai.yaml b/plugins/financial-markets/skills/economic-impact-report/agents/openai.yaml
deleted file mode 100644
index b4f8db2..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Economic Impact Report"
-  short_description: "Public-equity impact reports for macro, policy, commodity, geopolitical, and industry shocks."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/economic-impact-report/references/DASHBOARD_PACK.md
deleted file mode 100644
index 8264591..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# Economic Impact Report Dashboard Pack
-
-Use this pack only when the user explicitly selects a standardized dashboard, reusable dashboard template, or structured payload-driven render for an economic, macro, policy, rates, commodity, geopolitics, or demand shock. For an ordinary standalone HTML economic-impact report, follow the flexible HTML artifact standard and the owning skill's event-to-equity guidance instead of this fixed module map. The dashboard must terminate in public-equity issuer, sector, earnings, valuation, positioning, and portfolio implications.
-
-## Producer Role
-
-`economic-impact-report` owns the public-equity transmission logic, affected issuers/sectors, scenario framing, source/freshness posture, estimate and valuation read-through, positioning/flow impact, and PM action implications. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `economic_impact_report`
-- `layout`: `single_page` for full impact maps unless the user explicitly requests tabs
-- `hero.callout`: the shock/transmission question and most exposed public-equity debate
-- `snapshot`: shock magnitude, affected equity universe, top beneficiary, top risk, time horizon, source freshness, and PM action posture
-- If no portfolio/watchlist/thesis was provided, include a general exposure map for industries, countries/currencies, public companies, relevant private companies, commodities, suppliers/customers, and second-order peers; label portfolio-specific action unavailable.
-- `sources`: official economic releases first, then company filings, market data, trusted news/research, and user assumptions
-- `Source posture`, `Missing evidence`, `Line item affected`, `Priced-in status`, and `PM action` must be visible in the payload rather than buried in prose.
-- On this explicitly selected dashboard path, the HTML dashboard/report is the human deliverable; JSON, Markdown, CSV, and run logs are support artifacts.
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `impact-thesis`
-   - `executive_summary`: shock, public-equity mechanism, likely listed-equity winners/losers, time horizon, and confidence
-   - `decision_box`: PM bottom line, portfolio action posture, and what would change the view
-2. `transmission-map`
-   - `cards`: demand, pricing, margin, balance-sheet, rate, FX, commodity, regulatory, or funding channels, each ending in issuer/sector/line-item implications
-   - `timeline`: expected sequence and data-release checkpoints
-3. `issuer-sector-exposure`
-   - `table`: issuers/sectors with exposure driver, first financial line item affected, sensitivity, evidence, valuation/risk implication, priced-in status, and confidence
-   - `bar_chart`: exposure concentration, scenario sensitivity, or estimate-revision dispersion only when source-backed
-   - `table`: no-portfolio fallback rows should include exposure bucket, affected industries/countries/currencies/companies/commodities, equity mechanism, first estimate or valuation line affected, positioning/flow read-through, and next workflow
-4. `scenarios`
-   - `scenario_map`: bull/base/bear or shock-size cases with public-equity read-throughs and falsifiers
-5. `watch-items`
-   - `market_events`: relevant policy, data, macro, company, sector, or regulatory events
-   - `question_list`: next data checks, source requests, and research questions
-   - `missing_evidence`: stale macro releases, unsupported issuer exposures, model gaps, and unavailable market data
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every macro datapoint, policy date, issuer exposure, sensitivity, estimate, and market move.
-- Label stale releases, proxy exposures, model-derived sensitivities, and assumptions.
-- Make cross-asset references explicit inputs: rate, FX, commodity, credit, option, or futures data may support equity impact, but the dashboard should not become a non-equity trade-construction artifact.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not turn a macro note into generic commentary with no issuer/security linkage.
-- Do not turn the dashboard into a standalone rates, FX, commodity, futures, options, or credit-security dashboard.
-- Do not overclaim causality when evidence only supports correlation or a plausible channel.
-- Do not present standalone rates, FX, commodity, futures, options, credit-security, CDS, bond, loan, spread/yield, covenant, recovery, or debt-security analysis as the Public Equity Investing output.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the dashboard has a clear transmission map, issuer/sector exposure table, earnings/valuation/positioning read-through, scenarios, and freshness labels.
-- Confirm unsupported exposures appear in `missing_evidence`, not hidden in narrative.
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/domain-checklists.md b/plugins/financial-markets/skills/economic-impact-report/references/domain-checklists.md
deleted file mode 100644
index 7e036cf..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/domain-checklists.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Domain Checklists
-
-Use these checklists only when they materially improve the report. Do not force filler.
-
-## Public Equity Issuers
-For each listed issuer you include, answer:
-- What is the transmission channel?
-- Is the effect direct, second-order, third-order, or fourth-order?
-- Which line item changes first: revenue, gross margin, opex, working capital, free cash flow, valuation multiple, balance sheet, or optionality?
-- Is the impact on volume, price, mix, utilization, backlog, customer behavior, supplier costs, inventory needs, capex, funding costs, or regulatory burden?
-- What is the time horizon?
-- What part may already be in the stock?
-- Is this a consensus expression, a less obvious second-order expression, or a crowded trade to avoid?
-- Does the impact change estimate direction, target-price support, rating/stance, sizing, hedge, or watchlist status?
-
-## Sector And Peer Group
-Ask:
-- Which listed peer group, industry, index, or thematic basket is most exposed?
-- Is the impact cyclical, secular, regulatory, positioning-led, or purely event-driven?
-- Does it alter demand, pricing, margins, capex, working capital, capital return, or valuation convention?
-- Is the impact industry-wide, or only in a subsegment, geography, customer cohort, or business model?
-- Does the shock create estimate revision dispersion, multiple dispersion, or a pair-trade read-through?
-- What sector KPI should move first, and what would falsify the read-through?
-
-## Portfolio And Positioning
-Ask:
-- Is the affected security an owned position, watchlist name, benchmark weight, ETF/index constituent, pair leg, or hedge candidate?
-- Does the event change active weight, factor exposure, liquidity/capacity, crowding, short interest, borrow risk, or passive-flow risk?
-- Does it create a need to add, trim, exit, cover, hedge, wait for proof, or re-underwrite?
-- What is the first market signal that would prove the equity market has already repriced the event?
-
-If no portfolio, watchlist, thesis, benchmark, or positions were provided:
-- Do not infer holdings or active weights.
-- Build a general exposure map across industries, countries/currencies, public companies, relevant private companies, commodities, supplier/customer groups, and second-order peers.
-- Mark portfolio-specific action as unavailable and use watchlist, research queue, wait for proof, pass, or re-underwrite candidate.
-- State which holdings, active weights, mandate, cost basis, and risk limits are needed before giving add/trim/exit/hedge guidance.
-
-## Macro, FX, Rates, Commodities, Options, And Futures As Inputs
-Use these only as transmission variables for public-equity conclusions. Ask:
-- Does the event shift the growth-inflation mix?
-- Does it change fiscal stance, current account, terms of trade, or political stability?
-- Which issuers have revenue, cost, balance-sheet, duration, FX translation, commodity, or financing exposure?
-- Is the rate/FX/commodity/volatility move already in consensus, spot price, or the multiple?
-- Is the event mispriced in implied volatility?
-- Does it change gap risk, skew, or event-risk premium for the affected equities?
-- Do futures curves, inventories, hedging behavior, or delivery constraints change issuer margins, cash flow, or customer behavior?
-
-## Credit Signals As Equity Read-Through
-Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt-security analysis. In this skill, credit signals are allowed only as a common-equity downside signal or valuation/earnings read-through when they inform:
-- refinancing stress or liquidity pressure that affects common equity;
-- maturity-wall risk, rating pressure, or covenant headlines that change downside;
-- funding-cost changes that affect earnings, FCF, buybacks, M&A, or multiple support;
-- public-equity read-through from a Credit Markets output.
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/impact-framework.md b/plugins/financial-markets/skills/economic-impact-report/references/impact-framework.md
deleted file mode 100644
index 290758f..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/impact-framework.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Economic Impact Framework
-
-Use this reference after `economic-impact-report` is selected. Keep the main skill compact; load this for full causal mapping, timing, and public-equity transmission discipline. Cross-asset facts are inputs here, not the final product.
-
-## Causal Spine
-
-A strong report moves:
-
-1. Event -> what is new.
-2. What is new -> transmission channel.
-3. Transmission channel -> first variable likely to reprice.
-4. First repricing -> direct beneficiaries and losers.
-5. Direct effects -> second-, third-, and fourth-order consequences.
-6. Consequences -> scenarios, catalysts, issuer/sector implications, and portfolio actionability.
-
-If you cannot name the channel, you do not yet understand the impact well enough.
-
-## Timing Buckets
-
-- Immediate: 0 to 5 trading days.
-- Near term: 1 to 12 weeks.
-- Medium term: 1 to 4 quarters.
-- Structural: 1 year+.
-
-## Confidence Labels
-
-- High: strong evidence, clear mechanism, limited dependency chain.
-- Medium: good mechanism, some unresolved assumptions or pricing uncertainty.
-- Low: plausible but evidence-light or highly path-dependent.
-
-## Directness Labels
-
-- Direct: immediate mechanical effect on the event participants or primary exposed assets.
-- Second-order: suppliers, customers, competitors, substitutes, financing channels, insurers, distributors, or policy response.
-- Third-order: labor, inventories, credit quality, capex, cross-border flows, second-round inflation/growth effects, or market structure knock-ons.
-- Fourth-order: regime effects, industrial policy, reserve/capital-flow shifts, strategic behavior changes, or long-duration capital allocation consequences.
-
-## Transmission Channels
-
-Choose the 2 to 5 channels that actually drive the thesis. Common channels:
-
-- demand
-- supply, capacity, logistics, throughput
-- pricing power
-- regulation, litigation, policy precedent
-- financing conditions, refinancing, collateral, credit availability
-- labor, wages, staffing
-- commodities, energy, freight, raw materials
-- geopolitics, sanctions, tariffs, trade restrictions
-- inventories, backlog, working capital
-- fiscal response, subsidies, taxation, sovereign issuance
-- central bank response and rate path as they affect equity duration, funding costs, margins, capital return, or multiples
-- cross-border flows, reserve dynamics, external balance, and FX translation as they affect listed issuers
-- positioning, systematic flows, hedging, crowding
-- balance-sheet optionality, capital allocation, buybacks, dividends, M&A, and equity downside
-
-## Quantification Rules
-
-Prefer approximate magnitude over vague direction. Quantify when relevant:
-
-- revenue exposure
-- cost pass-through and margin sensitivity
-- EPS, FCF, ROIC, capital return, and multiple sensitivity
-- import reliance and export mix
-- refinancing needs and funding cost sensitivity
-- inventory, backlog, utilization
-- working-capital effects
-- rate, FX, commodity sensitivity
-- curve shape, basis, carry, spread, and commodity implications when they feed issuer economics
-- implied move, implied volatility, skew, term structure
-- scenario probabilities and time-to-truth
-
-Use explicit labels: `Known`, `Inferred`, `Assumed`, `Disconfirmers`, and `Action / monitoring`.
-
-## Domain Checks
-
-Cover only domains that are material, but check each one internally:
-
-- Listed issuers: direction, directness, mechanism, first line item affected, timing, confidence, what is priced, and action implication.
-- Sectors and peer groups: structural versus sentiment-driven effect, subsegment impact, cyclical/secular/regulatory/event-driven nature, and likely estimate-revision dispersion.
-- Portfolio and positioning: owned/watchlist/benchmark exposure, active weight, liquidity, crowding, short interest, ETF/index flow, and hedge relevance.
-- Macro/FX/rates/commodities/options/futures: include only when they transmit into revenue, costs, margins, funding, discount rate, valuation, volatility, or equity positioning.
-- Credit signals: use only as common-equity downside or solvency read-through; route credit instruments, spreads/yields relative value, covenants, recovery, and debt-security selection to Credit Markets.
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/quality-bar.md b/plugins/financial-markets/skills/economic-impact-report/references/quality-bar.md
deleted file mode 100644
index aefc96a..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/quality-bar.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# Quality Bar And Failure Checks
-
-## Gold Standard
-A strong economic impact report is not a news summary, macro essay, or cross-asset trade note. It is a public-equity decision document.
-
-It should tell a senior public-equity investor:
-- what changed;
-- why it matters for listed equities;
-- what should reprice first across issuers, peer groups, sectors, factors, or benchmark/ETF exposures;
-- what is still uncertain;
-- where the highest-conviction consequences sit across earnings, estimates, valuation, positioning, liquidity, and portfolio action.
-
-It should move from event to mechanism to public-equity implication. It should separate facts from judgment, direct effects from higher-order effects, and near-term equity consequences from long-duration strategic consequences.
-
-For a substantial standalone HTML report, the first read should center on event status, market baseline, the event-to-equity transmission map, and the ranked equity impact map rather than a fixed dashboard module inventory.
-
-## Evidence Standards
-- Prefer primary material.
-- Include a visible `Source And Freshness` section with data cut-off/as-of date, sources used, evidence posture, stale or missing data, and source conflicts.
-- Include a visible `Event Status And Market Baseline` block that separates confirmed facts, reported or assumed events, and time-sensitive market observations. When material intraday observations inform the report, state an exact research cut-off time and time zone. For commodity shocks, state the benchmark and distinguish spot, futures, intraday, and closing observations where relevant.
-- Tie every major claim to a public-equity mechanism.
-- Label judgments as fact, inference, or scenario when uncertainty matters.
-- Include rough magnitude estimates whenever possible.
-- Use historical analogs to bound outcomes, not to assume repetition.
-- Let confidence track evidence quality, not writing confidence.
-- Surface unknowns explicitly.
-- Keep citations decision-useful: cite material facts near use, then avoid repeated citation chips that make tables or monitoring queues harder to read.
-- Group representative listed exposures only when the transmission channel, first affected line item, and directional read-through are genuinely shared; thematic adjacency is not enough.
-- Judge what is priced in primarily from the affected equities/sectors, revisions, valuation movement, and relevant transmission markets; treat broad-index performance as context rather than primary evidence for an exposure call.
-
-## Delivery-Mode Source Gate
-Final delivery must not pass with missing, stale, or weak evidence posture. Run:
-
-```bash
-python scripts/check_economic_impact_report.py --mode delivery path/to/support_note.md
-```
-
-Delivery mode fails when the report lacks a dedicated `Source And Freshness` section, omits a data cut-off/as-of date, omits sources used, omits the stale/missing-data assessment, or discloses load-bearing evidence that is stale, missing, unknown, unsupported, unverified, preliminary, assumption-led, secondary-only, or otherwise weak. Use `--mode draft` for work-in-progress reports where those issues should remain warnings.
-
-## Self-QA Before Answering
-Ask internally:
-1. Did I identify what is genuinely new relative to the baseline?
-2. Did I explain why now and the likely repricing sequence?
-3. Did I name the transmission channels?
-4. Did I state what the equity market is likely to care about first?
-5. Did I separate direct from second-, third-, and fourth-order effects?
-6. Did I rank impacts by sign, magnitude, timing, confidence, and directness?
-7. Did I quantify where a reasonable range was possible?
-8. Did I identify issuer, sector, earnings, valuation, positioning, and portfolio implications?
-9. Did I explain what may already be priced and what may be mispriced in the affected equities?
-10. Did I include the strongest counterargument?
-11. Did I include scenarios, catalysts, and what would change the view?
-12. Does the source/freshness posture pass delivery mode rather than merely warning in draft mode?
-13. If no portfolio/watchlist was provided, did I build a general exposure map across industries, countries/currencies, companies, commodities, supplier/customer groups, and second-order peers rather than inventing holdings?
-14. Could a senior public-equity investor make a sharper decision after reading this?
-
-If any answer is no, revise.
-
-## Common Failure Modes
-Revise any output that does one or more of the following:
-- It lacks issuer, sector, earnings, valuation, positioning, or portfolio implications.
-- It recommends a standalone rates, FX, options, futures, commodity, or credit-security expression instead of a public-equity conclusion or explicit handoff.
-- It fails to state what is already priced in.
-- It does not identify what would change the PM action.
-- summarizes the article instead of analyzing public-equity impact;
-- lists impacted names without explaining the transmission channel;
-- confuses headline intensity with economic importance;
-- ignores what was already expected or already priced;
-- treats rates, FX, options, futures, commodities, or credit as standalone strategy outputs instead of public-equity transmission inputs;
-- over-focuses on the named company and misses suppliers, customers, competitors, peers, sector baskets, or financing channels;
-- collapses all time horizons into one conclusion;
-- gives speculative higher-order effects the same confidence as direct effects;
-- uses qualitative language where a rough range is possible;
-- names a non-equity asset class as the answer without routing trade construction out of Public Equity Investing;
-- omits the strongest counterargument;
-- omits falsifiers or monitoring signals;
-- sounds balanced by making every impact equally important.
-- defaults a substantive report into rigid dashboard modules when the user did not request a standardized dashboard;
-- treats grouped issuer examples as security-level investment conclusions without issuer-specific supporting evidence;
-- combines exposures with materially different operating or valuation transmission channels into one ranked row;
-- uses broad-index performance as the principal evidence that an issuer or sector shock is priced in;
-- lets repeated source chips or internal evidence machinery overwhelm the investor readout.
-
-## Tone Check
-The prose should be:
-- senior;
-- concise;
-- bottom-line first;
-- evidence-led;
-- explicit about uncertainty without becoming vague.
-
-Avoid:
-- throat-clearing;
-- generic macro filler;
-- repetitive "this could be positive or negative" hedging;
-- name-dropping companies without mechanisms;
-- false precision when the data is weak.
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/report-template.md b/plugins/financial-markets/skills/economic-impact-report/references/report-template.md
deleted file mode 100644
index b3cb11c..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/report-template.md
+++ /dev/null
@@ -1,130 +0,0 @@
-# Public Equity Economic Impact Report Template
-
-Use this full structure by default. Shorter or narrower output requires explicit user wording such as summary, quick read, one-pager, brief, TL;DR, or a single requested section.
-
-```markdown
-# [Event Title]: Public Equity Impact
-
-## Executive Call
-[One tight paragraph: what happened, what is genuinely new, why it matters for listed equities, the primary transmission channel, what reprices first, and the highest-conviction issuer/sector/portfolio implication.]
-
-## Event Status And Market Baseline
-- Event status: confirmed / reported / assumed scenario / disputed / unresolved
-- Key event evidence and timestamp:
-- Research cut-off: include exact time and time zone when material intraday observations inform the report
-- Relevant market observation: identify benchmark and, when relevant, spot vs futures, contract/month, intraday vs close, timestamp, and source
-- What is already in the starting price or consensus:
-- Material verification gaps:
-
-## Source And Freshness
-- Data cut-off:
-- Sources used:
-- Evidence posture: decision-grade / research-grade / preliminary / assumption-led / not supportable
-- Stale or missing data: none material, or list gaps and keep the report in draft mode
-- Source conflicts:
-
-## Public Equity Bottom Line
-- Most exposed long candidates:
-- Most exposed short/avoid candidates:
-- Sector or peer-group read-through:
-- Portfolio action posture: add / press / hold / trim / exit / hedge / watchlist / pass / wait for proof / re-underwrite
-- If no portfolio/watchlist was provided: portfolio-specific action unavailable; use general exposure map, watchlist, research queue, wait for proof, pass, or re-underwrite candidate.
-- What is probably already priced:
-- What may still be mispriced:
-
-## General Exposure Map If No Portfolio Was Provided
-| Exposure bucket | Industries / countries / currencies / companies / commodities affected | Public-equity mechanism | First estimate or valuation line affected | Positioning / flow read-through | Next workflow |
-|---|---|---|---|---|---|
-
-## What Is New Vs. Expected
-- Baseline / consensus before event:
-- Genuine delta:
-- Why the delta matters for equities:
-- What looks stale, background, or already known:
-
-## Why Now
-- Immediate vs near-term vs medium-term vs structural:
-- Likely repricing sequence:
-- What should fade vs persist:
-
-## Event-To-Equity Transmission Map
-### Transmission Map
-1. [Event] -> [channel] -> [issuer/sector driver] -> [financial line item] -> [stock/portfolio implication]
-2. [Event] -> [channel] -> [issuer/sector driver] -> [financial line item] -> [stock/portfolio implication]
-3. [Event] -> [channel] -> [issuer/sector driver] -> [financial line item] -> [stock/portfolio implication]
-
-## Ranked Equity Impact Map
-### Issuer And Sector Impact
-Label named issuers as representative exposure candidates or research-queue names unless company-specific evidence supports a security-level conclusion.
-Combine exposures in one row only when they share the same transmission channel, first affected line item, and directional read-through; otherwise split them into separate candidates.
-
-| Rank | Issuer / sector candidate | Direction | Directness | Mechanism | First line item affected | Timing | Confidence | Priced-in status | Research / PM action |
-|---|---|---|---|---|---|---|---|---|---|
-
-## Earnings, Valuation, And Positioning Read-Through
-- Estimate revision path:
-- Margin / FCF / balance-sheet impact:
-- Valuation or multiple implication:
-- Factor, benchmark, ETF/index, ownership, crowding, liquidity, or short-interest implication:
-- Credit, rates, FX, commodity, volatility, or futures signal used as public-equity input:
-
-## Public Equity Framing
-- What the equity market is likely to care about first:
-- Where consensus may be stale:
-- Where the obvious trade may be crowded or noisy:
-- Best equity expression:
-- Non-equity signal to monitor, if relevant:
-
-## What Is Priced In Vs. What Requires Proof
-- What appears reflected in current equity or commodity pricing:
-- Primary priced-in evidence from affected equities/sectors, estimate revisions, valuation movement, or the relevant transmission market:
-- Broad-index context, if used, clearly labeled as supporting rather than primary evidence:
-- What remains an inference or scenario:
-- What evidence would support an estimate revision or multiple change:
-- What evidence would reverse or weaken the screen:
-
-## Second-, Third-, And Fourth-Order Equity Effects
-### Second-order
-- ...
-### Third-order
-- ...
-### Fourth-order
-- ...
-
-## Strongest Counterargument
-- Counter-thesis:
-- Why it could be right:
-- Why it is weaker, later, already priced, or unresolved:
-
-## Scenario Matrix
-### Base Case ([x]%)
-- Assumptions:
-- Equity consequences:
-- What moves most:
-
-### Upside / Bull Case ([x]%)
-- Assumptions:
-- Equity consequences:
-- What moves differently:
-
-### Downside / Bear Case ([x]%)
-- Assumptions:
-- Equity consequences:
-- What moves differently:
-
-## Monitoring Triggers And Research Queue
-- Specific datapoints, filings, economic releases, management commentary, policy decisions, market signals, estimate revisions, and positioning signals that would invalidate or materially revise the thesis.
-- Research queue: convert representative exposure candidates into security-level work only after obtaining the relevant issuer evidence.
-
-## What Would Change The View
-- State the specific evidence threshold that invalidates or materially revises the thesis and the action posture it would change.
-
-## Bottom-Line Judgment
-- What matters most:
-- What is noise:
-- What is actionable now:
-- What deserves continued monitoring:
-- Recommended next Public Equity Investing workflow:
-```
-
-For standalone HTML reports, keep the first-read layer compact: `Executive Call`, `Event Status And Market Baseline`, the transmission map, and ranked equity impact map should be visible before detailed scenario, monitoring, and source-ledger material. Cite load-bearing facts near use, but prefer a short section-level source note over repeated source chips in every adjacent module.
diff --git a/plugins/financial-markets/skills/economic-impact-report/references/workflow.md b/plugins/financial-markets/skills/economic-impact-report/references/workflow.md
deleted file mode 100644
index 4b0db9e..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/references/workflow.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Economic Impact Workflow
-
-Use this reference for full-form reports and high-stakes public-equity market-event work.
-
-1. Define the event exactly and separate it from surrounding noise.
-2. Identify what is genuinely new relative to baseline or consensus.
-3. Name the public-equity decision affected: issuer diligence, sector view, earnings setup, valuation support, position sizing, hedge/watchlist, or portfolio review.
-4. Explain why now: immediate repricing, medium-term earnings/policy effect, structural shift, or likely fade.
-5. Build the first-pass transmission map: event -> channel -> issuer/sector driver -> financial line item -> stock/portfolio implication.
-6. Write hypotheses before over-collecting facts. For each key channel, define hypothesis, first equity-market variable, confirmers, and falsifiers.
-7. Gather evidence to test and rank the hypotheses. Prefer primary material and evidence that answers the highest-value unresolved questions.
-8. Rank impacts by sign, magnitude, timing, confidence, directness, and priced-in status.
-9. Cover materially relevant equity domains: listed issuers, peer groups, sectors, estimate paths, valuation/multiple support, factor/benchmark/ETF exposure, positioning, and portfolio action.
-10. Use macro, rates, FX, commodities, options, futures, and credit signals only as public-equity inputs. If the best work product is a non-equity trade or credit-security view, route out.
-11. Identify what is priced and what is not across spot price, consensus, volatility, financing, supply-chain/regulatory precedent, ownership, passive flows, short interest, and crowding.
-12. Write the strongest counterargument and explain whether it is stronger, weaker, delayed, already priced, or unresolved.
-13. Build base, upside, and downside cases with approximate probabilities where supportable.
-14. End with monitoring signals, falsifiers, and what would change portfolio action.
-15. Run the checker in `--mode draft` while iterating, then `--mode delivery` before final delivery. Do not deliver if source/freshness posture fails for missing, stale, or weak evidence.
-
-## No Portfolio / Watchlist Fallback
-
-When the user provides no holdings, thesis, watchlist, benchmark, or active weights, build a general exposure screen instead of pretending to know the book. The fallback must:
-
-- infer the affected industries, countries/currencies, commodities, public companies, relevant private companies, supplier/customer groups, and second-order peer groups;
-- label portfolio-specific action as unavailable without holdings, active weights, mandate, and risk limits;
-- use `watchlist`, `wait for proof`, `pass`, `research queue`, or `re-underwrite candidate` rather than add/trim/exit recommendations;
-- identify the next data needed to convert the general screen into portfolio action;
-- still end every channel in issuer, sector, earnings/estimate, valuation/multiple, positioning/flow, and next-workflow implications.
-
-## Common Failure Modes
-
-Reject or revise outputs that:
-
-- summarize an article instead of analyzing economic impact
-- list impacted names without explaining transmission channels
-- confuse headline intensity with economic importance
-- ignore what was expected or priced
-- treat rates, FX, options, futures, commodities, or credit as standalone outputs instead of public-equity transmission inputs
-- over-focus on the named company and miss suppliers, customers, competitors, or financing channels
-- collapse time horizons into one blurry conclusion
-- present speculative higher-order effects with the same confidence as direct effects
-- use qualitative language where a rough range is possible
-- omit issuer/sector, earnings, valuation, positioning, or portfolio implications
-- omit what would falsify the thesis
diff --git a/plugins/financial-markets/skills/economic-impact-report/scripts/check_economic_impact_report.py b/plugins/financial-markets/skills/economic-impact-report/scripts/check_economic_impact_report.py
deleted file mode 100644
index cf53958..0000000
--- a/plugins/financial-markets/skills/economic-impact-report/scripts/check_economic_impact_report.py
+++ /dev/null
@@ -1,350 +0,0 @@
-#!/usr/bin/env python3
-"""Check economic-impact report/support-note text for required structure and source posture."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-from pathlib import Path
-
-REQUIRED_SECTIONS = [
-    "executive call",
-    "event status and market baseline",
-    "what is new vs expected",
-    "public equity framing",
-    "ranked equity impact map",
-    "what is priced in vs what requires proof",
-    "scenario matrix",
-    "monitoring triggers and research queue",
-    "what would change the view",
-    "bottom line judgment",
-]
-
-REQUIRED_SECTION_ALIASES = {
-    "transmission map": {"transmission map", "event to equity transmission map"},
-}
-
-SOURCE_TERMS = [
-    "source and freshness",
-    "data cut-off",
-    "data cutoff",
-    "as of",
-    "sources used",
-    "source register",
-    "retrieved",
-    "published",
-]
-
-SOURCE_SECTION_HEADINGS = {
-    "source and freshness",
-    "sources and freshness",
-    "source freshness",
-    "source posture",
-    "source register",
-    "source inventory",
-    "evidence posture",
-    "evidence and freshness",
-}
-
-SOURCE_FIELD_ALIASES = {
-    "data_cutoff": {
-        "data cut off",
-        "data cutoff",
-        "data cut",
-        "as of",
-        "as of date",
-        "accessed",
-        "accessed at",
-        "retrieved",
-        "retrieved at",
-        "freeze time",
-        "data freeze",
-    },
-    "sources_used": {
-        "source used",
-        "sources used",
-        "sources",
-        "source register",
-        "source inventory",
-        "source ids",
-        "source id",
-    },
-    "stale_or_missing_data": {
-        "stale or missing data",
-        "missing or stale data",
-        "stale missing data",
-        "stale data",
-        "missing data",
-        "open evidence gaps",
-        "evidence gaps",
-        "missing evidence",
-        "open source gaps",
-        "source gaps",
-    },
-    "evidence_posture": {
-        "evidence posture",
-        "source posture",
-        "source quality",
-        "evidence quality",
-    },
-}
-
-WEAK_EVIDENCE_PATTERNS = [
-    r"\bweak\b",
-    r"\blow confidence\b",
-    r"\bnot supportable\b",
-    r"\bassumption[- ]led\b",
-    r"\bassumption[_ -]inferred\b",
-    r"\bassumption[_ -]user[_ -]provided\b",
-    r"\bpreliminary\b",
-    r"\bunverified\b",
-    r"\buncorroborated\b",
-    r"\brumou?r\b",
-    r"\bnews only\b",
-    r"\bsecondary only\b",
-    r"\bsingle source\b",
-    r"\bno primary\b",
-    r"\bunknown\b",
-    r"\bunknown freshness\b",
-    r"\bunsourced\b",
-    r"\bnot available\b",
-    r"\bunavailable\b",
-    r"\bpending\b",
-    r"\bmissing_required_source\b",
-    r"\bunknown\b",
-]
-
-STALE_EVIDENCE_PATTERNS = [
-    r"\bstale\b",
-    r"\bstale_source\b",
-    r"\boutdated\b",
-    r"\bsuperseded\b",
-    r"\bpotentially superseded\b",
-    r"\bold quote\b",
-    r"\bold price\b",
-    r"\bno timestamp\b",
-    r"\bno as[- ]of\b",
-]
-
-NO_MATERIAL_GAP_PATTERNS = [
-    r"^none$",
-    r"^none material$",
-    r"^not material$",
-    r"^n/?a$",
-    r"^no material gaps?$",
-    r"^no material source gaps?$",
-    r"^no material evidence gaps?$",
-    r"^no stale or missing data$",
-    r"^no material stale or missing data$",
-]
-
-PLACEHOLDER_PATTERNS = [
-    r"\[[^\]]+\]",
-    r"\bTBD\b",
-    r"\bTODO\b",
-    r"\.\.\.",
-]
-
-
-def normalize_heading(text: str) -> str:
-    text = re.sub(r"^#+\s*", "", text.strip()).lower()
-    return re.sub(r"[^a-z0-9]+", " ", text).strip()
-
-
-def headings(markdown: str) -> set[str]:
-    out: set[str] = set()
-    for line in markdown.splitlines():
-        if line.startswith("## "):
-            out.add(normalize_heading(line))
-    return out
-
-
-def has_source_posture(markdown: str) -> bool:
-    lowered = markdown.lower()
-    return any(term in lowered for term in SOURCE_TERMS)
-
-
-def source_section(markdown: str) -> str:
-    section_lines: list[str] = []
-    in_section = False
-    for line in markdown.splitlines():
-        if line.startswith("## "):
-            if in_section:
-                break
-            in_section = normalize_heading(line) in SOURCE_SECTION_HEADINGS
-            continue
-        if in_section:
-            section_lines.append(line)
-    return "\n".join(section_lines).strip()
-
-
-def compact_value(value: str) -> str:
-    value = re.sub(r"\s+", " ", value.strip().lower())
-    return value.strip(" .;:-")
-
-
-def is_placeholder_value(value: str) -> bool:
-    compact = compact_value(value)
-    if compact in {
-        "",
-        "tbd",
-        "todo",
-        "unknown",
-        "pending",
-        "not available",
-        "unavailable",
-        "n/a",
-        "na",
-    }:
-        return True
-    return any(
-        re.fullmatch(pattern, value.strip(), flags=re.IGNORECASE)
-        for pattern in PLACEHOLDER_PATTERNS
-    )
-
-
-def matches_any(value: str, patterns: list[str]) -> bool:
-    return any(re.search(pattern, value, flags=re.IGNORECASE) for pattern in patterns)
-
-
-def no_material_gap(value: str) -> bool:
-    compact = compact_value(value)
-    return any(
-        re.fullmatch(pattern, compact, flags=re.IGNORECASE) for pattern in NO_MATERIAL_GAP_PATTERNS
-    )
-
-
-def source_field_values(section: str) -> dict[str, list[str]]:
-    values: dict[str, list[str]] = {key: [] for key in SOURCE_FIELD_ALIASES}
-    for line in section.splitlines():
-        stripped = re.sub(r"^\s*[-*]\s*", "", line.strip())
-        if ":" not in stripped:
-            continue
-        raw_key, raw_value = stripped.split(":", 1)
-        key = normalize_heading(raw_key)
-        value = raw_value.strip()
-        for field, aliases in SOURCE_FIELD_ALIASES.items():
-            if key in aliases:
-                values[field].append(value)
-                break
-    return values
-
-
-def joined(values: list[str]) -> str:
-    return " ".join(value for value in values if value).strip()
-
-
-def source_freshness_issues(markdown: str) -> list[str]:
-    section = source_section(markdown)
-    if not section:
-        if has_source_posture(markdown):
-            return ["source/freshness posture is not in a dedicated Source And Freshness section"]
-        return ["missing source/freshness posture: add a Source And Freshness section"]
-
-    values = source_field_values(section)
-    issues: list[str] = []
-
-    data_cutoff = joined(values["data_cutoff"])
-    if not data_cutoff:
-        issues.append("missing data cut-off/as-of date in Source And Freshness")
-    elif is_placeholder_value(data_cutoff):
-        issues.append(
-            "weak source/freshness posture: data cut-off/as-of date is blank, unknown, or placeholder"
-        )
-
-    sources_used = joined(values["sources_used"])
-    if not sources_used:
-        issues.append("missing sources used/source register in Source And Freshness")
-    elif is_placeholder_value(sources_used) or matches_any(sources_used, WEAK_EVIDENCE_PATTERNS):
-        issues.append(
-            "weak source evidence: sources used/source register is blank, unknown, or relies on weak evidence"
-        )
-
-    stale_or_missing = joined(values["stale_or_missing_data"])
-    if not stale_or_missing:
-        issues.append("missing stale/missing-data assessment in Source And Freshness")
-    elif not no_material_gap(stale_or_missing):
-        if is_placeholder_value(stale_or_missing) or matches_any(
-            stale_or_missing, WEAK_EVIDENCE_PATTERNS
-        ):
-            issues.append(
-                "missing evidence disclosed in Source And Freshness: unresolved source gaps remain"
-            )
-        elif matches_any(stale_or_missing, STALE_EVIDENCE_PATTERNS):
-            issues.append("stale evidence disclosed in Source And Freshness")
-
-    evidence_posture = joined(values["evidence_posture"])
-    if evidence_posture and (
-        is_placeholder_value(evidence_posture)
-        or matches_any(evidence_posture, WEAK_EVIDENCE_PATTERNS)
-    ):
-        issues.append("weak evidence posture disclosed in Source And Freshness")
-
-    return issues
-
-
-def placeholder_hits(markdown: str) -> list[str]:
-    hits: list[str] = []
-    for pattern in PLACEHOLDER_PATTERNS:
-        for match in re.findall(pattern, markdown, flags=re.IGNORECASE):
-            hits.append(str(match))
-    return hits[:25]
-
-
-def check(markdown: str, mode: str = "delivery") -> dict:
-    if mode not in {"draft", "delivery"}:
-        raise ValueError("mode must be 'draft' or 'delivery'")
-    found = headings(markdown)
-    missing = [section for section in REQUIRED_SECTIONS if section not in found]
-    missing.extend(
-        label for label, aliases in REQUIRED_SECTION_ALIASES.items() if found.isdisjoint(aliases)
-    )
-    placeholders = placeholder_hits(markdown)
-    warnings: list[str] = []
-    source_issues = source_freshness_issues(markdown)
-    if mode == "delivery":
-        errors: list[str] = source_issues.copy()
-    else:
-        errors = []
-        warnings.extend(source_issues)
-    if len(markdown.split()) < 250:
-        warnings.append("report appears short for a decision-grade economic impact report")
-    if missing:
-        errors.append("missing required sections: " + ", ".join(missing))
-    if placeholders:
-        errors.append("unresolved placeholders detected")
-    return {
-        "valid": not errors,
-        "mode": mode,
-        "errors": errors,
-        "warnings": warnings,
-        "source_freshness_issues": source_issues,
-        "missing_sections": missing,
-        "placeholder_hits": placeholders,
-    }
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Validate economic-impact report markdown structure."
-    )
-    parser.add_argument(
-        "--mode",
-        choices=["draft", "delivery"],
-        default="delivery",
-        help="draft warns on source/freshness gaps; delivery fails missing, stale, or weak evidence posture",
-    )
-    parser.add_argument("report_md", type=Path)
-    args = parser.parse_args()
-    try:
-        result = check(args.report_md.read_text(encoding="utf-8"), mode=args.mode)
-    except Exception as exc:
-        print(json.dumps({"valid": False, "errors": [str(exc)], "warnings": []}, indent=2))
-        return 1
-    print(json.dumps(result, indent=2))
-    return 0 if result["valid"] else 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/equity-model-update/SKILL.md b/plugins/financial-markets/skills/equity-model-update/SKILL.md
deleted file mode 100644
index e0d3ff1..0000000
--- a/plugins/financial-markets/skills/equity-model-update/SKILL.md
+++ /dev/null
@@ -1,101 +0,0 @@
----
-name: equity-model-update
-description: Safely update public-company Excel model copies from source-to-model maps; emits XLSX as the hero artifact and CSV/log/manifest as support. Do not use for pure earnings notes or broad workbook audits.
----
-
-# Equity Model Update
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is an XLSX equity model workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-Use this skill when the real job is updating a listed-equity model from reported actuals, guidance, consensus, transcript/KPI disclosures, or user assumptions, then translating the changed lines into estimate revisions, valuation support, target-price implications, and PM action.
-
-## Use When
-
-Use this skill to refresh public-company model inputs as a controlled source-to-model process. It owns safe copied-workbook updates, source-to-model maps, change logs, stale/missing data flags, pre/post-print model update checklists, and support CSV artifacts.
-
-When an Excel model is supplied, default to a copied updated workbook or workbook control pack as the primary human deliverable. CSV, run-log, JSON citations, and manifest outputs are support artifacts. The user-facing handoff should lead with the updated `.xlsx` package, model implication, estimate-revision path, valuation/target-price read-through, source posture, changed/blocked cells, and update dashboard; for substantial reusable update packages, render an HTML dashboard/report alongside the workbook or hand off to the relevant workbook builder so the workbook `Cover` carries the insight view.
-
-For dashboard handoffs, use `references/DASHBOARD_PACK.md`. `equity-model-update` owns source-to-model mapping, changed-line interpretation, stale/missing flags, and model implication, valuation read-through, and PM action implication; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV/run-log support files behind the HTML dashboard unless explicitly requested.
-
-## Do Not Use For
-
-Use neighboring skills for pure earnings notes, post-print interpretation, formula audits, raw cleanup, trade pitches, and memo prose.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` for reported actuals, guidance, consensus, source timestamps, and claim labels. Use `financials-normalizer` before building the source-to-model map from messy release tables, transcript KPI tables, segment schedules, share-count support, net debt, or capital allocation data. Use `excel-data-cleaner` before relying on malformed model-update exports. Use `model-audit-tieout` for formula integrity, recalc/cache state, external links, circularity, and final tie-out. Support artifacts stay secondary to the copied workbook/control pack and update dashboard.
-
-## Workflow
-
-1. Identify company, period, event, user model status, and decision.
-2. Use user files first, company primary sources next, connected consensus/market data when available, then public fallback.
-3. Label numbers as reported actual, guidance, consensus, user estimate, agent estimate, market-implied, inferred, or judgment.
-4. Map each source metric to a model line and assign `mapping_treatment` before calculating a delta: `safe_update` for true input updates, `reference_only` for facts that inform the review but cannot replace a model input, `missing_model_architecture` when the workbook lacks the required schedule, `assumption_required` when an investor judgment is needed, or `rebuild_required` when the model cannot express the update safely. Only `safe_update` rows may populate proposed model values or estimate-change deltas.
-5. For EPS model lines, separate reported GAAP EPS, adjusted/operating EPS, recurring EPS drivers, tax/share-count effects, and below-the-line/non-recurring items before updating forward EPS.
-6. If a workbook is supplied, run a preflight and write only to a copied workbook. Never mutate the original workbook. Update mapped non-formula input cells only when sheet/cell, prior value, new value, source ID, freshness, and confidence are sufficient.
-7. If workbook editing is unsafe, under-mapped, protected, formula-targeted, stale, or ambiguous, create a workbook-based control pack with `Update_Cover`, `Source_Map`, `Rebuild_Requirements`, `Change_Log`, `Tie_Out`, and `Stale_Data` review tabs instead of editing model cells.
-8. When the supplied workbook lacks the operating schedules required for an estimate refresh, state that refreshed estimates and valuation are unavailable, retain cached legacy valuation only as a stale reference, and identify the missing architecture or rebuild requirements. Do not present reported quarterly metrics or net-cash reference facts as changes to an annual DCF output formula.
-9. Surface recalc/cache warnings: formula workbook versus cached values, stale workbook calculation state, external links, formula overwrite risk, source timestamp, and whether Excel/Sheets recalculation is still required. Require recalculation after copied input edits that affect formulas; do not require recalculation merely because an unchanged control-pack workbook contains cached formula values. Never imply formulas were recalculated if the runtime only inspected cached values or edited copied input cells.
-10. Route formula integrity, external-link checks, circularity, final tie-out, and rebuilt model work to `model-audit-tieout`, DCF, three-statement, comps, or public-model builders as appropriate.
-11. When a final presentation workbook is restyled, renamed, or exported after materialization, make that final `.xlsx` the only user-facing primary workbook. Update `run_log.json`, `manifest.json`, and `model_update_citations.json` to point to the final workbook and its final sheet names before handoff; do not lead with an unstyled intermediate control pack.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: new source extraction, estimate/guidance deltas, model input changes, source labels, and change-log QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Executable Contract
-
-Safe copied-workbook helper:
-
-```bash
-python scripts/materialize_workbook_update.py input.csv --workbook uploaded_model.xlsx --out output
-```
-
-Default workbook modes:
-
-- `xlsx_update_copy`: updates safe mapped input cells in `updated_model.xlsx`.
-- `xlsx_control_pack`: preserves model cells, opens on the `Update_Cover` review tab, and adds review tabs in `model_update_control_pack.xlsx`.
-- `csv_update_map_export`: mapping/log fallback when no workbook is supplied or the user explicitly asks for support artifacts only.
-
-Support-only helper:
-
-```bash
-python scripts/materialize_model_update.py input.csv --out output
-```
-
-Workbook outputs: `updated_model.xlsx` or `model_update_control_pack.xlsx`, plus `source_to_model.csv`, `change_log.csv`, `tieout_checklist.csv`, `model_update_citations.json`, `run_log.json`, and `manifest.json`. The manifest marks the workbook as `primary_human_deliverable`; if a polished successor workbook is exported, promote that successor consistently across the manifest, run log, and model citations.
-
-The workbook helper reports `calc_chain_present`, `formula_cell_count`, `cached_formula_value_count`, `calc_mode`, and `recalc_required` in the run log and cover tab. It separately labels artifact readiness and model readiness: a completed control pack may be ready for review while the model remains not updated and requires mapping or rebuild. If mapped inputs are changed and formulas exist, treat downstream formula outputs as stale until Excel/Sheets recalculation and `model-audit-tieout` are complete.
-
-Support-only outputs: `source_to_model.csv`, `change_log.csv`, `tieout_checklist.csv`, `run_log.json`, and `manifest.json`. The run log includes `status`, `model_status`, source basis, warnings, hard failures, output paths, and output manifest. Hard failures: missing `source_id`, missing `model_line`, malformed/empty input. Stale data is flagged.
-
-## Output Standard
-
-Lead with the workbook artifact when a model was supplied. Include model implication, changed cells, blocked cells, source/data-quality note, comparison table when available, KPI/model-driver dashboard, source map, change log, tie-out checklist, and post-print actions.
-
-References: `references/workflow.md`, `references/templates.md`, `references/sector-kpis.md`, `references/executable-contract.md`.
-
-## Equity Valuation PM Standard
-
-Load `shared/equity-valuation-pm-standard.md` and `shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, what changes target, rating, sizing, hedge, trim, exit, or watchlist status, and what evidence is missing.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instrument underwriting, or distressed claim valuation.
diff --git a/plugins/financial-markets/skills/equity-model-update/agents/openai.yaml b/plugins/financial-markets/skills/equity-model-update/agents/openai.yaml
deleted file mode 100644
index b4d0799..0000000
--- a/plugins/financial-markets/skills/equity-model-update/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Equity Model Update"
-  short_description: "Safely update copied public-company Excel models, with source-to-cell maps, change logs, and review packs."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/equity-model-update/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/equity-model-update/references/DASHBOARD_PACK.md
deleted file mode 100644
index 8837d3d..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# Equity Model Update Dashboard Pack
-
-Use this when a public-company model refresh, source-to-model update, post-print change log, KPI dashboard, or model-input QA package needs an HTML dashboard/report.
-
-## Producer Role
-
-`equity-model-update` owns safe copied-workbook update status, source-to-model mapping, changed input identification, stale/missing data labels, model implication, and handoff to workbook/model owners. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `equity_model_update`
-- `layout`: `single_page` for reusable model-update packages unless the user explicitly requests tabs
-- `hero.callout`: the most important model implication or unresolved update risk
-- `snapshot`: primary workbook artifact, separate artifact and model readiness, changed driver count, applied/blocked/reference-only cell count, KPI/source posture, biggest valid estimate impact, stale/missing data count, recalc/cache warning, formula cell count, external-link status, next action
-- `model_citations_path`: point this to `model_update_citations.json` so workbook/sheet/cell provenance generated by `materialize_workbook_update.py` renders in the source ledger
-- `sources`: release/filing/deck/transcript first, model/user files next, consensus/market data next, and assumptions clearly labeled
-- Raw JSON, Markdown notes, CSV exports, run logs, and manifests are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `model-read`
-   - `decision_box`: model implication, readiness, primary workbook artifact, key update, blocked fields, and next handoff
-   - `metric_tiles`: changed drivers, applied cells, blocked cells, reference-only facts, stale fields, source confidence, tie-out status, formula cells, cached values, calc-chain status, recalc required, external links, and separate model/workbook status
-2. `changed-lines`
-   - `table`: model line, workbook sheet/cell, prior value, new value, applied/blocked status, source, basis, period, change driver, and PM read
-3. `kpi-driver-dashboard`
-   - `key_metrics`: company-specific KPIs, driver changes, source basis, and model relevance
-   - `growth_trajectory`: driver acceleration/deceleration when source-backed and comparable
-4. `source-map`
-   - `table`: source-to-model mapping, source confidence, stale/missing flags, and unsupported assumptions
-5. `actions`
-   - `timeline`: post-print/model-update tasks, workbook handoff, audit checks, and review triggers
-   - `missing_evidence`: missing sources, stale data, unsupported line items, and unavailable workbook/model files
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every changed model line, KPI, estimate, source timestamp, and model implication.
-- Cite workbook-derived claims to `model_update_citations.json` so chips can show workbook, sheet, cell, prior value, new value, source ID, and block status.
-- Label source-reported, model-derived, user-provided, assumption, stale, and unsupported fields.
-- Label formula workbook versus cached values. Never imply formula outputs were recalculated if the runtime only inspected cached formula values or edited copied inputs; show Excel/Sheets recalculation required only after copied inputs were changed, and keep an unchanged control pack labeled as not a refreshed valuation.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not make the dashboard look like the original workbook was mutated; only copied workbook artifacts can be represented as edited.
-- Do not present a blocked control pack as if model cells were updated.
-- Do not hide stale or missing source fields behind a clean model implication.
-- Do not make raw JSON, Markdown notes, CSV sidecars, run logs, or manifests the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm model implication, primary workbook artifact, changed lines, applied/blocked cells, source map, stale/missing data, and workbook handoff are visible.
-- Confirm workbook citations point to changed or blocked cells rather than only CSV rows.
-- Confirm workbook/formula integrity, recalc/cache, external-link, formula-overwrite, and stale calculation-state issues route to the appropriate model/audit skill.
diff --git a/plugins/financial-markets/skills/equity-model-update/references/dashboard-map.md b/plugins/financial-markets/skills/equity-model-update/references/dashboard-map.md
deleted file mode 100644
index 6294592..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/dashboard-map.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Dashboard Map: Equity Model Update
-
-Use `dashboard-builder` for safe model-update dashboards when the user needs to see changed cells, blocked cells, stale sources, tie-out status, and workbook implications.
-
-## Decision Question
-
-Which source-backed model updates were safely applied to a copied workbook, which were blocked, and what needs analyst review?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `equity_model_update`
-- `metadata.citation_policy`: `strict`
-- `model_citations_path`: `model_update_citations.json`
-- `sources`: update source pack, uploaded workbook copy, source-to-model map, and stale/blocked evidence records
-
-## Recommended Tabs And Modules
-
-1. Update Verdict: `decision_box`, `metric_tiles`.
-2. Changed Cells: `table`.
-3. Blocked Or Needs Review: `missing_evidence`, `table`.
-4. Source Freshness: `source_list`, `table`.
-5. Tie-Out And Handoffs: `timeline`, `question_list`.
-
-## Required Evidence
-
-Each changed cell should cite workbook, sheet, cell, prior value, new value, source ID, source label/date, freshness, and review status.
-
-## Do Not
-
-- Do not mutate the original uploaded workbook.
-- Do not overwrite formulas without explicit approval.
-- Do not make CSV/log/manifest files the lead output when a safe XLSX copy or control workbook exists.
diff --git a/plugins/financial-markets/skills/equity-model-update/references/executable-contract.md b/plugins/financial-markets/skills/equity-model-update/references/executable-contract.md
deleted file mode 100644
index 7d4a8c0..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/executable-contract.md
+++ /dev/null
@@ -1,133 +0,0 @@
-# Equity Model Update Executable Contract
-
-Use `scripts/materialize_workbook_update.py` when the user supplies an Excel model and source/model mapping rows. Use `scripts/materialize_model_update.py` only when no workbook is supplied or the user explicitly wants support/control artifacts only.
-
-## Boundary
-
-The workbook helper creates a copied workbook package. It does not fetch market data, mutate the original workbook, choose investment assumptions, rebuild formulas, or replace `model-audit-tieout`.
-
-Formula integrity, external links, circularity, model checks, and final workbook reliability still route to `model-audit-tieout`. When mapped cells are unsafe, the helper produces a workbook control pack instead of editing model cells.
-
-## Workbook Command
-
-```bash
-python scripts/materialize_workbook_update.py input.csv --workbook uploaded_model.xlsx --out output --run-date 2026-05-12 --stale-days 45
-```
-
-Workbook modes:
-
-- `xlsx_update_copy`: writes safe mapped input-cell updates into `updated_model.xlsx`.
-- `xlsx_control_pack`: writes review/control tabs into `model_update_control_pack.xlsx`, opens on `Update_Cover`, and leaves model cells unchanged.
-
-## Support-Only Command
-
-```bash
-python scripts/materialize_model_update.py input.csv --out output --run-date 2026-05-12 --stale-days 45
-```
-
-## Input CSV
-
-Required columns:
-
-- `source_id`
-- `model_line`
-- `source_metric`
-- `source_value`
-
-Recommended columns:
-
-- `company`
-- `ticker`
-- `fiscal_period`
-- `model_section`
-- `workbook_sheet`
-- `workbook_cell`
-- `current_model_value`
-- `prior_value`
-- `source_name`
-- `source_location`
-- `evidence_label`
-- `as_of_date`
-- `confidence`
-- `update_action`
-- `mapping_treatment`: `safe_update`, `reference_only`, `missing_model_architecture`, `assumption_required`, or `rebuild_required`
-- `overwrite_formula_allowed`
-- `earnings_basis` or `gaap_flag` for EPS/net-income rows
-- `notes` for tax, share-count, below-the-line, non-operating, or non-recurring EPS items
-
-Legacy workbook-intent columns:
-
-- `workbook_write_requested`
-- `direct_workbook_write`
-- `write_to_workbook`
-
-Any truthy value in a legacy workbook-intent column is treated as a handoff reminder in the support-only helper. For workbook editing, use `materialize_workbook_update.py`; it always writes to a copy.
-
-Workbook updater safety behavior:
-
-- Only `safe_update` rows carry proposed values and deltas into the update/change path. `reference_only` rows remain informational, while missing-architecture and rebuild-required rows remain blocked without becoming estimate changes.
-- Updates mapped target cells only when `workbook_sheet` and `workbook_cell` are exact and present.
-- Preserves the original uploaded workbook.
-- Blocks protected sheets, missing sheets, missing cells, merged cells, stale prior values, missing new values, and formula cells unless `overwrite_formula_allowed` is explicit.
-- Adds review tabs: `Update_Cover`, `Source_Map`, `Rebuild_Requirements`, `Change_Log`, `Tie_Out`, and `Stale_Data`.
-- Emits `model_update_citations.json` with workbook/sheet/cell provenance for applied and blocked targets.
-
-## Outputs
-
-`updated_model.xlsx`
-
-Primary human deliverable when at least one mapped model-input cell was safely updated in the copied workbook.
-
-`model_update_control_pack.xlsx`
-
-Primary human deliverable when no model cells could be safely edited. It preserves the workbook and adds review/control tabs.
-
-If a reader-facing workbook is restyled, renamed, or re-exported after this helper runs, it replaces the intermediate workbook as `primary_human_deliverable`. Before handoff, update `run_log.json`, `manifest.json`, and `model_update_citations.json` to use the final workbook path and any final sheet names.
-
-`source_to_model.csv`
-
-Maps each supplied source row to a model line with treatment, source value, proposed value and delta only when updateable, workbook target, applied/blocked/reference-only status, freshness status, confidence, and issue flags.
-
-`change_log.csv`
-
-Includes only rows that require update, add/map, or input-required review. Every change row marks `requires_model_audit_tieout=yes`.
-
-`tieout_checklist.csv`
-
-Always includes a first-row handoff to `model-audit-tieout`, plus per-line source/model tie-out checks and freshness checks where needed. `reference_only` rows are informational/caveated rather than blocked update failures.
-
-`run_log.json`
-
-Records status, artifact readiness, model readiness, generated time, input path, output paths, warnings, hard failures, workbook preflight, update counts, and the capability boundary. A review-ready control pack does not imply an updated model.
-
-## Failure Rules
-
-Hard failure:
-
-- missing `source_id`
-- missing `model_line`
-- malformed or empty input CSV
-
-Warning / issue flag:
-
-- stale source data
-- missing `as_of_date`
-- missing `source_metric`
-- missing/non-numeric proposed value
-- EPS/net-income row missing basis
-- EPS/net-income row whose notes indicate non-operating or non-recurring items that require EPS-quality review
-- workbook update requested through the support-only helper
-
-Workbook control-pack blockers:
-
-- target sheet or cell missing
-- protected sheet
-- merged target cell
-- formula target without explicit overwrite approval
-- supplied prior value does not match the workbook cell
-- macro-enabled workbook requiring manual review
-- input validation failures
-
-## Handoff
-
-After materialization, send workbook formula integrity, output reliability, and final model tie-out to `model-audit-tieout`. Send source hierarchy or stale-source conflicts to `financial-source-of-truth`.
diff --git a/plugins/financial-markets/skills/equity-model-update/references/sector-kpis.md b/plugins/financial-markets/skills/equity-model-update/references/sector-kpis.md
deleted file mode 100644
index e2932ad..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/sector-kpis.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# Sector KPI Cheat Sheet
-
-Select only the KPIs that affect the current model debate. For each selected metric, show last actual, guide/consensus, user model when available, bull/bear threshold, affected model line, freshness, and confidence.
-
-## Software / SaaS
-
-- KPIs: ARR, subscription revenue, NRR/DBNRR, gross retention, churn, cRPO/RPO, billings, new ARR, large-customer adds, cloud usage, gross margin, operating margin, FCF margin, sales efficiency.
-- Red flags: billings decelerates ahead of revenue, NRR weakens without segment detail, margin beat comes from underinvestment, RPO duration flatters growth.
-
-## Internet / Marketplaces / Digital Ads
-
-- KPIs: GMV/GTV/bookings, take rate, active users, frequency, conversion, ad pricing, impressions, CAC/TAC, contribution margin, cohort retention, supply-demand liquidity.
-- Red flags: revenue beats only from take-rate expansion, engagement deteriorates, supply quality weakens, CAC rises faster than LTV.
-
-## Semis / Hardware
-
-- KPIs: units, ASP, content growth, backlog, book-to-bill, inventory, channel inventory, gross margin, utilization, capex, node/product mix.
-- Red flags: backlog cancellations, inventory rebuild without sell-through, margin supported by temporary mix, capex cycle outruns demand.
-
-## Banks / Diversified Financials
-
-- KPIs: NII, NIM, deposits, beta, loan growth, fee income, credit costs, NCOs, reserves, CET1, AOCI, RWA, tangible book value.
-- Red flags: deposit costs reset faster than assets, reserve release flatters EPS, credit migration outpaces charge-offs, capital return masks RWA pressure.
-
-## Insurance
-
-- KPIs: combined ratio, loss ratio, expense ratio, renewal pricing, retention, catastrophe losses, reserve development, book value, ROE, investment yield.
-- Red flags: prior-year reserve releases support earnings, pricing lags loss cost trend, cat normalization too benign, investment income hides underwriting weakness.
-
-## Asset Managers / Exchanges / Market Structure
-
-- KPIs: AUM, flows, fee rate, performance fees, transaction volumes, open interest, data/analytics revenue, operating leverage.
-- Red flags: mix shift lowers fee rate, organic growth masked by markets, volume spike is non-recurring, expenses reset higher.
-
-## Payments / Fintech
-
-- KPIs: TPV, transactions, take rate, active accounts, interchange/spread, fraud losses, funding costs, CAC, cohort profitability.
-- Red flags: volume growth slows as take rate compresses, credit/fraud losses lag volume, funding cost sensitivity understated.
-
-## Consumer / Retail / Restaurants / CPG
-
-- KPIs: comps, traffic, ticket, price/mix, units, margin, inventory, shrink, promo intensity, same-store sales, restaurant-level margin.
-- Red flags: price drives comp while traffic falls, gross margin beat from temporary commodity relief, inventory markdowns deferred.
-
-## Industrials / Transportation
-
-- KPIs: orders, backlog, book-to-bill, price/cost, utilization, shipments, mix, productivity, working capital, FCF conversion.
-- Red flags: backlog quality weakens, price/cost turns negative, volume deleverage appears before management guide resets.
-
-## Energy / Natural Resources
-
-- KPIs: production, realized price, LOE, capex, decline rates, reserves, hedges, FCF, leverage, shareholder returns.
-- Red flags: production held by unsustainable capex, hedge book masks price exposure, maintenance capex understated.
-
-## Utilities / Power
-
-- KPIs: rate base, allowed ROE, load growth, capex, regulatory lag, earned ROE, fuel recovery, balance sheet, dividend coverage.
-- Red flags: capex requires equity, regulatory outcomes lag plan, storm/fuel recovery timing strains liquidity.
-
-## REITs / Real Estate
-
-- KPIs: occupancy, rent growth, leasing spreads, NOI, AFFO, cap rates, development yields, leverage, debt maturities.
-- Red flags: occupancy bought with concessions, cap-rate expansion not in NAV, floating-rate exposure pressures AFFO.
-
-## Healthcare Services / Managed Care
-
-- KPIs: medical loss ratio, membership, utilization, rate updates, acuity, labor cost, admissions, payer mix, reimbursement.
-- Red flags: utilization acceleration, rate mismatch, labor inflation, payer mix deterioration.
-
-## Biotech / Pharma / Medtech
-
-- KPIs: trial milestones, enrollment, efficacy/safety endpoints, launch curve, script trends, gross-to-net, pipeline spend, patent cliffs.
-- Red flags: endpoint ambiguity, safety signal, slow adoption, channel inventory, gross-to-net deterioration.
-
-## Media / Telecom / Cable
-
-- KPIs: subscribers, ARPU, churn, broadband net adds, ad revenue, affiliate fees, content spend, capex, FCF.
-- Red flags: promo-driven subs, ARPU/churn tradeoff worsens, content costs outpace monetization, capex intensity rises.
diff --git a/plugins/financial-markets/skills/equity-model-update/references/templates.md b/plugins/financial-markets/skills/equity-model-update/references/templates.md
deleted file mode 100644
index 29d7e22..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/templates.md
+++ /dev/null
@@ -1,123 +0,0 @@
-# Equity Model Update Templates
-
-## Model Refresh Summary
-
-```markdown
-# [Company] ([Ticker]) Equity Model Update - [Period]
-
-## Bottom line
-[What changed, which model lines move, and whether this is ready for analyst review.]
-
-## Source/data-quality note
-| Source | Source ID | Date | Label | Freshness | Caveat |
-|---|---|---:|---|---|---|
-
-## Source-to-model map
-| Source metric | Model section | Model line | Workbook target | Current | Proposed | Delta | Label | Confidence | Workbook status | Issue flags |
-|---|---|---|---|---:|---:|---:|---|---|---|---|
-
-## EPS-quality bridge when EPS lines move
-Use when any updated model line touches EPS, net income, tax rate, share count, or below-the-line income/expense.
-
-| Step | Source metric / model line | Value | Evidence label | Treatment | Source ID | Confidence |
-|---|---|---:|---|---|---|---|
-| Reported GAAP diluted EPS |  |  | fact_source_reported | Starting point |  |  |
-| Adjusted / operating EPS, if provided |  |  | management_adjusted | Compare definition and reconciliation |  |  |
-| Non-operating / non-recurring items |  |  | analyst_adjusted / issuer_management_claim | Exclude, isolate, or monitor |  |  |
-| Tax and diluted share-count effects |  |  | derived_calculation | Normalize only with support |  |  |
-| Recurring EPS driver change |  |  | analyst_interpretation | Update forward EPS only if recurring |  |  |
-
-## Change log
-| Change | Model line | Workbook target | Action | Reason | Source ID | Status | Applied to copy | Tie-out required |
-|---|---|---|---|---|---|---|---|---|
-
-## Tie-out checklist
-| Check | Area | Model line | Workbook target | Owner | Status | Handoff |
-|---|---|---|---|---|---|---|
-```
-
-## Pre-Print Model Checklist
-
-```markdown
-## Core question
-[What will arbitrate the model or stock debate?]
-
-## Threshold table
-| Metric | Bear / thesis break | In line | Bull / upside | Model line | Why it matters |
-|---|---:|---:|---:|---|---|
-
-## Consensus / guidance / user model
-| Metric | Last actual | Company guide | Consensus | User model | User vs street | Importance |
-|---|---:|---:|---:|---:|---:|---|
-
-## Post-print update plan
-| When | Item to update | Source | Model line | Handoff |
-|---|---|---|---|---|
-```
-
-## Post-Release / Pre-Call Questions
-
-Use model-linked questions only:
-
-| Question | Model line affected | Why it matters | Bullish answer | Bearish answer |
-|---|---|---|---|---|
-
-## Executable Input CSV
-
-Minimum columns:
-
-- `source_id`
-- `model_line`
-- `mapping_treatment`: `safe_update`, `reference_only`, `missing_model_architecture`, `assumption_required`, or `rebuild_required`
-
-Recommended columns:
-
-- `company`
-- `ticker`
-- `fiscal_period`
-- `model_section`
-- `workbook_sheet`
-- `workbook_cell`
-- `source_metric`
-- `source_value`
-- `current_model_value`
-- `prior_value`
-- `proposed_model_value`
-- `source_name`
-- `source_location`
-- `evidence_label`
-- `as_of_date`
-- `confidence`
-- `overwrite_formula_allowed`
-- `earnings_basis` or `gaap_flag` for EPS-related rows
-- `notes` for tax, share-count, below-the-line, non-operating, or non-recurring EPS issues
-
-Only `safe_update` rows should carry `proposed_model_value`, `new_value`, or estimate-change deltas. Use `reference_only` for source facts that are decision-relevant but not valid replacements for a workbook input, including incompatible period comparisons or facts mapped only to output formulas.
-
-Legacy workbook-intent columns treated as handoff reminders in the support-only helper:
-
-- `workbook_write_requested`
-- `direct_workbook_write`
-- `write_to_workbook`
-
-## Workbook Output Tabs
-
-When a workbook is supplied, lead with either `updated_model.xlsx` or `model_update_control_pack.xlsx`.
-
-Required review tabs:
-
-- `Update_Cover`: preflight status, applied/blocked/reference-only counts, artifact versus model readiness, warnings, handoff; first visible tab for a control pack.
-- `Source_Map`: source-to-model rows with workbook sheet/cell and applied/blocked status.
-- `Rebuild_Requirements`: missing architecture, assumption-required, or rebuild-required rows that prevent a refreshed valuation.
-- `Change_Log`: change rows with source IDs and tie-out status.
-- `Tie_Out`: model-owner checks and handoff skills.
-- `Stale_Data`: stale or missing source rows.
-
-Support artifacts:
-
-- `source_to_model.csv`
-- `change_log.csv`
-- `tieout_checklist.csv`
-- `model_update_citations.json`
-- `run_log.json`
-- `manifest.json`
diff --git a/plugins/financial-markets/skills/equity-model-update/references/workflow.md b/plugins/financial-markets/skills/equity-model-update/references/workflow.md
deleted file mode 100644
index 0fe56fd..0000000
--- a/plugins/financial-markets/skills/equity-model-update/references/workflow.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# Equity Model Update Workflow
-
-## Boundary
-
-Own safe copied-workbook updates, source-to-model refresh maps, model change logs, and post-update tie-out checklists for public-company models. Do not own pure earnings prose, full formula audits, original workbook mutation, trade pitches, or memo synthesis.
-
-## Operating Modes
-
-### No User Model
-
-- Produce a screen-grade model update checklist.
-- State that user-model deltas are unavailable.
-- Use public or connected sources only when available.
-- Focus on likely model lines, KPI thresholds, missing data, and post-print update tasks.
-- Use `csv_update_map_export` only; do not imply an Excel model was updated.
-
-### Partial Context
-
-- Preserve user-provided assumptions and focus areas.
-- Separate sourced facts from agent estimates.
-- Flag missing model lines, source IDs, fiscal periods, units, and stale consensus.
-- Produce a first-pass change map rather than pretending the model has been updated.
-- If a workbook exists but mappings are incomplete, create `model_update_control_pack.xlsx` with review tabs instead of editing model cells.
-- If source facts cannot map to comparable workbook inputs, classify them as `reference_only` or `missing_model_architecture`; do not populate proposed values or estimate-change deltas.
-
-### Existing Workbook Or Model
-
-- Audit before changing.
-- Default to `xlsx_update_copy` when source/model/workbook mapping is sufficient.
-- Always preserve the uploaded original; write only to a copied workbook.
-- Preserve tabs, formulas, workbook structure, source files, hidden sheets, names, charts, pivots, and user hardcodes.
-- Update only mapped input cells with exact sheet/cell references, source IDs, freshness labels, confidence, and prior/new values.
-- Do not overwrite formulas unless the user explicitly approves through `overwrite_formula_allowed`; even then, label it for model-owner review.
-- If target cells are formulas, missing, protected, merged, stale, or ambiguous, create `model_update_control_pack.xlsx` with `Update_Cover`, `Source_Map`, `Rebuild_Requirements`, `Change_Log`, `Tie_Out`, and `Stale_Data` tabs.
-- When required revenue, margin, EPS, segment, share-count, or net-cash schedules do not exist, describe the workbook as structurally insufficient for a refreshed valuation and specify rebuild requirements.
-- Route formula integrity, circularity, external links, and tie-out checks to `model-audit-tieout`.
-
-### Pre-Print Setup
-
-- Compare user model, guidance, consensus, revision path, peer prints, price action, and options-implied move where available.
-- Identify model lines that will matter most after the print.
-- Build thresholds for clean beat, messy beat, in-line, miss/reset, and thesis-break cases.
-
-### Post-Release / Pre-Call
-
-- Map reported actuals and updated guidance to model lines.
-- Separate accounting noise from durable driver changes.
-- For EPS lines, bridge reported GAAP EPS to adjusted/operating or recurring EPS before changing forward EPS. Isolate tax, share-count, below-the-line, mark-to-market, and non-recurring items.
-- Keep valuation categories separate: unchanged legacy cached valuation, informational/mechanical reference bridge where supportable, and refreshed target value only when the forecast architecture can be updated and tied out.
-- Generate management questions tied to the model update map.
-- When workbook mappings are safe, return the copied updated workbook first, then the model-update dashboard/control outputs.
-
-### Post-Call Update
-
-- Incorporate transcript clarifications.
-- Update the source-to-model map and change log.
-- Preserve unresolved items in the tie-out checklist.
-- Hand final workbook integrity to `model-audit-tieout`.
-- If the control pack or updated workbook is restyled, renamed, or otherwise re-exported for final handoff, promote the final exported workbook in `manifest.json`, `run_log.json`, and `model_update_citations.json`, including final sheet-name references.
-- Re-run workbook preflight before applying any second-pass updates; prior model cells may have moved after analyst edits.
-
-## Source Hierarchy
-
-Use this order unless the user instructs otherwise:
-
-1. User-provided model, files, notes, and assumptions.
-2. Company primary sources: filings, releases, supplements, decks, transcripts, investor days.
-3. Connected market-data, consensus, and research apps when available.
-4. Broker/internal research and prior user work.
-5. Industry, macro, government, and alternative datasets.
-6. Public web fallback.
-
-## Required Labels
-
-Label values as reported actual, company guidance, consensus, user estimate, agent estimate, market-implied, inferred/calculated, or judgment. For EPS, also label GAAP, adjusted, operating, or recurring basis. Show the calculation for inferred values and flag stale or missing source IDs.
-
-## Neighboring Skills
-
-- `financial-source-of-truth`: source hierarchy, evidence posture, freshness gaps.
-- `financials-normalizer`: messy financial statement extraction or normalization.
-- `excel-data-cleaner`: raw tabular cleanup before mapping.
-- `earnings-preview`: event setup when no model update contract is needed.
-- `earnings-deep-dive`: post-print interpretation and narrative.
-- `scenario-sensitivity-generator`: model sensitivity tables.
-- `thesis-tracker`: confirm/disconfirm evidence ledger.
-- `long-short-pitch`: investable recommendation, sizing, add/trim/exit.
-- `memo-builder`: formal IC/client/PM written memo.
-- `model-audit-tieout`: workbook formula, link, check, and tie-out integrity.
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/materialize_model_update.py b/plugins/financial-markets/skills/equity-model-update/scripts/materialize_model_update.py
deleted file mode 100644
index 84d12ac..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/materialize_model_update.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-
-from model_update_core import materialize, output_paths
-from model_update_dates import parse_date
-from model_update_io import write_run_log
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description="Materialize equity-model-update source-to-model artifacts from a supplied CSV."
-    )
-    parser.add_argument("input_csv", type=Path, help="CSV with source/model mapping rows.")
-    parser.add_argument("--out", type=Path, default=Path("output"), help="Output directory.")
-    parser.add_argument(
-        "--run-date",
-        default=datetime.now(timezone.utc).date().isoformat(),
-        help="Run date used by freshness checks, YYYY-MM-DD.",
-    )
-    parser.add_argument("--stale-days", type=int, default=45, help="Stale threshold in days.")
-    return parser
-
-
-def main() -> int:
-    args = build_parser().parse_args()
-    run_date = parse_date(args.run_date)
-    if run_date is None:
-        print("ERROR: --run-date must be parseable as YYYY-MM-DD")
-        return 1
-    try:
-        return materialize(args.input_csv, args.out, run_date, args.stale_days)
-    except Exception as exc:
-        args.out.mkdir(parents=True, exist_ok=True)
-        write_run_log(
-            args.out / "run_log.json",
-            status="failed",
-            input_path=args.input_csv,
-            row_count=0,
-            outputs=output_paths(args.out),
-            warnings=[],
-            failures=[str(exc)],
-        )
-        print(f"ERROR: {exc}")
-        return 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/materialize_workbook_update.py b/plugins/financial-markets/skills/equity-model-update/scripts/materialize_workbook_update.py
deleted file mode 100644
index 3fad48e..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/materialize_workbook_update.py
+++ /dev/null
@@ -1,973 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-import zipfile
-from copy import deepcopy
-from dataclasses import dataclass
-from datetime import datetime, timezone
-from pathlib import Path
-from xml.etree import ElementTree as ET
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-
-from model_update_artifacts import build_change_log, build_tieout_checklist
-from model_update_core import output_paths
-from model_update_dates import parse_date
-from model_update_fields import CHANGE_LOG_FIELDS, SOURCE_TO_MODEL_FIELDS, TIEOUT_FIELDS, TRUTHY
-from model_update_format import text
-from model_update_io import load_rows, write_csv, write_run_log
-from model_update_rows import build_source_to_model_rows
-
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_DOC_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-NS_PKG_REL = "http://schemas.openxmlformats.org/package/2006/relationships"
-NS_CONTENT_TYPES = "http://schemas.openxmlformats.org/package/2006/content-types"
-NS_XML = "http://www.w3.org/XML/1998/namespace"
-
-REL_TYPE_WORKSHEET = "http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet"
-WORKSHEET_CONTENT_TYPE = "application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"
-
-CELL_RE = re.compile(r"^([A-Za-z]{1,3})([1-9][0-9]*)$")
-
-
-def qn(ns: str, name: str) -> str:
-    return f"{{{ns}}}{name}"
-
-
-def xml_bytes(root: ET.Element) -> bytes:
-    return ET.tostring(root, encoding="utf-8", xml_declaration=True)
-
-
-def col_to_num(col: str) -> int:
-    number = 0
-    for char in col.upper():
-        number = number * 26 + ord(char) - ord("A") + 1
-    return number
-
-
-def num_to_col(number: int) -> str:
-    col = ""
-    while number:
-        number, rem = divmod(number - 1, 26)
-        col = chr(ord("A") + rem) + col
-    return col
-
-
-def parse_cell_ref(cell_ref: str) -> tuple[int, int] | None:
-    match = CELL_RE.match(cell_ref.strip())
-    if not match:
-        return None
-    return col_to_num(match.group(1)), int(match.group(2))
-
-
-def normalize_cell_ref(cell_ref: str) -> str:
-    parsed = parse_cell_ref(cell_ref)
-    if parsed is None:
-        return cell_ref.strip()
-    col, row = parsed
-    return f"{num_to_col(col)}{row}"
-
-
-def cell_in_range(cell_ref: str, range_ref: str) -> bool:
-    if ":" not in range_ref:
-        return normalize_cell_ref(cell_ref) == normalize_cell_ref(range_ref)
-    start_ref, end_ref = range_ref.split(":", 1)
-    cell = parse_cell_ref(cell_ref)
-    start = parse_cell_ref(start_ref)
-    end = parse_cell_ref(end_ref)
-    if cell is None or start is None or end is None:
-        return False
-    cell_col, cell_row = cell
-    start_col, start_row = start
-    end_col, end_row = end
-    return min(start_col, end_col) <= cell_col <= max(start_col, end_col) and min(
-        start_row, end_row
-    ) <= cell_row <= max(start_row, end_row)
-
-
-def values_equivalent(left: str, right: str) -> bool:
-    left_clean = left.strip()
-    right_clean = right.strip()
-    if left_clean == right_clean:
-        return True
-    try:
-        return abs(float(left_clean.replace(",", "")) - float(right_clean.replace(",", ""))) < 1e-9
-    except ValueError:
-        return False
-
-
-def is_truthy(value: str) -> bool:
-    return text(value).lower() in TRUTHY
-
-
-def unique_sheet_name(base: str, existing: set[str]) -> str:
-    candidate = base[:31]
-    counter = 2
-    while candidate in existing:
-        suffix = f"_{counter}"
-        candidate = f"{base[: 31 - len(suffix)]}{suffix}"
-        counter += 1
-    existing.add(candidate)
-    return candidate
-
-
-def slug(value: str) -> str:
-    clean = re.sub(r"[^a-z0-9]+", "-", value.lower()).strip("-")
-    return clean or "row"
-
-
-@dataclass
-class WorkbookInspection:
-    sheet_paths: dict[str, str]
-    first_visible_sheet: str
-    hidden_sheets: list[str]
-    protected_sheets: list[str]
-    named_ranges: list[str]
-    external_links: list[str]
-    has_vba: bool
-    calc_chain_present: bool
-    formula_cell_count: int
-    cached_formula_value_count: int
-    calc_mode: str
-    full_calc_on_load: str
-    force_full_calc: str
-    max_sheet_id: int
-    max_relationship_id: int
-    used_sheet_numbers: set[int]
-
-    def as_dict(self, *, recalc_required: bool = False) -> dict[str, object]:
-        return {
-            "sheet_names": list(self.sheet_paths),
-            "first_visible_sheet": self.first_visible_sheet,
-            "hidden_sheets": self.hidden_sheets,
-            "hidden_sheet_count": len(self.hidden_sheets),
-            "protected_sheets": self.protected_sheets,
-            "protected_sheet_count": len(self.protected_sheets),
-            "named_ranges": self.named_ranges,
-            "named_range_count": len(self.named_ranges),
-            "external_links": self.external_links,
-            "external_link_count": len(self.external_links),
-            "has_vba": self.has_vba,
-            "calc_chain_present": self.calc_chain_present,
-            "formula_cell_count": self.formula_cell_count,
-            "cached_formula_value_count": self.cached_formula_value_count,
-            "calc_mode": self.calc_mode,
-            "full_calc_on_load": self.full_calc_on_load,
-            "force_full_calc": self.force_full_calc,
-            "recalc_required": recalc_required,
-        }
-
-
-def load_zip_entries(path: Path) -> dict[str, bytes]:
-    with zipfile.ZipFile(path) as zf:
-        return {name: zf.read(name) for name in zf.namelist()}
-
-
-def relationship_target_to_path(target: str) -> str:
-    target = target.replace("\\", "/")
-    if target.startswith("/"):
-        return target.lstrip("/")
-    if target.startswith("xl/"):
-        return target
-    return f"xl/{target}"
-
-
-def inspect_workbook(
-    entries: dict[str, bytes],
-) -> tuple[WorkbookInspection, ET.Element, ET.Element, ET.Element]:
-    required = {"xl/workbook.xml", "xl/_rels/workbook.xml.rels", "[Content_Types].xml"}
-    missing = sorted(required - set(entries))
-    if missing:
-        raise ValueError(f"workbook is missing required XLSX parts: {', '.join(missing)}")
-
-    workbook_root = ET.fromstring(entries["xl/workbook.xml"])
-    rels_root = ET.fromstring(entries["xl/_rels/workbook.xml.rels"])
-    content_root = ET.fromstring(entries["[Content_Types].xml"])
-
-    rel_targets = {
-        rel.attrib["Id"]: relationship_target_to_path(rel.attrib.get("Target", ""))
-        for rel in rels_root.findall(qn(NS_PKG_REL, "Relationship"))
-    }
-    sheets_parent = workbook_root.find(qn(NS_MAIN, "sheets"))
-    if sheets_parent is None:
-        raise ValueError("workbook has no sheets collection")
-
-    sheet_paths: dict[str, str] = {}
-    hidden_sheets: list[str] = []
-    first_visible_sheet = ""
-    max_sheet_id = 0
-    used_sheet_numbers: set[int] = set()
-
-    for sheet in sheets_parent.findall(qn(NS_MAIN, "sheet")):
-        name = sheet.attrib.get("name", "")
-        state = sheet.attrib.get("state", "visible")
-        rid = sheet.attrib.get(qn(NS_DOC_REL, "id"), "")
-        target = rel_targets.get(rid, "")
-        if not name or not target:
-            continue
-        sheet_paths[name] = target
-        if state != "visible":
-            hidden_sheets.append(name)
-        elif not first_visible_sheet:
-            first_visible_sheet = name
-        try:
-            max_sheet_id = max(max_sheet_id, int(sheet.attrib.get("sheetId", "0")))
-        except ValueError:
-            pass
-        match = re.search(r"sheet([0-9]+)\.xml$", target)
-        if match:
-            used_sheet_numbers.add(int(match.group(1)))
-
-    protected_sheets: list[str] = []
-    formula_cell_count = 0
-    cached_formula_value_count = 0
-    for name, path in sheet_paths.items():
-        if path in entries:
-            root = ET.fromstring(entries[path])
-            if root.find(qn(NS_MAIN, "sheetProtection")) is not None:
-                protected_sheets.append(name)
-            for cell in root.iter(qn(NS_MAIN, "c")):
-                formula = cell.find(qn(NS_MAIN, "f"))
-                if formula is not None:
-                    formula_cell_count += 1
-                    value = cell.find(qn(NS_MAIN, "v"))
-                    if value is not None and value.text not in (None, ""):
-                        cached_formula_value_count += 1
-
-    named_ranges: list[str] = []
-    defined_names = workbook_root.find(qn(NS_MAIN, "definedNames"))
-    if defined_names is not None:
-        for defined_name in defined_names.findall(qn(NS_MAIN, "definedName")):
-            name = defined_name.attrib.get("name", "")
-            if name:
-                named_ranges.append(name)
-
-    max_relationship_id = 0
-    for rel in rels_root.findall(qn(NS_PKG_REL, "Relationship")):
-        match = re.match(r"rId([0-9]+)$", rel.attrib.get("Id", ""))
-        if match:
-            max_relationship_id = max(max_relationship_id, int(match.group(1)))
-
-    calc_pr = workbook_root.find(qn(NS_MAIN, "calcPr"))
-    calc_mode = calc_pr.attrib.get("calcMode", "auto") if calc_pr is not None else "auto"
-    full_calc_on_load = calc_pr.attrib.get("fullCalcOnLoad", "") if calc_pr is not None else ""
-    force_full_calc = calc_pr.attrib.get("forceFullCalc", "") if calc_pr is not None else ""
-
-    inspection = WorkbookInspection(
-        sheet_paths=sheet_paths,
-        first_visible_sheet=first_visible_sheet,
-        hidden_sheets=hidden_sheets,
-        protected_sheets=protected_sheets,
-        named_ranges=named_ranges,
-        external_links=sorted(name for name in entries if name.startswith("xl/externalLinks/")),
-        has_vba="xl/vbaProject.bin" in entries,
-        calc_chain_present="xl/calcChain.xml" in entries,
-        formula_cell_count=formula_cell_count,
-        cached_formula_value_count=cached_formula_value_count,
-        calc_mode=calc_mode,
-        full_calc_on_load=full_calc_on_load,
-        force_full_calc=force_full_calc,
-        max_sheet_id=max_sheet_id,
-        max_relationship_id=max_relationship_id,
-        used_sheet_numbers=used_sheet_numbers,
-    )
-    return inspection, workbook_root, rels_root, content_root
-
-
-def read_shared_strings(entries: dict[str, bytes]) -> list[str]:
-    if "xl/sharedStrings.xml" not in entries:
-        return []
-    root = ET.fromstring(entries["xl/sharedStrings.xml"])
-    values: list[str] = []
-    for si in root.findall(qn(NS_MAIN, "si")):
-        fragments = [node.text or "" for node in si.findall(f".//{qn(NS_MAIN, 't')}")]
-        values.append("".join(fragments))
-    return values
-
-
-def find_cell(sheet_root: ET.Element, cell_ref: str) -> ET.Element | None:
-    normalized = normalize_cell_ref(cell_ref)
-    for cell in sheet_root.iter(qn(NS_MAIN, "c")):
-        if cell.attrib.get("r") == normalized:
-            return cell
-    return None
-
-
-def merged_ranges(sheet_root: ET.Element) -> list[str]:
-    container = sheet_root.find(qn(NS_MAIN, "mergeCells"))
-    if container is None:
-        return []
-    return [
-        merge.attrib.get("ref", "")
-        for merge in container.findall(qn(NS_MAIN, "mergeCell"))
-        if merge.attrib.get("ref")
-    ]
-
-
-def cell_display_value(cell: ET.Element, shared_strings: list[str]) -> str:
-    formula = cell.find(qn(NS_MAIN, "f"))
-    if formula is not None:
-        return f"={formula.text or ''}"
-    cell_type = cell.attrib.get("t", "")
-    if cell_type == "inlineStr":
-        text_nodes = cell.findall(f".//{qn(NS_MAIN, 't')}")
-        return "".join(node.text or "" for node in text_nodes)
-    value = cell.find(qn(NS_MAIN, "v"))
-    if value is None or value.text is None:
-        return ""
-    if cell_type == "s":
-        try:
-            return shared_strings[int(value.text)]
-        except (ValueError, IndexError):
-            return value.text
-    if cell_type == "b":
-        return "TRUE" if value.text == "1" else "FALSE"
-    return value.text
-
-
-def set_cell_value(cell: ET.Element, value: str) -> None:
-    for child in list(cell):
-        if child.tag in {qn(NS_MAIN, "f"), qn(NS_MAIN, "v"), qn(NS_MAIN, "is")}:
-            cell.remove(child)
-
-    raw = value.strip()
-    try:
-        float(raw.replace(",", ""))
-        is_number = raw not in {"", ".", "-", "+"}
-    except ValueError:
-        is_number = False
-
-    if is_number:
-        cell.attrib.pop("t", None)
-        value_node = ET.SubElement(cell, qn(NS_MAIN, "v"))
-        value_node.text = raw.replace(",", "")
-    else:
-        cell.attrib["t"] = "inlineStr"
-        inline = ET.SubElement(cell, qn(NS_MAIN, "is"))
-        text_node = ET.SubElement(inline, qn(NS_MAIN, "t"))
-        if raw != value:
-            text_node.attrib[qn(NS_XML, "space")] = "preserve"
-        text_node.text = value
-
-
-def content_namespace(root: ET.Element) -> str:
-    if root.tag.startswith("{"):
-        return root.tag[1:].split("}", 1)[0]
-    return NS_CONTENT_TYPES
-
-
-def table_sheet_xml(rows: list[list[object]]) -> bytes:
-    worksheet = ET.Element(qn(NS_MAIN, "worksheet"))
-    sheet_data = ET.SubElement(worksheet, qn(NS_MAIN, "sheetData"))
-    for row_index, row_values in enumerate(rows, start=1):
-        row = ET.SubElement(sheet_data, qn(NS_MAIN, "row"), {"r": str(row_index)})
-        for col_index, value in enumerate(row_values, start=1):
-            cell_ref = f"{num_to_col(col_index)}{row_index}"
-            cell = ET.SubElement(row, qn(NS_MAIN, "c"), {"r": cell_ref, "t": "inlineStr"})
-            inline = ET.SubElement(cell, qn(NS_MAIN, "is"))
-            text_node = ET.SubElement(inline, qn(NS_MAIN, "t"))
-            cell_text = "" if value is None else str(value)
-            if cell_text != cell_text.strip():
-                text_node.attrib[qn(NS_XML, "space")] = "preserve"
-            text_node.text = cell_text
-    return xml_bytes(worksheet)
-
-
-def next_sheet_number(used: set[int]) -> int:
-    number = 1
-    while number in used:
-        number += 1
-    used.add(number)
-    return number
-
-
-def shift_sheet_scoped_defined_names_for_inserted_first_sheet(workbook_root: ET.Element) -> None:
-    defined_names = workbook_root.find(qn(NS_MAIN, "definedNames"))
-    if defined_names is None:
-        return
-    for defined_name in defined_names.findall(qn(NS_MAIN, "definedName")):
-        local_sheet_id = defined_name.attrib.get("localSheetId")
-        if local_sheet_id is None:
-            continue
-        try:
-            defined_name.set("localSheetId", str(int(local_sheet_id) + 1))
-        except ValueError:
-            continue
-
-
-def append_review_sheets(
-    entries: dict[str, bytes],
-    workbook_root: ET.Element,
-    rels_root: ET.Element,
-    content_root: ET.Element,
-    inspection: WorkbookInspection,
-    tables: dict[str, list[list[object]]],
-    *,
-    promote_cover: bool = False,
-) -> None:
-    sheets_parent = workbook_root.find(qn(NS_MAIN, "sheets"))
-    if sheets_parent is None:
-        raise ValueError("workbook has no sheets collection")
-
-    existing_names = set(inspection.sheet_paths)
-    sheet_id = inspection.max_sheet_id
-    rel_id = inspection.max_relationship_id
-    ct_ns = content_namespace(content_root)
-
-    for base_name, table_rows in tables.items():
-        sheet_id += 1
-        rel_id += 1
-        sheet_number = next_sheet_number(inspection.used_sheet_numbers)
-        sheet_name = unique_sheet_name(base_name, existing_names)
-        sheet_path = f"xl/worksheets/sheet{sheet_number}.xml"
-        target = f"worksheets/sheet{sheet_number}.xml"
-        rid = f"rId{rel_id}"
-
-        sheet = ET.Element(
-            qn(NS_MAIN, "sheet"),
-            {"name": sheet_name, "sheetId": str(sheet_id), qn(NS_DOC_REL, "id"): rid},
-        )
-        if promote_cover and base_name == "Update_Cover":
-            shift_sheet_scoped_defined_names_for_inserted_first_sheet(workbook_root)
-            sheets_parent.insert(0, sheet)
-            book_views = workbook_root.find(qn(NS_MAIN, "bookViews"))
-            if book_views is None:
-                book_views = ET.Element(qn(NS_MAIN, "bookViews"))
-                workbook_root.insert(list(workbook_root).index(sheets_parent), book_views)
-            workbook_view = book_views.find(qn(NS_MAIN, "workbookView"))
-            if workbook_view is None:
-                workbook_view = ET.SubElement(book_views, qn(NS_MAIN, "workbookView"))
-            workbook_view.set("activeTab", "0")
-            workbook_view.set("firstSheet", "0")
-        else:
-            sheets_parent.append(sheet)
-        ET.SubElement(
-            rels_root,
-            qn(NS_PKG_REL, "Relationship"),
-            {"Id": rid, "Type": REL_TYPE_WORKSHEET, "Target": target},
-        )
-        ET.SubElement(
-            content_root,
-            qn(ct_ns, "Override"),
-            {"PartName": f"/{sheet_path}", "ContentType": WORKSHEET_CONTENT_TYPE},
-        )
-        entries[sheet_path] = table_sheet_xml(table_rows)
-
-
-def source_basis(source_rows: list[dict[str, str]]) -> list[dict[str, str]]:
-    return [
-        {
-            "source_id": row.get("source_id", ""),
-            "source_name": row.get("source_name", ""),
-            "source_location": row.get("source_location", ""),
-            "evidence_label": row.get("evidence_label", ""),
-            "as_of_date": row.get("as_of_date", ""),
-            "confidence": row.get("confidence", ""),
-        }
-        for row in source_rows
-        if row.get("source_id")
-    ]
-
-
-def build_citation(row: dict[str, str], index: int, workbook_path: Path) -> dict[str, object]:
-    sheet = row.get("workbook_sheet", "")
-    cell = row.get("workbook_cell", "")
-    source_id = row.get("source_id", "")
-    model_line = row.get("model_line", "")
-    citation_id = (
-        f"model-update:{slug(source_id or model_line or str(index))}:{slug(sheet)}:{slug(cell)}"
-    )
-    treatment = row.get("mapping_treatment", "")
-    if treatment == "reference_only":
-        citation_title = f"{model_line} workbook reference"
-        citation_label = "Model Reference"
-        citation_type = "model_reference_cell"
-    elif treatment in {"missing_model_architecture", "rebuild_required"}:
-        citation_title = f"{model_line} rebuild requirement"
-        citation_label = "Rebuild Required"
-        citation_type = "model_rebuild_requirement"
-    elif treatment == "assumption_required":
-        citation_title = f"{model_line} assumption review"
-        citation_label = "Assumption Review"
-        citation_type = "model_assumption_review"
-    else:
-        citation_title = f"{model_line} workbook update"
-        citation_label = "Model Update"
-        citation_type = "model_update_cell"
-    return {
-        "id": citation_id,
-        "title": citation_title,
-        "short_label": f"{citation_label}: {sheet}!{cell}" if sheet and cell else citation_label,
-        "type": citation_type,
-        "workbook_path": str(workbook_path),
-        "sheet": sheet,
-        "cell": cell,
-        "range": cell,
-        "prior_value": row.get("prior_value", ""),
-        "new_value": row.get("new_value", "") or row.get("proposed_model_value", ""),
-        "model_line": model_line,
-        "source_id": source_id,
-        "source_label": row.get("source_name", "") or row.get("evidence_label", ""),
-        "source_date": row.get("as_of_date", ""),
-        "freshness": row.get("freshness_status", ""),
-        "confidence": row.get("confidence", ""),
-        "update_action": row.get("update_action", ""),
-        "mapping_treatment": treatment,
-        "applied": row.get("applied_to_workbook") == "yes",
-        "blocked_reason": row.get("blocked_reason", ""),
-    }
-
-
-def apply_workbook_updates(
-    entries: dict[str, bytes],
-    inspection: WorkbookInspection,
-    source_rows: list[dict[str, str]],
-    force_review_reason: str | None,
-) -> tuple[list[dict[str, str]], list[str], dict[str, int]]:
-    updated_rows = [deepcopy(row) for row in source_rows]
-    shared_strings = read_shared_strings(entries)
-    warnings: list[str] = []
-    counts = {
-        "target_row_count": 0,
-        "applied_count": 0,
-        "blocked_count": 0,
-        "no_change_count": 0,
-        "reference_only_count": 0,
-        "missing_architecture_count": 0,
-        "rebuild_required_count": 0,
-        "assumption_required_count": 0,
-    }
-    parsed_sheets: dict[str, ET.Element] = {}
-    dirty_sheet_paths: set[str] = set()
-
-    for row in updated_rows:
-        treatment = text(row.get("mapping_treatment"))
-        if treatment == "reference_only":
-            row["review_status"] = "reference_only"
-            row["applied_to_workbook"] = "no"
-            row["blocked_reason"] = ""
-            counts["reference_only_count"] += 1
-            continue
-        if treatment in {
-            "missing_model_architecture",
-            "rebuild_required",
-            "assumption_required",
-        }:
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            if treatment == "missing_model_architecture":
-                counts["missing_architecture_count"] += 1
-            elif treatment == "rebuild_required":
-                counts["rebuild_required_count"] += 1
-            else:
-                counts["assumption_required_count"] += 1
-            continue
-        sheet = text(row.get("workbook_sheet"))
-        cell_ref = normalize_cell_ref(text(row.get("workbook_cell")))
-        new_value = text(row.get("new_value")) or text(row.get("proposed_model_value"))
-        action = text(row.get("update_action"))
-        if not sheet and not cell_ref:
-            row["blocked_reason"] = "missing_workbook_target"
-            row["review_status"] = "control_pack_review"
-            counts["blocked_count"] += 1
-            continue
-
-        counts["target_row_count"] += 1
-        row["workbook_cell"] = cell_ref
-
-        if force_review_reason:
-            row["blocked_reason"] = force_review_reason
-        elif not sheet:
-            row["blocked_reason"] = "missing_workbook_sheet"
-        elif not cell_ref or parse_cell_ref(cell_ref) is None:
-            row["blocked_reason"] = "missing_or_invalid_workbook_cell"
-        elif not new_value:
-            row["blocked_reason"] = "missing_new_value"
-        elif sheet not in inspection.sheet_paths:
-            row["blocked_reason"] = "workbook_sheet_not_found"
-        elif sheet in inspection.protected_sheets:
-            row["blocked_reason"] = "protected_sheet"
-        elif action == "no_change":
-            row["review_status"] = "no_change"
-            row["applied_to_workbook"] = "no"
-            counts["no_change_count"] += 1
-            continue
-
-        if row.get("blocked_reason"):
-            row["review_status"] = "control_pack_review"
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            continue
-
-        sheet_path = inspection.sheet_paths[sheet]
-        sheet_root = parsed_sheets.get(sheet_path)
-        if sheet_root is None:
-            sheet_root = ET.fromstring(entries[sheet_path])
-            parsed_sheets[sheet_path] = sheet_root
-
-        if any(cell_in_range(cell_ref, range_ref) for range_ref in merged_ranges(sheet_root)):
-            row["blocked_reason"] = "merged_cell_target"
-            row["review_status"] = "control_pack_review"
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            continue
-
-        cell = find_cell(sheet_root, cell_ref)
-        if cell is None:
-            row["blocked_reason"] = "workbook_cell_not_found"
-            row["review_status"] = "control_pack_review"
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            continue
-
-        formula = cell.find(qn(NS_MAIN, "f"))
-        if formula is not None and not is_truthy(row.get("overwrite_formula_allowed", "")):
-            row["blocked_reason"] = "formula_cell_not_overwritten_without_approval"
-            row["review_status"] = "control_pack_review"
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            continue
-
-        prior_from_workbook = cell_display_value(cell, shared_strings)
-        supplied_prior = text(row.get("prior_value")) or text(row.get("current_model_value"))
-        row["prior_value"] = prior_from_workbook
-        if (
-            supplied_prior
-            and prior_from_workbook
-            and not values_equivalent(supplied_prior, prior_from_workbook)
-        ):
-            row["blocked_reason"] = "prior_value_mismatch"
-            row["review_status"] = "control_pack_review"
-            row["applied_to_workbook"] = "no"
-            counts["blocked_count"] += 1
-            continue
-
-        if formula is not None:
-            warnings.append(
-                f"{sheet}!{cell_ref}: formula overwritten because overwrite_formula_allowed was explicit"
-            )
-        set_cell_value(cell, new_value)
-        row["applied_to_workbook"] = "yes"
-        row["blocked_reason"] = ""
-        row["review_status"] = "applied_to_copy"
-        counts["applied_count"] += 1
-        dirty_sheet_paths.add(sheet_path)
-
-    for sheet_path in dirty_sheet_paths:
-        entries[sheet_path] = xml_bytes(parsed_sheets[sheet_path])
-    return updated_rows, warnings, counts
-
-
-def cover_rows(
-    *,
-    mode: str,
-    original_workbook: Path,
-    output_workbook: Path,
-    inspection: WorkbookInspection,
-    counts: dict[str, int],
-    warnings: list[str],
-    failures: list[str],
-) -> list[list[object]]:
-    artifact_readiness = "not_ready" if failures else "ready_for_review"
-    model_readiness = (
-        "not_ready_validation_failure"
-        if failures
-        else "updated_requires_tieout"
-        if mode == "xlsx_update_copy"
-        else "not_updated_requires_mapping_or_rebuild"
-    )
-    rows: list[list[object]] = [
-        ["Equity Model Update", "Safe workbook update copy"],
-        ["Status", "failed" if failures else "completed"],
-        ["Artifact readiness", artifact_readiness],
-        ["Model readiness", model_readiness],
-        ["Workbook mode", mode],
-        ["Original workbook", str(original_workbook)],
-        ["Output workbook", str(output_workbook)],
-        ["First visible sheet preserved", inspection.first_visible_sheet],
-        ["Target rows", counts.get("target_row_count", 0)],
-        ["Applied updates", counts.get("applied_count", 0)],
-        ["Blocked updates", counts.get("blocked_count", 0)],
-        ["Reference-only facts", counts.get("reference_only_count", 0)],
-        ["Missing model architecture", counts.get("missing_architecture_count", 0)],
-        ["Rebuild-required rows", counts.get("rebuild_required_count", 0)],
-        ["Assumption-required rows", counts.get("assumption_required_count", 0)],
-        ["No-change rows", counts.get("no_change_count", 0)],
-        ["External links detected", len(inspection.external_links)],
-        ["Formula cells detected", inspection.formula_cell_count],
-        ["Cached formula values detected", inspection.cached_formula_value_count],
-        ["Calc chain present", "yes" if inspection.calc_chain_present else "no"],
-        ["Workbook calc mode", inspection.calc_mode],
-        [
-            "Excel/Sheets recalculation required",
-            "yes" if counts.get("applied_count") and inspection.formula_cell_count else "no",
-        ],
-        ["Hidden sheets detected", len(inspection.hidden_sheets)],
-        ["Protected sheets detected", len(inspection.protected_sheets)],
-        ["Named ranges detected", len(inspection.named_ranges)],
-        ["VBA/macros detected", "yes" if inspection.has_vba else "no"],
-        ["Next handoff", "model-audit-tieout for formula integrity and final tie-out"],
-        [],
-        ["Warnings"],
-    ]
-    rows.extend([[warning] for warning in warnings] or [["None"]])
-    if failures:
-        rows.append([])
-        rows.append(["Hard failures"])
-        rows.extend([[failure] for failure in failures])
-    return rows
-
-
-def dict_rows_table(
-    title: str, rows: list[dict[str, str]], fields: list[str]
-) -> list[list[object]]:
-    return [[title], fields] + [[row.get(field, "") for field in fields] for row in rows]
-
-
-def stale_rows(source_rows: list[dict[str, str]]) -> list[dict[str, str]]:
-    return [
-        row
-        for row in source_rows
-        if row.get("freshness_status") in {"stale", "unknown"}
-        or "missing" in row.get("issue_flags", "")
-    ]
-
-
-def rebuild_rows(source_rows: list[dict[str, str]]) -> list[dict[str, str]]:
-    return [
-        row
-        for row in source_rows
-        if row.get("mapping_treatment")
-        in {"missing_model_architecture", "rebuild_required", "assumption_required"}
-    ]
-
-
-def write_workbook(entries: dict[str, bytes], output_path: Path) -> None:
-    with zipfile.ZipFile(output_path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
-        for name, payload in entries.items():
-            zf.writestr(name, payload)
-
-
-def workbook_output_paths(out_dir: Path, workbook_key: str, workbook_path: Path) -> dict[str, str]:
-    paths = output_paths(out_dir)
-    return {
-        workbook_key: str(workbook_path),
-        "source_to_model": paths["source_to_model"],
-        "change_log": paths["change_log"],
-        "tieout_checklist": paths["tieout_checklist"],
-        "model_update_citations": str(out_dir / "model_update_citations.json"),
-        "run_log": paths["run_log"],
-        "manifest": paths["manifest"],
-    }
-
-
-def materialize_workbook_update(
-    input_csv: Path,
-    workbook: Path,
-    out_dir: Path,
-    run_date: datetime,
-    stale_days: int,
-) -> int:
-    rows = load_rows(input_csv)
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    source_rows, warnings, failures = build_source_to_model_rows(rows, run_date, stale_days)
-    entries = load_zip_entries(workbook)
-    inspection, workbook_root, rels_root, content_root = inspect_workbook(entries)
-    source_rows, workbook_warnings, counts = apply_workbook_updates(
-        entries,
-        inspection,
-        source_rows,
-        force_review_reason=(
-            "input_validation_failure"
-            if failures
-            else "macro_enabled_workbook_requires_manual_review"
-            if inspection.has_vba
-            else None
-        ),
-    )
-    warnings.extend(workbook_warnings)
-    warnings.extend(
-        [
-            "workbook has external links; final tie-out should confirm links/calculation integrity"
-            if inspection.external_links
-            else "",
-            "workbook contains formulas with cached values; this runtime edited copied input cells but does not recalculate downstream formula caches. Open in Excel/Sheets and recalculate, then run model-audit-tieout before relying on formula outputs."
-            if counts["applied_count"] and inspection.formula_cell_count
-            else "",
-            "workbook formula outputs were inspected as cached values; no model inputs were updated in this control pack, so it is not a refreshed valuation."
-            if not counts["applied_count"] and inspection.formula_cell_count
-            else "",
-            "workbook calc mode is manual; recalculation is required after applied input updates"
-            if counts["applied_count"] and inspection.calc_mode.lower() == "manual"
-            else "",
-            "calcChain.xml is present; downstream formula dependency chain may be stale after copied input updates"
-            if counts["applied_count"] and inspection.calc_chain_present
-            else "",
-            "macro-enabled workbook detected; model cells were routed to review instead of edited"
-            if inspection.has_vba
-            else "",
-        ]
-    )
-    warnings = [warning for warning in warnings if warning]
-    mode = "xlsx_update_copy" if counts["applied_count"] else "xlsx_control_pack"
-    workbook_key = "updated_model" if mode == "xlsx_update_copy" else "model_update_control_pack"
-    suffix = ".xlsm" if workbook.suffix.lower() == ".xlsm" or inspection.has_vba else ".xlsx"
-    workbook_name = f"{workbook_key}{suffix}"
-    output_workbook = out_dir / workbook_name
-    outputs = workbook_output_paths(out_dir, workbook_key, output_workbook)
-
-    change_log = build_change_log(source_rows)
-    tieout = build_tieout_checklist(source_rows)
-    review_tables = {
-        "Update_Cover": cover_rows(
-            mode=mode,
-            original_workbook=workbook,
-            output_workbook=output_workbook,
-            inspection=inspection,
-            counts=counts,
-            warnings=warnings,
-            failures=failures,
-        ),
-        "Source_Map": dict_rows_table("Source-to-model map", source_rows, SOURCE_TO_MODEL_FIELDS),
-        "Rebuild_Requirements": dict_rows_table(
-            "Missing architecture or rebuild requirements",
-            rebuild_rows(source_rows),
-            SOURCE_TO_MODEL_FIELDS,
-        ),
-        "Change_Log": dict_rows_table("Change log", change_log, CHANGE_LOG_FIELDS),
-        "Tie_Out": dict_rows_table("Tie-out checklist", tieout, TIEOUT_FIELDS),
-        "Stale_Data": dict_rows_table(
-            "Stale or missing source rows", stale_rows(source_rows), SOURCE_TO_MODEL_FIELDS
-        ),
-    }
-    append_review_sheets(
-        entries,
-        workbook_root,
-        rels_root,
-        content_root,
-        inspection,
-        review_tables,
-        promote_cover=mode == "xlsx_control_pack",
-    )
-    entries["xl/workbook.xml"] = xml_bytes(workbook_root)
-    entries["xl/_rels/workbook.xml.rels"] = xml_bytes(rels_root)
-    entries["[Content_Types].xml"] = xml_bytes(content_root)
-
-    write_workbook(entries, output_workbook)
-    write_csv(out_dir / "source_to_model.csv", source_rows, SOURCE_TO_MODEL_FIELDS)
-    write_csv(out_dir / "change_log.csv", change_log, CHANGE_LOG_FIELDS)
-    write_csv(out_dir / "tieout_checklist.csv", tieout, TIEOUT_FIELDS)
-
-    citation_rows = [
-        build_citation(row, index, output_workbook)
-        for index, row in enumerate(source_rows, start=1)
-        if row.get("workbook_sheet") or row.get("workbook_cell")
-    ]
-    citations = {
-        "model_citations": citation_rows,
-        "model_update_citations": citation_rows,
-    }
-    (out_dir / "model_update_citations.json").write_text(
-        json.dumps(citations, indent=2) + "\n", encoding="utf-8"
-    )
-
-    status = "failed" if failures else "completed"
-    write_run_log(
-        out_dir / "run_log.json",
-        status=status,
-        input_path=input_csv,
-        row_count=len(rows),
-        outputs=outputs,
-        warnings=warnings,
-        failures=failures,
-        source_basis=source_basis(source_rows),
-        artifact_level="workbook_update",
-        workbook_mode=mode,
-        primary_human_deliverable=str(output_workbook),
-        workbook_preflight=inspection.as_dict(
-            recalc_required=bool(counts.get("applied_count") and inspection.formula_cell_count)
-        ),
-        workbook_update_summary=counts,
-        recalculation_warning={
-            "recalc_required": bool(counts.get("applied_count") and inspection.formula_cell_count),
-            "formula_cell_count": inspection.formula_cell_count,
-            "cached_formula_value_count": inspection.cached_formula_value_count,
-            "calc_chain_present": inspection.calc_chain_present,
-            "calc_mode": inspection.calc_mode,
-            "runtime_recalculated_formulas": False,
-            "required_next_step": (
-                "Open the copied workbook in Excel/Sheets, recalculate, save, and run model-audit-tieout before relying on formula outputs."
-                if counts.get("applied_count") and inspection.formula_cell_count
-                else "No recalculation is required from this run because no model cells were changed; map safe inputs or rebuild the forecast architecture before refreshing valuation."
-            ),
-        },
-        capability_boundary=(
-            "safe copied-workbook updater; original workbook is never mutated, formulas are preserved unless "
-            "overwrite_formula_allowed is explicit, blocked targets are surfaced in review tabs, and formula caches are not recalculated by this runtime"
-        ),
-    )
-    if failures:
-        for failure in failures:
-            print(f"ERROR: {failure}")
-        return 1
-    print(f"Wrote equity model update workbook package to {out_dir}")
-    return 0
-
-
-def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description="Safely materialize equity-model-update artifacts into a copied workbook package."
-    )
-    parser.add_argument("input_csv", type=Path, help="CSV with source/model/workbook mapping rows.")
-    parser.add_argument(
-        "--workbook",
-        required=True,
-        type=Path,
-        help="Uploaded XLSX/XLSM workbook to copy and update.",
-    )
-    parser.add_argument("--out", type=Path, default=Path("output"), help="Output directory.")
-    parser.add_argument(
-        "--run-date",
-        default=datetime.now(timezone.utc).date().isoformat(),
-        help="Run date used by freshness checks, YYYY-MM-DD.",
-    )
-    parser.add_argument("--stale-days", type=int, default=45, help="Stale threshold in days.")
-    return parser
-
-
-def main() -> int:
-    args = build_parser().parse_args()
-    run_date = parse_date(args.run_date)
-    if run_date is None:
-        print("ERROR: --run-date must be parseable as YYYY-MM-DD")
-        return 1
-    try:
-        return materialize_workbook_update(
-            args.input_csv, args.workbook, args.out, run_date, args.stale_days
-        )
-    except Exception as exc:
-        args.out.mkdir(parents=True, exist_ok=True)
-        outputs = output_paths(args.out)
-        write_run_log(
-            args.out / "run_log.json",
-            status="failed",
-            input_path=args.input_csv,
-            row_count=0,
-            outputs=outputs,
-            warnings=[],
-            failures=[str(exc)],
-            artifact_level="workbook_update",
-            workbook_mode="xlsx_update_failed",
-            capability_boundary="safe copied-workbook updater failed before workbook materialization",
-        )
-        print(f"ERROR: {exc}")
-        return 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_artifacts.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_artifacts.py
deleted file mode 100644
index 4506401..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_artifacts.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from __future__ import annotations
-
-
-def build_change_log(source_rows: list[dict[str, str]]) -> list[dict[str, str]]:
-    change_rows: list[dict[str, str]] = []
-    counter = 1
-    change_actions = {"update", "add_or_map", "input_required"}
-    for row in source_rows:
-        if row["update_action"] not in change_actions:
-            continue
-        status = "ready_for_review"
-        if row.get("applied_to_workbook") == "yes":
-            status = "applied_to_copy"
-        elif row["issue_flags"] or row.get("blocked_reason"):
-            status = "blocked"
-        change_rows.append(
-            {
-                "change_id": f"CHG-{counter:03d}",
-                "company": row["company"],
-                "ticker": row["ticker"],
-                "fiscal_period": row["fiscal_period"],
-                "model_section": row["model_section"],
-                "model_line": row["model_line"],
-                "workbook_sheet": row.get("workbook_sheet", ""),
-                "workbook_cell": row.get("workbook_cell", ""),
-                "current_model_value": row["current_model_value"],
-                "proposed_model_value": row["proposed_model_value"],
-                "delta": row["delta"],
-                "delta_pct": row["delta_pct"],
-                "update_action": row["update_action"],
-                "change_reason": "source-to-model refresh mapping",
-                "source_id": row["source_id"],
-                "confidence": row["confidence"],
-                "status": status,
-                "applied_to_workbook": row.get("applied_to_workbook", ""),
-                "blocked_reason": row.get("blocked_reason", ""),
-                "requires_model_audit_tieout": "yes",
-            }
-        )
-        counter += 1
-    return change_rows
-
-
-def build_tieout_checklist(source_rows: list[dict[str, str]]) -> list[dict[str, str]]:
-    checks: list[dict[str, str]] = [
-        {
-            "check_id": "TIE-001",
-            "check_area": "model_audit_handoff",
-            "related_model_line": "all changed lines",
-            "workbook_sheet": "all workbook targets",
-            "workbook_cell": "",
-            "check_description": "Run model-audit-tieout before relying on workbook formulas.",
-            "owner": "model owner",
-            "status": "required",
-            "handoff_skill": "model-audit-tieout",
-        }
-    ]
-    for idx, row in enumerate(source_rows, start=2):
-        treatment = row.get("mapping_treatment", "")
-        if treatment == "reference_only":
-            check_status = "reference_only"
-            check_description = (
-                f"Confirm {row['model_line']} is retained only as a cited reference "
-                f"from {row['source_id']}."
-            )
-        else:
-            check_status = "blocked" if row["issue_flags"] or row.get("blocked_reason") else "open"
-            check_description = f"Verify {row['model_line']} maps to source {row['source_id']}."
-        checks.append(
-            {
-                "check_id": f"TIE-{idx:03d}",
-                "check_area": "source_to_model",
-                "related_model_line": row["model_line"],
-                "workbook_sheet": row.get("workbook_sheet", ""),
-                "workbook_cell": row.get("workbook_cell", ""),
-                "check_description": check_description,
-                "owner": "analyst",
-                "status": check_status,
-                "handoff_skill": "model-audit-tieout",
-            }
-        )
-        if row["freshness_status"] in {"stale", "unknown"}:
-            checks.append(
-                {
-                    "check_id": f"TIE-{idx:03d}F",
-                    "check_area": "freshness",
-                    "related_model_line": row["model_line"],
-                    "workbook_sheet": row.get("workbook_sheet", ""),
-                    "workbook_cell": row.get("workbook_cell", ""),
-                    "check_description": f"Refresh/caveat {row['model_line']}; freshness is {row['freshness_status']}.",
-                    "owner": "analyst",
-                    "status": "open",
-                    "handoff_skill": "financial-source-of-truth",
-                }
-            )
-    return checks
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_core.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_core.py
deleted file mode 100644
index 63825e1..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_core.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from datetime import datetime
-from pathlib import Path
-
-from model_update_artifacts import build_change_log, build_tieout_checklist
-from model_update_fields import CHANGE_LOG_FIELDS, SOURCE_TO_MODEL_FIELDS, TIEOUT_FIELDS
-from model_update_io import load_rows, write_csv, write_run_log
-from model_update_rows import build_source_to_model_rows
-
-
-def output_paths(out_dir: Path) -> dict[str, str]:
-    return {
-        "source_to_model": str(out_dir / "source_to_model.csv"),
-        "change_log": str(out_dir / "change_log.csv"),
-        "tieout_checklist": str(out_dir / "tieout_checklist.csv"),
-        "run_log": str(out_dir / "run_log.json"),
-        "manifest": str(out_dir / "manifest.json"),
-    }
-
-
-def log_failure(
-    out_dir: Path,
-    input_csv: Path,
-    row_count: int,
-    outputs: dict[str, str],
-    warnings: list[str],
-    failures: list[str],
-) -> int:
-    write_run_log(
-        out_dir / "run_log.json",
-        status="failed",
-        input_path=input_csv,
-        row_count=row_count,
-        outputs=outputs,
-        warnings=warnings,
-        failures=failures,
-    )
-    for failure in failures:
-        print(f"ERROR: {failure}")
-    return 1
-
-
-def materialize(input_csv: Path, out_dir: Path, run_date: datetime, stale_days: int) -> int:
-    rows = load_rows(input_csv)
-    out_dir.mkdir(parents=True, exist_ok=True)
-    outputs = output_paths(out_dir)
-    source_rows, warnings, failures = build_source_to_model_rows(rows, run_date, stale_days)
-
-    if failures:
-        return log_failure(out_dir, input_csv, len(rows), outputs, warnings, failures)
-
-    write_csv(out_dir / "source_to_model.csv", source_rows, SOURCE_TO_MODEL_FIELDS)
-    write_csv(out_dir / "change_log.csv", build_change_log(source_rows), CHANGE_LOG_FIELDS)
-    write_csv(out_dir / "tieout_checklist.csv", build_tieout_checklist(source_rows), TIEOUT_FIELDS)
-    source_basis = [
-        {
-            "source_id": row.get("source_id", ""),
-            "source_name": row.get("source_name", ""),
-            "source_location": row.get("source_location", ""),
-            "evidence_label": row.get("evidence_label", ""),
-            "as_of_date": row.get("as_of_date", ""),
-            "confidence": row.get("confidence", ""),
-        }
-        for row in source_rows
-        if row.get("source_id")
-    ]
-    write_run_log(
-        out_dir / "run_log.json",
-        status="completed",
-        input_path=input_csv,
-        row_count=len(rows),
-        outputs=outputs,
-        warnings=warnings,
-        failures=[],
-        source_basis=source_basis,
-    )
-    print(f"Wrote equity model update artifacts to {out_dir}")
-    return 0
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_dates.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_dates.py
deleted file mode 100644
index 01bfa10..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_dates.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-from datetime import datetime, timezone
-
-from model_update_format import text
-
-
-def parse_date(value: object) -> datetime | None:
-    raw = text(value)
-    if not raw:
-        return None
-    formats = ("%Y-%m-%d", "%m/%d/%Y", "%Y/%m/%d")
-    for date_format in formats:
-        try:
-            return datetime.strptime(raw, date_format).replace(tzinfo=timezone.utc)
-        except ValueError:
-            pass
-    try:
-        parsed = datetime.fromisoformat(raw)
-    except ValueError:
-        return None
-    if parsed.tzinfo:
-        return parsed
-    return parsed.replace(tzinfo=timezone.utc)
-
-
-def freshness(row: dict[str, str], run_date: datetime, stale_days: int) -> tuple[str, str | None]:
-    explicit = text(row.get("freshness_status"))
-    if explicit:
-        return explicit, None
-    as_of = parse_date(row.get("as_of_date") or row.get("source_as_of_date"))
-    if as_of is None:
-        return "unknown", "missing_as_of_date"
-    age_days = (run_date - as_of).days
-    if age_days > stale_days:
-        return "stale", f"source data is {age_days} days old"
-    return "current", None
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_fields.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_fields.py
deleted file mode 100644
index 73e3a62..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_fields.py
+++ /dev/null
@@ -1,68 +0,0 @@
-SOURCE_TO_MODEL_FIELDS = [
-    "company",
-    "ticker",
-    "fiscal_period",
-    "model_section",
-    "model_line",
-    "mapping_treatment",
-    "workbook_sheet",
-    "workbook_cell",
-    "source_metric",
-    "source_value",
-    "current_model_value",
-    "proposed_model_value",
-    "prior_value",
-    "new_value",
-    "delta",
-    "delta_pct",
-    "update_action",
-    "overwrite_formula_allowed",
-    "source_id",
-    "source_name",
-    "source_location",
-    "evidence_label",
-    "as_of_date",
-    "freshness_status",
-    "confidence",
-    "review_status",
-    "applied_to_workbook",
-    "blocked_reason",
-    "issue_flags",
-]
-
-CHANGE_LOG_FIELDS = [
-    "change_id",
-    "company",
-    "ticker",
-    "fiscal_period",
-    "model_section",
-    "model_line",
-    "workbook_sheet",
-    "workbook_cell",
-    "current_model_value",
-    "proposed_model_value",
-    "delta",
-    "delta_pct",
-    "update_action",
-    "change_reason",
-    "source_id",
-    "confidence",
-    "status",
-    "applied_to_workbook",
-    "blocked_reason",
-    "requires_model_audit_tieout",
-]
-
-TIEOUT_FIELDS = [
-    "check_id",
-    "check_area",
-    "related_model_line",
-    "workbook_sheet",
-    "workbook_cell",
-    "check_description",
-    "owner",
-    "status",
-    "handoff_skill",
-]
-
-TRUTHY = {"1", "true", "yes", "y", "direct", "write", "edit"}
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_format.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_format.py
deleted file mode 100644
index 64b6223..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_format.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from __future__ import annotations
-
-import math
-import re
-from typing import Any
-
-INVALID_NUMBERS = {"na", "n/a", "nm", "-", "--", "input required"}
-
-
-def text(value: Any) -> str:
-    return "" if value is None else str(value).strip()
-
-
-def first_text(row: dict[str, str], fields: tuple[str, ...]) -> str:
-    for field in fields:
-        value = text(row.get(field))
-        if value:
-            return value
-    return ""
-
-
-def parse_number(value: Any) -> float | None:
-    raw = text(value)
-    if not raw:
-        return None
-    if raw.lower() in INVALID_NUMBERS:
-        return None
-    negative = raw.startswith("(") and raw.endswith(")")
-    cleaned = raw.replace(",", "").replace("$", "").replace("%", "").strip("() ")
-    match = re.search(r"[-+]?\d+(?:\.\d+)?", cleaned)
-    if not match:
-        return None
-    number = float(match.group(0))
-    if negative:
-        return -abs(number)
-    return number
-
-
-def first_number(row: dict[str, str], fields: tuple[str, ...]) -> float | None:
-    for field in fields:
-        parsed = parse_number(row.get(field))
-        if parsed is not None:
-            return parsed
-    return None
-
-
-def fmt_number(value: float | None) -> str:
-    if value is None or not math.isfinite(value):
-        return ""
-    rendered = f"{value:.1f}" if abs(value) >= 100 else f"{value:.2f}"
-    return rendered.rstrip("0").rstrip(".")
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_io.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_io.py
deleted file mode 100644
index 1c84d2c..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_io.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from __future__ import annotations
-
-import csv
-import json
-from datetime import datetime, timezone
-from pathlib import Path
-
-
-def load_rows(path: Path) -> list[dict[str, str]]:
-    with path.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        if not reader.fieldnames:
-            raise ValueError("input CSV must include a header row")
-        rows = [dict(row) for row in reader]
-    if not rows:
-        raise ValueError("input CSV must include at least one row")
-    return rows
-
-
-def write_csv(path: Path, rows: list[dict[str, str]], fields: list[str]) -> None:
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fields)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def write_run_log(
-    path: Path,
-    *,
-    status: str,
-    input_path: Path,
-    row_count: int,
-    outputs: dict[str, str],
-    warnings: list[str],
-    failures: list[str],
-    source_basis: list[dict[str, str]] | None = None,
-    artifact_level: str = "deterministic_export",
-    workbook_mode: str = "update_map_export",
-    primary_human_deliverable: str | None = None,
-    support_artifacts: list[str] | None = None,
-    workbook_preflight: dict[str, object] | None = None,
-    workbook_update_summary: dict[str, object] | None = None,
-    recalculation_warning: dict[str, object] | None = None,
-    capability_boundary: str | None = None,
-    artifact_readiness: str | None = None,
-    model_readiness: str | None = None,
-) -> None:
-    descriptions = {
-        "updated_model": "Copied XLSX model with safe mapped input updates and review tabs.",
-        "model_update_control_pack": "Copied XLSX workbook with review/control tabs; model cells were not edited.",
-        "source_to_model": "Source-to-model mapping with treatment and updateable-value deltas.",
-        "change_log": "Review-ready model change log.",
-        "tieout_checklist": "Model audit handoff checklist.",
-        "model_update_citations": "Workbook/sheet/cell provenance for changed and blocked model targets.",
-        "run_log": "Status, warnings, hard failures, workbook preflight, and output manifest.",
-        "manifest": "Machine-readable output manifest.",
-    }
-    output_manifest = [
-        {
-            "key": key,
-            "path": artifact_path,
-            "required": True,
-            "written": key in {"run_log", "manifest"} or Path(artifact_path).exists(),
-            "description": descriptions.get(key, "Generated artifact."),
-        }
-        for key, artifact_path in outputs.items()
-    ]
-    support_artifacts = support_artifacts or [
-        artifact_path
-        for artifact_path in outputs.values()
-        if primary_human_deliverable is None or artifact_path != primary_human_deliverable
-    ]
-    if model_readiness is None:
-        if failures:
-            model_readiness = "not_ready_validation_failure"
-        elif workbook_mode == "xlsx_control_pack":
-            model_readiness = "not_updated_requires_mapping_or_rebuild"
-        elif workbook_mode == "xlsx_update_copy":
-            model_readiness = "updated_requires_tieout"
-        else:
-            model_readiness = "mapping_package_ready_for_review"
-    if artifact_readiness is None:
-        artifact_readiness = "not_ready" if failures else "ready_for_review"
-    payload = {
-        "status": status,
-        "model_status": model_readiness,
-        "artifact_readiness": artifact_readiness,
-        "model_readiness": model_readiness,
-        "artifact_level": artifact_level,
-        "workbook_mode": workbook_mode,
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "input_path": str(input_path),
-        "row_count": row_count,
-        "source_basis": source_basis or [],
-        "outputs": outputs,
-        "primary_human_deliverable": primary_human_deliverable,
-        "support_artifacts": support_artifacts,
-        "output_manifest": output_manifest,
-        "warnings": warnings,
-        "hard_failures": failures,
-        "missing_source_id_count": sum("source_id is required" in item for item in failures),
-        "workbook_write_rejected_count": sum(
-            "original workbook mutation" in item for item in failures
-        ),
-        "model_audit_tieout_required": True,
-        "handoff_skill": "model-audit-tieout",
-        "capability_boundary": capability_boundary
-        or "CSV update-map materializer only; use the workbook materializer for safe copied-XLSX updates",
-    }
-    if workbook_preflight is not None:
-        payload["workbook_preflight"] = workbook_preflight
-    if workbook_update_summary is not None:
-        payload["workbook_update_summary"] = workbook_update_summary
-    if recalculation_warning is not None:
-        payload["recalculation_warning"] = recalculation_warning
-    path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
-    manifest_path = path.parent / "manifest.json"
-    manifest_payload = {
-        "artifact_mode": workbook_mode,
-        "primary_human_deliverable": primary_human_deliverable,
-        "human_deliverables": [primary_human_deliverable] if primary_human_deliverable else [],
-        "support_artifacts": support_artifacts,
-        "outputs": output_manifest,
-    }
-    manifest_path.write_text(json.dumps(manifest_payload, indent=2) + "\n", encoding="utf-8")
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_rows.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_rows.py
deleted file mode 100644
index d4d3487..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_rows.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from __future__ import annotations
-
-import re
-from datetime import datetime
-
-from model_update_dates import freshness
-from model_update_format import first_number, first_text, fmt_number, text
-from model_update_validate import resolve_action, validate_mapping_row
-
-EPS_DISTORTION_TERMS = (
-    "asset sale",
-    "below-the-line",
-    "equity investment",
-    "fair value",
-    "fx",
-    "gain",
-    "impairment",
-    "litigation",
-    "loss",
-    "mark-to-market",
-    "non-operating",
-    "non-recurring",
-    "one-time",
-    "pension",
-    "restructuring",
-    "share count",
-    "tax",
-)
-
-NON_UPDATE_TREATMENTS = {
-    "reference_only",
-    "missing_model_architecture",
-    "rebuild_required",
-    "assumption_required",
-}
-
-
-def mapping_treatment(row: dict[str, str]) -> str:
-    raw = first_text(row, ("mapping_treatment", "model_treatment", "updateability")).lower()
-    normalized = re.sub(r"[-\s]+", "_", raw).strip("_")
-    aliases = {
-        "informational": "reference_only",
-        "missing_architecture": "missing_model_architecture",
-        "missing_model_line": "missing_model_architecture",
-        "safe": "safe_update",
-        "update_candidate": "safe_update",
-    }
-    if normalized:
-        return aliases.get(normalized, normalized)
-    action = text(row.get("update_action")).lower()
-    if action in NON_UPDATE_TREATMENTS:
-        return action
-    return "safe_update"
-
-
-def treatment_action(treatment: str, fallback_action: str) -> str:
-    return {
-        "reference_only": "reference_only",
-        "missing_model_architecture": "rebuild_required",
-        "rebuild_required": "rebuild_required",
-        "assumption_required": "input_required",
-    }.get(treatment, fallback_action)
-
-
-def treatment_status(treatment: str, flags: list[str]) -> tuple[str, str]:
-    if treatment == "reference_only":
-        return "reference_only", ""
-    if treatment == "missing_model_architecture":
-        return "blocked_missing_model_architecture", "missing_model_architecture"
-    if treatment == "rebuild_required":
-        return "blocked_rebuild_required", "rebuild_required"
-    if treatment == "assumption_required":
-        return "blocked_assumption_required", "assumption_required"
-    return ("blocked", "") if flags else ("ready_for_review", "")
-
-
-def eps_quality_flags(row: dict[str, str], model_line: str, source_metric: str) -> list[str]:
-    combined_metric = f"{model_line} {source_metric}".lower()
-    if "eps" not in combined_metric and "net income" not in combined_metric:
-        return []
-
-    flags: list[str] = []
-    basis = first_text(row, ("earnings_basis", "gaap_flag", "metric_basis"))
-    notes = " ".join(
-        first_text(row, (field,)) for field in ("notes", "commentary", "source_note", "issue_flags")
-    ).lower()
-
-    if not basis:
-        flags.append("eps_basis_missing")
-    if any(term in notes for term in EPS_DISTORTION_TERMS):
-        flags.append("eps_quality_review_required")
-    normalized_basis = basis.lower().replace("_", "-").strip()
-    if (
-        normalized_basis.startswith("gaap")
-        and not normalized_basis.startswith("non-gaap")
-        and "adjust" not in notes
-        and "operating" not in notes
-    ):
-        flags.append("gaap_eps_requires_recurring_driver_check")
-
-    return flags
-
-
-def build_source_to_model_row(
-    row: dict[str, str],
-    model_line: str,
-    source_metric: str,
-    source_id: str,
-    proposed: float | None,
-    current: float | None,
-    treatment: str,
-    fresh_status: str,
-    flags: list[str],
-) -> dict[str, str]:
-    is_update_candidate = treatment not in NON_UPDATE_TREATMENTS
-    delta = (
-        proposed - current
-        if is_update_candidate and proposed is not None and current is not None
-        else None
-    )
-    delta_pct = delta / abs(current) if delta is not None and current not in (None, 0) else None
-    flags = flags + eps_quality_flags(row, model_line, source_metric)
-    action = treatment_action(treatment, resolve_action(row, proposed, current, delta))
-    confidence = text(row.get("confidence")) or ("low" if flags else "medium")
-    current_text = first_text(row, ("current_model_value", "model_value"))
-    proposed_text = fmt_number(proposed) if is_update_candidate else ""
-    workbook_sheet = first_text(row, ("workbook_sheet", "target_sheet", "model_sheet", "sheet"))
-    workbook_cell = first_text(row, ("workbook_cell", "target_cell", "model_cell", "cell"))
-    review_status, blocked_reason = treatment_status(treatment, flags)
-
-    return {
-        "company": text(row.get("company")),
-        "ticker": text(row.get("ticker")),
-        "fiscal_period": first_text(row, ("fiscal_period", "period")),
-        "model_section": first_text(row, ("model_section", "tab")),
-        "model_line": model_line,
-        "mapping_treatment": treatment,
-        "workbook_sheet": workbook_sheet,
-        "workbook_cell": workbook_cell,
-        "source_metric": source_metric,
-        "source_value": first_text(row, ("source_value", "actual_value")),
-        "current_model_value": current_text,
-        "proposed_model_value": proposed_text,
-        "prior_value": first_text(row, ("prior_value",)) or current_text,
-        "new_value": (first_text(row, ("new_value",)) or proposed_text)
-        if is_update_candidate
-        else "",
-        "delta": fmt_number(delta),
-        "delta_pct": "" if delta_pct is None else f"{delta_pct:.2%}",
-        "update_action": action,
-        "overwrite_formula_allowed": first_text(
-            row,
-            ("overwrite_formula_allowed", "allow_formula_overwrite", "formula_overwrite_allowed"),
-        ),
-        "source_id": source_id,
-        "source_name": text(row.get("source_name")),
-        "source_location": text(row.get("source_location")),
-        "evidence_label": text(row.get("evidence_label")) or "fact_source_reported",
-        "as_of_date": first_text(row, ("as_of_date", "source_as_of_date")),
-        "freshness_status": fresh_status,
-        "confidence": confidence,
-        "review_status": review_status,
-        "applied_to_workbook": "",
-        "blocked_reason": blocked_reason,
-        "issue_flags": "; ".join(flags),
-    }
-
-
-def build_source_to_model_rows(
-    rows: list[dict[str, str]],
-    run_date: datetime,
-    stale_days: int,
-) -> tuple[list[dict[str, str]], list[str], list[str]]:
-    output: list[dict[str, str]] = []
-    warnings: list[str] = []
-    failures: list[str] = []
-
-    for index, row in enumerate(rows, start=2):
-        source_id = text(row.get("source_id"))
-        model_line = first_text(row, ("model_line", "model_line_item"))
-        source_metric = first_text(row, ("source_metric", "metric"))
-        treatment = mapping_treatment(row)
-        proposed = (
-            first_number(row, ("proposed_model_value", "source_value", "actual_value"))
-            if treatment not in NON_UPDATE_TREATMENTS
-            else None
-        )
-        current = first_number(row, ("current_model_value", "model_value"))
-        fresh_status, fresh_warning = freshness(row, run_date, stale_days)
-        row_flags, row_warnings, row_failures = validate_mapping_row(
-            row,
-            index,
-            source_id,
-            model_line,
-            source_metric,
-            proposed,
-            fresh_warning,
-            proposed_required=treatment not in NON_UPDATE_TREATMENTS,
-        )
-
-        warnings.extend(row_warnings)
-        failures.extend(row_failures)
-        output.append(
-            build_source_to_model_row(
-                row,
-                model_line,
-                source_metric,
-                source_id,
-                proposed,
-                current,
-                treatment,
-                fresh_status,
-                row_flags,
-            )
-        )
-
-    return output, warnings, failures
diff --git a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_validate.py b/plugins/financial-markets/skills/equity-model-update/scripts/model_update_validate.py
deleted file mode 100644
index 9aafe4c..0000000
--- a/plugins/financial-markets/skills/equity-model-update/scripts/model_update_validate.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from __future__ import annotations
-
-from model_update_fields import TRUTHY
-from model_update_format import text
-
-
-def wants_workbook_write(row: dict[str, str]) -> bool:
-    fields = ("workbook_write_requested", "direct_workbook_write", "write_to_workbook")
-    return any(text(row.get(field)).lower() in TRUTHY for field in fields)
-
-
-def resolve_action(
-    row: dict[str, str], proposed: float | None, current: float | None, delta: float | None
-) -> str:
-    requested = text(row.get("update_action"))
-    if requested:
-        return requested
-    if proposed is None:
-        return "input_required"
-    if current is None:
-        return "add_or_map"
-    if abs(delta or 0.0) > 1e-9:
-        return "update"
-    return "no_change"
-
-
-def validate_mapping_row(
-    row: dict[str, str],
-    index: int,
-    source_id: str,
-    model_line: str,
-    source_metric: str,
-    proposed: float | None,
-    fresh_warning: str | None,
-    proposed_required: bool = True,
-) -> tuple[list[str], list[str], list[str]]:
-    flags: list[str] = []
-    warnings: list[str] = []
-    failures: list[str] = []
-
-    if not source_id:
-        flags.append("missing_source_id")
-        failures.append(f"row {index}: source_id is required")
-    if wants_workbook_write(row):
-        flags.append("workbook_update_requires_safe_copy")
-        warnings.append(
-            f"row {index}: workbook update requested; use materialize_workbook_update.py for a copied XLSX update"
-        )
-    if not model_line:
-        flags.append("missing_model_line")
-        failures.append(f"row {index}: model_line is required")
-    if not source_metric:
-        flags.append("missing_source_metric")
-        warnings.append(f"row {index}: source_metric is missing")
-    if proposed_required and proposed is None:
-        flags.append("missing_proposed_value")
-        warnings.append(f"row {index}: proposed value is missing or non-numeric")
-    if fresh_warning:
-        flags.append(fresh_warning.split()[0])
-        warnings.append(f"row {index}: {fresh_warning}")
-    return flags, warnings, failures
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/SKILL.md b/plugins/financial-markets/skills/event-driven-analyzer/SKILL.md
deleted file mode 100644
index 69f28a7..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/SKILL.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-name: event-driven-analyzer
-description: Use when analyzing dated public-equity event paths, probabilities, payoffs, and expected returns. Do not use for generic catalyst lists, risk sizing, hedges, capital structure, covenants, or credit recovery.
----
-
-# Event Driven Analyzer
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML event-driven analysis report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For an explicit full event analysis, full report, or reusable/source-heavy special-situations package, the default resolves the presentation surface to a polished standalone HTML event report unless the user requests an alternate surface, a quick/no-file answer, or model/math output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML event report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Convert a public-equity catalyst into a path-dependent underwriting view:
-
-`event -> conditions -> timing -> scenarios -> payoffs -> probabilities -> expected return -> trade expression -> monitoring plan`
-
-This skill owns event-driven analysis for mergers, spins, split-offs, activism, regulatory/litigation events, tenders, index events, rights offerings, lockups, de-SPACs, stubs, warrants, preferreds, and other dated public special situations.
-
-Ownership boundary: this skill owns dated public-equity event paths, probability trees, payoff math, expected value, timing, trade expression, and monitoring. Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis. If a distressed or restructuring event needs both, this skill can own the equity event path while Credit Markets supplies capital-structure, covenant, and recovery inputs.
-
-## Do Not Use
-
-- General long/short thesis without a controlling event: use `long-short-pitch`.
-- Calendar/watchlist without underwriting: use `catalyst-calendar`.
-- Credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, debt security selection, capital structure, priority, liquidity, fulcrum, or recovery waterfall work: use Credit Markets.
-- Portfolio exposure, hedge, or sizing work: use `portfolio-risk-management`.
-- Macro/rates/FX/commodity transmission map: use `economic-impact-report`.
-- Ongoing thesis monitoring after underwriting: use `thesis-tracker`.
-
-## Required Posture
-
-- Do not invent an event. If no company/security/event is supplied and no active event is found, return an intake request or event-scan framing.
-- Timestamp market-sensitive facts: price, spread, borrow, bond/loan level, option price, regulatory status, and court date.
-- Prioritize primary documents over press commentary: merger agreements, 8-Ks, S-4/F-4, proxies, Schedule TO/14D-9, Form 10s, 13D/Gs, indentures, court/regulator records, and company releases.
-- Separate `Fact`, `Assumption`, and `Judgment` for important inputs.
-- Scenario probabilities must sum to 100% before presenting probability-weighted conclusions.
-- Calibrate recommendation language to evidence stage. Without timestamped pricing and execution inputs required for the proposed expression, such as price/volume, spread, borrow, liquidity, when-issued trading, index treatment, or hedge inputs, present an underwriting framework or entry screen and use `Wait`, `Monitor`, or `Diligence to initiate` rather than an executable `Buy`, `Own`, or `Initiate` headline.
-- If possible MNPI is supplied, do not recommend a trade from it; flag compliance/legal review.
-
-## Workflow
-
-1. **Classify the event and security.** Identify event type, sub-type, right security or spread expression, and whether a playbook reference is needed.
-2. **Build the fact pack.** Capture parties, terms, current price, unaffected price, timing, outside date, approvals, conditions, litigation/regulatory posture, borrow/liquidity, and sources.
-3. **Build the timeline.** Separate known dates from estimated windows and highlight the next real catalyst.
-4. **Compute market pricing when supportable.** Use gross spread, annualized spread, market-implied probability, downside/break price, borrow/financing/dividend adjustments, hedge ratio, and expected return only where source-backed inputs exist. Otherwise state the missing trade-ready inputs and build an entry screen or gating-items view.
-5. **Define terminal cases.** Use unaffected, peer-adjusted unaffected, standalone value, litigation/regulatory outcomes, SOTP, or Credit Markets-supplied recovery values only when the final question is a listed-equity event view.
-6. **Build a supported event path.** Use a scenario tree with probability, timing, terminal value, return, rationale, and signposts only when the necessary inputs are supportable. Otherwise show mechanics, valuation screens, gating evidence, and the inputs needed for trade readiness without false precision.
-7. **Identify gating items.** Name the controller, evidence, next signpost, and action threshold.
-8. **Recommend expression and monitoring.** State buy/avoid/watch, implementation, sizing caveats, review thresholds, and monitoring plan.
-9. **Red-team.** Explain how the trade loses money, what the market may know, what is stale/missing, and what changes the recommendation.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: fact timeline, market pricing, regulatory/legal conditions, probability/payoff math, trade expression, and red-team review. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Output Modes
-
-- **Full event report:** default analytical route for broad or substantial reusable event-driven requests, especially those with many dates, sources, or tables. For an explicit full event analysis, full report, or reusable/source-heavy special-situations package, produce a polished standalone HTML event report following `../../shared/html-artifact-standard.md`; let the event type determine the report structure. Include event mechanics, evidence-backed valuation or pricing where available, trade or entry posture, monitoring, red team, open questions, and sources.
-- **Standardized dashboard:** use only when the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render. For that path, use `references/DASHBOARD_PACK.md`: `event-driven-analyzer` owns the event facts, supported scenario math, timing, probability/payoff view, trade construction, and monitoring judgment; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-- **Explicit quick PM read:** use only when the user explicitly asks for quick, short, summary, one-page, brief, or TL;DR. Include recommendation, setup, pricing, implied probability, gating item, downside, expected return, next catalyst, and kill criteria.
-- **Model/math mode:** deterministic spread, annualized return, implied probability, hedge ratio, CVR/spin/scenario EV, dated event probability, and payoff math. Use `scripts/event_math.py` with `references/event-math-schema.md`; use Credit Markets for capital structure, covenant, debt-security, and recovery math.
-- **Red-team mode:** attack downside, probabilities, gate risk, source quality, timing, crowding, liquidity, borrow, and sizing.
-- **Monitoring/update mode:** state what changed and whether probability, sizing, or recommendation changes.
-
-Do not use quick mode just because context is sparse. Sparse context should produce a full screen-grade event report with source gaps, assumptions, and missing inputs.
-
-## HTML Guidance
-
-For a substantive HTML full event report, load `../../shared/html-artifact-standard.md` and use these event-specific requirements:
-
-- Lead with a recommendation headline calibrated to the evidence stage, a decision/action-posture box, three to five event-specific evidence tiles, and the event type's primary analytical object.
-- If required pricing or execution inputs are missing, make visible that the report is an underwriting framework or entry screen, name the evidence needed for an actionable recommendation, and do not make the title more actionable than the evidence.
-- For a spin-off or split-off, lead with distribution mechanics and hard dates; a value-allocation bridge that keeps distributed stake, retained stake, and cash/debt transfer separate; SpinCo and RemainCo starting points; confirmed evidence versus the technical-supply thesis; and the entry screen, expression, monitoring, and red-team case.
-- For merger arbitrage or tender work, lead with spread, downside, implied probability, and scenario tree only when sourced inputs support them. For litigation or regulatory work, lead with procedural path, remedy risk, and payoff implications. For flow or technical events, lead with confirmed flow mechanics, liquidity, execution evidence, and exit path.
-- Keep `Fact`, `Assumption`, `Derived Calculation`, and `PM Judgment` visibly distinct.
-- Include probability-weighted scenarios, expected-return math, and trade-execution modules only when inputs are sourced and supportable; otherwise replace them with event mechanics, valuation screens, evidence gaps, and gating inputs.
-- Visually inspect the HTML according to the shared artifact standard before delivery and iterate on hierarchy, legibility, clipping, crowding, and decision clarity.
-
-## Deterministic Helper
-
-Run event math when the user provides structured inputs:
-
-```bash
-python scripts/event_math.py --mode cash_merger --input path/to/input.json --pretty
-python scripts/event_math.py --mode scenario_ev --input path/to/input.json --pretty
-python scripts/event_math.py --mode scenario_ev --input path/to/input.json --allow-probability-sum-mismatch --pretty
-```
-
-The helper performs repeatable math only. It does not fetch prices, verify terms, assign legal/regulatory probabilities, or replace PM judgment.
-For `scenario_ev`, probabilities must sum to 1.0 by default. A mismatch hard-fails unless `--allow-probability-sum-mismatch` is passed for diagnostic, non-memo-ready output.
-
-## Reference Map
-
-- `references/event_taxonomy.md`: event labels and routing.
-- `references/source_hierarchy.md`: source hierarchy by event type.
-- `references/scenario_math.md`: spread, probability, annualization, EV, and hedge math.
-- `references/event-math-schema.md`: deterministic helper input contract.
-- `references/merger_arb_playbook.md`: mergers, tenders, collars, CVRs, deal breaks.
-- `references/spins_activism_playbook.md`: spins, split-offs, activism, proxy fights.
-- `references/litigation_regulatory_playbook.md`: court, agency, antitrust, CFIUS, IP, settlement.
-- `references/restructuring_special_situations_playbook.md`: distressed, exchange offers, bankruptcy, technical events.
-- `references/output_templates.md`: memo and dashboard templates.
-- `references/quality_checks.md`: final QA.
-
-## Final QA
-
-Before finalizing, confirm event/security clarity, timestamped market data or caveat, fact/assumption/judgment split, primary-source priority, probabilities summing to 100%, explicit downside methodology, specific gating item, actionable monitoring plan, and a red-team section.
-
-## Public Equity PM Judgment Layer
-
-For substantial event work, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Index / ETF / Passive Flow Event analysis is an explicit event-driven equity path.
-
-Required PM judgment:
-- State whether the edge is underwriting, timing, flow, legal/process, or mispriced optionality.
-- Include base-rate awareness: what usually happens in this event class and what makes this case different.
-- Include trade expression menu: common equity, pair, spread, options, stub, when-issued, rights, warrants, basket hedge, or avoid.
-- Require path risk: mark-to-market pain, liquidity, borrow, crowding, event delay, financing cost, and exit liquidity.
-- For flow events, include flow-vs-ADV, price/volume timestamp, float, market cap, borrow, ETF/index AUM assumptions, and exit plan.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/agents/openai.yaml b/plugins/financial-markets/skills/event-driven-analyzer/agents/openai.yaml
deleted file mode 100644
index a31a78d..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Event Driven Analyzer"
-  short_description: "PM-quality dated event path, probability, payoff, and expected-return analysis for public-equity special situations."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/assets/event_memo_template.md b/plugins/financial-markets/skills/event-driven-analyzer/assets/event_memo_template.md
deleted file mode 100644
index d3d0ab1..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/assets/event_memo_template.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Event-Driven Analysis: [Company / Ticker]
-
-## PM Decision Summary
-
-## Facts / Assumptions / Judgments
-
-## Situation Overview
-
-## Timeline
-
-## Market Pricing
-
-## Downside / Terminal Value
-
-## Scenario Tree
-
-## Event-Specific Analysis
-
-## Trade Construction and Risk
-
-## Monitoring Dashboard
-
-## Red-Team / Bear Case
-
-## Open Questions
-
-## Sources and Assumptions
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/assets/monitoring_dashboard_template.csv b/plugins/financial-markets/skills/event-driven-analyzer/assets/monitoring_dashboard_template.csv
deleted file mode 100644
index 6f5e121..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/assets/monitoring_dashboard_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-Catalyst,Date or Window,Source,Positive Signal,Negative Signal,Action,Owner,Status
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/event-driven-analyzer/references/DASHBOARD_PACK.md
deleted file mode 100644
index 770d067..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Event Driven Analyzer Dashboard Pack
-
-Use this pack only for an explicitly selected standardized dashboard, reusable dashboard template, or structured payload-driven render for an event-driven situation. For an ordinary standalone HTML full event report, follow the flexible HTML artifact standard and the owning skill's event-specific guidance instead of this fixed module map.
-
-## Producer Role
-
-`event-driven-analyzer` owns event facts, scenario math, timing, probability judgment, market pricing, trade construction, and monitoring plan. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `event_driven_analyzer`
-- `layout`: `single_page` for full event packages unless the user explicitly requests tabs
-- `hero.callout`: the key event question and expected value/risk skew
-- `snapshot`: event date/window, market-implied probability or spread if sourced, base case, downside case, timing, data confidence
-- `sources`: filings/regulatory/court/company sources first, then market pricing, trusted news, expert/user materials, and assumptions
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `event-view`
-   - `decision_box`: event stance, expected value/readiness, key risk, timing, and action posture
-   - `metric_tiles`: spread/probability, timing, downside, upside, liquidity, and source confidence
-2. `fact-pattern`
-   - `timeline`: event chronology, upcoming milestones, regulatory/court/company deadlines
-   - `market_events`: source-backed event news and market reactions
-3. `scenario-tree`
-   - `scenario_map`: approval/failure/delay/remedy/settlement cases, probabilities if supportable, price outcomes, and falsifiers
-   - `table`: scenario math with source labels and probability-sum checks
-4. `trade-monitoring`
-   - `cards`: trade expression, implementation caveats, hedge/sizing considerations, and red-team risks
-   - `question_list`: diligence checks and monitoring triggers
-5. `gaps`
-   - `missing_evidence`: missing pricing, probability support, event documents, legal/regulatory data, or terminal-only fields
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every event date, legal/regulatory fact, spread, price, probability, scenario assumption, and trade-readiness claim.
-- Label facts, assumptions, model-derived fields, stale pricing, legal uncertainty, and missing documents.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not use probabilities that fail the event-math quality gate without an explicit diagnostic.
-- Do not imply legal certainty or final trade readiness from incomplete event evidence.
-- Do not use an executable `Buy`, `Own`, or `Initiate` headline when required live pricing or execution evidence is absent; show a `Wait`, `Monitor`, or entry-screen posture and the missing evidence.
-- Do not force scenario-tree or expected-return modules from unsupported pricing, terminal-value, or probability inputs.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm fact pattern, scenario math, timing, market pricing, trade caveats, and missing evidence are visible.
-- Confirm material legal/regulatory uncertainty is labeled.
-- Confirm the decision posture and hero headline do not imply more actionability than the evidence supports.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/event-math-schema.md b/plugins/financial-markets/skills/event-driven-analyzer/references/event-math-schema.md
deleted file mode 100644
index c1097e8..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/references/event-math-schema.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Event Math Schema
-
-Use this reference when preparing JSON inputs for `scripts/event_math.py`. The helper calculates deterministic event math from supplied terms only; it does not fetch market data or validate legal probability.
-
-## CLI
-
-```bash
-python scripts/event_math.py --mode cash_merger --input input.json --pretty
-python scripts/event_math.py --mode stock_deal --input input.json --pretty
-python scripts/event_math.py --mode scenario_ev --input input.json --pretty
-python scripts/event_math.py --mode scenario_ev --input input.json --allow-probability-sum-mismatch --pretty
-python scripts/event_math.py --mode cvr --input input.json --pretty
-```
-
-`scenario_ev` validates that scenario probabilities sum to 1.0 and hard-fails mismatches by default. Use `--allow-probability-sum-mismatch` only for diagnostic output when intentionally inspecting an incomplete or non-normalized tree; do not use that output as a probability-weighted conclusion in a memo.
-
-## `cash_merger`
-
-Required:
-
-- `current_price`
-- `deal_price`
-
-Optional:
-
-- `days_to_close`
-- `break_price`
-- `expected_dividends`
-- `financing_cost`
-- `other_carry`
-
-Outputs include adjusted deal value, gross return, annualized return, and market-implied probability when `break_price` is supplied.
-
-## `stock_deal`
-
-Required:
-
-- `target_price`
-- `acquirer_price`
-- `exchange_ratio`
-
-Optional:
-
-- `target_shares`
-- `days_to_close`
-- `expected_target_dividends`
-- `expected_acquirer_dividends`
-- `borrow_cost`
-
-Outputs include deal value, adjusted deal value, gross return, annualized return, and hedge shares per target shares.
-
-## `scenario_ev`
-
-Required:
-
-- `current_price`
-- `scenarios`
-
-Each scenario requires:
-
-- `name`
-- `probability`
-- `terminal_value`
-
-Each scenario may also include `days_to_resolution`. Probabilities should be decimals between 0.0 and 1.0 and must sum to 1.0 unless the CLI is run with `--allow-probability-sum-mismatch`. With the default strict validation, bad sums return a non-zero exit code instead of JSON output. With the explicit allow flag, the output includes `probabilities_sum_to_100pct: false` and a diagnostic note; revise the inputs before using the math in a PM memo.
-
-## `cvr`
-
-Required:
-
-- `milestones`
-
-Each milestone requires:
-
-- `name`
-- `payment`
-- `probability`
-- `years`
-
-Optional:
-
-- `discount_rate`
-- `liquidity_discount`
-
-Outputs include gross present value and net present value after liquidity discount.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/event_taxonomy.md b/plugins/financial-markets/skills/event-driven-analyzer/references/event_taxonomy.md
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--- a/plugins/financial-markets/skills/event-driven-analyzer/references/event_taxonomy.md
+++ /dev/null
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-# Event Taxonomy and Classification
-
-Use this reference when the event type is ambiguous or when a situation has multiple overlapping catalysts.
-
-## Classification rule
-
-Classify by the investment path, not by headline. A setup can have several labels. Example: a cash merger can also be an antitrust event, litigation event, financing event, and shareholder vote event.
-
-Ownership boundary: use this skill when the central work is dated event path, probability, timing, payoff, expected-return, and monitoring math. Use Credit Markets when the central work is capital structure, covenant/document review, liquidity, maturity wall, priority, fulcrum-security, or recovery-waterfall analysis.
-
-## Primary event types
-
-### Cash merger arbitrage
-Core question: Will the deal close, when, and what is the break price?
-Key outputs: spread, annualized spread, implied close probability, break downside, close/delay/break/recut scenarios.
-
-### Stock-for-stock merger arbitrage
-Core question: Is the spread attractive after exchange ratio, acquirer hedge, collar, dividends, borrow, and acquirer risk?
-Key outputs: deal value, hedge ratio, spread, collar sensitivity, acquirer risk, vote risk.
-
-### Tender offer
-Core question: Will tender conditions be satisfied, and what happens if the minimum condition or regulatory condition fails?
-Key outputs: tender deadline, proration, minimum condition, withdrawal rights, extension path.
-
-### CVR or contingent consideration
-Core question: What is the probability-weighted value of future milestones, and are incentives aligned?
-Key outputs: milestone tree, discounting, control-party incentive analysis, liquidity discount.
-
-### Spin-off or split-off
-Core question: What are SpinCo and RemainCo worth separately, and what technical flows create opportunity?
-Key outputs: SOTP, distribution mechanics, forced-selling analysis, index eligibility, capital structure, management incentives.
-
-### Activism
-Core question: Can the activist force value creation or governance change, and what outcome is priced in?
-Key outputs: campaign demands, vote math, board vulnerability, settlement probability, annual meeting timeline, value plan.
-
-### Restructuring / distressed event
-Core question: Which dated restructuring, exchange, court, maturity, or liability-management catalyst changes value, when, and with what payoff?
-Key outputs: event path, process timeline, probability tree, sourced recovery/payoff assumptions, scenario EV, monitoring thresholds. Use Credit Markets for the capital structure, covenants, liquidity, maturity wall, priority, fulcrum-security, and recovery waterfall that support those payoffs.
-
-### Litigation
-Core question: What is the probability-weighted investment impact of trial, ruling, settlement, appeal, injunction, or damages?
-Key outputs: merits matrix, damages/remedy range, procedural timeline, settlement incentives, security impact.
-
-### Regulatory event
-Core question: Which regulator controls the path, what process applies, and what remedies or delays are likely?
-Key outputs: agency map, review stage, statutory timeline, theory of harm, remedy feasibility, interaction with outside date.
-
-### Index / technical event
-Core question: What flow is expected, who must buy/sell, and what is already priced?
-Key outputs: inclusion/deletion logic, rebalance date, expected flow, liquidity, crowding, unwind plan.
-
-### De-SPAC or SPAC event
-Core question: What is the post-close equity really worth after redemption, PIPE, sponsor promote, warrant dilution, and lockups?
-Key outputs: trust value, redemption, PIPE, minimum cash, sponsor economics, float, lockups, dilution, projections skepticism.
-
-### Capital return / Dutch tender / exchange offer
-Core question: What is the economic effect after proration, tax, participation, leverage, and signaling?
-Key outputs: tender range, proration scenarios, accretion/dilution, balance-sheet impact, shareholder behavior.
-
-## Intake questions by context
-
-Ask only for information that is necessary and unavailable. Prefer best-effort with assumptions when the user has already provided enough to start.
-
-Minimal intake:
-- Company/ticker and security.
-- Event type if known.
-- User's position or intended trade, if relevant.
-- Output mode only if the user wants something other than the default full memo: explicit quick read, model/math, red-team, or monitoring update.
-- Allowed sources and whether to use callable connected routes, user-provided exports, or public web.
-
-Event-specific missing facts:
-- Merger: deal price, current price, expected close, outside date, break price, approvals.
-- Stock deal: exchange ratio, acquirer price, collar, dividends, borrow.
-- Spin: distribution ratio, SpinCo/RemainCo financials, dates, debt allocation.
-- Activism: activist, ownership, demands, annual meeting date, nomination deadline.
-- Litigation: venue, claim, procedural posture, next hearing/trial date, damages theory.
-- Regulatory: agency, filing status, review stage, outside date, remedy path.
-- Restructuring/distressed event: dated catalyst, court/RSA/exchange/maturity status, next process date, security expression, and any recovery/payoff assumptions already available. If debt stack, liquidity, maturities, prices, covenant status, or recovery waterfall are not already available and are central to the answer, route that work to Credit Markets.
-
-## Multi-label examples
-
-- `Cash merger + antitrust + financing`: cash sponsor take-private with high leverage and overlapping assets.
-- `Spin-off + index technical + balance-sheet event`: parent distributes small levered SpinCo that index funds cannot hold.
-- `Activism + sale process + governance`: activist seeks board seats and pushes strategic alternatives.
-- `Litigation + regulatory + merger arb`: regulator sues to block a transaction and the trial schedule drives deal timing.
-- `Distressed exchange + litigation + equity stub`: company attempts LME while creditor group challenges transaction.
-
-## Output implication of classification
-
-The classification determines which analysis must be included:
-- Merger arb requires spread/probability/break-price math.
-- Spin requires SOTP and forced-selling analysis.
-- Activism requires vote math and campaign path.
-- Litigation requires merits/timing/damages/settlement analysis.
-- Regulatory requires agency process and remedy analysis.
-- Restructuring under this skill requires a dated process path, probability/payoff tree, and monitoring plan; capital structure, covenant, and recovery waterfall work belongs in Credit Markets.
-- Technical events require flow/liquidity/crowding analysis.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/litigation_regulatory_playbook.md b/plugins/financial-markets/skills/event-driven-analyzer/references/litigation_regulatory_playbook.md
deleted file mode 100644
index 4fcd3a6..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/references/litigation_regulatory_playbook.md
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-# Litigation and Regulatory Event Playbook
-
-Use this reference for litigation, regulatory approvals, antitrust, CFIUS, sector regulators, trials, settlements, appeals, injunctions, damages, and agency decisions.
-
-## Important boundary
-
-Provide investment analysis, not legal advice. Identify where counsel input is required. If the user provides possible material non-public information, do not give a trade recommendation based on it.
-
-## Litigation objective
-
-Answer: What are the probability-weighted security impacts of legal outcomes, timing, settlement incentives, appeals, damages, and remedies?
-
-## Litigation required fact pack
-
-- Company/security.
-- Claim type and venue.
-- Parties.
-- Judge or tribunal if public.
-- Procedural posture: complaint, motion to dismiss, discovery, summary judgment, trial, post-trial, appeal, settlement.
-- Key dates: hearing, trial, ruling, appeal deadline.
-- Damages theory and remedy sought.
-- Injunction or business-model impact.
-- Company disclosures and reserves if available.
-- Insurance, indemnity, or financing capacity if disclosed.
-- Prior settlements or comparable cases if available.
-- Current market-implied move or damages if inferable.
-
-## Litigation workflow
-
-1. Define the legal issue in investor language.
-2. Map procedural posture and timeline.
-3. Identify possible outcomes: win, partial loss, loss, injunction, settlement, appeal, delay.
-4. Estimate economics by outcome: cash damages, royalty, injunction, business-model change, balance-sheet pressure, financing need.
-5. Analyze settlement incentives and timing.
-6. Infer what the market prices using equity move, options, credit, or comparable securities.
-7. Build probability tree and expected value.
-8. Recommend trade expression: common, options, bonds, pair trade, avoid, or wait.
-9. Build monitoring plan.
-
-## Litigation senior judgment questions
-
-- Is the market focused on the right legal question?
-- Is the event truly binary, or is settlement the base case?
-- Does the plaintiff need an injunction, damages, or negotiating leverage?
-- Does the defendant have willingness and capacity to settle?
-- Is the key risk merits, damages, remedy, appeal, or timing?
-- Is the equity impact over- or under-discounting likely cash impact?
-- Is options exposure cleaner than common equity?
-
-## Regulatory objective
-
-Answer: Which regulator controls the outcome, what process applies, what remedy or delay is likely, and whether the current price compensates for the path risk.
-
-## Regulatory required fact pack
-
-- Company/security and transaction or business event.
-- Regulator(s) and jurisdiction(s).
-- Filing status and review stage.
-- Statutory or expected timeline.
-- Decision standard and key issue.
-- Theory of harm or agency concern.
-- Customer/competitor/political opposition.
-- Remedy feasibility.
-- Litigation likelihood.
-- Interaction with outside date, financing, and company liquidity.
-- Precedent matters if relevant.
-
-## Regulatory workflow
-
-1. Identify the controlling agency and process.
-2. Define the core issue: antitrust, national security, sector license, safety, data/privacy, reimbursement, drug approval, environmental, utility, banking, telecom, defense, or foreign ownership.
-3. Map official status and likely timeline.
-4. Identify remedies, concessions, or conditions that could solve the issue.
-5. Evaluate whether delay changes economics.
-6. Build outcomes: cleared, cleared with remedy, delayed, challenged/blocked, abandoned, recut.
-7. Translate outcomes into security values.
-8. Recommend trade expression and monitoring.
-
-## Regulatory senior judgment questions
-
-- Is the real risk agency review, court litigation, political pressure, or remedy economics?
-- Does the outside date allow enough time?
-- Can the buyer accept the remedy without destroying deal rationale?
-- Is the regulatory issue structural, behavioral, national security, or process timing?
-- Are other jurisdictions likely to coordinate or diverge?
-- Is there a credible path to settlement?
-- What would force a recut or abandonment?
-
-## Antitrust underwriting prompts
-
-Consider:
-- Horizontal concentration and market shares.
-- Vertical foreclosure.
-- Potential or nascent competition.
-- Platform/ecosystem effects.
-- Labor-market effects.
-- Serial acquisition/roll-up concerns.
-- Customer harm evidence.
-- Customer/competitor complaints.
-- Divestiture buyer feasibility.
-- Behavioral remedy credibility.
-- Litigation venue and judge if challenged.
-
-## CFIUS / national security prompts
-
-Consider:
-- Foreign ownership/control.
-- Sensitive personal data.
-- Critical technology.
-- Critical infrastructure.
-- Government contracts.
-- Proximity to sensitive facilities.
-- Mitigation agreement feasibility.
-- Political environment.
-- Interaction with financing and outside date.
-
-## Sector regulator prompts
-
-For banking, utilities, telecom, defense, healthcare, energy, insurance, airlines, and other regulated sectors, identify:
-- Required approvals.
-- Public-interest standard.
-- Local/state/federal overlap.
-- Ratepayer/customer impact.
-- License transfer issues.
-- Foreign ownership restrictions.
-- Political stakeholders.
-- Historical approval/remedy pattern.
-
-## Output requirements
-
-- Clear statement of controlling issue.
-- Timeline with known and estimated dates.
-- Scenario tree with probabilities and values.
-- Remedy/settlement path.
-- Market-implied vs analyst probability where possible.
-- Trade expression.
-- Monitoring plan with official sources.
-- Legal-advice caveat where appropriate.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/merger_arb_playbook.md b/plugins/financial-markets/skills/event-driven-analyzer/references/merger_arb_playbook.md
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-# Merger Arbitrage Playbook
-
-Use this reference for cash mergers, stock deals, tender offers, collars, competing bids, recuts, deal breaks, and CVRs.
-
-## PM objective
-
-Answer: Is the spread paying us for the real risk after timing, downside, financing, regulatory path, shareholder vote, acquirer risk, and liquidity?
-
-## Required fact pack
-
-- Buyer, target, ticker, security, exchange.
-- Announcement date and unaffected date.
-- Current target price and timestamp.
-- Consideration: cash, stock, mixed, election, collar, CVR, tender.
-- Deal value per share and transaction value.
-- Current gross spread and annualized spread.
-- Expected close date and outside date.
-- Required approvals: target vote, acquirer vote, antitrust, CFIUS, sector regulator, foreign approvals.
-- Conditions: financing, no injunction, material adverse effect, minimum tender, tax opinion, listing approval.
-- Break fee, reverse termination fee, ticking fee if any.
-- Financing commitments and expiration.
-- Litigation status.
-- Borrow and options availability if hedging.
-- Source documents reviewed and missing documents.
-
-## Cash deal analysis
-
-Must produce:
-- Gross spread.
-- Annualized spread.
-- Market-implied close probability.
-- Analyst probability tree.
-- Break price/downside methodology.
-- Expected value and expected annualized return.
-- Gating item and next catalyst.
-- Trade recommendation.
-
-Market-implied probability:
-`(current target price - break price) / (cash deal price - break price)`
-
-Senior checks:
-- Is the break price leak-adjusted and peer-adjusted?
-- Is downside worse because the target issued weak earnings or guidance after announcement?
-- Is the reverse termination fee large enough to show buyer commitment?
-- Does buyer have a financing out or conditional funding?
-- Does the outside date allow enough time for litigation or remedies?
-- Is the spread wide because of real risk, crowding, illiquidity, small-cap neglect, tax, index deletion, or borrow mechanics?
-
-## Stock-for-stock deal analysis
-
-Must produce:
-- Exchange ratio and deal value.
-- Long target / short acquirer hedge ratio.
-- Gross spread and annualized spread.
-- Collar sensitivity if applicable.
-- Dividend and borrow adjustment.
-- Acquirer vote risk.
-- Acquirer fundamental risk and stock volatility.
-- Residual beta and factor exposure.
-
-Senior checks:
-- Does the acquirer short create borrow, dividend, or squeeze risk?
-- Does the collar change the hedge at different acquirer prices?
-- Does the acquirer shareholder base support the transaction?
-- Is the acquirer overpaying in a way that creates short-leg risk if the deal breaks?
-- Is the target really the cleanest expression, or is the acquirer mispriced?
-
-## Tender offer analysis
-
-Must produce:
-- Offer price and expiration.
-- Minimum tender condition.
-- Proration mechanics.
-- Withdrawal rights.
-- Extension path.
-- Regulatory conditions.
-- Back-end merger or squeeze-out process.
-- Tax considerations if relevant.
-
-Senior checks:
-- Can the tender close without a shareholder meeting?
-- Is the minimum condition likely to be satisfied?
-- Are arbs or insiders blocking tender participation?
-- Does the buyer have enough financing and legal ability to extend?
-
-## Regulatory underwriting
-
-Do not write `regulatory risk` generically. Identify:
-- Agency and jurisdiction.
-- Review stage.
-- Theory of harm.
-- Market definition.
-- Customer/competitor opposition.
-- Remedy feasibility.
-- Political sensitivity.
-- Litigation probability.
-- Interaction with outside date and financing.
-
-Common issue types:
-- Horizontal concentration.
-- Vertical foreclosure.
-- Potential competition.
-- Nascent competition.
-- Platform/ecosystem entrenchment.
-- Labor-market effects.
-- Buyer power or monopsony.
-- Serial acquisition roll-up.
-- National security/data sensitivity.
-
-## Deal document review checklist
-
-From the merger agreement or tender documents, extract:
-- Consideration and adjustment mechanisms.
-- Outside date and extension mechanics.
-- Regulatory efforts covenant: reasonable best efforts, hell-or-high-water, divestiture cap.
-- No-shop, go-shop, fiduciary out.
-- Matching rights.
-- Break fee and reverse termination fee.
-- Financing cooperation and debt commitment terms.
-- MAC definition and carveouts.
-- Interim operating covenants.
-- Termination rights.
-- Specific performance availability.
-- Shareholder support agreements.
-
-## Scenario tree template
-
-Scenarios should usually include:
-- Close on time.
-- Close with delay/remedy.
-- Price recut.
-- Competing bid/higher bid if plausible.
-- Break.
-
-For each scenario, include probability, timing, terminal value, return, rationale, and signposts.
-
-## Recommendation language examples
-
-- `Attractive, but size as a regulatory trade rather than a clean arb. The spread pays for a second-request path, but not for full litigation.`
-- `Avoid despite optical annualized spread. Break downside is understated and buyer financing is vulnerable.`
-- `Prefer options because the event is binary and timeline uncertain.`
-- `Prefer long target / short acquirer at deal ratio; residual acquirer vote risk argues for lower sizing.`
-
-## Merger arb final checklist
-
-Before finalizing:
-- Deal terms verified from primary source or marked unverified.
-- Current price and spread timestamped.
-- Break price is triangulated, not blindly unaffected.
-- Market-implied probability shown.
-- Our probability tree shown and sums to 100%.
-- Gating item is specific.
-- Outside date and expected close date distinguished.
-- Trade expression and sizing/risk comments included.
-- Monitoring plan has next date and action.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/output_templates.md b/plugins/financial-markets/skills/event-driven-analyzer/references/output_templates.md
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-# Output Templates
-
-Use these templates as content guides for PM-quality event output. For an explicit full event analysis, full report, or reusable/source-heavy special-situations package, default to a polished standalone HTML event report following `../../../shared/html-artifact-standard.md` and the event-specific HTML guidance in the owning skill. Do not force a fixed section inventory when evidence or event type calls for a different structure. Use the quick read only when the user explicitly asks for a quick, short, summary, one-page, brief, or TL;DR output.
-
-## HTML Full Event Report
-
-Use flexible standalone HTML for a substantive full event report. Lead with a decision posture that matches the evidence stage and put the event type's primary analytical object near the top. If live pricing or execution evidence required to act is missing, label the artifact as an underwriting framework or entry screen, state what is needed to initiate, and avoid an executable trade headline.
-
-For a spin-off or split-off, prioritize distribution mechanics and hard dates, the value-allocation bridge, SpinCo and RemainCo starting points, confirmed technical-flow evidence versus thesis, entry screen, monitoring, and red team. For merger arbitrage, show spread, downside, implied probability, and scenario math only when supportable. For regulatory or litigation events, prioritize procedural path, remedy risk, and payoff implications.
-
-Use the full content inventory below as applicable. Replace unsupported market-pricing, probability, scenario, or expected-return modules with evidence gaps, valuation screens, and gating inputs rather than presenting false precision.
-
-## Explicit Quick PM Read
-
-```markdown
-# Event-Driven Quick Read: [Company / Ticker] - [Event Type]
-
-**Recommendation:** [Buy / Avoid / Watch / Add / Trim / Use options / Wait]
-**Preferred expression:** [Security/trade]
-**Sizing view:** [Starter / core / small due to gap risk / no position]
-**Market data timestamp:** [Date/time/source or unavailable]
-
-## Setup
-- Event:
-- Current price:
-- Event value / deal value / target value:
-- Gross spread / expected return:
-- Expected timing:
-- Next catalyst:
-
-## PM View
-[3-6 bullets that state the variant view, not just facts.]
-
-## Market-Implied vs Our View
-- Market-implied probability:
-- Our probability:
-- Key difference:
-
-## Downside / Break Value
-- Downside value:
-- Methodology:
-- Main weakness in downside estimate:
-
-## Scenario Tree
-| Scenario | Probability | Timing | Value | Return | Rationale | Signposts |
-|---|---:|---:|---:|---:|---|---|
-
-## Gating Item
-- Controlling issue:
-- Who controls it:
-- What evidence matters next:
-
-## Trade Construction
-- Instrument:
-- Hedge:
-- Liquidity/borrow/carry:
-- Review threshold:
-
-## Monitoring Plan
-| Catalyst | Date/window | Source | Positive signal | Negative signal | Action |
-|---|---:|---|---|---|---|
-
-## Red Team
-- How we lose money:
-- What would change our mind:
-```
-
-## Full Event Content Inventory
-
-```markdown
-# Event-Driven Analysis: [Company / Ticker] - [Event Type]
-
-## 1. PM Decision Summary
-- Recommendation:
-- Preferred expression:
-- Suggested sizing:
-- Current price:
-- Event value:
-- Gross spread / expected return:
-- Annualized return if relevant:
-- Market-implied probability:
-- Our probability:
-- Primary gating item:
-- Next catalyst:
-- Kill criteria:
-
-## 2. Facts / Assumptions / Judgments
-### Facts
-- [Source-backed facts]
-### Assumptions
-- [User or model assumptions]
-### Judgments
-- [Analyst/PM estimates]
-
-## 3. Situation Overview
-- Event:
-- Parties:
-- Securities:
-- Announcement date:
-- Consideration / transaction mechanics:
-- Expected timing:
-- Required approvals:
-- Source documents reviewed:
-- Missing critical sources:
-
-## 4. Timeline
-| Date/window | Event | Known or estimated | Why it matters | Source |
-|---|---|---|---|---|
-
-## 5. Market Pricing
-- Current price and timestamp:
-- Spread / return:
-- Carry, dividends, borrow, financing:
-- Market-implied probability:
-- Sensitivity to timing:
-
-## 6. Downside / Terminal Value
-- Method 1:
-- Method 2:
-- Selected downside:
-- Why selected:
-- Key downside uncertainty:
-
-## 7. Scenario Tree
-| Scenario | Probability | Timing | Terminal value | Return | Rationale | Signposts |
-|---|---:|---:|---:|---:|---|---|
-
-## 8. Event-Specific Analysis
-[Use the relevant playbook: merger, spin, activism, litigation/regulatory, restructuring, technical situation.]
-
-## 9. Trade Construction and Risk
-- Best expression:
-- Alternative expression:
-- Hedge:
-- Sizing:
-- Liquidity:
-- Borrow/carry:
-- Factor/beta exposure:
-- Mark-to-market path:
-
-## 10. Monitoring Dashboard
-| Monitor | Date/window | Source | Positive signal | Negative signal | Action |
-|---|---:|---|---|---|---|
-
-## 11. Red-Team / Bear Case
-- Strongest bear argument:
-- What the market may know:
-- Hidden risks:
-- Crowding/liquidity/path risk:
-- Kill criteria:
-
-## 12. Open Questions
-- [Question]
-- Why it matters:
-- How to resolve:
-
-## 13. Sources and Assumptions
-- Sources:
-- Market data timestamp:
-- Unverified items:
-```
-
-## Monitoring update
-
-```markdown
-# Event-Driven Monitoring Update: [Company / Ticker]
-
-## What changed
-- New fact:
-- Source:
-- Date:
-
-## Impact on thesis
-- Prior probability:
-- Updated probability:
-- Prior recommendation:
-- Updated recommendation:
-- Why:
-
-## Updated scenario tree
-| Scenario | Prior probability | Updated probability | Value | Comment |
-|---|---:|---:|---:|---|
-
-## Action
-- Add / hold / trim / exit / wait:
-- Review threshold:
-- Next catalyst:
-```
-
-## Red-team template
-
-```markdown
-# Event-Driven Red Team: [Company / Ticker]
-
-## Thesis being tested
-[State the bull/base thesis in one paragraph.]
-
-## Biggest ways this loses money
-1. [Risk]
-2. [Risk]
-3. [Risk]
-
-## Weakest assumptions
-| Assumption | Why it may be wrong | Evidence needed | Impact if wrong |
-|---|---|---|---|
-
-## Downside challenge
-- User downside:
-- Alternative downside:
-- Difference:
-
-## Probability challenge
-- User probability:
-- Market-implied probability:
-- Alternative probability:
-- Why:
-
-## Path risk
-- Delay:
-- Liquidity:
-- Borrow/carry:
-- Crowding:
-- Forced selling:
-
-## Revised recommendation
-[Decision and why.]
-```
-
-## Event PM Judgment Output
-
-State edge type: underwriting, timing, flow, legal/process, or mispriced optionality. Include base-rate awareness, trade expression menu, event probability, downside methodology, liquidity/borrow/crowding, path risk, and exit plan.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/quality_checks.md b/plugins/financial-markets/skills/event-driven-analyzer/references/quality_checks.md
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-# Quality Checks
-
-Use this before finalizing any event-driven output for senior review.
-
-## Mandatory checks
-
-- Event type is explicit and, if multi-label, all relevant labels are named.
-- Security expression is explicit.
-- Current market data is timestamped or clearly unavailable.
-- Facts, assumptions, and judgments are separated.
-- Primary source documents are used when available.
-- Missing critical documents are named.
-- Timeline distinguishes known dates from estimated dates.
-- Gating item is specific and tied to a decision-maker or process.
-- Downside/break value methodology is explicit.
-- Scenario probabilities sum to 100%; `scripts/event_math.py --mode scenario_ev` hard-fails mismatches unless `--allow-probability-sum-mismatch` is explicitly used for diagnostic output.
-- Expected return and annualized return are not used without downside.
-- Recommendation is explicit, or no-trade rationale is explicit.
-- Monitoring plan includes date/window, source, signal, and action.
-- Red-team section states how the trade loses money.
-
-## Event-specific checks
-
-### Merger arb
-- Deal price and current price confirmed or marked unverified.
-- Spread and annualized spread calculated.
-- Market-implied probability calculated if downside is available.
-- Merger agreement/proxy status checked.
-- Outside date and expected close date distinguished.
-- Break fee/reverse termination fee and financing condition reviewed or marked missing.
-- Regulatory approvals and vote thresholds named.
-- Acquirer risk and hedge ratio included for stock deals.
-
-### Spin-off
-- Distribution mechanics and dates included.
-- SpinCo and RemainCo financials separated.
-- Debt/cash/liability allocation considered.
-- Stranded costs and dis-synergies considered.
-- Forced selling/index/holder base analyzed.
-- Entry timing considered.
-
-### Activism
-- Activist ownership and demands stated.
-- Governance and nomination mechanics considered.
-- Vote math and shareholder base assessed.
-- Settlement vs proxy fight probability included.
-- Value creation plan judged, not just summarized.
-
-### Litigation/regulatory
-- Venue/agency and procedural posture stated.
-- Timeline and next official date included.
-- Remedy/damages/settlement path analyzed.
-- Legal-advice boundary respected.
-- Official sources prioritized.
-
-### Restructuring/distressed
-- Dated catalyst or process path is explicit.
-- Probability/payoff tree uses sourced or clearly labeled recovery assumptions.
-- If capital structure, covenants, liquidity, maturity wall, fulcrum security, priority, or recovery waterfall are central, the output routes to or incorporates work from Credit Markets.
-- Priming/LME/class vote/court risk is considered as event-path risk, with legal conclusions reserved for counsel.
-
-### Technical/special situations
-- Flow mechanics and dates included.
-- Flow size compared with liquidity.
-- Crowding and pre-positioning considered.
-- Exit plan stated.
-
-## Senior-taste checks
-
-Ask yourself:
-- Would a PM know what to do after reading the first page?
-- Is the variant view clear?
-- Did the output identify the real gating item, or only generic risks?
-- Is downside intellectually honest?
-- Is the trade expression practical in real market conditions?
-- Are we over-precise relative to source quality?
-- Did we state what would change our mind?
-
-## Common failure modes
-
-- Summarizing news instead of underwriting outcomes.
-- Treating annualized spread as the conclusion.
-- Failing to calculate market-implied probability.
-- Accepting unaffected price as break price without adjustment.
-- Ignoring timing risk.
-- Ignoring stock-deal acquirer risk.
-- Ignoring borrow/liquidity/carry.
-- Using generic regulatory or litigation risk language.
-- Omitting a monitoring plan.
-- Giving a trade recommendation based on possible MNPI.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/restructuring_special_situations_playbook.md b/plugins/financial-markets/skills/event-driven-analyzer/references/restructuring_special_situations_playbook.md
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-# Restructuring and Special Situations Playbook
-
-Use this reference for dated public special-situation events adjacent to distressed securities, exchange offers, liability management, bankruptcy process milestones, index events, de-SPACs, rights offerings, warrants, preferreds, and technical situations.
-
-Boundary: this event-driven skill owns the dated public-equity event path, probability tree, timing, payoff bridge, expected return, listed-equity expression, and monitoring. Credit Markets owns capital structure mapping, covenant/document review, liquidity, maturity wall, priority, fulcrum-security analysis, and recovery waterfalls. If those credit outputs are not already available and drive the answer, route that portion to Credit Markets and use the results here as terminal payoff inputs.
-
-## Distressed / restructuring objective
-
-Answer: Which dated restructuring, exchange, court, maturity, or liability-management event changes price before the market updates, and what are the scenario payoffs and probabilities?
-
-## Required fact pack
-
-- Issuer, tickers, securities.
-- Dated catalyst: exchange deadline, forbearance date, maturity, coupon date, RSA milestone, court hearing, vote, plan confirmation, rights offering, index/technical date, or lockup/float event.
-- Current security price, timestamp, liquidity, borrow, and expected holding period.
-- Existing terminal payoff or recovery assumptions, clearly labeled by source.
-- Debt prices, yields, spreads, CDS if available.
-- Equity market cap and enterprise value.
-- Exchange offer / LME / RSA / bankruptcy status.
-- Court dates, creditor groups, DIP, plan, disclosure statement if applicable.
-- Credit inputs to source from Credit Markets when needed: capital structure, maturity wall, liquidity runway, covenant headroom, collateral, guarantees, structural subordination, intercreditor terms, priority, value break, fulcrum security, recovery values, and plan valuation.
-
-## Distressed workflow
-
-1. Confirm whether the task is an event-path question or a credit/recovery question. Route credit/recovery work to Credit Markets.
-2. Identify the dated catalyst: maturity, covenant test date, interest payment, lawsuit, exchange deadline, RSA, court hearing, vote, confirmation, or technical event.
-3. Convert supplied credit/recovery assumptions into event terminal payoffs.
-4. Build scenario probabilities, timing, expected value, and downside.
-5. Assess legal/process risks as event-path risks: priming, uptier, drop-down, fraudulent transfer, class vote, cramdown, appeal.
-6. Recommend listed-equity or event expression only if payoff inputs are sufficiently sourced or clearly caveated; route debt-security expression to Credit Markets.
-7. Build monitoring plan and kill criteria.
-
-## Distressed senior judgment questions
-
-- Is equity a real option or simply retail optionality?
-- If a fulcrum security matters, has Credit Markets supplied the liquidity and ownership view?
-- Can the company execute an out-of-court exchange?
-- Who has blocking position or control?
-- Is there priming risk or collateral leakage?
-- Does the recovery depend on business value or litigation leverage?
-- Is the price compensating for process complexity and time?
-
-## Index and technical events
-
-Use when the catalyst is forced buying/selling, index inclusion/deletion, rebalance, lockup expiry, flow event, convert technical, warrant exercise, rights offering, or float change.
-
-Analyze:
-- Event date and mechanics.
-- Who must buy/sell.
-- Estimated flow vs average daily value traded.
-- Float and liquidity.
-- Crowding and pre-positioning.
-- Borrow and options.
-- Fundamental anchor after the flow.
-- Exit plan.
-
-Senior questions:
-- Is the flow already priced?
-- Is there enough liquidity to monetize the dislocation?
-- Does the trade have a fundamental floor after the technical pressure ends?
-- Is the flow one-day, multi-day, or persistent?
-
-## De-SPAC / SPAC situations
-
-Analyze:
-- Trust value.
-- Redemption deadline and expected redemption rate.
-- PIPE size and conditions.
-- Minimum cash condition.
-- Sponsor promote and earnout.
-- Warrant dilution.
-- Lockups.
-- Float and borrow.
-- Projections quality.
-- Post-close liquidity and financing need.
-
-Senior questions:
-- Is the target viable without projected growth?
-- Does redemption break the minimum cash condition?
-- Is float so small that price action is technical rather than fundamental?
-- Are warrants or converts a better expression than common?
-
-## Rights offerings and capital raises
-
-Analyze:
-- Subscription price.
-- Oversubscription rights.
-- Backstop party and fees.
-- Use of proceeds.
-- Dilution.
-- Shareholder participation incentives.
-- Trading value of rights.
-- Balance-sheet impact.
-
-Senior questions:
-- Is the capital raise value creating or a rescue financing?
-- Is the backstop extracting economics from non-participating shareholders?
-- Are rights mispriced relative to common?
-
-## Preferreds, warrants, converts, and stubs
-
-Analyze:
-- Conversion/exercise terms.
-- Call/put features.
-- Dividends/coupons and arrears.
-- Change-of-control provisions.
-- Liquidity and borrow.
-- Path to monetization.
-- Control-party incentives.
-
-## Output requirements
-
-- Security expression and any required handoff to Credit Markets.
-- Event/catalyst timeline.
-- Scenario tree.
-- Recovery, SOTP, or payoff value only where sourced, supplied, or incorporated from Credit Markets.
-- Best expression and why.
-- Liquidity/crowding/borrow assessment.
-- Monitoring plan.
-- Red-team critique.
-
-## Credit Markets Boundary
-
-Distressed exchange, bankruptcy, covenant, recovery, and debt-security selection route to Credit Markets. Public Equity Investing may use Credit Markets outputs only as inputs to a listed-equity event view.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/scenario_math.md b/plugins/financial-markets/skills/event-driven-analyzer/references/scenario_math.md
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-# Scenario Math and Calculation Frameworks
-
-Use this reference when calculating dated event spread, annualized return, market-implied probability, stock-deal hedge ratio, scenario EV, CVR value, spin SOTP, and payoff math. For capital structure, covenants, priority, fulcrum-security, and recovery-waterfall analysis, use Credit Markets; this skill may consume those outputs as terminal-value assumptions.
-
-For repeatable calculations, use `scripts/event_math.py` when available. It accepts JSON input and emits JSON output.
-
-## Basic return math
-
-### Gross return
-
-`gross_return = terminal_value / current_price - 1`
-
-### Annualized return
-
-`annualized_return = (1 + gross_return) ** (365 / days_to_resolution) - 1`
-
-Use simple annualization only if the user expects it:
-
-`simple_annualized_return = gross_return * 365 / days_to_resolution`
-
-Caveat: Annualized return can mislead for binary trades. Always pair annualized spread with downside and expected value.
-
-## Cash merger arbitrage
-
-Inputs:
-- Current target price.
-- Cash deal price.
-- Estimated close date or days to close.
-- Break price/downside.
-- Dividends, financing cost, borrow if relevant.
-
-Core outputs:
-- Gross spread.
-- Annualized spread.
-- Market-implied close probability.
-- Probability-weighted expected return.
-
-Formula:
-
-`market_implied_probability = (current_price - break_price) / (deal_price - break_price)`
-
-Interpretation:
-- If implied probability is lower than the analyst's probability and downside is defensible, spread may be attractive.
-- If implied probability is low because downside is understated, the trade may be a value trap.
-
-Adjustments:
-- Add expected dividends if holder receives them.
-- Subtract financing/carry costs.
-- Adjust terminal values for recut or remedy scenarios.
-- Use delay scenarios when timing is uncertain.
-
-## Stock-for-stock merger arbitrage
-
-Inputs:
-- Target price.
-- Acquirer price.
-- Exchange ratio.
-- Collar terms if any.
-- Expected dividends.
-- Borrow cost and hedge availability.
-- Days to close.
-
-Formulas:
-- `deal_value = acquirer_price * exchange_ratio`
-- `gross_spread = deal_value / target_price - 1`
-- `hedge_shares = target_shares * exchange_ratio`
-
-Adjust for:
-- Fixed exchange ratio vs floating exchange ratio.
-- Collars and walk-away thresholds.
-- Proration and election mechanics.
-- Dividend mismatch.
-- Borrow cost on short acquirer leg.
-- Acquirer vote risk and acquirer fundamental risk.
-- Residual exposure if hedge ratio is partial or collar-dependent.
-
-## CVR valuation
-
-Inputs by milestone:
-- Payment amount.
-- Probability of achievement.
-- Expected payment date.
-- Discount rate.
-- Transferability/liquidity discount.
-- Enforcement or sponsor incentive risk.
-
-Formula:
-
-`cvr_value = sum(payment_i * probability_i / (1 + discount_rate) ** years_i) - liquidity_discount`
-
-Senior checks:
-- Are milestones independent or conditional?
-- Does management/control party have incentive to maximize or minimize payout?
-- Is there reporting transparency?
-- Is the CVR tradeable?
-- Are there comparable outcomes?
-
-## Scenario tree EV
-
-Each scenario must include:
-- Scenario name.
-- Probability.
-- Timing in days or months.
-- Terminal value.
-- Rationale.
-- Signposts.
-
-Formulas:
-- `expected_terminal_value = sum(probability_i * terminal_value_i)`
-- `expected_return = expected_terminal_value / current_price - 1`
-- `expected_annualized_return = time-weighted or scenario-weighted annualized return`
-
-Probabilities must sum to 100%. `scripts/event_math.py --mode scenario_ev` hard-fails bad sums by default. Use `--allow-probability-sum-mismatch` only for diagnostic output, and do not present probability-weighted conclusions until the tree is corrected or explicitly caveated as non-normalized.
-
-## Downside / break price methods
-
-Use at least two methods for high-stakes trades:
-
-1. Unaffected price.
-2. Peer-adjusted unaffected price.
-3. Market-adjusted unaffected price.
-4. Fundamental standalone value.
-5. Historical trading range.
-6. Bear-case valuation.
-7. Debt recovery or liquidation value from Credit Markets when capital structure, covenants, priority, or recovery waterfalls drive the terminal case.
-8. Litigation loss or damages-adjusted value.
-
-Preferred merger break-price formula:
-
-`peer_adjusted_break = unaffected_price * (1 + peer_return_since_unaffected_date) + idiosyncratic_adjustment`
-
-Do not accept the unaffected price blindly if there was deal leakage, sector movement, earnings, guidance, macro shock, or unrelated company-specific news.
-
-## Spin-off SOTP
-
-Inputs:
-- SpinCo revenue, EBITDA, EBIT, FCF, net debt.
-- RemainCo revenue, EBITDA, EBIT, FCF, net debt.
-- Peer multiples.
-- Dis-synergies, stranded costs, separation costs.
-- Tax leakage, pension, litigation, environmental liabilities.
-- Share count and distribution ratio.
-
-Formula:
-
-`enterprise_value = metric * selected_multiple`
-
-`equity_value = enterprise_value - net_debt - other_claims`
-
-`per_share_value = equity_value / pro_forma_share_count`
-
-Senior checks:
-- Which shareholder base will own SpinCo after distribution?
-- Is forced selling likely?
-- Is the best entry pre-spin, when-issued, or post-distribution?
-- Is the balance sheet fair or value-shifting?
-
-## Distressed event bridge
-
-When a dated distressed catalyst depends on capital structure or recovery, do not build the credit stack inside this skill. Hand off to or rely on Credit Markets for:
-- Enterprise value range, cash, debt, secured/unsecured status, guarantees, collateral, DIP/new-money claims, rights offering economics, covenants, maturity wall, priority, and recovery waterfall.
-- Fulcrum security, value-break location, priming risk, structural subordination, intercreditor terms, class vote dynamics, and court/process recovery risks.
-
-This skill then owns the event bridge:
-1. Identify the dated catalyst or process step.
-2. Convert sourced credit/recovery outputs into terminal payoffs.
-3. Assign scenario probabilities and timing.
-4. Compute expected return, annualized return where useful, and monitoring thresholds.
-
-## Position sizing bridge
-
-A simple event-driven sizing conversation should include:
-- Expected return.
-- Downside gap.
-- Probability of adverse scenario.
-- Liquidity and ability to exit.
-- Correlation to existing book.
-- Mark-to-market path volatility.
-- Catalyst date certainty.
-- Borrow/financing constraints.
-
-Possible language:
-`The expected value is attractive, but the break downside and regulatory path argue for a starter position until the next gating item clears.`
-
-## Flow Event Math
-
-Add `flow_event` fields for estimated shares to buy/sell, ADV, days-to-trade, flow-vs-ADV, impact bands, assumed participation rate, borrow/financing cost, expected reversal, and liquidity exit plan. The helper is math-only; PM probability and market impact remain judgment.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/source_hierarchy.md b/plugins/financial-markets/skills/event-driven-analyzer/references/source_hierarchy.md
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-# Source Hierarchy and Evidence Discipline
-
-Use this reference whenever source quality, citations, or missing data affect the analysis.
-
-## Core principle
-
-Event-driven investing is document driven. Press is useful for context, but primary documents define terms, rights, deadlines, remedies, and process. Treat market prices as evidence of market belief, not as truth.
-
-## Global source hierarchy
-
-1. User-provided materials: uploaded filings, models, memos, trackers, notes, call transcripts, and pasted text.
-2. Callable connected routes or user-provided exports: Google Drive, Slack, Gmail, Notion, internal research stores, broker data, or market-data connectors.
-3. Primary public records: filings, court dockets, regulator pages, company releases, exchange notices, and official calendars.
-4. Market data: prices, spreads, borrow, options, bonds, loans, CDS, rates, consensus, peer multiples, and factor data.
-5. Company materials: investor decks, transcripts, annual reports, proxy materials, activist decks, spin information statements, lender presentations.
-6. Third-party commentary: sell-side, legal alerts, press, specialist newsletters, expert call notes, and web search.
-
-## Evidence weighting rules
-
-- Signed transaction documents beat press releases.
-- Definitive proxy/S-4/offer documents beat announcement decks.
-- Court orders beat legal commentary.
-- Regulator calendars and official decisions beat management timing optimism.
-- Ordinary-course company documents can be more probative than advocacy materials.
-- Market price reflects consensus and constraints; it does not answer the underwriting question.
-- User assumptions may be used, but must be labeled as user assumptions.
-
-## Source map by event type
-
-### Merger arbitrage
-Primary sources:
-- Merger agreement exhibit to 8-K or equivalent.
-- S-4/F-4 for stock deals.
-- Preliminary and definitive proxy.
-- Schedule TO and 14D-9 for tender offers.
-- Press release and investor presentation.
-- Antitrust/regulatory filings and agency updates.
-- Shareholder meeting materials and vote results.
-
-Must extract:
-- Consideration, exchange ratio, collar, election mechanics.
-- Conditions, outside date, termination rights, break fee, reverse termination fee.
-- Financing commitments, debt marketing, buyer funding obligations.
-- Required approvals and vote thresholds.
-- Background/process details that indicate buyer commitment or renegotiation risk.
-
-### Spin-offs and split-offs
-Primary sources:
-- Form 10, information statement, proxy/prospectus, 8-Ks, parent filings.
-- Separation agreement, tax matters agreement, employee matters agreement, transition services agreement.
-- Capitalization table and pro forma financials.
-- When-issued trading notices and exchange/index announcements.
-
-Must extract:
-- Distribution ratio, record date, distribution date, tax treatment, retained stake.
-- Debt allocation, cash transfer, stranded costs, dis-synergies, transition costs.
-- Management incentives, board composition, segment financials.
-- Index eligibility and expected holder base.
-
-### Activism
-Primary sources:
-- 13D/G and amendments.
-- Activist letters, presentations, proxy materials, definitive proxy cards.
-- Company response letters and governance documents.
-- Bylaws, advance notice deadlines, rights plans, director nomination requirements.
-- Annual meeting notice, record date, ISS/Glass Lewis if available.
-
-Must extract:
-- Ownership, derivatives, campaign demands, board seats sought.
-- Nomination deadlines, annual meeting date, settlement terms.
-- Shareholder base and vote math.
-- Activist track record and credibility.
-
-### Litigation
-Primary sources:
-- Complaint, answer, motions, orders, trial calendar, docket entries.
-- Settlement agreements, judgments, appellate briefs, damages reports where public.
-- Company disclosures on contingencies and risk factors.
-
-Must extract:
-- Venue, judge, procedural posture, key motions, trial/appeal dates.
-- Damages/remedy theory, injunction risk, settlement path.
-- Balance-sheet impact and insurance/indemnity if disclosed.
-
-### Regulatory
-Primary sources:
-- Agency pages and official calendars.
-- Company disclosures, transaction documents, remedy proposals.
-- Consent orders, complaints, statements of objections, phase decisions, press releases.
-
-Must extract:
-- Which regulator controls the path.
-- Filing status, review stage, statutory timeline, likely remedy, political sensitivity.
-- Interaction with outside date, financing commitments, and transaction conditions.
-
-### Restructuring and distressed special situations
-Primary sources:
-- 10-K/Q, debt agreements, indentures, credit agreements, exchange-offer memoranda, RSA, bankruptcy petitions, first-day motions, disclosure statement, plan, court docket.
-- Bond/loan prices, CDS, maturity schedules, covenant tests, collateral docs.
-
-Must extract:
-- Dated catalyst, court/process status, exchange or RSA terms, next hearing/vote/deadline, security price, payoff assumptions, and source date.
-- If claims stack, maturity wall, liquidity, covenant headroom, collateral, guarantees, priority, creditor groups, class votes, DIP, rights offering, backstop, or recovery waterfall are central and not already supplied, route that capital-structure/recovery work to Credit Markets.
-
-## Timestamping rules
-
-Always timestamp:
-- Equity price, deal value, spread, annualized return.
-- Bond/loan/CDS levels.
-- Borrow cost and locate status.
-- Options prices and implied volatility.
-- Regulatory status and court status.
-- Consensus, peer multiples, and market data.
-
-Use wording such as: `Market data timestamp: [date/time/source]. If unavailable, current market math could not be verified.`
-
-## Missing data language
-
-Use direct language:
-- `Unverified: merger agreement not reviewed, so termination rights and reverse termination fee are not confirmed.`
-- `Assumption: current price supplied by user; no live quote available in this environment.`
-- `Judgment: downside uses peer-adjusted unaffected price because the unaffected price appears leak-influenced.`
-
-## Citations and auditability
-
-When the runtime supports citations, cite the source for:
-- Deal terms.
-- Filing dates and deadlines.
-- Regulatory process status.
-- Court rulings or docket events.
-- Financial metrics, consensus, prices, and valuation data.
-- Ownership and vote thresholds.
-
-Do not over-cite generic judgment, but make the factual substrate auditable.
-
-## Index / ETF / Passive Flow Event Sources
-
-Use index methodology docs, official constituent announcements, exchange notices, ETF holdings/rebalance files where available, issuer float/share-count disclosures, short interest/borrow, ADV/liquidity, and timestamped price/volume data. Flow math requires market data timestamps for price, ADV, float, market cap, borrow, options, and ETF/index AUM assumptions.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/references/spins_activism_playbook.md b/plugins/financial-markets/skills/event-driven-analyzer/references/spins_activism_playbook.md
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-# Spin-Off, Split-Off, and Activism Playbook
-
-Use this reference for spin-offs, split-offs, RemainCo/SpinCo trades, activist campaigns, proxy fights, board contests, capital-return campaigns, and strategic-alternative pushes.
-
-## Spin-off and split-off objective
-
-Answer: Are SpinCo and RemainCo mispriced because of separation, capital structure, incentives, investor-base rotation, tax constraints, or forced selling?
-
-## Spin required fact pack
-
-- Parent, SpinCo, RemainCo, tickers if available.
-- Transaction type: spin, split-off, carve-out, IPO, retained stake, Reverse Morris Trust.
-- Record date, distribution date, when-issued trading date.
-- Distribution ratio.
-- Tax treatment and tax constraints.
-- Form 10 / information statement status.
-- Pro forma financials by entity.
-- Debt allocation and cash transfer.
-- Stranded costs, dis-synergies, transition service agreements.
-- Management teams and incentives.
-- Board composition.
-- Retained stake and monetization plan.
-- Index eligibility and likely forced selling.
-- Liquidity, expected float, short interest/borrow if available.
-
-## Spin analysis workflow
-
-1. Separate the businesses.
-2. Normalize segment financials and pro forma costs.
-3. Allocate debt, cash, pensions, environmental, litigation, and tax liabilities.
-4. Select peer sets for SpinCo and RemainCo.
-5. Build SOTP value range.
-6. Analyze technical flows: holder base, index eligibility, size, liquidity, mandate fit.
-7. Assess management incentives and capital allocation.
-8. Decide entry point: pre-spin, when-issued, post-distribution, or wait.
-9. Recommend trade: long parent, long SpinCo, long RemainCo, pair trade, avoid.
-10. Build monitoring plan.
-
-## Spin senior judgment questions
-
-- Does the current shareholder base want the spun asset?
-- Is SpinCo too small, levered, cyclical, low-margin, or off-index for forced holders?
-- Are stranded costs understated?
-- Is the balance sheet fair, or did the parent over-lever SpinCo?
-- Does management have real incentive alignment?
-- Is the story a real pure-play re-rating or just conglomerate complexity moved elsewhere?
-- Is the better trade before the spin, in when-issued trading, or after forced selling?
-- Are tax constraints limiting strategic alternatives?
-
-## Spin output requirements
-
-- PM recommendation and timing.
-- Spin mechanics and timeline.
-- SpinCo valuation range.
-- RemainCo valuation range.
-- Pro forma capitalization.
-- Forced-selling and index-flow assessment.
-- Management incentive assessment.
-- Trade expression and monitoring plan.
-
-## Activism objective
-
-Answer: Can the activist force value creation, board change, sale, breakup, buyback, margin improvement, governance reform, or settlement, and what outcome is priced in?
-
-## Activism required fact pack
-
-- Company, ticker, market cap, sector.
-- Activist identity, track record, style, and prior campaigns.
-- Ownership percentage, cost basis if known, derivatives/swaps, filing history.
-- Campaign demands.
-- Board composition, tenure, independence, committee structure.
-- Governance: staggered board, poison pill, bylaws, advance notice, nomination window, universal proxy setup.
-- Shareholder base, passive ownership, insider ownership.
-- Company response.
-- Annual meeting date, record date, nomination deadline.
-- ISS/Glass Lewis timing and likely stance if available.
-- Prior underperformance and operational issues.
-- Value creation levers: sale, breakup, margin, capital allocation, management change, governance.
-
-## Activism analysis workflow
-
-1. Identify campaign type: sale process, board refresh, margin plan, capital return, breakup/spin, management change, governance, or strategic review.
-2. Assess activist credibility and track record.
-3. Build value-creation bridge: what could change numbers or multiple?
-4. Analyze shareholder base and vote math.
-5. Assess board vulnerability and defense posture.
-6. Estimate settlement probability vs proxy contest probability.
-7. Build scenarios: no change, settlement, partial win, full win, sale, downside.
-8. Recommend trade expression and sizing.
-9. Build catalyst timeline.
-
-## Activism senior judgment questions
-
-- Is the activist's plan economically credible or just financial engineering?
-- Is the board vulnerable because of underperformance, governance, capital allocation, or strategy?
-- Will long-only and passive holders support change?
-- Is management credible enough to preempt the campaign?
-- Is a settlement more likely than a full proxy fight?
-- What is the downside if the activist loses?
-- Does the stock already discount a sale or board change?
-
-## Activism output requirements
-
-- Campaign summary.
-- Activist credibility.
-- Value-creation plan assessment.
-- Vote math and board vulnerability.
-- Settlement/proxy fight probabilities.
-- Expected value by scenario.
-- Trade recommendation.
-- Next dates and monitoring plan.
-
-## Common mistakes to avoid
-
-- Treating all spin-offs as automatically value creating.
-- Ignoring pro forma stranded costs and debt allocation.
-- Assuming forced selling without checking size, index, holder base, and liquidity.
-- Treating activist letters as facts.
-- Ignoring bylaws, nomination windows, record dates, and vote math.
-- Assuming a sale process is feasible without buyer universe, antitrust, financing, or board willingness.
diff --git a/plugins/financial-markets/skills/event-driven-analyzer/scripts/event_math.py b/plugins/financial-markets/skills/event-driven-analyzer/scripts/event_math.py
deleted file mode 100755
index 73904d5..0000000
--- a/plugins/financial-markets/skills/event-driven-analyzer/scripts/event_math.py
+++ /dev/null
@@ -1,299 +0,0 @@
-#!/usr/bin/env python3
-"""Event-driven math helpers.
-
-Usage:
-  python scripts/event_math.py --mode cash_merger --input input.json
-  python scripts/event_math.py --mode stock_deal --input input.json
-  python scripts/event_math.py --mode scenario_ev --input input.json
-  python scripts/event_math.py --mode scenario_ev --input input.json --allow-probability-sum-mismatch
-  python scripts/event_math.py --mode cvr --input input.json
-
-All numeric probabilities should be decimals, e.g. 0.75 for 75%.
-This script performs deterministic math only; it does not fetch market data.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from pathlib import Path
-from typing import Any
-
-PROBABILITY_SUM_TOLERANCE = 1e-6
-
-
-def _as_float(
-    data: dict[str, Any], key: str, required: bool = True, default: float | None = None
-) -> float:
-    value = data.get(key, default)
-    if value is None:
-        if required:
-            raise ValueError(f"missing required numeric field: {key}")
-        return float("nan")
-    try:
-        return float(value)
-    except (TypeError, ValueError) as exc:
-        raise ValueError(f"field {key} must be numeric, got {value!r}") from exc
-
-
-def _safe_div(num: float, den: float) -> float | None:
-    if den == 0:
-        return None
-    return num / den
-
-
-def _as_probability(value: Any, context: str) -> float:
-    try:
-        probability = float(value)
-    except (TypeError, ValueError) as exc:
-        raise ValueError(f"{context} probability must be numeric, got {value!r}") from exc
-    if probability < 0.0 or probability > 1.0:
-        raise ValueError(f"{context} probability must be between 0 and 1, got {probability}")
-    return probability
-
-
-def annualized_return(gross_return: float, days: float) -> float | None:
-    if days <= 0:
-        return None
-    if gross_return <= -1:
-        return -1.0
-    return (1.0 + gross_return) ** (365.0 / days) - 1.0
-
-
-def cash_merger(data: dict[str, Any]) -> dict[str, Any]:
-    current_price = _as_float(data, "current_price")
-    deal_price = _as_float(data, "deal_price")
-    days_to_close = _as_float(data, "days_to_close", required=False, default=float("nan"))
-    break_price = _as_float(data, "break_price", required=False, default=float("nan"))
-    expected_dividends = _as_float(data, "expected_dividends", required=False, default=0.0)
-    financing_cost = _as_float(data, "financing_cost", required=False, default=0.0)
-    other_carry = _as_float(data, "other_carry", required=False, default=0.0)
-
-    adjusted_deal_value = deal_price + expected_dividends - financing_cost - other_carry
-    gross_spread = _safe_div(adjusted_deal_value, current_price)
-    gross_return = None if gross_spread is None else gross_spread - 1.0
-
-    implied_probability = None
-    if not math.isnan(break_price):
-        implied_probability = _safe_div(
-            current_price - break_price, adjusted_deal_value - break_price
-        )
-
-    annualized = None
-    if gross_return is not None and not math.isnan(days_to_close):
-        annualized = annualized_return(gross_return, days_to_close)
-
-    return {
-        "mode": "cash_merger",
-        "current_price": current_price,
-        "adjusted_deal_value": adjusted_deal_value,
-        "gross_return": gross_return,
-        "annualized_return": annualized,
-        "market_implied_probability": implied_probability,
-        "notes": [
-            "Market-implied probability depends heavily on break_price.",
-            "Annualized return should be paired with downside and scenario EV.",
-        ],
-    }
-
-
-def stock_deal(data: dict[str, Any]) -> dict[str, Any]:
-    target_price = _as_float(data, "target_price")
-    acquirer_price = _as_float(data, "acquirer_price")
-    exchange_ratio = _as_float(data, "exchange_ratio")
-    target_shares = _as_float(data, "target_shares", required=False, default=1.0)
-    days_to_close = _as_float(data, "days_to_close", required=False, default=float("nan"))
-    expected_target_dividends = _as_float(
-        data, "expected_target_dividends", required=False, default=0.0
-    )
-    expected_acquirer_dividends = _as_float(
-        data, "expected_acquirer_dividends", required=False, default=0.0
-    )
-    borrow_cost = _as_float(data, "borrow_cost", required=False, default=0.0)
-
-    deal_value = acquirer_price * exchange_ratio
-    adjusted_deal_value = (
-        deal_value
-        + expected_target_dividends
-        - (exchange_ratio * expected_acquirer_dividends)
-        - borrow_cost
-    )
-    gross_return = _safe_div(adjusted_deal_value, target_price)
-    if gross_return is not None:
-        gross_return -= 1.0
-    hedge_shares = target_shares * exchange_ratio
-    annualized = None
-    if gross_return is not None and not math.isnan(days_to_close):
-        annualized = annualized_return(gross_return, days_to_close)
-
-    return {
-        "mode": "stock_deal",
-        "target_price": target_price,
-        "acquirer_price": acquirer_price,
-        "exchange_ratio": exchange_ratio,
-        "deal_value": deal_value,
-        "adjusted_deal_value": adjusted_deal_value,
-        "gross_return": gross_return,
-        "annualized_return": annualized,
-        "hedge_shares_per_target_shares": hedge_shares,
-        "notes": [
-            "Review collar, election, dividend, borrow, and acquirer vote risk separately.",
-        ],
-    }
-
-
-def scenario_ev(
-    data: dict[str, Any], allow_probability_sum_mismatch: bool = False
-) -> dict[str, Any]:
-    current_price = _as_float(data, "current_price")
-    scenarios: list[dict[str, Any]] = data.get("scenarios", [])
-    if not scenarios:
-        raise ValueError("scenario_ev requires a non-empty scenarios list")
-
-    probability_sum = 0.0
-    expected_terminal_value = 0.0
-    weighted_annualized = 0.0
-    rows = []
-
-    for scenario in scenarios:
-        name = scenario.get("name", "unnamed")
-        if "probability" not in scenario:
-            raise ValueError(f"scenario {name!r} missing required probability")
-        probability = _as_probability(scenario["probability"], f"scenario {name!r}")
-        terminal_value = float(scenario["terminal_value"])
-        days = float(
-            scenario.get("days_to_resolution", data.get("days_to_resolution", float("nan")))
-        )
-        ret = terminal_value / current_price - 1.0
-        ann = None if math.isnan(days) else annualized_return(ret, days)
-        probability_sum += probability
-        expected_terminal_value += probability * terminal_value
-        if ann is not None:
-            weighted_annualized += probability * ann
-        rows.append(
-            {
-                "name": name,
-                "probability": probability,
-                "terminal_value": terminal_value,
-                "return": ret,
-                "days_to_resolution": None if math.isnan(days) else days,
-                "annualized_return": ann,
-            }
-        )
-
-    probabilities_sum_to_100pct = abs(probability_sum - 1.0) < PROBABILITY_SUM_TOLERANCE
-    if not probabilities_sum_to_100pct and not allow_probability_sum_mismatch:
-        raise ValueError(
-            "scenario probabilities must sum to 1.0; "
-            f"got {probability_sum:.6f}. "
-            "Pass --allow-probability-sum-mismatch to emit diagnostic, non-memo-ready output."
-        )
-
-    notes = [
-        "Scenario-weighted annualized return can be misleading when timing varies materially.",
-    ]
-    if not probabilities_sum_to_100pct:
-        notes.insert(
-            0,
-            "Probability sum mismatch was explicitly allowed; revise before using probability-weighted conclusions.",
-        )
-
-    expected_return = expected_terminal_value / current_price - 1.0
-    return {
-        "mode": "scenario_ev",
-        "current_price": current_price,
-        "probability_sum": probability_sum,
-        "probabilities_sum_to_100pct": probabilities_sum_to_100pct,
-        "expected_terminal_value": expected_terminal_value,
-        "expected_return": expected_return,
-        "probability_weighted_annualized_return": weighted_annualized,
-        "scenarios": rows,
-        "notes": notes,
-    }
-
-
-def cvr(data: dict[str, Any]) -> dict[str, Any]:
-    milestones: list[dict[str, Any]] = data.get("milestones", [])
-    if not milestones:
-        raise ValueError("cvr requires a non-empty milestones list")
-    default_discount_rate = _as_float(data, "discount_rate", required=False, default=0.12)
-    liquidity_discount = _as_float(data, "liquidity_discount", required=False, default=0.0)
-
-    pv = 0.0
-    rows = []
-    for milestone in milestones:
-        name = milestone.get("name", "unnamed")
-        payment = float(milestone["payment"])
-        probability = _as_probability(milestone["probability"], f"milestone {name!r}")
-        years = float(milestone["years"])
-        discount_rate = float(milestone.get("discount_rate", default_discount_rate))
-        value = payment * probability / ((1.0 + discount_rate) ** years)
-        pv += value
-        rows.append(
-            {
-                "name": name,
-                "payment": payment,
-                "probability": probability,
-                "years": years,
-                "discount_rate": discount_rate,
-                "present_value": value,
-            }
-        )
-    pv_after_discount = pv - liquidity_discount
-    return {
-        "mode": "cvr",
-        "gross_present_value": pv,
-        "liquidity_discount": liquidity_discount,
-        "net_present_value": pv_after_discount,
-        "milestones": rows,
-        "notes": [
-            "Review milestone dependency, sponsor incentives, reporting rights, transferability, and enforcement risk.",
-        ],
-    }
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Event-driven math helpers")
-    parser.add_argument(
-        "--mode", required=True, choices=["cash_merger", "stock_deal", "scenario_ev", "cvr"]
-    )
-    parser.add_argument("--input", help="Path to JSON input. If omitted, reads stdin.")
-    parser.add_argument("--pretty", action="store_true", help="Pretty-print JSON output")
-    parser.add_argument(
-        "--allow-probability-sum-mismatch",
-        action="store_true",
-        help="Allow scenario_ev probabilities that do not sum to 1.0; output is diagnostic and not memo-ready.",
-    )
-    args = parser.parse_args()
-
-    try:
-        if args.input:
-            data = json.loads(Path(args.input).read_text())
-        else:
-            data = json.load(sys.stdin)
-
-        if args.mode == "cash_merger":
-            result = cash_merger(data)
-        elif args.mode == "stock_deal":
-            result = stock_deal(data)
-        elif args.mode == "scenario_ev":
-            result = scenario_ev(
-                data,
-                allow_probability_sum_mismatch=args.allow_probability_sum_mismatch,
-            )
-        elif args.mode == "cvr":
-            result = cvr(data)
-        else:
-            raise AssertionError(args.mode)
-    except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc:
-        print(f"error: {exc}", file=sys.stderr)
-        return 1
-
-    print(json.dumps(result, indent=2 if args.pretty else None, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/financials-normalizer/SKILL.md b/plugins/financial-markets/skills/financials-normalizer/SKILL.md
deleted file mode 100644
index 2d27867..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/SKILL.md
+++ /dev/null
@@ -1,110 +0,0 @@
----
-name: financials-normalizer
-description: Use when normalizing public-company financials from source materials. Do not use for private data rooms or non-financial cleanup.
----
-
-# Financials Normalizer
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is an XLSX normalization workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new standalone reader-facing normalization deliverable should it, before source gathering, analysis, or rendering, load `../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved preferences.
-
-## Purpose
-
-Load `shared/equity-research-support-standard.md` and `shared/support-layer-routing-contract.md` before substantial source, data, QA, or style work.
-
-
-Turn messy public-company source financials into auditable, model-ready normalized statements, KPI schedules, consensus/guidance inputs, segment schedules, share-count support, net-debt and capital-allocation support, source citations, assumptions, conflicts, and QA flags for downstream Public Equity Investing workflows.
-
-Boundary: shipped scripts create `Source_Index.csv`, `Normalized_Financials_Long.csv`, and `Normalization_Issues.csv`. Wide statements, KPI schedules, adjustment logs, conflict logs, assumption registers, and workbook/deck-ready tabs are instruction-led unless explicitly built from staging data.
-
-For a standalone request for **model-ready normalized financials**, do not treat a long-form staging CSV alone as the analyst deliverable. Create a model-loading package from the staged rows: full-scope wide schedules relevant to the supplied financials, a disclosure/comparability bridge when definitions or presentation changed, material QA flags, and logged validation checks. Use XLSX when the user requests a workbook or will load/review the output in a workbook; otherwise a clearly organized CSV package plus a concise review summary is appropriate. This is a data-first skill; do not force an HTML artifact unless the user requests one.
-
-## Embedded Support Routing
-
-This is an embedded service under the owning workflow unless the user explicitly asks for standalone normalization. Preserve the `owning_workflow` internally, such as `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `earnings-preview`, `earnings-deep-dive`, `memo-builder`, `thesis-tracker`, `scenario-sensitivity-generator`, `portfolio-risk-management`, or `dashboard-builder`.
-
-For substantial embedded work, preserve `decision_impact`, `readiness_effect`, `artifact_role`, and `hidden_unless_requested` in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own the valuation, memo, earnings, or recommendation; state in natural language how normalization issues change estimate confidence, valuation support, target support, sizing, model readiness, or circulation readiness. `Source_Index.csv`, `Normalized_Financials_Long.csv`, `Normalization_Issues.csv`, run logs, manifests, and support notes are secondary/support artifacts when invoked by an owning workflow.
-
-## Non-Negotiables
-
-- Preserve raw/source materials.
-- Prefer user files/context, callable runtime apps/connectors when actually available, primary public sources, user-provided provider exports, then labeled assumptions. Never imply live provider access when it is unavailable.
-- Never invent missing financials; mark unavailable values as `missing_required_source`.
-- Keep normalized values traceable to source ID, source name/location, retrieved-at date, period, units, currency, and evidence label.
-- Missing `source_id` must remain visible as `SRC-UNSPECIFIED`, produce a QA flag, and block decision-grade handoff.
-- Retain conflicts rather than silently choosing values.
-- Flag stale, preliminary, unaudited, OCR-derived, or low-confidence data.
-- Do not infer fiscal period-end dates from quarter labels alone; use an explicit source date or mark the date missing and flag it.
-- Keep issuer outlook or guidance in `kpi_schedule` with `issuer_management_claim`; reserve `consensus_estimate` with `estimate_consensus` for externally sourced consensus estimates.
-- When a segment, KPI, non-GAAP definition, or balance-sheet presentation changes, preserve both bases and create a comparability bridge before calling any series model-loadable.
-- Apply comparability status at the affected series or line-item level. A changed cash presentation does not recast unrelated balance-sheet rows.
-- Use `comparable_rounded` when an unchanged reported series is comparable across periods but only available in rounded narrative units; disclose that it is unsuitable for exact tie-out.
-- Do not backsolve an undisclosed comparable value from rounded amounts or percentage growth for model loading. A labeled directional calculation may appear separately only when useful.
-- Material open exceptions must be surfaced in `QA_Flags` and in the readiness summary; an empty technical `Normalization_Issues` file does not mean the financials are clear for downstream use.
-
-## Workflow
-
-1. **Classify job.** Public-equity issuer financials, earnings/model update, consensus/provider export, ETF/index constituent support, portfolio/market-data support, or equity-risk debt/liquidity context.
-2. **Build source index.** Capture source ID, name/type, owner/provider, period, as-of date, retrieved-at date, location, source rank, freshness, and notes.
-3. **Extract long-form staging.** Use `Normalized_Financials_Long` before wide statements; preserve original line labels beside canonical labels.
-4. **Normalize periods, scale, currency, signs, and labels.** Keep reported, adjusted, pro forma, provider-standardized, estimated, and analyst-adjusted values separate.
-5. **Reconcile disclosure changes.** Identify renamed, regrouped, newly introduced, discontinued, recast, or definition-changed segments/KPIs/non-GAAP lines; preserve legacy and new bases; create a disclosure/comparability bridge with model treatment.
-6. **Reconcile and QA.** Check subtotals, roll-forwards, balance sheet balance, cash flow bridge, units/currency, duplicate periods, missing sources, stale/conflicting values, signs, unsupported KPIs, and completeness of the intended model-loading scope. Log performed checks and results; do not claim a check count that is not preserved in an output.
-7. **Produce package.** For standalone model-ready work, produce loadable wide schedules plus `QA_Flags` and `Validation_Checks` from the audited staging layer; include a disclosure/comparability bridge whenever presentation changed. For narrower extraction/support work, return only the deterministic CSV outputs or instruction-led tabs actually created.
-8. **Hand off.** State what is loadable, what is audit-only, and what remains partial or blocked before routing to models, earnings, comps, memo, thesis, scenario, risk, ETF/index, or deck/report skills. Route covenant/recovery/debt-security normalization to Credit Markets.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source inventory, line-item mapping, period/unit normalization, conflict log, and QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-When embedded in a broader workflow, "lead" means lead for normalization only; the owning workflow remains the investment-artifact owner.
-
-
-## Evidence Labels
-
-Use exact labels from `references/normalization-schema.md`, including `fact_source_reported`, `fact_provider_standardized`, `derived_calculation`, `issuer_management_claim`, `management_adjusted`, `analyst_adjusted`, `analyst_interpretation`, `assumption_user_provided`, `assumption_inferred`, `estimate_consensus`, `stale_source`, `contradicted_source`, `missing_required_source`, and `unknown`.
-
-Confidence labels are `high`, `medium`, or `low`.
-
-## Scripts
-
-```bash
-python scripts/normalize_extracted_financials.py path/to/input.csv --output-dir output
-python scripts/validate_normalized_financials.py output/Normalized_Financials_Long.csv
-```
-
-For workbook inputs, first extract the relevant tab/range with spreadsheet tools into a table/CSV; scripts must not destructively modify workbooks.
-
-## Final Response
-
-Return:
-
-1. what was normalized: entity, sources, periods, units, currency, scope;
-2. outputs created;
-3. what can be loaded into a model and what remains audit-only, partial, or blocked;
-4. material QA findings and disclosure/comparability breaks;
-5. fact versus assumption summary and validation checks actually performed;
-6. recommended next step or missing source.
-
-## Reference Map
-
-- `references/source-protocol.md`: hierarchy, stale data, citations, conflicts.
-- `references/normalization-schema.md`: output schema, signs, scales, labels.
-- `references/line-item-taxonomy.md`: statement/KPI mappings.
-- `references/qa-rules.md`: reconciliation tests and red flags.
-- `references/integration-guide.md`: downstream Public Equity Investing handoffs.
diff --git a/plugins/financial-markets/skills/financials-normalizer/agents/openai.yaml b/plugins/financial-markets/skills/financials-normalizer/agents/openai.yaml
deleted file mode 100644
index 4407940..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/agents/openai.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-interface:
-  display_name: Financials Normalizer
-  short_description: normalize messy Public Equity Investing financial source data into model-ready long-form outputs, source indexes, and qa flags.
-  icon_small: assets/icon.svg
-  icon_large: assets/icon.svg
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/financials-normalizer/assets/icon.svg b/plugins/financial-markets/skills/financials-normalizer/assets/icon.svg
deleted file mode 100644
index 5bb120e..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/assets/icon.svg
+++ /dev/null
@@ -1,7 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="64" height="64" viewBox="0 0 64 64" role="img" aria-label="financials normalizer icon">
-  <rect x="8" y="8" width="48" height="48" rx="12" fill="#0f766e"/>
-  <path d="M18 22h28M18 32h28M18 42h28" stroke="#ffffff" stroke-width="4" stroke-linecap="round"/>
-  <circle cx="26" cy="22" r="3" fill="#ccfbf1"/>
-  <circle cx="38" cy="32" r="3" fill="#ccfbf1"/>
-  <circle cx="30" cy="42" r="3" fill="#ccfbf1"/>
-</svg>
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/integration-guide.md b/plugins/financial-markets/skills/financials-normalizer/references/integration-guide.md
deleted file mode 100644
index 2972186..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/integration-guide.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Integration guide
-
-This skill is a shared-core feeder for Public Equity Investing skills. It should create reliable inputs, not complete the downstream analysis itself.
-
-## Shared core handoffs
-
-- `financial-source-of-truth`: use for evidence policy, source access caveats, conflict policy, connector honesty, and citation discipline. `financials-normalizer` applies those rules to financial statement, KPI, consensus, and market-data support.
-- `excel-data-cleaner`: use first when spreadsheet structure blocks extraction. Examples: merged headers, multiple tables per tab, blank rows, malformed dates, export artifacts, or broken table shapes.
-- `model-audit-tieout`: use after normalized data is placed into a workbook/model to audit formulas, links, hardcodes, checks, and sign conventions.
-- `scenario-sensitivity-generator`: use after a base case exists to build downside/upside cases, sensitivities, breakevens, and stress tests.
-- `memo-builder`: use after normalized tables and QA findings are complete to generate decision narratives.
-- `deck-report-qc`: use after normalized outputs are inserted into decks/reports.
-- `style-guide-adapter`: use after content is correct to match firm/client formatting and language.
-
-## Public Equity Investing
-
-Use normalized outputs for `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `long-short-pitch`, `thesis-tracker`, `portfolio-risk-management`, `economic-impact-report`, `comps-valuation`, `dcf-model-builder`, `three-statement-model-builder`, `scenario-sensitivity-generator`, `memo-builder`, and `sector-context-overlay`.
-
-Recommended extra fields: filing/press release dates, period type, guidance, consensus/estimate source, provider/export as-of timestamp, company-defined KPIs, segment disclosures, share count, buybacks/SBC, net debt, capital allocation, index/ETF constituent support when relevant, and source filing references.
-
-## Credit Markets handoff / equity-risk debt and liquidity context
-
-Keep debt, liquidity, maturity, rating, CDS/spread, and refinancing fields only when they support common-equity downside, solvency, valuation bridge, or risk sizing. Recommended equity-context fields: net debt, cash, revolver availability, maturity wall summary, rating or CDS/spread signal, liquidity runway, refinancing date, and source document. Route covenant packages, recovery waterfalls, distressed-security value, bond/loan/CDS pricing, spread/yield relative value, and debt-security selection to Credit Markets.
-
-## When not to hand off
-
-Do not hand off as ready when there are blocker flags for missing periods, missing source refs, unresolved source conflicts, unknown currency/scale, material tie-out breaks, or unknown actual/forecast status. Hand off as partial only with explicit limitations.
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/line-item-taxonomy.md b/plugins/financial-markets/skills/financials-normalizer/references/line-item-taxonomy.md
deleted file mode 100644
index cb24c12..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/line-item-taxonomy.md
+++ /dev/null
@@ -1,109 +0,0 @@
-# Line-item taxonomy
-
-Use this starter taxonomy to map source labels into normalized financials while preserving the exact source label.
-
-## Mapping principles
-
-- Keep `line_item_original` exactly as found in the source.
-- Map to `line_item_standard` only when the mapping is clear or reasonably supported.
-- If uncertain, set confidence `low` and add a QA flag rather than forcing the mapping.
-- Preserve company-defined non-GAAP metrics separately from reconstructed metrics.
-- Do not collapse operating, non-operating, discontinued operations, minority interest, preferred dividends, or noncontrolling interest unless explicitly requested.
-
-## Income statement starter map
-
-| line_item_id | line_item_standard | Common labels |
-|---|---|---|
-| revenue | Revenue | revenue, net sales, sales, net revenue, total revenues, operating revenue |
-| cogs | Cost of revenue | cost of revenue, cost of sales, cogs, cost of services |
-| gross_profit | Gross profit | gross profit |
-| sales_marketing | Sales and marketing | sales and marketing, selling expense, marketing |
-| research_development | Research and development | r&d, research and development, product development |
-| general_admin | General and administrative | g&a, general and administrative, administrative expense |
-| opex_total | Total operating expenses | operating expenses, total opex |
-| operating_income | Operating income | operating income, income from operations, operating profit |
-| depreciation_amortization | Depreciation and amortization | d&a, depreciation, amortization |
-| ebitda_company_defined | EBITDA, company-defined | ebitda, adjusted ebitda, company adjusted ebitda |
-| stock_based_comp | Stock-based compensation | sbc, share-based compensation |
-| interest_expense | Interest expense | interest expense, finance costs |
-| interest_income | Interest income | interest income, investment income |
-| other_income_expense | Other income / expense | other income, other expense, non-operating income |
-| pretax_income | Income before taxes | pretax income, income before income taxes |
-| tax_expense | Income tax expense | provision for income taxes, income tax expense |
-| net_income | Net income | net income, net earnings, profit attributable to shareholders |
-| eps_basic | EPS basic | basic EPS, basic earnings per share |
-| eps_diluted | EPS diluted | diluted EPS, diluted earnings per share |
-| shares_basic | Weighted average shares basic | basic weighted average shares |
-| shares_diluted | Weighted average shares diluted | diluted weighted average shares |
-
-## Balance sheet starter map
-
-| line_item_id | line_item_standard | Common labels |
-|---|---|---|
-| cash_equivalents | Cash and equivalents | cash, cash equivalents, cash and cash equivalents |
-| short_term_investments | Short-term investments | marketable securities, short-term investments |
-| accounts_receivable | Accounts receivable | trade receivables, accounts receivable, ar |
-| inventory | Inventory | inventory, inventories |
-| prepaid_other_current_assets | Prepaids and other current assets | prepaid expenses, other current assets |
-| total_current_assets | Total current assets | current assets, total current assets |
-| ppne_net | Net PP&E | property and equipment, pp&e, fixed assets |
-| goodwill | Goodwill | goodwill |
-| intangibles_net | Intangible assets | intangible assets, acquired intangibles |
-| total_assets | Total assets | total assets |
-| accounts_payable | Accounts payable | trade payables, accounts payable, ap |
-| accrued_expenses | Accrued expenses | accrued liabilities, accrued expenses |
-| deferred_revenue_current | Current deferred revenue | current deferred revenue, current contract liabilities |
-| short_term_debt | Short-term debt | current debt, current maturities, short-term borrowings |
-| total_current_liabilities | Total current liabilities | current liabilities, total current liabilities |
-| long_term_debt | Long-term debt | long-term debt, borrowings, notes payable |
-| lease_liabilities | Lease liabilities | operating lease liabilities, finance lease liabilities |
-| deferred_revenue_noncurrent | Noncurrent deferred revenue | long-term deferred revenue, noncurrent contract liabilities |
-| total_liabilities | Total liabilities | liabilities, total liabilities |
-| noncontrolling_interest | Noncontrolling interest | minority interest, nci |
-| total_equity | Total equity | shareholders' equity, stockholders' equity, net assets |
-| liabilities_equity | Total liabilities and equity | liabilities and equity, liabilities and shareholders' equity |
-
-## Cash flow starter map
-
-| line_item_id | line_item_standard | Common labels |
-|---|---|---|
-| net_income_cf | Net income in cash flow | net income, net earnings |
-| depreciation_amortization_cf | D&A in cash flow | depreciation and amortization |
-| stock_based_comp_cf | Stock-based compensation in cash flow | stock-based compensation, share-based compensation |
-| deferred_taxes | Deferred taxes | deferred income taxes |
-| accounts_receivable_change | Change in accounts receivable | accounts receivable change, receivables |
-| inventory_change | Change in inventory | inventory change |
-| accounts_payable_change | Change in accounts payable | accounts payable change |
-| other_working_capital_change | Other working capital change | other operating assets and liabilities |
-| cfo | Cash flow from operations | net cash provided by operating activities, cfo |
-| capex | Capital expenditures | purchases of property and equipment, capex |
-| acquisitions | Acquisitions | business acquisitions, acquisition of subsidiaries |
-| cfi | Cash flow from investing | net cash used in investing activities |
-| debt_issuance_repayment | Debt issuance / repayment | proceeds from debt, repayment of debt |
-| equity_issuance | Equity issuance | issuance of common stock, proceeds from shares |
-| dividends | Dividends | dividends paid |
-| share_repurchases | Share repurchases | repurchases of stock, buybacks |
-| cff | Cash flow from financing | net cash provided by financing activities |
-| fx_cash_effect | FX effect on cash | effect of exchange rates on cash |
-| net_change_cash | Net change in cash | net increase/decrease in cash |
-| beginning_cash | Beginning cash | cash at beginning of period |
-| ending_cash | Ending cash | cash at end of period |
-| free_cash_flow | Free cash flow | fcf, cfo less capex |
-
-## KPI handling
-
-Treat sector KPIs as definition-sensitive. Store company definition or data-provider definition when available.
-
-Common KPI categories:
-- SaaS/software: ARR, NRR, GRR, churn, billings, bookings, RPO, customers, ACV, ARPU, CAC, LTV, payback.
-- Banking/financials: net interest income, NIM, deposits, loans, CET1, provisions, charge-offs, efficiency ratio.
-- Insurance: premiums, loss ratio, combined ratio, reserves, investment income.
-- Real estate/REITs: NOI, same-store NOI, occupancy, rent PSF, AFFO, FFO, cap rate.
-- Energy: production, reserves, realized price, lifting cost, DD&A, capex by basin.
-- Marketplaces: GMV, take rate, active users, transactions, contribution profit.
-- Healthcare/biotech: patients, procedures, trial milestones, R&D by program, cash runway.
-
-
-## Equity-risk debt and liquidity context
-
-Use only for common-equity read-through: net debt, cash, restricted cash, maturity wall summary, interest expense, revolver availability, refinancing date, rating or CDS/spread signal, and liquidity runway. Route covenant models, recovery waterfalls, bond/loan/CDS pricing, spread/yield relative value, and debt-security selection to Credit Markets.
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/line_item_aliases.csv b/plugins/financial-markets/skills/financials-normalizer/references/line_item_aliases.csv
deleted file mode 100644
index 724d9aa..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/line_item_aliases.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-line_item_id,line_item_standard,statement,alias
-revenue,Revenue,income_statement,revenue
-revenue,Revenue,income_statement,net sales
-revenue,Revenue,income_statement,net revenue
-cogs,Cost of revenue,income_statement,cost of revenue
-cogs,Cost of revenue,income_statement,cost of sales
-gross_profit,Gross profit,income_statement,gross profit
-operating_income,Operating income,income_statement,operating income
-operating_income,Operating income,income_statement,income from operations
-net_income,Net income,income_statement,net income
-eps_diluted,EPS diluted,income_statement,diluted eps
-cash_equivalents,Cash and equivalents,balance_sheet,cash and cash equivalents
-accounts_receivable,Accounts receivable,balance_sheet,accounts receivable
-inventory,Inventory,balance_sheet,inventory
-total_assets,Total assets,balance_sheet,total assets
-accounts_payable,Accounts payable,balance_sheet,accounts payable
-long_term_debt,Long-term debt,balance_sheet,long-term debt
-total_liabilities,Total liabilities,balance_sheet,total liabilities
-total_equity,Total equity,balance_sheet,stockholders equity
-liabilities_equity,Total liabilities and equity,balance_sheet,total liabilities and stockholders equity
-cfo,Cash flow from operations,cash_flow,net cash provided by operating activities
-capex,Capital expenditures,cash_flow,capital expenditures
-capex,Capital expenditures,cash_flow,purchases of property and equipment
-free_cash_flow,Free cash flow,cash_flow,free cash flow
-ending_cash,Ending cash,cash_flow,cash at end of period
-arr,ARR,kpi_schedule,annual recurring revenue
-nrr,Net revenue retention,kpi_schedule,nrr
-gmv,GMV,kpi_schedule,gross merchandise value
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/normalization-schema.md b/plugins/financial-markets/skills/financials-normalizer/references/normalization-schema.md
deleted file mode 100644
index c196593..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/normalization-schema.md
+++ /dev/null
@@ -1,129 +0,0 @@
-# Normalization schema
-
-Use this schema for model-ready financial outputs. Prefer long-form staging first; pivot to wide statements only after QA.
-
-## Source_Index
-
-Required columns:
-- `source_id`: stable identifier such as `SRC-001`.
-- `source_name`: file, system, report, filing, provider, or source title.
-- `source_type`: `uploaded_file`, `callable_connected_system`, `user_provided_export`, `filing`, `earnings_release`, `transcript`, `investor_deck`, `provider_export`, `web`, `user_prompt`, `assumption`. Use `uploaded_file` for a supplied provenance note or source-support file; use `user_prompt` only for facts or instructions contained directly in the user's message.
-- `owner_or_provider`: source owner, provider, or uploader when known.
-- `period_covered`: period(s) covered by source.
-- `as_of_date`: report date, filing date, close date, data snapshot date, or version date.
-- `retrieved_at`: date/time the assistant accessed or used the source.
-- `file_tab_page_url_or_location`: page, tab, cell, row, URL, connected table, or object reference.
-- `source_rank`: rank from source protocol.
-- `freshness_status`: `current`, `acceptable_for_period`, `preliminary`, `stale`, `unknown`.
-- `notes`: caveats, version notes, limitations.
-
-`source_id` cannot be blank for decision-grade outputs. If deterministic helpers emit `SRC-UNSPECIFIED`, treat it as a blocking provenance error until a real `SRC-###` ID from `Source_Index` is assigned.
-
-## Normalized_Financials_Long
-
-Required columns:
-- `entity`
-- `source_id`
-- `statement`: `income_statement`, `balance_sheet`, `cash_flow`, `kpi_schedule`, `segment`, `equity_risk_debt_liquidity_context`, `share_count`, `working_capital`, `capital_allocation`, `consensus_estimate`, `etf_index_context`, `adjustment`
-- `line_item_original`
-- `line_item_standard`
-- `line_item_id`
-- `period_end`: `YYYY-MM-DD` when known.
-- `period_label`: source period label such as FY2025, Q1-2026, LTM Sep-2025, Apr-2026.
-- `period_type`: `annual`, `quarterly`, `monthly`, `ytd`, `ltm`, `forecast`, `budget`, `pro_forma`, `scenario`.
-- `currency`
-- `units`: source or normalized unit such as ones, $000, $mm, %, bps.
-- `source_value`: exact extracted value where possible.
-- `normalized_value`: numeric value in normalized unit/sign convention.
-- `normalization_method`: e.g. `as_reported`, `scaled_or_sign_normalized`, `calculated`, `mapped`, `currency_converted`.
-- `source_location`: page, table, tab, cell, row, URL, or system object.
-- `evidence_label`: one of the required canonical labels in SKILL.md.
-- `confidence`: `high`, `medium`, or `low`.
-- `normalization_note`
-
-Use `kpi_schedule` with `issuer_management_claim` for issuer outlook or guidance. Use `consensus_estimate` only for an external consensus estimate or provider forecast, with `estimate_consensus`; do not classify company guidance as consensus.
-
-## Wide outputs
-
-Pivot long-form values into these sheets only after staging:
-- `Normalized_IS`
-- `Normalized_BS`
-- `Normalized_CF`
-- `KPI_Schedule`
-- `Equity_Risk_Debt_Liquidity_Context`
-- `Share_Count`
-- `Segment_Financials`
-- `Working_Capital`
-
-Each wide output should retain source/citation columns or companion note columns if cell-level comments are not supported.
-
-## Standalone model-ready package
-
-When the user explicitly requests model-ready normalized financials, the staged long-form file is the audit foundation, not the complete human deliverable. Create:
-
-- complete wide load schedules for the sourced scope, such as income statement, balance sheet, cash flow, segment/KPI, guidance, adjustments, and share-count schedules where supplied;
-- `Disclosure_Comparability_Bridge` when segment, KPI, non-GAAP, or presentation definitions changed;
-- `QA_Flags` containing every material open readiness exception;
-- `Validation_Checks` recording each performed tie-out/control and its result; and
-- `Source_Index` and `Normalized_Financials_Long` as traceable supporting data.
-
-A compact headline-metric schedule may be included for review, but it must not be labeled a complete model-input package when material statement or driver lines were sourced and omitted.
-
-For any loadable wide schedule, retain or accompany each affected series with `comparison_status` (`comparable`, `comparable_rounded`, `recast_comparable`, `legacy_only`, `directional_only`, `missing_required_source`, or `not_comparable`) and `model_treatment`. Apply the status line by line: a presentation change should affect only the lines actually re-presented or redefined.
-
-## Sign conventions
-
-Default institutional modeling convention:
-- Revenue, gross profit, EBITDA, operating income, pretax income, net income: positive when income/profit.
-- Expenses: positive as line-item values unless a downstream model explicitly requires negative expenses.
-- Assets, liabilities, equity, cash, debt: positive balances.
-- Cash-flow inflows: positive. Cash-flow outflows: negative.
-- Capex: negative in cash-flow outputs; positive as a separate operating driver if requested by downstream model.
-- Changes in working capital: preserve source cash-flow sign; do not convert to balance-sheet delta without documenting method.
-- Share count and per-share data: preserve units separately from currency.
-
-## Scaling and currency
-
-- Preserve source scale in `source_value` and normalized scale/sign in `normalized_value` / `units`.
-- Default to `$mm` for institutional finance work unless user/source/downstream model specifies another unit. The shipped helper can infer common USD/GBP/EUR markers, common quarter/year period labels, `$000` / `$mm` / `$bn` scale, and cash-flow capex sign convention.
-- Do not perform full currency conversion unless requested or needed for comparability. When converting, include FX rate, date, and source in `normalization_note` and `Source_Index`.
-
-## Deterministic helper output contract
-
-The shipped `scripts/normalize_extracted_financials.py` helper emits:
-- `Source_Index.csv`
-- `Normalized_Financials_Long.csv`
-- `Normalization_Issues.csv`
-
-It does not automatically create wide statements, KPI schedules, adjustment logs, conflict logs, assumption registers, workbook tabs, or full FX conversions. Those are instruction-led artifacts that should be built only when the agent has enough source context and explicitly creates them.
-
-If an instruction-led XLSX workbook is created from normalized outputs, make `Cover` the first visible tab and summarize source posture, normalized period/entity scope, key metrics, conflict count, adjustment count, unresolved assumptions, and the workbook map before statement/detail tabs.
-
-## Required companion logs
-
-### Adjustments_Log
-Use for all normalized, pro forma, issuer/management-defined, provider-standardized, or analyst adjustments. Include: `adjustment_id`, `entity`, `period`, `metric`, `amount`, `direction`, `reason`, `source_id`, `evidence_label`, `confidence`, `included_in_output`.
-
-### Conflict_Log
-Use for material conflicting values. Include: `conflict_id`, `entity`, `metric`, `period`, `source_a`, `value_a`, `source_b`, `value_b`, `conflict_type`, `working_value`, `resolution_basis`, `open_question`.
-
-### Assumptions_Register
-Use for user or inferred assumptions. Include: `assumption_id`, `assumption`, `source_or_owner`, `rationale`, `affected_outputs`, `evidence_label`, `confidence`, `replacement_source_needed`.
-
-### QA_Flags
-Use for exceptions. Include: `flag_id`, `severity`, `entity`, `period`, `area`, `issue`, `impact`, `recommended_fix`, `source_id`, `status`.
-
-### Disclosure_Comparability_Bridge
-Use whenever disclosures are renamed, regrouped, newly introduced, discontinued, recast, or definition-changed. Include: `area`, `metric_or_framework`, `current_period`, `prior_period`, `current_basis`, `prior_basis`, `comparison_status`, `current_value`, `prior_value`, `model_treatment`, `required_source`, `source_id`.
-
-Preserve legacy components and current-framework lines even when no exact bridge is possible. Do not create loadable historical values by deriving undisclosed amounts from rounded disclosures or narrative growth rates.
-
-Use `comparable_rounded` rather than `directional_only` when the issuer reports the same retained metric across both periods and only rounding prevents an exact total tie-out. For example, a retained top-level platform series may be comparable for trend analysis even while new sub-platform history remains unavailable.
-
-### Validation_Checks
-Use for controls actually performed. Include: `check_id`, `area`, `period`, `test`, `expected_value`, `observed_value`, `variance`, `result`, `source_id`, `notes`.
-
-Distinguish helper processing issues from analytical readiness exceptions: `Normalization_Issues` records extraction/schema/provenance processing problems, while `QA_Flags` records substantive downstream-readiness issues. An empty `Normalization_Issues` output never overrides open `QA_Flags`.
-
-
-Credit-security schedules such as covenant packages, recovery waterfalls, bond/loan/CDS pricing, spread/yield relative value, and debt-security selection belong in Credit Markets. This schema may hold net debt, maturities, liquidity, ratings, or CDS/spread signals only as common-equity risk context.
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/qa-rules.md b/plugins/financial-markets/skills/financials-normalizer/references/qa-rules.md
deleted file mode 100644
index 9017339..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/qa-rules.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# QA rules
-
-Use these checks before any normalized financials are handed to modeling, memo, deck/report, equity-model-update, scenario, risk, ETF/index, or earnings skills.
-
-## Required QA outputs
-
-Create `QA_Flags` with: `flag_id`, `severity`, `entity`, `period`, `area`, `issue`, `impact`, `recommended_fix`, `source_id`, and `status`.
-
-Create `Validation_Checks` for checks actually performed, with: `check_id`, `area`, `period`, `test`, `expected_value`, `observed_value`, `variance`, `result`, `source_id`, and `notes`. Do not report a number of passed checks unless those checks are preserved here or in an equivalent audit output.
-
-When disclosure definitions or presentation bases changed, create `Disclosure_Comparability_Bridge` before downstream handoff. Preserve the legacy and current bases, identify any recast comparable series, and state which lines are loadable, historical/audit-only, directional-only, or missing required source.
-
-Severity:
-- `blocker`: cannot use downstream until resolved.
-- `high`: materially affects valuation, rating/target support, investment memo, earnings interpretation, equity risk, or public-equity reporting.
-- `medium`: should be reviewed before senior/client/committee use.
-- `low`: documentation, labeling, or immaterial cleanup.
-
-## Source checks
-
-- Every material value has a source ID and source location.
-- Every source ID exists in `Source_Index`.
-- Every source has source date, period coverage, and source type.
-- User, management, provider, calculated, adjusted, estimated, and inferred values are separately labeled.
-- Issuer guidance is labeled `issuer_management_claim` in `kpi_schedule`; `consensus_estimate` is reserved for externally sourced estimates labeled `estimate_consensus`.
-- A user-supplied provenance note or source-support file is classified as `uploaded_file`, not `user_prompt`.
-- Conflicting source values are retained in `Conflict_Log`.
-- Stale or preliminary values are flagged but not automatically discarded.
-
-## Period checks
-
-- Fiscal year-end is identified or marked unknown.
-- Annual, quarter, month, YTD, LTM/TTM, budget, forecast, estimate, and pro forma periods are labeled distinctly.
-- Period end dates align with fiscal labels.
-- Fiscal period-end dates are sourced explicitly; do not assume calendar quarter ends from labels such as `Q1 FY2027`.
-- Stub periods are not mixed with full periods without disclosure.
-- Restated values supersede prior values when restatement status is clear.
-
-## Scale, unit, and currency checks
-
-- Currency and units are identified for every value.
-- Source units and normalized units are preserved.
-- Factor-of-1,000 / 1,000,000 differences are flagged.
-- Per-share, percentage, bps, count, and dollar values are not mixed.
-- FX conversions include rate, date, and source.
-
-## Income statement checks
-
-- Revenue - COGS ties to gross profit when all values are available.
-- Gross profit - operating expenses ties to operating income when all values are available.
-- Pretax income - tax expense ties to net income when all values are available.
-- EPS directionally ties to net income and share counts when available.
-- Non-GAAP values are not treated as GAAP facts.
-- Discontinued operations, minority interest, preferred dividends, and non-operating items are isolated when material.
-
-## Balance sheet checks
-
-- Total assets tie to total liabilities plus equity when available.
-- Current assets and current liabilities tie to component sums when available.
-- Ending cash ties to cash-flow statement ending cash when available.
-- Debt, leases, preferred stock, and noncontrolling interest are not hidden inside generic liabilities/equity without notes.
-
-## Cash flow checks
-
-- CFO + CFI + CFF + FX effect ties to net change in cash when available.
-- Beginning cash + net change ties to ending cash when available.
-- Capex sign is explicit.
-- FCF formula is explicit and not assumed to match company definition.
-- Working-capital cash-flow signs preserve source convention unless clearly converted and documented.
-
-## KPI and segment checks
-
-- KPI definitions are captured or marked missing.
-- Segment totals tie to consolidated totals when source provides reconciliations.
-- Company-defined metrics are labeled company-defined.
-- Sector metrics include units and definition: ARR, NRR, RPO, GMV, AUM, NIM, loss ratio, NOI, production, reserves, bookings, backlog, cohort data, and similar.
-- New, renamed, regrouped, discontinued, or recast KPIs/segments appear in `Disclosure_Comparability_Bridge` with both available disclosure bases and a model treatment.
-- Use `comparable_rounded` for a retained same-definition series available across periods in rounded narrative units; reserve `directional_only` for data that cannot support a comparable series.
-- Narrative growth rates or rounded current-period disclosures do not silently become exact historical model inputs.
-
-## Presentation-change checks
-
-- Apply `recast_comparable` only to rows changed or re-presented on a comparable basis; do not apply a changed cash-and-securities presentation to unrelated balance-sheet lines.
-
-## Downstream readiness
-
-Mark readiness as `ready`, `partial`, or `not_ready` for relevant downstream skills.
-
-For standalone model-ready requests, state readiness by schedule or series as well as overall. A consolidated actuals schedule may be `ready` while a recast segment driver schedule is `partial`; do not label the full package model-ready without this distinction.
-
-- Valuation/DCF: revenue, margins/EBIT/EBITDA, tax, D&A, capex, working capital, cash, debt, shares, and forecast basis.
-- Public valuation/modeling: historicals, revenue, EBITDA/EBIT, EPS, FCF, capex, working capital, cash/debt, tax, share count, and forecast drivers.
-- Normalization adjustments: financials, trial balance/detail where available, management adjustments, add-back support, customer/vendor concentration, and NWC support.
-- Earnings: reported financials, guidance/KPIs, transcript/source commentary if requested, and consensus if available.
-- Credit Markets handoff / equity-risk context: net debt, cash, liquidity runway, maturity wall summary, rating or CDS/spread signal, refinancing pressure, and common-equity downside drivers. Route covenant disclosures, collateral coverage, recovery, bond/loan/CDS pricing, and debt-security analysis to Credit Markets.
-- Internal/source exports: actuals, forecast/estimate, account/cost center mappings, department or segment owners, and refresh timestamp.
diff --git a/plugins/financial-markets/skills/financials-normalizer/references/source-protocol.md b/plugins/financial-markets/skills/financials-normalizer/references/source-protocol.md
deleted file mode 100644
index c94019a..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/references/source-protocol.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# Source protocol
-
-Use this reference when deciding what evidence to use, whether source data is stale, how to cite values, and how to handle source conflicts.
-
-## Source hierarchy
-
-Prefer the highest-ranked available source that the user is permitted to access. If a lower-ranked source is used because a higher-ranked source is unavailable, disclose that limitation.
-
-| Rank | Source type | Examples | Default treatment |
-|---:|---|---|---|
-| 1 | User-provided source package | Uploaded model, company financial workbook, issuer supplement, provider export, consensus extract, management report, internal public-company support file | Treat as governing for the user's task unless contradicted by explicit instructions or a higher-control connected system. |
-| 2 | Callable connected internal system of record | ERP/GL, planning/EPM, data warehouse, HRIS, CRM, procurement, treasury, subledger when actually available in runtime | Treat as authoritative only when the connector/tool is callable and scoped correctly; otherwise request/export and label the gap. |
-| 3 | Primary public-company source | 10-K, 10-Q, 8-K, earnings release, investor deck, transcript from company/primary provider | Treat as authoritative for public reported financials. |
-| 4 | Trusted financial data provider export or callable provider connector | User-provided FactSet, S&P Global, LSEG, Daloopa, CapIQ-like, Bloomberg-like, consensus, or market-data export; callable provider only if available in runtime | Treat as standardized; cite provider/as-of timestamp and verify critical values against primary sources when material. Do not imply live access. |
-| 5 | Secondary public source | News, market-data sites, aggregator pages, broker summaries, web snippets | Use only for context or fallback; mark lower confidence. |
-| 6 | User assumption | User-provided forecast, case, target, or scenario assumption | Label as `assumption_user_provided`. |
-| 7 | Assistant inference | Mapping inference, calculated placeholder, rough estimate | Avoid when possible; label `assumption_inferred`, disclose, and keep confidence low. |
-
-## Stale-data checks
-
-Assign `freshness_status` in `Source_Index`:
-
-| Status | Use when |
-|---|---|
-| `current` | Source date covers the requested latest period or the user-requested period. |
-| `acceptable_for_period` | Source is not the latest available but is appropriate because the user asked for that period. |
-| `preliminary` | Source is from a press release, flash report, unaudited package, soft close, draft model, or management preliminary view. |
-| `stale` | A newer filing/release/report/version likely exists for the requested period. |
-| `unknown` | Date, period, version, or retrieval metadata cannot be determined. |
-
-Default stale-data rules:
-- Public companies: latest-quarter work should prefer the latest 10-Q/10-K and earnings release; mark prior-period-only data as stale if a newer period is available.
-- Earnings work: pre-print data becomes stale once the company reports; post-print data is preliminary until filing and transcript are available.
-- Internal company or accounting exports: actuals are preliminary until close status is final or the user accepts a soft-close view.
-- User-provided source packages: data can be stale if the package lacks a recent reporting period, as-of date, provider timestamp, or updated model support.
-- Treasury/cash: bank balance and liquidity data can become stale quickly; mark older-than-requested snapshots as stale.
-
-## Citation format
-
-For every material value, preserve enough locator detail for a reviewer to find the source:
-
-`source_id | source_name | source_type | source_location | period | currency | units | retrieved_at`
-
-`source_id` is required for every normalized row. `SRC-UNSPECIFIED` is allowed only as a visible blocker placeholder in deterministic script outputs; it is not acceptable for decision-grade downstream model, memo, deck, or trading work.
-
-Examples:
-- `SRC-001 | FY2025 10-K | filing | p. 82 consolidated statements of operations | FY2025 | USD | $mm | 2026-05-06`
-- `SRC-004 | ERP actuals export | connected_system | tab GL_Detail rows 1042-1088 | Apr-2026 | USD | ones | 2026-05-06`
-- `SRC-007 | Uploaded model support | uploaded_xlsx | tab Revenue row 24 col FY2027E | FY2027E | USD | $000 | 2026-05-06`
-
-Use citation text in workbook notes/comments or dedicated citation columns. In chat, cite files/web/callable connectors using the environment's required citation syntax when available; otherwise use source IDs and request the missing export.
-
-For source typing, a provenance note, uploaded supporting document, or other user-supplied file is an `uploaded_file` even if its content was authored by the user. Reserve `user_prompt` for information stated directly in the conversation rather than provided as a file.
-
-## Source conflicts
-
-A source conflict exists when two sources give different values for the same entity, metric, period, and accounting basis beyond immaterial rounding.
-
-When conflicts appear:
-1. Preserve both values in `Conflict_Log`.
-2. Identify conflict type: `timing`, `definition`, `scale`, `currency`, `restatement`, `pro_forma`, `reported_vs_adjusted`, `provider_standardization`, `mapping`, or `unknown`.
-3. Choose a working value only if source hierarchy and task context support it.
-4. Label the working value's evidence and confidence.
-5. Explain unresolved conflicts before downstream handoff.
-
-## Fact vs assumption handling
-
-- Use `fact_source_reported` only for values directly visible in a cited source or connected system.
-- Use `fact_provider_standardized` for provider-normalized values even if they are based on filings.
-- Use `derived_calculation` when the value is mathematically calculated from cited inputs.
-- Use `issuer_management_claim` for issuer, company, or management assertions that need evidence support.
-- Store issuer outlook or guidance as `kpi_schedule` rows labeled `issuer_management_claim`; do not treat it as `consensus_estimate`.
-- Use `management_adjusted` for company-defined adjusted metrics or management model adjustments.
-- Use `analyst_adjusted` for normalization, add-backs, reclasses, or user/assistant analytical changes.
-- Use `analyst_interpretation` for synthesis based on cited facts but not directly stated by a source.
-- Use `assumption_user_provided` for explicit user assumptions.
-- Use `assumption_inferred` only when necessary; explain why and how to replace it with evidence.
-- Use `estimate_consensus` with `consensus_estimate` only for externally sourced consensus/provider forecasts.
-- Use `stale_source` when a source may be superseded for the current decision.
-- Use `contradicted_source` when source conflict is material and unresolved.
-- Use `missing_required_source` instead of filling unsupported blanks.
-- Use `unknown` only when evidence type cannot be determined.
diff --git a/plugins/financial-markets/skills/financials-normalizer/scripts/build_source_index.py b/plugins/financial-markets/skills/financials-normalizer/scripts/build_source_index.py
deleted file mode 100755
index 7daa9db..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/scripts/build_source_index.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/env python3
-"""Create a starter Source_Index CSV from a folder of source files."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-from datetime import datetime, timezone
-from pathlib import Path
-
-HEADER = [
-    "source_id",
-    "source_name",
-    "source_type",
-    "owner_or_provider",
-    "period_covered",
-    "as_of_date",
-    "retrieved_at",
-    "file_tab_page_url_or_location",
-    "source_rank",
-    "freshness_status",
-    "notes",
-]
-
-EXT_TYPES = {
-    ".pdf": "uploaded_file",
-    ".xlsx": "uploaded_file",
-    ".xls": "uploaded_file",
-    ".csv": "uploaded_file",
-    ".json": "uploaded_file",
-    ".docx": "uploaded_file",
-    ".pptx": "uploaded_file",
-    ".txt": "uploaded_file",
-    ".md": "uploaded_file",
-}
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Build a starter Source_Index CSV from a source folder"
-    )
-    parser.add_argument("source_folder", type=Path)
-    parser.add_argument("output_csv", type=Path)
-    args = parser.parse_args()
-    if not args.source_folder.exists() or not args.source_folder.is_dir():
-        raise SystemExit(f"source folder not found: {args.source_folder}")
-    files = sorted(
-        [p for p in args.source_folder.rglob("*") if p.is_file() and not p.name.startswith(".")]
-    )
-    retrieved_at = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    args.output_csv.parent.mkdir(parents=True, exist_ok=True)
-    with args.output_csv.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=HEADER)
-        writer.writeheader()
-        for idx, path in enumerate(files, start=1):
-            writer.writerow(
-                {
-                    "source_id": f"SRC-{idx:03d}",
-                    "source_name": path.name,
-                    "source_type": EXT_TYPES.get(path.suffix.lower(), "uploaded_file"),
-                    "owner_or_provider": "user_provided",
-                    "period_covered": "",
-                    "as_of_date": "",
-                    "retrieved_at": retrieved_at,
-                    "file_tab_page_url_or_location": str(path),
-                    "source_rank": "1",
-                    "freshness_status": "unknown",
-                    "notes": "starter index; review period/as-of/reliability after inspection",
-                }
-            )
-    print(f"wrote {len(files)} sources to {args.output_csv}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/financials-normalizer/scripts/normalize_extracted_financials.py b/plugins/financial-markets/skills/financials-normalizer/scripts/normalize_extracted_financials.py
deleted file mode 100755
index 1159d74..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/scripts/normalize_extracted_financials.py
+++ /dev/null
@@ -1,607 +0,0 @@
-#!/usr/bin/env python3
-"""Normalize already-extracted financial rows into the financials-normalizer long-form schema.
-
-Inputs may be CSV or JSON. This script is intentionally conservative: it preserves
-source labels and signs, maps only clear aliases, and leaves low-confidence items
-flagged for review rather than forcing an analyst judgment.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import re
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-OUTPUT_COLUMNS = [
-    "entity",
-    "source_id",
-    "statement",
-    "line_item_original",
-    "line_item_standard",
-    "line_item_id",
-    "period_end",
-    "period_label",
-    "period_type",
-    "currency",
-    "units",
-    "source_value",
-    "normalized_value",
-    "normalization_method",
-    "source_location",
-    "evidence_label",
-    "confidence",
-    "normalization_note",
-]
-
-ISSUE_COLUMNS = [
-    "severity",
-    "issue_type",
-    "row_number",
-    "field",
-    "source_id",
-    "description",
-    "recommended_action",
-]
-
-SOURCE_INDEX_COLUMNS = [
-    "source_id",
-    "source_name",
-    "source_type",
-    "owner_or_provider",
-    "period_covered",
-    "as_of_date",
-    "retrieved_at",
-    "file_tab_page_url_or_location",
-    "source_rank",
-    "freshness_status",
-    "notes",
-]
-
-ALLOWED_STATEMENTS = {
-    "income_statement",
-    "balance_sheet",
-    "cash_flow",
-    "kpi_schedule",
-    "segment",
-    "equity_risk_debt_liquidity_context",
-    "share_count",
-    "working_capital",
-    "capital_allocation",
-    "consensus_estimate",
-    "etf_index_context",
-    "adjustment",
-}
-
-EVIDENCE_LABELS = {
-    "fact_source_reported",
-    "fact_provider_standardized",
-    "derived_calculation",
-    "issuer_management_claim",
-    "management_adjusted",
-    "analyst_adjusted",
-    "analyst_interpretation",
-    "assumption_user_provided",
-    "assumption_inferred",
-    "estimate_consensus",
-    "stale_source",
-    "contradicted_source",
-    "missing_required_source",
-    "unknown",
-}
-
-LOCATION_OPTIONAL_LABELS = {
-    "assumption_user_provided",
-    "assumption_inferred",
-    "missing_required_source",
-    "unknown",
-}
-
-
-def norm(value: Any) -> str:
-    return "" if value is None else str(value).strip()
-
-
-def slug(value: str) -> str:
-    value = value.lower().replace("&", " and ")
-    value = re.sub(r"[^a-z0-9]+", "_", value)
-    return re.sub(r"_+", "_", value).strip("_")
-
-
-def parse_number(value: Any) -> str:
-    parsed = parse_numeric(value)
-    if parsed is None:
-        return ""
-    return format_number(parsed)
-
-
-def parse_numeric(value: Any) -> float | None:
-    text = norm(value)
-    if not text or text.lower() in {"na", "n/a", "nm", "-", "--"}:
-        return None
-    neg = text.startswith("(") and text.endswith(")")
-    cleaned = text.replace(",", "").replace("$", "").replace("%", "")
-    cleaned = cleaned.strip("() ")
-    match = re.search(r"[-+]?\d+(?:\.\d+)?", cleaned)
-    if not match:
-        return None
-    val = float(match.group(0))
-    if neg:
-        val = -abs(val)
-    return val
-
-
-def format_number(value: float | None) -> str:
-    if value is None:
-        return ""
-    rendered = f"{value:.6f}".rstrip("0").rstrip(".")
-    return "0" if rendered in {"-0", ""} else rendered
-
-
-def infer_period_type(period_label: str, period_type: str) -> str:
-    if period_type:
-        return period_type
-    p = period_label.lower()
-    if re.search(r"outlook|guidance|budget|forecast|plan|case|scenario|estimate", p):
-        return "forecast"
-    if "ltm" in p:
-        return "ltm"
-    if "ytd" in p:
-        return "ytd"
-    if re.search(r"q[1-4]|quarter", p):
-        return "quarterly"
-    if re.search(r"fy|year", p):
-        return "annual"
-    if re.search(r"jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec", p):
-        return "monthly"
-    return ""
-
-
-def infer_period_end(period_end: str, period_label: str) -> str:
-    if period_end:
-        return period_end
-    label = period_label.lower().replace(" ", "")
-    year_match = re.search(r"(20\d{2}|19\d{2})", label)
-    short_year = re.search(r"(?:fy|cy|q[1-4])'?(\d{2})", label)
-    year = int(year_match.group(1)) if year_match else None
-    if year is None and short_year:
-        year = 2000 + int(short_year.group(1))
-    if year is None:
-        return ""
-    # A fiscal-quarter label alone does not identify an issuer's calendar date.
-    is_calendar_period = "cy" in label or "calendar" in label
-    if not is_calendar_period:
-        return ""
-    quarter_ends = {"q1": "03-31", "q2": "06-30", "q3": "09-30", "q4": "12-31"}
-    for quarter, month_day in quarter_ends.items():
-        if quarter in label:
-            return f"{year}-{month_day}"
-    if "fy" in label or "cy" in label or "year" in label:
-        return f"{year}-12-31"
-    return ""
-
-
-def normalize_currency(currency: str, units: str, raw_value: str) -> str:
-    raw = " ".join([currency, units, raw_value]).lower()
-    if currency:
-        return currency.upper()
-    if "$" in raw or "usd" in raw:
-        return "USD"
-    if "€" in raw or "eur" in raw:
-        return "EUR"
-    if "£" in raw or "gbp" in raw:
-        return "GBP"
-    return ""
-
-
-def normalize_source_type(source_type: str, source_name: str) -> str:
-    normalized = norm(source_type) or "uploaded_file"
-    if normalized == "user_prompt" and Path(source_name).suffix:
-        return "uploaded_file"
-    return normalized
-
-
-def infer_units(units: str, raw_value: str) -> str:
-    raw = " ".join([units, raw_value]).lower().replace(" ", "")
-    if "%" in raw:
-        return "%"
-    if "bps" in raw or raw.endswith("bp"):
-        return "bps"
-    if "x" in raw and re.search(r"\d", raw):
-        return "x"
-    if any(token in raw for token in ["$bn", "usdbn", "billion", "bn"]):
-        return "$mm"
-    if any(token in raw for token in ["$000", "$k", "thousand", "usd000"]):
-        return "$mm"
-    if any(token in raw for token in ["$mm", "$m", "million", "usdmm"]):
-        return "$mm"
-    return units
-
-
-def scale_factor_to_normalized_units(units: str, raw_value: str) -> float:
-    raw = " ".join([units, raw_value]).lower().replace(" ", "")
-    if any(token in raw for token in ["$bn", "usdbn", "billion", "bn"]):
-        return 1000.0
-    if any(token in raw for token in ["$000", "$k", "thousand", "usd000"]):
-        return 0.001
-    return 1.0
-
-
-def apply_sign_convention(
-    statement: str, original: str, value: float | None
-) -> tuple[float | None, str]:
-    if value is None:
-        return None, ""
-    original_slug = slug(original)
-    capex_terms = ("capex", "capital_expenditure", "capital_expenditures")
-    if (
-        statement == "cash_flow"
-        and any(term in original_slug for term in capex_terms)
-        and value > 0
-    ):
-        return -value, "sign_flipped_for_cash_flow_capex"
-    return value, ""
-
-
-def load_aliases(path: Path) -> dict[str, dict[str, str]]:
-    aliases: dict[str, dict[str, str]] = {}
-    with path.open(newline="", encoding="utf-8-sig") as f:
-        for row in csv.DictReader(f):
-            alias = slug(norm(row.get("alias")))
-            if alias:
-                aliases[alias] = {
-                    "line_item_id": norm(row.get("line_item_id")),
-                    "line_item_standard": norm(row.get("line_item_standard")),
-                    "statement": norm(row.get("statement")),
-                }
-    return aliases
-
-
-def load_input(path: Path) -> list[dict[str, Any]]:
-    if path.suffix.lower() == ".csv":
-        with path.open(newline="", encoding="utf-8-sig") as f:
-            return [dict(row) for row in csv.DictReader(f)]
-    if path.suffix.lower() == ".json":
-        data = json.loads(path.read_text(encoding="utf-8"))
-        if isinstance(data, list):
-            return [dict(row) for row in data]
-        if isinstance(data, dict):
-            rows = data.get("rows") or data.get("records") or data.get("data") or []
-            if isinstance(rows, list):
-                metadata = data.get("metadata") or {}
-                return [dict(metadata, **dict(row)) for row in rows]
-        raise ValueError("json input must be a list or an object with rows/records/data")
-    raise ValueError("input must be .csv or .json")
-
-
-def normalize_row(row: dict[str, Any], aliases: dict[str, dict[str, str]]) -> dict[str, str]:
-    original = norm(
-        row.get("line_item_original")
-        or row.get("label")
-        or row.get("line_item")
-        or row.get("source_label")
-    )
-    alias = aliases.get(slug(original), {})
-    source_value_raw = norm(row.get("source_value") or row.get("value") or row.get("amount"))
-    source_numeric = parse_numeric(source_value_raw)
-    statement = norm(row.get("statement") or alias.get("statement"))
-    legacy_debt_schedule = statement == "debt_schedule"
-    if legacy_debt_schedule:
-        statement = "equity_risk_debt_liquidity_context"
-    if statement not in ALLOWED_STATEMENTS:
-        statement = alias.get("statement", statement)
-    line_item_id = norm(row.get("line_item_id") or alias.get("line_item_id"))
-    line_item_standard = norm(row.get("line_item_standard") or alias.get("line_item_standard"))
-    confidence = norm(row.get("confidence")) or "high"
-    note_parts = []
-    if legacy_debt_schedule:
-        note_parts.append("legacy debt_schedule migrated to equity-risk debt/liquidity context")
-    if not line_item_id:
-        line_item_id = "unmapped_" + (slug(original) or "unknown")
-        line_item_standard = original or "Unmapped"
-        confidence = "low"
-        note_parts.append("mapping requires analyst review")
-    if not statement or statement not in ALLOWED_STATEMENTS:
-        raw_value = norm(row.get("source_value") or row.get("value"))
-        statement = (
-            "kpi_schedule" if "margin" in slug(original) or "%" in raw_value else "income_statement"
-        )
-        confidence = "low"
-        note_parts.append("statement inferred; review")
-
-    source_location = norm(
-        row.get("source_location") or row.get("locator") or row.get("cell") or row.get("page")
-    )
-    evidence_label = norm(row.get("evidence_label")) or "fact_source_reported"
-    if evidence_label == "management_claim":
-        evidence_label = "issuer_management_claim"
-    if evidence_label not in EVIDENCE_LABELS:
-        confidence = "low"
-        note_parts.append("unrecognized evidence_label; review")
-    if statement == "consensus_estimate" and evidence_label == "issuer_management_claim":
-        statement = "kpi_schedule"
-        note_parts.append("issuer guidance retained separately from external consensus")
-    elif statement == "consensus_estimate" and evidence_label != "estimate_consensus":
-        evidence_label = "estimate_consensus"
-        confidence = "low"
-        note_parts.append(
-            "consensus evidence label normalized to estimate_consensus; verify external source"
-        )
-    source_id = norm(row.get("source_id"))
-    if not source_location and evidence_label not in LOCATION_OPTIONAL_LABELS:
-        confidence = "low"
-        note_parts.append("source location missing")
-    if not source_id:
-        confidence = "low"
-        note_parts.append("source_id missing; provenance must be fixed before downstream use")
-    source_units = norm(row.get("units") or row.get("unit") or row.get("scale"))
-    units = infer_units(source_units, source_value_raw)
-    scaled = None
-    if source_numeric is not None:
-        scaled = source_numeric * scale_factor_to_normalized_units(source_units, source_value_raw)
-    normalized_numeric, sign_note = apply_sign_convention(statement, original, scaled)
-    normalized_value = format_number(normalized_numeric)
-    methods = ["as_reported"]
-    if source_numeric is not None and normalized_numeric != source_numeric:
-        methods.append("scaled_or_sign_normalized")
-    if sign_note:
-        note_parts.append(sign_note)
-    method = "+".join(methods)
-    period_label = norm(row.get("period_label") or row.get("period"))
-    note = norm(row.get("normalization_note"))
-    if note:
-        note_parts.insert(0, note)
-
-    return {
-        "entity": norm(row.get("entity") or row.get("entity_name") or row.get("company")),
-        "source_id": source_id or "SRC-UNSPECIFIED",
-        "statement": statement,
-        "line_item_original": original,
-        "line_item_standard": line_item_standard,
-        "line_item_id": line_item_id,
-        "period_end": infer_period_end(norm(row.get("period_end")), period_label),
-        "period_label": period_label,
-        "period_type": infer_period_type(period_label, norm(row.get("period_type"))),
-        "currency": normalize_currency(norm(row.get("currency")), source_units, source_value_raw),
-        "units": units,
-        "source_value": source_value_raw,
-        "normalized_value": normalized_value,
-        "normalization_method": method,
-        "source_location": source_location,
-        "evidence_label": evidence_label,
-        "confidence": confidence,
-        "normalization_note": "; ".join(p for p in note_parts if p),
-    }
-
-
-def build_normalization_issues(
-    rows: list[dict[str, Any]], normalized: list[dict[str, str]]
-) -> list[dict[str, str]]:
-    issues: list[dict[str, str]] = []
-    for idx, (raw, normed) in enumerate(zip(rows, normalized), start=2):
-        raw_source_id = norm(raw.get("source_id"))
-        source_id = normed.get("source_id", "")
-        if not raw_source_id:
-            issues.append(
-                {
-                    "severity": "error",
-                    "issue_type": "missing_source_id",
-                    "row_number": str(idx),
-                    "field": "source_id",
-                    "source_id": source_id or "SRC-UNSPECIFIED",
-                    "description": (
-                        "Input row is missing source_id; normalized output used "
-                        "SRC-UNSPECIFIED as a visible placeholder."
-                    ),
-                    "recommended_action": (
-                        "Assign a stable source_id from Source_Index before using this row downstream."
-                    ),
-                }
-            )
-        elif not raw_source_id.startswith("SRC-"):
-            issues.append(
-                {
-                    "severity": "warning",
-                    "issue_type": "nonstandard_source_id",
-                    "row_number": str(idx),
-                    "field": "source_id",
-                    "source_id": raw_source_id,
-                    "description": "source_id does not follow the expected SRC-### convention.",
-                    "recommended_action": (
-                        "Use a stable SRC-### source_id so downstream citations and tie-outs are consistent."
-                    ),
-                }
-            )
-        if (
-            not normed.get("source_location")
-            and normed.get("evidence_label") not in LOCATION_OPTIONAL_LABELS
-        ):
-            issues.append(
-                {
-                    "severity": "warning",
-                    "issue_type": "missing_source_location",
-                    "row_number": str(idx),
-                    "field": "source_location",
-                    "source_id": source_id,
-                    "description": "Source-backed row is missing page, tab, cell, URL, or other source locator.",
-                    "recommended_action": "Add a source_location before relying on the value for decision-grade work.",
-                }
-            )
-        if not normed.get("period_end"):
-            issues.append(
-                {
-                    "severity": "warning",
-                    "issue_type": "missing_period_end",
-                    "row_number": str(idx),
-                    "field": "period_end",
-                    "source_id": source_id,
-                    "description": (
-                        "Period end is not explicitly sourced; fiscal labels are not "
-                        "converted to assumed calendar dates."
-                    ),
-                    "recommended_action": (
-                        "Add the issuer's reported fiscal period-end date before loading "
-                        "the row downstream."
-                    ),
-                }
-            )
-    return issues
-
-
-def write_csv(path: Path, rows: list[dict[str, str]], columns: list[str]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    with path.open("w", newline="", encoding="utf-8") as f:
-        writer = csv.DictWriter(f, fieldnames=columns)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def write_source_index(path: Path, rows: list[dict[str, Any]]) -> None:
-    sources: dict[str, dict[str, str]] = {}
-    retrieved_at = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    for row in rows:
-        raw_source_id = norm(row.get("source_id"))
-        source_id = raw_source_id or "SRC-UNSPECIFIED"
-        if source_id not in sources:
-            if raw_source_id:
-                notes = "review source rank and freshness before relying on output"
-            else:
-                notes = "ERROR: source_id missing; assign a stable SRC-### id before downstream use"
-            sources[source_id] = {
-                "source_id": source_id,
-                "source_name": norm(row.get("source_name") or row.get("source")),
-                "source_type": normalize_source_type(
-                    norm(row.get("source_type")),
-                    norm(
-                        row.get("source_name")
-                        or row.get("source")
-                        or row.get("source_location")
-                        or row.get("locator")
-                        or row.get("file")
-                    ),
-                ),
-                "owner_or_provider": norm(row.get("owner_or_provider") or row.get("provider")),
-                "period_covered": norm(row.get("period_label") or row.get("period")),
-                "as_of_date": norm(row.get("as_of_date") or row.get("source_date")),
-                "retrieved_at": norm(row.get("retrieved_at")) or retrieved_at,
-                "file_tab_page_url_or_location": norm(
-                    row.get("source_location") or row.get("locator") or row.get("file")
-                ),
-                "source_rank": norm(row.get("source_rank")),
-                "freshness_status": norm(row.get("freshness_status")) or "unknown",
-                "notes": notes,
-            }
-    write_csv(path, list(sources.values()), SOURCE_INDEX_COLUMNS)
-
-
-def write_support_manifest(
-    output_dir: Path, *, standalone: bool, issues: list[dict[str, str]]
-) -> None:
-    outputs = {
-        "normalized_financials": output_dir / "Normalized_Financials_Long.csv",
-        "source_index": output_dir / "Source_Index.csv",
-        "normalization_issues": output_dir / "Normalization_Issues.csv",
-        "run_log": output_dir / "run_log.json",
-        "manifest": output_dir / "manifest.json",
-    }
-    primary = str(outputs["normalized_financials"]) if standalone else None
-    output_manifest = [
-        {
-            "key": key,
-            "path": str(path),
-            "required": True,
-            "written": path.exists() or key in {"run_log", "manifest"},
-            "artifact_role": "primary_human_deliverable"
-            if primary and str(path) == primary
-            else "support_artifact",
-            "hidden_unless_requested": not (primary and str(path) == primary),
-            "description": "Financials-normalizer support artifact for Public Equity Investing workflows.",
-        }
-        for key, path in outputs.items()
-    ]
-    hard_failures = [issue["description"] for issue in issues if issue.get("severity") == "error"]
-    run_log = {
-        "status": "failed" if hard_failures else "completed",
-        "model_status": "screen-grade" if issues else "senior-review-ready",
-        "artifact_level": "standalone_support_request"
-        if standalone
-        else "embedded_support_artifact",
-        "workbook_mode": "csv_normalization_support",
-        "primary_human_deliverable": primary,
-        "support_artifacts_user_visible_default": False,
-        "warnings": [issue["description"] for issue in issues if issue.get("severity") != "error"],
-        "hard_failures": hard_failures,
-        "output_manifest": output_manifest,
-        "final_response_guidance": {
-            "lead_with": "normalization_package" if standalone else "owning_workflow_hero_artifact",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-    }
-    outputs["run_log"].write_text(json.dumps(run_log, indent=2) + "\n", encoding="utf-8")
-    outputs["manifest"].write_text(
-        json.dumps(
-            {
-                "outputs": output_manifest,
-                "primary_human_deliverable": primary,
-                "support_artifacts_user_visible_default": False,
-                "final_response_guidance": run_log["final_response_guidance"],
-            },
-            indent=2,
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Normalize extracted financial rows into long-form CSV"
-    )
-    parser.add_argument("input_file", type=Path, help="Extracted financial rows as CSV or JSON")
-    parser.add_argument("output_dir", type=Path, nargs="?", help="Directory for output CSV files")
-    parser.add_argument(
-        "--output-dir", dest="output_dir_flag", type=Path, help="Directory for output CSV files"
-    )
-    parser.add_argument(
-        "--aliases",
-        type=Path,
-        default=Path(__file__).resolve().parents[1] / "references" / "line_item_aliases.csv",
-    )
-    parser.add_argument(
-        "--standalone-normalization-package",
-        action="store_true",
-        help="Mark the normalized CSV package as the explicit support-task deliverable.",
-    )
-    args = parser.parse_args()
-    output_dir = args.output_dir_flag or args.output_dir
-    if output_dir is None:
-        parser.error("output directory is required")
-
-    aliases = load_aliases(args.aliases)
-    raw_rows = load_input(args.input_file)
-    normalized = [normalize_row(row, aliases) for row in raw_rows]
-    issues = build_normalization_issues(raw_rows, normalized)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    write_csv(output_dir / "Normalized_Financials_Long.csv", normalized, OUTPUT_COLUMNS)
-    write_source_index(output_dir / "Source_Index.csv", raw_rows)
-    write_csv(output_dir / "Normalization_Issues.csv", issues, ISSUE_COLUMNS)
-    write_support_manifest(
-        output_dir, standalone=args.standalone_normalization_package, issues=issues
-    )
-    print(f"wrote {len(normalized)} rows to {output_dir / 'Normalized_Financials_Long.csv'}")
-    error_count = sum(1 for issue in issues if issue.get("severity") == "error")
-    if error_count:
-        print(
-            "ERROR: "
-            f"{error_count} provenance issue(s) require remediation; "
-            f"see {output_dir / 'Normalization_Issues.csv'}"
-        )
-        return 1
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/financials-normalizer/scripts/validate_normalized_financials.py b/plugins/financial-markets/skills/financials-normalizer/scripts/validate_normalized_financials.py
deleted file mode 100755
index 80ae7c0..0000000
--- a/plugins/financial-markets/skills/financials-normalizer/scripts/validate_normalized_financials.py
+++ /dev/null
@@ -1,160 +0,0 @@
-#!/usr/bin/env python3
-"""Lightweight schema validator for financials-normalizer CSV outputs."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-import sys
-from pathlib import Path
-
-REQUIRED = {
-    "entity",
-    "source_id",
-    "statement",
-    "line_item_original",
-    "line_item_standard",
-    "line_item_id",
-    "period_end",
-    "period_label",
-    "period_type",
-    "currency",
-    "units",
-    "source_value",
-    "normalized_value",
-    "normalization_method",
-    "source_location",
-    "evidence_label",
-    "confidence",
-}
-EVIDENCE = {
-    "fact_source_reported",
-    "fact_provider_standardized",
-    "derived_calculation",
-    "issuer_management_claim",
-    "management_adjusted",
-    "analyst_adjusted",
-    "analyst_interpretation",
-    "assumption_user_provided",
-    "assumption_inferred",
-    "estimate_consensus",
-    "stale_source",
-    "contradicted_source",
-    "missing_required_source",
-    "unknown",
-}
-STATEMENTS = {
-    "income_statement",
-    "balance_sheet",
-    "cash_flow",
-    "kpi_schedule",
-    "segment",
-    "equity_risk_debt_liquidity_context",
-    "share_count",
-    "working_capital",
-    "capital_allocation",
-    "consensus_estimate",
-    "etf_index_context",
-    "adjustment",
-}
-PERIOD_TYPES = {
-    "annual",
-    "quarterly",
-    "monthly",
-    "ytd",
-    "ltm",
-    "forecast",
-    "budget",
-    "pro_forma",
-    "scenario",
-}
-LOCATION_OPTIONAL = {
-    "assumption_user_provided",
-    "assumption_inferred",
-    "missing_required_source",
-    "unknown",
-}
-CONFIDENCE = {"high", "medium", "low"}
-
-
-def is_number(value: str) -> bool:
-    try:
-        number = float(str(value).replace(",", ""))
-    except Exception:
-        return False
-    return math.isfinite(number)
-
-
-def validate(path: Path) -> dict:
-    errors, warnings = [], []
-    with path.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        fields = set(reader.fieldnames or [])
-        missing = sorted(REQUIRED - fields)
-        if missing:
-            return {
-                "ok": False,
-                "rows": 0,
-                "errors": ["missing columns: " + ", ".join(missing)],
-                "warnings": [],
-            }
-        rows = list(reader)
-    for i, row in enumerate(rows, start=2):
-        label = f"row {i}"
-        source_id = row.get("source_id", "")
-        if not source_id or source_id == "SRC-UNSPECIFIED":
-            errors.append(
-                f"{label}: missing source_id; SRC-UNSPECIFIED is not decision-grade provenance"
-            )
-        elif not source_id.startswith("SRC-"):
-            warnings.append(f"{label}: source_id should start with SRC-")
-        if row.get("evidence_label") not in EVIDENCE:
-            errors.append(f"{label}: invalid evidence_label {row.get('evidence_label')!r}")
-        if row.get("statement") not in STATEMENTS:
-            errors.append(f"{label}: invalid statement {row.get('statement')!r}")
-        if (
-            row.get("statement") == "consensus_estimate"
-            and row.get("evidence_label") != "estimate_consensus"
-        ):
-            errors.append(
-                f"{label}: consensus_estimate is reserved for external consensus "
-                "labeled estimate_consensus"
-            )
-        if row.get("period_type") not in PERIOD_TYPES:
-            errors.append(f"{label}: invalid period_type {row.get('period_type')!r}")
-        if row.get("confidence") not in CONFIDENCE:
-            errors.append(f"{label}: invalid confidence {row.get('confidence')!r}")
-        if row.get("evidence_label") != "missing_required_source" and not is_number(
-            row.get("normalized_value", "")
-        ):
-            errors.append(f"{label}: normalized_value is not numeric")
-        if row.get("evidence_label") not in LOCATION_OPTIONAL and not row.get("source_location"):
-            warnings.append(f"{label}: missing source_location")
-    return {"ok": not errors, "rows": len(rows), "errors": errors, "warnings": warnings}
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("csv_path", type=Path)
-    parser.add_argument("--json", action="store_true")
-    args = parser.parse_args()
-    if not args.csv_path.exists():
-        print(f"file not found: {args.csv_path}", file=sys.stderr)
-        return 2
-    result = validate(args.csv_path)
-    if args.json:
-        print(json.dumps(result, indent=2))
-    else:
-        print("ok" if result["ok"] else "failed")
-        print(f"rows: {result['rows']}")
-        for error in result["errors"]:
-            print(f"error: {error}")
-        for warning in result["warnings"]:
-            print(f"warning: {warning}")
-    return 0 if result["ok"] else 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/idea-generation/SKILL.md b/plugins/financial-markets/skills/idea-generation/SKILL.md
deleted file mode 100644
index 889d501..0000000
--- a/plugins/financial-markets/skills/idea-generation/SKILL.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-name: idea-generation
-description: Use when triaging public-equity idea candidates. Do not use for final trade recommendations, pitches, memos, or models.
----
-
-# Idea Generation For Public Equity Investing
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML idea-triage report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive idea screen, market map, watchlist review, or reusable/source-heavy candidate set, the default resolves the presentation surface to a polished standalone HTML idea-triage report unless the user requests another surface, a quick/no-file answer, or workbook/tracker output. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML idea-triage report and `Full working analysis` and disclose those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Triage public-equity idea candidates and screen outputs into research-worthy longs, shorts, pairs, catalysts, relative-value ideas, watchlist items, and rejected false positives. This is a PM-style research-prioritization skill, not a final recommendation engine.
-
-A strong output explains why a security surfaced now, possible variant perception, what is priced in, what would make it investable, first rejection risk, and the next workflow.
-
-For a new standalone reader-facing idea screen, market map, watchlist review, or candidate set, produce a polished standalone HTML idea-triage report following `../../shared/html-artifact-standard.md`; let the mandate and screening question determine the structure. Use chat only when the user explicitly requests a lightweight response. Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render.
-
-## Operating Rules
-
-- Never present a screen result as a final recommendation.
-- Label outputs as idea candidates, watchlist items, deeper-research candidates, or rejected false positives.
-- Preserve user portfolios, watchlists, models, notes, and spreadsheets; add outputs rather than overwriting source work.
-- Use prompt/connected/user files first, primary sources next, market/consensus data next, and web fallback only when current public data is needed.
-- Do not fabricate unavailable market data or metrics.
-- Use `crowded` only when supported by positioning, ownership, flow, short-interest, or comparably direct evidence. If the evidence is limited to price appreciation, narrative visibility, or simple valuation, use `expectations-heavy`, `valuation-gated`, or `crowding-risk candidate`.
-- Do not advance a candidate solely because the stock rallied after a thematic catalyst or management mentioned the theme. For an advanced name, show a source-backed link from the driver to orders, backlog, revenue, margins, or estimate revisions; otherwise label it `needs exposure attribution`.
-
-## Workflow
-
-1. **Assess context.** With no context, use liquid public-equity defaults and state assumptions. With partial context, infer mandate/style and label assumptions. With rich context, treat provided portfolios, screens, notes, files, or connected data as primary evidence.
-2. **Set mandate and universe.** Identify long-only, long/short, market-neutral, event, credit, region, sector, cap size, liquidity, benchmark, horizon, and allowed instruments.
-3. **Normalize candidates.** Resolve tickers, ADRs, share classes, currencies, fiscal calendars, sectors, liquidity, benchmark membership, and portfolio/watchlist overlap.
-4. **Screen by archetype and beneficiary pathway.** Use long, short, pair, relative value, catalyst, and watchlist archetypes rather than one generic rank. For thematic screens, map candidates by beneficiary pathway before ranking, such as electrical/power distribution, construction, networking, cooling, grid/generation, or colocation/real estate.
-5. **Score if structured data exists.** Use `scripts/score_ideas.py` only for user-provided candidate rows and scoring fields.
-6. **Apply PM triage.** Test denominator quality, estimate credibility, catalyst path, valuation support, risk compensation, verified positioning versus inferred expectations risk, liquidity, and false-positive risk.
-7. **Output priorities and routing.** Lead with a candidate funnel and route candidates to model update, earnings, pitch, thesis tracker, hedge, event, credit, macro, scenario, memo, or QC workflows as appropriate.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: screen construction, source QC, variant view, catalyst path, risk flags, and prioritization. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Deterministic Scoring Helper
-
-```bash
-python scripts/score_ideas.py candidates.csv --output-dir output
-```
-
-The helper materializes supplied scores into ranked support outputs and warnings. It does not create proprietary market data or replace PM judgment. If no numeric score fields are supplied, treat output as qualitative triage.
-
-For standardized dashboard handoffs only, use `references/DASHBOARD_PACK.md`. `idea-generation` owns candidate triage, screen interpretation, and workflow routing; `dashboard-builder` owns the shared shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML artifact unless explicitly requested.
-
-## Output Contract
-
-Default output:
-
-- scope and assumptions;
-- methodology and data/source caveats;
-- headline research-priority conclusion and candidate funnel;
-- beneficiary-pathway map when relevant to a thematic screen;
-- ranked idea table with exposure proof and expectations risk;
-- PM-style idea cards for highest-priority names;
-- rejected/deprioritized false positives;
-- cross-idea themes and risk observations;
-- next research step for each important candidate;
-- downstream routing.
-
-Ranking buckets: `A - immediate research candidate`, `B - watchlist / needs trigger`, `C - screen flag only`, and `Reject`.
-
-For thematic or source-heavy idea screens, express the funnel as `Advance to deeper work`, `Valuation / expectations gated`, `Exposure not yet proven`, and `Deprioritized or reject`. Make clear that `Advance to deeper work` is a research-priority status, not an investment recommendation, approved position, or attractive entry point; an advanced candidate may still be valuation-gated or entry-gated. A candidate without a quantified or source-backed link to the theme stays in `Exposure not yet proven` and is labeled `needs exposure attribution`.
-
-## HTML Guidance
-
-For a substantive HTML idea-triage report, load `../../shared/html-artifact-standard.md` and apply these workflow-specific rules:
-
-- Lead with the research-priority conclusion and candidate funnel; make the funnel or an exposure-versus-expectations matrix the primary visual object.
-- Use three to five first-read tiles only when each has a distinct job, such as universe size, advance count, valuation-gated count, unproven-exposure count, or the most important evidence gap.
-- Keep methodology and evidence-posture commentary compact or place it below the candidate funnel when it would otherwise delay the primary research-prioritization visual.
-- Put the ranked candidate board near the top. For thematic screens, organize candidates by beneficiary pathway before or alongside rank.
-- Show `Exposure proof`, `Expectations risk`, `First rejection`, and `Next workflow` in the table or visible candidate summaries. Reserve longer idea cards for the highest-priority names.
-- Distinguish verified positioning from inferred crowding risk; do not make the headline or status label more certain than the evidence.
-- Keep citations traceable but readable: do not fragment years, ticker symbols, ranges, product names, or guidance values into separately linked tokens, and avoid an oversized citation run in the hero.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on legibility, hierarchy, clipping, citation rendering, and whitespace before delivery.
-
-## Reference Map
-
-- `references/source-qc.md`: source hierarchy, stale data, citations, data caveats.
-- `references/workflow.md`: full workflow and context adaptation.
-- `references/screen-archetypes.md`: long, short, pair, catalyst, and watchlist archetypes.
-- `references/sector-overlays.md`: sector metrics and traps.
-- `references/output-standards.md`: report and idea-card templates.
-- `references/downstream-routing.md`: local handoff patterns.
-- `references/scoring-materializer.md`: deterministic scoring contract.
-
-## Public Equity PM Judgment Layer
-
-For substantial screens or idea queues, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Every top idea must include `Actionability`, `Variant Wedge`, `Why Now`, `First Rejection`, `What Would Make It Investable`, `What Would Kill It`, and `Next Workflow`.
-
-Required PM judgment:
-- Long-only: benchmark fit, active share, quality durability, downside versus benchmark, liquidity, and add/trim path.
-- Long/short hedge fund: catalyst path, shortability, borrow, gross/net impact, factor crowding, and cover discipline.
-- Sell-side: rating-change potential, estimate revisions, target-price debate, client relevance, and risk-to-rating.
-- ETF/index: index methodology, constituent weight, rebalance effects, liquidity, passive-flow relevance, and factor exposure.
-- Public equity diligence: source pack completeness, thesis readiness, diligence queue, and next evidence needed.
diff --git a/plugins/financial-markets/skills/idea-generation/agents/openai.yaml b/plugins/financial-markets/skills/idea-generation/agents/openai.yaml
deleted file mode 100644
index 3fdba08..0000000
--- a/plugins/financial-markets/skills/idea-generation/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Idea Generation"
-  short_description: "PM-grade public equity markets idea funnel"
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/idea-generation/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/idea-generation/references/DASHBOARD_PACK.md
deleted file mode 100644
index 3f88497..0000000
--- a/plugins/financial-markets/skills/idea-generation/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Idea Generation Dashboard Pack
-
-Use this pack only for an explicitly selected standardized dashboard, reusable dashboard template, or structured payload-driven render for an idea screen, market map, watchlist review, or candidate funnel. For an ordinary standalone HTML idea-triage report, follow the flexible HTML artifact standard and the owning skill's idea-specific guidance instead of this fixed module map.
-
-## Producer Role
-
-`idea-generation` owns mandate interpretation, candidate scoring, false-positive rejection, variant-view triage, and workflow routing. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `idea_generation`
-- `layout`: `single_page` for reusable idea screens unless the user explicitly requests tabs
-- `hero.callout`: why the screen matters now and what decision it supports
-- `snapshot`: universe size, number of actionable candidates, top long/short/watchlist candidate, rejection rate, source/data-quality status
-- `sources`: user screen/portfolio/watchlist first, then market/filing/consensus/public sources with as-of labels
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `screen-summary`
-   - `decision_box`: screen posture, top candidates, what is investable now, and what needs deeper work
-   - `metric_tiles`: universe, candidates, rejects, data quality, style/mandate tags
-2. `candidate-board`
-   - `table`: candidates with ticker, beneficiary pathway where relevant, archetype, exposure proof, thesis stub, why now, expectations/valuation risk, catalyst path, first rejection, and next workflow
-3. `triage`
-   - `cards`: top long, short, pair, event, and watchlist ideas with variant view and first rejection risk
-   - `scenario_map`: candidate paths where a few cases explain upside/downside or follow-up sequencing
-4. `rejection-log`
-   - `table`: rejected false positives and why they failed
-5. `next-actions`
-   - `question_list`: first diligence questions, source requests, and routing to model, earnings, pitch, hedge, event, or thesis-tracker workflows
-   - `missing_evidence`: stale or missing data that blocks upgrade from candidate to research idea
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite any screen rule, market value, estimate, catalyst, liquidity metric, portfolio/watchlist overlap, or claimed reason a security surfaced.
-- Label candidates as candidate, watchlist, deeper-research, or rejected; never as final recommendations.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not present screen output as a final trade recommendation.
-- Do not invent unavailable market data, consensus, liquidity, borrow, or option-chain fields.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-- Do not use `crowded` without verified positioning, ownership, flow, short-interest, or comparably direct support; use `expectations-heavy` or `crowding-risk candidate` when the evidence is indirect.
-- Do not upgrade a name to deeper research without source-backed exposure proof; label unquantified beneficiaries `needs exposure attribution`.
-- Do not render citations that fragment years, ticker symbols, numeric ranges, product names, or guidance values, or overwhelm the hero with a citation run.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the candidate funnel, beneficiary pathways where relevant, exposure proof, expectations-risk posture, false-positive risks, next workflow routing, and missing evidence are visible.
-- Confirm verified positioning is distinguished from inferred crowding risk and citations remain readable.
-- Confirm the dashboard preserves user-provided screens/watchlists without overwriting them.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/idea-generation/references/downstream-routing.md b/plugins/financial-markets/skills/idea-generation/references/downstream-routing.md
deleted file mode 100644
index 6cfaf49..0000000
--- a/plugins/financial-markets/skills/idea-generation/references/downstream-routing.md
+++ /dev/null
@@ -1,200 +0,0 @@
-# Downstream Workflow Routing
-
-Use this reference to route idea-generation candidates to the right next skill or workstream. The goal is to make idea generation the top-of-funnel router, not to over-solve every idea in the initial screen.
-
-## Public Equity Investing skills
-
-### `equity-model-update`
-
-Route here when the next step is to update or build the company model.
-
-Triggers:
-- Consensus bridge, guidance mismatch, margin bridge, EPS/EBITDA/FCF revision work.
-- Segment model required.
-- Valuation depends on normalized earnings or KPI rebuild.
-- Recent filing, earnings release, transcript, KPI disclosure, or investor day changed the numbers.
-
-Handoff fields:
-- Ticker, setup type, key model debate, source documents, consensus concern, KPIs to update, base/upside/downside questions.
-
-### `earnings-preview`
-
-Route here when the candidate has an upcoming print or guidance event.
-
-Triggers:
-- Earnings within the catalyst window.
-- The idea depends on whether the next print validates revisions, margin improvement, demand slowdown, or guidance reset.
-
-Handoff fields:
-- Consensus expectations, buy-side debate, KPIs to watch, likely questions, stock setup, upside/downside scenarios.
-
-### `earnings-deep-dive`
-
-Route here for post-print moves or results-driven screens.
-
-Triggers:
-- Large post-earnings stock move.
-- Estimate revisions after print.
-- Guidance change.
-- Market reaction appears too harsh or too optimistic.
-
-Handoff fields:
-- Reported vs consensus, guidance delta, transcript issues, stock reaction, thesis implication.
-
-### `long-short-pitch`
-
-Route here when an idea is sufficiently developed to become an investable recommendation.
-
-Triggers:
-- Variant view is defined.
-- Model or valuation work exists or can be completed.
-- Catalyst path and risks are clear.
-- PM wants a full long/short recommendation.
-
-Handoff fields:
-- Thesis, variant perception, valuation/risk-reward, catalyst path, key risks, position framing, hedge considerations.
-
-### `thesis-tracker`
-
-Route here for ongoing monitoring.
-
-Triggers:
-- Candidate moves from screen to active research or portfolio watch.
-- Need confirm/disconfirm signals, KPIs, catalysts, and kill criteria.
-
-Handoff fields:
-- Thesis statement, must-be-true items, invalidation tests, KPIs, catalyst dates, risk flags, current status.
-
-### `portfolio-risk-management` - `hedge_design`
-
-Route here when the idea is attractive but has unwanted exposure.
-
-Triggers:
-- Sector beta, market beta, macro, rates, FX, commodity, factor, or style exposure should be neutralized.
-- Pair trade or basket construction is needed.
-- Options hedge, proxy hedge, or basis-risk analysis is needed.
-
-Handoff fields:
-- Core alpha thesis, exposures to hedge, acceptable basis risk, time horizon, liquidity constraints, candidate peers/indices.
-
-### `event-driven-analyzer`
-
-Route here when an event drives the setup.
-
-Triggers:
-- M&A, merger arb, spin, split-off, activism, strategic review, litigation, regulatory, restructuring, index event, lockup, tender, buyback, or recapitalization.
-
-Handoff fields:
-- Event type, timeline, probability, downside if failed/delayed, key approvals, legal/regulatory milestones, valuation of outcomes.
-
-### Credit Markets
-
-Route here when credit instruments, creditworthiness, or capital-structure priority are the primary question; retain only listed-equity read-through locally.
-
-Triggers:
-- Maturity wall, refinancing risk, covenant risk, high leverage, CDS/spread dislocation, debt trading poorly, liquidity runway issue, restructuring risk, or equity-credit divergence that could change common-equity downside or require Credit Markets work.
-
-Handoff fields:
-- Debt stack, maturities, liquidity, CDS/spread or debt-price signals, leverage, FCF, covenant concerns, suspected value break, and explicit Credit Markets handoff need.
-
-### `economic-impact-report`
-
-Route here when the idea depends on macro variables.
-
-Triggers:
-- Rates, yield curve, inflation, central bank policy, FX, commodities, credit spreads, sovereign risk, or cross-asset transmission.
-
-Handoff fields:
-- Macro variable, sensitivity, transmission channel, securities affected, scenario questions.
-
-### `catalyst-calendar`
-
-Route here when event timing needs to be tracked.
-
-Triggers:
-- Multiple names with upcoming earnings, conferences, trial data, regulatory decisions, lockups, product launches, investor days, M&A deadlines, or macro prints.
-
-Handoff fields:
-- Ticker, event, date/window, importance, expected evidence, owner, status.
-
-### `portfolio-risk-management` - `position_sizing`
-
-Route here when the user asks how to size or risk-manage an idea.
-
-Triggers:
-- Position size, drawdown, beta, factor risk, stop-loss, liquidity, scenario P&L, VaR, or portfolio exposure.
-
-Handoff fields:
-- Thesis, scenarios, liquidity, volatility, factor exposures, portfolio constraints, hedge candidates.
-
-## Shared-core skills
-
-### `financial-source-of-truth`
-
-Use when source hierarchy, stale-data review, citations, or fact/assumption labeling is central.
-
-### `financials-normalizer`
-
-Use when inputs include messy filings, PDFs, VDR exports, statements, or non-standard financials that must be normalized before screening.
-
-### `excel-data-cleaner`
-
-Use when raw spreadsheet data needs cleaning, headers/units/date repair, or QA flags before analysis.
-
-### `model-audit-tieout`
-
-Use when a user-supplied model must be audited before relying on it for idea ranking.
-
-### `scenario-sensitivity-generator`
-
-Use when a candidate needs upside/downside, breakeven, stress, or scenario ranges.
-
-### `memo-builder`
-
-Use when the output should become an IC, investment, credit, client, or committee memo.
-
-### `deck-report-qc`
-
-Use before sending a deck/report to senior or external audiences.
-
-### `style-guide-adapter`
-
-Use when the user wants the output to match a firm/client precedent.
-
-### `company-tearsheet`
-
-Use to establish a concise source-backed company profile before deeper work.
-
-### `meeting-prep`
-
-Use when the idea-generation output needs to become meeting questions, management diligence asks, or PM discussion prep.
-
-## Valuation, credit, and diligence intersections
-
-Use local Public Equity Investing skills when an idea needs deeper work:
-
-- `comps-valuation`, `dcf-model-builder`, `three-statement-model-builder`, and `scenario-sensitivity-generator`: if the user needs valuation, model rebuild, SOTP, comps, DCF, downside, or sensitivity work.
-- Credit Markets: if recovery, claim priority, liquidity, maturity wall, refinancing, or distressed-security value drives the idea.
-- `company-tearsheet`, `financial-source-of-truth`, `financials-normalizer`, and `meeting-prep`: if the candidate requires profile, evidence, normalization, or management/expert-call readiness before deeper analysis.
-
-## Handoff format
-
-When routing, provide a compact handoff block:
-
-- Candidate:
-- Direction/setup:
-- Why it surfaced:
-- Variant wedge:
-- Why now/catalyst:
-- Main risks:
-- Data gaps:
-- Next workflow:
-- Specific question for next workflow:
-
-Example:
-
-"Route ABC to `equity-model-update`: rebuild the FY2 segment margin bridge and compare management cost-save targets to consensus. The idea only graduates from watchlist if the model shows EBITDA revisions can continue without assuming heroic revenue recovery."
-
-## PM Routing
-
-Route screen triage to idea-generation, investability and expression to long-short-pitch, formal memos and sell-side/client notes to memo-builder, portfolio monitoring to thesis-tracker, event math to event-driven-analyzer, and credit-first work to Credit Markets.
diff --git a/plugins/financial-markets/skills/idea-generation/references/output-standards.md b/plugins/financial-markets/skills/idea-generation/references/output-standards.md
deleted file mode 100644
index 6074f17..0000000
--- a/plugins/financial-markets/skills/idea-generation/references/output-standards.md
+++ /dev/null
@@ -1,181 +0,0 @@
-# Output Standards and Templates
-
-Use this reference when producing a PM pack, idea cards, exportable idea log, or examples of senior-investor language. For a substantial screen, market map, watchlist review, or source-heavy candidate set, default to a flexible standalone HTML idea-triage report following `../../../shared/html-artifact-standard.md`; use the standardized dashboard pack only when that dashboard path is explicitly selected.
-
-## HTML idea-triage report
-
-Use a candidate funnel or exposure-versus-expectations matrix as the primary visual object rather than a generic dashboard summary. Lead with the research-priority conclusion, then classify names as `Advance to deeper work`, `Valuation / expectations gated`, `Exposure not yet proven`, or `Deprioritized or reject`.
-
-For thematic screens, show beneficiary pathways before or alongside rank, such as electrical/power distribution, construction, networking, cooling, grid/generation, and colocation/real estate. A name advances only when evidence connects the demand driver to orders, backlog, revenue, margins, or estimate revisions; otherwise mark it `needs exposure attribution`.
-
-Recommended HTML sequence:
-
-- Research-priority conclusion and three to five distinct summary tiles.
-- Candidate funnel or exposure-versus-expectations matrix.
-- Ranked candidate board with beneficiary pathway, exposure proof, expectations risk, first rejection, and next workflow.
-- Short idea cards for highest-priority names only.
-- Deprioritized or false-positive list, missing evidence, and source ledger.
-
-Use `crowded` only for supported positioning, ownership, flow, short-interest, or comparably direct evidence. With indirect evidence only, write `expectations-heavy`, `valuation-gated`, or `crowding-risk candidate`.
-
-## Default report structure
-
-Use this structure unless the user requests a shorter or different format:
-
-# Public Equity Investing Idea Generation
-
-## Scope and assumptions
-- Universe, region, sector, market cap, liquidity, instruments.
-- Mandate, direction, horizon, and constraints.
-- Sources used and freshness caveats.
-- Assumptions inferred from missing context.
-
-## Executive summary
-- Top 3-5 conclusions.
-- Most actionable ideas.
-- Best long, short, pair, event, and watchlist candidates if relevant.
-- Biggest false positives or data issues.
-- Recommended next work.
-
-## Methodology
-- Archetypes used.
-- Factor dimensions.
-- Sector-specific adjustments.
-- Ranking logic.
-- Data-quality limitations.
-
-## Ranked idea table
-Include columns appropriate to available data:
-
-| Rank | Ticker | Company | Direction | Pathway | Priority | Exposure proof | Expectations risk | Variant wedge | Why now / catalyst | First rejection test | Next workflow |
-|---:|---|---|---|---|---|---|---|---|---|---|---|
-
-## Top idea cards
-Use the idea-card template below.
-
-## Rejected / deprioritized names
-Explain why names that screened well were killed or deprioritized.
-
-## Cross-idea themes and risk observations
-Discuss sector, macro, factor, liquidity, crowding, benchmark, and portfolio implications.
-
-## Recommended next workflows
-Map candidates to downstream skills or human workstreams.
-
-## Data gaps and caveats
-State missing data, stale data, assumptions, and required follow-up.
-
-## Idea-card template
-
-### [Ticker] - [Company] - [Direction] - [Setup type] - [Priority]
-
-**Security snapshot**
-- Region/sector/industry:
-- Market cap/liquidity:
-- Benchmark/portfolio/watchlist status if known:
-- Source date and data caveats:
-
-**Why it surfaced**
-- Name the factor triggers, not just the rank.
-- Mention valuation, growth, revisions, quality, momentum, catalysts, risk, liquidity, crowding, and portfolio fit only where relevant.
-
-**Exposure proof**
-- State the source-backed link between the theme or driver and orders, backlog, revenue, margins, or estimate revisions.
-- If that link is unavailable or unquantified, label the candidate `needs exposure attribution`.
-
-**Potential thesis wedge / variant perception**
-- What the market appears to believe.
-- What the investor might believe differently.
-- Whether the possible edge is analytical, mosaic, time-horizon, behavioral, structural, or variant interpretation.
-
-**Why now**
-- Catalyst, revision inflection, price dislocation, narrative shift, flow/technical event, or watchlist trigger.
-
-**What must be true**
-- Fundamental conditions required for the idea to work.
-
-**What would invalidate it**
-- Disconfirming evidence or kill criteria.
-
-**Main false-positive risk**
-- The first smart reason a senior PM might reject the idea.
-- State whether crowding is verified by direct evidence or only an inferred expectations risk.
-
-**Next highest-value work**
-- Model, filings, transcript review, consensus bridge, peer work, catalyst timeline, borrow check, channel work, valuation scenarios, credit review, hedge analysis, or memo.
-
-**Recommended routing**
-- Downstream skill or human owner/workstream.
-
-## Structured idea log schema
-
-When the user wants an exportable database or recurring tracker, use fields like:
-
-- idea_id
-- date_generated
-- ticker
-- company
-- security_type
-- region
-- sector
-- industry
-- market_cap
-- liquidity
-- direction
-- setup_type
-- rank
-- priority
-- why_surfaced
-- valuation_summary
-- growth_summary
-- revision_summary
-- quality_summary
-- momentum_summary
-- catalyst_summary
-- risk_summary
-- variant_perception
-- why_now
-- what_must_be_true
-- what_would_invalidate
-- main_false_positive_risk
-- next_research_step
-- recommended_downstream_skill
-- data_sources
-- data_gaps
-- status
-
-If creating a spreadsheet, preserve source tabs and write output to a new tab or new file.
-
-## PM-grade language examples
-
-Weak:
-"ABC is cheap and has strong growth. It may be a good long idea."
-
-Strong:
-"ABC screens as a possible revision-inflection long, not simply a cheap stock. The stock remains near trough valuation while the first positive FY2 EBITDA revisions in four quarters suggest consensus may be moving from estimate reset to recovery. The potential wedge is that the market still treats the margin improvement as temporary, while segment disclosure may support a more durable mix/cost bridge. The idea is not underwritten yet: rebuild the FY2 margin bridge, compare consensus to guidance, and test downside if revenue weakness offsets cost saves. First rejection risk: this is a value trap if the denominator is still too high."
-
-Weak:
-"XYZ has poor momentum and high valuation, so it is a short."
-
-Strong:
-"XYZ is a possible over-earning short. The setup depends on consensus margin assumptions proving too high, not just valuation mean reversion. Margins appear above normalized levels while revenue momentum is decelerating and the stock still trades at a premium multiple. Before advancing, check borrow cost, short interest, customer concentration, and buyback support. First rejection risk: a low bar next print could trigger a squeeze before the fundamental short thesis plays out."
-
-Weak:
-"DEF is a good company but expensive."
-
-Strong:
-"DEF belongs on watchlist, not active research, unless price or revisions create a better entry point. Business quality appears high, but the current setup lacks variant perception: the market already prices durable growth and clean execution. Move to active research if a drawdown creates a valuation dislocation without degradation in retention, margins, or bookings."
-
-## Tone and concision
-
-- Be direct, skeptical, and decision-oriented.
-- Use plain investment language.
-- Avoid cheerleading.
-- Say when an idea is merely interesting rather than actionable.
-- Distinguish verified positioning from valuation-gated or expectations-heavy exposure.
-- Prefer a sharp top five over a long list of generic names.
-- Use "candidate," "screen flag," "watchlist," and "requires diligence" unless the user has requested a final pitch or memo.
-
-## PM Output Standard
-
-Every top idea should include actionability, variant wedge, why now, what is priced in, first smart rejection reason, what would make it investable, what would kill it, and next workflow owner.
diff --git a/plugins/financial-markets/skills/idea-generation/references/scoring-materializer.md b/plugins/financial-markets/skills/idea-generation/references/scoring-materializer.md
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index 511ec30..0000000
--- a/plugins/financial-markets/skills/idea-generation/references/scoring-materializer.md
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-# Scoring Materializer
-
-Use `scripts/score_ideas.py` when the user provides a structured candidate list and wants a ranked idea log. The script does not fetch market data and does not create a final recommendation; it materializes supplied scores into a repeatable PM triage artifact.
-
-Default output directory is `/tmp/public_equity_investing_idea_generation_output`; pass `--output-dir` for a project-specific location.
-
-## Input
-
-Required:
-
-- `ticker` or `security`
-
-Optional descriptive fields:
-
-- `company`
-- `idea_type`
-- `direction`
-- `sector`
-- `variant_view`
-- `catalyst`
-- `first_rejection_risk`
-- `next_step`
-- `source`
-- `source_as_of`, `source_date`, or `as_of_date` in `YYYY-MM-DD` format
-
-Optional numeric score fields, preferably 0-5:
-
-- `valuation_score`
-- `growth_score`
-- `revisions_score`
-- `quality_score`
-- `momentum_score`
-- `catalyst_score`
-- `risk_reward_score`
-- `liquidity_score`
-- `crowding_score`
-- `portfolio_fit_score`
-- `variant_perception_score`
-
-Scores above 5 are allowed and normalized against a 100-point scale. Invalid numeric values are not coerced to zero; they are excluded from the composite and surfaced in the warnings column.
-
-## Outputs
-
-The script writes:
-
-- `ranked_ideas.csv`
-- `idea_scorecard.json`
-- `idea_scorecard_support_note.md`
-
-Use `--run-date YYYY-MM-DD` in tests or dated research packets so stale/future source warnings are deterministic.
-
-Rows with no valid numeric scores are labeled `needs_scoring_inputs`. The scorecard should be followed by human PM triage before any pitch, memo, or model handoff.
diff --git a/plugins/financial-markets/skills/idea-generation/references/screen-archetypes.md b/plugins/financial-markets/skills/idea-generation/references/screen-archetypes.md
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index 44526c8..0000000
--- a/plugins/financial-markets/skills/idea-generation/references/screen-archetypes.md
+++ /dev/null
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-# Screen Archetypes
-
-Use this reference to choose the right screen logic and avoid treating all public-equity ideas as one factor rank.
-
-## Long idea archetypes
-
-### Quality compounder at a fair price
-
-Find durable businesses that can compound value over multiple years.
-
-Signals:
-- High and durable ROIC/ROE/ROTCE.
-- Organic growth with pricing power or reinvestment runway.
-- Strong FCF conversion and clean accounting.
-- Stable or expanding margins.
-- Conservative balance sheet.
-- Rational capital allocation.
-- Valuation reasonable relative to growth, quality, and history.
-
-False positives:
-- Crowded ownership.
-- Multiple still too high for slowing growth.
-- FCF inflated by underinvestment or working capital.
-- SBC, M&A, or accounting obscures true economics.
-
-PM framing:
-- Do not call this cheap just because it derated.
-- Explain whether the entry point compensates for quality and durability.
-
-### GARP / growth at a reasonable price
-
-Find names where growth is attractive relative to valuation.
-
-Signals:
-- Above-peer growth.
-- Valuation below growth-adjusted peer range.
-- Positive revisions or resilient guidance.
-- Margin stability or operating leverage.
-- Moderate leverage and good FCF conversion.
-
-False positives:
-- Growth decelerating faster than consensus.
-- Revenue quality weak.
-- Competitive intensity rising.
-- Market already expects acceleration.
-
-### Revision inflection long
-
-Find companies where earnings expectations may be turning upward.
-
-Signals:
-- First positive revisions after downgrade cycle.
-- Guidance stabilization or raise.
-- KPI improvement before EPS revisions fully catch up.
-- Peers confirm demand improvement.
-- Valuation still near trough or unchanged despite improved outlook.
-
-False positives:
-- One-time FX, commodity, tax, or accounting effect.
-- Revisions driven by one analyst.
-- Price already reacted more than estimates.
-- Late-cycle bounce mistaken for durable improvement.
-
-### Derated quality / temporary dislocation
-
-Find good businesses sold off for reasons that may be temporary.
-
-Signals:
-- Large drawdown or relative underperformance.
-- Quality metrics intact.
-- Balance sheet strong.
-- Estimate reset likely complete.
-- Catalyst for narrative repair.
-
-False positives:
-- Structural impairment.
-- Management credibility broken.
-- Competitive moat weaker than history suggests.
-- Margin or growth reset not finished.
-
-### Self-help / margin expansion
-
-Find companies where internal actions can drive earnings independent of macro.
-
-Signals:
-- Cost program, restructuring, new CFO/CEO, portfolio simplification, pricing discipline, procurement savings, SG&A leverage, mix shift.
-- Consensus does not fully reflect target savings or margin bridge.
-- Management credibility and measurement path are clear.
-
-False positives:
-- Savings already in consensus.
-- Revenue deterioration offsets costs.
-- Execution history poor.
-- Restructuring costs recurring.
-
-### Capital allocation catalyst
-
-Find companies where capital deployment may change market perception.
-
-Signals:
-- Excess cash or underlevered balance sheet.
-- New buyback, dividend, leverage target, asset sale, divestiture, or M&A discipline.
-- Activist pressure.
-- High FCF yield and clear shareholder return capacity.
-
-False positives:
-- Governance blocks action.
-- Empire-building management.
-- Capital trapped or restricted.
-- Cyclical FCF overestimated.
-
-### Sum-of-the-parts / hidden asset value
-
-Find companies where consolidated valuation obscures asset or segment value.
-
-Signals:
-- Multiple segments with different peer sets.
-- Non-core assets, stakes, real estate, infrastructure, or unconsolidated assets.
-- Possible spin, divestiture, simplification, or activist pressure.
-
-False positives:
-- Holding-company discount justified.
-- Taxes/leakage high.
-- Segment disclosure inadequate.
-- No catalyst to close discount.
-
-### Post-earnings overreaction long
-
-Find names where the market reaction appears disproportionate to fundamental change.
-
-Signals:
-- Large price move on small estimate change.
-- Misread KPI or temporary issue.
-- Guidance conservative.
-- Peer read-throughs supportive.
-- Management commentary better than headline results.
-
-False positives:
-- Market correctly repricing long-term risk.
-- Management credibility damaged.
-- Hidden quality issue or new disclosure.
-
-## Short idea archetypes
-
-### Over-earning / peak-margin short
-
-Find companies with earnings above normalized level.
-
-Signals:
-- Margins above history or peer norms.
-- Temporary pricing, inventory, freight, commodity, stimulus, or demand pull-forward benefit.
-- Consensus extrapolates peak earnings.
-- Valuation support depends on unsustainable denominator.
-
-False positives and risks:
-- Peak lasts longer than expected.
-- Buybacks support EPS.
-- Low float or crowded short.
-- Takeout or strategic interest.
-
-### Deteriorating revisions short
-
-Find companies with estimate risk not fully reflected.
-
-Signals:
-- Negative revisions beginning or accelerating.
-- KPI deterioration, guidance risk, channel weakness, negative peer read-throughs.
-- Valuation remains high or stock has not de-rated enough.
-
-False positives and risks:
-- Bar already reset.
-- Easy comps ahead.
-- Cost cuts offset revenue weakness.
-- Market looks through near-term pressure.
-
-### Quality trap
-
-Find companies priced as high quality while fundamentals degrade.
-
-Signals:
-- Margin pressure, churn, slowing bookings/orders, working capital stress, falling returns, management turnover, aggressive adjustments.
-- Narrative remains strong despite evidence.
-
-False positives and risks:
-- Temporary investment cycle.
-- Strong moat absorbs pressure.
-- Activist or M&A event.
-
-### Balance sheet / refinancing short
-
-Find names where equity may not reflect credit risk.
-
-Signals:
-- High leverage, maturity wall, rising interest expense, weak FCF, covenant pressure, poor liquidity, debt trading down, spread widening.
-
-False positives and risks:
-- Refinancing window reopens.
-- Asset sale or sponsor support.
-- Liability management transaction.
-- Equity dilution may fix credit risk but change short thesis.
-
-Route to Credit Markets when capital structure analysis matters.
-
-### Narrative / valuation excess short
-
-Find companies where expectations are heroic relative to fundamentals.
-
-Signals:
-- Extreme multiple, low FCF, insider selling, promotional narrative, sell-side optimism, weak unit economics, decelerating growth.
-
-False positives and risks:
-- Momentum/squeeze.
-- Real technological inflection.
-- Retail/options flows.
-- Strategic buyer.
-
-### Accounting / cash-conversion short
-
-Find companies where reported earnings may overstate economics.
-
-Signals:
-- High accruals, receivables growing faster than revenue, inventory build, capitalized costs, repeated one-time add-backs, weak FCF conversion, auditor issues, related-party transactions.
-
-False positives and risks:
-- Accounting concerns lack near-term catalyst.
-- Market ignores complexity while growth continues.
-- Company improves disclosure or cash conversion.
-
-## Relative-value and pair archetypes
-
-### Peer mispricing
-
-Compare companies with similar exposures and diverging valuation or revisions.
-
-Evaluate:
-- Growth, margins, ROIC, revisions, balance sheet, catalysts, liquidity, ownership, valuation, and factor exposure.
-
-Output should explain why the spread exists and why it should close or widen.
-
-### Long quality / short deteriorating peer
-
-Use when market beta or sector exposure should be neutralized.
-
-Screen for:
-- Long candidate with better durability, cleaner revisions, or stronger balance sheet.
-- Short candidate with weaker revisions, worse quality, or overstated earnings.
-- Basis risk: different end markets, geography, customer base, liquidity, beta, and factor exposures.
-
-### Basket idea
-
-Use when the theme is broader than one name.
-
-Output:
-- Long basket, short basket, or paired baskets.
-- Common thesis and name-specific caveats.
-- Factor/correlation risks.
-
-Route to `portfolio-risk-management` for hedge construction and basis-risk work.
-
-## Catalyst and event archetypes
-
-Use when timing or event outcome drives the setup more than steady-state fundamentals.
-
-Events:
-- Earnings, guidance, analyst day, product launch, regulatory decision, litigation, M&A, spin, divestiture, activism, buyback, dividend, index change, debt refinancing, restructuring, management change, strategic review, macro print, commodity inflection, FDA/clinical event.
-
-Classify:
-- Hard vs soft catalyst.
-- Timing and probability.
-- Magnitude and directionality.
-- Whether priced.
-- Path dependency and downside if failed/delayed.
-
-Route complex events to `event-driven-analyzer`.
-
-## Watchlist archetypes
-
-Use watchlist status when the business or setup is interesting but not actionable.
-
-Common watchlist triggers:
-- Attractive company but valuation too high.
-- Cheap stock but revisions still negative.
-- Catalyst timing unclear.
-- Data gap unresolved.
-- Liquidity/capacity insufficient.
-- Need one more print, guide, regulatory decision, or management proof point.
-
-Output the exact condition that would move it to active research.
-
-## Mandate-Specific Screens
-
-Long-only screens should evaluate benchmark relevance, active weight, quality durability, downside, and liquidity. Hedge fund screens should evaluate catalyst, shortability, borrow, crowding, and gross/net fit. Sell-side screens should evaluate rating-change potential, estimate revisions, and client relevance. ETF/index screens should evaluate methodology, constituent weight, rebalance effects, passive flow, factor exposure, and liquidity.
diff --git a/plugins/financial-markets/skills/idea-generation/references/sector-overlays.md b/plugins/financial-markets/skills/idea-generation/references/sector-overlays.md
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-# Sector Overlays for Public-Market Idea Generation
-
-Use this reference when the user specifies a sector, when a company is in a sector with specialized metrics, or when generic multiples would mislead.
-
-## General rule
-
-Never apply one generic factor framework across all sectors. Use the metrics that actually drive value and estimate revisions in the relevant business model.
-
-## Software / SaaS / Internet
-
-Relevant metrics:
-- ARR growth, RPO/cRPO growth, bookings, billings, net revenue retention, gross retention.
-- Gross margin, FCF margin, operating margin, Rule of 40, CAC payback, sales efficiency.
-- SBC as percent of revenue, dilution, deferred revenue, contract duration.
-- Enterprise vs SMB exposure, consumption vs seat-based model, customer concentration.
-- AI disruption/tailwind, pricing power, platform consolidation, churn, new logo trends.
-
-Valuation:
-- EV/revenue, EV/gross profit, FCF yield, growth-adjusted revenue multiple, mature-state margin framework.
-
-Traps:
-- FCF boosted by deferred revenue timing or underinvestment.
-- High NRR hiding weak new-logo growth.
-- SBC masking true economics.
-- AI narrative without monetization.
-- Consumption model cyclicality mistaken for secular deceleration.
-
-## Semiconductors / Hardware
-
-Relevant metrics:
-- End-market mix, backlog, book-to-bill, inventory days, channel inventory, utilization, wafer starts.
-- ASPs, gross margin cycle, capex cycle, customer concentration, China/export-control exposure.
-- AI/datacenter exposure, gaming/PC/mobile/auto/industrial cycle.
-
-Valuation:
-- Through-cycle P/E, EV/sales, EV/EBITDA, gross-margin-normalized earnings, cycle-adjusted multiples.
-
-Traps:
-- Peak margins capitalized as normal.
-- Inventory correction hidden by backlog.
-- Double ordering.
-- Export control or customer concentration risk underpriced.
-
-## Banks
-
-Relevant metrics:
-- P/TBV, ROTCE, CET1, NIM, deposit beta, loan growth, deposit flows, fee income.
-- Credit costs, reserve build/release, nonperforming assets, CRE exposure, AOCI marks, duration risk.
-
-Valuation:
-- P/TBV relative to sustainable ROTCE and capital return; avoid EV/EBITDA.
-
-Traps:
-- Low P/E because credit costs are too low.
-- Tangible book quality impaired by unrealized losses.
-- NIM expansion temporary.
-- Capital return constrained by regulation or losses.
-
-## Insurance
-
-Relevant metrics:
-- Combined ratio, loss ratio, expense ratio, reserve development, premium growth, pricing cycle.
-- ROE, book value growth, investment yield, cat exposure, capital adequacy.
-
-Valuation:
-- P/B, P/E, ROE spread, reserve-adjusted book value.
-
-Traps:
-- Reserve releases flattering earnings.
-- Cat normalization too optimistic.
-- Investment gains masking underwriting weakness.
-
-## Consumer / Retail / Restaurants
-
-Relevant metrics:
-- Same-store sales, traffic vs ticket, gross margin, promotions, inventory, shrink, loyalty, credit exposure.
-- Unit growth, restaurant-level margin, franchise mix, wage/input costs, lease-adjusted leverage.
-
-Valuation:
-- EV/EBITDAR, P/E, FCF yield, unit economics, normalized margin.
-
-Traps:
-- Price-driven comps mistaken for demand.
-- Margin benefit from temporary freight/input relief.
-- Inventory markdown risk.
-- Consumer credit stress lagging.
-
-## Industrials
-
-Relevant metrics:
-- Organic growth, backlog, book-to-bill, segment margins, aftermarket mix, price/cost spread.
-- Restructuring savings, capex cycle, automation, aerospace/auto/construction exposure, China/Europe exposure.
-
-Valuation:
-- Mid-cycle P/E, EV/EBITDA, EV/EBIT, FCF yield, SOTP for diversified industrials.
-
-Traps:
-- Late-cycle order strength.
-- Backlog not converting.
-- Margin expansion from price/cost tailwind that fades.
-- Acquisition integration risk.
-
-## Energy / E&P / Oilfield Services
-
-Relevant metrics:
-- FCF at strip, production growth, decline rates, reserve life, lifting costs, breakeven prices.
-- Hedge book, capex intensity, inventory depth, service cost inflation, leverage, capital return framework.
-
-Valuation:
-- EV/DACF, FCF yield at strip and mid-cycle, NAV, reserve value, recycle ratio.
-
-Traps:
-- Spot commodity price overstates sustainable FCF.
-- Maintenance capex understated.
-- Inventory quality deteriorating.
-- Political/regulatory risk.
-
-## Metals and Mining
-
-Relevant metrics:
-- Commodity exposure, cost curve position, reserve life, grade, production profile, capex commitments.
-- Jurisdiction risk, permitting, balance sheet, hedging, byproduct credits.
-
-Valuation:
-- NAV, EV/EBITDA at spot and long-term commodity prices, FCF yield, replacement value.
-
-Traps:
-- Using spot prices as normalized.
-- Understated sustaining capex.
-- Mine-life and permitting risk.
-- Country/geopolitical risk.
-
-## Healthcare Services / Medtech / Pharma / Biotech
-
-Relevant metrics:
-- Procedure volumes, reimbursement, payer mix, utilization, pricing, pipeline, patent cliffs.
-- Trial readouts, regulatory calendar, R&D productivity, product cycles, physician adoption.
-
-Valuation:
-- P/E, EV/EBITDA, pipeline risk-adjusted NPV, sum-of-parts, patent-cliff-adjusted earnings.
-
-Traps:
-- Binary clinical/regulatory risk hidden by screens.
-- Patent cliff underappreciated.
-- Reimbursement pressure.
-- Pipeline probability too optimistic.
-
-For pre-commercial biotech, avoid generic screens unless the user explicitly requests biotech event ideas.
-
-## REITs
-
-Relevant metrics:
-- AFFO, FFO, NAV, implied cap rate, occupancy, leasing spreads, same-store NOI.
-- Lease duration, tenant concentration, debt maturity, floating-rate debt, development pipeline.
-
-Valuation:
-- P/AFFO, NAV discount/premium, dividend yield quality, implied cap rate vs private market cap rates.
-
-Traps:
-- Dividend yield as false support.
-- NAV based on stale cap rates.
-- Refinancing risk.
-- Tenant concentration.
-
-## Telecom / Cable / Media
-
-Relevant metrics:
-- Subscribers, churn, ARPU, broadband net adds, content costs, advertising, leverage, capex, spectrum.
-- FCF, pricing, competitive intensity, cord-cutting, streaming losses/profitability.
-
-Valuation:
-- FCF yield, EV/EBITDA, leverage-adjusted equity value, sum-of-parts.
-
-Traps:
-- FCF masked by underinvestment.
-- Leverage overwhelms equity value.
-- Subscriber trends deteriorating faster than consensus.
-
-## Utilities / Infrastructure
-
-Relevant metrics:
-- Rate base growth, allowed ROE, regulatory jurisdiction, capex plan, balance sheet, rate cases.
-- Renewable exposure, project execution, wildfire/storm/liability risk.
-
-Valuation:
-- P/E, dividend yield, premium/discount to regulated utility peers, rate-base growth-adjusted multiples.
-
-Traps:
-- Regulatory lag.
-- Equity issuance needs.
-- Catastrophe/liability risk.
-- Dividend not covered by FCF.
-
-## Payments / Exchanges / Marketplaces
-
-Relevant metrics:
-- TPV, take rate, volume growth, active users, frequency, monetization, mix, chargebacks.
-- Operating leverage, competitive intensity, regulatory risk, network effects.
-
-Valuation:
-- P/E, EV/EBITDA, FCF yield, EV/gross profit, growth-adjusted multiples.
-
-Traps:
-- Volume growth without take-rate durability.
-- Regulatory fee pressure.
-- Marketplace quality weakening.
-- Competition compressing margins.
-
-## Credit Markets Handoff / Equity-Risk Signal
-
-Relevant public-equity signal metrics:
-- Maturity wall, leverage, interest coverage, liquidity runway, rating pressure, CDS/spread warning signals, refinancing windows.
-- Equity downside mechanism, estimate risk, valuation support, and catalyst timing tied to credit stress.
-
-Local idea-generation use:
-- Flag listed-equity ideas where credit stress may create downside, optionality, catalyst pressure, or a handoff need.
-- Route credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, debt security analysis, or capital-structure priority to Credit Markets.
-
-Traps:
-- Treating a bond/loan/CDS setup as a Public Equity idea.
-- Using credit stress without an equity impairment mechanism.
-- Screening for distress without security-level or covenant support from Credit Markets.
diff --git a/plugins/financial-markets/skills/idea-generation/references/source-qc.md b/plugins/financial-markets/skills/idea-generation/references/source-qc.md
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-# Source, Evidence, and Data-Handling Standards
-
-Use this reference when the request depends on user files, connected apps, current market data, filings, models, portfolios, watchlists, or any factual data that could be stale.
-
-## Source hierarchy
-
-Prefer sources in this order:
-
-1. Prompt-provided context: tickers, constraints, portfolio holdings, watchlists, uploaded tables, models, notes, and user instructions.
-2. Callable apps/connectors and internal context the user has authorized: drive/docs, sheets, slides, email, chat, calendar, notebooks, research repositories, callable financial data connectors, or other linked systems.
-3. User-uploaded files: spreadsheets, PDFs, models, filings, decks, tearsheets, research notes, and CSV exports.
-4. Primary company sources: 10-k, 10-q, 20-f, 6-k, 8-k, earnings releases, transcripts, investor presentations, proxy statements, guidance, and capital allocation announcements.
-5. Market and consensus data: prices, volume, market cap, enterprise value, valuation multiples, analyst estimates, revisions, short interest, borrow, ownership, options, equity-risk credit signals such as CDS/spread context, peer data, and benchmark membership.
-6. News and external context: regulatory updates, litigation, macro releases, industry reports, supply-chain data, pricing data, web/app data, and other alternative data.
-7. User clarification: ask only if the missing context would materially change the answer and cannot be reasonably inferred.
-
-Always label which level of the hierarchy was used. If relying on fallback sources or assumptions, say so.
-
-## Connected-app behavior
-
-When connected apps are available, use them before public web search if the user's request implies private or firm-specific context. Examples:
-
-- Existing portfolio, watchlist, restrictions, or benchmark exposures.
-- Internal research notes, prior thesis trackers, PM comments, model outputs, or idea logs.
-- Firm style, templates, prior memos, and sector coverage conventions.
-- Meeting notes, earnings call notes, diligence questions, or analyst feedback.
-
-Do not assume connected-app context is complete. If an app search returns nothing, state the gap and continue with available public/user-provided data.
-
-## Current data and web fallback
-
-Use current web or market-data tools whenever facts may have changed, including:
-
-- Prices, market caps, volume, valuation multiples, estimates, and revisions.
-- Earnings dates, conference appearances, regulatory decisions, litigation milestones, index changes, and corporate actions.
-- Management changes, guidance updates, filings, financing events, debt maturities, buybacks, dividends, M&A, and activism.
-- Laws, regulations, sanctions, tariffs, central bank decisions, macro data, commodity prices, rates, FX, and sector news.
-
-If current market data is not available, provide a methodological output with placeholders and clearly say which metrics must be populated before ranking can be relied upon.
-
-## Non-destructive handling
-
-Never delete, overwrite, or destructively edit user data unless the user explicitly requests it. For files and spreadsheets:
-
-- Preserve the original file.
-- Create a copy, new tab, new output file, or appended section for generated screens.
-- Keep raw source data separate from cleaned, normalized, and output tabs.
-- Add data-quality flags rather than silently fixing source issues.
-- Do not remove rows, names, formulas, tabs, comments, or prior workpapers unless specifically instructed.
-- If cleaning is required, route to or follow `excel-data-cleaner` / `financials-normalizer` style handling before analysis.
-
-## Evidence labels
-
-Use these labels or equivalents:
-
-- Fact: directly sourced or provided.
-- Assumption: inferred because the user did not specify.
-- Estimate: derived from a model, consensus, or calculation.
-- Inference: analytical conclusion based on facts and assumptions.
-- Data gap: unavailable, stale, inconsistent, or incomplete.
-- PM judgment: qualitative conclusion that reflects professional interpretation.
-
-## Stale-data checks
-
-Before relying on a metric, check:
-
-- Date of source.
-- Reporting period and fiscal year.
-- Whether consensus is before or after the latest print.
-- Whether company guidance has changed.
-- Whether capital structure changed through debt issuance, buyback, equity issuance, M&A, spin, or divestiture.
-- Whether prices and market caps are current.
-- Whether estimates are calendarized or fiscal-year based.
-- Whether the metric uses adjusted or GAAP/IFRS definitions.
-- Whether reported financials are pro forma for recent acquisitions or divestitures.
-
-Flag stale data in the output rather than smoothing it over.
-
-## Public-equity data normalization checks
-
-Normalize or flag:
-
-- Ticker changes, share classes, dual listings, ADR ratios, depositary receipts, and local vs U.S. lines.
-- Currency and reporting currency.
-- Fiscal year ends and calendar-year mismatches.
-- Enterprise value components, including cash, debt, leases, pensions, minority interests, preferred stock, and unconsolidated investments.
-- Sector-specific metric definitions: e.g. ebitda is not meaningful for banks; affo matters for reits; fcf at strip matters for energy.
-- One-time items, adjusted ebitda add-backs, stock-based compensation, restructuring charges, working capital swings, tax rates, and non-cash gains/losses.
-- Recently IPO'd companies, spin-offs, restatements, discontinued operations, and post-merger financials.
-
-## Citation discipline
-
-Cite retrieved facts and current market data. Do not cite unsupported calculations as if sourced; cite the inputs and explain the calculation. If source confidence is low, explicitly say so.
-
-## Investment framing
-
-Use research language rather than trade instructions:
-
-- Good: "candidate for further work," "possible revision-inflection long," "watchlist pending catalyst," "short-screen flag requiring borrow check."
-- Avoid: "buy this," "sell this," "guaranteed upside," or personalized portfolio instruction without appropriate context.
-
-## Public Equity Source QC
-
-Label facts, management claims, consensus, market data, assumptions, and PM judgment. Price, consensus, short interest, borrow, options, ETF holdings, index rules, liquidity, and ownership require as-of dates.
diff --git a/plugins/financial-markets/skills/idea-generation/references/workflow.md b/plugins/financial-markets/skills/idea-generation/references/workflow.md
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-# PM-Grade Idea Generation Workflow
-
-Use this reference for full PM-pack outputs, portfolio-aware screens, watchlist refreshes, or any request where the user expects a systematic workflow rather than a quick list.
-
-## 1. Establish mandate and universe
-
-Capture or infer:
-
-- Mandate: long-only, long/short, market-neutral, event-driven, Credit Markets, sector research, equity research, or multi-asset.
-- Objective: research queue, trade candidate, benchmark-relative idea, hedge, watchlist, earnings setup, or sector screen.
-- Direction: long, short, long/short, pair, relative value, catalyst, or watchlist.
-- Time horizon: days/weeks, 1-3 months, 3-12 months, or 1-3 years.
-- Geography, sector, industry, market cap, liquidity, benchmark, and instrument type.
-- Constraints: minimum average daily value, max position size, restricted sectors, ESG/restricted lists, leverage, borrow, options availability, currency, country, and benchmark limits.
-
-If unspecified, use liquid public equities and a 3-18 month fundamental horizon. State assumptions.
-
-## 2. Build or validate the investable universe
-
-Before scoring, resolve:
-
-- Tickers, identifiers, exchanges, local lines, ADRs, and share classes.
-- Active listings vs stale listings.
-- Market cap and liquidity.
-- Sector, industry, region, and benchmark membership.
-- Portfolio/watchlist/coverage overlap.
-- Data sufficiency and freshness.
-- Exclusions: illiquid microcaps, highly binary pre-commercial biotech, special purpose vehicles, funds, non-operating shells, or companies with inadequate data, unless requested.
-
-Flag special cases:
-
-- Recent IPO, spin-off, merger, divestiture, bankruptcy emergence, restatement, fiscal-year change, or suspended guidance.
-- Financials, REITs, E&P, biotech, insurers, and banks requiring sector-specific metrics.
-
-## 3. Choose archetypes before scoring
-
-Do not run one generic composite score. Select multiple archetypes based on mandate:
-
-- Long: quality compounder, GARP, revision inflection, derated quality, self-help, capital allocation, SOTP, post-earnings overreaction.
-- Short: over-earning, deteriorating revisions, quality trap, balance sheet/refinancing, narrative excess, accounting/cash-conversion.
-- Relative value: peer mispricing, pair trade, long quality/short deteriorating peer, sector-neutral basket.
-- Event: M&A, spin, activism, litigation, regulatory, restructuring, product launch, index event, debt refinancing.
-- Watchlist: good setup missing price, catalyst, data confirmation, or risk clarity.
-
-Use `references/screen-archetypes.md` for details.
-
-## 4. Score the relevant dimensions
-
-Score qualitatively or quantitatively, but explain the drivers. Default dimensions:
-
-1. Valuation: absolute, historical, peer-relative, growth-adjusted, normalized, SOTP, or sector-specific.
-2. Growth: rate, durability, organic quality, unit economics, operating leverage, and reinvestment runway.
-3. Revisions: direction, magnitude, breadth, recency, dispersion, guidance delta, and whether revisions may continue.
-4. Quality: ROIC/ROE, margins, FCF conversion, accounting quality, balance sheet, management, moat, and capital allocation.
-5. Momentum: price, relative strength, volume, post-earnings drift, and fundamental momentum.
-6. Catalysts: timing, probability, magnitude, path dependency, and whether priced.
-7. Risk: balance sheet, liquidity, crowding, borrow, squeeze, accounting, governance, regulatory, litigation, macro, factor, and portfolio risk.
-8. Portfolio fit: benchmark fit, diversifying exposure, factor impact, position capacity, and overlap with existing holdings.
-9. Data quality: source freshness, completeness, comparability, and confidence.
-
-## 5. Apply PM triage
-
-For every top candidate, ask:
-
-- Why did it screen well?
-- Is that reason economically meaningful or just statistical?
-- Is the metric distorted by accounting, cyclicality, one-time items, commodity/FX/tax effects, or stale consensus?
-- Is the valuation denominator credible?
-- What is the market debate?
-- What is consensus likely pricing?
-- Is there a real variant perception or merely a known fact?
-- Is there a catalyst or reason the market will care?
-- Is the risk compensated?
-- Is the idea investable given liquidity, borrow, capacity, mandate, and portfolio context?
-- What is the first smart reason to reject it?
-- What is the next highest-value research step?
-
-Kill weak ideas aggressively. A shorter, sharper list is better than a broad undifferentiated screen.
-
-## 6. Classify priority
-
-Use these buckets:
-
-- A - immediate research candidate: strong setup, clear why-now, credible variant view, manageable risk, next work obvious.
-- B - watchlist / needs trigger: interesting but missing catalyst, price, data confirmation, risk clarity, or valuation support.
-- C - screen flag only: statistically interesting but weak actionability or weak variant view.
-- Reject: false positive, stale data, mandate mismatch, poor liquidity, uninvestable risk, no edge, or factor trap.
-
-Do not overstate precision. Narrative judgment outweighs a numeric score.
-
-## 7. Build the output
-
-Default PM-pack structure:
-
-1. Scope and assumptions.
-2. Methodology and source caveats.
-3. Executive summary and top findings.
-4. Ranked idea table.
-5. Idea cards for top A/B candidates.
-6. Rejected false positives.
-7. Cross-idea themes and risk observations.
-8. Recommended downstream routing.
-9. Data gaps and next research asks.
-
-Quick-scan structure:
-
-- One-paragraph scope and caveats.
-- Ranked table of 5-20 ideas.
-- One-line PM rationale and rejection risk for each.
-- Next step/routing.
-
-Watchlist-refresh structure:
-
-- Names that moved up in priority.
-- Names that moved down or should be killed.
-- New catalysts and revision changes.
-- Price/valuation range changes.
-- Thesis tracker updates and next actions.
-
-Portfolio-aware structure:
-
-- Additive ideas.
-- Duplicative exposures.
-- Hedges/pairs.
-- Factor and sector implications.
-- Liquidity/capacity observations.
-- Names that improve portfolio construction vs just standalone appeal.
-
-## 8. Maintain an idea log when appropriate
-
-For recurring use, append an idea log with:
-
-- Date generated.
-- Security and ticker.
-- Setup type and direction.
-- Price/date if available.
-- Rank and priority.
-- Why surfaced.
-- Variant perception.
-- Catalyst path.
-- Next work.
-- Assigned owner if provided.
-- Status: rejected, watchlist, active research, invested, exited.
-- Later outcome and lessons learned.
-
-A mature process reviews hit rate by archetype and false-positive patterns over time.
-
-## PM Triage Upgrade
-
-Top ideas require Actionability, Variant Wedge, Why Now, First Rejection, What Would Make It Investable, What Would Kill It, and Next Workflow. Separate interesting from actionable.
diff --git a/plugins/financial-markets/skills/idea-generation/scripts/score_ideas.py b/plugins/financial-markets/skills/idea-generation/scripts/score_ideas.py
deleted file mode 100644
index 0b7a449..0000000
--- a/plugins/financial-markets/skills/idea-generation/scripts/score_ideas.py
+++ /dev/null
@@ -1,258 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize a deterministic public-equity-investing idea scorecard from supplied CSV inputs."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-from datetime import date, datetime
-from pathlib import Path
-from typing import Any
-
-SCORE_WEIGHTS = {
-    "variant_perception_score": 1.25,
-    "catalyst_score": 1.15,
-    "valuation_score": 1.10,
-    "revisions_score": 1.00,
-    "quality_score": 0.90,
-    "growth_score": 0.85,
-    "risk_reward_score": 1.15,
-    "liquidity_score": 0.55,
-    "crowding_score": 0.55,
-    "momentum_score": 0.50,
-    "portfolio_fit_score": 0.70,
-}
-
-
-OUTPUT_FIELDS = [
-    "rank",
-    "ticker",
-    "security",
-    "company",
-    "idea_type",
-    "direction",
-    "sector",
-    "composite_score",
-    "bucket",
-    "valid_score_count",
-    "variant_view",
-    "catalyst",
-    "first_rejection_risk",
-    "next_step",
-    "source",
-    "source_as_of",
-    "freshness_status",
-    "warnings",
-]
-
-
-def parse_score(value: Any, field: str, warnings: list[str]) -> float | None:
-    text = str(value or "").strip().replace(",", "")
-    if not text:
-        return None
-    try:
-        score = float(text)
-    except ValueError:
-        warnings.append(f"{field} invalid numeric value {value!r}")
-        return None
-    if not math.isfinite(score):
-        warnings.append(f"{field} is not finite")
-        return None
-    if score < 0:
-        warnings.append(f"{field} negative score excluded")
-        return None
-    if score <= 5:
-        return score / 5.0 * 100.0
-    if score <= 100:
-        return score
-    warnings.append(f"{field} above 100 capped at 100")
-    return 100.0
-
-
-def bucket_for(score: float | None) -> str:
-    if score is None:
-        return "needs_scoring_inputs"
-    if score >= 75:
-        return "A - immediate research candidate"
-    if score >= 60:
-        return "B - watchlist / needs trigger"
-    if score >= 45:
-        return "C - screen flag only"
-    return "Reject / low-priority false positive"
-
-
-def parse_iso_date(value: Any, field: str, warnings: list[str]) -> date | None:
-    text = str(value or "").strip()
-    if not text:
-        return None
-    try:
-        return datetime.strptime(text, "%Y-%m-%d").date()
-    except ValueError:
-        warnings.append(f"{field} invalid date {value!r}; expected YYYY-MM-DD")
-        return None
-
-
-def source_freshness(row: dict[str, str], run_date: date, warnings: list[str]) -> tuple[str, str]:
-    source_as_of = str(
-        row.get("source_as_of") or row.get("source_date") or row.get("as_of_date") or ""
-    ).strip()
-    if not source_as_of:
-        warnings.append("missing source_as_of")
-        return "", "missing"
-    parsed = parse_iso_date(source_as_of, "source_as_of", warnings)
-    if parsed is None:
-        return source_as_of, "invalid"
-    if parsed > run_date:
-        warnings.append("source_as_of is after run_date")
-        return source_as_of, "future"
-    if (run_date - parsed).days > 180:
-        warnings.append("source_as_of is stale")
-        return source_as_of, "stale"
-    return source_as_of, "current"
-
-
-def load_rows(path: Path) -> list[dict[str, str]]:
-    with path.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        if not reader.fieldnames:
-            raise ValueError("input CSV must include a header row")
-        rows = [dict(row) for row in reader]
-    if not rows:
-        raise ValueError("input CSV must include at least one candidate row")
-    return rows
-
-
-def score_row(row: dict[str, str], index: int, run_date: date) -> dict[str, Any]:
-    warnings: list[str] = []
-    ticker = str(row.get("ticker") or "").strip()
-    security = str(row.get("security") or "").strip()
-    if not ticker and not security:
-        raise ValueError(f"row {index}: ticker or security is required")
-
-    weighted_score = 0.0
-    weight_total = 0.0
-    valid_count = 0
-    for field, weight in SCORE_WEIGHTS.items():
-        score = parse_score(row.get(field), field, warnings)
-        if score is None:
-            continue
-        weighted_score += score * weight
-        weight_total += weight
-        valid_count += 1
-
-    composite = round(weighted_score / weight_total, 2) if weight_total else None
-    if valid_count == 0:
-        warnings.append("no valid numeric score fields provided")
-    if not str(row.get("variant_view") or "").strip():
-        warnings.append("missing variant_view")
-    if not str(row.get("first_rejection_risk") or "").strip():
-        warnings.append("missing first_rejection_risk")
-    source_as_of, freshness_status = source_freshness(row, run_date, warnings)
-
-    return {
-        "rank": 0,
-        "ticker": ticker,
-        "security": security,
-        "company": str(row.get("company") or "").strip(),
-        "idea_type": str(row.get("idea_type") or "").strip(),
-        "direction": str(row.get("direction") or "").strip(),
-        "sector": str(row.get("sector") or "").strip(),
-        "composite_score": composite,
-        "bucket": bucket_for(composite),
-        "valid_score_count": valid_count,
-        "variant_view": str(row.get("variant_view") or "").strip(),
-        "catalyst": str(row.get("catalyst") or "").strip(),
-        "first_rejection_risk": str(row.get("first_rejection_risk") or "").strip(),
-        "next_step": str(row.get("next_step") or "").strip(),
-        "source": str(row.get("source") or "").strip(),
-        "source_as_of": source_as_of,
-        "freshness_status": freshness_status,
-        "warnings": "; ".join(warnings),
-    }
-
-
-def materialize(rows: list[dict[str, str]], run_date: date | None = None) -> list[dict[str, Any]]:
-    run_date = run_date or date.today()
-    scored = [score_row(row, idx, run_date) for idx, row in enumerate(rows, start=2)]
-    scored.sort(
-        key=lambda row: (
-            -1 if row["composite_score"] is None else -row["composite_score"],
-            row["ticker"] or row["security"],
-        )
-    )
-    for rank, row in enumerate(scored, start=1):
-        row["rank"] = rank
-    return scored
-
-
-def write_csv(path: Path, rows: list[dict[str, Any]]) -> None:
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=OUTPUT_FIELDS)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow(
-                {
-                    field: "" if row.get(field) is None else row.get(field, "")
-                    for field in OUTPUT_FIELDS
-                }
-            )
-
-
-def markdown(rows: list[dict[str, Any]]) -> str:
-    lines = [
-        "# Public Equity Investing Idea Scorecard",
-        "",
-        "> Deterministic materializer from supplied inputs only. Scores rank research candidates, not final recommendations.",
-        "",
-        "| Rank | Ticker/Security | Type | Score | Bucket | Variant View | Catalyst | First Rejection Risk | Warnings |",
-        "|---:|---|---|---:|---|---|---|---|---|",
-    ]
-    for row in rows:
-        label = row["ticker"] or row["security"]
-        score = "" if row["composite_score"] is None else f"{row['composite_score']:.2f}"
-        lines.append(
-            f"| {row['rank']} | {label} | {row['idea_type']} | {score} | {row['bucket']} | {row['variant_view']} | {row['catalyst']} | {row['first_rejection_risk']} | {row['warnings']} |"
-        )
-    return "\n".join(lines) + "\n"
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Score supplied public-equity idea candidates into a deterministic PM triage log."
-    )
-    parser.add_argument("input_csv", type=Path)
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=Path("/tmp/public_equity_investing_idea_generation_output"),
-    )
-    parser.add_argument(
-        "--run-date",
-        default=date.today().isoformat(),
-        help="Freshness date for source_as_of checks, YYYY-MM-DD.",
-    )
-    args = parser.parse_args()
-
-    try:
-        run_date = datetime.strptime(args.run_date, "%Y-%m-%d").date()
-        rows = materialize(load_rows(args.input_csv), run_date)
-    except Exception as exc:
-        print(f"ERROR: {exc}")
-        return 1
-
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    write_csv(args.output_dir / "ranked_ideas.csv", rows)
-    (args.output_dir / "idea_scorecard.json").write_text(
-        json.dumps({"rows": rows}, indent=2) + "\n", encoding="utf-8"
-    )
-    (args.output_dir / "idea_scorecard_support_note.md").write_text(
-        markdown(rows), encoding="utf-8"
-    )
-    print(f"Wrote idea scorecard outputs to {args.output_dir}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/initiating-coverage/SKILL.md b/plugins/financial-markets/skills/initiating-coverage/SKILL.md
deleted file mode 100644
index 0e2a03a..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/SKILL.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-name: initiating-coverage
-description: Use when building public-equity-investing initiating coverage reports. Do not use for trade pitches, memos, earnings notes, models, or tearsheets.
----
-
-# Initiating Coverage
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML initiation report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Build a full public-equity-investing initiation report that is thesis-led, model-backed, valuation-aware, source-disciplined, and explicit about what is proven versus assumed.
-
-This skill owns initiation-style research architecture. It can produce a report outline, full initiation report, structured internal handoff, source request list, model/valuation workplan, or deck-ready storyboard. For a new standalone reader-facing initiation package, default to a polished standalone HTML initiation report following `../../shared/html-artifact-standard.md`, unless the user requests another surface, a quick/no-file answer, or a model/workbook-first deliverable. Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable validated template, or structured payload-driven render. Structured JSON is support/audit material, not the final user-facing artifact. This skill does not own live trade expression, generic memo writing, earnings notes, model-only work, or one-page issuer profiles.
-
-## Do Not Use
-
-- Live trade pitch, pair trade, add/trim/exit rules, short-cover logic, or variant-perception trade expression: use `long-short-pitch`.
-- IC memo, investment memo, committee note, client memo, PM update, or memo rewrite: use `memo-builder`.
-- Pre-earnings or post-earnings note: use `earnings-preview` or `earnings-deep-dive`.
-- Model refresh, DCF, 3-statement model, comps workbook, or sensitivity-only task: use the relevant model or scenario skill.
-- One-page public issuer profile: use `company-tearsheet`.
-
-## Non-Negotiables
-
-- Preserve user files and models. Create additive outputs or marked-up recommendations unless the user explicitly asks for edits.
-- Prioritize user-provided material, connected/approved sources, primary filings/releases/transcripts, trusted providers, then clearly labeled assumptions.
-- Include report date, data cut-off, evidence confidence, underwriting status, unresolved conflicts, and major assumptions. Keep evidence confidence distinct from underwriting readiness; do not describe investability as source confidence.
-- Label facts, company claims, street estimates, model-derived values, PM judgment, assumptions, mixed claims, and missing sources.
-- Do not make personalized investment advice or compliance/legal/tax conclusions. Treat rating/target price language as research output requiring human review.
-- If context is thin, produce a skeleton, research agenda, model architecture, source request list, and thesis hypotheses rather than fabricating facts.
-
-## Workflow
-
-1. **Classify the mode.** Choose `sell_side_initiation`, `buy_side_deep_dive`, `long_only_initiation`, `hedge_fund_initiation`, `credit_adjacent_initiation`, `sector_initiation`, `model_first_initiation`, or `report_refresh`. Load `references/report-modes.md` only when mode-specific requirements matter.
-2. **Assess context depth.** For no-context prompts, ask one targeted clarification only if company/security or output mode is unclear. For partial/full context, preserve supplied facts, identify missing inputs, and build a source register and assumption register.
-3. **Complete market data and valuation inputs.** Before finalizing a substantive initiation, retrieve current price with an as-of timestamp, market capitalization, diluted share-count inputs, enterprise-value inputs, and available consensus or estimate context from approved accessible sources. Load `references/source-and-evidence.md` for required treatment. If an input is unavailable, label it missing and explain what valuation conclusion it prevents; do not omit available market context simply because the report remains preliminary.
-4. **Coordinate with local skills.** Use `financial-source-of-truth` for evidence posture, `company-tearsheet` for issuer baseline, `financials-normalizer` or `excel-data-cleaner` for source prep, model skills for valuation work, `scenario-sensitivity-generator` for cases, and `thesis-tracker` after initiation.
-5. **Frame the senior thesis.** Answer what the market is missing, what must be true, what falsifies the thesis, which KPI/catalyst matters, why the valuation method fits, and what would change the view.
-6. **Apply the financed-growth gate when material.** When capital intensity, debt, lease liabilities, dilution, customer-funded buildout, or financing risk is central to the equity debate, require a pro forma fully diluted capitalization / enterprise-value bridge and after-financing return evidence before a positive ownership conclusion or target price. Use normalized FCF, ROIC or return on invested capacity, capacity-cohort cash returns, leverage, and interest burden as applicable. Treat equity-value-to-revenue or other revenue multiples as preliminary market context only when material financed obligations are not yet incorporated.
-7. **Build the report.** Default structure: cover/view, executive summary, key debates, company overview, industry position, model summary, valuation, catalysts, risks/disconfirmers, scenarios, and source appendix. In standalone HTML, make the first read a single research-posture block, the central debate, the valuation or capital-return gate, the essential evidence, and what remains missing. Do not repeat the same conclusion in a hero, metric tile, PM-answer block, and decision box.
-8. **Apply sector context.** Load `references/sector-overlays.md` for report-mode framing and use `sector-context-overlay` only when sector nuance affects KPIs, valuation, or red flags.
-9. **Run QA.** Confirm the report has a real view, falsifiable thesis, sourced metrics, visible data cut-off, source conflicts, appropriate sector metrics, and non-boilerplate risks. For a local HTML file, inspect the opening viewport and important downstream sections with local headless-browser screenshots rather than the in-app Browser plugin; iterate on hierarchy, clipping, citation noise, and readability.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: evidence gathering, company and industry analysis, model/valuation, thesis and risk, and report QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Artifact Contract
-
-Default initiation output should include:
-
-- MD / PM-level answer with recommendation or research posture.
-- Investment thesis with 3-5 evidence-linked claims.
-- Key debates and variant perception.
-- Company and industry overview.
-- Model and forecast driver table.
-- Valuation / target price methodology, sensitivity, and backup method.
-- Current market data and valuation-input status, including as-of time and explicit missing inputs.
-- Earnings quality and EPS basis when P/E or EPS-driven valuation is used.
-- Catalysts, risks, disconfirming evidence, and monitoring items.
-- Source register, conflicts, assumptions, and open evidence requests.
-
-For a long-only initiation where valuation, the capital stack, or after-financing cash economics remain materially unproven, label the output a `Preliminary initiation underwrite` or `Watchlist initiation`. State the evidence and modeling required before ownership rather than implying a completed positive initiation or supplying an unsupported target price. This posture does not excuse omitting current market data that is obtainable from approved accessible sources.
-
-For standalone HTML artifacts, keep citations traceable but readable: do not fragment tickers, fiscal periods, dates, prices, ranges, multiples, or metric names with inline source links. Cite a complete figure or statement, or use a compact nearby source note.
-
-Structured handoffs should follow `references/output-schema.md` and can be validated with:
-
-```bash
-python scripts/validate_initiation_json.py path/to/initiation.json
-```
-
-For publication-looking reports, run `python scripts/validate_initiation_json.py path/to/initiation.json --publication-ready` so unresolved placeholders, weak source metadata, and unknown source IDs hard-fail. Use the validated structure to render or support the human-readable report; do not make raw JSON the lead deliverable unless explicitly requested.
-
-Use `scripts/calculate_price_target.py` only when valuation assumptions are available in JSON; the helper calculates math, not analyst judgment.
-
-If the user explicitly asks for a standardized dashboard, use `references/DASHBOARD_PACK.md`. `initiating-coverage` owns the thesis, report architecture, and valuation judgment; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-
-## Reference Map
-
-- `references/report-modes.md`: mode-specific output standards.
-- `references/report-architecture.md`: full report structures and section variants.
-- `references/thesis-framework.md`: senior thesis, debate, and falsification prompts.
-- `references/valuation-and-modeling.md`: valuation methods, assumptions, and price-target logic.
-- `references/source-and-evidence.md`: source hierarchy, labels, stale data, and evidence requests.
-- `references/sector-overlays.md`: sector KPI, valuation, and red-flag overlays.
-- `references/pm-md-standards.md`: quality bar for PM/MD-level initiations.
-- `references/output-templates.md`: reusable report sections.
-- `references/quality-checklist.md`: final QA.
-- `references/integration-guide.md`: local handoff patterns.
-
-## Final Note
-
-End each initiation with evidence confidence, underwriting status, data cut-off, unresolved conflicts, major assumptions, and the recommended next handoff such as `thesis-tracker`, `equity-model-update`, `earnings-preview`, or `long-short-pitch`.
-
-## Public Equity PM Judgment Layer
-
-For substantial initiation work, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Required PM judgment:
-- State the decision hinge, variant perception, what is priced in, valuation basis, downside mechanism, ownership/positioning, benchmark relevance, and falsifiers.
-- Add `etf_index_constituent_diligence` when the user is assessing an issuer as an ETF/index constituent, index exposure, passive-flow risk, or benchmark-relative holding.
-- Include market cap, float/liquidity, ownership, short interest, factor exposure, index membership, and capital allocation when source-backed or clearly missing.
-- Fund/product diligence is out of scope; public issuer or constituent diligence is in scope.
diff --git a/plugins/financial-markets/skills/initiating-coverage/agents/openai.yaml b/plugins/financial-markets/skills/initiating-coverage/agents/openai.yaml
deleted file mode 100644
index 2f79d8e..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Initiating Coverage"
-  short_description: "Builds public-equity-investing initiation reports with thesis, model, valuation, catalysts, risks, and target price."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/initiating-coverage/assets/icon.svg b/plugins/financial-markets/skills/initiating-coverage/assets/icon.svg
deleted file mode 100644
index 7485531..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/assets/icon.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" role="img" aria-label="Initiating Coverage icon">
-  <rect width="64" height="64" rx="14" fill="#244C8A"/>
-  <path d="M16 45h32" stroke="#FFFFFF" stroke-width="4" stroke-linecap="round"/>
-  <path d="M18 38l8-10 8 6 12-16" fill="none" stroke="#FFFFFF" stroke-width="4" stroke-linecap="round" stroke-linejoin="round"/>
-  <circle cx="18" cy="38" r="3" fill="#BFD7FF"/>
-  <circle cx="26" cy="28" r="3" fill="#BFD7FF"/>
-  <circle cx="34" cy="34" r="3" fill="#BFD7FF"/>
-  <circle cx="46" cy="18" r="3" fill="#BFD7FF"/>
-</svg>
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/initiating-coverage/references/DASHBOARD_PACK.md
deleted file mode 100644
index 01a3610..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# Initiating Coverage Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, reusable validated template, or structured payload-driven render for an initiation, buy-side deep dive, coverage launch, or sector initiation. An ordinary substantial initiation is a polished standalone HTML initiation report following `../../../shared/html-artifact-standard.md`.
-
-## Producer Role
-
-`initiating-coverage` owns the report architecture, thesis, evidence hierarchy, model/valuation framing, and research judgment. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `initiating_coverage`
-- `layout`: `single_page` for full initiation packages unless the user explicitly requests tabs
-- `hero.callout`: the variant view or investment debate that makes the report worth reading
-- `snapshot`: rating/research posture if applicable, target/valuation range if supportable, upside/downside skew, key catalyst, key risk, data cut-off
-- `sources`: filings, company materials, model outputs, consensus/market data, expert/user materials, and assumptions clearly separated
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `view`
-   - `executive_summary`: thesis-led initiation summary with what is proven, assumed, and still unresolved
-   - `decision_box`: recommendation or research posture, valuation stance, thesis change triggers, and next catalyst
-2. `thesis`
-   - `cards`: 3-5 evidence-linked thesis pillars, variant perception, and falsifiers
-   - `scenario_map`: bull/base/bear cases with drivers, valuation implications, and breakpoints
-3. `company-industry`
-   - `key_metrics`: issuer-specific KPIs and operating drivers
-   - `table`: company/industry positioning, segment economics, market share, or end-market exposure
-4. `model-valuation`
-   - `table`: forecast drivers, valuation methods, multiple/DCF support, sensitivity outputs, and basis labels
-   - `bar_chart`: valuation bridge or sensitivity only when the sourced data is complete
-5. `catalysts-risks`
-   - `timeline`: catalyst path, earnings, investor day, regulatory, product, or macro events
-   - `cards`: key risks, disconfirming evidence, and monitoring triggers
-6. `evidence`
-   - `missing_evidence`: unresolved conflicts, stale data, model gaps, and open source requests
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every metric, valuation input, target/range, estimate, market-size claim, and catalyst date.
-- Label facts, company claims, estimates, model-derived values, PM judgment, assumptions, and missing evidence.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not let dashboard modules invent report logic that is absent from the initiation.
-- Do not convert an ordinary initiation report into an action-rules dashboard simply because the research is source-heavy or intended for PM review.
-- Do not present unsupported rating or price-target language as decision-ready.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the dashboard contains a real thesis, valuation method, catalysts, risks, source confidence, and open evidence requests.
-- Confirm model/workbook outputs are clearly labeled when they are support artifacts rather than native dashboard calculations.
-- Confirm citation rendering remains readable and does not fragment tickers, fiscal periods, dates, prices, ranges, multiples, or metric names.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/input-schema.md b/plugins/financial-markets/skills/initiating-coverage/references/input-schema.md
deleted file mode 100644
index a50f6e2..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/input-schema.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# Input Schema
-
-Use this schema when the user or another skill provides structured initiation inputs.
-
-```json
-{
-  "company": "string",
-  "ticker": "string or null",
-  "exchange": "string or null",
-  "sector": "string or null",
-  "report_mode": "sell_side_initiation | buy_side_deep_dive | long_only_initiation | hedge_fund_initiation | credit_adjacent_initiation | sector_initiation | model_first_initiation | report_refresh",
-  "audience": "string or null",
-  "data_cutoff": "YYYY-MM-DD or null",
-  "current_price": 0.0,
-  "currency": "string",
-  "rating_framework": "string or null",
-  "user_view": "string or null",
-  "thesis_hypotheses": ["string"],
-  "sources": [
-    {
-      "source_id": "S1",
-      "source_name": "string",
-      "source_type": "filing | transcript | earnings_release | investor_deck | model | consensus | provider | web | user_provided | other",
-      "date_published": "YYYY-MM-DD or null",
-      "period_covered": "string or null"
-    }
-  ],
-  "financials": {
-    "historical": [],
-    "consensus": [],
-    "forecast": []
-  },
-  "valuation": {
-    "primary_method": "string or null",
-    "target_price": 0.0,
-    "upside_case": 0.0,
-    "downside_case": 0.0,
-    "assumptions": []
-  },
-  "catalysts": [],
-  "risks": [],
-  "open_questions": []
-}
-```
-
-Required minimum fields for structured validation:
-- company,
-- report_mode,
-- sources,
-- thesis_hypotheses or user_view.
-
-If `target_price` is included, `current_price`, `currency`, and valuation assumptions should be present.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/integration-guide.md b/plugins/financial-markets/skills/initiating-coverage/references/integration-guide.md
deleted file mode 100644
index a5ddfb6..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/integration-guide.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Integration Guide
-
-## Upstream skills
-
-### financial-source-of-truth
-
-Use first when sources are messy, conflicting, stale, or mixed between user files, filings, provider data, and web. It supplies evidence hierarchy and citation protocol.
-
-### company-tearsheet
-
-Use to create a concise baseline profile before a full initiation, especially when the user provides little context.
-
-### financials-normalizer
-
-Use when historical financials are embedded in filings, PDFs, spreadsheets, data-room exports, or messy tables.
-
-### excel-data-cleaner
-
-Use when the source workbook has messy tabs, headers, units, dates, hidden rows, broken formats, or inconsistent mappings.
-
-### equity-model-update
-
-Use to update or build an operating model from latest filings, earnings releases, transcripts, KPIs, and consensus estimates.
-
-### earnings-preview / earnings-deep-dive
-
-Use when the initiation depends on a recent or upcoming earnings event.
-
-## Parallel or downstream skills
-
-### Valuation skills
-
-Use for detailed DCF, comps, SOTP, valuation range, and target price support when valuation is the core work product.
-
-### scenario-sensitivity-generator
-
-Use for bull/base/bear, price target sensitivities, downside case, and break-even assumptions.
-
-### long-short-pitch
-
-Use after initiation when the user wants a trade expression: position, catalyst, hedge, risk/reward, and kill signals.
-
-### thesis-tracker
-
-Use after initiation to track KPIs, catalysts, estimate revisions, confirm/disconfirm signals, and thesis status.
-
-### portfolio-risk-management
-
-Use when a thesis needs pair trade, factor hedge, proxy hedge, options hedge, or exposure management.
-
-### memo-builder
-
-Use to convert the initiation into an IC memo, PM memo, committee note, or client-ready summary.
-
-### deck-report-qc / style-guide-adapter
-
-Use before publishing or sharing externally to align formatting, sources, disclosures, and style.
-
-## Handoff fields
-
-When handing off to another skill, provide:
-- company/ticker,
-- report mode,
-- thesis pillars,
-- key debates,
-- model drivers,
-- valuation method and target price,
-- catalysts,
-- risks and disconfirming signals,
-- source register,
-- assumptions register,
-- stale-data log,
-- open questions.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/output-schema.md b/plugins/financial-markets/skills/initiating-coverage/references/output-schema.md
deleted file mode 100644
index cba8df6..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/output-schema.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# Output Schema
-
-Use this schema for structured handoff to `thesis-tracker`, `equity-model-update`, `long-short-pitch`, `memo-builder`, or deck workflows.
-
-```json
-{
-  "company": "string",
-  "ticker": "string or null",
-  "report_mode": "string",
-  "prepared_date": "YYYY-MM-DD",
-  "data_cutoff": "YYYY-MM-DD or null",
-  "evidence_confidence": "high | medium | low",
-  "rating_or_view": "string or null",
-  "target_price": 0.0,
-  "currency": "string or null",
-  "md_pm_answer": "string",
-  "thesis_pillars": [
-    {
-      "title": "string",
-      "claim": "string",
-      "evidence_label": "fact | company_claim | street_estimate | model_derived | banker_or_pm_judgment | assumption | mixed | needs_source",
-      "confidence": "high | medium | low",
-      "source_ids": ["S1"],
-      "disconfirming_signal": "string"
-    }
-  ],
-  "key_debates": [
-    {
-      "debate": "string",
-      "consensus_view": "string",
-      "our_view": "string",
-      "evidence": "string",
-      "what_would_change_our_mind": "string",
-      "source_ids": ["S1"]
-    }
-  ],
-  "model_drivers": [
-    {
-      "driver": "string",
-      "historical_baseline": "string",
-      "forecast_assumption": "string",
-      "sensitivity": "string",
-      "evidence_label": "string",
-      "source_ids": ["S1"]
-    }
-  ],
-  "valuation": {
-    "primary_method": "string",
-    "target_price_math": "string",
-    "implied_multiple": "string or null",
-    "upside_case": "string",
-    "downside_case": "string",
-    "key_sensitivities": []
-  },
-  "catalysts": [],
-  "risks": [],
-  "source_register": [],
-  "assumptions_register": [],
-  "stale_data_flags": [],
-  "open_questions": []
-}
-```
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/output-templates.md b/plugins/financial-markets/skills/initiating-coverage/references/output-templates.md
deleted file mode 100644
index 45a9ace..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/output-templates.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# Output Templates
-
-## Standalone HTML Long-Only Initiation Report
-
-Use this as the normal first-read initiation surface. It should open with one clear research posture, the central ownership debate, a concise evidence snapshot, and the gating work still required. Keep one decision block rather than repeating the conclusion across dashboard-style tiles.
-
-For a capital-intensive or financing-risk thesis, include an early `Capital Return And Funding Gate` with capex, pro forma fully diluted capitalization, debt/lease/dilution exposure, interest burden, cash conversion or after-financing return proof, valuation implication, and missing evidence. Treat equity-value-to-revenue metrics as preliminary context until this gate is supported.
-
-When a target price or ownership recommendation cannot yet be supported, use `Preliminary initiation underwrite` or `Watchlist initiation` as the visible posture and identify the work needed to complete coverage.
-
-Separate `Evidence confidence` from `Underwriting status`: source reliability and investability are different judgments. Include current price, market capitalization, enterprise-value inputs, consensus context, and their as-of timestamps when obtainable; show unavailable valuation inputs as explicit missing evidence rather than silently excluding market context.
-
-Use plain-English metric labels in the first-read layer. For example, prefer `Pro Forma Net Debt + Recorded Leases` to shorthand such as `PF Recognized Claims`.
-
-## Initiation Report Support Note / Renderer Input
-
-```markdown
-# [Company / Ticker] Initiating Coverage
-Prepared: [date] | Data cut-off: [date/time] | Mode: [mode]
-Rating/View: [rating] | Target price: [target] | Current price: [price] | Implied return: [%]
-Market data as-of: [date/time and source] | Market cap / EV: [values or missing inputs]
-Evidence confidence: [high/medium/low] | Underwriting status: [complete/preliminary/watchlist/more work required]
-
-## MD / PM-level answer
-[one paragraph]
-
-## Investment thesis
-1. **[pillar]** - [claim] [source/evidence]
-2. **[pillar]** - [claim] [source/evidence]
-3. **[pillar]** - [claim] [source/evidence]
-
-## Key debates and variant perception
-| Debate | Consensus view | Our view | Evidence | What would change our mind |
-|---|---|---|---|---|
-
-## Business and industry overview
-[concise overview]
-
-## Model and forecast drivers
-| Driver | Historical baseline | Forecast assumption | Sensitivity | Evidence |
-|---|---|---|---|---|
-
-## Valuation and target price
-[method, math, implied multiple, sensitivities]
-
-## Catalysts and timeline
-| Timing | Catalyst | Expected read-through | Evidence | Risk |
-|---|---|---|---|---|
-
-## Risks and disconfirming evidence
-| Risk | Thesis impact | Leading indicator | Downside case | Mitigant/monitoring |
-|---|---|---|---|---|
-
-## Source register, conflicts, and assumptions
-[table]
-```
-
-## Buy-side PM memo format
-
-```markdown
-# [Company / Ticker] Buy-Side Initiation
-
-## Decision
-[long / short / watchlist / avoid / more work needed]
-
-## Variant perception
-[what we believe vs market]
-
-## Why now
-[catalyst and timing]
-
-## Risk/reward
-[base/upside/downside, path, skew]
-
-## Thesis kill signals
-[signals]
-
-## Position considerations
-[liquidity, sizing considerations, hedge/pair ideas if requested]
-```
-
-## Sell-side initiation summary box
-
-```markdown
-| Field | Value |
-|---|---|
-| Rating | [rating] |
-| Target price | [target] |
-| Current price | [price] |
-| Upside/downside | [%] |
-| Market cap / EV | [values] |
-| Primary valuation | [method] |
-| Key catalyst | [catalyst] |
-| Key risk | [risk] |
-| Data cut-off | [date] |
-```
-
-## Source request checklist output
-
-```markdown
-## Missing sources needed before publication
-- Latest 10-K / 10-Q / interim report
-- Latest earnings release and transcript
-- Investor deck
-- Consensus estimates
-- Current share price and diluted shares
-- Net debt and non-operating assets/liabilities
-- Peer set and trading multiples
-- Sector-specific KPIs
-- Existing model or forecast assumptions
-- Compliance/disclosure language if external publication
-```
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/pm-md-standards.md b/plugins/financial-markets/skills/initiating-coverage/references/pm-md-standards.md
deleted file mode 100644
index eedbcf1..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/pm-md-standards.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# PM / MD-Level Standards
-
-## What senior-quality looks like
-
-The report should feel like it was reviewed by a sector head or top-tier PM. It should:
-- have a clear view,
-- focus on the debate that matters,
-- avoid generic company-description filler,
-- tie thesis to model drivers,
-- show what would change the view,
-- be honest about weak evidence,
-- respect valuation and downside,
-- understand investor psychology and timing.
-
-## The senior lens
-
-Before finalizing, ask:
-
-1. Would this change how a PM sizes or prioritizes the idea?
-2. Is the thesis actually different from consensus?
-3. Is the downside case credible and specific?
-4. Are catalysts strong enough to close the valuation gap?
-5. Does valuation already price in the good news?
-6. Which estimate line matters most?
-7. Which KPI is the tell?
-8. Where is management most likely to disappoint?
-9. What would a skeptical short or bull say?
-10. What would make us admit we are wrong?
-
-## Language standards
-
-Prefer:
-- "The debate is not whether growth is high; it is whether NRR can stabilize without incremental CAC pressure."
-- "Our target multiple requires confidence in margin durability; absent that, the stock deserves a lower range."
-- "The next two prints should resolve whether the guide is conservative or demand is weakening."
-
-Avoid:
-- "The company is a leader in a large and growing market."
-- "Management is executing well."
-- "Valuation appears attractive" without explaining relative to what, why, and what breaks it.
-
-## Rating / view discipline
-
-When providing a rating, view, or target price:
-- define the rating framework if user has not supplied one,
-- connect rating to expected return and risk/reward,
-- avoid false precision,
-- show target price sensitivity,
-- show downside case,
-- flag that publication requires human/compliance review.
-
-## Research taste
-
-A great initiation is not the longest report. It is the report that makes the central risk/reward obvious.
-
-Cut or move to appendix:
-- generic company history,
-- broad market-size discussion with no financial impact,
-- long management bios unless governance/capital allocation matters,
-- peer tables with no conclusion,
-- risks that apply to every company in the sector.
-
-## Senior Public Equity PM Standards
-
-Require variant perception, evidence strength, what is priced in, valuation support, downside mechanism, catalyst path, ownership/positioning, liquidity, factor exposure, benchmark/active risk, short interest, and falsifiers. Name what the market is paying for and where the PM edge sits.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/quality-checklist.md b/plugins/financial-markets/skills/initiating-coverage/references/quality-checklist.md
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index 7431194..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/quality-checklist.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# Quality Checklist
-
-Run this checklist before final delivery.
-
-## Research view
-
-- The report has a clear rating/view or explains why no rating/view is provided.
-- The central debate is explicit.
-- The variant perception is specific and not just bullish/bearish adjectives.
-- The thesis is falsifiable.
-- The disconfirming evidence is real and measurable.
-
-## Model and valuation
-
-- Forecast drivers are visible.
-- Consensus bridge is included when estimates are available.
-- Target price method is appropriate to the sector and company maturity.
-- Target price math ties to share count, net debt, and valuation assumptions.
-- Sensitivities show what matters most.
-- Downside case is credible, not token.
-- Non-GAAP metrics are labeled.
-- EPS-driven valuation uses the right basis: recurring/operating EPS when GAAP EPS is distorted, with accepted/rejected adjustments visible.
-- For capital-intensive or externally financed growth, the report shows the pro forma fully diluted capitalization, financing burden, and after-financing return evidence before any positive ownership or target-price conclusion.
-- Revenue multiples are labeled preliminary market context rather than stand-alone valuation support when material debt, leases, or dilution are unresolved.
-
-## Sector nuance
-
-- Sector-specific metrics are used where relevant.
-- Sector-specific risks are included.
-- Generic TAM / market leader language is cut unless it affects model or valuation.
-- Peer set is appropriate and caveated.
-
-## Evidence and sources
-
-- Data cut-off is shown.
-- Current price, market capitalization, diluted share-count inputs, enterprise-value inputs, and available consensus context were retrieved from approved accessible sources or explicitly labeled unavailable with the conclusion they prevent.
-- Source register is included.
-- Every key metric and claim has source support or an evidence label.
-- Stale data is flagged.
-- Source conflicts are visible.
-- Assumptions are separated from facts.
-
-## Senior polish
-
-- Evidence confidence is separate from underwriting status; source confidence is not used as a proxy for investability.
-- Executive summary is concise and decision-useful.
-- Section titles convey conclusions.
-- Risks are ranked by thesis impact.
-- Catalysts have timing and read-through.
-- The output is appropriate for sell-side, buy-side, long-only, hedge fund, or credit use.
-- A standalone HTML initiation uses one clear posture and avoids repeating the same decision across dashboard-style modules.
-- Citation rendering remains readable and does not fragment tickers, fiscal periods, dates, prices, ranges, multiples, or metric names.
-- Local HTML output has been checked with local headless-browser screenshots, including the opening viewport and a decision-critical downstream section.
-- First-read capitalization labels are plain English, such as `Pro Forma Net Debt + Recorded Leases`, rather than internal shorthand.
-
-## Non-destructive behavior
-
-- No original user file is overwritten.
-- Any edits are additive or versioned.
-- Any suggested changes to existing models/reports are clearly marked.
-
-## Public Equity PM Acceptance
-
-Require a real view, target/valuation basis, downside case, variant perception, what is priced in, falsifiers, source register, and audience-specific implications for long-only, hedge fund, sell-side, ETF/index, or public diligence users.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/report-architecture.md b/plugins/financial-markets/skills/initiating-coverage/references/report-architecture.md
deleted file mode 100644
index a6832bf..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/report-architecture.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Report Architecture
-
-A strong initiation report should feel like an argument, not a fact book.
-
-## Standalone HTML Long-Only Initiation Report
-
-For the normal user-facing HTML deliverable, create a polished standalone report rather than a standardized dashboard. The opening viewport should show one research posture, the central debate, what must be true for ownership, the valuation or capital-return gate, and the most important evidence limitation. Do not repeat the same posture through a hero banner, summary tile, PM answer, and decision box.
-
-When capital intensity or financing risk is part of the thesis, place a `Capital Return And Funding Gate` near the top of the report. Summarize capex, pro forma debt and lease obligations, prospective dilution, interest burden, the after-financing return or cash-conversion proof available, and what is still missing from the model. If those items cannot support a target price or positive ownership conclusion, label the document `Preliminary initiation underwrite` or `Watchlist initiation` and state the work required to complete coverage.
-
-## Default section order
-
-1. Cover / summary box
-2. MD or PM-level answer
-3. Thesis and recommendation
-4. Key debates and variant perception
-5. Company overview
-6. Industry structure and competitive position
-7. Financial model and forecast drivers
-8. Valuation and target price
-9. Catalysts and timeline
-10. Risks and disconfirming evidence
-11. Scenario analysis and sensitivity
-12. Appendix and source register
-
-## Cover / summary box
-
-Include:
-- company / ticker / exchange,
-- sector / subsector,
-- date and data cut-off,
-- rating or view if requested,
-- target price or target range if requested,
-- current price / market cap / enterprise value when available,
-- current market-data as-of time and any explicitly missing valuation inputs,
-- 12-month return implied by target price,
-- evidence confidence,
-- underwriting status, stated separately from evidence confidence,
-- primary valuation methodology.
-
-## MD / PM-level answer
-
-One paragraph only. It should answer:
-- what should the reader believe or do,
-- why now,
-- what the market is missing,
-- what valuation says,
-- what would change the view.
-
-## Thesis section
-
-Use 3-5 thesis pillars. Each pillar needs:
-- title,
-- supporting evidence,
-- model/valuation consequence,
-- counterargument,
-- signal to monitor.
-
-Bad: "large TAM and strong management."
-Good: "enterprise mix shift can drive 300 bps of gross margin expansion if renewal cohort NRR holds above 115%, but the thesis breaks if new logo CAC payback remains above 30 months."
-
-## Key debates and variant perception
-
-For each debate:
-- consensus view,
-- our view,
-- evidence,
-- why the market may be wrong,
-- next event that resolves or reframes the debate.
-
-## Company and industry overview
-
-Keep concise. Only include business description that supports the investment debate.
-
-Required:
-- revenue model,
-- customer / end-market mix,
-- cost structure,
-- margin structure,
-- competitive position,
-- cyclicality/regulation/technology exposure,
-- management or capital allocation points if material.
-
-## Model and forecast drivers
-
-Required:
-- historical baseline,
-- consensus bridge,
-- forecast assumptions,
-- model sensitivities,
-- driver link to valuation.
-
-## Valuation and target price
-
-Required:
-- primary method and why it fits,
-- backup method,
-- target price calculation,
-- implied multiple or valuation bridge,
-- sensitivity table,
-- upside/downside cases,
-- limitations.
-
-For externally financed or capital-intensive growth, a revenue multiple cannot replace the capital-return and funding gate. Present an equity-value-to-revenue multiple as preliminary market context only until the fully diluted capitalization, enterprise-value bridge, financing burden, and after-financing returns are supportable.
-
-## Catalysts
-
-Separate:
-- hard-dated events,
-- recurring events,
-- soft catalysts,
-- thesis milestones,
-- regulatory/legal/policy events,
-- estimate revision catalysts.
-
-## Risks and disconfirming evidence
-
-Do not use generic risk boilerplate. Include:
-- risk description,
-- why it matters to thesis,
-- leading indicator,
-- downside impact,
-- mitigation or monitoring plan.
-
-## Appendix
-
-Include only items that support the report:
-- model assumptions,
-- peer set,
-- valuation tables,
-- source register,
-- data caveats,
-- glossary,
-- disclosure placeholders.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/report-modes.md b/plugins/financial-markets/skills/initiating-coverage/references/report-modes.md
deleted file mode 100644
index ca9162e..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/report-modes.md
+++ /dev/null
@@ -1,126 +0,0 @@
-# Report Modes
-
-Use this reference to adapt the initiating coverage workflow to the user's audience and decision.
-
-## Sell-side initiation
-
-Purpose: publishable or publication-style research report for institutional clients.
-
-Required content:
-- rating / recommendation framework if requested,
-- target price and methodology,
-- expected return and rating rationale,
-- investment thesis,
-- key debates,
-- model summary,
-- valuation summary,
-- catalysts,
-- risks,
-- compliance/disclosure placeholders.
-
-Tone: polished, client-facing, evidence-heavy, balanced. Avoid sounding like a trade recommendation unless the user wants buy-side framing.
-
-## Buy-side deep dive
-
-Purpose: PM-ready internal research packet.
-
-Required content:
-- variant perception,
-- risk/reward and expected value,
-- what the market is missing,
-- positionability and liquidity considerations,
-- catalyst path,
-- disconfirming evidence,
-- sizing considerations if requested,
-- explicit monitoring plan.
-
-Tone: direct, skeptical, decision-oriented. Less boilerplate, more debate and falsifiability.
-
-## Long-only initiation
-
-Purpose: determine whether the company can compound capital over a multi-year horizon.
-
-Required content:
-- durability of growth,
-- moat and competitive position,
-- management and capital allocation,
-- balance sheet quality,
-- reinvestment runway,
-- return on incremental invested capital and after-financing cash economics when growth is capital intensive or externally funded,
-- capital-stack, lease, and dilution gate when financing obligations matter to the equity case,
-- valuation discipline,
-- downside protection,
-- multi-year thesis milestones.
-
-Tone: quality and time-horizon focused, but not blind to valuation risk.
-
-When the evidence cannot establish capitalization and after-financing returns, use a preliminary or watchlist initiation posture rather than a positive ownership conclusion or unsupported target price.
-
-## Hedge fund initiation
-
-Purpose: decide whether a long, short, pair, event, or catalyst trade should be pursued.
-
-Required content:
-- variant view,
-- catalyst calendar,
-- setup into next prints,
-- estimate revision risk,
-- positioning/technical context when available,
-- downside/upside asymmetry,
-- hedge or pair considerations,
-- thesis kill signals.
-
-Tone: sharper and less encyclopedic. Every section should tie to return path.
-
-## Credit-adjacent initiation
-
-Purpose: analyze an issuer or public company through a credit lens.
-
-Required content:
-- capital structure,
-- debt maturity wall,
-- secured/unsecured hierarchy,
-- leverage and coverage,
-- FCF and liquidity,
-- ratings/spreads when available,
-- refinancing risk,
-- downside/recovery considerations.
-
-Tone: cash-flow and protection focused. Equity upside is secondary unless explicitly requested.
-
-## Sector initiation
-
-Purpose: launch coverage of a sector or subsector.
-
-Required content:
-- industry structure,
-- coverage universe,
-- key themes,
-- stock selection framework,
-- top picks / avoids if requested,
-- relative valuation,
-- sector KPIs,
-- macro/regulatory/technology drivers,
-- debate map and catalyst calendar.
-
-Tone: map the battlefield before naming winners.
-
-## Model-first initiation
-
-Purpose: create a model-backed initiation where the workbook and target price are the central deliverable.
-
-Required content:
-- model architecture,
-- forecast drivers,
-- historical normalization,
-- consensus bridge,
-- valuation methodology,
-- sensitivities,
-- model checks,
-- written investment conclusion.
-
-Tone: assumptions and mechanics first, narrative second.
-
-## Audience Context Modes
-
-Add `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_constituent_diligence`, and `public_equity_diligence`. Fund/product diligence is out of scope; public issuer and constituent diligence is in scope.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/sector-overlays.md b/plugins/financial-markets/skills/initiating-coverage/references/sector-overlays.md
deleted file mode 100644
index b126330..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/sector-overlays.md
+++ /dev/null
@@ -1,158 +0,0 @@
-# Sector Overlays
-
-Use `sector-context-overlay` to avoid generic public-company analysis when the issuer matches a supported sector lens.
-
-## SaaS / software
-
-Key metrics:
-- ARR, subscription revenue, RPO/cRPO, billings,
-- net revenue retention, gross retention, churn,
-- CAC payback, LTV/CAC, sales efficiency,
-- gross margin, free cash flow margin,
-- rule of 40, magic number,
-- enterprise vs SMB mix,
-- seat vs usage vs transaction pricing.
-
-Red flags:
-- billings deceleration masked by revenue recognition,
-- NRR decay,
-- rising CAC payback,
-- customer concentration,
-- deferred revenue weakness,
-- stock-based compensation dilution.
-
-Valuation:
-- EV/revenue, EV/gross profit, FCF yield, DCF for mature names.
-
-## Banks
-
-Key metrics:
-- NII, NIM, deposit beta, loan growth,
-- credit costs, NCOs, reserves, criticized loans,
-- CET1, tangible common equity, RWA,
-- efficiency ratio, fee income,
-- TBV/share and ROTCE.
-
-Red flags:
-- deposit mix deterioration,
-- commercial real estate exposure,
-- unrealized securities losses,
-- reserve underbuild,
-- capital return constraints.
-
-Valuation:
-- P/TBV vs ROTCE, excess capital, dividend/buyback capacity.
-
-## Insurance
-
-Key metrics:
-- premiums, combined ratio, loss ratio,
-- reserve development,
-- investment income,
-- book value growth,
-- capital adequacy and catastrophe exposure.
-
-Valuation:
-- P/B, P/E, ROE, embedded value where relevant.
-
-## REITs
-
-Key metrics:
-- NOI, same-store NOI, occupancy,
-- leasing spreads, rent growth,
-- AFFO/share, payout ratio,
-- NAV, cap rates,
-- debt/EBITDA, secured debt, maturity schedule.
-
-Red flags:
-- tenant concentration,
-- lease rollover cliffs,
-- refinancing costs,
-- capex leakage,
-- cap rate expansion.
-
-Valuation:
-- NAV, implied cap rate, AFFO multiple, dividend yield.
-
-## Energy / E&P
-
-Key metrics:
-- production, realized prices, lifting cost,
-- decline rate, inventory depth,
-- capex, FCF, hedges,
-- reserves, finding/development cost,
-- leverage and shareholder returns.
-
-Valuation:
-- EV/EBITDA, FCF yield, NAV, reserve value.
-
-## Biotech
-
-Key metrics:
-- pipeline stage, trial design, endpoints,
-- probability of success,
-- TAM and peak sales,
-- launch curve, patent life,
-- cash runway and dilution.
-
-Red flags:
-- underpowered trial,
-- weak endpoint relevance,
-- financing overhang,
-- binary event risk,
-- competitive standard-of-care shift.
-
-Valuation:
-- rNPV, probability-weighted DCF, sum of pipeline assets.
-
-## Consumer / retail
-
-Key metrics:
-- comparable sales, traffic, ticket,
-- units, pricing, mix,
-- gross margin, inventory, promotions,
-- store growth, digital mix,
-- loyalty/retention metrics.
-
-Red flags:
-- inventory build,
-- promotional intensity,
-- traffic deterioration,
-- channel mix margin dilution,
-- consumer credit stress.
-
-Valuation:
-- P/E, EV/EBITDA, FCF yield, DCF for stable names.
-
-## Industrials / business services
-
-Key metrics:
-- organic growth, backlog, book-to-bill,
-- price/cost, utilization,
-- segment margins,
-- working capital,
-- M&A contribution,
-- cycle exposure.
-
-Red flags:
-- order slowdown,
-- margin lag from input costs,
-- integration issues,
-- project delays,
-- customer concentration.
-
-Valuation:
-- EV/EBITDA, P/E, FCF yield, SOTP.
-
-## Exchanges / financial infrastructure
-
-Key metrics:
-- volumes, open interest, take rate,
-- data and analytics revenue,
-- clearing revenue/risk,
-- market share,
-- operating leverage,
-- regulatory risk.
-
-Valuation:
-- P/E, EV/EBITDA, FCF yield, DCF for durable compounders.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/source-and-evidence.md b/plugins/financial-markets/skills/initiating-coverage/references/source-and-evidence.md
deleted file mode 100644
index 0c174c2..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/source-and-evidence.md
+++ /dev/null
@@ -1,84 +0,0 @@
-# Source and Evidence Protocol
-
-## Source hierarchy
-
-Use sources in this order unless the user instructs otherwise:
-
-1. User-provided context and files.
-2. Callable connected routes and user-provided exports.
-3. Company primary sources: 10-K, 10-Q, 8-K, S-1, prospectus, proxy, investor deck, earnings release, transcript, press release.
-4. Callable financial-data provider routes or user-provided consensus/estimate exports.
-5. Exchange, regulator, rating agency, central bank, or government sources where relevant.
-6. Reputable public web and news.
-7. Explicit assumptions.
-
-## Required source fields
-
-For each source-register entry capture:
-- source_id,
-- source_name,
-- source_type,
-- date_published,
-- date_accessed,
-- period_covered,
-- document_section_or_page when available,
-- reliability tier,
-- stale flag,
-- notes.
-
-## Evidence labels
-
-Use one of:
-- `fact`: directly supported by a reliable source.
-- `company_claim`: stated by the company or management but not independently verified.
-- `street_estimate`: consensus, sell-side estimate, or third-party forecast.
-- `model_derived`: calculated from sourced inputs or a provided model.
-- `banker_or_pm_judgment`: professional interpretation based on evidence.
-- `assumption`: user/model/assistant assumption not directly sourced.
-- `mixed`: combines supported facts with assumptions or judgment.
-- `needs_source`: material claim still missing evidence.
-
-## Confidence labels
-
-Use one of:
-- `high`: primary source or multiple trusted sources, recent and directly relevant.
-- `medium`: credible but indirect, older, partial, or requires some interpretation.
-- `low`: weak evidence, incomplete, stale, or assumption-heavy.
-
-## Stale-data checks
-
-Flag stale data when:
-- public-company market data is not current as of the report date,
-- latest fiscal period is missing,
-- estimates are pre-earnings when post-earnings estimates should be available,
-- transcript/earnings data is more than one quarter old for current setup claims,
-- macro/commodity/rate data is not aligned to current report date,
-- industry data predates a major regulatory, macro, or company event.
-
-## Market Data And Valuation Inputs Completion Step
-
-Before delivering a substantive initiation report, attempt to source the current price with an as-of timestamp, market capitalization, reported and fully diluted share-count inputs, cash and debt/lease inputs required for enterprise value, and available consensus or estimate context. Use approved connected or accessible market-data sources where available and retain source IDs and freshness labels.
-
-If a required input cannot be retrieved, mark it `needs_source`, explain which valuation or ownership conclusion cannot be supported without it, and keep the initiation posture appropriately preliminary. Do not omit retrievable current market data merely because the capital-return thesis remains unproven.
-
-Keep two judgments separate in the report header and conclusion:
-- `Evidence confidence`: confidence in reported operating, financing, market-data, and estimate inputs.
-- `Underwriting status`: whether the available evidence is sufficient for ownership, a target price, watchlist status, or more work.
-
-## Conflict handling
-
-If sources disagree:
-1. Identify the conflict explicitly.
-2. Prefer primary source for reported historicals.
-3. Prefer latest consensus provider for current estimates if user approved that provider.
-4. Preserve user-provided internal estimates as `user_assumption` or `model_derived`, not objective fact.
-5. Show the impact of the conflict if material.
-6. Do not average conflicting sources unless there is a clear reason.
-
-## Citation style
-
-Use consistent footnotes or inline citations according to the user's environment. When building an output that cannot preserve clickable citations, include a source register with source IDs and map each material claim to a source ID.
-
-Example:
-- Revenue was $X in FY2025 [S1].
-- We assume revenue CAGR of Y% from FY2025-FY2028 based on pipeline commentary [S2] and user model assumptions [A1].
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/thesis-framework.md b/plugins/financial-markets/skills/initiating-coverage/references/thesis-framework.md
deleted file mode 100644
index 718e2a9..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/thesis-framework.md
+++ /dev/null
@@ -1,101 +0,0 @@
-# Thesis Framework
-
-## The initiation must answer the PM questions
-
-1. What is the central underwriting question?
-2. What is mispriced or misunderstood?
-3. What evidence supports our view?
-4. What evidence would disprove it?
-5. What will change investor expectations?
-6. What is the path to return?
-7. What is the downside if wrong?
-8. How does valuation compensate for the risk?
-
-## Thesis quality bar
-
-A strong thesis is:
-- specific: tied to measurable drivers,
-- variant: different from consensus or sharper than consensus,
-- falsifiable: clear disconfirming signals,
-- financial: tied to estimates, cash flow, multiples, or balance sheet,
-- timed: has catalysts or time horizon,
-- risk-aware: knows the most dangerous counterargument.
-
-## Common thesis types
-
-### Quality compounder
-
-Core question: can the company reinvest at high returns for longer than expected?
-
-Watch:
-- organic growth durability,
-- incremental margins,
-- retention / pricing power,
-- reinvestment runway,
-- capital allocation.
-
-### Turnaround / margin expansion
-
-Core question: is the margin bridge real or just cyclical noise?
-
-Watch:
-- cost saves executed vs promised,
-- mix shift,
-- restructuring costs,
-- revenue leakage,
-- operating discipline.
-
-### Cyclical recovery
-
-Core question: where are we in the cycle and how much is priced in?
-
-Watch:
-- volume indicators,
-- pricing/spreads,
-- inventories,
-- end-market demand,
-- operating leverage.
-
-### Estimate revision
-
-Core question: are numbers too low or too high?
-
-Watch:
-- leading KPIs,
-- bookings/backlog/RPO,
-- price/cost trend,
-- guidance conservatism,
-- consensus dispersion.
-
-### Multiple re-rating
-
-Core question: what unlocks a higher or lower multiple?
-
-Watch:
-- growth/margin consistency,
-- business mix,
-- capital returns,
-- index inclusion,
-- governance,
-- narrative shift.
-
-### Special situation
-
-Core question: does the event change value or merely timing?
-
-Watch:
-- merger approval,
-- spin economics,
-- litigation/regulatory decisions,
-- restructuring steps,
-- capital allocation.
-
-## Debate table template
-
-| Debate | Consensus view | Our view | Evidence | Disconfirming signal | Next catalyst |
-|---|---|---|---|---|---|
-| [driver] | [what market believes] | [variant view] | [source-backed facts] | [what breaks thesis] | [date/event] |
-
-## Thesis Scorecard
-
-Score variant perception, evidence strength, valuation support, catalyst path, downside mechanism, timing, ownership/positioning, benchmark relevance, and falsifiers. Do not recommend without measurable disconfirmers and a downside case.
diff --git a/plugins/financial-markets/skills/initiating-coverage/references/valuation-and-modeling.md b/plugins/financial-markets/skills/initiating-coverage/references/valuation-and-modeling.md
deleted file mode 100644
index 57d4a5c..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/references/valuation-and-modeling.md
+++ /dev/null
@@ -1,139 +0,0 @@
-# Valuation and Modeling
-
-## Minimum viable initiation model
-
-At minimum, define:
-- historical revenue and segment drivers,
-- gross margin / EBITDA / EBIT or equivalent profitability,
-- tax, D&A, capex, working capital where relevant,
-- shares, net debt, and non-operating items,
-- consensus estimates and variance from user's view,
-- normalized earnings quality: GAAP EPS, adjusted/operating EPS, tax/share-count effects, non-operating items, and whether EPS is recurring,
-- base/upside/downside cases,
-- target price methodology,
-- sensitivity table.
-
-## Capital-Intensive And Financed Growth Gate
-
-Apply this gate when the equity debate depends on large capex, external funding, debt, lease liabilities, converts or prospective dilution, customer concentration, or contracted capacity still requiring construction.
-
-Before a positive ownership conclusion or target price, establish:
-- a pro forma fully diluted capitalization bridge, including options, converts, expected issuance, and other material claims when relevant,
-- enterprise value incorporating debt, lease commitments, cash, and non-operating items,
-- interest burden, maturity/refinancing exposure, and debt-service capacity,
-- maintenance and growth capex, depreciation/useful-life assumptions, and cash conversion,
-- after-financing economics through normalized FCF, ROIC / return on invested capacity, or capacity-cohort cash returns, as appropriate,
-- a sensitivity that shows how financing cost, utilization, pricing, margin, or dilution changes equity value.
-
-Equity-value-to-revenue or revenue multiples may be shown as preliminary market context, but they are not primary valuation support when material financed obligations remain outside the comparison. If the inputs above are not available, deliver a preliminary or watchlist initiation underwrite and list the proof required before ownership.
-
-## Method selection
-
-### DCF
-
-Best when:
-- cash flow can be forecast with some confidence,
-- terminal economics matter,
-- company has stable or maturing model,
-- user needs intrinsic value.
-
-Required caveats:
-- WACC and terminal value sensitivity,
-- margin normalization,
-- reinvestment/capex requirements,
-- forecast horizon credibility.
-
-### Trading comparables
-
-Best when:
-- business has relevant public peers,
-- market-relative view matters,
-- mature or sector-standard valuation methods exist.
-
-Required caveats:
-- peer set quality,
-- growth/margin/risk differences,
-- accounting differences,
-- calendarization and one-time adjustments.
-
-### Sum-of-the-parts
-
-Best when:
-- segments have different economics,
-- conglomerate discount or breakup case matters,
-- multiple business lines require different valuation metrics.
-
-Required caveats:
-- segment disclosure quality,
-- allocated costs,
-- intersegment dependencies,
-- tax/leakage and corporate overhead.
-
-### NAV / book / tangible book
-
-Best for:
-- banks,
-- insurers,
-- REITs,
-- asset-heavy companies,
-- financials and real assets.
-
-Required caveats:
-- asset marks,
-- reserve adequacy,
-- credit/impairment risk,
-- cap rate / spread sensitivity,
-- capital requirements.
-
-### Probability-weighted valuation
-
-Best for:
-- biotech,
-- litigation-driven companies,
-- binary regulatory or development events,
-- special situations.
-
-Required caveats:
-- probability assumptions,
-- timing assumptions,
-- capital needs/dilution,
-- downside if event fails.
-
-## Target price bridge
-
-Required outputs:
-- current price,
-- target price,
-- implied upside/downside,
-- valuation method,
-- key assumptions,
-- implied multiple,
-- sensitivity range,
-- comparison to consensus target if available.
-
-For P/E or EPS-driven target prices, use recurring or operating EPS when GAAP EPS is materially distorted. If GAAP EPS is used, explain why it is representative. If adjusted EPS is used, cite the reconciliation and state which adjustments are accepted, rejected, or still unproven.
-
-## Sensitivities
-
-Common sensitivities:
-- revenue CAGR vs terminal margin,
-- WACC vs terminal growth,
-- EBITDA multiple vs EBITDA,
-- NTM multiple vs estimate revision,
-- NIM vs credit cost for banks,
-- cap rate vs NOI for REITs,
-- probability of success vs peak sales for biotech,
-- commodity price vs production/cost for energy.
-
-## Model QA expectations
-
-Before presenting valuation:
-- share count ties to latest filing or estimate source,
-- net debt ties to latest filing or model,
-- enterprise value math checks,
-- multiples use consistent calendar year / NTM / LTM definitions,
-- non-GAAP metrics are labeled,
-- EPS basis and quality are explicit before applying a P/E multiple,
-- source dates are visible,
-- no target price is presented as more precise than the inputs justify,
-- capital-intensive or externally financed growth clears the pro forma fully diluted capitalization and after-financing return gate before a positive ownership conclusion.
diff --git a/plugins/financial-markets/skills/initiating-coverage/scripts/build_initiation_markdown.py b/plugins/financial-markets/skills/initiating-coverage/scripts/build_initiation_markdown.py
deleted file mode 100644
index c24bd5d..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/scripts/build_initiation_markdown.py
+++ /dev/null
@@ -1,173 +0,0 @@
-#!/usr/bin/env python3
-"""Build an initiating coverage Markdown support note from structured JSON."""
-
-import argparse
-import json
-from pathlib import Path
-
-PLACEHOLDER_TERMS = ("TBD", "TODO", "MISSING", "[PLACEHOLDER]")
-
-
-def fmt(value, default="not provided"):
-    if value is None or value == "":
-        return default
-    return str(value)
-
-
-def rows(items, columns):
-    if not items:
-        return "_No entries provided._\n"
-    out = []
-    out.append("| " + " | ".join(columns) + " |")
-    out.append("|" + "|".join(["---"] * len(columns)) + "|")
-    for item in items:
-        out.append("| " + " | ".join(fmt(item.get(c, "")) for c in columns) + " |")
-    return "\n".join(out) + "\n"
-
-
-def build(data):
-    company = fmt(data.get("company"))
-    ticker = data.get("ticker")
-    title = f"{company}"
-    if ticker:
-        title += f" ({ticker})"
-    title += " Initiating Coverage"
-    prepared = fmt(data.get("prepared_date"))
-    cutoff = fmt(data.get("data_cutoff"))
-    mode = fmt(data.get("report_mode"))
-    evidence = fmt(data.get("evidence_confidence"))
-    rating = fmt(data.get("rating_or_view") or data.get("user_view"))
-    target = fmt(data.get("target_price"))
-    currency = fmt(data.get("currency"), "")
-
-    md = []
-    md.append(f"# {title}\n")
-    md.append(
-        f"Prepared: {prepared} | Data cut-off: {cutoff} | Mode: {mode} | Evidence confidence: {evidence}\n"
-    )
-    md.append(f"Rating/View: {rating} | Target price: {currency}{target}\n")
-    md.append("## MD / PM-level answer\n")
-    md.append(
-        fmt(
-            data.get("md_pm_answer"),
-            "not provided - add the core recommendation, central debate, valuation implication, and what would change the view.",
-        )
-        + "\n"
-    )
-
-    md.append("## Investment thesis\n")
-    thesis = data.get("thesis_pillars") or []
-    if thesis:
-        for i, item in enumerate(thesis, 1):
-            md.append(
-                f"{i}. **{fmt(item.get('title'))}** - {fmt(item.get('claim'))} "
-                f"[{fmt(item.get('evidence_label'))}; {fmt(item.get('confidence'))}]\n"
-            )
-            if item.get("disconfirming_signal"):
-                md.append(f"   - Disconfirming signal: {item['disconfirming_signal']}\n")
-    else:
-        md.append("_No thesis pillars provided._\n")
-
-    md.append("\n## Key debates and variant perception\n")
-    md.append(
-        rows(
-            data.get("key_debates", []),
-            ["debate", "consensus_view", "our_view", "evidence", "what_would_change_our_mind"],
-        )
-    )
-
-    md.append("\n## Model and forecast drivers\n")
-    md.append(
-        rows(
-            data.get("model_drivers", []),
-            [
-                "driver",
-                "historical_baseline",
-                "forecast_assumption",
-                "sensitivity",
-                "evidence_label",
-            ],
-        )
-    )
-
-    valuation = data.get("valuation") or {}
-    md.append("\n## Valuation and target price\n")
-    if valuation:
-        md.append(f"- Primary method: {fmt(valuation.get('primary_method'))}\n")
-        md.append(f"- Target price math: {fmt(valuation.get('target_price_math'))}\n")
-        md.append(f"- Implied multiple: {fmt(valuation.get('implied_multiple'))}\n")
-        md.append(f"- Upside case: {fmt(valuation.get('upside_case'))}\n")
-        md.append(f"- Downside case: {fmt(valuation.get('downside_case'))}\n")
-    else:
-        md.append("_No valuation block provided._\n")
-
-    md.append("\n## Catalysts and timeline\n")
-    md.append(
-        rows(
-            data.get("catalysts", []),
-            ["timing", "catalyst", "expected_read_through", "evidence", "risk"],
-        )
-    )
-
-    md.append("\n## Risks and disconfirming evidence\n")
-    md.append(
-        rows(
-            data.get("risks", []),
-            ["risk", "thesis_impact", "leading_indicator", "downside_case", "monitoring_plan"],
-        )
-    )
-
-    md.append("\n## Source register, conflicts, and assumptions\n")
-    sources = data.get("source_register") or data.get("sources") or []
-    md.append(
-        rows(
-            sources, ["source_id", "source_name", "source_type", "date_published", "period_covered"]
-        )
-    )
-
-    if data.get("stale_data_flags"):
-        md.append("\n### Stale data flags\n")
-        for flag in data["stale_data_flags"]:
-            md.append(f"- {flag}\n")
-    if data.get("open_questions"):
-        md.append("\n### Open questions\n")
-        for q in data["open_questions"]:
-            md.append(f"- {q}\n")
-
-    return "".join(md)
-
-
-def contains_placeholder(text):
-    upper = text.upper()
-    return any(term in upper for term in PLACEHOLDER_TERMS)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Build an initiating coverage Markdown support note from structured JSON."
-    )
-    parser.add_argument("input_json", help="Path to structured initiating coverage JSON")
-    parser.add_argument("output_md", help="Output Markdown support-note path")
-    parser.add_argument(
-        "--publication-ready",
-        action="store_true",
-        help="Fail if generated support note contains unresolved placeholders.",
-    )
-    args = parser.parse_args()
-
-    try:
-        data = json.loads(Path(args.input_json).read_text(encoding="utf-8"))
-        output = build(data)
-    except Exception as exc:
-        print(f"ERROR: {exc}")
-        return 1
-    if args.publication_ready and contains_placeholder(output):
-        print("ERROR: generated support note contains unresolved placeholder text")
-        return 1
-    Path(args.output_md).write_text(output, encoding="utf-8")
-    print(f"wrote {args.output_md}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/initiating-coverage/scripts/build_source_request_checklist.py b/plugins/financial-markets/skills/initiating-coverage/scripts/build_source_request_checklist.py
deleted file mode 100644
index d24b384..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/scripts/build_source_request_checklist.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python3
-"""Generate a source request checklist for initiating coverage.
-
-Usage:
-    python build_source_request_checklist.py [mode] [sector]
-"""
-
-import sys
-
-BASE = [
-    "latest annual report / 10-K / 20-F",
-    "latest quarterly report / 10-Q / interim filing",
-    "latest earnings release and transcript",
-    "latest investor presentation",
-    "current share price, market cap, diluted shares, and net debt",
-    "consensus estimates and revisions history",
-    "historical segment financials and KPIs",
-    "peer set and trading multiples",
-    "existing model or forecast assumptions",
-    "management guidance and long-term targets",
-]
-
-MODE_EXTRA = {
-    "sell_side_initiation": [
-        "rating framework",
-        "disclosure language",
-        "publishing/compliance requirements",
-    ],
-    "buy_side_deep_dive": [
-        "portfolio constraints",
-        "position sizing framework",
-        "existing PM view",
-        "risk/hedge constraints",
-    ],
-    "hedge_fund_initiation": [
-        "near-term catalyst calendar",
-        "short interest / positioning if available",
-        "borrow/liquidity context if short",
-    ],
-    "credit_adjacent_initiation": [
-        "debt schedule",
-        "ratings reports",
-        "bond/loan prices",
-        "maturity wall",
-        "covenants if relevant",
-    ],
-    "sector_initiation": [
-        "coverage universe",
-        "sector KPI data",
-        "industry market-size and share data",
-        "sector factor exposures",
-    ],
-}
-
-SECTOR_EXTRA = {
-    "saas": [
-        "ARR / RPO / billings",
-        "NRR and gross retention",
-        "CAC payback",
-        "cohort and sales efficiency data",
-    ],
-    "banks": [
-        "NII / NIM data",
-        "deposit beta",
-        "loan growth and credit costs",
-        "CET1 / TBV / ROTCE",
-    ],
-    "insurance": [
-        "combined ratio",
-        "reserve development",
-        "investment income",
-        "book value and capital data",
-    ],
-    "reit": ["NOI", "occupancy", "leasing spreads", "AFFO", "NAV and cap rate assumptions"],
-    "energy": ["production volumes", "realized prices", "hedges", "reserves", "capex and FCF"],
-    "biotech": [
-        "pipeline assets",
-        "trial data",
-        "probability assumptions",
-        "cash runway",
-        "patent/market exclusivity",
-    ],
-}
-
-
-def main():
-    if any(arg in {"-h", "--help"} for arg in sys.argv[1:]):
-        print("Usage: python build_source_request_checklist.py [mode] [sector]")
-        print("Prints an initiating-coverage source request checklist.")
-        return 0
-    mode = sys.argv[1] if len(sys.argv) > 1 else "sell_side_initiation"
-    sector = (sys.argv[2] if len(sys.argv) > 2 else "").lower()
-    items = list(BASE)
-    items += MODE_EXTRA.get(mode, [])
-    items += SECTOR_EXTRA.get(sector, [])
-    print(f"# Source request checklist: {mode}" + (f" / {sector}" if sector else ""))
-    for item in items:
-        print(f"- {item}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/initiating-coverage/scripts/calculate_price_target.py b/plugins/financial-markets/skills/initiating-coverage/scripts/calculate_price_target.py
deleted file mode 100644
index 7aac04a..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/scripts/calculate_price_target.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python3
-"""Simple deterministic price target helper.
-
-Supports three methods:
-- multiple: enterprise value from target multiple * metric, minus net debt, divided by diluted shares
-- pe: target EPS * target P/E multiple
-- dcf: equity value divided by diluted shares
-
-Usage:
-    python calculate_price_target.py assumptions.json
-"""
-
-import argparse
-import json
-from pathlib import Path
-
-
-def require(d, key):
-    if key not in d or d[key] is None:
-        raise ValueError(f"missing required field: {key}")
-    try:
-        return float(d[key])
-    except (TypeError, ValueError) as exc:
-        raise ValueError(f"{key} must be numeric") from exc
-
-
-def require_positive(d, key):
-    value = require(d, key)
-    if value <= 0:
-        raise ValueError(f"{key} must be positive")
-    return value
-
-
-def calculate(data):
-    method = data.get("method")
-    if method == "multiple":
-        metric = require(data, "metric")
-        multiple = require(data, "target_multiple")
-        net_debt = float(data.get("net_debt", 0) or 0)
-        non_operating_adjustments = float(data.get("non_operating_adjustments", 0) or 0)
-        shares = require_positive(data, "diluted_shares")
-        enterprise_value = metric * multiple
-        equity_value = enterprise_value - net_debt + non_operating_adjustments
-        price = equity_value / shares
-        return {
-            "method": method,
-            "enterprise_value": enterprise_value,
-            "equity_value": equity_value,
-            "price_target": price,
-            "formula": "((metric * target_multiple) - net_debt + non_operating_adjustments) / diluted_shares",
-        }
-    if method == "pe":
-        eps = require(data, "target_eps")
-        pe = require(data, "target_pe")
-        return {"method": method, "price_target": eps * pe, "formula": "target_eps * target_pe"}
-    if method == "dcf":
-        equity_value = require(data, "equity_value")
-        shares = require_positive(data, "diluted_shares")
-        return {
-            "method": method,
-            "price_target": equity_value / shares,
-            "formula": "equity_value / diluted_shares",
-        }
-    raise ValueError("method must be one of: multiple, pe, dcf")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Calculate a deterministic initiation price target from structured assumptions."
-    )
-    parser.add_argument(
-        "assumptions_json",
-        type=Path,
-        help="JSON file containing method-specific price-target assumptions.",
-    )
-    args = parser.parse_args()
-    try:
-        with args.assumptions_json.open("r", encoding="utf-8") as f:
-            data = json.load(f)
-        result = calculate(data)
-    except Exception as exc:
-        print(f"ERROR: {exc}")
-        return 1
-    print(json.dumps(result, indent=2))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/initiating-coverage/scripts/validate_initiation_json.py b/plugins/financial-markets/skills/initiating-coverage/scripts/validate_initiation_json.py
deleted file mode 100644
index 3d64f8c..0000000
--- a/plugins/financial-markets/skills/initiating-coverage/scripts/validate_initiation_json.py
+++ /dev/null
@@ -1,179 +0,0 @@
-#!/usr/bin/env python3
-"""Validate structured initiating coverage JSON."""
-
-import argparse
-import json
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-VALID_EVIDENCE = {
-    "fact",
-    "company_claim",
-    "street_estimate",
-    "model_derived",
-    "banker_or_pm_judgment",
-    "assumption",
-    "mixed",
-    "needs_source",
-}
-VALID_CONFIDENCE = {"high", "medium", "low"}
-VALID_MODES = {
-    "sell_side_initiation",
-    "buy_side_deep_dive",
-    "long_only_initiation",
-    "hedge_fund_initiation",
-    "credit_adjacent_initiation",
-    "sector_initiation",
-    "model_first_initiation",
-    "report_refresh",
-}
-PLACEHOLDER_TERMS = {"TBD", "TODO", "MISSING", "[PLACEHOLDER]", "PLACEHOLDER"}
-SOURCE_DATE_FIELDS = ("date_accessed", "date_published", "as_of_date", "source_as_of")
-
-
-def load_json(path):
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def is_placeholder(value: Any) -> bool:
-    if not isinstance(value, str):
-        return False
-    text = value.strip().upper()
-    return text in PLACEHOLDER_TERMS or text.startswith("TBD -")
-
-
-def placeholder_paths(value: Any, prefix: str = "") -> list[str]:
-    paths: list[str] = []
-    if isinstance(value, dict):
-        for key, child in value.items():
-            child_prefix = f"{prefix}.{key}" if prefix else str(key)
-            paths.extend(placeholder_paths(child, child_prefix))
-    elif isinstance(value, list):
-        for idx, child in enumerate(value):
-            child_prefix = f"{prefix}[{idx}]"
-            paths.extend(placeholder_paths(child, child_prefix))
-    elif is_placeholder(value):
-        paths.append(prefix or "<root>")
-    return paths
-
-
-def valid_iso_date(value: Any) -> bool:
-    if not isinstance(value, str) or not value.strip():
-        return False
-    try:
-        datetime.strptime(value.strip(), "%Y-%m-%d")
-    except ValueError:
-        return False
-    return True
-
-
-def validate(data, publication_ready=False):
-    errors = []
-    warnings = []
-
-    if not data.get("company"):
-        errors.append("missing required field: company")
-
-    mode = data.get("report_mode")
-    if not mode:
-        errors.append("missing required field: report_mode")
-    elif mode not in VALID_MODES:
-        warnings.append(f"report_mode '{mode}' is not one of expected modes")
-
-    sources = data.get("sources") or data.get("source_register") or []
-    if not isinstance(sources, list) or len(sources) == 0:
-        errors.append("at least one source/source_register entry is required")
-
-    source_ids = set()
-    for i, src in enumerate(sources):
-        sid = src.get("source_id") if isinstance(src, dict) else None
-        if not sid:
-            message = f"source {i} missing source_id"
-            (errors if publication_ready else warnings).append(message)
-        else:
-            source_ids.add(sid)
-        if publication_ready and isinstance(src, dict):
-            if not any(src.get(field) for field in SOURCE_DATE_FIELDS):
-                errors.append(f"source {sid or i} missing source date/as_of metadata")
-            for field in SOURCE_DATE_FIELDS:
-                if src.get(field) and not valid_iso_date(src.get(field)):
-                    errors.append(f"source {sid or i} {field} must be YYYY-MM-DD")
-            if not (src.get("reliability") or src.get("reliability_tier")):
-                errors.append(f"source {sid or i} missing reliability/reliability_tier")
-            if not (src.get("stale_flag") is not None or src.get("freshness_status")):
-                errors.append(f"source {sid or i} missing stale_flag/freshness_status")
-
-    thesis = data.get("thesis_pillars") or data.get("thesis_hypotheses") or []
-    user_view = data.get("user_view") or data.get("md_pm_answer")
-    if not thesis and not user_view:
-        errors.append("provide thesis_pillars/thesis_hypotheses or user_view/md_pm_answer")
-
-    for section_name in ["thesis_pillars", "model_drivers"]:
-        for i, item in enumerate(data.get(section_name, []) or []):
-            if not isinstance(item, dict):
-                errors.append(f"{section_name}[{i}] must be an object")
-                continue
-            ev = item.get("evidence_label")
-            if ev and ev not in VALID_EVIDENCE:
-                errors.append(f"{section_name}[{i}] invalid evidence_label: {ev}")
-            conf = item.get("confidence")
-            if conf and conf not in VALID_CONFIDENCE:
-                errors.append(f"{section_name}[{i}] invalid confidence: {conf}")
-            for sid in item.get("source_ids", []) or []:
-                if source_ids and sid not in source_ids:
-                    message = f"{section_name}[{i}] references unknown source_id: {sid}"
-                    (errors if publication_ready else warnings).append(message)
-            if ev in {"fact", "company_claim", "street_estimate", "model_derived"} and not item.get(
-                "source_ids"
-            ):
-                warnings.append(
-                    f"{section_name}[{i}] evidence_label {ev} should include source_ids"
-                )
-
-    valuation = data.get("valuation") or {}
-    if (
-        data.get("target_price")
-        or valuation.get("target_price")
-        or valuation.get("target_price_math")
-    ):
-        if not (data.get("current_price") or valuation.get("current_price")):
-            warnings.append("target price provided without current_price")
-        if not (data.get("currency") or valuation.get("currency")):
-            warnings.append("target price provided without currency")
-        if not (valuation.get("primary_method") or valuation.get("target_price_math")):
-            warnings.append("target price provided without primary valuation method or math")
-
-    if publication_ready:
-        for path in placeholder_paths(data):
-            errors.append(f"publication-ready output contains unresolved placeholder at {path}")
-
-    return errors, warnings
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Validate structured initiating coverage JSON.")
-    parser.add_argument("input_json", help="Path to structured initiating coverage JSON")
-    parser.add_argument(
-        "--publication-ready",
-        action="store_true",
-        help="Escalate placeholder, source freshness, and unknown source-id issues to errors.",
-    )
-    args = parser.parse_args()
-
-    path = Path(args.input_json)
-    data = load_json(path)
-    errors, warnings = validate(data, publication_ready=args.publication_ready)
-    for w in warnings:
-        print(f"WARNING: {w}")
-    for e in errors:
-        print(f"ERROR: {e}")
-    if errors:
-        return 1
-    print("validation passed")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/long-short-pitch/SKILL.md b/plugins/financial-markets/skills/long-short-pitch/SKILL.md
deleted file mode 100644
index 1fa38a1..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/SKILL.md
+++ /dev/null
@@ -1,187 +0,0 @@
----
-name: long-short-pitch
-description: Use when building PM-facing Public Equity Investing trade pitches, including sparse-context or partial-section requests. Do not use for formal memos; use memo-builder.
----
-
-# Long / Short Pitch
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML trade-pitch report. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive reusable trade pitch or explicit HTML pitch, the default resolves the presentation surface to a polished standalone HTML trade-pitch report unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML trade-pitch report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Goal
-
-Turn a public-equity-investing idea into an investable trade pitch. This is the buy-side pitch muscle: what is mispriced, how to express it, why now, how much can be made or lost, what proves us wrong, and what action discipline governs add, trim, cover, hedge, or exit decisions.
-
-Default deliverable: full PM-facing trade pitch. A new standalone reader-facing pitch should become a polished standalone HTML trade-pitch report following `../../shared/html-artifact-standard.md`; use chat only when the user explicitly requests a lightweight response. Let the trade decision, expression, and implementation gates determine the hierarchy. The output should sound like a seasoned portfolio manager forming a research posture, not an analyst filling a template. Do not compress to a quick pitch unless the user explicitly asks for `quick`, `short`, `summary`, `one-pager`, `TL;DR`, red-team-only review, or one specific section.
-
-## Use When
-
-Use this skill when the user asks to:
-
-- pitch a public-equity long, short, pair, relative-value equity, or event-driven equity idea
-- build variant perception, trade expression, catalyst path, sizing considerations, add/trim/exit/cover rules, or investability
-- pressure-test weak catalysts, poor risk/reward, missing disconfirmers, crowding, borrow, carry, or bad exit discipline
-- upgrade a hedge-fund-style idea note rather than a formal memo
-- answer only part of a pitch, such as variant perception, why now, expression, sizing logic, disconfirmers, or cover rules
-
-Do not use for:
-
-- formal IC memos, investment memos, committee notes, client notes, research notes, or polished PM updates; use `memo-builder`
-- pure merger-arb spread math or regulatory timeline; use `event-driven-analyzer` first
-- pure capital-structure, covenant, recovery, maturity-wall, bond, loan, CDS, or debt-security work; use Credit Markets
-- model refresh, earnings preview/deep dive, source update, or scenario table generation with no pitch synthesis
-- personal investment advice or trade execution
-
-## Boundary With `memo-builder`
-
-`long-short-pitch` owns trade construction and pitch discipline. `memo-builder` owns formal written artifacts and final memo synthesis.
-
-Use this skill first when the unresolved question is: what is the trade, variant perception, expression, catalyst, and action discipline?
-
-Hand off to `memo-builder` when the pitch needs to become an IC memo, investment memo, committee note, client note, PM update, or formal equity research note. Credit-first memos route to Credit Markets.
-
-## Operating Defaults
-
-- Produce one polished standalone HTML trade-pitch report for new standalone reader-facing pitch work unless another format takes precedence; do not force it through a fixed dashboard module inventory.
-- Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render. For that optional path, use `references/DASHBOARD_PACK.md`: `long-short-pitch` owns trade construction, variant perception, expression, risk/reward, and monitoring rules; `dashboard-builder` owns the standardized shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-- Keep the pitch PM-usable; expand mechanics, not background.
-- Never invent real-world facts. Unknowns must be labeled `TBD`, `not provided`, or explicit assumptions.
-- Every material number must tie to a cited source, connector output, user-provided input, model output, or explicit assumption.
-- If source access is incomplete, state the source posture and build a screen-grade pitch with missing data.
-- Do not produce a blank template. If context is sparse, give a provisional stance, the best expression logic available, what would change the stance, and the exact missing data needed to upgrade conviction.
-- If the user asks for only one section, answer that section directly with enough source posture, assumptions, and next data requests to make it usable. Otherwise default to the full pitch spine even when source context is thin.
-- Ask a clarifying question only when there is no identifiable issuer, instrument, sector archetype, or trade direction and answering would be misleading.
-- This skill can discuss proposed trade framing, but must not present account-specific advice or execute trades.
-
-Load `references/intake-and-source-policy.md` for source tiers, request types, pitch modes, and minimum inputs.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` before final variant claims, market data, source conflicts, or management assertions. Use `financials-normalizer` or `excel-data-cleaner` before relying on messy financials, consensus/provider exports, ownership/short-interest tables, or event data. Use `deck-report-qc` before circulating a pitch deck/report and `style-guide-adapter` only after the pitch substance is locked. Support artifacts stay secondary; this skill owns trade construction, variant wedge, expression, catalyst path, and action discipline.
-
-## Sparse Context And Partial Requests
-
-When key facts are missing, lead with:
-
-`Screen-grade only; placeholder assumptions used.`
-
-Then give a PM stance before any metric table:
-
-- `Actionability`: actionable candidate, watchlist, pass for now, or red-team only
-- `Reason`: the one or two conditions that make the idea investable or not investable
-- `What would change the stance`: evidence that would upgrade, downgrade, or kill the idea
-- `Missing data`: exact items needed, not a generic diligence list
-
-Sparse context does not mean short. Use the full pitch spine unless the user explicitly asks for a narrow section or compressed format.
-
-For partial requests, do not force the full pitch spine. Deliver the requested slice, then add `Implications For The Trade` and `Missing Data To Upgrade Conviction`.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: variant perception, valuation/scenario work, catalyst path, risk/sizing/hedge, action rules, and red-team review. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Deterministic Scenario Support
-
-This skill is primarily instruction-led, but it ships a scenario materializer for expected-value tables.
-
-Use `scripts/materialize_trade_scenarios.py` when the user provides scenario rows or asks for scenario/EV math. The script accepts `references/scenario-ev-schema.md` and writes:
-
-- `trade_scenarios_support_note.md`
-- `trade_scenarios.json`
-
-Default paths are under `/tmp/public_equity_investing_long_short_pitch/` unless explicit output paths are supplied. These are support artifacts. The helper is math-only, and the Markdown support note / JSON outputs are not the final user-facing pitch unless the user explicitly requests those formats. The final pitch still needs variant perception, expression, catalyst path, risk/reward, sizing considerations, disconfirmers, and add/trim/exit/cover rules.
-
-## Output Spine
-
-Use this full structure unless the user explicitly asks for a quick/short pitch, red-team-only review, or one section:
-
-1. `Trade Recommendation`
-2. `Variant Perception`
-3. `Security / Expression`
-4. `Why Now / Catalyst Path`
-5. `Scenario Price Targets / Returns`
-6. `Risk / Reward and Sizing Considerations`
-7. `Disconfirmers and Kill Criteria`
-8. `Add / Trim / Exit / Cover Rules`
-9. `Monitoring Dashboard`
-10. `Open Items / Data Requests`
-
-Load `references/output-contract.md` for full section requirements and `references/strategy-playbooks.md` for long, short, pair, and event-driven equity pitch rules.
-
-For sparse-context outputs, use the full spine with `screen-grade` labels and visible missing data. Use the narrower contracts in `references/output-contract.md` only when the user explicitly requests a partial section or compressed format.
-
-## HTML Guidance
-
-For a substantive standalone HTML trade-pitch report, load `../../shared/html-artifact-standard.md` and apply these pitch-specific requirements:
-
-- Make actionability the primary visual object. Lead with the proposed posture (`initiate`, `watchlist`, `pass`, `cover`, or `wait for proof`), side and preferred expression if supportable, horizon, variant wedge, and the decisive implementation caveat.
-- Immediately after the opening verdict, show a compact `Implementation Gate` block covering the trade-critical checks: catalyst, valuation or price anchor, liquidity, borrow/carry and squeeze/buyback risk for shorts, option cost/skew when relevant, and hedge ratio/residual exposure for pairs. Mark each as `Cleared`, `Not cleared`, `Missing`, or `Illustrative only`.
-- Give the verdict, variant wedge, implementation gate, scenario skew, catalyst path, and action rules distinct jobs. Do not repeat the same recommendation in multiple large panels.
-- Keep `What Is Priced In` explicit: distinguish demonstrated operating improvement from the expectation or valuation assumption a proposed trade would challenge.
-- For shorts, put borrow, carry, crowding, squeeze/buyback exposure, preferred defined-risk expression if assessable, catalyst timing, and cover rule near the top rather than burying them in monitoring.
-- For partnership, capacity, purchase, cloud-spend, or customer-commitment metrics shown in the first-read layer, name the economic direction explicitly. For example, label an issuer spending commitment `AWS Infrastructure Commitment` or `Snowflake AWS Spend Commitment` and state that it is not incremental revenue guidance unless the source establishes revenue.
-- Title unsupported target/probability work `Illustrative Scenario Skew` when valuation support, borrow/carry, option pricing, or other implementation evidence is incomplete. Label targets and probabilities as analyst assumptions rather than company guidance, consensus, or externally validated price targets. Do not describe expected return as actionable until holding costs and constraints have been addressed.
-- In standalone HTML, title the monitoring section `Monitoring Triggers` or `Evidence To Watch`; reserve `Monitoring Dashboard` for the explicitly selected standardized-dashboard path. When the proposed posture is `watchlist`, `pass`, or `wait for proof` with no current position, title action discipline `Conditional Action Rules` rather than implying an active position.
-- Render evidence posture in plain investor language such as `Reported`, `Company guidance`, `Derived`, `Analyst assumption`, or `Not yet sourced`; do not expose internal evidence or schema labels in the visible report.
-- Keep citations traceable but readable: do not fragment tickers, fiscal years, dates, percentages, numeric ranges, metric names, or product labels into separately linked tokens. Do not make an analyst assumption register appear to be an external evidentiary source.
-- Do not render empty scenario fields, empty lists, decorative monitoring inventory, or repeated panels that do not sharpen expression or action discipline.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, density, clipping, citation rendering, and whether the proposed trade decision is immediately usable.
-
-## Handoffs
-
-- Source discipline: `financial-source-of-truth`, `financials-normalizer`
-- Earnings/post-print: `earnings-preview`, `earnings-deep-dive`
-- Valuation/model inputs: `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `scenario-sensitivity-generator`
-- Trade risk: `portfolio-risk-management`, `thesis-tracker`
-- Event foundations: `event-driven-analyzer`
-- Equity-risk credit-signal inputs: use Credit Markets when credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, or debt-security analysis drive the case
-- Formal memo synthesis: `memo-builder`
-
-Use `sector-context-overlay` only when the issuer clearly matches a supported sector; use `references/sector-overlays.md` only for pitch-specific framing.
-
-## QA Self-Check
-
-Before finalizing:
-
-- recommendation, side, expression, horizon, and caveat are clear
-- sparse-context outputs lead with a stance rather than a placeholder template
-- variant perception is not just "good company / bad company"
-- catalyst path is actionable
-- upside/downside math is internally consistent and probabilities sum to 100% when presented
-- short pitches include borrow/carry/squeeze/cover discipline when data is available
-- pair trades include hedge ratio, residual exposure, and break conditions
-- credit-first pitches route to Credit Markets unless the final expression is common equity and credit is only an input
-- missing live market data is labeled before any metric table
-- partial-section requests answer only the requested section, plus trade implications and missing data; ambiguous broad pitch requests default to the full pitch
-- action rules are evidence-based and not account-specific instructions
-- ordinary standalone HTML pitches use actionability-first hierarchy and do not expose internal evidence labels or empty dashboard modules
-- illustrative scenarios are clearly labeled when valuation anchors or implementation inputs are incomplete
-- standalone HTML watchlist or no-position pitches use conditional action and monitoring-trigger language rather than dashboard or active-position headings
-
-## Public Equity PM Judgment Layer
-
-For substantial pitches, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Use this PM-native spine: `Proposed Trade / Actionability`, `Variant Wedge`, `What Is Priced In`, `Expression and Risk Budget`, `Scenario Skew`, `Catalyst Path`, `Disconfirmers`, `Add / Trim / Exit / Cover`, `Monitoring`.
-
-Required PM judgment:
-- Shorts require borrow, carry, squeeze path, buyback/low-float risk, catalyst timing, and cover rules when data is available or clearly missing.
-- Pairs require hedge ratio, residual exposures, liquidity mismatch, catalyst symmetry, and break conditions.
-- ETF/index can be a hedge, basket expression, benchmark-relative pair, or index rebalance/event setup.
-- Preserve non-advice posture: discuss proposed expression and risk controls, not account-specific trade execution.
diff --git a/plugins/financial-markets/skills/long-short-pitch/agents/openai.yaml b/plugins/financial-markets/skills/long-short-pitch/agents/openai.yaml
deleted file mode 100644
index 06adf76..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: Long Short Pitch
-  short_description: Build PM-facing trade pitches with variant perception, expression, catalysts, sparse-context stance, and exit rules.
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/long-short-pitch/references/DASHBOARD_PACK.md
deleted file mode 100644
index 70fa370..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# Long Short Pitch Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render for a long, short, pair, catalyst, or PM-facing trade pitch. Ordinary substantive or explicitly requested HTML pitches should use the flexible standalone HTML trade-pitch report in `../SKILL.md` instead.
-
-## Producer Role
-
-`long-short-pitch` owns trade construction, variant perception, expression, risk/reward, sizing considerations, and add/trim/exit/cover rules. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `long_short_pitch`
-- `layout`: `single_page` for full PM pitch packages unless the user explicitly requests tabs
-- `hero.callout`: the variant perception and what the market is missing
-- `snapshot`: direction, instrument, expected return/skew if supportable, next catalyst, stop/falsifier, hedge/sizing caveat
-- `sources`: user thesis/materials first, then filings, market/consensus data, news/events, model outputs, and assumptions
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `pitch`
-   - `decision_box`: action posture, thesis, trade expression, catalyst path, risk/reward, and thesis-break trigger
-   - `executive_summary`: concise PM read with what is variant and what is already priced in
-   - For shorts, put the implementation gate near the decision box: catalyst, valuation anchor, borrow/carry, options feasibility when relevant, squeeze/buyback risk, and cover rule.
-2. `thesis`
-   - `cards`: thesis pillars, evidence, variant perception, and disconfirmers
-   - `table`: KPI/valuation/setup evidence where a table improves diligence speed
-3. `scenarios`
-   - `scenario_map`: bull/base/bear or long/short/pair cases with drivers, probabilities where supportable, and payoff/skew; label assumption-driven incomplete-underwriting work as `Illustrative Scenario Skew`
-4. `catalysts-risk`
-   - `timeline`: catalysts, add/trim/exit/cover triggers, and review dates
-   - `market_events`: source-backed company/sector/macro/regulatory events that affect the pitch
-5. `monitoring`
-   - `question_list`: must-answer diligence questions and checks
-   - `missing_evidence`: stale market data, unavailable consensus, missing borrow/options/liquidity, and model gaps
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every price, valuation, estimate, catalyst, market event, scenario assumption, and risk/reward figure.
-- Label assumptions, model-derived values, user-provided views, stale market data, and unsupported claims.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not turn a narrow conversational pitch into files unless the user asks or the work is substantial/reusable.
-- Do not hide sizing, hedge, borrow, liquidity, or catalyst caveats when they affect implementation readiness.
-- Do not label issuer purchase, cloud-spend, capacity, or infrastructure commitments as revenue or demand evidence without supporting disclosure of incremental economics.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-- Do not expose internal evidence-label strings, fragment dates or fiscal years with citation links, render empty scenario fields, or repeat the same recommendation across dashboard panels.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm variant perception, trade expression, scenario skew, catalysts, disconfirmers, and monitoring rules are visible.
-- Confirm output sounds like PM judgment rather than a generic template.
-- Confirm shorts surface implementation gates near the recommendation and assumption-driven scenarios are identified as illustrative rather than actionable.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/intake-and-source-policy.md b/plugins/financial-markets/skills/long-short-pitch/references/intake-and-source-policy.md
deleted file mode 100644
index af72763..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/intake-and-source-policy.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Long / Short Pitch Intake And Source Policy
-
-## Source Priority
-
-1. Connected or institutional-grade sources available in the session.
-2. Company filings, releases, transcripts, investor decks, and official financials.
-3. Market-data or consensus feeds available to the user.
-4. User-provided models, notes, or research.
-5. Web search or public summaries as a labeled fallback.
-
-Rules:
-
-- Prefer primary company sources over commentary.
-- Verify recency for current prices, borrow, short interest, options pricing, consensus, and event spreads when tools are available.
-- If a number is stale, approximate, or unavailable, label it.
-- Keep fact, inference, and assumption separate.
-
-## Request Types
-
-- `new-trade-pitch`: build a new long, short, pair, event-driven, or equity special-situation pitch; route credit-first pitches to Credit Markets.
-- `pitch-upgrade`: preserve the user's thesis while sharpening expression, catalyst, sizing, downside, and exit discipline.
-- `explicit-short-pitch`: compressed pitch only when the user explicitly asks for quick/short/summary/one-pager/TL;DR.
-- `qa-red-team`: findings-first investability review.
-- `re-underwrite`: update after earnings, news, price movement, thesis drift, or new data.
-- `partial-section`: answer only the requested part of the pitch, such as variant perception, why now, expression, sizing, disconfirmers, or cover rules.
-- `sparse-context-screen`: turn incomplete facts into a provisional PM stance and data request list without pretending the pitch is fully underwritten.
-
-## Pitch Modes
-
-- `full-trade-pitch`: default mode; include scenario work, risk budget, monitoring, add/trim/exit rules.
-- `explicit-short-pitch`: variant perception, expression, upside/downside, catalyst, kill criteria only when the user asks for a compressed version.
-- `sparse-context-screen`: actionability, provisional stance, core mispricing hypothesis, why now, what would change the stance, and missing data.
-- `partial-section`: requested section only, plus implications for the trade and missing data to upgrade conviction.
-- `short-pitch`: catalyst to expose mispricing, borrow/carry, squeeze risk, cover rules.
-- `pair-trade-pitch`: leg logic, hedge ratio, residual exposure, break conditions.
-- `event-driven-pitch`: probability tree, timing, spread, break price, milestones.
-- `distressed-equity-special-situation`: listed-equity expression, event path, liquidity runway, catalyst/default/refi read-through, and Credit Markets handoff for credit-security or recovery work.
-- `qa-red-team`: severity-ordered findings and investability verdict.
-
-## Minimum Inputs
-
-- Security, issuer, ticker, instrument, or spread.
-- Proposed side: long, short, pair, relative value, event-driven, distressed-equity special situation, Credit Markets handoff, or unknown.
-- Current price, event spread, FCF/dividend yield, or valuation reference, plus as-of date; route debt yield/spread analysis to Credit Markets.
-- Horizon or catalyst window.
-- Core thesis or observed mispricing.
-- Known valuation anchor or scenario frame.
-- Key risks, constraints, or data gaps.
-
-If inputs are missing, do not stall unless needed to avoid a misleading pitch. State `Screen-grade only; placeholder assumptions used.` before the first metric table and list missing data in `Open Items / Data Requests`.
-
-## Sparse Context Rules
-
-Sparse context is common in PM conversations. Do not respond with a blank pitch template.
-
-When the user gives only a company, theme, sector, or rough thesis:
-
-- infer the pitch mode from the user's wording when reasonable
-- provide a provisional `Actionability` view: actionable candidate, watchlist, pass for now, or red-team only
-- separate fact, assumption, and judgment
-- make the missing-data list specific enough for an analyst to gather
-- explain what evidence would upgrade, downgrade, or kill the idea
-- avoid precise valuation, borrow, short interest, consensus, or live-price claims unless sourced
-
-Ask one clarifying question only when no issuer, instrument, side, sector archetype, or thesis can be inferred.
-
-## Partial Section Rules
-
-When the user asks for only part of the report, answer that part directly.
-
-Examples:
-
-- `what is the variant perception?`: give consensus belief, contrary evidence, timing, and falsifiers
-- `how would a PM size this?`: discuss conviction, liquidity, downside gap, catalyst timing, crowding, borrow/carry, and risk-budget constraints without giving account-specific advice
-- `what would make us cover?`: give measurable cover triggers, time stops, squeeze/borrow triggers, and thesis-failure evidence
-- `why now?`: give catalyst path, expected evidence windows, and what would make timing premature
-
-End partial outputs with:
-
-- `Implications For The Trade`
-- `Missing Data To Upgrade Conviction`
-
-## PM Source And Intake Additions
-
-Capture mandate lens, portfolio role, benchmark relevance, position context, borrow/short interest, liquidity, options, ETF/index exposure, catalyst date quality, and data as-of times. Missing market data must be labeled before any pitch-ready conclusion.
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/output-contract.md b/plugins/financial-markets/skills/long-short-pitch/references/output-contract.md
deleted file mode 100644
index 7c21aaa..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/output-contract.md
+++ /dev/null
@@ -1,170 +0,0 @@
-# Long / Short Pitch Output Contract
-
-Use this reference after `long-short-pitch` is selected. Broad pitch requests default to the full pitch; quick or section-only outputs require explicit user wording.
-
-## Standard Pitch Spine
-
-1. `Trade Recommendation`
-2. `Variant Perception`
-3. `Security / Expression`
-4. `Why Now / Catalyst Path`
-5. `Scenario Price Targets / Returns`
-6. `Risk / Reward and Sizing Considerations`
-7. `Disconfirmers and Kill Criteria`
-8. `Add / Trim / Exit / Cover Rules`
-9. `Monitoring Dashboard`
-10. `Open Items / Data Requests`
-
-## Standalone HTML Trade-Pitch Report
-
-Use flexible standalone HTML for a substantial reusable trade pitch or when the user explicitly requests HTML. The first-read layer should answer whether risk can be put on now, not reproduce a fixed monitoring dashboard.
-
-Visible first-read order:
-
-1. `Proposed Trade / Actionability`: posture, side/expression if supportable, horizon, core reason, and decisive caveat.
-2. `Variant Wedge` and `What Is Priced In`: what operating evidence has changed and what expectation the trade would challenge.
-3. `Implementation Gate`: the trade-critical evidence that is cleared, not cleared, missing, or illustrative only.
-4. `Illustrative Scenario Skew` or supported scenario returns, clearly distinguished according to evidence quality.
-5. Catalyst path, disconfirmers, and add/trim/exit/cover discipline.
-6. Compact `Monitoring Triggers` or `Evidence To Watch` section and open items, followed by readable sources and limitations.
-
-For short ideas, the `Implementation Gate` must make catalyst failure, valuation anchor, borrow/carry, defined-risk option feasibility where relevant, and squeeze/buyback exposure immediately visible. For pairs, replace short-specific fields with hedge ratio, residual exposure, liquidity mismatch, and catalyst symmetry.
-
-Do not repeat the recommendation across a hero panel, action tile, bottom-line panel, and trade-recommendation section. Do not display internal evidence labels, fragmented fiscal-year/date/range citations, empty scenario cards, or decorative monitoring modules.
-
-For a `watchlist`, `pass`, or `wait for proof` posture where no position is proposed, use the visible heading `Conditional Action Rules` instead of `Add / Trim / Exit / Cover Rules`. Reserve `Monitoring Dashboard` as a visible heading for the optional standardized-dashboard path; standalone HTML should use `Monitoring Triggers` or `Evidence To Watch`.
-
-## Sparse Context Contract
-
-Use this when the user provides only a company, theme, sector archetype, or rough thesis. Sparse context changes the confidence label, not the depth default: provide the full pitch spine where possible and label gaps.
-
-Lead with:
-
-`Screen-grade only; placeholder assumptions used.`
-
-Then include:
-
-1. `PM Stance`
-2. `What The Market May Be Missing`
-3. `Best Expression From Current Facts`
-4. `Why Now / What Needs To Happen`
-5. `What Would Upgrade Conviction`
-6. `What Would Kill Or Downgrade The Idea`
-7. `Missing Data To Underwrite`
-
-`PM Stance` should choose one:
-
-- `actionable candidate`: enough evidence exists to frame a proposed expression and next diligence
-- `watchlist`: idea is interesting but needs a catalyst, valuation anchor, or source support
-- `pass for now`: setup does not clear risk/reward, catalyst, liquidity, or evidence quality
-- `red-team only`: user wants critique, not a proposed trade
-
-Do not hide behind "need more data" if a provisional stance is possible. State the stance, then say exactly what would change it.
-
-## Partial Request Contract
-
-If the user explicitly asks for only one part of the pitch, do not return the full pitch spine. Answer the requested slice and add only the context needed to make it investable.
-
-Use:
-
-1. Requested section, using the user's heading when possible
-2. `Implications For The Trade`
-3. `Missing Data To Upgrade Conviction`
-
-Examples:
-
-- Variant perception: consensus belief, mispricing, evidence edge, timing, falsifiers.
-- Expression: instrument, hedge needs, liquidity, factor/sector exposure, borrow/carry if short.
-- Sizing logic: conviction, downside gap, liquidity, catalyst timing, crowding, and risk-budget framing.
-- Cover rules: measurable thesis-failure evidence, catalyst time stop, price/risk-reward threshold, borrow/crowding change.
-- Add/trim/exit rules: evidence threshold, valuation gap, catalyst progress, and risk/reward change.
-
-## Trade Recommendation
-
-Lead with one sentence that includes side, instrument/expression, current price/spread/yield if known, horizon, expected return or risk/reward, and primary condition/caveat. Use `proposed expression`, not personal-advice language.
-
-If live market data is missing, use `current price not provided` rather than inventing one. If a directional stance is still possible, call it `screen-grade`.
-
-## Variant Perception
-
-Include consensus belief, what the market is mispricing, why timing is actionable, and what evidence would make the view consensus or wrong.
-
-A strong variant perception is specific enough to be falsified. It should name the KPI, event, behavioral bias, accounting issue, capital-market constraint, or consensus blind spot that creates the edge.
-
-## Security / Expression
-
-Specify common equity, ADR, option, bond, loan, CDS, pair, basket, or event spread. Name liquidity constraints and factor/sector/commodity/FX/rates/credit exposures that must be hedged or accepted.
-
-For pair trades, show long leg, short leg, hedge ratio, residual exposures, and break conditions.
-
-## Scenario Price Targets / Returns
-
-Include base, upside, and downside with probabilities that sum to 100% unless the user asks for a qualitative pitch.
-
-| Scenario | Probability | Key drivers | Target price / spread / recovery | Total return | Trigger / timing |
-|---|---:|---|---:|---:|---|
-
-For shorts, show adverse upside risk as the loss case. If probabilities do not sum to 100%, flag the table as not pitch-ready.
-
-When targets and probabilities are based on analyst assumptions and the pitch lacks a sourced valuation anchor or required implementation inputs such as borrow/carry or option pricing, title the object `Illustrative Scenario Skew`. State plainly that it screens the payoff shape but does not clear the trade for implementation.
-
-## Risk / Reward and Sizing Considerations
-
-Discuss sizing as a risk-management frame:
-
-- confidence and evidence quality
-- liquidity and time-to-exit
-- downside gap risk
-- catalyst timing uncertainty
-- factor or sector exposure
-- borrow, carry, squeeze, or financing cost
-- gross/net exposure or pair residual exposure where relevant
-
-Use sizing language as a proposed risk frame only. Do not tell the user what to trade in a personal account.
-
-For a short pitch, show this implementation screen near the initial recommendation when relevant:
-
-| Gate | Status | Why It Matters |
-|---|---|---|
-| Catalyst failure | Cleared / Not cleared / Missing | A valuation-only short is rarely sufficient after improved operating evidence. |
-| Borrow / carry | Cleared / Not cleared / Missing | Determines whether the short can be held economically. |
-| Defined-risk options | Cleared / Not cleared / Missing | Determines whether adverse squeeze exposure can be bounded at an acceptable cost. |
-| Valuation anchor | Cleared / Illustrative only / Missing | Separates expensive from mispriced and timely. |
-| Squeeze / buyback risk | Cleared / Not cleared / Missing | Determines the adverse path and cover discipline. |
-
-## Disconfirmers and Action Rules
-
-Disconfirmers must be measurable. Avoid vague claims like "execution improves" unless tied to a metric, date, or catalyst.
-
-Action discipline:
-
-- add if evidence improves and risk/reward remains attractive
-- trim if price target gap closes faster than evidence
-- exit if thesis fails or risk/reward no longer clears the hurdle
-- cover if short thesis fails, catalyst passes, borrow/crowding risk changes, or downside skew flips
-- hedge if thesis remains intact but factor or event exposure becomes dominant
-
-## Missing Data Quality Bar
-
-Missing data lists should be short and specific. Prefer:
-
-- current price, market cap, net debt, liquidity, borrow fee, short interest, or option skew as applicable
-- latest filing, transcript, investor deck, consensus snapshot, and model date
-- KPIs that directly prove or disprove the variant perception
-- event dates, lockups, regulatory milestones, refinancing maturities, or catalyst windows
-- ownership/crowding, liquidity, and factor exposure for expression and sizing
-
-Avoid generic asks like "more financials" or "more diligence" unless the user truly provided no context.
-
-## Reader-Facing Evidence And HTML QC
-
-- In user-facing HTML, use `Reported`, `Company guidance`, `Derived`, `Analyst assumption`, and `Not yet sourced` rather than internal evidence-label strings.
-- Name partnership and commitment metrics according to their economics. An issuer purchase, cloud-spend, infrastructure, or capacity commitment must not be presented as revenue, bookings, or customer demand unless directly supported; state `not incremental revenue guidance` where that distinction could be missed in a headline tile.
-- Keep fiscal years, tickers, dates, percentages, ranges, metric names, and product labels visually intact when attaching citations.
-- Cite analyst scenarios as assumptions in the nearby explanation rather than presenting an internal assumption register like an independent source.
-- Do not render empty headings, empty bullet lists, or empty scenario-card elements.
-- Visually inspect substantive local HTML using local headless-browser screenshots before delivery.
-
-## PM-Native Pitch Spine
-
-Use `Proposed Trade / Actionability`, `Variant Wedge`, `What Is Priced In`, `Expression and Risk Budget`, `Scenario Skew`, `Catalyst Path`, `Disconfirmers`, `Add / Trim / Exit / Cover`, and `Monitoring`. Always include missing evidence and what would change the stance.
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/scenario-ev-schema.md b/plugins/financial-markets/skills/long-short-pitch/references/scenario-ev-schema.md
deleted file mode 100644
index b933bd0..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/scenario-ev-schema.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# Scenario / Expected-Value Table Schema
-
-Use this schema when a long/short pitch needs deterministic scenario math.
-
-## CSV columns
-
-Required:
-- `scenario`: case name such as `Bull`, `Base`, `Bear`, `Deal closes`, `Break`, or `Restructuring`.
-- `probability`: decimal or percent probability; probabilities must sum to 100%.
-- `current_value`: current price, spread reference, bond price, recovery reference, or other entry value.
-- `target_value`: scenario target price, spread, recovery, or payoff value.
-- `key_drivers`: concise driver description.
-- `timing`: expected catalyst or realization window.
-
-Optional:
-- `trigger`: event or evidence that realizes the scenario.
-- `source`: source tag, model output, or explicit assumption label.
-- `source_as_of` or `source_date`: `YYYY-MM-DD` freshness date for the evidence behind the row.
-- `retrieval_date`: optional date the source was retrieved.
-- `borrow_fee`, `carry_cost`, `holding_period_days`: short-side borrow/carry math. Percent values are accepted.
-- `break_price`: downside/break value for event-driven pitches.
-- `recovery_value`, `bond_price`, `yield_or_spread`: Credit Markets pitch fields.
-
-## Script
-
-Run:
-
-```bash
-python scripts/materialize_trade_scenarios.py scenarios.csv --side long --markdown-out scenarios_support_note.md --json-out scenarios.json
-```
-
-Supported `--side` values:
-- `long`: return = `(target_value - current_value) / current_value`
-- `short`: gross return = `(current_value - target_value) / current_value`; net return subtracts borrow and carry over the holding period when supplied
-- `event`: keeps event posture, calculates spread-style return, and preserves `break_price`
-- `credit`: keeps credit posture and preserves recovery, bond-price, and spread fields
-
-Default outputs are written to `/tmp/public_equity_investing_long_short_pitch/` unless explicit output paths are passed. Use `--run-date YYYY-MM-DD` for deterministic source freshness checks.
-
-## Required pitch interpretation
-
-The table is not the pitch. It only materializes scenario math. The final pitch still needs:
-- variant perception
-- expression and constraints
-- catalyst path
-- sizing/risk discussion
-- borrow/carry for shorts
-- disconfirmers and kill criteria
-- add/trim/exit/cover rules
-- monitoring dashboard
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/sector-overlays.md b/plugins/financial-markets/skills/long-short-pitch/references/sector-overlays.md
deleted file mode 100644
index 989851b..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/sector-overlays.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# Long / Short Pitch Sector Overlays
-
-Load only the overlay that matches the prompt. These overlays sharpen trade judgment; they do not replace the core pitch workflow.
-
-## SaaS / Subscription Software Long
-Use when the pitch is a public software long, especially vertical SaaS, application software, payments software, or workflow software.
-
-### What a PM cares about
-- whether growth durability is mispriced
-- whether retention, expansion, pricing, and seat/module growth support the target multiple
-- whether cash conversion is better or worse than headline ARR / revenue growth implies
-- whether margin expansion is operating leverage or temporary cost timing
-- whether SBC, restructuring, implementation costs, or deferred revenue distort economic FCF
-
-### Must-have pitch facts
-- revenue growth, ARR/MRR if available, NRR, gross retention, churn, and backlog where relevant
-- gross margin, EBITDA margin, FCF margin, SBC load, and rule-of-40 context
-- net cash/debt and diluted share count
-- peer valuation and the company's own trading history
-- next 1-3 catalysts: print, guide, product event, price increase, investor day, margin reset
-
-### Common failure modes
-- calling a stock cheap without explaining normalized growth
-- leaning on AI/product narrative before it is economically measurable
-- using EBITDA without addressing SBC or FCF conversion
-- assuming rerating without a catalyst or peer/history support
-
-### Monitoring triggers
-- retention / NRR
-- cRPO / bookings / billings
-- gross margin and FCF margin
-- large-customer commentary
-- guide revisions and consensus estimate drift
-
-## Consumer Internet / Platform Short
-Use when the pitch is a short in consumer internet, marketplaces, ad platforms, fintech platforms, or other network-effect assets.
-
-### What a short seller cares about
-- what still supports the multiple and why that support should break
-- whether deterioration is cyclical, execution-driven, regulatory, or structural
-- whether the catalyst path is strong enough to offset borrow, crowding, and timing risk
-- whether a healthier segment or cash balance can keep the equity alive longer than expected
-
-### Must-have pitch facts
-- key usage metric: MAU/DAU, payer count, transactions, GMV, engagement, conversion, retention, or monetization
-- segment split between deteriorating and healthy assets
-- revenue growth, margin trend, guidance quality, and intervention intensity
-- borrow/carry/crowding if available
-- scenario price targets including adverse upside risk
-
-### Common failure modes
-- descriptive bear case with no catalyst
-- ignoring the offsetting healthy segment
-- no cover discipline
-- assuming multiple compression without explaining what forces it
-
-### Monitoring triggers
-- user/payer metrics
-- monetization intervention quality
-- guide revisions
-- regulatory milestones
-- next one or two earnings prints
-
-## Pair Trade / Relative Value
-Use when the pitch compares two securities, two baskets, or one security against a benchmark hedge.
-
-### What a PM cares about
-- whether the trade is genuinely relative value or just two unrelated directional bets
-- whether factor, beta, sector, rates, FX, commodity, or liquidity exposure overwhelms the spread logic
-- whether the long and short legs have comparable catalysts and time horizons
-- whether the hedge ratio is explainable
-
-### Must-have pitch facts
-- long leg and short leg thesis
-- valuation spread, earnings revision spread, KPI spread, or credit spread differential
-- hedge ratio, beta residual, sector/factor exposure, and liquidity mismatch
-- catalyst path for both legs
-- break conditions for each leg
-
-### Common failure modes
-- pair trade is actually net long or net short with no disclosure
-- using a valuation spread without explaining why it should close
-- ignoring idiosyncratic event risk in one leg
-- no rule for what happens if only one leg works
-
-### Monitoring triggers
-- spread / ratio movement
-- relative EPS revision trend
-- catalyst slippage for either leg
-- factor exposure drift
-- borrow or liquidity changes
-
-## Event-Driven / Special Situation
-Use when the pitch is tied to merger arbitrage, spin-offs, litigation, regulatory approval, restructuring, index inclusion, tender offers, or other discrete events.
-
-### What a PM cares about
-- expected value after probability, timing, and break downside
-- what the market-implied probability is versus the pitch probability
-- whether the legal, regulatory, shareholder, financing, or process path is correctly mapped
-- whether the trade can be resized before the final binary event
-
-### Must-have pitch facts
-- current spread or price
-- consideration, timing, outside date, and break price
-- probability tree
-- required approvals and milestone calendar
-- downside if the event fails
-
-### Common failure modes
-- treating management confidence as probability
-- ignoring timing drag
-- no break price
-- no interim milestones or resize triggers
-
-### Monitoring triggers
-- regulatory docket updates
-- shareholder vote timing
-- financing conditions
-- court / agency milestones
-- spread movement versus news
-
-## Credit Markets Handoff / Equity-Risk Signal
-Use only when credit data is an input to a listed-equity pitch, not the security being recommended.
-
-### What an equity PM cares about
-- whether refinancing stress, maturity walls, liquidity runway, rating pressure, or CDS/spread signals change common-equity downside
-- whether equity optionality survives under realistic enterprise value and liquidity stress
-- whether the credit read-through changes sizing, hedge posture, catalyst timing, or the kill case
-
-### Required local framing
-- state the equity-security implication first
-- label credit data as equity-risk context or Credit Markets handoff
-- route bond, loan, CDS, covenant, recovery, restructuring, spread/yield, and debt-security analysis to Credit Markets
-
-### Common failure modes
-- turning a credit instrument into a local Public Equity recommendation
-- using headline spread or CDS widening without an equity downside mechanism
-- implying recovery or covenant analysis without Credit Markets support
-
-### Monitoring triggers
-- liquidity runway and maturity-wall updates
-- refinancing announcements or rating actions
-- CDS/spread signals that move with equity drawdown risk
-- Credit Markets handoff output that changes the equity case
diff --git a/plugins/financial-markets/skills/long-short-pitch/references/strategy-playbooks.md b/plugins/financial-markets/skills/long-short-pitch/references/strategy-playbooks.md
deleted file mode 100644
index ef5a435..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/references/strategy-playbooks.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Long / Short Strategy Playbooks
-
-## Public Long
-
-- Show upside, downside, and expected return from price target scenarios.
-- Identify the one or two KPIs that unlock the variant perception.
-- Include add and trim evidence thresholds.
-- Do not rely on rerating alone unless peer/history support is explicit.
-- Under sparse context, state whether the long is actionable now, a watchlist idea, or a pass until valuation/catalyst support improves.
-
-## Public Short
-
-- Include borrow/carry, short interest, crowding, squeeze risk, and preferred expression where available.
-- Make the catalyst path explicit. A short with no catalyst is not investable.
-- Show cover rules and adverse price target risk.
-- Separate structural short thesis from cyclical disappointment.
-- Under sparse context, do not call a short actionable without at least a catalyst path, borrow/crowding caveat, and measurable cover triggers.
-- For HTML pitches, surface a compact `Implementation Gate` immediately after the proposed posture: catalyst failure, valuation anchor, borrow/carry, defined-risk option feasibility where relevant, and squeeze/buyback exposure.
-- If reported fundamentals improved and implementation gates are missing, prefer `wait for proof`, `watchlist`, or `pass for now` to a valuation-only short posture.
-- When scenario targets or probabilities are analyst assumptions and implementation is not underwritten, use `Illustrative Scenario Skew` and do not present expected return as actionable.
-- For a no-position or watchlist HTML pitch, use `Conditional Action Rules` and `Monitoring Triggers`; do not imply that an active short already exists.
-- Describe strategic agreements according to their actual economic direction: an issuer cloud-spend or infrastructure commitment is not short-negative revenue evidence unless incremental revenue or economics are disclosed.
-
-## Pair Trade / Relative Value
-
-- State the pair thesis in one sentence.
-- Show why the long leg should outperform the short leg.
-- Identify hedge ratio, factor/sector neutrality, beta residual, and liquidity mismatch.
-- Include spread, ratio, or relative multiple entry/exit logic.
-- Define break conditions for either leg independently.
-- Under sparse context, frame the pair as a watchlist relative-value setup until hedge ratio, liquidity, factor residual, and valuation spread are known.
-
-## Event-Driven
-
-- Show probability-weighted expected value, spread, timing, break price, downside, and event milestones.
-- Map regulatory, legal, shareholder, financing, or process risks.
-- Use `event-driven-analyzer` first when the ask is mostly spread math or legal/regulatory timeline.
-- Under sparse context, separate confirmed milestones from assumed milestones before presenting expected value.
-
-## Credit Markets Handoff / Equity-Risk Signal
-
-- Do not build a credit pitch, debt-security recommendation, covenant review, recovery waterfall, or restructuring view in this plugin.
-- Retain credit data only when it changes the listed-equity thesis: refinancing stress, solvency pressure, liquidity runway, maturity-wall risk, rating pressure, CDS/spread signal, or downside equity impairment.
-- Route bonds, loans, CDS, spread/yield relative value, capital-structure priority, distressed-security selection, covenants, and recovery work to Credit Markets.
-- Under sparse context, label the credit read-through as screen-grade equity-risk context and request the Credit Markets output before making it a trade driver.
-
-## Hard Gates
-
-Shorts require borrow, carry, squeeze path, buyback or low-float risk, catalyst timing, and cover rules. Pairs require hedge ratio, residual exposures, liquidity mismatch, catalyst symmetry, and break conditions. ETF/index expressions may be hedges, baskets, benchmark-relative pairs, or index-event setups.
diff --git a/plugins/financial-markets/skills/long-short-pitch/scripts/materialize_trade_scenarios.py b/plugins/financial-markets/skills/long-short-pitch/scripts/materialize_trade_scenarios.py
deleted file mode 100644
index 933cc6b..0000000
--- a/plugins/financial-markets/skills/long-short-pitch/scripts/materialize_trade_scenarios.py
+++ /dev/null
@@ -1,276 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize deterministic trade scenario expected-value tables.
-
-Input CSV columns:
-- scenario
-- probability
-- current_value
-- target_value
-- key_drivers
-- timing
-
-Optional columns:
-- trigger
-- source
-- source_as_of / source_date / retrieval_date
-- borrow_fee / carry_cost / holding_period_days for shorts
-- break_price for event pitches
-- recovery_value / bond_price / yield_or_spread for credit pitches
-
-Probabilities may be decimals (0.25) or percentages (25 / 25%).
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-import sys
-from datetime import date, datetime
-from pathlib import Path
-from typing import Any
-
-REQUIRED = ["scenario", "probability", "current_value", "target_value", "key_drivers", "timing"]
-
-
-def parse_number(value: Any, field: str) -> float:
-    text = str(value or "").strip().replace(",", "").replace("$", "")
-    if text.endswith("%"):
-        text = text[:-1]
-        scale = 0.01
-    else:
-        scale = 1.0
-    try:
-        number = float(text) * scale
-    except ValueError as exc:
-        raise ValueError(f"{field} must be numeric, got {value!r}") from exc
-    if not math.isfinite(number):
-        raise ValueError(f"{field} must be finite")
-    return number
-
-
-def parse_optional_number(value: Any, field: str, default: float | None = None) -> float | None:
-    if str(value or "").strip() == "":
-        return default
-    return parse_number(value, field)
-
-
-def parse_probability(value: Any) -> float:
-    p = parse_number(value, "probability")
-    if p > 1.0:
-        p = p / 100.0
-    if p < 0:
-        raise ValueError("probability cannot be negative")
-    return p
-
-
-def parse_source_date(value: Any, field: str, run_date: date, warnings: list[str]) -> str:
-    text = str(value or "").strip()
-    if not text:
-        warnings.append(f"missing {field}")
-        return ""
-    try:
-        parsed = datetime.strptime(text, "%Y-%m-%d").date()
-    except ValueError:
-        warnings.append(f"{field} invalid date {text!r}; expected YYYY-MM-DD")
-        return text
-    if parsed > run_date:
-        warnings.append(f"{field} is after run_date")
-    elif (run_date - parsed).days > 180:
-        warnings.append(f"{field} is stale")
-    return text
-
-
-def load_rows(path: Path) -> list[dict[str, str]]:
-    with path.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        fields = [str(f or "").strip() for f in (reader.fieldnames or [])]
-        missing = [field for field in REQUIRED if field not in fields]
-        if missing:
-            raise ValueError("missing required columns: " + ", ".join(missing))
-        rows = [dict(row) for row in reader]
-    if not rows:
-        raise ValueError("input CSV must include at least one scenario row")
-    return rows
-
-
-def scenario_return(side: str, current_value: float, target_value: float) -> float:
-    if current_value == 0:
-        raise ValueError("current_value cannot be zero")
-    if side == "short":
-        return (current_value - target_value) / current_value
-    return (target_value - current_value) / current_value
-
-
-def materialize(
-    rows: list[dict[str, str]], side: str, run_date: date | None = None
-) -> dict[str, Any]:
-    run_date = run_date or date.today()
-    output_rows: list[dict[str, Any]] = []
-    total_probability = 0.0
-    expected_return = 0.0
-
-    for idx, row in enumerate(rows, start=2):
-        scenario = str(row.get("scenario", "")).strip()
-        if not scenario:
-            raise ValueError(f"row {idx}: scenario is required")
-        probability = parse_probability(row.get("probability"))
-        current_value = parse_number(row.get("current_value"), "current_value")
-        target_value = parse_number(row.get("target_value"), "target_value")
-        gross_return = scenario_return(
-            "short" if side == "short" else "long", current_value, target_value
-        )
-        warnings: list[str] = []
-
-        holding_period_days = parse_optional_number(
-            row.get("holding_period_days"), "holding_period_days", 365.0
-        )
-        if holding_period_days is not None and holding_period_days <= 0:
-            raise ValueError(f"row {idx}: holding_period_days must be positive")
-        borrow_fee = parse_optional_number(row.get("borrow_fee"), "borrow_fee", 0.0) or 0.0
-        carry_cost = parse_optional_number(row.get("carry_cost"), "carry_cost", 0.0) or 0.0
-        financing_cost = 0.0
-        if side == "short":
-            financing_cost = (borrow_fee + carry_cost) * ((holding_period_days or 365.0) / 365.0)
-
-        net_return = gross_return - financing_cost
-        weighted_return = probability * net_return
-        total_probability += probability
-        expected_return += weighted_return
-
-        source_as_of = parse_source_date(
-            row.get("source_as_of") or row.get("source_date"), "source_as_of", run_date, warnings
-        )
-        retrieval_date = str(row.get("retrieval_date") or "").strip()
-        if retrieval_date:
-            parse_source_date(retrieval_date, "retrieval_date", run_date, warnings)
-
-        output_rows.append(
-            {
-                "scenario": scenario,
-                "probability": probability,
-                "current_value": current_value,
-                "target_value": target_value,
-                "gross_return": gross_return,
-                "financing_cost": financing_cost,
-                "net_return": net_return,
-                "weighted_return": weighted_return,
-                "event_spread": (target_value - current_value) / current_value
-                if side == "event"
-                else None,
-                "break_price": parse_optional_number(row.get("break_price"), "break_price"),
-                "recovery_value": parse_optional_number(
-                    row.get("recovery_value"),
-                    "recovery_value",
-                    target_value if side == "credit" else None,
-                ),
-                "bond_price": parse_optional_number(
-                    row.get("bond_price"), "bond_price", current_value if side == "credit" else None
-                ),
-                "yield_or_spread": str(row.get("yield_or_spread") or "").strip(),
-                "borrow_fee": borrow_fee if side == "short" else None,
-                "carry_cost": carry_cost if side == "short" else None,
-                "holding_period_days": holding_period_days,
-                "key_drivers": str(row.get("key_drivers", "")).strip(),
-                "timing": str(row.get("timing", "")).strip(),
-                "trigger": str(row.get("trigger", "")).strip(),
-                "source": str(row.get("source", "")).strip(),
-                "source_as_of": source_as_of,
-                "retrieval_date": retrieval_date,
-                "warnings": "; ".join(warnings),
-            }
-        )
-
-    probability_error = abs(total_probability - 1.0)
-    return {
-        "side": side,
-        "total_probability": total_probability,
-        "probability_ok": probability_error <= 0.0001,
-        "probability_error": probability_error,
-        "expected_return": expected_return,
-        "rows": output_rows,
-    }
-
-
-def pct(value: float) -> str:
-    return f"{value * 100:.1f}%"
-
-
-def markdown_table(result: dict[str, Any]) -> str:
-    lines = [
-        "| Scenario | Probability | Current | Target | Gross return | Financing cost | Net return | Weighted return | Key drivers | Timing | Trigger | Source | Source as-of | Warnings |",
-        "|---|---:|---:|---:|---:|---:|---:|---:|---|---|---|---|---|---|",
-    ]
-    for row in result["rows"]:
-        lines.append(
-            "| {scenario} | {probability} | {current:.2f} | {target:.2f} | {gross} | {cost} | {net} | {weighted} | {drivers} | {timing} | {trigger} | {source} | {source_as_of} | {warnings} |".format(
-                scenario=row["scenario"],
-                probability=pct(row["probability"]),
-                current=row["current_value"],
-                target=row["target_value"],
-                gross=pct(row["gross_return"]),
-                cost=pct(row["financing_cost"]),
-                net=pct(row["net_return"]),
-                weighted=pct(row["weighted_return"]),
-                drivers=row["key_drivers"],
-                timing=row["timing"],
-                trigger=row["trigger"],
-                source=row["source"],
-                source_as_of=row["source_as_of"],
-                warnings=row["warnings"],
-            )
-        )
-    lines.append("")
-    lines.append(f"Expected return: **{pct(result['expected_return'])}**")
-    lines.append(f"Probability sum: **{pct(result['total_probability'])}**")
-    if not result["probability_ok"]:
-        lines.append("")
-        lines.append(
-            "> QA warning: probabilities do not sum to 100%. Fix before using in a PM pitch."
-        )
-    return "\n".join(lines)
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Materialize long/short scenario and expected-value tables."
-    )
-    parser.add_argument("input_csv", type=Path)
-    parser.add_argument("--side", choices=["long", "short", "event", "credit"], default="long")
-    parser.add_argument(
-        "--markdown-out",
-        type=Path,
-        default=Path(
-            "/tmp/public_equity_investing_long_short_pitch/trade_scenarios_support_note.md"
-        ),
-    )
-    parser.add_argument(
-        "--json-out",
-        type=Path,
-        default=Path("/tmp/public_equity_investing_long_short_pitch/trade_scenarios.json"),
-    )
-    parser.add_argument(
-        "--run-date",
-        default=date.today().isoformat(),
-        help="Freshness date for source_as_of checks, YYYY-MM-DD.",
-    )
-    args = parser.parse_args()
-
-    try:
-        run_date = datetime.strptime(args.run_date, "%Y-%m-%d").date()
-        result = materialize(load_rows(args.input_csv), args.side, run_date)
-    except Exception as exc:
-        print(f"ERROR: {exc}", file=sys.stderr)
-        return 1
-
-    args.markdown_out.parent.mkdir(parents=True, exist_ok=True)
-    args.json_out.parent.mkdir(parents=True, exist_ok=True)
-    args.markdown_out.write_text(markdown_table(result) + "\n", encoding="utf-8")
-    args.json_out.write_text(json.dumps(result, indent=2), encoding="utf-8")
-    print(f"Wrote {args.markdown_out} and {args.json_out}")
-    return 0 if result["probability_ok"] else 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/meeting-prep/SKILL.md b/plugins/financial-markets/skills/meeting-prep/SKILL.md
deleted file mode 100644
index e19f3d9..0000000
--- a/plugins/financial-markets/skills/meeting-prep/SKILL.md
+++ /dev/null
@@ -1,118 +0,0 @@
----
-name: meeting-prep
-description: Use when creating Public Equity Investing meeting prep briefs. Do not use for private diligence, IB, FP&A, legal, or scheduling-only tasks.
----
-
-# Meeting Prep
-
-## Skill Configuration
-
-### User Context Preflight
-
-Invoke `public-equity-investing:user-context` in preflight mode by loading `skills/user-context/SKILL.md` from the plugin root and running `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root before searching connectors, retrieving evidence, or drafting output. Set the working directory before the first attempt; do not probe alternate relative paths. Use the returned envelope as authoritative for `saved_context`, `source_category_plan`, and `next_action`. Apply relevant `saved_context`. Do not read or reinterpret raw plugin state files unless preflight fails or the user explicitly asks for raw state inspection. Missing, malformed, or uninitialized context must not block meeting prep.
-
-During ordinary meeting prep, do not initialize state or run onboarding or broad source setup. If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append the router's one-line optional setup offer only once. Leave other onboarding steps to the explicit `user-context` flow.
-
-### Source Resolution
-
-Use `source_category_plan` from preflight to resolve only catalogued source categories needed for the current meeting. Prefer a user-named source first, then an active saved route when available. Attempt the smallest useful native read only when the workflow needs that source. If a route needs auth, connection, or setup, state the practical limitation and continue from prompt context, active artifacts, pasted or exported material, and public sources when the meeting brief can still be useful. Do not inspect unrelated source categories, run broad source setup, write connector readiness, or create, read, migrate, or update `category-state.json`.
-
-The source-category plan covers the catalogued Public Equity Investing sources below. Use `references/context-and-sources.md` for the broader evidence hierarchy, including optional meeting-logistics connectors.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. These are semantic source categories, not fixed connector names.
-
-- `company_filings_ir`: filings, IR materials, and reported financials needed for the meeting baseline.
-- `earnings_transcripts_presentations`: transcripts, presentations, events, and recent management commentary needed for question planning.
-- `internal_research`: internal notes, expert context, and team discussions when they materially improve the meeting plan.
-- `portfolio_models_trackers`: portfolio context, watchlists, models, and thesis trackers only when they materially change the investor stance or follow-up.
-- `market_data_estimates`: market data, consensus, estimates, and ownership only when they materially change the investor stance or questions.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML live-meeting brief. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive reusable meeting-prep packet or explicit HTML meeting brief, the default resolves the presentation surface to a polished standalone HTML live-meeting brief unless the user requests another surface, a quick/no-file answer, or a standardized dashboard. In interactive runs, ask only remaining material choices such as depth, audience/use, meeting type, or focus; in non-interactive runs, default to the HTML live-meeting brief and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Mission
-
-Create decision-useful Public Equity Investing meeting readiness: what to ask first, what to listen for, how to follow up if the answer is evasive, what not to say, what evidence to request, what decisions to drive, and what follow-ups to send. For a live meeting, the prep sheet is a conversation tool, not a general research report.
-
-Default prep bundle:
-
-1. meeting objective, investor stance, and compact verified baseline;
-2. conversation flow: opening frame, lead question, pressure-test, and close;
-3. three or four must-ask questions ranked by decision impact, with listen-fors and evasive-answer follow-ups;
-4. compact must-know context, time-permitting questions, and evidence requests;
-5. likely pushbacks, compliance boundaries, and what not to ask or say;
-6. follow-up/action tracker;
-7. source log and material open gaps.
-
-Preserve existing briefs, decks, memos, spreadsheets, trackers, and notes. Add new outputs, comments, suggested edits, speaker notes, or change logs unless the user explicitly asks to modify the original.
-
-For a new standalone reader-facing prep packet, produce a polished standalone HTML live-meeting brief following `../../shared/html-artifact-standard.md`; let the meeting type and decision needed determine the hierarchy. Use chat only when the user explicitly requests a lightweight response. Keep background research proportional to its utility in the conversation: do not turn meeting prep into an earnings deep dive, tearsheet, or initiation report merely because HTML space is available.
-
-Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, or structured payload-driven render. For that optional route, use `references/DASHBOARD_PACK.md`: `meeting-prep` owns the meeting objective, context, question sequencing, evidence requests, pushbacks, and follow-up logic; `dashboard-builder` owns the standardized shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV support files behind the HTML dashboard unless explicitly requested.
-
-## Public Equity Investing Boundary
-
-Use this skill for meetings tied to public securities, listed issuers, equity-risk credit signals, event-driven situations, sector research, PM/risk reviews, earnings, investor days, conferences, expert calls, and public-equity-investing client or committee discussions. Route credit-instrument, creditworthiness, covenant, recovery, restructuring, and debt-security meetings to Credit Markets.
-
-Do not use this skill as the lead owner for private-markets diligence, investment-banking pitch or process meetings, corporate FP&A or operating-cadence meetings, legal/regulatory/contract meetings, or scheduling-only tasks.
-
-If the user asks for those non-local workflows, route to the appropriate plugin/tool when available or state that the workflow is non-local to Public Equity Investing.
-
-## Workflow
-
-1. **Determine mode.** Build from scratch, refresh existing prep, turn analysis into call prep, prepare for a specific meeting, prepare follow-ups, or review/upgrade an existing prep packet.
-2. **Infer audience and meeting type.** Management/IR, issuer, PM, client, committee, expert, earnings, investor day, credit, risk, portfolio review, model review, catalyst prep, or research kickoff. Load `references/meeting-type-playbooks.md` when needed.
-3. **Build context pack.** Start with the prompt and active artifacts. Use runtime apps/connectors only when they are actually callable. Do not imply calendar, email, Slack, Drive, market-data, or research connectors are available if they are not.
-4. **Handle sparse context.** With no context, produce starter prep plus data request list. With partial context, label assumptions and prioritize decision-changing gaps. With full context, produce tailored questions, evidence requests, likely responses, and follow-ups. Do not reproduce a long issuer or earnings background unless it changes the meeting plan.
-5. **Compose with local skills.** Use source, tearsheet, memo, style, QC, scenario, audit, model, earnings, event, risk, hedge, thesis, or sector skills when meeting prep depends on their domain. Use Credit Markets as a handoff for credit-first meeting prep.
-6. **Create brief.** State why the meeting matters now, what decision/information is needed, what is known/assumed, the must-ask questions in meeting order, what to listen for, what follow-up to use if an answer remains qualitative or evasive, evidence requests, pushbacks, and action items.
-7. **Prepare follow-ups if post-meeting.** Capture decisions, new facts, changed assumptions, open questions, evidence requests, commitments, owners, dates, dependencies, and draft follow-up language if useful.
-
-## First-Class Meeting Modes
-
-Support these modes explicitly: `management_ir`, `expert_call`, `pm_internal_review`, `investment_committee`, `client_update`, `sell_side_call`, `earnings_call`, `investor_day`, `portfolio_watchlist_review`, `model_review`, `research_kickoff`, and `post_meeting_follow_up`.
-
-For every mode, rank questions by decision impact, include what not to ask or say, identify evidence requests, anticipate likely pushbacks, and assign follow-up actions. For a finite live conversation, make three or four questions the must-ask set and place additional questions in an `If Time Permits` block. Tie every must-ask question to an estimate, valuation, thesis, catalyst, sizing, or monitoring consequence. The senior PM layer should press on what would change the view, what evidence is missing, what answer would be evasive, what the market already knows, and what should route to a model, memo, thesis tracker, sizing review, or Credit Markets handoff.
-
-## HTML Guidance
-
-For a substantive standalone HTML meeting brief, load `../../shared/html-artifact-standard.md` and apply these meeting-specific requirements:
-
-- Make the live conversation plan the primary visual object. Lead with the meeting objective, one-sentence investor stance, core decision or information gap, and a compact disclosed baseline.
-- Place a compact `Conversation Flow` block immediately after the opening stance/baseline and before detailed question cards or extended background. It should show the opening frame, lead question, pressure-test, and close/evidence request at a glance.
-- For `management_ir`, `sell_side_call`, `expert_call`, `earnings_call`, or `investor_day`, display three or four must-ask questions after the conversation flow and before extended background. For each, show `Why it matters`, `Listen for`, `If evasive`, and `Model / thesis implication` or the closest decision consequence.
-- Keep each must-ask subfield concise enough for live use: generally one short sentence, and no more than two short sentences where material nuance would otherwise be lost.
-- Put secondary questions in a compact `If Time Permits` block. Do not render a long list of equally weighted question cards.
-- Keep the disclosed baseline short: usually four or five facts or metrics that make the questions intelligible. Detailed historical tables, scenario work, and extended industry context belong only when they change a live question or follow-up.
-- Include a usable conversation-flow block before detailed questions: opening frame, first question, productive follow-up if management remains qualitative, and closing evidence request or next public disclosure to monitor.
-- Keep the compliance boundary visible for external meetings: public-disclosure-only posture, prohibited asks, and clean ways to restate questions without seeking material nonpublic information.
-- Give the headline, stance box, baseline, and question plan distinct jobs. Do not repeat the same conclusion across several visible panels.
-- Keep citations traceable but readable: do not fragment company names, product names, periods, dates, numeric ranges, or metric labels into separately linked tokens.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, density, clipping, citation rendering, and whether the must-ask questions are quickly usable in a live meeting.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` for verified facts, claims, citations, and source gaps; use `company-tearsheet`, `financials-normalizer`, or `excel-data-cleaner` for issuer/table prep; use `deck-report-qc` for pre-read/circulation review; use `style-guide-adapter` only after substantive prep is locked. Support artifacts stay secondary to the live-meeting brief or optional standardized dashboard.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source pack, company and market context, key questions, risk flags, and follow-up tracker. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Quality Bar
-
-The objective is explicit, the brief is usable during the meeting, facts and assumptions are separated, material source gaps are visible, must-ask questions are sequenced by decision impact, evasive-answer follow-ups and decision consequences are clear, evidence requests are specific, likely objections are covered, action items have owner/timing where available, and no source artifact is modified or deleted without permission.
-
-## Reference Map
-
-- `references/context-and-sources.md`: source hierarchy and optional connector behavior. Treat connectors as optional runtime inputs.
-- `references/meeting-type-playbooks.md`: persona and meeting-type modules.
-- `references/question-and-evidence-bank.md`: question and evidence-request patterns.
-- `references/follow-up-and-action-tracking.md`: debrief and tracker fields.
-- `references/output-templates.md`: standard brief formats.
-- `references/safety-and-integrations.md`: non-destructive editing and circulation rules.
diff --git a/plugins/financial-markets/skills/meeting-prep/agents/openai.yaml b/plugins/financial-markets/skills/meeting-prep/agents/openai.yaml
deleted file mode 100644
index 9121139..0000000
--- a/plugins/financial-markets/skills/meeting-prep/agents/openai.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-interface:
-  display_name: Meeting Prep
-  short_description: Create public-equity-investing meeting briefs, question lists, evidence requests, follow-ups, and call prep.
-  icon_small: assets/icon.svg
-  icon_large: assets/icon.svg
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/meeting-prep/assets/icon.svg b/plugins/financial-markets/skills/meeting-prep/assets/icon.svg
deleted file mode 100644
index c43bcdf..0000000
--- a/plugins/financial-markets/skills/meeting-prep/assets/icon.svg
+++ /dev/null
@@ -1,7 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" role="img" aria-label="meeting prep icon">
-  <rect width="64" height="64" rx="14" fill="#153B50"/>
-  <rect x="14" y="14" width="36" height="42" rx="4" fill="#F7F2E8"/>
-  <path d="M22 24h20M22 32h20M22 40h14" stroke="#153B50" stroke-width="4" stroke-linecap="round"/>
-  <circle cx="46" cy="18" r="8" fill="#63C7B2"/>
-  <path d="M42.5 18.5l2.5 2.5 5-6" fill="none" stroke="#153B50" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"/>
-</svg>
diff --git a/plugins/financial-markets/skills/meeting-prep/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/meeting-prep/references/DASHBOARD_PACK.md
deleted file mode 100644
index bfe3c89..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Meeting Prep Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, reusable dashboard template, or structured payload-driven render for Public Equity Investing meeting prep. Ordinary substantive or explicitly requested HTML meeting briefs should use the flexible standalone HTML live-meeting brief in `../SKILL.md` instead.
-
-## Producer Role
-
-`meeting-prep` owns meeting objective, context synthesis, question prioritization, evidence requests, safety/circulation posture, and follow-up logic. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `meeting_prep`
-- `layout`: `single_page` for reusable prep packets unless the user explicitly requests tabs
-- `hero.callout`: what decision or information the meeting must unlock
-- `snapshot`: meeting type, audience, objective, highest-impact question, top evidence gap, follow-up owner/status if known
-- `snapshot`: include explicit mode such as `management_ir`, `expert_call`, `pm_internal_review`, `sell_side_call`, `earnings_call`, `investor_day`, `portfolio_watchlist_review`, or `model_review`
-- `sources`: prompt/user artifacts first, connected-app context only when actually available, then filings, market data, research artifacts, and assumptions
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `meeting-frame`
-   - `decision_box`: objective, one-sentence investor stance, desired decision, opening frame, and what would make the meeting successful
-   - `metric_tiles`: meeting type, audience, date/window if known, issuer/security, confidence, key source gap
-   - For a standardized live-meeting dashboard, place the compact conversation-flow object before detailed question modules.
-2. `must-know-context`
-   - `executive_summary`: concise context pack with only the known facts, assumptions, and why-now framing needed for the meeting plan
-   - `cards`: likely agenda, decision frame, risks to avoid, and circulation/safety notes
-3. `questions`
-   - `question_list`: three or four must-ask questions ranked by decision impact with concise why-it-matters, listen-for, evasive-answer follow-up, model/thesis implication, and evidence-request fields; place secondary questions in `If Time Permits`
-4. `pushbacks-actions`
-   - `table`: likely pushbacks, suggested responses, what not to ask/say, evidence support, and follow-up owner/timing when available
-   - `timeline`: follow-up actions, commitments, dependencies, and review dates
-5. `open-gaps`
-   - `missing_evidence`: missing sources, meeting details, model files, prior notes, market data, or evidence requests
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every verified fact, metric, meeting date/time, claim from prior artifacts, and source-backed context item.
-- Label assumptions and connector availability. Do not imply calendar, email, Slack, Drive, market-data, or research connectors are available unless they are.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not modify source artifacts unless the user asks.
-- Do not include internal strategy, restricted information, or trading intent in external-clean materials without explicit direction.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-- Do not turn a live meeting plan into an earnings deep dive or render a long list of equally weighted questions.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm objective, facts versus assumptions, source gaps, ranked questions, evidence requests, pushbacks, and follow-ups are visible.
-- Confirm circulation-sensitive content is labeled or removed for external-facing prep.
diff --git a/plugins/financial-markets/skills/meeting-prep/references/context-and-sources.md b/plugins/financial-markets/skills/meeting-prep/references/context-and-sources.md
deleted file mode 100644
index 2b8e656..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/context-and-sources.md
+++ /dev/null
@@ -1,103 +0,0 @@
-# Context and sources
-
-## Table of contents
-1. Source hierarchy
-2. Connected-source intake
-3. Stale-data checks
-4. Citation and source-log behavior
-5. Conflict handling
-6. Fact, assumption, and inference labels
-7. No-context and partial-context fallback
-
-## 1. Source hierarchy
-Use the highest-quality source available for the specific claim.
-
-### Public Equity Investing meeting logistics and intent
-1. User instructions in the current prompt.
-2. Current calendar invite, agenda, attachments, attendee list, meeting title, and location/video link.
-3. Recent email or chat threads discussing the meeting.
-4. Research, project, thesis, or action tracker status.
-5. Prior meeting notes or transcripts.
-6. Inference from context, labeled as an assumption.
-
-### Company, issuer, or investment facts
-1. User-provided source documents and active artifacts.
-2. Primary source materials: company financials, management presentations, filings, press releases, earnings releases, transcripts, investor presentations, court/regulatory records, and Credit Markets documents.
-3. Connected financial or market data apps available to the user.
-4. Internal research, prior memos, prior models, research notes, committee materials, and evidence trackers.
-5. Reputable public sources and web search.
-6. Model output, third-party summaries, or management claims, labeled and cross-checked.
-
-### People and relationship context
-1. Calendar attendee list and invite metadata.
-2. Recent emails, chat messages, prior meeting transcripts, research notes, and relationship notes.
-3. User-provided background.
-4. Public professional profiles or company bios if connected context is unavailable and web use is allowed.
-
-## 2. Connected-source intake
-Treat connectors as optional runtime inputs, not guaranteed dependencies. When connectors are enabled and available to the user, gather only what materially improves the prep. Useful connector categories include:
-- calendar: timing, attendees, agenda, recurrence, attachments, urgency;
-- email and chat: objectives, open issues, relationship tone, prior commitments, last-mile updates;
-- drive/docs/slides/sheets: pre-reads, models, decks, memos, evidence trackers, templates;
-- transcripts/meeting records: prior questions, commitments, unresolved issues, tone;
-- research/project trackers: stage, owners, next milestones, issuer/investor/counterparty status;
-- finance/data apps: filings, earnings, consensus, market data, portfolio/risk reports, credit documents, and internal source exports.
-
-Do not claim access to files, calendars, chats, emails, or market-data feeds unless the connector is actually enabled and callable. Do not ask for files or dates that are clearly available in the connected context. If connectors are unavailable or insufficient, say what was missing and proceed with a fallback brief based on user-provided materials, public sources, and clearly labeled assumptions.
-
-## 3. Stale-data checks
-Flag data as potentially stale when:
-- meeting logistics differ across invite, email, and user prompt;
-- financials predate a newer filing, earnings release, forecast, guidance update, investor day, rating action, or credit document;
-- market data, share price, spreads, yields, FX, or commodity prices are not current for a market-sensitive meeting;
-- consensus, guidance, KPI, or estimate data predates the latest print, guide, investor day, or major announcement;
-- catalyst status, event timeline, research action trackers, or evidence requests are older than the latest source communication;
-- prior notes are contradicted by newer source documents.
-
-Use concrete language: "potentially stale," "superseded by," "verify before meeting," or "latest available source reviewed."
-
-## 4. Citation and source-log behavior
-Cite facts that anchor the prep: financials, security terms, dates, deadlines, guidance, catalyst status, meeting objective, prior commitments, and evidence responses. Generic questions and judgment do not need citations, but should be traceable to the relevant issue.
-
-If the environment supports inline citations, cite factual claims inline. If not, include a source log with fields:
-- claim or fact,
-- source name,
-- source type,
-- date or version,
-- confidence,
-- notes or conflicts.
-
-For external-clean outputs, include citations or source descriptions that are appropriate to share. Keep internal emails, chat threads, and privileged strategy notes out of external-facing source logs unless the user explicitly asks and sharing is appropriate.
-
-## 5. Conflict handling
-When sources conflict:
-1. identify the conflict plainly;
-2. prefer current primary sources over summaries;
-3. prefer signed/final/current documents over drafts;
-4. prefer direct company or system data over manual notes;
-5. preserve the lower-confidence item as a question or verification item;
-6. do not silently blend conflicting figures.
-
-Example phrasing:
-- "Revenue is shown as $120m in the latest company deck but $116m in the older model. I used $120m for the brief and added a pre-call tie-out item."
-- "The calendar invite says this is an issuer update, while the email thread frames it as a liquidity review. Treating liquidity as the likely decision point; verify at the start."
-
-## 6. Fact, assumption, and inference labels
-Use these labels consistently:
-- **Verified fact**: supported by a cited source.
-- **Management claim**: stated by company/issuer/management but not independently verified.
-- **Model output**: calculated from a workbook or assumptions; cite model tab/range or describe source.
-- **Assumption**: needed to proceed but not verified.
-- **Inference**: reasonable conclusion from facts but not directly stated.
-- **Question**: open issue to resolve in the meeting.
-- **Recommendation**: suggested stance or action.
-
-## 7. No-context and partial-context fallback
-If no reliable source is available, still help. Produce:
-- a starter objective based on the user's wording,
-- a likely meeting-type assumption,
-- a short list of missing context that would most improve the brief,
-- a generic but senior question set for that meeting type,
-- a source log that says "no source provided" rather than inventing provenance.
-
-Do not block completion unless a missing fact is essential to safety, confidentiality, legal compliance, or the user's explicit deliverable.
diff --git a/plugins/financial-markets/skills/meeting-prep/references/follow-up-and-action-tracking.md b/plugins/financial-markets/skills/meeting-prep/references/follow-up-and-action-tracking.md
deleted file mode 100644
index aa97bae..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/follow-up-and-action-tracking.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Follow-up and action tracking
-
-## Table of contents
-1. When to switch from prep to debrief
-2. Debrief structure
-3. Action item taxonomy
-4. Follow-up email and message drafting
-5. Tracker fields
-6. Carry-forward logic
-
-## 1. When to switch from prep to debrief
-If the user provides notes, transcript, or says the meeting already happened, create a debrief. Do not keep preparing for a meeting that is over unless the user asks for a retroactive prep or next-call prep.
-
-## 2. Debrief structure
-A useful debrief answers:
-- What was decided?
-- What new facts were learned?
-- What assumptions changed?
-- What remains open?
-- What evidence was promised or requested?
-- What commitments were made by each party?
-- What should happen next, by whom, and by when?
-
-Default debrief:
-
-```markdown
-# Meeting debrief
-
-## Decisions and outcomes
-- [Decision / outcome]
-
-## New facts learned
-- [Fact] - Source: meeting notes / transcript / attendee
-
-## Open questions
-- [Question] - Owner: [owner] - Needed for: [decision]
-
-## Follow-up asks
-| Ask | Owner/source | Due | Why needed | Status |
-|---|---|---|---|---|
-
-## Action tracker
-| Action | Owner | Due | Dependency | Status | Notes |
-|---|---|---|---|---|---|
-
-## Draft follow-up
-[Draft email or message]
-```
-
-## 3. Action item taxonomy
-Classify each item so the tracker is useful:
-- **Decision**: a choice to be made by a person or committee.
-- **Evidence request**: evidence, document, data, or support needed.
-- **Analysis task**: work the user or team must perform.
-- **Relationship follow-up**: message, thank-you, intro, or stakeholder update.
-- **Process milestone**: committee date, earnings event, investor day, rating action, court/regulatory milestone, model update, or client deadline.
-- **Watch item**: event or trigger to monitor.
-- **Escalation**: issue that needs senior, legal, compliance, accounting, tax, or specialist review.
-
-## 4. Follow-up email and message drafting
-Draft follow-ups with the correct circulation mode.
-
-### External-clean follow-up
-Use for counterparties, management teams, issuers, clients, investors, experts, or advisors. Include:
-- thanks and purpose;
-- concise recap of agreed next steps;
-- specific asks with owners and deadlines;
-- neutral language;
-- no internal strategy, valuation bottoms, privileged views, trading intent, position sizing, or restricted information.
-
-### Internal follow-up
-Use for team, PM, committee, credit/risk group, or investment team. Include:
-- candid readout;
-- what changed;
-- risks and concerns;
-- recommended actions;
-- owners, deadlines, and escalation items;
-- what not to share externally.
-
-Do not send messages automatically unless the current environment and user instruction explicitly support sending. Prefer drafting for review.
-
-## 5. Tracker fields
-For high-stakes meetings, use these fields:
-
-| Field | Purpose |
-|---|---|
-| Action | What needs to happen |
-| Type | Decision, ask, analysis, process, relationship, watch item, escalation |
-| Owner | Named person or team if known |
-| Counterparty owner | External owner if applicable |
-| Due date | Concrete date or relative milestone |
-| Dependency | What must happen first |
-| Source | Where action came from |
-| Status | Not started, requested, in progress, blocked, complete |
-| Decision supported | Why it matters |
-| Notes | Context or risk |
-
-## 6. Carry-forward logic
-When updating a tracker or prior prep:
-- preserve existing open items unless marked complete or user asks to remove them;
-- add a status-change log if materially changing items;
-- do not delete historical items from a source tracker by default;
-- distinguish old unresolved asks from new asks;
-- call out stale or orphaned actions with no owner or due date.
diff --git a/plugins/financial-markets/skills/meeting-prep/references/meeting-type-playbooks.md b/plugins/financial-markets/skills/meeting-prep/references/meeting-type-playbooks.md
deleted file mode 100644
index 091bbd6..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/meeting-type-playbooks.md
+++ /dev/null
@@ -1,208 +0,0 @@
-# Meeting type playbooks
-
-## Table of contents
-1. Management or issuer meeting
-2. Research, PM, or client update
-3. Investor or counterparty meeting
-4. Credit, distressed, or risk committee meeting
-5. Investment committee or internal research review
-6. Earnings, investor day, or public-equity meeting
-7. Expert call
-8. Portfolio or watchlist review
-9. Forecast, model, valuation, or research review
-10. Research kickoff or evidence-request planning
-
-## First-class mode names
-
-Use these routing labels in outputs and dashboards: `management_ir`, `expert_call`, `pm_internal_review`, `investment_committee`, `client_update`, `sell_side_call`, `earnings_call`, `investor_day`, `portfolio_watchlist_review`, `model_review`, `research_kickoff`, and `post_meeting_follow_up`.
-
-All modes should rank questions by decision impact and include what not to ask/say, evidence requests, likely pushbacks, follow-up actions, and the next owning workflow.
-
-## 1. Management or issuer meeting
-Objective: test the company story, management credibility, KPIs, quality of growth, risks, and what evidence is needed next.
-
-Must know:
-- business model, product, customer base, market, unit economics, revenue quality, margin profile, cash flow, capex, working capital, and management incentives;
-- what the model or memo assumes;
-- issuer or management claims that need verification.
-
-Question focus:
-- growth drivers and sustainability;
-- customer concentration, churn, pipeline, pricing, retention, cohort behavior;
-- gross margin and operating leverage drivers;
-- non-recurring revenue, add-backs, revenue recognition, backlog, bookings, ARR/MRR, NRR/GRR as relevant;
-- cash conversion, working capital, capex, debt, covenants, liquidity;
-- what management would cut first in a downside case.
-
-Live-use output for a public-company management or IR meeting:
-- lead with the investor stance, purpose of the conversation, and the evidence or disclosure that would change the view;
-- place a compact conversation-flow sequence before the detailed must-ask question cards so the brief can be used during the meeting;
-- make three or four questions the must-ask sequence and move additional diligence topics to `If Time Permits`;
-- for each must-ask, concisely specify what answer to listen for, the clean follow-up if the answer stays qualitative or evasive, and the model or thesis implication;
-- keep the public-disclosure boundary explicit: do not seek intra-quarter performance, customer-specific contract terms, undisclosed pricing, or other material nonpublic information;
-- keep background to the handful of disclosed facts necessary to make the call plan useful, and route broader post-print or pre-print analysis to the relevant earnings workflow.
-
-Useful companion skills: `company-tearsheet`, `financial-source-of-truth`, `financials-normalizer`, `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `scenario-sensitivity-generator`, `thesis-tracker`, `sector-context-overlay`.
-
-## 2. Research, PM, or client update
-Objective: prepare the user to explain the current view, defend the evidence, update a recommendation, or clarify next analytical steps with a PM, client, or research team.
-
-Must know:
-- current thesis, recommendation or stance, source posture, model status, catalysts, risks, and decision-maker concerns;
-- relevant comps, valuation range, estimates, key KPI changes, market setup, and peer read-throughs;
-- what the user should ask for to sharpen the research, model, or client discussion.
-
-Question focus:
-- what changed since the prior view;
-- key estimate, valuation, and catalyst debates;
-- model and source gaps;
-- risk controls, hedges, or sizing constraints;
-- materials needed to refresh the research packet.
-
-Useful companion skills: `memo-builder`, `long-short-pitch`, `comps-valuation`, `dcf-model-builder`, `equity-model-update`, `earnings-preview`, `earnings-deep-dive`, `deck-report-qc`, `style-guide-adapter`.
-
-## 3. Investor, client, or counterparty meeting
-Objective: help the user understand mandate fit, thesis concerns, evidence gaps, and what would move an investor, client, or counterparty toward action.
-
-Must know:
-- counterparty mandate, sector focus, liquidity needs, risk appetite, valuation sensitivity, and prior behavior;
-- why this opportunity fits or does not fit;
-- likely research objections and decision process.
-
-Question focus:
-- investment mandate and non-negotiables;
-- valuation, growth, margin, balance sheet, catalyst, and risk assumptions;
-- evidence priorities and timeline;
-- committee or PM process and gating concerns;
-- reasons they might pass or size smaller.
-
-Useful companion skills: `company-tearsheet`, `memo-builder`, `long-short-pitch`, `comps-valuation`, `dcf-model-builder`, `scenario-sensitivity-generator`, `portfolio-risk-management`, `thesis-tracker`.
-
-## 4. Credit, distressed, or risk committee meeting
-Objective: pressure-test public issuer credit quality, downside protection, capital structure, liquidity, recovery, and risk limits.
-
-Must know:
-- capital structure, debt terms, maturity wall, collateral if disclosed, covenant package, liquidity, cash burn, forecast, and issuer case;
-- EBITDA adjustments, non-GAAP definitions, capex, cash taxes, refinancing risk, recovery downside, and trading levels;
-- covenant headroom when public or user-provided, rating trajectory, and instrument priority.
-
-Question focus:
-- what protects creditors or security holders in the downside;
-- covenant cushion if disclosed or user-provided;
-- liquidity runway and revolver availability;
-- non-GAAP adjustment support and recurring EBITDA;
-- collateral and recovery value;
-- required monitoring signals.
-
-Useful companion skills: `scenario-sensitivity-generator`, `event-driven-analyzer`, `portfolio-risk-management`, `equity-model-update`, `model-audit-tieout`, `memo-builder`. Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-
-## 5. Investment committee or internal research review
-Objective: prepare the user to make or defend a recommendation, expose the decision needed, and resolve the highest-impact open questions.
-
-Must know:
-- thesis, variant view, key risks, valuation, downside, catalysts, evidence status, kill criteria;
-- what has changed since last review;
-- what the committee must decide today.
-
-Question focus:
-- what has to be true to win;
-- biggest disconfirming evidence;
-- downside case and breakeven points;
-- valuation sensitivity and exit assumptions;
-- evidence gaps and who owns them;
-- go/no-go recommendation.
-
-Useful companion skills: `memo-builder`, `long-short-pitch`, `scenario-sensitivity-generator`, `thesis-tracker`, `portfolio-risk-management`, `deck-report-qc`.
-
-## 6. Earnings, investor day, or public-equity meeting
-Objective: prepare for a public-company event, management meeting, sell-side conference, or post-print investor discussion.
-
-Must know:
-- latest print, guidance, consensus, KPIs, transcript commentary, estimate revisions, valuation, debate points, catalysts;
-- what the market expected versus what changed;
-- what question would test the variant view.
-
-Question focus:
-- guidance bridge and key drivers;
-- demand, pricing, volume, mix, margin, capex, cash flow, buybacks, balance sheet;
-- KPI definitions and comparability;
-- competitive dynamics and channel signals;
-- risks management is not emphasizing;
-- what would cause estimates to move.
-
-Live-use output:
-- distinguish three or four meeting questions from the fuller earnings research agenda;
-- tie each priority question to the expected estimate, valuation, thesis, or monitoring consequence;
-- show the follow-up to use if management restates prepared commentary without addressing the investor debate.
-
-Useful companion skills: `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `long-short-pitch`, `thesis-tracker`, `portfolio-risk-management`, `sector-context-overlay`.
-
-## 7. Expert call
-Objective: turn limited expert time into specific evidence on market structure, competitive position, customer behavior, operational reality, or industry change.
-
-Must know:
-- expert background and potential bias;
-- hypotheses to test;
-- what evidence is observable by the expert versus outside their scope;
-- compliance restrictions and topics to avoid.
-
-Question focus:
-- observable facts and examples rather than opinions;
-- triangulation against company claims;
-- customer behavior, switching costs, procurement, pricing, sales cycles, churn, channel dynamics;
-- competitor positioning;
-- leading indicators and inflection points.
-
-Useful companion skills: `company-tearsheet`, `long-short-pitch`, `idea-generation`, `thesis-tracker`, `financial-source-of-truth`, `sector-context-overlay`.
-
-## 8. Portfolio or watchlist review
-Objective: identify what changed in a public-equity position or watchlist name, what actions are needed, and what the PM/research lead should do next.
-
-Must know:
-- price move, estimate revisions, earnings/KPI trend, liquidity/credit movement, catalyst path, risk/sizing, hedge status, and thesis drift;
-- prior action items and ownership;
-- management explanations, source quality, and disconfirming evidence.
-
-Question focus:
-- what changed versus thesis and what actually matters;
-- add, trim, hold, exit, hedge, or keep on watchlist;
-- revised estimate/valuation confidence;
-- catalysts, break conditions, and downside triggers;
-- source gaps that must be resolved before changing exposure.
-
-Useful companion skills: `thesis-tracker`, `equity-model-update`, `scenario-sensitivity-generator`, `portfolio-risk-management`, `model-audit-tieout`, `memo-builder`.
-
-## 9. Forecast, model, valuation, or research review
-Objective: prepare the user for decisions on forecast changes, model assumptions, valuation, financial performance, or public-equity-investing research communication.
-
-Must know:
-- current model, actuals, forecast, variance drivers, consensus, guidance, targets, and model version;
-- what changed since the prior forecast, model update, or research note;
-- what decisions are needed and by when.
-
-Question focus:
-- source of variance and controllability;
-- recurring versus one-time impact;
-- forecast tradeoffs, revenue/margin/FCF drivers, capital allocation, valuation, and risk controls;
-- forecast confidence and downside plan;
-- decisions, owner, and escalation path.
-
-Useful companion skills: `financials-normalizer`, `excel-data-cleaner`, `model-audit-tieout`, `equity-model-update`, `three-statement-model-builder`, `dcf-model-builder`, `scenario-sensitivity-generator`, `memo-builder`, `deck-report-qc`.
-
-## 10. Research kickoff or evidence-request planning
-Objective: align on research responsibilities, gating questions, timeline, workstreams, and evidence needed.
-
-Must know:
-- research question, coverage universe, deadlines, source package, required model work, open issues;
-- who owns each workstream;
-- next milestone and decision gate.
-
-Question focus:
-- timeline and deliverables;
-- owners and dependencies;
-- top disconfirming evidence and workstream priorities;
-- source, model, and data gaps;
-- approval or PM review process;
-- communication cadence.
-
-Useful companion skills: `financial-source-of-truth`, `company-tearsheet`, `financials-normalizer`, `equity-model-update`, `comps-valuation`, `dcf-model-builder`, `scenario-sensitivity-generator`, `memo-builder`, `thesis-tracker`.
diff --git a/plugins/financial-markets/skills/meeting-prep/references/output-templates.md b/plugins/financial-markets/skills/meeting-prep/references/output-templates.md
deleted file mode 100644
index b8bd024..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/output-templates.md
+++ /dev/null
@@ -1,233 +0,0 @@
-# Output templates
-
-## Table of contents
-1. Default one-page meeting brief
-2. Detailed meeting prep packet
-3. Standalone HTML management / IR call sheet
-4. Question list
-5. Evidence request list
-6. Talk track
-7. No-context fallback
-8. Source log
-9. Existing-brief review
-
-## 1. Default one-page meeting brief
-Use when the user asks for prep without specifying format.
-
-```markdown
-# Meeting prep: [meeting / company / counterparty]
-
-## 1. Objective
-- [What we need from the meeting]
-- Decision or outcome to drive: [decision / commitment / information / relationship outcome]
-- Mode: [management_ir / expert_call / pm_internal_review / investment_committee / client_update / sell_side_call / earnings_call / investor_day / portfolio_watchlist_review / model_review / research_kickoff / post_meeting_follow_up]
-
-## 2. Must-know context
-- [Verified fact] ... [source]
-- [Verified fact] ... [source]
-- [Assumption / inference] ... [why reasonable]
-
-## 3. What matters most
-1. [Issue] - [so what]
-2. [Issue] - [so what]
-3. [Issue] - [so what]
-
-## 4. Must-ask questions
-1. [Question] - Why it matters: [decision impact] - Listen for: [answer signal]
-2. [Question] - Why it matters: [decision impact] - Listen for: [answer signal]
-3. [Question] - Why it matters: [decision impact] - Listen for: [answer signal]
-
-## 4A. If time permits
-- [Secondary question] - [why useful]
-
-## 4B. What not to ask or say
-- [Topic/wording to avoid] - Reason: [MNPI, disclosure, negotiation, source weakness, or PM strategy sensitivity]
-
-## 5. Evidence needed
-- [Request] - Owner/source: [owner] - Needed by: [date if known]
-
-## 6. Likely pushbacks and suggested responses
-- If they say: [pushback]
-  - Respond with: [response]
-  - Follow-up: [question]
-
-## 7. Follow-ups
-| Action | Owner | Due | Dependency | Status |
-|---|---|---|---|---|
-| [action] | [owner] | [date] | [dependency] | [not started] |
-
-## 8. Sources and gaps
-- Reviewed: [sources]
-- Verify before meeting: [gaps]
-```
-
-## 2. Detailed meeting prep packet
-Use for high-stakes investor, PM, credit/risk, committee, management, or client meetings.
-
-```markdown
-# Meeting prep packet
-
-## Executive view
-- Objective:
-- Recommended stance:
-- Decision needed:
-- Highest-risk unknown:
-- Best next step:
-
-## Attendees and likely motivations
-| Attendee | Role | Likely objective | What they may ask | Prep note |
-|---|---|---|---|---|
-
-## Compact context and source-backed facts
-| Fact | Source | Date/version | Confidence | Notes |
-|---|---|---|---|---|
-
-## Decision architecture
-| Decision / issue | Current view | Evidence | Open question | Recommended next step |
-|---|---|---|---|---|
-
-## Must-ask question plan
-| Priority | Question | Why it matters | Listen for | If evasive | Model / thesis implication |
-|---:|---|---|---|---|---|
-
-## If time permits
-| Question | Why useful | Evidence to request |
-|---|---|---|
-
-## Evidence requests
-| Request | Why needed | Source/owner | Timing | Format | Decision supported |
-|---|---|---|---|---|---|
-
-## Talking points
-- Opening:
-- First question:
-- Productive follow-up if the answer remains qualitative:
-- Close / next public evidence ask:
-
-## Risks and watch-outs
-- Do not say/concede:
-- Keep internal only:
-- Needs verification:
-- Escalate to specialist if:
-
-## Follow-up tracker
-| Action | Owner | Due | Dependency | Evidence/source | Status |
-|---|---|---|---|---|---|
-```
-
-## 3. Standalone HTML management / IR call sheet
-Use for substantive reusable or explicitly requested HTML prep for a public-company management or investor-relations meeting. This is a live-use brief: keep the call plan above supporting research and do not expand it into an earnings deep dive.
-
-Visible first-read order:
-
-1. Meeting objective, one-sentence investor stance, and core information gap.
-2. Four or five source-backed baseline facts or metrics needed to use the questions.
-3. Conversation flow: opening frame, first question, follow-up if management remains qualitative, and closing evidence request.
-4. Three or four must-ask questions, each with `Why it matters`, `Listen for`, `If evasive`, and `Model / thesis implication`.
-5. Compact `If Time Permits` questions and evidence requests.
-6. Likely pushbacks, public-disclosure boundary, and what not to ask or say.
-7. Follow-up tracker, material open gaps, and readable source notes.
-
-The `Conversation Flow` block must appear before the detailed must-ask question cards. Keep each detailed question subfield to one short sentence when possible and no more than two short sentences when needed for material nuance. Do not place a long earnings or company history section before the must-ask questions. Avoid eight equally weighted question cards, repetitive stance panels, or large tables that do not alter the conversation.
-
-## 4. Question list
-Use when the user asks only for questions.
-
-```markdown
-## Must-ask questions
-1. [Question]
-   - Why it matters:
-   - Listen for:
-   - If evasive:
-   - Model / thesis implication:
-   - Evidence to request:
-
-## If time permits
-- [Question] - Why useful: [decision impact]
-```
-
-## 5. Evidence request list
-Use when the user needs a sendable or internal evidence request list.
-
-```markdown
-## Evidence requests
-
-### Highest priority
-| Request | Purpose | Owner/source | Timing | Notes |
-|---|---|---|---|---|
-
-### Nice to have
-| Request | Purpose | Owner/source | Timing | Notes |
-|---|---|---|---|---|
-
-### Questions for the call
-- [Question]
-```
-
-## 6. Talk track
-Use for live-meeting readiness.
-
-```markdown
-## Suggested talk track
-- Open: [one sentence]
-- Frame objective: [one sentence]
-- First question: [highest-impact question]
-- If the answer remains qualitative: [productive follow-up that stays within disclosure boundaries]
-- Next must-ask: [second and third questions]
-- Close: [evidence request, next public disclosure, owners, next steps]
-```
-
-## 7. No-context fallback
-Use when context is sparse.
-
-```markdown
-# Starter meeting prep
-
-## Assumed meeting type
-- [Assumption based on user wording]
-
-## Useful objective
-- [Likely objective]
-
-## Questions to ask
-- [Senior generic question tailored to likely meeting type]
-
-## Context that would improve this most
-1. Meeting type and attendees
-2. Company/counterparty and objective
-3. Any pre-read, model, memo, deck, or prior notes
-4. Desired output length and audience
-
-## Caveat
-- This is a starter brief because no source materials were provided or available.
-```
-
-## 8. Source log
-Use when citations are unavailable or a traceable audit trail is needed.
-
-```markdown
-| Claim / input | Source | Source type | Date/version | Confidence | Notes |
-|---|---|---|---|---|---|
-```
-
-## 9. Existing-brief review
-Use when reviewing a draft prep packet.
-
-```markdown
-## Review summary
-- Overall readiness: [ready / needs work / not ready]
-- Biggest gap:
-- Biggest risk:
-- Highest-impact fix:
-
-## Recommended edits
-| Section | Issue | Proposed fix | Reason |
-|---|---|---|---|
-
-## Missing questions
-- [Question] - Why it matters:
-
-## Unsupported or stale claims
-| Claim | Issue | Source needed | Action |
-|---|---|---|---|
-```
diff --git a/plugins/financial-markets/skills/meeting-prep/references/question-and-evidence-bank.md b/plugins/financial-markets/skills/meeting-prep/references/question-and-evidence-bank.md
deleted file mode 100644
index ae9dd4f..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/question-and-evidence-bank.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# Question and evidence-request bank
-
-## Table of contents
-1. Question design rules
-2. Question matrix format
-3. Universal finance question categories
-4. Evidence request patterns
-5. Weak questions to avoid
-6. Escalation prompts
-
-## 1. Question design rules
-Good meeting questions are pointed, evidence-seeking, and decision-linked. For every key question, know why it matters.
-
-Use this hierarchy:
-1. decision-critical questions;
-2. questions that validate or invalidate the thesis;
-3. questions that expose downside, fraud, data quality, or execution risk;
-4. questions that clarify process, ownership, or timing;
-5. background questions only if needed.
-
-Avoid asking questions that can be answered from available sources unless the goal is to test consistency, credibility, or judgment.
-
-## 2. Question matrix format
-Use a matrix when the user needs rigor:
-
-| Priority | Topic | Question | Why it matters | Good answer | Concern answer | Evidence to request | Follow-up |
-|---:|---|---|---|---|---|---|---|
-
-For shorter prep, use bullets with "why it matters" after each question.
-
-## 3. Universal finance question categories
-
-### Business model and market
-- What are the two or three drivers that most explain growth over the last 12-24 months?
-- Which customer segments or channels are growing fastest, and which are deteriorating?
-- What competitor behavior has changed most recently?
-- What is the clearest evidence that the market is expanding versus the company taking share?
-
-### Revenue quality
-- How much growth is volume, price, mix, new logo, retention, upsell, usage, or acquisition driven?
-- What portion of revenue is recurring, contracted, usage-based, transactional, seasonal, or non-recurring?
-- Where are bookings, backlog, ARR/MRR, pipeline, or revenue recognition not comparable across periods?
-- What customer or contract cohorts are performing materially better or worse than plan?
-
-### Customer and sales motion
-- What are the largest customer concentration risks and renewal dates?
-- What is the sales cycle by segment, and how has it changed?
-- What are win rates, loss reasons, discounting, implementation time, and ramp times?
-- Where is pipeline quality strongest and weakest?
-
-### Margins and operating leverage
-- What explains margin movement: price, mix, labor, input cost, utilization, cloud/hosting, procurement, freight, or one-time items?
-- Which costs are truly variable versus fixed or step-function?
-- What cost actions are available in a downside case, and what growth would they impair?
-- Where are current margins above or below normalized potential?
-
-### Cash flow, working capital, capex, and liquidity
-- What drove cash conversion relative to EBITDA or net income?
-- Which working-capital movements are timing versus structural?
-- What capex is maintenance versus growth?
-- What liquidity stress case has management modeled, and what actions are available?
-
-### Non-GAAP and adjustment quality
-- Which adjustments are recurring, discretionary, one-time, run-rate, or unsupported?
-- Which revenue or cost items are subject to recognition, deferral, capitalization, or comparability judgment?
-- How do reported EBITDA, adjusted EBITDA, Street EBITDA, and internal-case EBITDA differ?
-- Which adjustments would fail under auditor, SEC, investor, or Credit Markets or equity-investor scrutiny?
-
-### Valuation, returns, and downside
-- What assumptions drive most of the valuation or return outcome?
-- What is the breakeven revenue, margin, exit multiple, leverage, or cost of capital?
-- What downside scenario is realistic rather than punitive?
-- What would make the investment unfinanceable, unattractive, or non-actionable?
-
-### Management credibility and incentives
-- What has management consistently under- or over-forecasted?
-- What incentives could bias the story?
-- What examples show operational discipline, customer insight, or capital allocation judgment?
-- What would management do differently with unlimited capital versus constrained capital?
-
-### Process and decision mechanics
-- Who is the decision-maker, who can block, and what is the approval path?
-- What is the next decision gate and what evidence is needed before it?
-- What are the timing constraints, confidentiality issues, and communication rules?
-- What would cause the process to stop?
-
-## 4. Evidence request patterns
-Separate asks by timing and burden.
-
-### Pre-meeting asks
-Use for documents that materially improve the call:
-- latest filing, earnings release, transcript, investor presentation, KPI supplement, guidance bridge, or model output;
-- customer concentration, cohort, churn, renewal, backlog, bookings, or pipeline data;
-- debt schedule, covenant/indenture extracts if public or user-provided, liquidity forecast, capex plan, credit rating report, or maturity schedule;
-- management bios, org chart, sales pipeline, product roadmap, litigation/regulatory summary when relevant and source-backed.
-
-### In-meeting asks
-Use for judgment, explanation, and prioritization:
-- what changed, why it changed, and how management knows;
-- which assumptions management has the least confidence in;
-- which issues are most likely to surprise investors, PMs, credit investors, or the committee;
-- what evidence management would provide to prove the claim.
-
-### Post-meeting asks
-Use for follow-through:
-- specific support file, source data, bridge, tie-out, or owner;
-- deadline and format;
-- who is responsible;
-- what decision the ask supports.
-
-## 5. Weak questions to avoid
-Replace generic questions with specific ones.
-
-- Weak: "What keeps you up at night?"
-  Better: "Which KPI in the latest forecast has the widest downside range, and what operational trigger would tell us by mid-quarter that plan is at risk?"
-
-- Weak: "How is the pipeline?"
-  Better: "What percent of next-quarter pipeline is committed versus best case, how does that compare with the last four quarters, and what is the conversion rate by segment?"
-
-- Weak: "Can you talk about margins?"
-  Better: "How much of the 300 bps margin improvement is price/mix versus one-time cost actions, and which portion should we underwrite as recurring?"
-
-- Weak: "What are the risks?"
-  Better: "What is the single assumption in our base case you would be least comfortable underwriting, and what data would change your view?"
-
-## 6. Escalation prompts
-Escalate or caveat when questions touch:
-- material non-public information, selective disclosure, or trading restrictions;
-- privileged, confidential, or competitively sensitive information;
-- legal, regulatory, antitrust, sanctions, tax, accounting, or audit issues requiring specialist review;
-- employee, customer, patient, or personal data;
-- topics that should not be asked in an expert call or external meeting.
diff --git a/plugins/financial-markets/skills/meeting-prep/references/safety-and-integrations.md b/plugins/financial-markets/skills/meeting-prep/references/safety-and-integrations.md
deleted file mode 100644
index 3a1be93..0000000
--- a/plugins/financial-markets/skills/meeting-prep/references/safety-and-integrations.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# Safety and integrations
-
-## Table of contents
-1. Non-destructive artifact rules
-2. Internal versus external outputs
-3. Sensitive finance and compliance topics
-4. Integration with other skills
-5. Editing and export guidance
-6. Final quality checks
-
-## 1. Non-destructive artifact rules
-Default to preserving source artifacts.
-
-Do:
-- create a new meeting brief, notes section, slide, tab, tracker, or memo section;
-- append comments or speaker notes instead of replacing content;
-- create a copy before broad rewrites;
-- provide a change log for edits;
-- preserve formulas, source links, hidden tabs, comments, footnotes, and existing citations.
-
-Do not:
-- delete source data, notes, formulas, slides, sections, citations, or trackers unless explicitly asked;
-- overwrite a live model, deck, memo, research tracker, or action tracker without confirmation;
-- hide uncertainty by rewriting assumptions as facts;
-- modify numbers for style or simplicity;
-- remove inconvenient risks, caveats, or open questions from senior-facing prep.
-
-## 2. Internal versus external outputs
-Choose a circulation mode.
-
-### Internal strategy mode
-Use for internal research team, investment team, PM, risk committee, credit committee, or client-prep work. It may include candid views, concerns, pushback strategy, valuation sensitivity, evidence gaps, risk limits, and what not to share.
-
-### External-clean mode
-Use for client, counterparty, management, issuer, investor, expert, or advisor-facing materials. Remove internal strategy, valuation bottoms, trading intent, position sizing, restricted-list issues, privileged mental impressions, sensitive internal messages, and anything not appropriate to circulate.
-
-### Review mode
-Use when the user provides a draft. Flag where the draft mixes internal and external content, creates record risk, overstates facts, lacks evidence, or buries a key decision.
-
-## 3. Sensitive finance and compliance topics
-Flag and handle carefully:
-- material non-public information, selective disclosure, or trading restrictions;
-- insider lists, wall-crossing, restricted lists, or confidential process information;
-- expert-call topics that could solicit prohibited information;
-- antitrust, competition, sanctions, export controls, tax, accounting, audit, employment, privacy, healthcare, insurance, bank regulatory, or legal issues needing specialist review;
-- personal data, customer-level data, patient data, or employee data;
-- trading intent, internal thesis, position sizing, hedge strategy, or restricted-list issues that should not be written externally.
-
-When in doubt, preserve the issue as an escalation item and draft clean questions that seek observable, non-restricted facts.
-
-## 4. Integration with other skills
-Use the relevant companion skill rather than duplicating deep analysis:
-- `financial-source-of-truth`: source hierarchy, stale-data checks, citations, and fact/assumption labeling.
-- `company-tearsheet`: baseline public company, issuer, listed fund/manager, peer, or counterparty overview.
-- `style-guide-adapter`: firm/client tone, precedent format, and clean presentation style.
-- `memo-builder`: decision memo or committee memo based on prep.
-- `deck-report-qc`: presentation materials and pre-read review.
-- `model-audit-tieout`: model integrity, formulas, hardcodes, and source tie-outs.
-- `scenario-sensitivity-generator`: downside, upside, breakeven, and stress-test questions.
-- Public Equity Investing vertical skills: use earnings, model update, valuation, event-driven equity, thesis-tracking, risk/sizing, hedge, macro-impact, or `sector-context-overlay` when the meeting is part of those workflows.
-
-## 5. Editing and export guidance
-When the user wants an artifact:
-- For a doc or memo: create a new document or section; preserve citations and source log.
-- For slides: create meeting-ready talking points, speaker notes, or appendix pages; do not distort template style.
-- For spreadsheets: add a new "Meeting Prep" or "Action Tracker" tab; do not change calculations unless explicitly asked.
-- For email/chat: draft the message and wait for user review unless the environment explicitly allows sending and the user instructs it.
-- For research/thesis/action trackers: propose updates or create an importable tracker; avoid writing directly to production records unless explicitly asked.
-
-## 6. Final quality checks
-Before finalizing, confirm:
-- the output is usable five minutes before the meeting;
-- top three questions are not generic;
-- open asks are tied to a decision;
-- facts are sourced and assumptions are labeled;
-- no internal-only content appears in an external-clean output;
-- no source artifact was overwritten;
-- follow-ups have owner and due date when available;
-- missing sources are stated rather than hidden.
diff --git a/plugins/financial-markets/skills/memo-builder/SKILL.md b/plugins/financial-markets/skills/memo-builder/SKILL.md
deleted file mode 100644
index 2d58ea7..0000000
--- a/plugins/financial-markets/skills/memo-builder/SKILL.md
+++ /dev/null
@@ -1,144 +0,0 @@
----
-name: memo-builder
-description: Use when drafting or reviewing formal public-equity investment memos. Do not use for live trade construction or credit-first memos; use long-short-pitch or Credit Markets as appropriate.
----
-
-# Memo Builder
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML investment memo. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Goal
-
-Produce decision-grade Public Equity Investing memos that are evidence-led, numerically auditable, and ready for PM, committee, client, or internal research review.
-
-This skill owns formal written artifacts and synthesis. It does not own raw trade construction; use `long-short-pitch` first when the user needs variant perception, trade expression, sizing considerations, add/trim/exit/cover rules, or pair-trade mechanics.
-
-## Use When
-
-Use this skill when the user asks for:
-
-- IC memo, investment memo, committee note, client memo, research note, PM update, or equity event note
-- memo rewrite, upgrade, shortening, pressure test, or QA review
-- synthesis of outputs from models, comps, scenarios, earnings, event-driven equity, balance-sheet-risk, or long/short pitch work
-- committee-ready framing with source posture, valuation, scenarios, risks, catalysts, open items, and recommendation
-
-Credit-first memos route out: Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis. This skill may synthesize Credit Markets output only when the final deliverable is a common-equity or listed-equity memo.
-
-Do not use when the user asks to "pitch this long/short," "make this investable," "build a pair trade," "what is the variant perception," or "what would make us cover/add/trim." Use `long-short-pitch`.
-
-## Artifact Boundary
-
-- Default deliverable: produce the full decision-grade memo. For a substantive investment committee memo, buy-side investment memo, or reusable/source-heavy public-equity memo, produce a polished standalone HTML investment committee memo following `../../shared/html-artifact-standard.md` unless the user requests another surface, a quick/no-file answer, `.docx`, or a standardized dashboard.
-- For other substantial written memo modes, produce a polished standalone HTML memo or report appropriate to the requested audience, following `../../shared/html-artifact-standard.md`, unless another surface is selected.
-- Chat can be the presentation surface only when the user explicitly requests a lightweight response; Markdown is formatting, not the deliverable contract.
-- This local skill does not ship native `.docx` or `.xlsx` templates/scripts. Route workbook-backed outputs to the model/workbook skills.
-- Create `.docx` or `.xlsx` artifacts only when the user explicitly asks and an appropriate document/spreadsheet tool, template, or user-provided file is available.
-- If no document/template support is available, provide the memo in chat or HTML report form and keep JSON/Markdown support files out of the lead handoff unless explicitly requested.
-- Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render. On that path, load `references/DASHBOARD_PACK.md`; `memo-builder` owns memo synthesis, narrative architecture, evidence selection, and audience framing, while `dashboard-builder` owns the standardized shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input.
-
-## Source And Mode Selection
-
-Use the strongest available source tier and label assumptions, stale data, proxies, and unsupported figures. Load `references/source-policy.md` for source priority and minimum inputs.
-
-Default to the full memo mode that best fits the decision. Use a compressed mode only when the user explicitly asks for a `summary`, `short`, `quick`, `one-page`, `brief`, `TL;DR`, or review-only output:
-
-- `ic-note`
-- `investment-memo`
-- `event-driven-committee-note`
-- `pm-update`
-- `client-research-note`
-- `qa-review`
-
-Load `references/memo-modes.md` for mode selection and `references/output-contracts.md` for section-level contracts.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` for source/data/QC/style support. Use `financial-source-of-truth` before final claims, metrics, citations, or management assertions. Use `financials-normalizer` before building from messy financials, consensus exports, guidance tables, segment data, share count, net debt, or capital allocation inputs. Use `excel-data-cleaner` before relying on malformed tables or mixed fiscal periods. Use `deck-report-qc` before circulating a memo-derived deck/report or source-heavy packet, and use `style-guide-adapter` only after substance is locked. These support artifacts stay secondary; `memo-builder` owns the memo and investment judgment.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: evidence, model/valuation, risks and catalysts, narrative synthesis, and memo QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Default Memo Spine
-
-Use this full spine unless another memo mode is clearly requested:
-
-1. `Recommendation / Decision Ask`
-2. `Executive Summary`
-3. `Thesis and Evidence`
-4. `What Must Be True`
-5. `Valuation / Scenario Work`
-6. `Risks, Disconfirmers, and Mitigants`
-7. `Catalysts and Monitoring`
-8. `Implementation Considerations`
-9. `Open Items / Data Requests`
-
-If the memo is screen-grade because source context is thin, say so before the first metric table.
-
-## Handoffs
-
-Upstream inputs this skill can synthesize:
-
-- source/evidence: `financial-source-of-truth`, `financials-normalizer`, `company-tearsheet`
-- models/valuation: `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `scenario-sensitivity-generator`
-- earnings/research: `earnings-preview`, `earnings-deep-dive`, `initiating-coverage`
-- trade/event/risk: `long-short-pitch`, `event-driven-analyzer`, `portfolio-risk-management`
-- monitoring and QC: `thesis-tracker`, `catalyst-calendar`, `deck-report-qc`, `style-guide-adapter`
-
-Use `sector-context-overlay` only when issuer economics clearly match a supported sector; load `references/sector-overlays.md` only for memo-specific sector framing.
-
-## Rendering And Compression
-
-- Lead with the decision ask or conclusion.
-- For a standalone HTML investment committee memo, make the first-read layer a compact memo opening: recommendation / decision ask, decision hinge, what is priced in, valuation or scenario skew, and any source limitation that changes the action. Do not repeat the same recommendation across a dashboard-style hero, summary tile, and full decision panel.
-- Keep background proportionate; preserve recommendation, scenario/downside, source posture, catalysts, monitoring, and open items.
-- Use tables only when they compress decision-critical information.
-- For long memos, put source caveats and open items near the end, but do not bury data limitations that affect the recommendation.
-- Sparse source context should produce a full screen-grade memo skeleton with assumptions, missing evidence, and upgrade path, not a shortened memo.
-- State source readiness in decision-specific terms when evidence supports only one direction of action, such as `sufficient to decline initiation today; insufficient to support initiation`, rather than using broad readiness labels that imply the entire underwrite is decision-grade.
-- Keep citations traceable but readable in HTML: do not fragment tickers, prices, EPS ranges, percentages, dates, multiples, or metric labels into separately linked tokens.
-- Before delivering standalone local HTML, visually inspect the opening viewport and decision-critical downstream sections through local headless-browser screenshots, not the in-app Browser plugin. Check desktop and narrow-screen legibility, clipping, citation density, whitespace, and that the page does not acquire horizontal overflow.
-
-## QA
-
-Before finalizing, verify:
-
-- recommendation and decision hinge are clear
-- material numbers are sourced, model-derived, or explicit assumptions
-- downside is mechanistic, not generic
-- `what must be true` items are measurable
-- valuation/pricing/recovery support matches memo type
-- a multi-year valuation using forward-period earnings and an exit or terminal multiple shows either present value using an explicit discount-rate / required-return assumption or annualized return / IRR versus a stated hurdle; undiscounted terminal price appreciation alone does not support initiation
-- disconfirmers and monitoring triggers are present
-- trade-construction asks are handed to `long-short-pitch`
-
-Load `references/quality-workflow.md` for the full quality bar and workflow.
-
-## Public Equity PM Judgment Layer
-
-For substantial memos, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Memo modes to support: `buy-side-investment-memo`, `pm-update`, `sell-side-research-note`, `client-research-note`, `etf-index-diligence-note`, and `public-equity-diligence-memo`.
-
-Required PM judgment:
-- Every full memo needs a decision hinge, what must be true, what is priced in, downside mechanism, measurable disconfirmers, source posture, and action discipline.
-- Build-from-scratch memo mode must include an intake checklist, source packet requirements, first-pass house view, variant wedge, priced-in debate, estimate path, valuation/skew, downside mechanism, catalysts, disconfirmers, action rules, and exact evidence needed to upgrade from screen-grade to decision-grade.
-- Sell-side notes need rating/target-price debate, estimate revision bridge, variant versus Street, risk-to-rating, and compliance-safe wording.
-- ETF/index notes need mandate, methodology, holdings/weights, tracking-error or benchmark implications, factor exposure, liquidity, and rebalance/corporate-action risk.
diff --git a/plugins/financial-markets/skills/memo-builder/agents/openai.yaml b/plugins/financial-markets/skills/memo-builder/agents/openai.yaml
deleted file mode 100644
index 2626234..0000000
--- a/plugins/financial-markets/skills/memo-builder/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: Public Equity Investing Memo Builder
-  short_description: Draft and review formal Public Equity Investing memos, committee notes, PM updates, and research notes.
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/memo-builder/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/memo-builder/references/DASHBOARD_PACK.md
deleted file mode 100644
index fa89fd2..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Memo Builder Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render for a memo, committee note, PM update, client-style research packet, or source-heavy written artifact. An ordinary substantive memo should remain a polished standalone HTML memo following `../../../shared/html-artifact-standard.md`.
-
-## Producer Role
-
-`memo-builder` owns memo synthesis, narrative architecture, argument quality, evidence selection, and audience framing. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `memo_builder`
-- `layout`: `single_page` for full memo/report packages unless the user explicitly requests tabs
-- `hero.callout`: the memo's main decision, recommendation, or debate
-- `snapshot`: recommendation/research posture, thesis status, valuation or risk skew if supportable, next catalyst, top unresolved issue, source confidence
-- `sources`: user materials, primary filings/releases/transcripts, model outputs, market/consensus data, and assumptions separated
-- Raw JSON, Markdown notes, CSV exports, and run logs are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `memo-summary`
-   - `executive_summary`: full memo summary with recommendation, evidence, caveats, and audience-specific read
-   - `decision_box`: decision, action posture, thesis change, estimate/model effect, and next catalyst
-2. `argument`
-   - `cards`: core arguments, evidence, counterarguments, and decision implications
-   - `table`: key metrics, valuation, model changes, or source-backed debate items
-3. `scenarios-risks`
-   - `scenario_map`: upside/base/downside, risk/reward, or decision cases
-   - `cards`: risks, mitigants, red-team points, and falsifiers
-4. `catalysts-monitoring`
-   - `timeline`: catalysts, review cadence, action triggers, and open decisions
-   - `market_events`: recent source-backed events that affect the memo
-5. `evidence`
-   - `missing_evidence`: unresolved source conflicts, stale data, unavailable model support, and open questions
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every material number, claim, event, valuation input, estimate, and model-derived value.
-- Label facts, assumptions, PM judgment, management claims, model output, stale values, and unsupported claims.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not convert an ordinary standalone HTML investment committee memo into a dashboard merely because it is substantial or source-heavy.
-- Do not use dashboard layout to change the memo's thesis or audience.
-- Do not create `.docx` or `.xlsx` artifacts unless the user explicitly asks and the right tool/template is available.
-- Do not make raw JSON, Markdown notes, or CSV sidecars the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm thesis, counterarguments, source confidence, risks, catalysts, and unresolved evidence are visible.
-- Confirm citation rendering remains readable and does not fragment ticker symbols, price or EPS ranges, multiples, percentages, or dates.
-- Confirm memo substance remains owned by `memo-builder`.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/memo-builder/references/memo-modes.md b/plugins/financial-markets/skills/memo-builder/references/memo-modes.md
deleted file mode 100644
index 44ce9b0..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/memo-modes.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Public Equity Investing Memo Modes
-
-Choose the full memo mode that fits the decision by default. Use a compressed mode only when the user explicitly asks for a short, quick, one-page, brief, TL;DR, or review-only output.
-
-| Mode | Use when | Posture |
-|---|---|---|
-| `ic-note` | explicitly requested compact committee discussion or one-page ask | decision-forward, tight, source-aware |
-| `investment-memo` | formal public equity, event-driven equity, PM update, client research, or sector memo | complete narrative, valuation, scenarios, risks, catalysts |
-| `event-driven-committee-note` | M&A arb, spin, litigation, regulatory, restructuring, index, tender, special situation | probability, timing, spread, break price, milestones |
-| `pm-update` | post-print, news, price move, thesis drift, model update, catalyst change | what changed, implication, action options, monitoring |
-| `client-research-note` | polished external or semi-external communication | clear narrative, source caveats, less trade-instruction-heavy |
-| `screen-grade-scratch-memo` | ticker/theme/sparse prompt with no complete source packet | build-from-scratch scaffold with provisional house view, source gaps, and upgrade path |
-| `qa-review` | review an existing memo | findings first, severity ordered |
-
-Escalation rules:
-
-- If the prompt asks for short/brief/one-page/first-pass, use `ic-note` unless a fuller mode is necessary.
-- If the user provides thin context and no source pack, produce a full screen-grade memo structure with visible assumptions and evidence requests.
-- If the user provides only a ticker, theme, headline, or partial thesis, use `screen-grade-scratch-memo`: provide the intake checklist, source packet requirements, first-pass house view, variant wedge, what is priced in, estimate path, valuation/skew, downside mechanism, catalysts, disconfirmers, action rules, and evidence needed to upgrade the memo.
-- Compress background first; do not cut recommendation, scenarios, downside, catalysts, monitoring, or open items.
-
-## Public Equity Memo Modes
-
-Add `buy-side-investment-memo`, `pm-update`, `sell-side-research-note`, `client-research-note`, `etf-index-diligence-note`, and `public-equity-diligence-memo`. Each full memo must include decision hinge, what must be true, what is priced in, downside mechanism, measurable disconfirmers, source posture, and action discipline.
diff --git a/plugins/financial-markets/skills/memo-builder/references/output-contracts.md b/plugins/financial-markets/skills/memo-builder/references/output-contracts.md
deleted file mode 100644
index ca8c361..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/output-contracts.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Memo Builder Output Contracts
-
-## Default Full Memo Spine
-
-1. `Recommendation / Decision Ask`
-2. `Executive Summary`
-3. `Thesis and Evidence`
-4. `What Must Be True`
-5. `Valuation / Scenario Work`
-6. `Risks, Disconfirmers, and Mitigants`
-7. `Catalysts and Monitoring`
-8. `Implementation Considerations`
-9. `Open Items / Data Requests`
-
-## `investment-memo`
-
-1. recommendation and decision context
-2. company / security / situation snapshot
-3. thesis claims with evidence, KPIs, falsifiers, and time-to-truth
-4. financial and operating setup
-5. valuation and scenario analysis
-6. catalyst path and monitoring plan
-7. risk register and downside case
-8. implementation considerations and constraints
-9. open items, data requests, and source caveats
-
-### Standalone HTML Investment Committee Memo
-
-For a substantive buy-side investment committee memo delivered as HTML, use a polished standalone memo layout rather than a dashboard shell. The first read should be compact and decision-forward:
-
-1. recommendation / decision ask and investability posture
-2. decision hinge and what is priced in
-3. valuation or scenario skew, including downside where supportable
-4. source posture or evidence limitation when it changes the action
-
-Continue with the full memo spine below that opening layer. Do not repeat the same recommendation through a hero callout, snapshot tile, and a second full recommendation panel. Use one clear decision block and reserve tables, scenario cards, and monitoring sections for information that advances the underwrite.
-
-When a valuation case relies on forward-period earnings and an exit or terminal multiple over a multi-year horizon, show either discounted present value using an explicit discount rate / required-return assumption or annualized return / IRR against a stated hurdle. Do not present undiscounted terminal value appreciation as sufficient expected-return support for initiation. If evidence supports waiting but not buying, state the posture plainly: sufficient to decline initiation today, insufficient to support initiation.
-
-For HTML citations, cite material figures and claims near their use without fragmenting tickers, prices, EPS ranges, percentages, dates, multiples, or metric labels into separately linked tokens. Visually inspect local HTML with local headless-browser screenshots before delivery, including the opening viewport, valuation/scenario work, and a narrow-screen view.
-
-
-## `event-driven-committee-note`
-
-1. recommendation and event setup
-2. spread, consideration, timing, and break price
-3. probability tree and expected value
-4. regulatory, legal, shareholder, financing, or process milestones
-5. downside and timing risk
-6. monitoring and decision gates
-7. open items
-
-## `pm-update`
-
-1. `What changed`
-2. `Why it matters`
-3. `Model / valuation impact`
-4. `Thesis impact`
-5. `Action options`
-6. `Monitoring / next catalyst`
-7. `Open items`
-
-If `action options` require trade expression, sizing, add/trim/exit/cover rules, or pair mechanics, hand off to `long-short-pitch`.
-
-## `screen-grade-scratch-memo`
-
-Use when the user wants a memo but supplies only sparse context. Do not collapse into a blank template. Produce:
-
-1. `Source Posture And Intake Checklist`
-2. `Minimum Source Packet Required`
-3. `First-Pass House View`
-4. `Variant Wedge`
-5. `What Is Priced In`
-6. `Estimate Path`
-7. `Valuation / Scenario Skew`
-8. `Downside Mechanism`
-9. `Catalysts And Time-To-Truth`
-10. `Disconfirmers`
-11. `Action Rules`
-12. `Evidence Needed To Upgrade From Screen-Grade`
-
-The associate gets the build path; the seasoned PM layer must still say what matters, what is missing, and what would change the decision.
-
-## `qa-review`
-
-Return critical findings first, ordered by severity, covering missing sections, weak logic, sourcing risks, downside gaps, catalyst gaps, and memo-readiness verdict: `ready`, `ready with conditions`, or `not ready`.
-
-## Support Table Shapes
-
-Use compact tables for:
-
-- scenario summary: base/upside/downside, probability, price target, expected return, or key valuation bridge, key drivers
-- risk register: risk, mechanism, severity, mitigant, monitoring trigger
-- source posture: claim, source, as-of, confidence, gap
-- what-must-be-true: claim, evidence, KPI, falsifier, timing
-
-## PM Judgment Sections
-
-Every full memo should include `Decision Hinge`, `What Must Be True`, `What Is Priced In`, `Downside Mechanism`, `Measurable Disconfirmers`, `Action Discipline`, and `Missing Evidence`. Sell-side notes include rating/target-price debate and estimate revision bridge. ETF/index notes include mandate, methodology, holdings/weights, tracking-error or benchmark implications, factor exposure, liquidity, and rebalance/corporate-action risk.
diff --git a/plugins/financial-markets/skills/memo-builder/references/quality-workflow.md b/plugins/financial-markets/skills/memo-builder/references/quality-workflow.md
deleted file mode 100644
index 9c76170..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/quality-workflow.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Memo Builder Quality And Workflow
-
-## Quality Bar
-
-1. Lead with a clear recommendation or decision ask.
-2. State the primary decision hinge in plain English.
-3. Include base, upside, and downside scenario framing when recommending action.
-4. Make downside mechanistic: `Shock -> Transmission -> Constraint -> Outcome`.
-5. Include measurable `What must be true` items tied to monitoring triggers.
-6. Tie every important number to a source, model output, or explicit assumption.
-7. Separate thesis drivers from upside-only hopes.
-8. Do not rely only on multiple expansion, market recognition, or rerating language.
-9. Include valuation, pricing, expected-value, or common-equity recovery read-through support appropriate to the memo; route credit recovery work to Credit Markets.
-10. For a multi-year valuation based on forward-period earnings and an exit or terminal multiple, show discounted present value with an explicit discount rate / required-return assumption or annualized return / IRR against a stated hurdle. Do not treat undiscounted terminal price appreciation alone as sufficient initiation support.
-11. Disclose quality caveats for adjusted EBITDA, adjusted EPS, adjusted FCF, or non-GAAP KPIs.
-12. Include disconfirming evidence and what would change the view.
-13. If uncertainty changes the decision, show action options, staging, hedges, smaller size, or data requests.
-14. Express asymmetric readiness plainly when relevant: the evidence may be sufficient to decline initiation today while remaining insufficient to support initiation.
-
-## Workflow
-
-1. Classify request type and memo mode.
-2. Decide whether this is a formal memo or actually a trade pitch. If pitch, route to `long-short-pitch`.
-3. Run the source gate and label stale/missing/assumed data.
-4. Define the economic engine: public equity, equity-risk credit signal, event-driven, macro/sector, or PM update; route credit-first engines to Credit Markets.
-5. Convert narrative into 2-5 falsifiable thesis claims: `claim -> evidence -> implication -> KPI -> falsifier -> time-to-truth`.
-6. Choose one primary valuation/pricing method and 1-2 cross-checks.
-7. Build scenarios, return-hurdle framing for multi-year forward valuation, and downside mechanism.
-8. Build risk register, catalysts, and monitoring plan.
-9. Load `references/sector-overlays.md` only for matching sectors.
-10. Render the memo in the chosen mode.
-11. Run final QA and compression.
-
-## Strategy Requirements
-
-- Public equity memo: valuation bridge, scenario price targets or present values, return hurdle / IRR framing for multi-year scenarios, KPI drivers, revision/catalyst path, downside mechanism.
-- Credit-first memo: route to Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-- Event-driven memo: probability tree, expected value, spread, break price, milestone calendar, timing risk.
-- Macro/rates/commodity memo: factor driver, transmission path, sensitivity, hedge/portfolio interaction, catalyst calendar.
-- PM update: what changed, model/valuation impact, thesis impact, action options, next monitoring trigger.
-- Client/research note: polished narrative, source caveats, clear conclusion, audience-appropriate trade detail.
-
-## PM Memo QA
-
-No recommendation should pass without downside mechanism, source posture, measurable disconfirmers, and an explicit action classification. Penalize generic good-company or cheap-stock prose that does not identify the variant wedge and what is priced in.
diff --git a/plugins/financial-markets/skills/memo-builder/references/sector-overlays.md b/plugins/financial-markets/skills/memo-builder/references/sector-overlays.md
deleted file mode 100644
index bacc406..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/sector-overlays.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# Memo Builder Sector Overlays
-
-Load only the overlay that matches the prompt. These overlays sharpen formal Public Equity Investing memo judgment; they do not replace the core memo workflow.
-
-## Software / Subscription Equity Memo
-Use for public software, SaaS, payments software, developer-platform software, cybersecurity, vertical software, or other recurring-revenue equity memos.
-
-### What a senior reviewer cares about
-- whether growth durability is proven or merely narrated
-- whether retention, expansion, pricing, seat/module growth, and churn support the valuation
-- whether margins reflect durable operating leverage or temporary cost timing
-- whether FCF conversion is real after SBC, capitalized software, restructuring, and working-capital timing
-- whether consensus revisions are more important than headline valuation
-
-### Must-have memo facts
-- revenue growth, ARR/MRR if available, NRR, gross retention, cRPO/bookings/billings if material
-- gross margin, EBITDA margin, FCF margin, SBC load, net cash/debt, diluted share count
-- peer valuation and the company's own trading history
-- guidance, consensus setup, and next catalyst window
-- model sensitivity to growth, margin, and terminal multiple assumptions
-
-### Common failure modes
-- rerating story without revision path
-- using EBITDA without quality caveats
-- no reconciliation of market cap to enterprise value
-- product narrative without economic evidence
-
-## Consumer Internet / Platform Memo
-Use for marketplaces, ad platforms, consumer internet, fintech platforms, e-commerce platforms, gig economy, and network-effect businesses.
-
-### What a senior reviewer cares about
-- whether engagement, monetization, retention, take rate, or unit economics drive the thesis
-- whether platform quality is improving or masking saturation
-- whether margin improvement is durable or caused by temporary spending cuts
-- whether regulation, platform dependency, or consumer behavior changes alter the value case
-
-### Must-have memo facts
-- MAU/DAU, payer count, transactions, GMV, AOV, take rate, conversion, retention, or monetization metrics as applicable
-- segment performance between mature, declining, and growth assets
-- revenue growth, incremental margin, FCF, and marketing efficiency
-- consensus expectations and next print/catalyst sensitivity
-- valuation bridge and scenario price targets
-
-### Common failure modes
-- broad platform language with no KPI proof
-- ignoring healthy segment offsets in a bearish memo
-- no catalyst path
-- no downside mechanism if growth or engagement disappoints
-
-## Credit Markets Handoff / Equity-Risk Memo Context
-Use only when credit data supports a common-equity or listed-equity memo. Credit-first committee notes, public-credit memos, bond/loan/CDS recommendations, covenant reviews, recovery waterfalls, restructuring views, and debt-security selection route to Credit Markets.
-
-### What a senior equity reviewer cares about
-- whether liquidity runway, maturity walls, refinancing risk, rating pressure, or CDS/spread signals change equity downside
-- whether debt pressure alters the valuation bridge, catalyst path, sizing, hedge posture, or kill case
-- what evidence comes from public-equity sources versus Credit Markets handoff output
-
-### Must-have local memo facts
-- common-equity implication and downside mechanism
-- source/as-of date for credit signal data used as evidence
-- explicit Credit Markets handoff when the memo depends on security selection, covenants, recovery, restructuring, or debt valuation
-
-### Common failure modes
-- writing a credit memo inside the Public Equity skill
-- implying covenant, recovery, or spread/yield expertise without Credit Markets support
-- using credit stress as a conclusion without linking it to equity impairment or estimate risk
-
-## Event-Driven / Special Situation Memo
-Use for merger arbitrage, spin-offs, litigation, regulatory approvals, restructurings, tender offers, index events, and other discrete-event memos.
-
-### What a senior reviewer cares about
-- expected value after probability, timing, and break downside
-- market-implied probability versus memo probability
-- whether legal, regulatory, shareholder, financing, and process milestones are mapped
-- what evidence changes the probability before the final event
-
-### Must-have memo facts
-- current spread or price
-- consideration, timing, outside date, and break price
-- probability tree and expected value
-- approval and milestone calendar
-- downside if the event fails
-- source posture for legal/regulatory assumptions
-
-### Common failure modes
-- treating management confidence as probability
-- no timing drag
-- no break price
-- missing interim milestones
-
-## Macro / Sector Memo
-Use for rate, commodity, FX, policy, factor, or sector-level memos that need to tie macro views to public securities.
-
-### What a senior reviewer cares about
-- the transmission mechanism from macro view to security-level earnings, valuation, credit risk, or factor exposure
-- whether the memo is actionable or just descriptive
-- sensitivity to rates, FX, commodity, inflation, policy, or factor shifts
-- portfolio interaction, correlation, and hedge implications
-
-### Must-have memo facts
-- driver and transmission path
-- exposed securities, sectors, or factors
-- sensitivity table or qualitative stress map
-- catalyst calendar
-- disconfirming data
-
-### Common failure modes
-- broad macro call with no security implication
-- no timing or catalyst
-- no sensitivity to alternative macro outcomes
-- no risk-management implication
diff --git a/plugins/financial-markets/skills/memo-builder/references/source-policy.md b/plugins/financial-markets/skills/memo-builder/references/source-policy.md
deleted file mode 100644
index f7c2e50..0000000
--- a/plugins/financial-markets/skills/memo-builder/references/source-policy.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Memo Builder Source Policy
-
-Use this reference after `memo-builder` is selected.
-
-## Source Priority
-
-1. Connected or institutional-grade sources available in the session.
-2. Company filings, releases, transcripts, investor decks, and official reported financials.
-3. Market-data or consensus feeds available to the user.
-4. User-provided models, notes, and research.
-5. Web search or public summaries as a labeled fallback.
-
-Rules:
-
-- Prefer primary documents over summaries and summaries over commentary.
-- Disclose fallback, proxy, stale, or substitution figures.
-- Keep fact, inference, model output, and assumption distinct.
-- If the memo is screen-grade, say so before the first metric table.
-- Create an `Open Items / Data Requests` section when important information is missing.
-
-## Minimum Inputs
-
-- Issuer, security, sector, instrument, or theme.
-- Memo type and intended audience.
-- Decision question: initiate, add, trim, exit, hold, underwrite, monitor, or explain.
-- Current price, spread, yield, valuation reference, or relevant event spread, plus as-of date.
-- Time horizon or decision window.
-- Thesis drivers and known risks.
-- Source pack or source posture.
-
-## Build-From-Scratch Source Packet
-
-For `screen-grade-scratch-memo`, request or label the absence of: latest filing/release/transcript, investor presentation, consensus snapshot, price/market-cap/EV as-of, share count, net debt, segment/KPI history, valuation comps or DCF source, short interest/borrow where relevant, ownership/index/ETF exposure, catalysts, prior house view, and known disconfirming evidence. Missing fields do not block a screen-grade memo, but they cap readiness and must appear in open items.
-
-## Public Equity Source Discipline
-
-Filings, releases, transcripts, proxies, and company IR lead for facts. Consensus, live price, short interest, borrow, options, ETF holdings, index rules, liquidity, and ownership require as-of dates. Separate fact, management claim, consensus, market data, model output, assumption, and PM judgment.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/SKILL.md b/plugins/financial-markets/skills/model-audit-tieout/SKILL.md
deleted file mode 100644
index 4ebc855..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/SKILL.md
+++ /dev/null
@@ -1,149 +0,0 @@
----
-name: model-audit-tieout
-description: Use when auditing existing Public Equity Investing models or spreadsheets. Do not use to build a new model from scratch.
----
-
-# Model Audit Tie-Out
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-## Internal Support
-
-When the audit requires evidence control, generic data cleaning, dashboard rendering, style application, or sector context, route that support through the visible `public-equity-investing` router and its bundled internal playbooks. This visible skill remains the owner of the model-review deliverable.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML model-audit report, while direct remediation preserves the source workbook format. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive standalone review of an existing model or explicit HTML model-audit request, the default resolves the presentation surface to a polished standalone HTML model-audit report unless the user requests an alternate surface, a quick/no-file answer, remediation output, or a standardized dashboard. In interactive runs, ask only remaining material choices such as depth, audience/use, materiality, or focus; in non-interactive runs, default to the HTML model-audit report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Review an existing public-equity model, workbook, forecast, valuation file, estimate update, comps workbook, sensitivity deck, or model-derived output and produce a decision-useful audit pack. This skill is a QA layer for formula integrity, source support, assumption hygiene, scenario quality, controls, and decision readiness.
-
-Do not rebuild the model unless the user explicitly asks for remediation. Default to an audit memo, issue log, source tie-out ledger, and prioritized fix list.
-
-The headline output is an audit verdict on permitted use: `Ready`, `Ready with caveats`, `Not ready`, or `Not assessable`. This skill does not turn an unsupported model into an investment recommendation. When required valuation, scenario, or market-price outputs are absent, state that the model or model package cannot support portfolio action until remediated and re-audited.
-
-## Core Principles
-
-- Review before changing; never overwrite formulas, assumptions, outputs, or tabs unless asked.
-- Separate model mechanics from investment judgment.
-- Tie material outputs to workbook tabs/cells, source documents, or explicit assumptions.
-- Prioritize by decision impact, not issue count.
-- Escalate unsupported decision drivers even when formulas are mechanically clean.
-- Distinguish a broken model from an undocumented or unsupported underwriting assumption, a stale forecast, a missing source bridge, and a missing decision-output layer.
-- A three-statement operating model need not itself contain target price or valuation; for portfolio-decision use, require a linked valuation/scenario decision output in the model package before concluding that forecasts support action.
-- Label auditor-created stresses as `Illustrative audit sensitivity` unless the user explicitly approves them as revised base-case assumptions.
-
-## Workflow
-
-1. **Confirm audit mandate.** Identify model type, intended model-package scope, decision context, materiality threshold, and requested output. Determine whether the workbook is expected to contain decision outputs or to feed a separate linked valuation/scenario layer.
-2. **Run workbook inspection when available.** For `.xlsx` workbooks, use the helper as a starting point:
-
-```bash
-python scripts/audit_workbook.py path/to/model.xlsx --out-dir audit_output
-```
-
-Dependency note: the helper requires `openpyxl`; install the local dependency file in a fresh environment:
-
-```bash
-python -m pip install -r scripts/requirements.txt
-```
-
-3. **Map key outputs.** Trace valuation, target-price, estimate, earnings, event, risk, or common-equity downside outputs back to assumptions, formulas, source tabs, and source documents.
-4. **Apply formula and workbook controls.** Check inconsistent formulas, hardcodes, broken links, hidden sheets, volatile functions, bypassed assumption tabs, circularity, plugs, unsupported checks, and formulas that do not tie to schedules.
-5. **Review first-tab decision readiness.** Flag missing or thin first-visible `Cover` tabs when net read, key outputs, scenario metrics, model status, source posture, warnings/hard failures, chart-ready data, and workbook navigation are buried.
-6. **Tie assumptions to evidence.** Use `references/tieout-and-source-checks.md` and `financial-source-of-truth` standards to label primary facts, claims, estimates, assumptions, inferences, and unsupported items.
-7. **Review scenarios and downside.** Test whether scenarios focus on true value/risk drivers and whether downside captures liquidity/refinancing risk as common-equity impairment, multiple compression, estimate cuts, cash-burn dilution, or event failure as relevant. Treat auditor-created replacement assumptions or stress math as diagnostic illustrations, not repaired model forecasts.
-8. **Build issue log.** Include severity, finding type, category, location, finding, decision impact, recommended fix, and owner. Use reader-facing finding types: `Formula/control defect`, `Source contradiction`, `Unsupported assumption`, `Missing forecast refresh`, `Missing decision output`, or `Not comparable without bridge`.
-9. **Deliver audit pack.** Choose rapid screen or full audit pack based on user need.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: workbook controls, formula review, source tie-out, scenario/downside review, and issue prioritization. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Output Contract
-
-**Rapid screen**
-
-- model health score;
-- top issues by severity;
-- decision readiness;
-- immediate fixes.
-
-**Full audit pack**
-
-Default substantial workbook audits to a reader-facing `model_audit_report.html`, produced as a polished standalone HTML model-audit report following `../../shared/html-artifact-standard.md`, plus an optional `model_audit_issues.xlsx` companion workbook. Keep `model_audit_findings.json`, `formula_exception_log.csv`, `source_tieout_ledger.csv`, raw workbook maps, and support notes behind the HTML/workbook unless the user asks for audit files. In the default delivery message, link only the primary HTML report and any requested user-facing workbook; mention supporting audit files without linking them unless the user asks.
-
-- executive summary;
-- readiness posture;
-- issue log;
-- formula/workbook controls;
-- source tie-out findings;
-- assumption and scenario critique;
-- sensitivity review;
-- remediation sequence;
-- open evidence requests;
-- appendix with formula exceptions and tie-out ledger.
-
-Use `dashboard-builder` and `references/dashboard-map.md` only when the user explicitly requests a standardized dashboard, reusable validated template, model-health cockpit, remediation tracker, or structured payload-driven render. A substantial ordinary audit should remain a flexible standalone HTML report rather than a fixed dashboard-module package.
-
-## HTML Guidance
-
-For a substantive standalone HTML model-audit report, load `../../shared/html-artifact-standard.md` and use this audit-specific hierarchy:
-
-1. Audit verdict and permitted use: whether the model may support the stated decision, the top blocker, the scope reviewed, and any limitations on recalculation or source verification.
-2. Critical blockers and what appears mechanically sound, separated clearly so a linked workbook is not confused with an underwritten forecast.
-3. Decision-output assessment: whether a linked valuation/scenario layer, current-price anchor, estimate bridge, or downside view exists and is supportable for the requested use.
-4. Priority issue log with workbook location, finding type, decision impact, and remediation.
-5. Source tie-out findings, diagnostic sensitivities, controls, remediation sequence, evidence requests, and method/limitations.
-
-Additional rules:
-
-- Keep the headline as an audit verdict. When decision outputs are absent or unreliable, say `Do not use for portfolio action until remediated and re-audited`; do not imply an `add`, `trim`, `exit`, `hedge`, or `wait for proof` recommendation from an audit-only mandate.
-- Classify decision-relevant findings as `Formula/control defect`, `Source contradiction`, `Unsupported assumption`, `Missing forecast refresh`, `Missing decision output`, or `Not comparable without bridge`; do not make every unsupported forecast assumption sound like a formula error.
-- Display auditor-created cash, leverage, EPS, valuation, or scenario stresses as `Illustrative audit sensitivity`; do not represent a diagnostic stress as a corrected forecast or revised base case.
-- When reviewing an operating or three-statement model, distinguish absence of valuation inside that workbook from absence of a linked valuation/scenario decision output in the overall package. The latter blocks portfolio-decision use when required by the mandate; the former is not automatically a model defect.
-- Do not feature a current-price or market-data anchor in the verdict, headline KPI row, or decision-output assessment unless the cited source has been verified for the correct security and as-of date; if it cannot be verified, state that the external price anchor is unavailable.
-- Cite critical findings with workbook sheet/cell or range plus controlling source location where available. Keep source/as-of posture and limitations such as cached outputs versus native recalculation visible.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, legibility, table density, citation rendering, and whether the audit verdict and top fixes are immediately visible.
-
-## Handoffs
-
-- `financial-source-of-truth`: evidence hierarchy, stale/conflicting data, citations, labels.
-- `excel-data-cleaner`: malformed tables or unclear row/column grain before audit.
-- Model/research builders: rebuild, refresh, expand, or stress-test after audit.
-- `memo-builder`: convert audit findings into a decision memo.
-- `deck-report-qc`: reconcile audited model outputs to decks, board packs, IC memos, or client presentations.
-
-## Resources
-
-- `scripts/audit_workbook.py`: static workbook inspection helper.
-- `scripts/requirements.txt`: workbook helper dependency declaration.
-- `references/audit-playbook.md`: audit modes by model type.
-- `references/formula-and-workbook-controls.md`: formula and architecture checks.
-- `references/tieout-and-source-checks.md`: source tie-out process and evidence labels.
-- `references/issue-taxonomy.md`: severity and issue categories.
-- `references/output-templates.md`: audit memo, issue log, tie-out ledger, and IC-ready templates.
-- `references/dashboard-map.md`: dashboard mapping for model health, formula exceptions, source tie-outs, and readiness.
-- `references/p0-integrations.md`: coordination with launch skills.
-
-## Final QA
-
-Confirm severity is tied to decision impact, model issues are separated from evidence issues, audit sensitivities are identified as illustrative rather than repaired forecasts, missing decision-layer scope is not confused with a formula defect, source/as-of dates are visible, unsupported assumptions are not buried, remediation steps are concrete, and no user workbook content was destructively changed.
-
-## Equity Valuation PM Standard
-
-Load `shared/equity-valuation-pm-standard.md` and `shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, what changes target, rating, sizing, hedge, trim, exit, or watchlist status, and what evidence is missing.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instrument underwriting, or distressed claim valuation.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/agents/openai.yaml b/plugins/financial-markets/skills/model-audit-tieout/agents/openai.yaml
deleted file mode 100644
index 6975177..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Model Audit Tie-out"
-  short_description: "Audit financial models for formulas, tie-outs, assumptions, sensitivities, and IC-ready QA."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/audit-playbook.md b/plugins/financial-markets/skills/model-audit-tieout/references/audit-playbook.md
deleted file mode 100644
index e9c8f14..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/references/audit-playbook.md
+++ /dev/null
@@ -1,142 +0,0 @@
-# Model Audit Playbook
-
-## Audit modes
-
-### Rapid screen
-Use when the user wants a quick view of model health, likely issues, or readiness for a meeting.
-
-Deliver:
-- model purpose and inferred decision context
-- health score: green, yellow, red, or not assessable
-- top 5 issues by decision impact
-- must-fix items before use
-- open questions and missing files
-
-### Formula integrity audit
-Use when the user asks whether formulas are right, whether a workbook is broken, or whether formulas tie across tabs.
-
-Check:
-- formulas copied consistently across rows and periods
-- formulas using source/assumption cells rather than embedded constants
-- external workbook links and stale links
-- hidden or very hidden tabs
-- volatile formulas: today, now, rand, offset, indirect, info, cell
-- circular references or iterative-calculation dependencies
-- balance sheet, cash flow, debt schedule, and covenant checks
-- formulas that point to blank cells or unused ranges
-- formulas overwritten by hardcoded values inside expected formula regions
-
-### Source tie-out audit
-Use when the user asks whether model values tie to filings, earnings documents, company supplements, market data, Credit Markets documents, provider exports, third-party data, or internal assumptions.
-
-Check:
-- each material historical number ties to a named source
-- each forecast driver has a source, bridge, or explicit assumption
-- stale market data and stale consensus are identified with as-of dates
-- issuer/management/provider claims are not treated as verified facts
-- conflicting sources are escalated rather than silently averaged
-- each final output has a source path from source document to model cell to final output
-
-### Scenario and sensitivity review
-Use when the user asks whether a model is appropriately stress-tested.
-
-Check:
-- base, downside, and upside are coherent cases, not isolated arbitrary toggles
-- sensitivity variables reflect the real value/risk drivers
-- downside includes plausible negative outcomes for the asset class
-- extreme cases do not break formulas or produce impossible outputs
-- output ranges are not presented with false precision
-- auditor-created replacement assumptions or stresses are labeled `Illustrative audit sensitivity` and are not represented as corrected forecasts or a revised base case
-
-### IC-ready QA
-Use when the model will support an investment memo, credit committee, client deck, board pack, or transaction decision.
-
-Combine:
-- workbook/formula audit
-- source tie-out and evidence labels
-- assumption critique
-- scenario and downside review
-- output traceability
-- issue log with severity and owners
-- decision-readiness posture
-
-## Model-specific audit focus
-
-### DCF / intrinsic value
-- historical actuals tie to filings or source documents
-- revenue, margin, capex, tax, working capital, and fcf assumptions are explicit
-- terminal growth and exit multiple assumptions are defensible and not duplicative
-- wacc, cost of equity, beta, risk-free rate, credit spread, and tax assumptions are sourced or clearly assumed
-- enterprise-to-equity bridge includes debt, cash, minority interest, preferred, pensions, leases, and other claims where material
-- sensitivity tables show the true drivers and do not overstate precision
-
-### Three-statement operating model
-- income statement, balance sheet, and cash flow statement link correctly
-- working capital, depreciation, capex, debt, taxes, and equity schedules are internally consistent
-- balance sheet balances in every period and checks are meaningful
-- cash flow statement ties to cash on the balance sheet
-- assumptions flow through all statements and do not create hidden plugs
-- determine whether valuation or scenario outputs are intended to be embedded or provided by a linked companion layer; do not classify missing target price inside an operating model as a mechanical defect without confirming package scope
-- when the stated use is an investment or portfolio decision, block decision readiness until a supportable linked decision layer exists
-
-### Event-driven / transaction model
-- transaction terms, consideration, dates, closing conditions, break fees, and spreads tie to primary documents or company releases
-- probability-weighted outcomes reconcile to deal terms, downside price, timing, borrow/carry, and expected return
-- regulatory, court, shareholder, financing, or other milestone assumptions are explicit and source-labeled
-- downside and break-price cases do not rely on stale unaffected prices or unsupported valuation assumptions
-- output return metrics tie to share price, consideration, timing, and probability assumptions
-- catalyst bridge separates definitive terms, market spread, timing, probability, downside, and risk controls
-
-### Comps / valuation range
-- peer universe is justified and not cherry-picked
-- market values, net debt, minority interest, preferred, leases, and other adjustments are current and sourced
-- ltm/ntm metrics are calendarized and normalized consistently
-- outliers are treated explicitly
-- implied valuation range is not presented as more precise than the peer set supports
-
-### Adjusted metrics / non-GAAP bridge
-- non-GAAP adjustments are source-supported and reconciled to reported metrics where possible
-- one-time, recurring, stock-based comp, restructuring, acquisition, FX, and pro forma adjustments are separated
-- company-defined metrics are not treated as verified economics without support
-- provider-standardized and analyst-adjusted views are not conflated
-- adjustments that materially change valuation, leverage, coverage, or thesis support are escalated
-
-### Credit Markets handoff / equity-risk debt context
-- debt capacity, leverage, interest coverage, fixed charge coverage, and liquidity are calculated consistently when they affect common-equity downside
-- covenant-pressure references cite the actual source definition, but covenant-package interpretation routes to Credit Markets
-- EBITDA add-backs and baskets are source-supported before they influence equity value, sizing, or risk
-- downside case tests debt service, liquidity, revolver usage, refinancing, and covenant-pressure risk as equity impairment inputs
-- collateral, guarantees, priority, and recovery assumptions are visible only as handoff inputs unless Credit Markets supplies the analysis
-
-### Restructuring / distressed-equity event context
-- debt stack, collateral, guarantees, maturity, priority, liens, and intercreditor mechanics are identified as Credit Markets handoff inputs
-- recovery waterfall, plan value, liquidation value, DIP, exit financing, fulcrum-security, and impaired-class analysis routes to Credit Markets
-- Public Equity may use supplied restructuring outputs only as dated-event payoff inputs, common-equity option value, or downside impairment context
-
-### Real estate / infrastructure / real assets
-- NOI, occupancy, rents, rent roll, capex, reserves, taxes, insurance, and leasing costs are sourced
-- DSCR, debt yield, LTV/LTC, and cap rate assumptions are current and supportable
-- tenant rollover, lease expirations, market rent, and occupancy risk are stress-tested
-- construction/project finance models include draw schedule, contingency, completion, and interest reserve logic
-
-### Macro, rates, fixed income, FX, commodities
-- prices, yields, curves, spreads, indices, and macro releases have as-of dates
-- duration, convexity, carry, roll-down, and spread assumptions are clear
-- base/downside/upside cases reflect coherent macro regimes
-- stale market data is not mixed with current market commentary
-- source conflicts across vendors, central bank data, and market feeds are flagged
-
-## Decision-readiness posture
-
-Use one of these labels:
-
-- **ready for decision:** no unresolved critical/high issues, material outputs have source support, assumptions and downside are clearly disclosed.
-- **ready with caveats:** usable if caveats are explicitly included in the memo/deck and listed fixes are not decision-changing.
-- **not ready:** critical or high issues block use for an ic, lender, client, or trading decision.
-- **not assessable:** missing workbook, key tabs, source documents, or outputs prevent reliable review.
-
-For an audit-only mandate, use readiness and permitted-use language. If decision outputs are missing or unreliable, state `Do not use for portfolio action until remediated and re-audited` rather than expressing an add, trim, exit, hedge, or wait-for-proof investment stance.
-
-## Public Equity Model Scope
-
-This skill audits equity models, public-company valuation files, estimate update files, comps workbooks, DCFs, three-statement models, and public-equity sensitivity decks. Credit, macro, and real-asset model sections are reviewed only when they feed a common-equity decision. Route primary credit-security valuation, covenant-package work, spread/yield analysis, recovery waterfall, distressed claim valuation, or debt comps to Credit Markets.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/dashboard-map.md b/plugins/financial-markets/skills/model-audit-tieout/references/dashboard-map.md
deleted file mode 100644
index 404f108..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/references/dashboard-map.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Dashboard Map: Model Audit Tie-Out
-
-Use `dashboard-builder` only when the user explicitly requests a standardized model-health dashboard, reusable validated template, model-health cockpit, remediation tracker, or structured payload-driven render. For an ordinary substantial workbook audit, produce a polished standalone HTML model-audit report following `../../../shared/html-artifact-standard.md`; finding count alone does not require a standardized dashboard.
-
-## Decision Question
-
-Is the uploaded or generated model reliable enough for the public-equity-investing decision, and what breaks confidence?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `model_audit_tieout`
-- `metadata.citation_policy`: `strict`
-- `model_citations_path`: include when audited outputs cite workbook cells
-- `sources`: include uploaded workbook, filings, releases, market data, consensus, and model-output ledgers where applicable
-
-## Recommended Tabs And Modules
-
-1. Overview: `decision_box`, `metric_tiles`, `missing_evidence`.
-2. Formula And Workbook Controls: `table`, `bar_chart`, `cards`.
-3. Source Tie-Out: `table`, `source_list`, `missing_evidence`.
-4. Scenario And Sensitivity Review: `scenario_map`, `table`.
-5. Remediation: `timeline`, `question_list`.
-
-## Required Evidence
-
-Every finding needs workbook location, evidence basis, why it matters, suggested fix, and owner route. Model-derived claims should cite workbook/sheet/cell/range via `model_citations` records.
-
-Use reader-facing finding types that keep mechanics separate from underwriting and scope: `Formula/control defect`, `Source contradiction`, `Unsupported assumption`, `Missing forecast refresh`, `Missing decision output`, and `Not comparable without bridge`. Label auditor-created stress calculations `Illustrative audit sensitivity`, not corrected forecast output.
-
-## Do Not
-
-- Do not mark the model decision-ready based on static inspection alone.
-- Do not bury missing model/source files in the bottom source ledger.
-- Do not lead with CSV/JSON support files when an HTML audit dashboard or issue workbook exists.
-- Do not treat the absence of valuation within a standalone operating model as a defect when a linked valuation/scenario layer may be the intended package design; determine whether the package lacks the decision output needed for its stated use.
-
-
-## Equity Model Audit Dashboard Modules
-
-Dashboards should surface PM decision readiness, current-price or target-price relevance, valuation/model output affected, issue severity by decision impact, source posture, scenario/downside weakness, action rules, and missing evidence.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/formula-and-workbook-controls.md b/plugins/financial-markets/skills/model-audit-tieout/references/formula-and-workbook-controls.md
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--- a/plugins/financial-markets/skills/model-audit-tieout/references/formula-and-workbook-controls.md
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-# Formula and Workbook Controls
-
-## Workbook architecture checks
-
-Check whether the workbook has a clear, reviewable architecture:
-- first visible tab is a decision-useful `Cover` or dashboard that surfaces key outputs, model status, source posture, warnings/hard failures, and a workbook map
-- assumptions and inputs are separated from calculations and outputs
-- historical actuals are separated from forecasts
-- source tabs are labeled and preserved
-- output tabs tie to calculation tabs
-- checks are visible and summarized
-- the workbook is not dependent on hidden tabs, external links, or undocumented macros
-
-## Formula integrity checks
-
-### Formula consistency
-Look for formulas that should copy across a row, column, or period but do not.
-
-Red flags:
-- one period has a different formula family from adjacent periods
-- one line item switches from formula to hardcode without explanation
-- copied formulas point to the wrong row, tab, or period
-- forward-year formulas still point to historical-year inputs
-
-### Hardcoded values inside formulas
-Hardcodes inside formulas can be acceptable for simple constants but should be reviewed when they drive material outputs.
-
-Usually acceptable if documented:
-- 0, 1, -1, 100, 1000, 12, 365, tax-rate conversions, basis-point conversions
-
-Review carefully:
-- margin percentages
-- growth rates
-- multiples
-- interest rates
-- debt terms
-- working capital assumptions
-- synergy, add-back, run-rate, or cost-out numbers
-- prices, shares, market caps, yields, spreads, or fx rates
-
-### External links
-External links are high-risk when they are stale, inaccessible, or undocumented.
-
-Escalate if:
-- the workbook refers to another workbook that was not uploaded
-- external-linked source data is used in final outputs
-- the link path suggests a personal drive or local machine
-- the linked file name implies a prior version, outdated model, or different transaction
-
-### Hidden and very hidden sheets
-Hidden sheets are not inherently wrong, but they must be explained when they contain assumptions, source data, formulas, or outputs.
-
-Escalate if:
-- a hidden sheet feeds key outputs
-- very hidden sheets exist
-- hidden sheets contain hardcoded assumptions or external-linked data
-- the workbook relies on hidden checks that are not summarized in visible tabs
-
-### Volatile formulas
-Volatile functions can make model outputs change unexpectedly.
-
-Review formulas using:
-- today
-- now
-- rand
-- randbetween
-- offset
-- indirect
-- cell
-- info
-
-### Circularity and iterative calculation
-Circular references are sometimes used intentionally in finance models, especially for interest expense and cash sweep logic. They still require documentation.
-
-Escalate if:
-- circularity is accidental
-- iterative calculation settings are unknown
-- the model breaks when circularity is disabled
-- interest, cash, debt, or working capital use opaque plugs
-
-## Control checks by schedule
-
-### Balance sheet
-- assets equal liabilities plus equity in every period
-- retained earnings ties to net income and dividends/distributions
-- cash ties to cash flow statement ending cash
-- debt schedule ties to balance sheet debt
-
-### Cash flow statement
-- net income ties to income statement
-- depreciation/amortization ties to capex/fixed asset schedules
-- working capital movements tie to balance sheet accounts
-- ending cash ties to balance sheet cash
-
-### Debt schedule
-- beginning debt plus draws minus repayments equals ending debt
-- interest rate and spread assumptions are correct by tranche
-- mandatory amortization, excess cash sweep, revolver draw/paydown, fees, and maturity are modeled
-- covenant calculations use defined terms, not generic EBITDA unless appropriate
-
-### Valuation and return outputs
-- enterprise value converts to equity value with all material claims included
-- per-share value uses correct diluted share count
-- irr and moic calculations use correct timing and equity invested
-- sensitivity tables link to the same output as the base case
-
-### Data tables and sensitivities
-- data tables actually change the intended input cells
-- row and column inputs are labeled clearly
-- sensitivity output cell is correct
-- sensitivities do not rely on stale manual pasted values
-
-## Recommended manual checks beyond the script
-
-The bundled script performs static inspection only. Always supplement it with judgment:
-- recalculate the workbook in Excel or another spreadsheet environment when possible
-- inspect key formulas directly in the workbook
-- trace precedents and dependents around material outputs
-- compare model outputs to deck/memo numbers
-- review formulas around recently edited cells
-- test downside cases and see whether checks still pass
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/issue-taxonomy.md b/plugins/financial-markets/skills/model-audit-tieout/references/issue-taxonomy.md
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-# Issue Taxonomy
-
-## Severity levels
-
-### Critical
-Use when the issue likely changes the public-equity investment decision, valuation range, liquidity conclusion, common-equity recovery read-through, or client recommendation; route credit-approval, covenant-package, and recovery-workflow issues to Credit Markets.
-
-Examples:
-- broken formula in final valuation or return output
-- debt schedule, covenant-pressure, or liquidity calculation materially wrong for common-equity downside; route covenant-package analysis to Credit Markets
-- key source value does not tie and changes the recommendation
-- issuer/management/provider claim treated as verified fact for a material driver
-- model omits a material claim in enterprise-to-equity bridge or common-equity recovery read-through; route recovery waterfalls to Credit Markets
-- final output uses stale market data that would materially change the answer
-
-### High
-Use when the issue may materially change the result or undermine confidence and should be fixed before ic, client, credit committee, or trading use.
-
-Examples:
-- formula inconsistency in a key forecast row
-- external links feed key outputs and source files are missing
-- material assumption lacks source support or sensitivity
-- downside case does not test relevant liquidity, covenant-pressure, margin, or exit risk
-- control checks are missing or inadequate for a key schedule
-
-### Medium
-Use when the issue matters for quality, traceability, or explanation but is unlikely to change the decision alone.
-
-Examples:
-- hardcoded constant inside a non-core formula
-- model lacks clear source labels for some historical inputs
-- sensitivity range is narrow but not central to the decision
-- output labels or units are ambiguous
-- hidden tabs exist but appear non-material
-
-### Low
-Use for minor cleanups that improve readability or handoff quality.
-
-Examples:
-- inconsistent formatting
-- unclear tab names
-- duplicated labels
-- minor rounding differences
-- incomplete but non-critical footnotes
-
-### Info
-Use for observations, optional improvements, or context.
-
-Examples:
-- model could benefit from a dashboard
-- add version-control notes
-- add optional sensitivities for presentation quality
-
-## Issue categories
-
-- **formula_integrity:** formula errors, overwritten formulas, inconsistent formulas, hardcodes, broken links, volatile formulas, circularity.
-- **source_tieout:** value does not tie to source, no source, stale source, conflicting sources, unclear source hierarchy.
-- **assumption_support:** key forecast or valuation driver lacks evidence, bridge, benchmark, or sensitivity.
-- **scenario_design:** base/downside/upside cases are incoherent, missing, too narrow, or not relevant to the decision.
-- **model_architecture:** workbook structure, tab flow, input/calculation/output separation, hidden sheets, macros, checks.
-- **output_presentation:** final outputs, dashboards, memo/deck values, units, labels, precision, footnotes.
-- **governance:** version control, review trail, source dates, signoffs, protected formulas, change log.
-- **formatting:** cosmetic or readability items.
-
-## Reader-Facing Finding Types
-
-Use one of these finding types for each decision-relevant issue so the report does not conflate mechanical errors, underwriting gaps, and package-scope omissions:
-
-- **Formula/control defect:** broken or misleading workbook mechanics, checks, links, formulas, or controls.
-- **Source contradiction:** a modeled value conflicts with a controlling source without a supported explanation.
-- **Unsupported assumption:** a forecast or valuation driver may be intentional but lacks evidence, approval, or sensitivity support.
-- **Missing forecast refresh:** new actuals, guidance, market data, or source developments have not been bridged into the forecast.
-- **Missing decision output:** the requested decision requires a linked valuation, scenario, price implication, or risk output not present in the reviewed package.
-- **Not comparable without bridge:** a model metric and disclosed metric use different definitions, periods, currencies, or adjustment bases.
-
-Label auditor-created replacement assumptions or stress calculations `Illustrative audit sensitivity`; they diagnose decision impact but do not constitute corrected model forecasts or a revised base case.
-
-## Owner defaults
-
-Assign likely owner when possible:
-- analyst: formula corrections, tie-outs, formatting, source labels
-- associate/vp: assumption logic, model architecture, scenario design, output framing
-- md/pm/ic member: decision posture, risk tolerance, recommendation, valuation/range choice
-- Credit Markets reviewer: covenant definitions, credit-approval standards, risk appetite, debt-security analysis
-- accounting/research lead: adjusted metrics, accounting treatment, restatement issues, reporting quality
-- issuer/management/provider: issuer claims, source support, provider extracts, missing evidence
-- legal/restructuring counsel: credit agreement, indenture, priority, intercreditor, bankruptcy plan issues
-- unknown: use only when owner is unclear
-
-## Escalation rules
-
-Escalate immediately when:
-- a critical issue affects a final model output
-- source documents are missing for a material claim
-- the model appears to use stale market data for a live decision
-- covenant-pressure, liquidity, debt capacity, or common-equity recovery read-through outputs are unsupported; route credit-security outputs to Credit Markets
-- the workbook contains hidden or external-linked data that drives outputs
-- formulas cannot be recalculated or reviewed
-- the user asks for a recommendation but the model is not decision-ready
-
-## Model health score
-
-Use this simple score for rapid screens:
-
-- **green:** no unresolved high/critical issues; model is reviewable and source-supported for the requested use.
-- **yellow:** some medium/high caveats; model can be used only with explicit caveats and targeted fixes.
-- **red:** high/critical issues block decision use.
-- **gray:** insufficient information to assess.
-
-## Equity Decision Severity Overlay
-
-Severity is driven by public-equity decision impact: wrong value/share, wrong EPS or FCF path, unsupported selected multiple, stale market data, broken source tie-out, false downside protection, or hidden model mechanics that would change target, rating, sizing, hedge, trim, exit, or watchlist status. Credit-specific issues route to Credit Markets unless they directly impair common-equity value.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/output-templates.md b/plugins/financial-markets/skills/model-audit-tieout/references/output-templates.md
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-# Output Templates
-
-## Rapid screen template
-
-```markdown
-# Model Audit Rapid Screen: [model/company/deal]
-
-## Readiness posture
-**Status:** [green/yellow/red/gray]
-**Decision use:** [ready / ready with caveats / not ready / not assessable]
-**Scope reviewed:** [workbook tabs, source docs, outputs, limitations]
-
-## Top issues
-| severity | issue | location | decision impact | fix |
-|---|---|---|---|---|
-| [critical/high/etc.] | [finding] | [tab/cell/source] | [impact] | [action] |
-
-## What appears solid
-- [area]
-- [area]
-
-## Must-fix before use
-1. [fix]
-2. [fix]
-3. [fix]
-
-## Open questions / missing files
-- [question]
-```
-
-## Full audit memo template
-
-```markdown
-# Model Audit Tie-out Memo: [model/company/deal]
-
-## Executive summary
-[3-6 bullets on model readiness, major issues, source support, and decision impact.]
-
-## Decision-readiness posture
-**Posture:** [ready for decision / ready with caveats / not ready / not assessable]
-**Reason:** [brief explanation]
-**Permitted use:** [use for decision / preliminary screen only / do not use for portfolio action until remediated and re-audited]
-**Reviewed for:** [ic, credit committee, client deck, research review, earnings, trading, board, etc.]
-**Materiality lens:** [what would change the decision]
-
-## Model overview
-| item | assessment |
-|---|---|
-| model type | [dcf/3-statement/comps/Credit Markets/event-driven/risk/etc.] |
-| workbook / files reviewed | [file names] |
-| key output(s) | [outputs] |
-| key tabs | [tabs] |
-| source documents reviewed | [sources] |
-| limitations | [what was not reviewed] |
-
-## Priority issue log
-| severity | finding_type | category | location | finding | why it matters | recommended fix | owner |
-|---|---|---|---|---|---|---|---|
-| [critical/high/etc.] | [Formula/control defect / Source contradiction / Unsupported assumption / Missing forecast refresh / Missing decision output / Not comparable without bridge] | [category] | [tab/cell/doc] | [finding] | [impact] | [fix] | [owner] |
-
-## Formula and workbook controls
-- **Formula consistency:** [findings]
-- **Hardcodes:** [findings]
-- **External links / hidden tabs:** [findings]
-- **Checks:** [findings]
-- **Circularity / volatility:** [findings]
-
-## Source tie-out findings
-| output_or_driver | model_location | model_value | source | source_value | tie_status | evidence_label | decision_impact |
-|---|---|---:|---|---:|---|---|---|
-| [driver] | [tab/cell] | [value] | [source] | [value] | [ties/etc.] | [label] | [impact] |
-
-## Assumption and scenario critique
-- **Base case:** [support and concerns]
-- **Downside case:** [support and concerns]
-- **Upside case:** [support and concerns]
-- **Sensitivities:** [true drivers vs missing drivers]
-- **Illustrative audit sensitivity:** [diagnostic stress clearly labeled as auditor-created; do not describe as the repaired base case]
-
-## Recommended remediation sequence
-1. [critical fix]
-2. [high fix]
-3. [medium fix]
-
-## Evidence requests
-- [source request]
-- [issuer/management/broker/provider/lender question]
-
-## Appendix: scope and method
-[Briefly describe workbook inspection, source documents reviewed, manual checks, and limitations.]
-```
-
-## Issue log row format
-
-Use this format for every issue. Finding type distinguishes broken mechanics from unsupported underwriting or missing package scope:
-
-```markdown
-| severity | finding_type | category | location | finding | why it matters | recommended fix | owner |
-|---|---|---|---|---|---|---|---|
-| high | Formula/control defect | formula_integrity | Debt Schedule!F42 | revolver paydown formula breaks in the downside case | understates liquidity trough and covenant pressure | correct formula across forecast periods and rerun downside | analyst |
-```
-
-## Formula exception format
-
-```markdown
-| sheet | cell | issue | formula/value | recommended review |
-|---|---|---|---|---|
-| [sheet] | [cell] | [hardcoded number in formula / external link / volatile function / inconsistent formula] | `[formula]` | [action] |
-```
-
-## Source tie-out ledger format
-
-```markdown
-| output_or_driver | model_location | model_value | source_name | source_location | source_value | tie_status | variance | evidence_label | as_of_date | decision_impact | recommended_action |
-|---|---|---:|---|---|---:|---|---:|---|---|---|---|
-| [driver] | [tab/cell] | [value] | [source] | [page/table] | [value] | [ties] | [variance] | [label] | [date] | [impact] | [action] |
-```
-
-## Decision-readiness language
-
-Use direct language:
-- "the model is not ready for ic use until the debt schedule and source tie-outs are fixed."
-- "the valuation output appears mechanically coherent, but the margin and terminal-value assumptions are assumption-led and need sensitivity support."
-- "the model can be used for a preliminary screen, but not for a final investment recommendation."
-- "do not use for portfolio action until remediated and re-audited."
-- "the Credit Markets handoff is blocked by missing covenant definitions and unsupported add-backs."
-
-For an audit-only mandate, lead with the audit verdict and permitted use rather than an investment stance. Use `add`, `trim`, `exit`, `hedge`, or `wait for proof` only when an investment decision output is actually being assessed.
-
-For operating and three-statement models, a linked valuation/scenario decision output may sit in a companion workbook or downstream package. Treat missing decision output as a package-readiness blocker when the stated use requires it; do not automatically treat absent target price inside the operating workbook as a formula or architecture defect.
-
-Avoid vague language:
-- "looks fine"
-- "probably okay"
-- "minor issues" when severity is unknown
-- "audited" unless a real audit was performed by qualified auditors
-
-## Follow-up remediation output
-
-When the user asks to fix issues after the audit, provide:
-
-```markdown
-# Remediation Plan
-
-## Changes I recommend making now
-1. [change]
-2. [change]
-
-## Changes requiring user/source confirmation
-1. [change]
-2. [change]
-
-## Changes I would not make without senior review
-1. [change]
-2. [change]
-
-## Files or data needed
-- [file]
-```
-
-If actually editing the workbook, preserve raw/source tabs where possible and document every changed cell/range.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/p0-integrations.md b/plugins/financial-markets/skills/model-audit-tieout/references/p0-integrations.md
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-# Public Equity Investing Skill Integrations
-
-## Role of model-audit-tieout
-
-This skill is the quality-control and review layer for existing public-equity financial models. It should not replace model-builder skills or Credit Markets. It should help decide whether a model is usable, what breaks, what does not tie, and what must be fixed before the output is used in an investment, client, PM, or committee decision.
-
-## Integration rules
-
-### financial-source-of-truth
-Use for source hierarchy, stale-data standards, citation format, evidence labels, assumption/fact separation, and source conflicts.
-
-Typical handoff:
-1. model-audit-tieout identifies key model drivers and unsupported or conflicting values.
-2. financial-source-of-truth establishes the controlling source and evidence posture.
-3. model-audit-tieout updates the issue log and tie-out ledger.
-
-### excel-data-cleaner
-Use before audit when the workbook or data extract is too messy to review reliably.
-
-Trigger examples:
-- source tabs have merged, duplicated, or ambiguous headers
-- row grain is unclear
-- imported data has mixed date/number formats
-- category labels are inconsistent
-- raw exports need normalization before tie-out
-
-### three-statement-model-builder
-Use after audit when an integrated operating model needs to be rebuilt, extended, or corrected.
-
-Audit focus before handoff:
-- balance sheet and cash flow checks
-- working capital, debt, capex, and tax logic
-- forecast driver support
-- scenario design
-
-### dcf-model-builder
-Use after audit when a valuation model needs to be built or remediated.
-
-Audit focus before handoff:
-- source-supported historicals
-- forecast assumption support
-- wacc and terminal value logic
-- ev-to-equity bridge
-- valuation sensitivity table integrity
-
-### comps-valuation
-Use when the model audit finds issues in peer selection, market data, valuation multiples, calendarization, or implied valuation ranges.
-
-Audit focus:
-- peer rationale
-- market cap, enterprise value, net debt, shares, and currency
-- ltm/ntm metric source and normalization
-- outlier treatment
-- valuation range precision
-
-### equity-model-update
-Use after audit when a public-company model needs to be refreshed from new actuals, guidance, consensus, transcripts, filings, KPIs, share count, net debt, or market data.
-
-Audit focus:
-- actuals and estimate bridge
-- guidance and consensus as-of dates
-- segment and KPI definitions
-- share count, net debt, and EV bridge
-- model change log and source support
-
-### Credit Markets
-Use after audit when credit-instrument, distressed, refinancing, maturity wall, recovery-waterfall, covenant-package, or capital-structure analysis needs to be corrected or expanded outside this plugin.
-
-Public Equity audit focus before handoff:
-- debt schedule and maturity wall as common-equity risk inputs
-- liquidity runway
-- covenant-pressure disclosure and source support
-- recovery read-through or valuation-cushion assumptions
-- CDS/spread/yield/price signals and source timestamps
-
-Route covenant-package interpretation, debt-security valuation, recovery waterfall, and credit memo work to Credit Markets.
-
-### memo-builder
-Use after audit to turn findings into a decision memo or IC section.
-
-Audit output to pass forward:
-- readiness posture
-- unresolved critical/high issues
-- key source-supported findings
-- assumptions requiring sensitivity
-- decision-impact caveats
-- open evidence requests
-
-### earnings-preview and earnings-deep-dive
-Use when the model supports pre-earnings or post-earnings analysis.
-
-Audit focus:
-- consensus and guidance as-of dates
-- quarter/period alignment
-- reported vs adjusted metrics
-- KPI definitions
-- model update bridge
-- transcript/source support
-
-### economic-impact-report
-Use when a market, macro, regulatory, geopolitical, rate, commodity, or policy shock drives model changes.
-
-Audit focus:
-- current market data
-- scenario coherence
-- second-order effects
-- exposure mapping
-- stale assumptions after the event
-
-### event-driven-analyzer and economic-impact-report
-Use when the model supports event-driven, special-situation, macro, policy, rate, commodity, FX, or cross-asset analysis.
-
-Audit focus:
-- transaction or event terms
-- probability and scenario math
-- timing, gating milestones, and downside
-- market/macro assumptions and source timestamps
-
-### deck-report-qc
-Use when model outputs appear in a deck, report, memo, board pack, or client deliverable.
-
-Audit focus before handoff:
-- model output values to deck values
-- units, periods, currency, and rounding
-- footnotes and source citations
-- chart labels and narrative consistency
-
-## Recommended P0 workflow examples
-
-### Public equity valuation review
-1. financial-source-of-truth validates filings, market data, consensus, and transcript support.
-2. model-audit-tieout audits 3-statement, DCF, and comps model outputs.
-3. dcf-model-builder, three-statement-model-builder, equity-model-update, or comps-valuation remediates if needed.
-4. memo-builder, long-short-pitch, or earnings-deep-dive uses only decision-ready outputs.
-
-### Credit Markets review
-1. financial-source-of-truth establishes source hierarchy for filings, credit docs, ratings, market data, and lender materials.
-2. model-audit-tieout audits debt schedule, covenant, liquidity, recovery, and downside calculations.
-3. Credit Markets prepares any credit-instrument memo, covenant-package review, or recovery waterfall.
-4. deck-report-qc reconciles the committee or client deck/report to the model.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/references/tieout-and-source-checks.md b/plugins/financial-markets/skills/model-audit-tieout/references/tieout-and-source-checks.md
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-# Tie-out and Source Checks
-
-## Purpose
-
-A model audit is incomplete unless material outputs and assumptions can be traced to reliable evidence. Use this reference to build a source tie-out ledger and identify whether the model is fact-supported, assumption-led, or unsupported.
-
-When available, use financial-source-of-truth for the source hierarchy, stale-data rules, source conflict handling, and fact/assumption labels.
-
-## Tie-out workflow
-
-1. Identify key outputs.
-2. Trace each output to the model tab, cell, and formula path.
-3. Identify the assumptions, source rows, and source documents that feed the output.
-4. Label each driver by evidence type.
-5. Flag missing, stale, conflicting, or unsupported evidence.
-6. State whether the output is decision-grade, research-grade, preliminary, assumption-led, or not supportable.
-
-## Evidence labels
-
-Use canonical labels consistently:
-
-- **fact_source_reported:** directly supported by a source document or connected system.
-- **fact_provider_standardized:** provider-standardized data from trusted market/financial data vendors.
-- **derived_calculation:** calculated from cited inputs with visible formula or bridge.
-- **issuer_management_claim:** statement from issuer management, earnings call, investor presentation, conference deck, or management interview.
-- **management_adjusted:** management-defined adjusted metric or add-back.
-- **analyst_adjusted:** analyst normalization, add-back, reclass, or pro forma change.
-- **analyst_interpretation:** reasoned conclusion based on facts but not directly stated by a source.
-- **assumption_user_provided:** assumption explicitly supplied by the user.
-- **assumption_inferred:** assumption inferred from incomplete context.
-- **estimate_consensus:** consensus/provider forecast or estimate.
-- **stale_source:** source may be superseded for the current decision.
-- **contradicted_source:** source conflicts with another material source.
-- **missing_required_source:** no reliable support found or provided for a required item.
-- **unknown:** evidence type cannot be determined.
-
-## Source tie-out ledger fields
-
-Use these fields for full audits:
-
-| field | purpose |
-|---|---|
-| output_or_driver | material output, assumption, or line item being tested |
-| model_location | workbook, tab, cell/range, formula, or memo/deck location |
-| model_value | value shown in the model |
-| source_name | filing, company supplement, transcript, Credit Markets document, market data source, model tab, or document |
-| source_location | page, section, table, note, exhibit, or citation |
-| source_value | value from the source |
-| tie_status | ties, ties_with_rounding, does_not_tie, unsupported, stale, conflicting, not_tested |
-| variance | difference between source and model value |
-| evidence_label | evidence type label |
-| as_of_date | date the source value was current |
-| decision_impact | low, medium, high, or critical |
-| recommended_action | fix, explain, sensitivity-test, source request, or escalate |
-
-## Tie status definitions
-
-- **ties:** model value matches source exactly or within immaterial rounding.
-- **ties_with_rounding:** variance explained by rounding, scaling, currency conversion, or period convention.
-- **does_not_tie:** variance is unexplained or material.
-- **unsupported:** no source has been provided or identified.
-- **stale:** source may have been valid historically but is not current for the decision.
-- **conflicting:** two or more sources disagree and the model does not explain the chosen source.
-- **not_tested:** outside current scope or source unavailable.
-
-## Staleness rules by source type
-
-Treat stale data as a model issue when it feeds a material decision driver.
-
-Examples:
-- market price, fx rate, yield, spread, index level, commodity price, public-equity multiple: must have a current as-of date.
-- consensus estimates and broker data: must identify estimate date and data provider/source.
-- financial statements: must identify fiscal period and whether annual data is audited or quarterly/interim.
-- company presentations, conference decks, Credit Markets materials, rating reports, and provider exports: must identify document date, provider/as-of date, and whether a newer version exists.
-- credit agreement, indenture, lease, purchase agreement, or court filing: must identify execution or filing date and whether amendments exist.
-
-## Source conflict handling
-
-When sources conflict:
-1. Do not silently average or choose the source that supports the thesis.
-2. Rank sources using financial-source-of-truth or the best available hierarchy.
-3. Show the competing values and source dates.
-4. Explain likely reasons for the variance: period, currency, scope, accounting treatment, pro forma adjustment, share count, denominator definition, or stale data.
-5. Mark decision impact.
-6. Recommend a tie-out fix or evidence request.
-
-## Common tie-out failure patterns
-
-- LTM metrics do not match the period used in comps or leverage calculations.
-- EBITDA or adjusted metric definition differs across valuation, credit, covenant, and source tabs.
-- Net debt excludes leases, preferred, minority interest, pensions, or earnouts inconsistently.
-- Shares outstanding use basic share count in one tab and diluted share count in another.
-- Market data is current in one tab but stale in another.
-- Issuer add-backs or adjusted metrics are treated as verified economics without support.
-- Model output ties to deck but neither ties to source documents.
-- Forecast assumptions are presented as sourced facts.
diff --git a/plugins/financial-markets/skills/model-audit-tieout/scripts/audit_workbook.py b/plugins/financial-markets/skills/model-audit-tieout/scripts/audit_workbook.py
deleted file mode 100755
index 38cfa6f..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/scripts/audit_workbook.py
+++ /dev/null
@@ -1,930 +0,0 @@
-#!/usr/bin/env python3
-"""
-Static workbook inspection helper for the model-audit-tieout skill.
-
-This script does not calculate Excel formulas. It inventories workbook structure,
-formulas, visible/hidden sheets, hardcodes inside formulas, external links,
-volatile functions, and simple formula-family inconsistencies. Use the output as
-an audit starting point, then apply financial-modeling judgment manually.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import re
-import sys
-from collections import Counter, defaultdict
-from dataclasses import asdict, dataclass
-from datetime import UTC, date, datetime
-from pathlib import Path
-from typing import Any, Iterable
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[3]
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from shared.artifact_packager import (  # noqa: E402
-    artifact_item,
-    dict_rows_to_sheet,
-    logs_dir,
-    support_dir,
-    write_artifact_manifest,
-    write_cover_first_workbook,
-    write_dashboard_contract,
-    write_report_html,
-)
-
-VOLATILE_FUNCTIONS = [
-    "TODAY",
-    "NOW",
-    "RAND",
-    "RANDBETWEEN",
-    "OFFSET",
-    "INDIRECT",
-    "CELL",
-    "INFO",
-]
-
-ERROR_LITERALS = ["#DIV/0!", "#N/A", "#NAME?", "#NULL!", "#NUM!", "#REF!", "#VALUE!"]
-COMMON_CONSTANTS = {0, 1, -1, 2, -2, 10, 12, 52, 100, 365, 360, 1000, 1000000}
-
-CELL_REF_RE = re.compile(r"(?<![A-Z0-9_])\$?[A-Z]{1,3}\$?\d+(?![A-Z0-9_])")
-RANGE_RE = re.compile(r"\$?[A-Z]{1,3}\$?\d+\s*:\s*\$?[A-Z]{1,3}\$?\d+", re.IGNORECASE)
-SHEET_REF_RE = re.compile(r"(?:'[^']+'|[A-Za-z0-9_ ]+)!\$?[A-Z]{1,3}\$?\d+", re.IGNORECASE)
-EXTERNAL_REF_RE = re.compile(
-    r"\[[^\]]+\]|\.xlsx|\.xlsm|\.xls|http[s]?://|\\\\|/[A-Za-z0-9_. -]+/", re.IGNORECASE
-)
-NUMERIC_LITERAL_RE = re.compile(r"(?<![A-Za-z0-9_\$])[-+]?\d+(?:\.\d+)?%?(?![A-Za-z0-9_])")
-DATE_HINT_RE = re.compile(
-    r"\b(as of|updated|last updated|date|period|source date|market data)\b", re.IGNORECASE
-)
-
-
-@dataclass
-class WorkbookMapRow:
-    sheet: str
-    state: str
-    max_row: int
-    max_column: int
-    used_cells: int
-    formula_cells: int
-    constant_cells: int
-    blank_cells_in_used_range: int
-    merged_ranges: int
-    freeze_panes: str
-    has_tables: bool
-    has_auto_filter: bool
-
-
-@dataclass
-class FormulaRow:
-    sheet: str
-    cell: str
-    formula: str
-    normalized_formula: str
-    formula_length: int
-    cached_value: str
-    has_external_ref: bool
-    volatile_functions: str
-    has_error_literal: bool
-    hardcoded_numbers: str
-
-
-@dataclass
-class IssueRow:
-    severity: str
-    category: str
-    sheet: str
-    cell_or_range: str
-    finding: str
-    why_it_matters: str
-    recommended_next_step: str
-
-
-def stringify(value: Any) -> str:
-    if value is None:
-        return ""
-    if isinstance(value, (datetime, date)):
-        return value.isoformat()
-    return str(value)
-
-
-def is_formula(value: Any) -> bool:
-    return isinstance(value, str) and value.startswith("=")
-
-
-def strip_strings(formula: str) -> str:
-    # Replace double-quoted strings to avoid false positives in formulas.
-    return re.sub(r'"(?:[^"]|"")*"', '"STR"', formula)
-
-
-def normalize_formula(formula: str) -> str:
-    s = strip_strings(formula.upper())
-    s = RANGE_RE.sub("RANGE", s)
-    s = SHEET_REF_RE.sub("SHEET!REF", s)
-    s = CELL_REF_RE.sub("REF", s)
-    s = re.sub(r"\s+", "", s)
-    return s
-
-
-def volatile_functions(formula: str) -> list[str]:
-    found = []
-    upper = formula.upper()
-    for fn in VOLATILE_FUNCTIONS:
-        if re.search(r"\b" + re.escape(fn) + r"\s*\(", upper):
-            found.append(fn)
-    return found
-
-
-def extract_hardcoded_numbers(formula: str) -> list[str]:
-    s = strip_strings(formula.upper())
-    # Remove cell refs/ranges first to avoid row numbers appearing as numeric literals.
-    s = RANGE_RE.sub(" RANGE ", s)
-    s = SHEET_REF_RE.sub(" SHEET_REF ", s)
-    s = CELL_REF_RE.sub(" REF ", s)
-    raw = NUMERIC_LITERAL_RE.findall(s)
-    values = []
-    for item in raw:
-        cleaned = item.rstrip("%")
-        try:
-            num = float(cleaned)
-        except ValueError:
-            continue
-        if num.is_integer():
-            comp = int(num)
-        else:
-            comp = num
-        if comp in COMMON_CONSTANTS:
-            continue
-        # Keep percentages and non-common numbers; these are often assumptions.
-        values.append(item)
-    # Deduplicate while preserving order.
-    seen = set()
-    result = []
-    for val in values:
-        if val not in seen:
-            seen.add(val)
-            result.append(val)
-    return result
-
-
-def write_csv(path: Path, rows: Iterable[dict[str, Any]], fieldnames: list[str]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    with path.open("w", newline="", encoding="utf-8") as f:
-        writer = csv.DictWriter(f, fieldnames=fieldnames)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow(row)
-
-
-def add_issue(
-    issues: list[IssueRow],
-    severity: str,
-    category: str,
-    sheet: str,
-    cell_or_range: str,
-    finding: str,
-    why: str,
-    next_step: str,
-) -> None:
-    issues.append(
-        IssueRow(
-            severity=severity,
-            category=category,
-            sheet=sheet,
-            cell_or_range=cell_or_range,
-            finding=finding,
-            why_it_matters=why,
-            recommended_next_step=next_step,
-        )
-    )
-
-
-def scan_workbook(
-    workbook_path: Path,
-) -> tuple[list[WorkbookMapRow], list[FormulaRow], list[IssueRow], dict[str, Any]]:
-    try:
-        from openpyxl import load_workbook
-    except Exception as exc:  # pragma: no cover
-        print("ERROR: openpyxl is required to run this script.", file=sys.stderr)
-        print("Install with: python -m pip install -r scripts/requirements.txt", file=sys.stderr)
-        print(str(exc), file=sys.stderr)
-        sys.exit(2)
-
-    wb_formula = load_workbook(workbook_path, data_only=False, keep_links=True)
-    try:
-        wb_values = load_workbook(workbook_path, data_only=True, keep_links=True)
-    except Exception:
-        wb_values = None
-
-    workbook_map: list[WorkbookMapRow] = []
-    formulas: list[FormulaRow] = []
-    issues: list[IssueRow] = []
-    date_hints: list[dict[str, str]] = []
-    formula_cells_by_sheet_row: dict[tuple[str, int], list[tuple[str, str, str]]] = defaultdict(
-        list
-    )
-
-    external_links_count = len(getattr(wb_formula, "_external_links", []) or [])
-    if external_links_count:
-        add_issue(
-            issues,
-            "high",
-            "formula_integrity",
-            "workbook",
-            "external links",
-            f"workbook contains {external_links_count} external link object(s)",
-            "external links can make audit results stale or unreproducible if source files are missing",
-            "review linked files, refresh status, and tie material linked values to uploaded sources",
-        )
-
-    if getattr(wb_formula.calculation, "iterate", False):
-        add_issue(
-            issues,
-            "high",
-            "model_architecture",
-            "workbook",
-            "calculation settings",
-            "iterative calculation appears enabled",
-            "iterative settings can hide circularity in interest, debt, cash sweep, or plug logic",
-            "document intentional circular references and test model behavior with iteration settings confirmed",
-        )
-
-    visible_sheets = [ws for ws in wb_formula.worksheets if ws.sheet_state == "visible"]
-    first_visible = visible_sheets[0] if visible_sheets else None
-    normalized_cover_title = (
-        first_visible.title.lower().replace(" ", "_") if first_visible is not None else ""
-    )
-    is_update_cover = re.fullmatch(r"update_cover(?:_\d+)?", normalized_cover_title) is not None
-    if first_visible is None:
-        add_issue(
-            issues,
-            "high",
-            "presentation_readiness",
-            "workbook",
-            "visible sheets",
-            "workbook has no visible sheets",
-            "reviewers cannot inspect key outputs, sources, or checks without changing workbook state",
-            "restore a visible review surface before relying on the workbook",
-        )
-    elif normalized_cover_title != "cover" and not is_update_cover:
-        add_issue(
-            issues,
-            "medium",
-            "presentation_readiness",
-            first_visible.title,
-            "first visible sheet",
-            "first visible sheet is not a Cover tab",
-            "PM and senior-review workbooks should open to a Cover insight dashboard before raw model, README, or control tabs",
-            "add a first-tab Cover tied to model outputs, sources, checks, and the investment decision",
-        )
-    else:
-        cover_text = " ".join(
-            stringify(value).lower()
-            for row in first_visible.iter_rows(max_row=60, max_col=10, values_only=True)
-            for value in row
-            if value not in (None, "")
-        )
-        required_terms = {
-            "executive read-through": [
-                "executive read-through",
-                "net read",
-                "decision question",
-                "recommendation",
-            ],
-            "headline output": [
-                "headline output",
-                "selected valuation",
-                "value per share",
-                "base revenue",
-                "liquidity trough",
-                "thesis status",
-            ],
-            "key metrics": [
-                "kpi",
-                "key metric",
-                "metric",
-                "revenue",
-                "ebitda",
-                "fcf",
-                "cash",
-                "multiple",
-            ],
-            "scenario or output table": [
-                "scenario",
-                "base",
-                "upside",
-                "downside",
-                "bull",
-                "bear",
-                "output",
-            ],
-            "source posture": ["source posture", "source count", "source", "evidence"],
-            "warnings or failures": ["warning", "hard failure", "qa", "check"],
-            "chart-ready or visual data": ["chart-ready", "chart ready", "visual", "sensitivity"],
-            "workbook map": ["workbook map", "tab map"],
-        }
-        if is_update_cover:
-            required_terms["executive read-through"].extend(["bottom line", "pm posture"])
-            required_terms["headline output"].extend(["estimate change", "valuation change"])
-            required_terms["scenario or output table"].extend(["rebuild", "blocked"])
-            required_terms["warnings or failures"].extend(["blocked", "stale"])
-            required_terms["chart-ready or visual data"].extend(["revenue period", "latest value"])
-        missing = [
-            label
-            for label, terms in required_terms.items()
-            if not any(term in cover_text for term in terms)
-        ]
-        if missing:
-            add_issue(
-                issues,
-                "medium",
-                "presentation_readiness",
-                first_visible.title,
-                "cover contents",
-                f"cover/dashboard may be missing: {', '.join(missing)}",
-                "a Cover can exist mechanically but still fail as an insight dashboard if the investment read-through, metrics, sources, warnings, visuals, or navigation are buried",
-                "expand the first tab to include net read, headline outputs, key metrics, scenarios, source posture, warnings/hard failures, chart-ready data, and workbook map",
-            )
-
-    for ws in wb_formula.worksheets:
-        ws_values = wb_values[ws.title] if wb_values and ws.title in wb_values.sheetnames else None
-        used_cells = 0
-        formula_cells = 0
-        constant_cells = 0
-        blank_cells = 0
-
-        if ws.sheet_state != "visible":
-            sev = "high" if ws.sheet_state == "veryHidden" else "medium"
-            add_issue(
-                issues,
-                sev,
-                "model_architecture",
-                ws.title,
-                "sheet visibility",
-                f"sheet is {ws.sheet_state}",
-                "hidden sheets can contain source data, assumptions, checks, or formulas not visible to reviewers",
-                "inspect hidden sheet contents and disclose whether it feeds material outputs",
-            )
-
-        for row in ws.iter_rows():
-            for cell in row:
-                val = cell.value
-                if val is None:
-                    continue
-                used_cells += 1
-                coord = cell.coordinate
-                if is_formula(val):
-                    formula_cells += 1
-                    cached = ""
-                    if ws_values is not None:
-                        try:
-                            cached = stringify(ws_values[coord].value)
-                        except Exception:
-                            cached = ""
-                    norm = normalize_formula(val)
-                    vols = volatile_functions(val)
-                    hardcodes = extract_hardcoded_numbers(val)
-                    has_external = bool(EXTERNAL_REF_RE.search(val))
-                    has_error = any(err in val.upper() for err in ERROR_LITERALS)
-                    formulas.append(
-                        FormulaRow(
-                            sheet=ws.title,
-                            cell=coord,
-                            formula=val,
-                            normalized_formula=norm,
-                            formula_length=len(val),
-                            cached_value=cached,
-                            has_external_ref=has_external,
-                            volatile_functions=";".join(vols),
-                            has_error_literal=has_error,
-                            hardcoded_numbers=";".join(hardcodes),
-                        )
-                    )
-                    formula_cells_by_sheet_row[(ws.title, cell.row)].append((coord, val, norm))
-
-                    if has_external:
-                        add_issue(
-                            issues,
-                            "high",
-                            "formula_integrity",
-                            ws.title,
-                            coord,
-                            "formula contains an external reference",
-                            "external references can produce stale or unreproducible values if the linked file is unavailable or outdated",
-                            "tie the value to the linked source file or replace with documented source-tab data",
-                        )
-                    if vols:
-                        add_issue(
-                            issues,
-                            "medium",
-                            "formula_integrity",
-                            ws.title,
-                            coord,
-                            f"formula uses volatile function(s): {', '.join(vols)}",
-                            "volatile functions can change outputs on recalculation and complicate audit reproducibility",
-                            "confirm volatility is intentional and does not drive material outputs without disclosure",
-                        )
-                    if hardcodes:
-                        sev = "medium" if len(hardcodes) <= 2 else "high"
-                        add_issue(
-                            issues,
-                            sev,
-                            "formula_integrity",
-                            ws.title,
-                            coord,
-                            f"formula contains review-worthy numeric literal(s): {', '.join(hardcodes[:8])}",
-                            "hardcoded numbers inside formulas may hide assumptions, rates, multiples, or adjustments",
-                            "move material assumptions to an input/source tab or document why the constant is appropriate",
-                        )
-                    if has_error:
-                        add_issue(
-                            issues,
-                            "high",
-                            "formula_integrity",
-                            ws.title,
-                            coord,
-                            "formula contains an error literal",
-                            "error literals may indicate broken references or intentionally suppressed errors",
-                            "inspect precedent cells and resolve or document the error treatment",
-                        )
-                else:
-                    constant_cells += 1
-                    if isinstance(val, str) and DATE_HINT_RE.search(val):
-                        # Capture local context around date/source labels for manual review.
-                        date_hints.append({"sheet": ws.title, "cell": coord, "text": val[:200]})
-                # blanks in used range are computed below
-
-        total_range_cells = ws.max_row * ws.max_column if ws.max_row and ws.max_column else 0
-        blank_cells = max(total_range_cells - used_cells, 0)
-        workbook_map.append(
-            WorkbookMapRow(
-                sheet=ws.title,
-                state=ws.sheet_state,
-                max_row=ws.max_row,
-                max_column=ws.max_column,
-                used_cells=used_cells,
-                formula_cells=formula_cells,
-                constant_cells=constant_cells,
-                blank_cells_in_used_range=blank_cells,
-                merged_ranges=len(list(ws.merged_cells.ranges)),
-                freeze_panes=stringify(ws.freeze_panes),
-                has_tables=bool(getattr(ws, "tables", None)),
-                has_auto_filter=bool(ws.auto_filter and ws.auto_filter.ref),
-            )
-        )
-
-        if len(list(ws.merged_cells.ranges)) > 0:
-            add_issue(
-                issues,
-                "low",
-                "formatting",
-                ws.title,
-                "merged ranges",
-                f"sheet contains {len(list(ws.merged_cells.ranges))} merged range(s)",
-                "merged cells can complicate formula review, data extraction, and tie-out automation",
-                "review whether merged ranges are cosmetic or interfere with source data / calculations",
-            )
-
-    # Identify simple formula-family inconsistencies by row.
-    for (sheet, rownum), row_formulas in formula_cells_by_sheet_row.items():
-        if len(row_formulas) < 4:
-            continue
-        norms = [item[2] for item in row_formulas]
-        counts = Counter(norms)
-        dominant_norm, dominant_count = counts.most_common(1)[0]
-        # Flag minority formulas when a clear dominant pattern exists.
-        if dominant_count >= max(3, int(len(row_formulas) * 0.65)) and len(counts) > 1:
-            minority = [
-                (coord, formula) for coord, formula, norm in row_formulas if norm != dominant_norm
-            ]
-            if 0 < len(minority) <= 5:
-                cells = ", ".join(coord for coord, _ in minority)
-                add_issue(
-                    issues,
-                    "medium",
-                    "formula_integrity",
-                    sheet,
-                    f"row {rownum}: {cells}",
-                    "row contains formula(s) that differ from the dominant copied formula pattern",
-                    "unexpected formula-family breaks can indicate overwritten formulas, wrong period references, or hidden assumptions",
-                    "inspect the minority cells and confirm whether the break is intentional",
-                )
-
-    # Flag sheets that are mostly constants but named like assumptions/source checks.
-    for row in workbook_map:
-        name = row.sheet.lower()
-        if (
-            row.used_cells
-            and row.formula_cells == 0
-            and any(token in name for token in ["source", "input", "assumption", "data"])
-        ):
-            add_issue(
-                issues,
-                "info",
-                "source_tieout",
-                row.sheet,
-                "entire sheet",
-                "sheet appears to be a source/input tab with no formulas",
-                "source/input tabs should be tied to underlying documents and as-of dates",
-                "verify source labels, source dates, and tie-outs for material values on this tab",
-            )
-
-    summary = {
-        "workbook": str(workbook_path.name),
-        "sheets": len(wb_formula.worksheets),
-        "hidden_sheets": sum(1 for r in workbook_map if r.state != "visible"),
-        "formula_cells": len(formulas),
-        "external_link_objects": external_links_count,
-        "issues_by_severity": dict(Counter(issue.severity for issue in issues)),
-        "issues_by_category": dict(Counter(issue.category for issue in issues)),
-        "date_source_hints": date_hints[:100],
-        "generated_at": datetime.now(UTC).replace(microsecond=0).isoformat().replace("+00:00", "Z"),
-        "notes": [
-            "static inspection only; this script does not calculate formulas or prove business logic is correct",
-            "use outputs as a triage starting point and supplement with manual model review",
-        ],
-    }
-    return workbook_map, formulas, issues, summary
-
-
-def make_markdown_report(
-    workbook_path: Path,
-    workbook_map: list[WorkbookMapRow],
-    formulas: list[FormulaRow],
-    issues: list[IssueRow],
-    summary: dict[str, Any],
-) -> str:
-    sev_order = {"critical": 0, "high": 1, "medium": 2, "low": 3, "info": 4}
-    sorted_issues = sorted(
-        issues, key=lambda x: (sev_order.get(x.severity, 99), x.category, x.sheet, x.cell_or_range)
-    )
-    visible_issue_count = len(sorted_issues)
-    high_count = sum(1 for issue in sorted_issues if issue.severity in {"critical", "high"})
-    posture = (
-        "red"
-        if any(i.severity == "critical" for i in sorted_issues)
-        else "yellow"
-        if high_count
-        else "green"
-    )
-    if not workbook_map:
-        posture = "gray"
-
-    lines: list[str] = []
-    lines.append(f"# Workbook Audit Report: {workbook_path.name}")
-    lines.append("")
-    lines.append("## Static inspection summary")
-    lines.append("")
-    lines.append(f"- **Generated:** {summary.get('generated_at', '')}")
-    lines.append(f"- **Sheets:** {summary.get('sheets', 0)}")
-    lines.append(f"- **Hidden sheets:** {summary.get('hidden_sheets', 0)}")
-    lines.append(f"- **Formula cells:** {summary.get('formula_cells', 0)}")
-    lines.append(f"- **External link objects:** {summary.get('external_link_objects', 0)}")
-    lines.append(f"- **Initial model health posture:** {posture}")
-    lines.append("")
-    lines.append(
-        "> This report is a static inspection only. It does not calculate Excel formulas or validate investment logic. Use it as an audit starting point."
-    )
-    lines.append("")
-
-    lines.append("## Workbook map")
-    lines.append("")
-    lines.append(
-        "| sheet | state | rows | columns | used cells | formulas | constants | merged ranges |"
-    )
-    lines.append("|---|---|---:|---:|---:|---:|---:|---:|")
-    for row in workbook_map:
-        lines.append(
-            f"| {row.sheet} | {row.state} | {row.max_row} | {row.max_column} | {row.used_cells} | {row.formula_cells} | {row.constant_cells} | {row.merged_ranges} |"
-        )
-    lines.append("")
-
-    lines.append("## Priority issue log")
-    lines.append("")
-    if visible_issue_count == 0:
-        lines.append(
-            "No mechanical issues were flagged by static inspection. Manual model audit is still required for business logic, source tie-outs, and decision-readiness."
-        )
-    else:
-        lines.append("| severity | category | location | finding | recommended next step |")
-        lines.append("|---|---|---|---|---|")
-        for issue in sorted_issues[:100]:
-            location = f"{issue.sheet} {issue.cell_or_range}".strip()
-            lines.append(
-                f"| {issue.severity} | {issue.category} | {location} | {issue.finding} | {issue.recommended_next_step} |"
-            )
-        if visible_issue_count > 100:
-            lines.append(
-                f"| info | summary | issue log | {visible_issue_count - 100} additional issues omitted from audit report | see issues.csv |"
-            )
-    lines.append("")
-
-    top_hardcodes = [f for f in formulas if f.hardcoded_numbers]
-    top_external = [f for f in formulas if f.has_external_ref]
-    top_volatile = [f for f in formulas if f.volatile_functions]
-
-    lines.append("## Formula exception summary")
-    lines.append("")
-    lines.append(f"- formulas with review-worthy hardcoded numeric literals: {len(top_hardcodes)}")
-    lines.append(f"- formulas with external references: {len(top_external)}")
-    lines.append(f"- formulas with volatile functions: {len(top_volatile)}")
-    lines.append("")
-    lines.append("See formulas.csv for full formula inventory.")
-    lines.append("")
-
-    if summary.get("date_source_hints"):
-        lines.append("## Date/source label hints")
-        lines.append("")
-        lines.append(
-            "These cells may contain source-date or as-of-date labels worth reviewing during source tie-out."
-        )
-        lines.append("")
-        lines.append("| sheet | cell | text |")
-        lines.append("|---|---|---|")
-        for hint in summary["date_source_hints"][:25]:
-            text = hint.get("text", "").replace("|", " ")
-            lines.append(f"| {hint.get('sheet', '')} | {hint.get('cell', '')} | {text} |")
-        lines.append("")
-
-    lines.append("## Recommended next audit steps")
-    lines.append("")
-    lines.append("1. Trace material outputs to precedent formulas and source tabs.")
-    lines.append("2. Build a source tie-out ledger for all decision-driving values.")
-    lines.append(
-        "3. Review assumptions and sensitivities for the model's actual public-equity investment decision."
-    )
-    lines.append(
-        "4. Resolve critical/high issues before using the model in an PM, IC, client, or trading decision."
-    )
-    lines.append("")
-    return "\n".join(lines)
-
-
-def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Static audit of an Excel workbook for model-audit-tieout."
-    )
-    parser.add_argument("workbook", help="Path to .xlsx or .xlsm workbook")
-    parser.add_argument(
-        "--out-dir", default="audit_output", help="Directory where audit outputs will be written"
-    )
-    return parser.parse_args(argv)
-
-
-def main(argv: list[str] | None = None) -> int:
-    args = parse_args(argv)
-    workbook_path = Path(args.workbook).expanduser().resolve()
-    if not workbook_path.exists():
-        print(f"ERROR: workbook not found: {workbook_path}", file=sys.stderr)
-        return 1
-    if workbook_path.suffix.lower() not in {".xlsx", ".xlsm"}:
-        print(
-            "ERROR: only .xlsx and .xlsm files are supported by this static inspector.",
-            file=sys.stderr,
-        )
-        return 1
-
-    out_dir = Path(args.out_dir).expanduser().resolve()
-    workbook_map, formulas, issues, summary = scan_workbook(workbook_path)
-
-    out_dir.mkdir(parents=True, exist_ok=True)
-    csv_dir = support_dir(out_dir)
-    log_dir = logs_dir(out_dir)
-    workbook_map_rows = [asdict(row) for row in workbook_map]
-    formula_rows = [asdict(row) for row in formulas]
-    issue_rows = [asdict(row) for row in issues]
-
-    write_csv(
-        csv_dir / "workbook_map.csv",
-        workbook_map_rows,
-        list(asdict(workbook_map[0]).keys())
-        if workbook_map
-        else [
-            "sheet",
-            "state",
-            "max_row",
-            "max_column",
-            "used_cells",
-            "formula_cells",
-            "constant_cells",
-            "blank_cells_in_used_range",
-            "merged_ranges",
-            "freeze_panes",
-            "has_tables",
-            "has_auto_filter",
-        ],
-    )
-    write_csv(
-        csv_dir / "formula_exception_log.csv",
-        formula_rows,
-        list(asdict(formulas[0]).keys())
-        if formulas
-        else [
-            "sheet",
-            "cell",
-            "formula",
-            "normalized_formula",
-            "formula_length",
-            "cached_value",
-            "has_external_ref",
-            "volatile_functions",
-            "has_error_literal",
-            "hardcoded_numbers",
-        ],
-    )
-    write_csv(
-        csv_dir / "model_audit_findings.csv",
-        issue_rows,
-        list(asdict(issues[0]).keys())
-        if issues
-        else [
-            "severity",
-            "category",
-            "sheet",
-            "cell_or_range",
-            "finding",
-            "why_it_matters",
-            "recommended_next_step",
-        ],
-    )
-    source_tieout_rows = [
-        {
-            "sheet": hint.get("sheet", ""),
-            "cell_or_range": hint.get("cell", ""),
-            "source_or_date_label": hint.get("text", ""),
-            "tie_out_status": "needs_review",
-            "review_note": "Source/date label detected by static scan; tie to controlling filing, market data, model source, or assumption support.",
-        }
-        for hint in summary.get("date_source_hints", [])
-    ]
-    write_csv(
-        csv_dir / "source_tieout_ledger.csv",
-        source_tieout_rows,
-        ["sheet", "cell_or_range", "source_or_date_label", "tie_out_status", "review_note"],
-    )
-    with (log_dir / "model_audit_findings.json").open("w", encoding="utf-8") as f:
-        json.dump(
-            {
-                "summary": summary,
-                "issues": issue_rows,
-                "formula_exceptions": formula_rows,
-                "source_tieout_ledger": source_tieout_rows,
-            },
-            f,
-            indent=2,
-        )
-
-    report = make_markdown_report(workbook_path, workbook_map, formulas, issues, summary)
-    support_note_path = csv_dir / "model_audit_support_note.md"
-    support_note_path.write_text(report, encoding="utf-8")
-    report_path = write_report_html(
-        out_dir / "model_audit_report.html", "Public Equity Investing Equity Model Audit", report
-    )
-    issue_workbook = out_dir / "model_audit_issues.xlsx"
-    write_cover_first_workbook(
-        issue_workbook,
-        [
-            ["Model Audit Issue Log"],
-            ["Audited workbook", str(workbook_path)],
-            ["Sheets", summary.get("sheets", 0)],
-            ["Formula cells", summary.get("formula_cells", 0)],
-            ["Issues", len(issues)],
-            [
-                "First read",
-                "Open model_audit_report.html first; this workbook is the companion issue log.",
-            ],
-        ],
-        {
-            "Issues": dict_rows_to_sheet(issue_rows),
-            "Workbook_Map": dict_rows_to_sheet(workbook_map_rows),
-            "Formula_Log": dict_rows_to_sheet(formula_rows),
-            "Source_Tieout": dict_rows_to_sheet(source_tieout_rows),
-        },
-    )
-    contract_path = log_dir / "model_audit_dashboard_contract.json"
-    write_dashboard_contract(
-        contract_path,
-        "model-audit-tieout",
-        "Public Equity Investing Equity Model Audit",
-        workbook_path.name,
-        "report_only",
-        report_path,
-        executive_summary="Model audit report with formula exceptions, workbook map, source tie-out hints, and prioritized fixes.",
-        hero_actions=[
-            {"label": "Open audit report", "path": str(report_path)},
-            {"label": "Open issue workbook", "path": str(issue_workbook)},
-        ],
-        supporting_outputs=[
-            {
-                "path": str(csv_dir / "model_audit_findings.csv"),
-                "label": "Issue CSV",
-                "role": "support",
-            }
-        ],
-        report_body=[{"heading": "Audit Summary", "body": report}],
-        readiness_posture="needs_review" if issues else "first-pass-clear",
-    )
-    write_artifact_manifest(
-        out_dir,
-        "model-audit-tieout",
-        "html_report",
-        report_path,
-        companion_deliverables=[
-            artifact_item(
-                issue_workbook,
-                "companion_deliverable",
-                "xlsx",
-                "Cover-first issue-log workbook.",
-                True,
-                True,
-            )
-        ],
-        support_artifacts=[
-            artifact_item(
-                csv_dir / "workbook_map.csv",
-                "support_artifact",
-                "csv",
-                "Workbook structure inventory.",
-                False,
-                True,
-                "CSV is audit support for filtering/import.",
-            ),
-            artifact_item(
-                csv_dir / "formula_exception_log.csv",
-                "support_artifact",
-                "csv",
-                "Formula inventory and exception leads.",
-                False,
-                True,
-                "CSV backs the audit report and workbook.",
-            ),
-            artifact_item(
-                csv_dir / "model_audit_findings.csv",
-                "support_artifact",
-                "csv",
-                "Raw model audit issue table.",
-                False,
-                True,
-                "CSV backs the audit report and issue workbook.",
-            ),
-            artifact_item(
-                csv_dir / "source_tieout_ledger.csv",
-                "support_artifact",
-                "csv",
-                "Source/date tie-out hints.",
-                False,
-                True,
-                "CSV supports source-to-cell review.",
-            ),
-            artifact_item(
-                csv_dir / "model_audit_support_note.md",
-                "support_artifact",
-                "markdown",
-                "Narrative support note backing the HTML report.",
-                False,
-                True,
-                "Markdown is narrative support, not the lead artifact.",
-            ),
-            artifact_item(
-                log_dir / "model_audit_findings.json",
-                "support_artifact",
-                "json",
-                "Machine-readable model audit findings.",
-                False,
-                True,
-                "JSON is audit support for downstream tooling.",
-            ),
-            artifact_item(
-                contract_path,
-                "support_artifact",
-                "json",
-                "Dashboard/report contract.",
-                False,
-                False,
-                "Contract JSON is renderer plumbing.",
-            ),
-        ],
-        blocked_or_partial_status={
-            "status": "partial",
-            "reason": "Static workbook inspection does not recalculate formulas or prove investment logic.",
-            "missing_inputs": [
-                "Manual formula trace",
-                "Source document tie-out",
-                "Scenario/downside review",
-            ],
-        },
-    )
-
-    print(f"Wrote audit outputs to: {out_dir}")
-    print(
-        f"Sheets: {summary.get('sheets', 0)} | Formula cells: {summary.get('formula_cells', 0)} | Issues: {len(issues)}"
-    )
-    print(f"Support note: {support_note_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/model-audit-tieout/scripts/requirements.txt b/plugins/financial-markets/skills/model-audit-tieout/scripts/requirements.txt
deleted file mode 100644
index 289437c..0000000
--- a/plugins/financial-markets/skills/model-audit-tieout/scripts/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-openpyxl>=3.1
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/SKILL.md b/plugins/financial-markets/skills/portfolio-risk-management/SKILL.md
deleted file mode 100644
index bb84075..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/SKILL.md
+++ /dev/null
@@ -1,154 +0,0 @@
----
-name: portfolio-risk-management
-description: Use when sizing Public Equity Investing positions, finding equity hedges, or building an integrated position-and-hedge risk plan from a listed-equity thesis. Do not use for thesis construction, standalone event underwriting, trade execution, personal investment advice, or credit-instrument risk.
----
-
-# Portfolio Risk Management
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing` router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML risk decision report, with an XLSX workbook taking precedence when reusable risk calculations or trackers are central. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive integrated position-and-hedge plan or explicit HTML risk-plan request, the default resolves the presentation surface to a polished standalone HTML risk decision report unless the user requests another surface, a quick/no-file answer, workbook output, or a standardized dashboard. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Turn a listed-equity investment view into an implementable risk posture: the right size, any appropriate hedge, the exposure intentionally retained, and the rules for resizing or removing protection. This skill owns equity longs, equity shorts, pair trades, ETFs/index constituents, listed options, equity factor hedges, futures/index overlays, and macro proxies when the decision remains a public-equity decision.
-
-Do not treat sizing and hedging as independent answers when one changes the other. Size from the tightest credible constraint; hedge only risks that can be reduced without destroying the intended alpha. A size-down/no-hedge outcome is valid when hedge basis risk, cost, liquidity, or complexity is worse than the unwanted exposure.
-
-For a substantive reusable risk package, produce a polished standalone HTML risk decision report following `../../shared/html-artifact-standard.md`; retain the structured decision-sheet strengths of sizing tables, hedge comparisons, scenarios, and monitoring triggers without forcing a standardized dashboard shell. Use `dashboard-builder` only when the user explicitly asks for a standardized dashboard, recurring monitoring dashboard, reusable validated template, or structured payload-driven render.
-
-## Mode Selection
-
-Infer the mode from the requested decision and available context:
-
-| Mode | Use when | Core output |
-| --- | --- | --- |
-| `position_sizing` | The user asks how much to own or short, add/trim/exit thresholds, loss budgets, liquidity capacity, or portfolio exposure impact. | Recommended size, binding constraint, scenario P&L, liquidity exit, monitoring rules. |
-| `hedge_design` | The user asks how to hedge, neutralize, protect, de-beta, rank hedge candidates, or compare hedge instruments. | Hedge objective, candidate set, basis-risk ledger, sizing method, readiness checks, exit rules. |
-| `integrated_risk_plan` | The user asks for both position size and hedge, or the hedge choice could materially change recommended size. | Unhedged versus hedged/size-down comparison, recommended package, retained exposure, action rules. |
-
-Do not ask the user to choose a mode when the prompt or supplied artifact makes the decision clear. Ask one targeted clarification only when selecting the wrong mode would materially change the deliverable, such as an ambiguous request to "manage risk" without a stated position, unwanted exposure, or requested action.
-
-## Public Equity PM/Risk Lens
-
-Load `../../shared/pm-judgment-heuristics.md` and `../../shared/credit-markets-handoff.md` before producing a substantial risk recommendation.
-
-Audience modes:
-
-- `long_only_pm`: emphasize benchmark active weight, tracking error, liquidity/capacity, drawdown tolerance, add/trim discipline, and portfolio role.
-- `long_short_hf`: emphasize gross/net/beta/factor impact, borrow, short squeeze risk, catalyst path, hedge fit, and cover/resize rules.
-- `market_neutral_pairs_pm`: emphasize leg sizing, beta/factor/driver neutrality, residual spread risk, borrow, and liquidity.
-- `event_driven_equity_pm`: emphasize adverse gap, timing delay, probability-weighted outcomes, options/borrow liquidity, and event slippage.
-- `etf_index_benchmark_pm`: emphasize constituent weight, active weight, ETF/index flow exposure, rebalance risk, liquidity, and tracking-error impact.
-
-Every substantive output must state intended alpha, unwanted risk, retained exposure, binding constraint, liquidity/exit posture, hedge or size-down tradeoff, monitoring triggers, implementation readiness, and missing evidence that could change the decision. Include basis risk and a hedge-failure scenario whenever protection is proposed.
-
-## Boundary With Credit Markets
-
-This skill may use CDS levels, credit spreads, ratings, maturity walls, refinancing pressure, covenant headlines, or liquidity stress only as common-equity downside signals.
-
-Use CDS/spreads only as common-equity risk context. CDS levels and credit spreads may appear only as equity-risk signals or common-equity downside context; never treat them as a locally owned hedge implementation.
-
-Use Credit Markets for CDS, bonds, loans, spread DV01/CS01, credit spread hedges, capital-structure hedges, distressed hedges, recovery waterfalls, covenant analysis or covenant hedges, debt-security sizing, and credit-security relative value. Do not present a credit instrument as a local hedge recommendation.
-
-## Do Not Use
-
-- Thesis, variant perception, or pitch construction: use `long-short-pitch` or `memo-builder`.
-- Dated event probability and payoff underwriting: use `event-driven-analyzer`.
-- Macro/economic transmission mapping: use `economic-impact-report`.
-- Catalyst calendar or monitoring-only work: use `catalyst-calendar` or `thesis-tracker`.
-- Personal investment advice, trade execution, or legal/compliance conclusions.
-
-## Context Modes
-
-| Context | Default behavior |
-| --- | --- |
-| No context | Request the security/exposure, desired action, horizon, constraints, and available data; provide a usable intake frame without inventing a recommendation. |
-| Partial context | Produce a conditional risk screen or screen-grade integrated plan with explicit assumptions and missing-data checks; do not recommend initiation when required implementation inputs are missing. |
-| Full source | Normalize inputs, tie out sources, run scenarios, and produce decision-ready risk guidance. |
-| Review or refresh | Preserve prior work, identify stale inputs and hidden exposures, and show what changes the size or hedge. |
-
-## Workflow
-
-1. **Define the decision.** Capture security, direction, current/proposed size, thesis exposure to retain, unwanted risk, price/as-of time, horizon/catalyst, portfolio context, mandate limits, liquidity, and requested action.
-2. **Resolve the loss-budget interpretation.** Distinguish a `scenario loss budget`, which constrains loss under a stated adverse move, from an `absolute loss cap`, which must remain satisfied even if the stock moves beyond that scenario. When the user gives a loss limit for a short but does not say which interpretation applies, ask one targeted clarification in an interactive run. In a non-interactive run, show both branches and do not silently turn an assumed squeeze magnitude into an initiation recommendation. For a short with an absolute cap, require priced defined-loss protection or recommend no position.
-3. **Map exposures.** Separate intended idiosyncratic, event, and chosen factor/macro exposures from unwanted beta, sector, factor, FX, commodity, liquidity, borrow, financing, crowding, gap, and portfolio-concentration risk.
-4. **Build risk cases.** Estimate upside/base/downside/stress outcomes, probability-weighted return when supportable, scenario P&L, liquidity/exit implications, and any hedge-failure case. Label assumed stress magnitudes as illustrative until confirmed by the user or a governing risk policy.
-5. **Apply the selected mode.**
-   - For `position_sizing`, triangulate loss-budget, volatility, liquidity, exposure-limit, benchmark/factor, borrow/squeeze, catalyst, and portfolio-fit constraints; recommend the most restrictive credible size.
-   - For `hedge_design`, classify exposures as keep, hedge, reduce, or monitor; evaluate direct equity, ETF/index, factor, pair, listed-option, futures/index, causal macro-proxy, portfolio-overlay, and no-hedge/size-down alternatives.
-   - For `integrated_risk_plan`, compare unhedged size, hedged size, and size-down/no-hedge outcomes; recommend the package with the clearest retained alpha and acceptable cost/basis risk.
-6. **Assess feasibility.** State market-data freshness, ADV/exit days, borrow/recall/squeeze constraints, option chain/Greeks where relevant, cost/carry, basis risk, mandate/compliance checks, and implementation-readiness gaps. For a short recommendation, current executable quote, ADV/exit capacity, locate/borrow terms, and current squeeze/crowding inputs are required before using `initiate` or other implementation-ready language; live option terms are additionally required before recommending an options hedge.
-7. **Recommend and monitor.** If implementation inputs are missing, label the result `Conditional risk screen` or `Not implementation-ready` and state the conditional size/package rather than instructing initiation. Otherwise state recommended size and/or hedge, action, binding constraint, retained exposure, rejected alternative where material, add/trim/exit/cover/resize/roll/remove triggers, confidence, and missing evidence.
-8. **Render and inspect when HTML is delivered.** In a standalone HTML report, front-load a compact constraint-interpretation table, conditional or executable action, hedge tradeoff, and missing inputs before entry. Keep citations readable: do not fragment tickers, dates, percentages, basis-point amounts, instrument terms such as `GLP-1`, or scenario labels with inline links. For a local HTML file, inspect the opening viewport and decision-critical downstream sections with local headless-browser screenshots rather than the in-app Browser plugin.
-
-## Reference Map
-
-Load only references needed by the selected mode:
-
-- All substantive modes: `references/source-and-context-protocol.md`, `references/exposure-and-factor-risk.md`, and `references/liquidity-drawdown-scenarios.md`.
-- `position_sizing`: `references/sizing-framework.md`, `references/strategy-nuance.md`, `references/position-sizing-output-templates.md`, and `references/quality-control.md`.
-- `hedge_design`: `references/hedge-workflow.md`, `references/instrument-playbooks.md`, `references/hedge-output-templates.md`, and `references/hedge-scorecard-schema.md`.
-- `integrated_risk_plan`: the sizing and hedge references necessary for the recommended package; avoid loading unused instrument playbooks.
-- Standalone HTML deliverables: `../../shared/html-artifact-standard.md` and the selected mode's output template.
-- Explicit standardized dashboard deliverables: `references/DASHBOARD_PACK.md`.
-
-## Deterministic Scripts
-
-Use bundled helpers when structured inputs are supplied or the user asks for templates/files:
-
-```bash
-python scripts/create_position_sizing_templates.py --out path/to/templates
-python scripts/position_sizing_calculator.py --input path/to/input.json --out path/to/outputs
-python scripts/score_hedge_candidates.py hedge_candidates.csv --output-dir path/to/outputs
-```
-
-The sizing helpers write transparent calculation/audit support files including `position_summary.csv`, `sizing_cases.csv`, `scenario_pnl.csv`, `exposure_impact.csv`, `liquidity_exit.csv`, `monitoring_rules.csv`, `support_note.md`, `run_log.json`, and `manifest.json`. The hedge helper writes `hedge_scorecard_support_note.md`, `hedge_scorecard.csv`, `hedge_scorecard.json`, and `basis_risk_ledger.csv`.
-
-These helpers do not fetch live data, run portfolio risk systems, satisfy compliance checks, or replace PM judgment. CSV, Markdown, JSON, run-log, and manifest files are support artifacts unless the user explicitly asks for those formats. For substantial reusable packages, lead with a polished standalone HTML risk decision report unless the user explicitly requests a standardized dashboard.
-
-## Dashboard Handoff
-
-If the user explicitly asks for a standardized or recurring dashboard, use `references/DASHBOARD_PACK.md`. `portfolio-risk-management` owns size, hedge, retained-exposure, binding-constraint, basis-risk, scenario, liquidity, and monitoring judgment; `dashboard-builder` owns rendering and validation. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input and keep support files behind the HTML dashboard unless explicitly requested.
-
-## Handoffs
-
-- `long-short-pitch`: establish the thesis and trade expression before risk construction.
-- `thesis-tracker`: maintain decision triggers after a position or hedge is adopted.
-- `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `economic-impact-report`, or `event-driven-analyzer`: refresh the underlying equity case before risk work when needed.
-- `scenario-sensitivity-generator`: build larger grids or breakevens.
-- `financial-source-of-truth`, `financials-normalizer`, `model-audit-tieout`, and `excel-data-cleaner`: de-risk source data or workbook inputs.
-- `memo-builder`, `deck-report-qc`, and `style-guide-adapter`: package or circulate a final artifact.
-
-## Output Contract
-
-Default output should include:
-
-1. Decision summary: mode, recommendation or conditional screen status, action readiness, confidence, and binding constraint.
-2. Exposure objective: intended alpha, unwanted risk, and retained exposure.
-3. Constraint interpretation: scenario loss budget versus absolute loss cap, the assumptions needed for each branch, and the compliant action.
-4. Risk/return cases: downside, stress, catalyst/path case, and hedge-failure case when relevant.
-5. Sizing recommendation or hedge package, including size-down/no-hedge comparison where material.
-6. Portfolio impact, liquidity/exit, cost/carry, basis risk, and readiness gaps.
-7. Monitoring rules: add, trim, exit, cover, resize, roll, remove, and re-underwrite triggers as applicable.
-8. Sources, assumptions, stale inputs, and open data requests.
-
-Before finalizing a sizing or integrated recommendation, apply `references/quality-control.md`. Before recommending a hedge, confirm it preserves desired thesis exposure, names basis risk and cost/carry, and includes a no-hedge/size-down alternative.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/agents/openai.yaml b/plugins/financial-markets/skills/portfolio-risk-management/agents/openai.yaml
deleted file mode 100644
index 982989f..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Portfolio Risk Management"
-  short_description: "Size public-equity positions and design thesis-preserving hedges."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/portfolio-risk-management/references/DASHBOARD_PACK.md
deleted file mode 100644
index b054d42..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# Portfolio Risk Management Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, recurring monitoring dashboard, reusable validated template, or structured payload-driven render for a position-sizing analysis, hedge design, integrated risk plan, drawdown map, liquidity check, or PM risk package. An ordinary substantial integrated plan is a polished standalone HTML risk decision report following `../../../shared/html-artifact-standard.md`.
-
-## Producer Role
-
-`portfolio-risk-management` owns size, hedge selection, retained-exposure judgment, binding constraints, basis risk, scenario P&L, liquidity analysis, and monitoring rules. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-| Skill mode | Payload mode | Lead decision |
-| --- | --- | --- |
-| `position_sizing` | `portfolio_risk_position_sizing` | Recommended size and binding constraint. |
-| `hedge_design` | `portfolio_risk_hedge_design` | Recommended hedge package, basis risk, and readiness. |
-| `integrated_risk_plan` | `portfolio_risk_integrated_plan` | Size-versus-hedge comparison and retained alpha. |
-
-Use `layout: single_page` for reusable risk packages unless the user explicitly requests tabs. The hero callout should state the recommended action, intended alpha, unwanted risk, retained exposure, binding constraint, and whether a size-down/no-hedge alternative is cleaner.
-
-## Tabs And Modules
-
-1. `risk-decision`
-   - `decision_box`: mode, recommended size and/or hedge, action, confidence, intended alpha, unwanted risk, retained exposure, binding constraint, basis risk, size-down/no-hedge alternative, and what changes the recommendation.
-   - `metric_tiles`: size, downside loss, liquidity exit, gross/net/beta/factor impact, active-weight impact, ADV/exit days, cost/carry, borrow/squeeze/crowding, and readiness.
-2. `exposure-map`
-   - `table`: keep, hedge, reduce, and monitor classifications with evidence and decision impact.
-3. `constraints-and-candidates`
-   - `table`: sizing constraints in `position_sizing` mode, hedge candidates and basis-risk ranking in `hedge_design` mode, or both in `integrated_risk_plan` mode.
-4. `risk-return`
-   - `scenario_map`: upside/base/downside/stress/catalyst-path cases and at least one hedge-failure case when protection is recommended.
-5. `liquidity-execution`
-   - `key_metrics`: ADV, participation rate, days to exit, borrow/financing, option liquidity where relevant, cost/carry, and implementation-readiness gaps.
-6. `monitoring`
-   - `timeline`: add, trim, exit, cover, resize, hedge, roll, remove, re-underwrite, and thesis-break triggers.
-   - `missing_evidence`: missing NAV/AUM, limits, price, ADV, volatility, borrow, option-chain, factor, downside, basis-risk, or Credit Markets handoff inputs.
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Cite every price, size, NAV/AUM, volatility, liquidity, exposure, borrow/financing, cost/carry, target/downside, hedge input, and scenario assumption.
-- Label assumptions, user-provided limits, model-derived fields, stale market data, and implementation-readiness gaps.
-- Credit instruments, CDS hedge implementation, bonds, loans, spread DV01/CS01, recovery, covenants, and capital-structure hedges require a visible Credit Markets handoff.
-
-## Do Not
-
-- Do not size from conviction alone or hide the binding constraint.
-- Do not turn an assumed adverse-move scenario into an implementation recommendation when the user's loss limit may be an absolute cap.
-- Do not recommend a hedge without showing retained exposure, basis risk, cost/carry, hedge-failure risk, and the size-down/no-hedge alternative.
-- Do not make JSON, Markdown, CSV, manifests, or run logs the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Confirm the PM decision box shows intended alpha, unwanted risk, retained exposure, binding constraint, and size-down/no-hedge alternative.
-- Confirm sizing output shows gross/net/beta/factor impact and liquidity exit when sizing is in scope.
-- Confirm hedge output shows basis risk, cost/carry, hedge-failure scenario, readiness, and exit rules when hedging is in scope.
-- Confirm an absolute loss cap on a short uses priced share-for-share call protection or concludes `do not initiate`.
-- Confirm citation rendering remains readable and does not fragment tickers, dates, percentages, basis-point amounts, instrument terms, or scenario labels.
-- Confirm support JSON, Markdown, CSV, and logs remain behind the dashboard or decision summary unless explicitly requested.
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/example-risk-position-input.json b/plugins/financial-markets/skills/portfolio-risk-management/references/example-risk-position-input.json
deleted file mode 100644
index 980209a..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/example-risk-position-input.json
+++ /dev/null
@@ -1,50 +0,0 @@
-{
-  "analysis_date": "2026-05-07",
-  "portfolio": {
-    "nav": 1000000000,
-    "currency": "USD",
-    "current_gross_exposure_pct": 145.0,
-    "current_net_exposure_pct": 48.0,
-    "max_single_name_pct_nav": 5.0,
-    "max_sector_exposure_pct_nav": 25.0,
-    "max_loss_bps_nav": 50,
-    "target_position_vol_contribution_bps": 35
-  },
-  "position": {
-    "security": "ExampleCo",
-    "ticker": "EXMPL",
-    "instrument_type": "equity",
-    "direction": "long",
-    "entry_price": 50.0,
-    "current_size_pct_nav": 0.0,
-    "proposed_size_pct_nav": 3.0,
-    "target_price": 70.0,
-    "base_price": 60.0,
-    "downside_price": 40.0,
-    "stress_price": 32.0,
-    "beta": 1.2,
-    "annualized_volatility_pct": 38.0,
-    "sector": "Software",
-    "current_sector_exposure_pct_nav": 18.0,
-    "sector_limit_pct_nav": 25.0,
-    "confidence": "medium"
-  },
-  "liquidity": {
-    "adv_shares": 2500000,
-    "price": 50.0,
-    "normal_participation_rate": 0.10,
-    "stress_participation_rate": 0.05,
-    "required_exit_days": 5
-  },
-  "scenarios": [
-    {"name": "upside", "price": 70.0, "probability": 0.30, "notes": "target reached on estimate revisions"},
-    {"name": "base", "price": 60.0, "probability": 0.40, "notes": "thesis progresses but multiple unchanged"},
-    {"name": "downside", "price": 40.0, "probability": 0.20, "notes": "growth disappoints"},
-    {"name": "stress", "price": 32.0, "probability": 0.10, "notes": "miss plus sector derating"}
-  ],
-  "monitoring_triggers": [
-    {"trigger_type": "trim", "metric": "position_size_pct_nav", "threshold": ">= 4.0", "action": "trim to target range unless new thesis evidence emerges"},
-    {"trigger_type": "review", "metric": "revenue_growth", "threshold": "below plan for two quarters", "action": "re-underwrite thesis and downside"},
-    {"trigger_type": "exit", "metric": "downside_price", "threshold": "<= 40", "action": "exit or reduce unless thesis-break refuted"}
-  ]
-}
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/exposure-and-factor-risk.md b/plugins/financial-markets/skills/portfolio-risk-management/references/exposure-and-factor-risk.md
deleted file mode 100644
index 0b12650..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/exposure-and-factor-risk.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# Exposure and Factor Risk
-
-## Purpose
-
-Use this reference to decompose position risk before recommending size. The central question is whether the position expresses the intended thesis or merely adds hidden exposure to the book.
-
-## Exposure map
-
-Create an exposure map across these dimensions:
-
-- Security/instrument exposure.
-- Issuer and related-issuer exposure.
-- Sector and sub-industry exposure.
-- Geography and currency exposure.
-- Market beta and benchmark active weight.
-- Style factors: value, growth, quality, momentum, size, low volatility, leverage, profitability, revisions, short interest, crowding.
-- Macro factors: rates, inflation, FX, commodities, CDS/credit-spread signals as equity-risk context, and liquidity regime.
-- Event factors: earnings, regulatory, litigation, M&A, product launch, trial data, lockup, index event.
-- Liquidity and financing factors: ADV, float, borrow, options depth, ETF liquidity, margin/financing, and balance-sheet stress signals.
-
-## Core calculations
-
-### Position exposure
-
-- Dollar exposure = shares/contracts x price x multiplier.
-- Percent NAV = dollar exposure / portfolio NAV.
-- Gross exposure contribution = absolute dollar exposure / NAV.
-- Net exposure contribution = signed dollar exposure / NAV.
-- Beta-adjusted exposure = signed percent NAV x beta.
-- Active weight = portfolio weight - benchmark weight.
-
-### Short exposure
-
-For shorts, show both market value and loss asymmetry:
-
-- Short market value % NAV.
-- Adverse move loss at upside/squeeze price.
-- Borrow cost drag.
-- Dividend/financing drag.
-- Days-to-cover and utilization if available.
-
-### Options exposure
-
-Show at least:
-
-- Premium at risk.
-- Delta-adjusted notional.
-- Gamma exposure around catalyst.
-- Vega exposure to implied volatility change.
-- Theta decay to catalyst/expiry.
-- Open interest and bid/ask/liquidity.
-
-### Equity-risk credit signals as common-equity context
-
-Show only the equity read-through:
-
-- CDS or credit-spread signal, with date and source, when it changes common-equity downside.
-- Rating, maturity wall, refinancing, covenant headline, or liquidity stress as a solvency or equity-impairment input.
-- Whether the signal changes common-equity size, hedge review status, or re-underwrite status.
-- Do not compute market-value/par credit exposure, CS01/DV01, recovery, debt seniority, or dealer-depth sizing here; route those to Credit Markets.
-
-## Factor risk questions
-
-Ask and answer:
-
-1. Is the proposed position adding market beta that is intended or incidental?
-2. Is sector exposure already crowded in the book?
-3. Is the thesis really a factor bet in disguise?
-4. Will the position work if the market, sector, or factor moves against it?
-5. Does historical correlation understate current common ownership or crowdedness?
-6. Does the position interact with existing longs, shorts, pairs, options, equity-risk credit signals, or Credit Markets outputs?
-
-## Long-only / mutual fund nuance
-
-For long-only and benchmark-aware users:
-
-- Active weight and tracking error matter as much as absolute size.
-- A 3% position can be underweight if benchmark weight is larger.
-- Illiquid active underweights can create risk if the name rallies sharply.
-- Concentration and diversification limits may bind before loss-budget sizing.
-- Include benchmark sector/country/industry exposure if available.
-
-## Hedge fund / long-short nuance
-
-For hedge funds:
-
-- Gross, net, beta-adjusted net, factor exposure, and liquidity buckets matter.
-- Long and short books can be balanced in dollars but not in beta or factor exposure.
-- A new short can hedge beta but add short-squeeze/crowding risk.
-- A new long can improve alpha but worsen factor concentration.
-- Include capital usage and margin/financing where relevant.
-
-## Event-driven nuance
-
-For special situations:
-
-- Exposure should be measured by spread/gap risk, not only beta.
-- The key risk can be deal break, regulatory block, court ruling, shareholder vote, financing failure, product outcome, or timing delay.
-- Liquidity often disappears at exactly the wrong time.
-- A small percent NAV can still be too large if the adverse gap is severe.
-
-## Sector-specific red flags
-
-### Banks and financials
-
-- Rate sensitivity, deposit beta, credit cycle, regulatory capital, liquidity, CRE exposure, marks, and capital return.
-- Book value and capital ratios can move faster than ordinary earnings estimates.
-
-### Insurance
-
-- Reserve risk, catastrophe exposure, investment portfolio duration, capital adequacy, pricing cycle, and ratings.
-
-### SaaS/software
-
-- Duration, revenue growth, NRR, CAC/payback, FCF conversion, seat/usage trends, and valuation multiple sensitivity.
-
-### Semiconductors/hardware
-
-- Cycle, inventory correction, customer concentration, capex cycle, geopolitics/export controls, and supply chain.
-
-### Biotech/pharma
-
-- Binary trial/regulatory risk, cash runway, dilution, patent cliffs, reimbursement, and pipeline concentration.
-
-### Energy/materials
-
-- Commodity beta, hedging, decline/cost curve, reserves, leverage, FX, and political/regulatory risk.
-
-### REITs/real estate
-
-- Rates, cap rates, leverage, refinancing, occupancy, NOI, tenant concentration, and asset-type cycle.
-
-## Output standard
-
-The exposure section should be concise but complete. It should not drown the user in factor jargon. Focus on the exposures that can change the recommended size.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-output-templates.md b/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-output-templates.md
deleted file mode 100644
index 38929e9..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-output-templates.md
+++ /dev/null
@@ -1,108 +0,0 @@
-# Hedge Finder Output Contracts
-
-Use compact structures; include only rows that matter for the decision.
-
-## Full Hedge Memo
-
-```markdown
-# Hedge Recommendation: [target/view]
-
-## Executive PM Summary
-- Position/view: [long/short/security/size if supplied]
-- Core thesis to preserve: [desired exposure]
-- Hedge objective: [risk, horizon/catalyst]
-- Recommended hedge: [primary package]
-- Main cost/carry: [premium/borrow/roll/upside give-up]
-- Main basis risk: [failure mode]
-- Confidence/readiness: [high/medium/low; ready/needs checks/screen-grade]
-- Decision needed: [price/check borrow/run risk/size down/do not hedge]
-
-## Assumptions And Data Quality
-| Item | Status | Notes/as-of |
-|---|---|---|
-| Position size / horizon / portfolio exposures / live pricing / option chain / borrow / risk model | provided/missing/stale/unavailable |  |
-
-## Objective And Exposure Classification
-"Reduce [unwanted exposure] over [horizon/catalyst] while preserving [desired thesis exposure]."
-
-| Exposure | Direction | Desired? | Evidence | Materiality | Treatment |
-|---|---|---|---|---|---|
-
-## Candidate Hedge Set
-| Rank | Hedge | Type | Risk hedged | Thesis preservation | Cost/carry | Liquidity | Basis risk | Readiness | Senior view |
-|---:|---|---|---|---|---|---|---|---|---|
-
-Readiness labels: ready, needs live pricing, needs borrow check, needs option-chain validation, needs risk-system check, needs mandate/compliance review, idea-generation only.
-
-For an equity short subject to an absolute loss cap, show a `Hard-Cap Package` row. The compliant direct-option structure is a share-for-share long call hedge, normally one listed call contract per 100 shares short, with maximum loss calculated from entry-to-strike loss plus premium, borrow/dividend cost, bid/ask and execution reserve, and applicable carry. If live terms cannot establish compliance, the output should say `do not initiate`, not recommend an unhedged short. A call spread is not compliant with an absolute cap because loss reopens above its written strike.
-
-## Recommended Package
-Primary: instrument, conceptual size/method, rationale, risk hedged, exposure retained, expected behavior, cost/carry, basis risk, readiness.
-
-Alternative/event/overlay: include only if useful.
-
-Rejected hedges:
-| Hedge | Why rejected |
-|---|---|
-
-## Sizing / Cost / Scenario / Basis / Exits
-- Sizing method and assumptions: [beta/factor/vol/dollar/driver/premium/loss-floor/scenario/etc.]
-- Required data before implementation: [pricing/borrow/chain/risk model/mandate]
-- Scenario behavior: include thesis-right, adverse, and hedge-failure cases.
-- Basis risk ledger: hedge, risk, severity, mitigation, trigger.
-- Exit rules: resize, roll, monetize, remove, re-underwrite, escalate.
-
-## Bottom Line
-Best answer: [primary action]. Use [alternative] if [constraint]. Do not use [rejected hedge] because [reason]. Confirm [checks] before implementation.
-```
-
-## Quick Outputs
-
-### One-Page Summary
-
-Recommendation, objective, why this hedge, conceptual sizing, cost/carry, basis risk, alternatives, rejected hedge, next action.
-
-### Candidate Screen
-
-| Hedge idea | Risk hedged | Pros | Cons | Basis risk | Next check |
-|---|---|---|---|---|---|
-
-Add: best first check, most dangerous false hedge, likely path if no data.
-
-### Options Comparison
-
-| Structure | Protection | Premium/carry | Upside retained | Best use | Main risk |
-|---|---|---|---|---|---|
-| Long put | full below strike | high | full | clean downside | decay |
-| Put spread | between strikes | lower | full | event gap range | capped protection |
-| Collar | below put strike | low/zero possible | capped | willing to sell upside | gives away upside |
-| Index put | broad beta | varies | full for single name | portfolio selloff | basis |
-| Call on short | squeeze cap | premium | n/a | crowded short | decay |
-
-Required commentary: IV richness, expiry vs catalyst, protected risk, monetization rule, data needed.
-
-For a capped-loss short, add coverage ratio and maximum package loss; require live strike, expiry, premium, bid/ask, liquidity and Greeks before recommending implementation.
-
-### Pair Trade
-
-| Pair candidate | Relative thesis | Hedge fit | Sizing basis | Borrow/liquidity | Catalyst match | Key basis risk | Verdict |
-|---|---|---|---|---|---|---|---|
-
-State that the short leg is a second investment thesis with independent risk.
-
-### Basis-Risk Only
-
-| Failure mode | Why it could happen | Impact | Early warning | Mitigation |
-|---|---|---|---|---|
-
-Conclude with most likely failure, most severe failure, whether hedge still deserves use, and what to monitor.
-
-## Tone
-
-Prefer: "QQQ is liquid and reduces mega-cap growth beta, but may over-hedge desired AI/growth exposure. If the thesis is company-specific share gain, use it only as a partial beta hedge; if the thesis is continued AI capex, a semi peer basket is cleaner."
-
-Avoid: "The stock is correlated with QQQ, so short QQQ."
-
-Prefer: "A put spread is better than an outright put when protection is needed for a normal earnings miss, not disaster protection; the tradeoff is capped downside protection."
-
-Avoid: "Buy puts."
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-scorecard-schema.md b/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-scorecard-schema.md
deleted file mode 100644
index 7ce8acf..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-scorecard-schema.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Hedge Scorecard Schema
-
-Use `scripts/score_hedge_candidates.py` when the user supplies structured hedge candidates and wants repeatable scoring or a basis-risk ledger.
-
-```bash
-python scripts/score_hedge_candidates.py hedge_candidates.csv --output-dir output
-```
-
-Required columns: `hedge`, `hedge_type`, `risk_hedged`.
-
-Optional descriptive columns: `target_position`, `retained_exposure`, `as_of`, `source`, `live_pricing_status`, `borrow_status`, `option_chain_status`, `risk_model_status`, `basis_risk`, `basis_risk_mitigation`, `monitoring_trigger`, `implementation_status`, `notes`.
-
-Optional scores: `exposure_fit_score`, `thesis_preservation_score`, `relationship_stability_score`, `downside_behavior_score`, `tenor_alignment_score`, `cost_carry_score`, `liquidity_score`, `implementation_complexity_score`, `basis_risk_score`.
-
-Scores may be 0-5 or 0-100. Invalid numerics are excluded and surfaced in warnings; they are not coerced to zero. For `basis_risk_score`, higher means lower basis risk.
-
-Outputs: `hedge_scorecard.csv`, `basis_risk_ledger.csv`, `hedge_scorecard.json`, `hedge_scorecard_support_note.md`.
-
-Readiness status:
-
-- `implementation-data-ready`: as-of, source, live pricing, borrow, option-chain, and risk-model fields are supplied and current/available/confirmed/validated/not_applicable.
-- `needs-targeted-checks`: readiness fields exist but one or more are stale, unavailable, or not ready.
-- `screen-grade`: missing as-of/source/readiness fields.
-- `route_to_credit_markets`: hedge type is CDS, bond, loan, spread DV01/CS01, credit spread hedge, capital-structure hedge, distressed hedge, recovery, covenant, or another credit instrument. Use only as public-equity risk context in this plugin.
-
-The scorecard is a PM triage artifact, not an execution instruction; confirm live pricing, liquidity, borrow, option chain, risk model, mandate permissions, and portfolio interactions before implementation. Credit hedge construction belongs in Credit Markets; Public Equity Investing may use those rows only as equity-risk context.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-workflow.md b/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-workflow.md
deleted file mode 100644
index 5acad18..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/hedge-workflow.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Hedge Workflow Contract
-
-Use when the task requires PM-grade hedge analysis rather than a short conceptual answer.
-
-## Intake
-
-Collect what is available without blocking: security/position/view, direction, size, entry/P&L/target/downside, thesis/variant view, catalysts/horizon, exposure to keep/reduce, benchmark, gross/net/sector/single-name limits, existing hedges, derivative/short permissions, premium/drawdown/liquidity/tax constraints, and data inputs for prices, risk model, correlations, options, borrow, ADV, catalysts, estimates, and macro variables.
-
-## Objective
-
-State: "Reduce `[unwanted exposure]` over `[horizon/catalyst]` while preserving `[desired thesis exposure]`." Add confidence from data quality.
-
-## Exposure Map
-
-Create columns: Exposure, Direction, Desired, Evidence, Materiality, Treatment.
-
-Inventory market beta, sector/subsector, style/factor, leverage/balance sheet, liquidity/crowding, revenue/margin/estimate/multiple, management/regulatory/litigation, rates/curve, inflation, FX, commodities, CDS/credit-spread signals as equity-risk context, country risk, volatility/liquidity regime, and event/catalyst risk.
-
-Treatments:
-
-- Keep: core return driver; do not hedge or only partially hedge.
-- Hedge: material unwanted risk reducible without damaging thesis.
-- Reduce: better managed by position size than by hedge.
-- Monitor: uncertain/low-confidence exposure; set trigger before forcing hedge.
-
-Senior rule: reject any hedge that removes the reason the position exists.
-
-## Candidate Universe
-
-Consider direct equity instruments, market/index overlays, sector/thematic ETFs, factor baskets, peer/pair hedges, customer/supplier/commodity/FX/rates proxies, listed options, macro/cross-asset hedges, portfolio overlays, Credit Markets handoffs for credit instruments, and no-hedge/size-down.
-
-Evaluate each candidate on exposure fit, thesis preservation, causal logic, regime stability, downside behavior, catalyst/tenor alignment, cost/carry, liquidity/capacity, basis risk, complexity, and exit clarity. Scores can help triage but PM judgment overrides.
-
-## Sizing Methods
-
-- Market/sector beta: target market value x beta to risk / hedge beta, adjusted for desired partial hedge, book constraints, liquidity, correlation instability, and catalyst window.
-- Factor: reduce specified factor exposures subject to liquidity, cost, concentration, mandate, and residual exposure constraints.
-- Pair/peer: dollar-, beta-, volatility-, factor-, or driver-neutral; check spread logic, valuation/revisions, borrow/dividends, catalyst match, and independent short risk.
-- ETF proxy: adjust for look-through overlap, constituent mismatch, market cap/geography/factor mismatch, rebalance drift, and stress liquidity.
-- Options: size by premium budget, loss floor, protection amount, delta target, scenario payoff, or event move; include expiry, strike, premium, breakeven, upside give-up, Greeks, liquidity, and monetization.
-- Futures: notional / (price x multiplier), adjusted for basis, expiry, roll, margin, and liquidity.
-- Credit Markets handoff: route CDS, bond, loan, spread DV01/CS01, recovery, covenant, distressed, and capital-structure hedge construction to Credit Markets. In this workflow, CDS/spread data is only an equity-risk signal that may argue for smaller equity size or a different equity hedge.
-- FX/commodity: use net economic exposure or physical/unit sensitivity, not headline revenue alone; check natural hedges, tenor, basis, seasonality, and roll.
-
-For an absolute loss cap on a short equity position, a direct long-call hedge must cover the short share-for-share: one listed call contract per 100 shares short, subject to contract multiplier and corporate-action adjustments. Calculate maximum package loss from the short sale entry price to the call strike plus premium, borrow/dividend cost, bid/ask and execution reserve, and other required carrying costs. A call spread is not a hard-cap solution because loss reopens above the written call strike.
-
-## Cost And Scenario Discipline
-
-Cost/carry items: borrow/dividends/rebate/recall, option premium/theta/skew/bid-ask/open interest/upside give-up, futures roll/basis/margin, swap financing/collateral/counterparty, FX forward points, commodity roll/location/grade basis, ETF expense/holdings drift, and Credit Markets handoff needs if a credit instrument appears.
-
-Run relevant scenarios: thesis right, idiosyncratic downside, market/sector selloff, factor unwind, rates/FX/commodity shock plus CDS/credit-spread signal stress, earnings/catalyst gap, volatility spike/collapse, liquidity stress, correlation break, short squeeze or hedge pain. Include at least one scenario where the hedge fails.
-
-## Basis Risk Ledger
-
-Columns: hedge, basis risk, severity, why it matters, mitigation, monitoring trigger.
-
-Common risks: product, factor, sector/subsector, geography, currency, timing/expiry, liquidity, volatility, catalyst, borrow/locate, crowding/squeeze, ETF constituent, curve/tenor, legal entity/capital-structure signal context. Credit instrument basis belongs in Credit Markets.
-
-Severity: low = direct/causal/monitorable; medium = proxy mismatch acceptable for cost/liquidity; high = may fail in the adverse scenario and should be sized conservatively or rejected.
-
-## Recommendation And Exits
-
-Recommend primary hedge, secondary alternative, tactical/event hedge, portfolio overlay if superior, rejected hedges, implementation checks, and readiness label. Do not recommend implementing an option hedge until strike, expiry, premium, bid/ask, liquidity, Greeks, coverage ratio, and maximum-loss math are available.
-
-Exit triggers: resize when exposure/position/beta/vol changes; roll when expiry/catalyst timing changes; monetize after protection works or vol spikes; remove after risk passes or hedge damages upside; re-underwrite when basis breaks, P&L is abnormal, borrow changes, or correlation fails.
-
-## QA
-
-Confirm objective, retained exposure, candidate breadth, basis risk, cost/liquidity, sizing logic, hedge-failure scenario, readiness label, missing data, and next action are explicit.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/instrument-playbooks.md b/plugins/financial-markets/skills/portfolio-risk-management/references/instrument-playbooks.md
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-# Hedge Instrument Playbooks
-
-Use to select and critique instruments. The aim is not exhaustive coverage; it is PM judgment on fit, cost, and basis risk.
-
-## Instrument Map
-
-| Instrument | Best use | Key checks | Failure modes |
-|---|---|---|---|
-| Factor hedge | Reduce beta, growth/value, momentum, quality, size, leverage, duration, liquidity, or crowding while retaining idiosyncratic alpha | factor definition/source, stability across regimes, liquidity, crowding, whether factor is part of alpha | stale/unstable exposure, over-hedging alpha, crowded basket, correlation breaks, hedge becomes main P&L |
-| Proxy hedge | Direct hedge unavailable, illiquid, too expensive, or too narrow | same driver, geography, currency, cycle, market cap, leverage, factor profile, liquidity, stress behavior | unrelated exposures dominate, fails in feared scenario, poor stress liquidity |
-| Pair/peer hedge | Express relative value while reducing market/sector/factor risk | similar end-market, clear relative thesis, catalyst, borrow, dividends, corporate actions, sizing basis | short leg M&A/squeeze, borrow spike, spread not mean-reverting, hidden factor bet, both legs fall |
-| Options | Nonlinear/event protection, defined downside, squeeze cap, upside preservation | expiry vs catalyst, strike vs downside, premium vs alpha, IV/skew/term, delta/gamma/vega/theta, liquidity, monetization | rich IV decay, wrong tenor, capped protection too soon, collar caps thesis upside, poor liquidity |
-| Macro/cross-asset | Rates, curve, inflation, FX, commodity, country, vol, or CDS/credit-spread signal risk is causal to the equity thesis | economic link, tenor/roll, natural hedges, portfolio-level overlap, whether the proxy preserves alpha | loose macro proxy, relationship regime shift, roll dominates, headline exposure offset elsewhere |
-| Credit Markets handoff / equity-risk signal | Use CDS levels, spreads, ratings, refinancing pressure, maturity walls, or covenant headlines only to judge common-equity downside; route implementation to Credit Markets | confirm the signal is causal to equity downside, dated, sourced, and not a substitute for credit-security analysis | treating a credit signal as an equity hedge, basis widens, capital-structure trade requires CDS/bond/loan expertise |
-| Portfolio overlay | Common unwanted risk across book or single-name hedges are too costly | book exposure, benchmark risk, liquidity, rebalancing rules | over-hedges existing offsets, less precise, changes active risk |
-| No hedge / size down | Hedge cost or basis risk is worse than exposure | opportunity cost, liquidity, tax/mandate, catalyst timing | missed protection if risk is hedgeable and material |
-
-## Options Structures
-
-| Structure | Best for | Tradeoff |
-|---|---|---|
-| Long put | Clean disaster/idiosyncratic downside | highest premium/theta |
-| Put spread | Normal adverse move or event gap range | capped protection |
-| Collar / put-spread collar | Lower net cost when upside can be sold | caps upside and/or disaster protection |
-| Index/sector put | Broad beta/sector selloff | basis to single name |
-| Long call on short | Cap squeeze risk when sized share-for-share and priced through maximum loss | premium drag and theta |
-| Call spread on short | Offset a defined squeeze range | loss reopens above written call strike; not an absolute cap |
-| Calendar / risk reversal | timing/vol or premium efficiency | more complex Greeks and obligations |
-
-## Pair Sizing Choices
-
-Dollar-neutral for similar risks; beta-neutral for market/sector beta; volatility-neutral for risk contribution; factor-neutral for style exposures; driver-neutral for units, revenue, commodity sensitivity, deposits/assets, or other economic drivers.
-
-Treat every pair as a second thesis, not just a hedge.
-
-For a short subject to an absolute loss cap, require one listed long-call contract per 100 shares short, adjusted for the contract multiplier and any corporate action. Maximum loss must include strike minus short sale entry, call premium, borrow/dividend cost, bid/ask and execution reserve, and applicable carry. If that package cannot be priced and shown within the cap, recommend no position rather than an unhedged or call-spread substitute.
-
-## Sector Hedge Cues
-
-| Sector | Likely exposures | Candidate hedges | Watch basis |
-|---|---|---|---|
-| SaaS/software | growth factor, rates/duration, NRR/bookings, IT spend, margins, AI disruption | software ETF, Nasdaq/growth basket, peer basket, earnings put spreads, rates proxy if duration matters | vertical vs horizontal, SMB vs enterprise, profitable vs unprofitable, seat vs usage, AI beneficiary vs disrupted |
-| Semis | AI capex, memory/equipment cycle, China/export controls, inventory, margins | semi ETF, equipment/memory/analog peers, customer/supplier proxies, Nasdaq, options, China/Asia proxy | fabless/foundry/equipment, AI leader vs cyclical analog, China exposure, cycle timing |
-| Banks | rates/curve, deposits, credit cycle, CRE, capital, regulation | bank ETF, regional/peer banks, curve hedge, event options; CDS/spreads as equity-downside signal only | asset sensitivity, deposit mix, CRE, capital ratios, HTM marks, regulatory category |
-| Biotech/pharma | clinical/regulatory binary events, patent cliff, M&A, rates, reimbursement | biotech/pharma ETF, event options, modality/TA basket, index puts for funding risk | binary idiosyncratic risk often not hedgeable by ETF; tenor must match event |
-| REITs | rates/cap rates, leverage/refi, property type, occupancy/rent growth | REIT ETF, property-type peers, rates futures, housing proxy; credit spreads as equity-risk signal only | property type and maturity structure dominate |
-| Energy/E&P | oil/gas, basis differentials, service costs, production mix, balance sheet | commodity futures, energy/E&P ETF, service proxy; credit spreads as equity-downside signal only | company hedge book, basin, transport, production mix |
-| Consumer/retail | consumer spend, wages/freight, inventory, promotions, rates/housing, FX | sector ETF, cohort/channel peers, rates/housing proxy, FX, earnings put spreads | luxury vs mass, online vs store, discretionary vs staple-like |
-| Industrials/transports | PMI/capex cycle, backlog, labor/fuel, supply chain | industrial ETF, subsector peers, oil/fuel, cyclical basket, rates proxy; credit spreads as signal only | diversified ETF may not match subsector |
-| Insurance | rates, investment income, catastrophe, reserves, pricing cycle | insurance ETF, line-specific peers, rates, cat-risk proxies | P&C/life/brokers/reinsurers differ |
-| Exchanges/brokers | volumes, volatility, rates, cash balances, crypto, listings, regulation | peer basket, vol proxy, rates, financials ETF | vol can help exchanges but hurt brokers depending mix |
-| Payments/fintech | consumer spend, cross-border, rates, credit losses, regulation, SMB, crypto | peer basket, financials/tech blend, consumer proxy, rates; credit-loss and spread signals as equity context only | networks/processors/lenders/wallets have different drivers |
-
-## Recommendation-Changing Red Flags
-
-Hedge cost exceeds expected alpha/protection value, hedge caps needed upside, hedge has more idiosyncratic risk than target, borrow is unavailable/unstable, option liquidity is poor, ETF overlap is weak, tenor misses catalyst, portfolio already offsets the risk, basis risk is highest in the adverse scenario, or size reduction is cleaner.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/liquidity-drawdown-scenarios.md b/plugins/financial-markets/skills/portfolio-risk-management/references/liquidity-drawdown-scenarios.md
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-# Liquidity, Drawdown, and Scenario Analysis
-
-## Purpose
-
-Position sizing is only useful if the user understands how much can be lost, how quickly, and whether the position can be exited or reduced during stress.
-
-## Liquidity analysis
-
-### Core equity liquidity metrics
-
-- Average daily volume (ADV), preferably 30-day and 90-day.
-- Dollar ADV = ADV x price.
-- Free float and float-adjusted volume.
-- Position as percent of ADV and percent of float.
-- Days to exit at 5%, 10%, and 20% ADV participation.
-- Bid/ask spread and block liquidity, if available.
-
-### Short liquidity and borrow
-
-- Borrow availability and borrow cost.
-- Utilization and short interest.
-- Days to cover.
-- Dividend cost.
-- Recall risk.
-- Squeeze/crowding risk.
-
-### Options liquidity
-
-- Open interest and daily option volume.
-- Bid/ask width.
-- Strike/expiry depth.
-- Market maker liquidity around catalyst.
-- Assignment/exercise considerations if relevant.
-
-### Credit Markets handoff / equity risk read-through
-
-- Do not analyze TRACE volumes, dealer depth, bid lists, lot size, issue size, spread DV01/CS01, recovery, or debt-security liquidity as a local sizing lane.
-- If credit liquidity matters, use it only as a common-equity stress signal: refinancing access, maturity wall, covenant headline, liquidity squeeze, or solvency risk that could force a smaller equity position.
-- Route credit instrument liquidity, debt exit capacity, and recovery/default sizing to Credit Markets.
-
-## Participation-rate discipline
-
-Use normal and stressed participation rates:
-
-- Normal exit: 5-15% ADV depending on liquidity.
-- Stress exit: 2.5-10% ADV, lower for event-driven, small-cap, crowded, balance-sheet-stressed, or options-heavy equity positions.
-- For positions with catalyst gap risk, assume exit happens after adverse move, not before.
-
-## Scenario design
-
-### Baseline scenarios
-
-At minimum:
-
-1. **Base case**: thesis works or unfolds as expected.
-2. **Downside case**: thesis delayed or partly wrong.
-3. **Stress case**: thesis wrong plus market/liquidity/regime pressure.
-
-### Scenario fields
-
-Each scenario should include:
-
-- Description.
-- Probability, if user provided or explicitly assumed.
-- Price/return move.
-- P&L dollars.
-- P&L as percent NAV.
-- Time horizon.
-- Liquidity/exit implication.
-- Decision rule.
-
-### Event scenarios
-
-Include:
-
-- Positive event outcome.
-- Neutral/delay outcome.
-- Negative event outcome.
-- Adverse market backdrop at event date.
-- Financing/borrow/liquidity impact.
-
-### Earnings scenarios
-
-Include:
-
-- Beat/raise, in-line, miss/lower.
-- Multiple expansion/contraction.
-- Estimate revision path.
-- Implied move/options market if available.
-- Post-print liquidity and gap risk.
-
-### Options scenarios
-
-Include:
-
-- Underlying price move.
-- Implied volatility change.
-- Time to expiry / theta decay.
-- Delta/gamma change.
-- Premium remaining and maximum loss.
-
-### Equity-risk credit signal scenarios
-
-Include only when they affect common equity:
-
-- CDS or credit-spread widening as an equity-downside warning signal.
-- Rating action, refinancing pressure, maturity wall, covenant headline, or liquidity squeeze as an equity impairment input.
-- Whether the signal changes add/trim/exit/hedge/re-underwrite status.
-- Route recovery/default valuation, spread DV01/CS01, bond, loan, CDS, and covenant-security analysis to Credit Markets.
-
-## Drawdown framing
-
-Calculate:
-
-- Ordinary downside loss.
-- Stress downside loss.
-- Loss as bps of NAV.
-- Contribution to expected portfolio drawdown if correlations rise.
-- Number of other correlated positions that may lose simultaneously.
-
-PM interpretation:
-
-- A position can be acceptable on standalone downside but unacceptable when correlated book losses are included.
-- A position can have attractive expected value but unacceptable path risk if it can force de-risking before payoff.
-- Stop-loss levels are less useful when liquidity is poor, catalyst is binary, or borrow can be recalled.
-
-## Monitoring triggers
-
-Good triggers are specific and connected to thesis or risk. Include:
-
-- Price levels: add, trim, review, stop, thesis break.
-- Fundamental KPIs: revenue, margins, orders, churn, same-store sales, credit metrics, cash burn, utilization.
-- Estimate revisions and consensus changes.
-- Factor/macro triggers: rates, FX, commodities, CDS/credit-spread signals tied to equity risk, inflation, curve, and funding markets.
-- Liquidity triggers: ADV decline, borrow cost spike, options liquidity deterioration.
-- Catalyst triggers: event date change, adverse legal/regulatory signal, deal spread widening.
-- Portfolio triggers: sector exposure breach, factor exposure breach, drawdown threshold, correlation spike.
-
-Avoid vague triggers like "monitor earnings" or "watch macro."
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/position-sizing-output-templates.md b/plugins/financial-markets/skills/portfolio-risk-management/references/position-sizing-output-templates.md
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-# Output Templates
-
-## Standalone HTML Integrated Risk Decision Report
-
-For a substantive integrated size-and-hedge decision, use a polished standalone HTML risk decision report following `../../../shared/html-artifact-standard.md`. Structured tables, scenarios, and monitoring triggers are appropriate; do not convert the ordinary report into a standardized dashboard unless explicitly requested.
-
-The first-read layer should include:
-
-| Required first-read block | Content |
-| --- | --- |
-| `Current Action` | `Conditional risk screen`, `Not implementation-ready`, `Initiate`, `Resize`, `Hedge`, or `Do not initiate`, with the reason. |
-| `Constraint Interpretation` | Whether the loss limit is a `scenario loss budget`, an `absolute loss cap`, or unresolved. |
-| `Illustrative Unhedged Size` | Show only when tied to a stated adverse-move assumption; never imply it satisfies an absolute cap for a short. |
-| `Hard-Cap Compliant Package` | For a short, priced share-for-share long calls or `no position`; include maximum-loss inputs. |
-| `Missing Inputs Before Entry` | Executable quote, ADV/exit capacity, borrow/locate and squeeze inputs, portfolio checks, and option terms when needed. |
-
-If current executable price, liquidity/exit capacity, short borrow/locate inputs, or required option-chain terms are missing, do not use `initiate` as the lead action. Label the output `Conditional risk screen` or `Not implementation-ready`, and state which conditional size or hedge package can be evaluated once inputs are available.
-
-For HTML readability, do not fragment tickers, dates, percentages, basis-point amounts, instrument terms such as `GLP-1`, or scenario labels with inline citation links. Prefer compact citations adjacent to complete figures, table-row source columns, or short section-level notes. Visually inspect local HTML with local headless-browser screenshots before delivery.
-
-## Default PM readout
-
-Use this structure unless the user asks for a spreadsheet, deck, or shorter answer.
-
-# Risk Position Sizing - [Security / Trade]
-
-## 1. Decision summary
-
-| Item | Recommendation |
-|---|---|
-| Action | initiate / add / hold / trim / hedge / avoid / watchlist |
-| Recommended size | [% NAV] / [$] / [shares/contracts/notional] |
-| Current/proposed size | [if provided] |
-| Binding constraint | loss budget / liquidity / volatility / exposure limit / conviction / portfolio fit |
-| Loss-limit interpretation | scenario loss budget / absolute loss cap / unresolved |
-| Implementation readiness | conditional risk screen / not implementation-ready / executable after checks |
-| Confidence | high / medium / low |
-| PM judgment | [1-3 sentences on why this is the right size] |
-
-## 2. Trade setup
-
-- Instrument/direction:
-- Thesis being expressed:
-- Time horizon/catalyst:
-- Target/base/downside:
-- Key data date/time:
-- User-provided facts:
-- Assumptions:
-
-## 3. Risk/return case
-
-| Case | Price/return | P&L $ | P&L % NAV | Timing | Notes |
-|---|---:|---:|---:|---|---|
-| Upside |  |  |  |  |  |
-| Base |  |  |  |  |  |
-| Downside |  |  |  |  |  |
-| Stress |  |  |  |  |  |
-
-## 4. Constraint interpretation and compliant package
-
-| Constraint branch | Size/package | Maximum loss treatment | Current action |
-|---|---:|---|---|
-| Scenario loss budget |  | Stated adverse move plus cost/reserve assumptions | Conditional size if scenario is accepted |
-| Absolute loss cap |  | Priced defined-loss package required for an equity short | Do not initiate without compliant protection |
-
-## 5. Sizing triangulation
-
-| Sizing lens | Implied max size | Constraint status | Interpretation |
-|---|---:|---|---|
-| Loss budget |  | binding / not binding |  |
-| Volatility budget |  | binding / not binding |  |
-| Liquidity / exit |  | binding / not binding |  |
-| Gross/net/beta limits |  | binding / not binding |  |
-| Factor/sector limits |  | binding / not binding |  |
-| Conviction/catalyst quality |  | binding / not binding |  |
-| Portfolio fit/correlation |  | binding / not binding |  |
-
-## 6. Portfolio impact
-
-| Exposure | Before | Incremental | After | Limit / context | Flag |
-|---|---:|---:|---:|---:|---|
-| Gross exposure |  |  |  |  |  |
-| Net exposure |  |  |  |  |  |
-| Beta-adjusted net |  |  |  |  |  |
-| Sector exposure |  |  |  |  |  |
-| Issuer exposure |  |  |  |  |  |
-| Factor exposure |  |  |  |  |  |
-| Liquidity bucket |  |  |  |  |  |
-
-## 7. Liquidity and execution
-
-- ADV / dollar ADV:
-- Position as % ADV:
-- Exit days at selected participation rate:
-- Stress-liquidity assumption:
-- Borrow/options/equity-risk signal notes:
-- Execution recommendation:
-
-## 8. Monitoring rules
-
-| Trigger | Threshold | Action | Owner / cadence |
-|---|---|---|---|
-| Add |  |  |  |
-| Trim |  |  |  |
-| Exit / thesis break |  |  |  |
-| Risk review |  |  |  |
-| Catalyst |  |  |  |
-
-## 9. Open items and QC flags
-
-- Missing data that matters:
-- Stale inputs:
-- Formula/source tie-out issues:
-- Compliance/mandate review items:
-
-## Deterministic support output schema
-
-The shipped calculator writes CSV support tables plus an optional support note, not a native multi-tab workbook. If a workbook is required, create it with a spreadsheet/workbook tool from these CSVs and preserve the source workbook.
-
-### `position_summary.csv`
-
-Columns: analysis_date, security, ticker, direction, entry_price, recommended_size_pct_nav, recommended_notional, recommended_shares_or_units, raw_binding_constraint, raw_binding_size_pct_nav, confidence, proposed_size_pct_nav, current_size_pct_nav
-
-### `sizing_cases.csv`
-
-Columns: sizing_lens, input_value, formula, implied_size_pct_nav, implied_notional, binding_flag, notes
-
-### `scenario_pnl.csv`
-
-Columns: scenario, probability, price_or_return, pnl_dollars, pnl_pct_nav, time_horizon, liquidity_assumption, action_rule, notes
-
-### `exposure_impact.csv`
-
-Columns: exposure_type, before, incremental, after, limit, status, source
-
-### `liquidity_exit.csv`
-
-Columns: security, price, adv_shares, adv_dollars, position_shares, position_dollars, position_pct_nav, participation_rate, days_to_exit, stressed_participation_rate, stressed_days_to_exit, notes
-
-### `monitoring_rules.csv`
-
-Columns: trigger_type, metric, threshold, action, owner, cadence, source
-
-### `support_note.md`
-
-Optional support note with recommended size, binding constraint, scenario P&L, liquidity, and PM caveat. For substantial reusable sizing work, the lead user-facing artifact should be a decision summary, HTML report/dashboard, or workbook/deck surface rather than Markdown.
-
-## Template CSV bundle
-
-`scripts/create_position_sizing_templates.py` writes blank intake CSVs: `trade_setup.csv`, `portfolio_context.csv`, `exposure_impact.csv`, `liquidity.csv`, `scenarios.csv`, `options_overlay.csv`, `pair_legs.csv`, `factor_exposures.csv`, `etf_index_context.csv`, `macro_proxy_inputs.csv`, `monitoring_rules.csv`, and `sources.csv`. Credit-security inputs route to Credit Markets rather than a local template.
-
-## Deck structure
-
-If creating a deck section, use this sequence:
-
-1. Executive sizing recommendation.
-2. Trade setup and thesis expression.
-3. Risk/return and scenario P&L.
-4. Sizing triangulation.
-5. Portfolio exposure impact.
-6. Liquidity/exit and execution plan.
-7. Monitoring rules and decision triggers.
-8. Appendix: sources, assumptions, and QC flags.
-
-## Existing-analysis critique format
-
-When reviewing an existing analysis, use:
-
-1. What is directionally right.
-2. What is missing or stale.
-3. Where the sizing math is fragile.
-4. Hidden exposures or basis risks.
-5. Liquidity/exit concerns.
-6. Recommended revised size and why.
-7. Specific edits to make the analysis PM-ready.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/quality-control.md b/plugins/financial-markets/skills/portfolio-risk-management/references/quality-control.md
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-# Quality Control Checklist
-
-Use this checklist before finalizing any `position_sizing` or `integrated_risk_plan` output from `portfolio-risk-management`.
-
-## Source QC
-
-- Are all prices, market data, portfolio values, exposures, betas, volatilities, liquidity, borrow, and options inputs dated?
-- Are user-provided facts separated from assumptions and sourced facts?
-- Are freshness-sensitive inputs flagged if stale or missing?
-- Are source conflicts identified instead of silently averaged?
-- Are formulas and units tied out if a workbook was used?
-
-## Sizing QC
-
-- Is the recommended size based on the tightest binding constraint?
-- Does the output distinguish a `scenario loss budget` from an `absolute loss cap`, rather than assuming an adverse-move magnitude defines the user's constraint?
-- For an ambiguous short loss limit, does an interactive run clarify the interpretation, or does a non-interactive output show conditional branches without recommending initiation?
-- Is downside loss shown in dollars and bps/% NAV?
-- Is stress loss shown separately from ordinary downside?
-- Does the analysis include gross, net, beta-adjusted, and relevant factor exposure?
-- Is liquidity/exit capacity calculated using realistic participation rates?
-- Are current, proposed, and recommended sizes clearly distinguished?
-- Does the output explain whether the trade should be initiated, added to, held, trimmed, hedged, avoided, or watchlisted?
-- If executable quote, liquidity/exit capacity, borrow/locate or required option terms are missing, is the output labeled `Conditional risk screen` or `Not implementation-ready` rather than `Initiate`?
-
-## PM judgment QC
-
-- Does the size preserve the intended alpha thesis?
-- Does it avoid over-hedging or over-sizing based on false precision?
-- Does it recognize catalyst/path risk, not just endpoint valuation?
-- Does it account for correlated positions and crowdedness?
-- Does it distinguish a starter position from a core position?
-- Does it explain what new evidence would justify scaling up?
-
-## Instrument-specific QC
-
-### Long equity
-
-- Downside tied to thesis-break value, not just arbitrary stop.
-- Sector/factor/beta exposure visible.
-- Liquidity acceptable for the proposed size.
-
-### Short equity
-
-- Borrow cost, availability, recall risk, dividend cost, short interest, days to cover, and squeeze risk addressed when relevant.
-- Stress case includes gap-up risk beyond base upside target.
-- Size smaller when loss is convex or exit risk is high.
-- An absolute loss cap is met only by a priced defined-loss package or no position; an unhedged short is not presented as cap-compliant.
-
-### Pair trade
-
-- Both legs sized consistently by beta, factor, sector, or dollar neutrality.
-- Residual net/gross exposure and borrow/liquidity shown.
-- The spread is thesis-linked, not merely historically correlated.
-
-### Options
-
-- Premium-at-risk and delta-adjusted exposure are both shown.
-- Expiry aligns with catalyst plus buffer.
-- IV crush, theta decay, and liquidity are addressed.
-- Greeks are not stale.
-- A hard-cap hedge for a short uses share-for-share long-call coverage and calculates maximum loss after entry-to-strike loss, premium, borrow/dividend cost, bid/ask and execution reserve, and applicable carry.
-- A call spread is not described as an absolute loss cap on a short because loss reopens above its written strike.
-
-### Credit Markets handoff and equity macro proxy
-
-- CDS, bonds, loans, spread DV01/CS01, recovery, covenant, capital-structure, and distressed hedging or sizing questions are routed to Credit Markets.
-- CDS/spreads/ratings/maturity/refinancing/covenant headlines are used only as common-equity risk signals.
-- Macro proxies are tied to the equity thesis and compared against size-down/no-hedge alternatives.
-- Basis between proxy and equity thesis is explained.
-
-## Monitoring QC
-
-- Are add/trim/exit rules specific and measurable?
-- Are thesis-break triggers distinguished from price-only stops?
-- Is there a review cadence around catalysts or data releases?
-- Are portfolio-level triggers included when relevant?
-
-## HTML and presentation QC
-
-- A substantive integrated risk plan defaults to a polished standalone HTML risk decision report unless the user explicitly requests a standardized dashboard.
-- The first-read layer shows current action, constraint interpretation, illustrative unhedged size when applicable, hard-cap compliant package, and missing inputs before entry.
-- Citations do not fragment tickers, dates, percentages, basis-point amounts, instrument terms such as `GLP-1`, or scenario labels.
-- Local HTML has been inspected with local headless-browser screenshots of the opening viewport and decision-critical sections before delivery.
-
-## Common failure modes to avoid
-
-- Sizing entirely from upside/downside without portfolio context.
-- Treating a stop loss as executable around binary events.
-- Using stale beta/correlation as if stable.
-- Ignoring liquidity in small-cap, crowded, event-driven, balance-sheet-stressed, or options positions.
-- Ignoring borrow and squeeze risk on shorts.
-- Failing to distinguish gross, net, active, and beta-adjusted exposure.
-- Recommending a large position while key inputs are assumptions.
-- Producing a generic thesis memo instead of a sizing decision.
-- Overwriting source data or prior analysis in a workbook.
-
-## Final answer test
-
-A PM should be able to read the final output and immediately know:
-
-1. Whether to put the position on.
-2. How large it should be.
-3. What loss is being tolerated.
-4. What exposure is being added.
-5. What would make the PM add, trim, hedge, or exit.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/sizing-framework.md b/plugins/financial-markets/skills/portfolio-risk-management/references/sizing-framework.md
deleted file mode 100644
index 9d5dc9f..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/sizing-framework.md
+++ /dev/null
@@ -1,213 +0,0 @@
-# Sizing Framework
-
-## Purpose
-
-This framework converts a thesis into a position size by combining math, market structure, and PM judgment. The recommended size should be the maximum prudent size, not the maximum possible size.
-
-## The six sizing lenses
-
-### 1. Loss-budget sizing
-
-Use when the user provides maximum loss tolerance, stop level, downside price, or portfolio loss limit.
-
-Formula:
-
-- Position value = loss budget dollars / absolute downside return
-- Percent NAV = position value / NAV
-
-For a long:
-
-- Downside return = (downside price / entry price) - 1
-
-For a short:
-
-- Adverse return = (upside squeeze price / entry price) - 1
-- Loss budget should include borrow cost, dividends, financing, and forced-cover risk if material.
-
-Interpret the loss constraint before converting it into a position size:
-
-- `Scenario loss budget`: the user accepts a stated stress case as the sizing boundary. Show the adverse-move assumption explicitly and describe the result as conditional on that scenario.
-- `Absolute loss cap`: loss cannot exceed the stated NAV amount even if a short squeezes beyond the modeled move. An unhedged short cannot meet this constraint; require priced defined-loss protection or recommend no position.
-- `Ambiguous constraint`: for an interactive request, clarify which interpretation applies before recommending entry. For a non-interactive run, show both branches and do not label an assumed-stress size as ready to initiate.
-
-PM judgment:
-
-- Use thesis-break downside if better than a mechanical stop.
-- Do not let a tight stop justify an oversized illiquid or gap-risk position.
-- For catalyst trades, use gap-loss sizing, not stop-loss sizing.
-- For shorts with an absolute cap, size the combined short-plus-call package from its maximum loss, not from an arbitrary squeeze percentage.
-
-### 2. Volatility-budget sizing
-
-Use when the user has volatility, target risk contribution, or risk-system output.
-
-Simple approximation:
-
-- Volatility contribution dollars = position value x annualized volatility x correlation adjustment
-- Position value = volatility budget dollars / annualized volatility
-
-If beta or factor volatility matters more than total volatility, use beta-adjusted or factor-adjusted exposure instead.
-
-PM judgment:
-
-- Volatility can understate risk for event-driven, crowded short, litigation, biotech, merger arb, and balance-sheet-stressed equity trades.
-- Historical beta and correlation can break during regime shifts or company-specific events.
-
-### 3. Liquidity sizing
-
-Use when ADV, float, trading volume, options open interest, ETF liquidity, or exit window matters.
-
-Formula:
-
-- Exit capacity = ADV x participation rate x exit days
-- Liquidity-constrained size = exit capacity x price, or notional capacity for listed options and approved macro proxies.
-
-Default participation rate assumptions if absent:
-
-- Highly liquid large-cap equity: 10-20% ADV for ordinary execution.
-- Mid-cap equity: 5-10% ADV.
-- Small-cap/illiquid equity: 2.5-5% ADV.
-- Balance-sheet-stressed equity or event-driven names: lower unless block liquidity is verified.
-
-PM judgment:
-
-- Use stressed liquidity for downside cases.
-- For shorts, liquidity must include borrow availability and recall risk.
-- For options, open interest and market depth can bind before underlying ADV.
-
-### 4. Exposure-limit sizing
-
-Use when mandate or portfolio constraints matter.
-
-Check:
-
-- Single-name max.
-- Sector/industry max.
-- Country/currency max.
-- Gross and net exposure.
-- Beta-adjusted gross/net.
-- Factor exposure and active risk.
-- Long/short book balance.
-- Liquidity buckets.
-- Benchmark active weight and tracking error, for long-only or mutual fund contexts.
-
-Formula examples:
-
-- Single-name capacity = max single-name % NAV - current issuer exposure % NAV.
-- Sector capacity = max sector exposure - current sector exposure.
-- Beta capacity = beta budget / security beta.
-
-PM judgment:
-
-- Limits are not a target. A position can be below limit and still be too large.
-- Add correlated positions and related instruments before applying limits.
-
-### 5. Conviction and thesis-quality sizing
-
-Use to scale the mathematically permitted size up or down based on quality of evidence.
-
-Conviction ladder:
-
-- **Starter**: thesis still forming, data incomplete, catalyst uncertain, high basis risk. Size small enough to learn.
-- **Core**: thesis is well-evidenced, downside understood, liquidity acceptable, monitoring rules clear.
-- **High-conviction**: variant view is strong, evidence is current, skew is favorable, path is robust, exit plan credible.
-- **Special situation / event**: size by event outcome distribution, not by ordinary beta/vol.
-- **No position / watchlist**: risk is unbounded, liquidity poor, thesis not falsifiable, or data insufficient.
-
-PM judgment:
-
-- Increase size only when downside is both quantified and tolerable.
-- A great thesis with poor liquidity or bad asymmetry deserves a smaller size.
-- A position with high expected return but unclear thesis-break rules should not be core size.
-
-### 6. Portfolio-fit sizing
-
-Use when the existing book already has overlapping exposure.
-
-Check:
-
-- Correlated longs/shorts.
-- Supply-chain and competitor exposure.
-- Same macro factor or thematic basket.
-- Same catalyst or regulatory outcome.
-- Crowded institutional ownership or hedge-fund hotel risk.
-- Short book squeeze correlation.
-- Benchmark and factor overlap.
-
-PM judgment:
-
-- Size incremental exposure, not the isolated line item.
-- If the thesis is a pure idiosyncratic view but the position adds significant factor risk, consider a hedge or lower gross size.
-
-## Recommended size construction
-
-1. Compute size under each lens.
-2. Identify the tightest binding constraint.
-3. Adjust for confidence and catalyst path.
-4. Compare to current/proposed size.
-5. Recommend action: initiate, add, hold, trim, hedge, avoid, or watchlist.
-6. Explain why the recommendation is not merely formulaic.
-
-## Sizing language
-
-Use precise language:
-
-- "Recommended starter size: 1.0-1.5% NAV, capped by downside loss budget and liquidity."
-- "Could scale to 3.0% only if the next data point confirms margin recovery and ADV remains above threshold."
-- "Proposed 5.0% size is too large because the stress case would cost 150 bps of NAV and require 12 trading days to exit at 10% ADV."
-- "Do not use a price stop as the primary control because catalyst gap risk makes the stop non-executable."
-- "Illustrative unhedged size under a +150% squeeze scenario: 20 bp NAV; not implementation-ready until the loss-cap interpretation, borrow, and liquidity checks are confirmed."
-
-Avoid weak language:
-
-- "Position looks attractive."
-- "Size according to conviction."
-- "Risk/reward is favorable" without quantification.
-- "Beta is low" without date, window, or regime caveat.
-
-## Situation-specific guidance
-
-### Long equity
-
-- Size by downside to thesis-break value, not just stop-loss.
-- Include market beta, sector, factor, and liquidity impact.
-- If upside is long-duration, avoid over-sizing into near-term estimate/catalyst risk.
-
-### Short equity
-
-- Size smaller than equivalent long when upside risk is uncapped, borrow is tight, catalyst timing uncertain, or squeeze risk is high.
-- Include borrow cost, dividends, recall, short interest, days to cover, and crowding.
-- Use stress case above mechanical upside target.
-
-### Pair trade
-
-- Size each leg by beta/factor/sector neutrality target and borrow/liquidity constraints.
-- Confirm the spread is linked to the thesis, not just historical correlation.
-- Include gross exposure and residual net exposure.
-
-### Options
-
-- Size premium-at-risk separately from delta-adjusted exposure.
-- Match expiry to catalyst timing with buffer.
-- Include IV crush, skew, theta, liquidity, and roll plan.
-- Do not recommend options only because downside is limited; premium decay and poor liquidity can make them bad hedges or bad expressions.
-
-### Event-driven
-
-- Size by probability-weighted outcome and adverse gap.
-- Include timing delay, break price, regulatory/legal outcomes, financing risk, borrow, and liquidity under stress.
-- Stop-losses can be unreliable around binary events.
-
-### Credit Markets handoff / equity-risk signals
-
-- Do not size CDS, bonds, loans, debt securities, distressed claims, recovery waterfalls, covenant trades, spread DV01/CS01, or capital-structure hedges in this plugin.
-- Use Credit Markets when the security being sized is credit or the recommended action is a credit trade.
-- Inside Public Equity Investing, CDS levels, credit spreads, rating actions, maturity walls, refinancing pressure, or covenant headlines may only lower equity size, change downside cases, trigger a hedge review, or force a re-underwrite of the common-equity thesis.
-- Best output: explain how the credit signal changes common-equity downside, liquidity, or sizing. Do not produce credit notional, spread DV01/CS01, recovery, or debt-security sizing.
-
-### Equity macro proxies
-
-- Use rates, FX, commodities, inflation, country risk, or volatility proxies only when they are causal to the public-equity thesis or portfolio exposure.
-- Size the equity position or approved macro proxy by scenario sensitivity, beta/regression, exposure mapping, option premium, or notional risk to the equity book.
-- Route standalone rates DV01, credit CS01, bond futures basis, loan hedges, CDS, or credit spread implementation to Credit Markets or the relevant macro workflow.
-- Include basis risk between the proxy and the equity thesis, and compare the proxy to a smaller equity size.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/source-and-context-protocol.md b/plugins/financial-markets/skills/portfolio-risk-management/references/source-and-context-protocol.md
deleted file mode 100644
index 167800e..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/source-and-context-protocol.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Source and Context Protocol
-
-## Purpose
-
-Use this protocol to make the skill robust across no-context, partial-context, full-source, and refresh workflows. The goal is to produce a useful PM-grade answer without hallucinating current market data or silently overwriting user work.
-
-## Context modes
-
-### No-context mode
-
-Trigger when the user gives no ticker, no position, no portfolio size, or no thesis.
-
-Output:
-
-1. Minimum intake checklist.
-2. Default analysis structure.
-3. Template tabs or CSV templates if the user is working in a spreadsheet.
-4. Examples of the few inputs that unlock a real recommendation.
-
-Do not invent a company, price, beta, volatility, current holdings, or recommended size.
-
-Minimum intake checklist:
-
-- Instrument/ticker and direction.
-- Portfolio NAV/AUM and benchmark, if relevant.
-- Current or proposed size.
-- Investment thesis and intended holding period.
-- Upside/base/downside or target/stop levels.
-- Maximum acceptable loss or risk budget.
-- For a short or option position, whether that maximum is a scenario loss budget or an absolute loss cap.
-- Liquidity constraint and required exit window.
-- Existing related exposures and hedges.
-- Mandate/risk limits.
-
-### Partial-context mode
-
-Trigger when the user provides a thesis or ticker but not a complete risk pack.
-
-Output:
-
-- A v0 sizing view with facts, assumptions, and missing data separated.
-- A preliminary recommended size only if enough information exists to compute a defensible range.
-- Confidence labels for price, beta, volatility, liquidity, borrow, factor, and scenario inputs.
-- A short list of data that would most improve the recommendation.
-- A conditional rather than executable action label when current quote, liquidity, borrow/locate, or required option inputs are not established.
-
-Use explicit labels:
-
-- **User-provided fact**: supplied by the user or in the active file.
-- **Sourced fact**: retrieved from a callable connected route, user-provided export, or cited source.
-- **Assumption**: inferred or defaulted because the source was missing.
-- **Placeholder**: intentionally blank until the user or source provides it.
-
-### Full-source mode
-
-Trigger when the user provides or enables access to a model, risk report, portfolio file, holdings export, data room, broker pack, or connected market data.
-
-Output:
-
-- Source inventory and source hierarchy.
-- Normalized trade, portfolio, and risk inputs.
-- Sizing recommendation with math trace.
-- Scenario P&L and liquidity/execution view.
-- Monitoring rules and open items.
-- QC flags for stale or inconsistent inputs.
-
-### Refresh mode
-
-Trigger when updating an existing sizing analysis or workbook.
-
-Output:
-
-- What changed since prior version.
-- Updated size and whether recommendation changed.
-- Drivers of change: price, target/downside, volatility, beta, liquidity, factor exposure, catalyst timing, portfolio NAV, current holding, or risk limits.
-- New tabs or sections; preserve prior versions.
-
-## Source hierarchy
-
-1. User-provided files, pasted context, active workbook/deck, and explicit assumptions.
-2. Callable connected routes or user-provided exports: portfolio holdings, risk systems, OMS/EMS exports, market data providers, research systems, broker files, and internal models.
-3. Primary market/company sources: filings, releases, presentations, transcripts, exchange/index/ETF sponsor data, issuer docs, exchange/index/ETF sponsor data, debt maturity schedules when relevant to common-equity risk, and official equity/security terms.
-4. Callable third-party routes or user-provided exports: Bloomberg, FactSet, LSEG, S&P Capital IQ, MSCI/Barra, Axioma, Refinitiv, broker estimates, rating agencies, TRACE/FINRA, OCC/options data, short interest data, and consensus providers.
-5. Public web fallback. Use only when better sources are unavailable and label the limitation.
-
-## Freshness rules
-
-Treat these as freshness-sensitive and date/time stamp them when possible:
-
-- Last price, market cap, shares outstanding, enterprise value.
-- Portfolio NAV, current holdings, gross/net exposure.
-- Beta, correlation, volatility, factor exposure.
-- ADV, float, short interest, borrow availability/cost, utilization, days to cover.
-- Options prices, implied volatility, skew, open interest, delta/gamma/vega/theta.
-- CDS levels, credit spreads, ratings, maturities, refinancing pressure, and covenant headlines only when used as common-equity risk signals; route yield, duration, recovery assumptions, spread DV01/CS01, and credit-security terms to Credit Markets.
-- Rates, FX, commodities, index weights, ETF holdings.
-- Earnings dates, regulatory events, trial dates, shareholder votes, merger close dates, lockups.
-- Consensus estimates and revisions.
-
-If data may be stale, say exactly which input is stale and why it matters for sizing.
-
-For short positions with a user-provided maximum NAV loss, classify the constraint as a `scenario loss budget`, `absolute loss cap`, or `unresolved` before recommending an implementable size. A scenario budget needs a visible adverse-move assumption. An absolute cap needs a priced defined-loss option package or a no-position conclusion.
-
-## Confidentiality and compliance
-
-- Treat portfolio holdings, trades, sizing, risk limits, LP/client context, and internal research as confidential.
-- Do not include client names or fund names in a clean external version unless the user requests it.
-- Do not present a recommendation as guaranteed or riskless.
-- For public-company analysis, avoid selective disclosure issues. If a source appears MNPI or restricted, flag it and do not use it unless the user confirms it is permitted for the workflow.
-- Keep the answer framed as analytical support for a professional user, not as retail investment advice.
-
-## Spreadsheet preservation
-
-When modifying spreadsheets:
-
-- Add versioned output tabs; do not overwrite source tabs.
-- Preserve formulas, hidden sheets, comments, named ranges, and formatting unless explicitly asked.
-- Put source notes and assumptions in separate tabs.
-- Create QC flags instead of silently changing questionable inputs.
-- If there are circular references, hardcodes, broken links, or stale external links, flag them before editing.
-
-## Confidence labels
-
-Use these labels in the output:
-
-- **High confidence**: sourced from user file or reliable connected source, recent, internally consistent.
-- **Medium confidence**: sourced but stale, estimated, or not fully tied to portfolio context.
-- **Low confidence**: inferred, public-web-only, missing key inputs, or inconsistent sources.
-
-## Missing data hierarchy
-
-If many inputs are missing, prioritize the missing data that most affects sizing:
-
-1. Portfolio NAV and current exposure.
-2. Downside/stop case and max acceptable loss.
-3. Liquidity/ADV and exit window.
-4. Beta/factor/sector exposure.
-5. Catalyst timing and gap risk.
-6. Borrow and options terms; if the missing inputs are CDS, bonds, loans, spread DV01/CS01, recovery, covenant, or debt-security terms, use a Credit Markets handoff instead of local sizing.
-7. Existing correlated positions and hedges.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/references/strategy-nuance.md b/plugins/financial-markets/skills/portfolio-risk-management/references/strategy-nuance.md
deleted file mode 100644
index 26d5e73..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/references/strategy-nuance.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# Strategy Nuance and PM Judgment
-
-## Purpose
-
-Use this reference when adapting position sizing to the type of public-equity-investing strategy. The right size is context-dependent: the same security can be a 50 bps event-driven stub, a 300 bps core long, a 100 bps short, or no position depending on mandate, catalyst, liquidity, and portfolio construction.
-
-## Long/short equity
-
-Key sizing judgment:
-
-- Size idiosyncratic alpha, not accidental net exposure.
-- Check long book and short book factor balance separately.
-- Avoid using a low-quality short as a beta hedge if it adds squeeze/crowding risk.
-- Treat earnings gaps, factor rotations, and borrow recalls as real loss events.
-- Use starter sizes when thesis evidence is not yet falsifiable.
-
-Best output:
-
-- Recommended gross and net contribution.
-- Beta-adjusted exposure.
-- Factor/crowding risks.
-- Add/trim triggers tied to thesis data and risk limits.
-
-## Long-only / mutual fund
-
-Key sizing judgment:
-
-- Benchmark active weight can matter more than absolute weight.
-- A position may be a risk-reducing add if the portfolio is structurally underweight a large benchmark constituent.
-- Liquidity and capacity matter because redemptions can force sales.
-- Drawdown and tracking error should be framed for the portfolio and mandate.
-
-Best output:
-
-- Absolute weight, active weight, and tracking-error implication if available.
-- Position rank in portfolio.
-- Benchmark-relative risk and sector/country impact.
-- Add/trim plan around valuation and thesis milestones.
-
-## Event-driven / special situations
-
-Key sizing judgment:
-
-- Size to adverse gap and timing delay, not ordinary volatility.
-- Stop-losses are often not executable around binary events.
-- Deal break, regulatory block, litigation, financing, shareholder vote, and timing risk dominate.
-- High expected value can still be too large if adverse outcome is severe or liquidity vanishes.
-
-Best output:
-
-- Probability-weighted outcome table.
-- Break price/adverse gap P&L.
-- Timeline and catalyst calendar.
-- Exit plan if event slips or spread widens.
-
-## Merger arbitrage
-
-Key sizing judgment:
-
-- Size by downside to unaffected/break price and probability of close.
-- Consider antitrust/regulatory, financing, shareholder, litigation, and timing risks.
-- Annualized spread can be misleading if downside is large or timeline uncertain.
-- Include borrow for stock deals and factor/market exposure for acquirer shares.
-
-Best output:
-
-- Gross spread, annualized spread, break spread, break price P&L.
-- Close probability and timing cases.
-- Hedge ratio if stock consideration exists.
-- Risk limit based on break loss.
-
-## Equity options / volatility
-
-Key sizing judgment:
-
-- Size premium-at-risk and delta/gamma/vega exposure separately.
-- Match expiry to catalyst with buffer; avoid false precision in short-dated options.
-- Consider IV crush after earnings/events and theta decay while waiting.
-- Options can cap downside but still be poor expressions if skew and liquidity are punitive.
-
-Best output:
-
-- Premium budget.
-- Delta-adjusted notional and Greeks.
-- Catalyst/expiry alignment.
-- Breakeven and scenario table with IV/time changes.
-
-## Credit Markets handoff / equity-risk signals
-
-Key sizing judgment:
-
-- Public Equity Investing does not size CDS, bonds, loans, distressed claims, recovery waterfalls, covenant trades, spread DV01/CS01, or capital-structure hedges.
-- CDS levels, credit spreads, rating actions, maturity walls, refinancing stress, and covenant headlines can still matter because they can impair common-equity value, liquidity, and downside gap risk.
-- If the next action is to buy, sell, hedge, or size a credit security, route to Credit Markets.
-
-Best output:
-
-- Equity impairment read-through from the credit signal.
-- Whether common-equity size should be smaller, hedged with equity instruments, or re-underwritten.
-- What evidence would restore equity sizing confidence.
-- Explicit Credit Markets handoff for any credit instrument, recovery, covenant, or spread DV01/CS01 work.
-
-## Equity macro proxies
-
-Key sizing judgment:
-
-- Rates, FX, commodities, inflation, country risk, volatility, and curve proxies belong here only when they are causal to a listed-equity thesis or portfolio exposure.
-- Size by equity scenario sensitivity, beta/regression, position notional, option premium, or factor exposure rather than standalone rates DV01 or credit CS01.
-- Correlations can invert in crisis or policy-shock regimes, so the proxy can fail exactly when the equity position is stressed.
-- A smaller equity position may be cleaner than a loose macro proxy.
-
-Best output:
-
-- Equity P&L sensitivity to the macro driver.
-- Proxy instrument, hedge ratio logic, basis risk, and liquidity caveat.
-- Scenario P&L for the equity book and proxy together.
-- Data/catalyst monitoring plan and size-down/no-hedge alternative.
-
-## Portfolio manager override principles
-
-Use these principles when the math and judgment disagree:
-
-- Do not scale simply because the upside/downside looks attractive if liquidity, gap risk, or source confidence is weak.
-- Do not cut a high-quality position solely because short-term volatility is high if downside is fundamental and tolerable.
-- Do not use a hedge or pair leg that removes the actual alpha driver.
-- Do not let a model produce a size that the team cannot psychologically or operationally hold through normal volatility.
-- Do not ignore concentration just because the name is liquid.
-- Do not recommend a core position until the thesis has explicit disconfirming evidence and exit rules.
-
-## Watchlist vs starter vs core
-
-- **Watchlist**: data incomplete, catalyst uncertain, liquidity/borrow poor, valuation not compelling, or risk not quantifiable.
-- **Starter**: enough evidence to begin learning; size small enough that being wrong is cheap.
-- **Core**: thesis, downside, liquidity, and portfolio fit are all sufficiently underwritten.
-- **High conviction / oversized only with governance**: requires exceptional evidence, favorable skew, clear risk controls, mandate fit, and explicit PM/risk approval.
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/scripts/create_position_sizing_templates.py b/plugins/financial-markets/skills/portfolio-risk-management/scripts/create_position_sizing_templates.py
deleted file mode 100755
index 1d70cb4..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/scripts/create_position_sizing_templates.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python3
-"""Create blank CSV templates for the portfolio-risk-management sizing mode."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-from pathlib import Path
-
-TEMPLATES = {
-    "trade_setup.csv": [
-        "analysis_date",
-        "security",
-        "ticker",
-        "instrument",
-        "direction",
-        "entry_price",
-        "target_price",
-        "base_price",
-        "downside_price",
-        "stress_price",
-        "holding_period",
-        "catalyst",
-        "thesis_summary",
-        "source",
-        "confidence",
-    ],
-    "portfolio_context.csv": [
-        "portfolio_name",
-        "nav",
-        "currency",
-        "benchmark",
-        "current_gross_exposure_pct",
-        "current_net_exposure_pct",
-        "current_active_weight_pct",
-        "max_single_name_pct_nav",
-        "max_sector_exposure_pct_nav",
-        "benchmark_active_weight_limit_pct",
-        "factor_limit_pct_nav",
-        "correlated_exposure_limit_pct_nav",
-        "borrow_squeeze_capacity_pct_nav",
-        "max_loss_bps_nav",
-        "target_position_vol_contribution_bps",
-        "notes",
-    ],
-    "exposure_impact.csv": [
-        "exposure_type",
-        "before",
-        "incremental",
-        "after",
-        "limit",
-        "status",
-        "source",
-    ],
-    "liquidity.csv": [
-        "security",
-        "price",
-        "adv_shares",
-        "adv_dollars",
-        "position_shares",
-        "position_dollars",
-        "normal_participation_rate",
-        "stress_participation_rate",
-        "required_exit_days",
-        "borrow_cost_pct",
-        "short_interest_pct_float",
-        "days_to_cover",
-        "borrow_availability",
-        "crowding_read",
-        "notes",
-    ],
-    "scenarios.csv": [
-        "scenario",
-        "probability",
-        "price_or_return",
-        "pnl_dollars",
-        "pnl_pct_nav",
-        "time_horizon",
-        "liquidity_assumption",
-        "action_rule",
-        "notes",
-    ],
-    "options_overlay.csv": [
-        "underlying",
-        "option_type",
-        "strike",
-        "expiry",
-        "contracts",
-        "premium",
-        "delta",
-        "gamma",
-        "vega",
-        "theta",
-        "implied_volatility",
-        "open_interest",
-        "bid_ask_spread",
-        "catalyst_alignment",
-        "notes",
-    ],
-    "pair_legs.csv": [
-        "leg",
-        "ticker",
-        "direction",
-        "price",
-        "current_size_pct_nav",
-        "proposed_size_pct_nav",
-        "beta",
-        "factor_exposure",
-        "borrow_cost_pct",
-        "adv_shares",
-        "liquidity_notes",
-        "thesis_link",
-        "source",
-    ],
-    "factor_exposures.csv": [
-        "factor",
-        "before",
-        "incremental",
-        "after",
-        "limit",
-        "factor_exposure_per_1pct_position",
-        "correlation_to_existing_book",
-        "source",
-        "as_of",
-        "notes",
-    ],
-    "etf_index_context.csv": [
-        "ticker",
-        "index_or_etf",
-        "benchmark_weight",
-        "active_weight",
-        "constituent_weight",
-        "etf_ownership_or_flow_signal",
-        "rebalance_event",
-        "liquidity_notes",
-        "source",
-    ],
-    "macro_proxy_inputs.csv": [
-        "proxy",
-        "risk_driver",
-        "direction",
-        "notional_or_exposure",
-        "price_or_level",
-        "rate_fx_commodity_or_index_level",
-        "equity_sensitivity",
-        "correlation_window",
-        "basis_risk",
-        "liquidity_notes",
-        "source",
-    ],
-    "monitoring_rules.csv": [
-        "trigger_type",
-        "metric",
-        "threshold",
-        "action",
-        "owner",
-        "cadence",
-        "source",
-    ],
-    "sources.csv": ["item", "source_name", "source_type", "date", "confidence", "notes"],
-}
-
-README = """# Risk Position Sizing Templates\n\nFill these CSV templates with the data available for the trade. Leave unknown fields blank rather than guessing.\n\nMinimum useful inputs: instrument/ticker, direction, entry price, downside/stress case, portfolio NAV, loss budget, liquidity/ADV, exit-window requirement, intended alpha, unwanted risk, and binding constraint. PM constraints should include gross/net/beta, active weight, factor exposure, correlated exposure, ADV/exit days, borrow/squeeze/crowding, catalyst gap risk, options Greeks when relevant, and size-down vs hedge. CDS, bond, loan, spread DV01/CS01, recovery, covenant, and capital-structure sizing belongs in Credit Markets.\n"""
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--out", default="risk_position_sizing_templates")
-    args = parser.parse_args()
-    out = Path(args.out)
-    out.mkdir(parents=True, exist_ok=True)
-    for name, headers in TEMPLATES.items():
-        with (out / name).open("w", newline="") as f:
-            writer = csv.writer(f)
-            writer.writerow(headers)
-    (out / "README.md").write_text(README)
-    print(f"Created {len(TEMPLATES)} templates in {out}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_calculator.py b/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_calculator.py
deleted file mode 100644
index 625821c..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_calculator.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#!/usr/bin/env python3
-"""Transparent CLI helper for portfolio-risk-management sizing JSON inputs."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-PLUGIN_ROOT = SCRIPT_DIR.parents[2]
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from position_sizing_core import (  # noqa: E402
-    exposure_rows,
-    liquidity_rows,
-    monitoring_rows,
-    scenario_rows,
-    sizing_rows,
-)
-from position_sizing_outputs import (  # noqa: E402
-    output_paths,
-    source_basis,
-    summarize_console,
-    write_output_bundle,
-)
-
-from shared.artifacts import build_run_log, output_manifest, write_run_log_bundle  # noqa: E402
-
-FILENAMES = {
-    "position_summary": "position_summary.csv",
-    "sizing_cases": "sizing_cases.csv",
-    "scenario_pnl": "scenario_pnl.csv",
-    "exposure_impact": "exposure_impact.csv",
-    "liquidity_exit": "liquidity_exit.csv",
-    "monitoring_rules": "monitoring_rules.csv",
-    "support_note": "support_note.md",
-    "run_log": "run_log.json",
-    "manifest": "manifest.json",
-}
-
-
-def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Materialize portfolio-risk-management sizing support outputs from JSON input."
-    )
-    parser.add_argument("--input", required=True)
-    parser.add_argument("--out", default="risk_position_sizing_outputs")
-    return parser.parse_args(argv)
-
-
-def build_failure_run_log(
-    out: Path, args: argparse.Namespace, paths: dict[str, str], message: str
-) -> dict[str, Any]:
-    return build_run_log(
-        hard_failures=[message],
-        warnings=[],
-        outputs=paths,
-        output_manifest_rows=output_manifest(
-            paths,
-            written={"run_log", "manifest"},
-            artifact_roles={
-                "support_note": "narrative_support",
-                "run_log": "machine_support",
-                "manifest": "machine_support",
-            },
-            hidden_unless_requested=set(paths),
-        ),
-        checks={"validation_errors": [message]},
-        model_status="not-decision-ready",
-        workbook_mode="csv_support_note_export",
-        extra={
-            "input_path": str(args.input),
-            "primary_human_deliverable": None,
-            "support_artifacts": [paths[key] for key in paths if key in {"run_log", "manifest"}],
-        },
-    )
-
-
-def main(argv: list[str] | None = None) -> int:
-    args = parse_args(argv)
-    out = Path(args.out)
-    out.mkdir(parents=True, exist_ok=True)
-    paths = output_paths(out)
-
-    try:
-        data = json.loads(Path(args.input).read_text(encoding="utf-8"))
-        sizing, summary = sizing_rows(data)
-        scenarios = scenario_rows(data, summary)
-        exposures = exposure_rows(data, summary)
-        liquidity = liquidity_rows(data, summary)
-        monitoring = monitoring_rows(data)
-    except Exception as exc:
-        write_run_log_bundle(out, build_failure_run_log(out, args, paths, str(exc)))
-        print(f"ERROR: {exc}")
-        return 1
-
-    try:
-        write_output_bundle(out, summary, sizing, scenarios, exposures, liquidity, monitoring)
-    except Exception as exc:
-        message = f"could not write output: {exc}"
-        write_run_log_bundle(out, build_failure_run_log(out, args, paths, message))
-        print(f"ERROR: {message}")
-        return 1
-
-    written = set(FILENAMES)
-    run_log = build_run_log(
-        hard_failures=[],
-        warnings=[
-            "Validate live prices, volatility, beta, liquidity, borrow, Greeks, limits, and correlations before trading."
-        ],
-        outputs=paths,
-        output_manifest_rows=output_manifest(
-            paths,
-            written=written,
-            artifact_roles={
-                "support_note": "narrative_support",
-                "run_log": "machine_support",
-                "manifest": "machine_support",
-            },
-            hidden_unless_requested=set(paths),
-        ),
-        source_basis=source_basis(data),
-        workbook_mode="csv_support_note_export",
-        extra={
-            "input_path": str(args.input),
-            "primary_human_deliverable": None,
-            "support_artifacts": [paths[key] for key in paths if key in written],
-        },
-    )
-    write_run_log_bundle(out, run_log)
-
-    print(f"Wrote outputs to {out}")
-    for line in summarize_console(summary):
-        print(line)
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_core.py b/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_core.py
deleted file mode 100644
index 001410d..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_core.py
+++ /dev/null
@@ -1,556 +0,0 @@
-"""Risk-position-sizing calculation helpers.
-
-This module owns the sizing math only. The CLI wrapper owns input/output and
-run-log behavior.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-
-def fnum(x: Any, default: float | None = None) -> float | None:
-    try:
-        if x is None or x == "":
-            return default
-        return float(x)
-    except (TypeError, ValueError):
-        return default
-
-
-def safe_div(a: float | None, b: float | None) -> float | None:
-    if a is None or b in (None, 0):
-        return None
-    return a / b
-
-
-def pct_to_notional(pct: float | None, nav: float) -> float | None:
-    return None if pct is None else nav * pct / 100.0
-
-
-def notional_to_pct(notional: float | None, nav: float) -> float | None:
-    return None if notional is None else notional / nav * 100.0
-
-
-def signed_return(direction: str, entry: float, price: float) -> float:
-    raw = price / entry - 1.0
-    return -raw if direction.lower() == "short" else raw
-
-
-def confidence_multiplier(label: str) -> float:
-    label = (label or "medium").lower()
-    return {"high": 1.0, "medium": 0.75, "low": 0.50}.get(label, 0.75)
-
-
-CREDIT_ROUTE_MESSAGE = (
-    "Use Credit Markets for CDS, bonds, loans, spread DV01/CS01, "
-    "capital-structure, distressed, recovery, covenant, or debt-security sizing. "
-    "Public Equity Investing may use CDS/spreads only as common-equity risk context."
-)
-
-CREDIT_INSTRUMENT_PATTERNS = {
-    "cds",
-    "credit default swap",
-    "bond",
-    "bonds",
-    "note",
-    "notes",
-    "debenture",
-    "loan",
-    "loans",
-    "bank loan",
-    "leveraged loan",
-    "term loan",
-    "high yield",
-    "investment grade",
-    "spread dv01",
-    "spread_dv01",
-    "cs01",
-    "dv01",
-    "capital structure",
-    "capital-structure",
-    "distressed debt",
-    "distressed claim",
-    "recovery waterfall",
-    "covenant",
-    "credit security",
-    "credit instrument",
-}
-
-
-def _normalized_text(value: Any) -> str:
-    return str(value or "").strip().lower().replace("_", " ").replace("/", " ").replace("-", " ")
-
-
-def is_credit_like_instrument(value: Any) -> bool:
-    text = _normalized_text(value)
-    if not text:
-        return False
-    return any(
-        pattern.replace("_", " ").replace("-", " ") in text
-        for pattern in CREDIT_INSTRUMENT_PATTERNS
-    )
-
-
-def _route_credit_instruments(position: dict[str, Any]) -> None:
-    for field in ["instrument_type", "security_type", "asset_class", "hedge_type", "instrument"]:
-        if is_credit_like_instrument(position.get(field)):
-            raise ValueError(CREDIT_ROUTE_MESSAGE)
-
-
-def _required_portfolio_inputs(
-    portfolio: dict[str, Any], position: dict[str, Any]
-) -> tuple[float, float, str]:
-    nav = fnum(portfolio.get("nav"))
-    entry = fnum(position.get("entry_price"))
-    if not nav or nav <= 0:
-        raise ValueError("portfolio.nav must be a positive number")
-    if not entry or entry <= 0:
-        raise ValueError("position.entry_price must be a positive number")
-    return nav, entry, str(position.get("direction", "long")).lower()
-
-
-def _loss_budget_rows(
-    portfolio: dict[str, Any],
-    position: dict[str, Any],
-    nav: float,
-    entry: float,
-    direction: str,
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    loss_bps = fnum(portfolio.get("max_loss_bps_nav"))
-    loss_budget = nav * loss_bps / 10000.0 if loss_bps is not None else None
-    loss_cases = [
-        ("loss_budget_downside", "downside_price"),
-        ("loss_budget_stress", "stress_price"),
-    ]
-    for case_name, field in loss_cases:
-        price = fnum(position.get(field))
-        if loss_budget is None or price is None:
-            continue
-        move = abs(signed_return(direction, entry, price))
-        notional = safe_div(loss_budget, move)
-        rows.append(
-            {
-                "sizing_lens": case_name,
-                "input_value": f"{loss_bps} bps NAV loss budget; {field}={price}",
-                "formula": "loss budget dollars / absolute adverse return",
-                "implied_size_pct_nav": notional_to_pct(notional, nav),
-                "implied_notional": notional,
-                "binding_flag": "",
-                "notes": "Use stress case for binary, crowded, or illiquid trades.",
-            }
-        )
-    return rows
-
-
-def _volatility_budget_rows(
-    portfolio: dict[str, Any], position: dict[str, Any], nav: float
-) -> list[dict[str, Any]]:
-    vol_pct = fnum(position.get("annualized_volatility_pct"))
-    vol_budget_bps = fnum(portfolio.get("target_position_vol_contribution_bps"))
-    if not vol_pct or not vol_budget_bps:
-        return []
-    vol_budget = nav * vol_budget_bps / 10000.0
-    notional = vol_budget / (vol_pct / 100.0)
-    return [
-        {
-            "sizing_lens": "volatility_budget",
-            "input_value": f"{vol_budget_bps} bps NAV vol budget; {vol_pct}% annualized vol",
-            "formula": "vol budget dollars / annualized volatility",
-            "implied_size_pct_nav": notional_to_pct(notional, nav),
-            "implied_notional": notional,
-            "binding_flag": "",
-            "notes": "Standalone approximation; factor model preferred when available.",
-        }
-    ]
-
-
-def _liquidity_capacity_rows(
-    liquidity: dict[str, Any], nav: float, entry: float
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    adv = fnum(liquidity.get("adv_shares"))
-    liq_price = fnum(liquidity.get("price"), entry)
-    exit_days = fnum(liquidity.get("required_exit_days"), 5.0) or 5.0
-    if not adv or not liq_price:
-        return []
-    liquidity_cases = [
-        ("liquidity_normal_exit", "normal_participation_rate", 0.10),
-        ("liquidity_stress_exit", "stress_participation_rate", 0.05),
-    ]
-    for lens, part_field, default in liquidity_cases:
-        part = fnum(liquidity.get(part_field), default) or default
-        notional = adv * liq_price * part * exit_days
-        rows.append(
-            {
-                "sizing_lens": lens,
-                "input_value": f"ADV={adv}; participation={part}; days={exit_days}",
-                "formula": "ADV x price x participation x exit days",
-                "implied_size_pct_nav": notional_to_pct(notional, nav),
-                "implied_notional": notional,
-                "binding_flag": "",
-                "notes": "Adjust downward for blocks, crowding, gap risk, or poor borrow/options liquidity.",
-            }
-        )
-    return rows
-
-
-def _limit_capacity_rows(
-    portfolio: dict[str, Any], position: dict[str, Any], nav: float
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    max_single = fnum(portfolio.get("max_single_name_pct_nav"))
-    current_pct = fnum(position.get("current_size_pct_nav"), 0.0) or 0.0
-    if max_single is not None:
-        cap = max(0.0, max_single - current_pct)
-        rows.append(
-            {
-                "sizing_lens": "single_name_limit_capacity",
-                "input_value": f"limit={max_single}% NAV; current={current_pct}% NAV",
-                "formula": "single-name limit minus current issuer exposure",
-                "implied_size_pct_nav": cap,
-                "implied_notional": pct_to_notional(cap, nav),
-                "binding_flag": "",
-                "notes": "Limit capacity is a cap, not a sizing target.",
-            }
-        )
-
-    sector_limit = fnum(
-        position.get("sector_limit_pct_nav"),
-        fnum(portfolio.get("max_sector_exposure_pct_nav")),
-    )
-    sector_current = fnum(position.get("current_sector_exposure_pct_nav"))
-    if sector_limit is not None and sector_current is not None:
-        cap = max(0.0, sector_limit - sector_current)
-        rows.append(
-            {
-                "sizing_lens": "sector_limit_capacity",
-                "input_value": f"limit={sector_limit}% NAV; current={sector_current}% NAV",
-                "formula": "sector limit minus current sector exposure",
-                "implied_size_pct_nav": cap,
-                "implied_notional": pct_to_notional(cap, nav),
-                "binding_flag": "",
-                "notes": "Check correlated names before relying on this capacity.",
-            }
-        )
-    return rows
-
-
-def _pm_constraint_rows(
-    portfolio: dict[str, Any],
-    position: dict[str, Any],
-    liquidity: dict[str, Any],
-    nav: float,
-    direction: str,
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    current_pct = fnum(position.get("current_size_pct_nav"), 0.0) or 0.0
-
-    active_limit = fnum(
-        position.get("benchmark_active_weight_limit_pct"),
-        fnum(
-            portfolio.get("benchmark_active_weight_limit_pct"),
-            fnum(portfolio.get("max_active_weight_pct")),
-        ),
-    )
-    current_active = (
-        fnum(
-            position.get("current_active_weight_pct"),
-            fnum(portfolio.get("current_active_weight_pct"), 0.0),
-        )
-        or 0.0
-    )
-    if active_limit is not None:
-        cap = max(0.0, active_limit - abs(current_active))
-        rows.append(
-            {
-                "sizing_lens": "benchmark_active_weight_capacity",
-                "input_value": f"active_weight_limit={active_limit}% NAV; current_active_weight={current_active}% NAV",
-                "formula": "active-weight limit minus absolute current active weight",
-                "implied_size_pct_nav": cap,
-                "implied_notional": pct_to_notional(cap, nav),
-                "binding_flag": "",
-                "notes": "Long-only and benchmark-aware PMs should size against active risk, not only absolute loss.",
-            }
-        )
-
-    factor_limit = fnum(
-        position.get("factor_limit_pct_nav"), fnum(portfolio.get("factor_limit_pct_nav"))
-    )
-    current_factor = fnum(position.get("current_factor_exposure_pct_nav"), 0.0) or 0.0
-    factor_per_pct = (
-        fnum(position.get("factor_exposure_per_1pct_position"), fnum(position.get("beta"), 1.0))
-        or 1.0
-    )
-    if factor_limit is not None and factor_per_pct:
-        cap = max(0.0, (factor_limit - abs(current_factor)) / abs(factor_per_pct))
-        rows.append(
-            {
-                "sizing_lens": "factor_limit_capacity",
-                "input_value": f"factor_limit={factor_limit}% NAV-equivalent; current={current_factor}; exposure_per_1pct={factor_per_pct}",
-                "formula": "(factor limit minus current factor exposure) / factor exposure per 1% position",
-                "implied_size_pct_nav": cap,
-                "implied_notional": pct_to_notional(cap, nav),
-                "binding_flag": "",
-                "notes": "Use a real factor model where available; this is a PM guardrail for unwanted factor exposure.",
-            }
-        )
-
-    correlation_limit = fnum(
-        position.get("correlated_exposure_limit_pct_nav"),
-        fnum(portfolio.get("correlated_exposure_limit_pct_nav")),
-    )
-    current_correlated = fnum(position.get("current_correlated_exposure_pct_nav"), 0.0) or 0.0
-    correlation = abs(fnum(position.get("correlation_to_existing_book"), 1.0) or 1.0)
-    if correlation_limit is not None:
-        cap = max(0.0, (correlation_limit - current_correlated) / max(correlation, 0.01))
-        rows.append(
-            {
-                "sizing_lens": "portfolio_fit_correlation_capacity",
-                "input_value": f"correlated_exposure_limit={correlation_limit}% NAV; current={current_correlated}% NAV; correlation={correlation}",
-                "formula": "(correlated exposure limit minus current correlated exposure) / correlation",
-                "implied_size_pct_nav": cap,
-                "implied_notional": pct_to_notional(cap, nav),
-                "binding_flag": "",
-                "notes": "Prevents a new name from becoming an accidental crowded factor/cluster bet.",
-            }
-        )
-
-    if direction == "short":
-        borrow_capacity = fnum(
-            position.get("borrow_squeeze_capacity_pct_nav"),
-            fnum(
-                portfolio.get("borrow_squeeze_capacity_pct_nav"),
-                fnum(portfolio.get("max_short_position_pct_nav")),
-            ),
-        )
-        if borrow_capacity is not None:
-            short_interest = fnum(
-                position.get("short_interest_pct_float"),
-                fnum(liquidity.get("short_interest_pct_float")),
-            )
-            days_to_cover = fnum(
-                position.get("days_to_cover"), fnum(liquidity.get("days_to_cover"))
-            )
-            borrow_cost = fnum(
-                position.get("borrow_cost_pct"), fnum(liquidity.get("borrow_cost_pct"))
-            )
-            cap = max(0.0, borrow_capacity - current_pct)
-            rows.append(
-                {
-                    "sizing_lens": "borrow_squeeze_capacity",
-                    "input_value": f"short_capacity={borrow_capacity}% NAV; current={current_pct}% NAV; short_interest={short_interest}; days_to_cover={days_to_cover}; borrow_cost={borrow_cost}",
-                    "formula": "borrow/squeeze capacity minus current short exposure",
-                    "implied_size_pct_nav": cap,
-                    "implied_notional": pct_to_notional(cap, nav),
-                    "binding_flag": "",
-                    "notes": "Short sizing must account for borrow availability, borrow cost, crowding, buyback/low-float risk, and squeeze path.",
-                }
-            )
-    return rows
-
-
-def _binding_constraint(rows: list[dict[str, Any]]) -> tuple[float | None, str]:
-    candidates = [
-        (fnum(r.get("implied_size_pct_nav")), r["sizing_lens"])
-        for r in rows
-        if fnum(r.get("implied_size_pct_nav")) and fnum(r.get("implied_size_pct_nav")) > 0
-    ]
-    return min(candidates, key=lambda x: x[0]) if candidates else (None, "insufficient data")
-
-
-def _summary(
-    position: dict[str, Any],
-    data: dict[str, Any],
-    nav: float,
-    entry: float,
-    direction: str,
-    raw_pct: float | None,
-    binding: str,
-) -> dict[str, Any]:
-    current_pct = fnum(position.get("current_size_pct_nav"), 0.0) or 0.0
-    confidence = str(position.get("confidence", "medium"))
-    mult = confidence_multiplier(confidence)
-    recommended_pct = raw_pct * mult if raw_pct is not None else None
-    recommended_notional = pct_to_notional(recommended_pct, nav)
-    return {
-        "analysis_date": data.get("analysis_date", ""),
-        "security": position.get("security", ""),
-        "ticker": position.get("ticker", ""),
-        "direction": direction,
-        "entry_price": entry,
-        "recommended_size_pct_nav": recommended_pct,
-        "recommended_notional": recommended_notional,
-        "recommended_shares_or_units": safe_div(recommended_notional, entry),
-        "raw_binding_constraint": binding,
-        "raw_binding_size_pct_nav": raw_pct,
-        "confidence": confidence,
-        "proposed_size_pct_nav": fnum(position.get("proposed_size_pct_nav")),
-        "current_size_pct_nav": current_pct,
-        "nav": nav,
-    }
-
-
-def _confidence_adjustment_row(summary: dict[str, Any]) -> dict[str, Any]:
-    confidence = str(summary.get("confidence", "medium"))
-    mult = confidence_multiplier(confidence)
-    return {
-        "sizing_lens": "confidence_adjustment",
-        "input_value": confidence,
-        "formula": "raw binding size x confidence multiplier",
-        "implied_size_pct_nav": summary.get("recommended_size_pct_nav"),
-        "implied_notional": summary.get("recommended_notional"),
-        "binding_flag": "final_adjustment"
-        if summary.get("recommended_size_pct_nav") is not None
-        else "",
-        "notes": f"Multiplier={mult}; PM may override based on evidence quality and mandate.",
-    }
-
-
-def sizing_rows(data: dict[str, Any]) -> tuple[list[dict[str, Any]], dict[str, Any]]:
-    portfolio = data.get("portfolio", {})
-    position = data.get("position", {})
-    _route_credit_instruments(position)
-    liquidity = data.get("liquidity", {})
-    nav, entry, direction = _required_portfolio_inputs(portfolio, position)
-
-    rows: list[dict[str, Any]] = []
-    rows.extend(_loss_budget_rows(portfolio, position, nav, entry, direction))
-    rows.extend(_volatility_budget_rows(portfolio, position, nav))
-    rows.extend(_liquidity_capacity_rows(liquidity, nav, entry))
-    rows.extend(_limit_capacity_rows(portfolio, position, nav))
-    rows.extend(_pm_constraint_rows(portfolio, position, liquidity, nav, direction))
-
-    raw_pct, binding = _binding_constraint(rows)
-    for row in rows:
-        if row["sizing_lens"] == binding:
-            row["binding_flag"] = "raw_binding_constraint"
-
-    summary = _summary(position, data, nav, entry, direction, raw_pct, binding)
-    rows.append(_confidence_adjustment_row(summary))
-    return rows, summary
-
-
-def scenario_rows(data: dict[str, Any], summary: dict[str, Any]) -> list[dict[str, Any]]:
-    rows = []
-    notional = summary.get("recommended_notional")
-    nav = summary["nav"]
-    for scenario in data.get("scenarios", []):
-        price = fnum(scenario.get("price"))
-        ret = fnum(scenario.get("return_pct"))
-        if price is not None:
-            result_return = signed_return(summary["direction"], summary["entry_price"], price)
-        elif ret is not None:
-            result_return = ret / 100.0
-            price = summary["entry_price"] * (1.0 + result_return)
-        else:
-            continue
-        pnl = notional * result_return if notional is not None else None
-        rows.append(
-            {
-                "scenario": scenario.get("name", "scenario"),
-                "probability": scenario.get("probability", ""),
-                "price_or_return": price,
-                "pnl_dollars": pnl,
-                "pnl_pct_nav": notional_to_pct(pnl, nav),
-                "time_horizon": scenario.get("time_horizon", ""),
-                "liquidity_assumption": scenario.get("liquidity_assumption", ""),
-                "action_rule": scenario.get("action_rule", ""),
-                "notes": scenario.get("notes", ""),
-            }
-        )
-    return rows
-
-
-def exposure_rows(data: dict[str, Any], summary: dict[str, Any]) -> list[dict[str, Any]]:
-    portfolio = data.get("portfolio", {})
-    position = data.get("position", {})
-    recommended_pct = summary.get("recommended_size_pct_nav") or 0.0
-    sign = -1 if summary["direction"] == "short" else 1
-    beta = fnum(position.get("beta"), 1.0) or 1.0
-    rows = []
-    exposure_cases = [
-        ("gross_exposure_pct_nav", fnum(portfolio.get("current_gross_exposure_pct"))),
-        ("net_exposure_pct_nav", fnum(portfolio.get("current_net_exposure_pct"))),
-        (
-            "active_weight_pct_nav",
-            fnum(
-                position.get("current_active_weight_pct"),
-                fnum(portfolio.get("current_active_weight_pct")),
-            ),
-        ),
-        ("factor_exposure_pct_nav", fnum(position.get("current_factor_exposure_pct_nav"))),
-        ("correlated_exposure_pct_nav", fnum(position.get("current_correlated_exposure_pct_nav"))),
-    ]
-    for name, before in exposure_cases:
-        if before is None:
-            continue
-        incremental = abs(recommended_pct) if name.startswith("gross") else sign * recommended_pct
-        rows.append(
-            {
-                "exposure_type": name,
-                "before": before,
-                "incremental": incremental,
-                "after": before + incremental,
-                "limit": "",
-                "status": "informational",
-                "source": "input",
-            }
-        )
-    rows.append(
-        {
-            "exposure_type": "beta_adjusted_incremental_pct_nav",
-            "before": "",
-            "incremental": sign * recommended_pct * beta,
-            "after": "",
-            "limit": "",
-            "status": "check risk model if available",
-            "source": "input beta or default",
-        }
-    )
-    return rows
-
-
-def liquidity_rows(data: dict[str, Any], summary: dict[str, Any]) -> list[dict[str, Any]]:
-    liquidity = data.get("liquidity", {})
-    price = fnum(liquidity.get("price"), summary["entry_price"])
-    adv = fnum(liquidity.get("adv_shares"))
-    notional = summary.get("recommended_notional")
-    if not price or not adv or notional is None:
-        return []
-    shares = notional / price
-    normal_participation = fnum(liquidity.get("normal_participation_rate"), 0.10) or 0.10
-    stress_participation = fnum(liquidity.get("stress_participation_rate"), 0.05) or 0.05
-    return [
-        {
-            "security": summary.get("security", ""),
-            "price": price,
-            "adv_shares": adv,
-            "adv_dollars": adv * price,
-            "position_shares": shares,
-            "position_dollars": notional,
-            "position_pct_nav": summary.get("recommended_size_pct_nav"),
-            "participation_rate": normal_participation,
-            "days_to_exit": safe_div(shares, adv * normal_participation),
-            "stressed_participation_rate": stress_participation,
-            "stressed_days_to_exit": safe_div(shares, adv * stress_participation),
-            "notes": "Simple ADV participation math; validate block liquidity and market impact.",
-        }
-    ]
-
-
-def monitoring_rows(data: dict[str, Any]) -> list[dict[str, Any]]:
-    fields = [
-        "trigger_type",
-        "metric",
-        "threshold",
-        "action",
-        "owner",
-        "cadence",
-        "source",
-    ]
-    return [
-        {field: trigger.get(field, "") for field in fields}
-        for trigger in data.get("monitoring_triggers", [])
-    ]
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_outputs.py b/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_outputs.py
deleted file mode 100644
index 43fd32d..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/scripts/position_sizing_outputs.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""Output helpers for the portfolio-risk-management sizing mode."""
-
-from __future__ import annotations
-
-import csv
-from pathlib import Path
-from typing import Any, Iterable
-
-from position_sizing_core import fnum
-
-
-def write_csv(path: Path, headers: list[str], rows: Iterable[dict[str, Any]]) -> None:
-    with path.open("w", newline="") as file:
-        writer = csv.DictWriter(file, fieldnames=headers)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow({header: row.get(header, "") for header in headers})
-
-
-def fmt(x: Any, digits: int = 2) -> str:
-    if isinstance(x, float):
-        return f"{x:.{digits}f}"
-    if x is None:
-        return ""
-    return str(x)
-
-
-def output_paths(out: Path) -> dict[str, str]:
-    return {
-        "position_summary": str(out / "position_summary.csv"),
-        "sizing_cases": str(out / "sizing_cases.csv"),
-        "scenario_pnl": str(out / "scenario_pnl.csv"),
-        "exposure_impact": str(out / "exposure_impact.csv"),
-        "liquidity_exit": str(out / "liquidity_exit.csv"),
-        "monitoring_rules": str(out / "monitoring_rules.csv"),
-        "support_note": str(out / "support_note.md"),
-        "run_log": str(out / "run_log.json"),
-        "manifest": str(out / "manifest.json"),
-    }
-
-
-def source_basis(data: dict[str, Any]) -> list[dict[str, Any]]:
-    rows = data.get("sources", [])
-    if not isinstance(rows, list):
-        return []
-    return [row for row in rows if isinstance(row, dict)]
-
-
-def write_report(
-    path: Path,
-    summary: dict[str, Any],
-    sizing: list[dict[str, Any]],
-    scenarios: list[dict[str, Any]],
-    liquidity: list[dict[str, Any]],
-) -> None:
-    lines = [
-        f"# Risk Position Sizing - {summary.get('security') or summary.get('ticker') or 'Position'}",
-        "",
-    ]
-    lines += [
-        "## Decision summary",
-        f"- Direction: {summary.get('direction')}",
-        f"- Recommended size: {fmt(summary.get('recommended_size_pct_nav'))}% NAV",
-        f"- Recommended notional: {fmt(summary.get('recommended_notional'), 0)}",
-        f"- Recommended shares/units: {fmt(summary.get('recommended_shares_or_units'), 0)}",
-        f"- Raw binding constraint: {summary.get('raw_binding_constraint')}",
-        f"- Confidence: {summary.get('confidence')}",
-        "",
-    ]
-    lines.append("## Sizing constraints")
-    for row in sizing:
-        if row.get("implied_size_pct_nav") != "":
-            lines.append(
-                f"- {row.get('sizing_lens')}: {fmt(row.get('implied_size_pct_nav'))}% NAV {row.get('binding_flag', '')}"
-            )
-    if scenarios:
-        lines += ["", "## Scenario P&L"]
-        for scenario in scenarios:
-            lines.append(
-                f"- {scenario.get('scenario')}: {fmt(scenario.get('pnl_pct_nav'))}% NAV P&L; {scenario.get('notes', '')}"
-            )
-    if liquidity:
-        lines += [
-            "",
-            "## Liquidity",
-            f"- Normal exit days: {fmt(liquidity[0].get('days_to_exit'))}",
-            f"- Stressed exit days: {fmt(liquidity[0].get('stressed_days_to_exit'))}",
-        ]
-    lines += [
-        "",
-        "## PM caveat",
-        "Validate live prices, beta, volatility, liquidity, borrow, Greeks, risk limits, and portfolio correlations before using this for a trading decision.",
-    ]
-    path.write_text("\n".join(lines))
-
-
-def write_output_bundle(
-    out: Path,
-    summary: dict[str, Any],
-    sizing: list[dict[str, Any]],
-    scenarios: list[dict[str, Any]],
-    exposures: list[dict[str, Any]],
-    liquidity: list[dict[str, Any]],
-    monitoring: list[dict[str, Any]],
-) -> None:
-    write_csv(
-        out / "position_summary.csv",
-        [
-            "analysis_date",
-            "security",
-            "ticker",
-            "direction",
-            "entry_price",
-            "recommended_size_pct_nav",
-            "recommended_notional",
-            "recommended_shares_or_units",
-            "raw_binding_constraint",
-            "raw_binding_size_pct_nav",
-            "confidence",
-            "proposed_size_pct_nav",
-            "current_size_pct_nav",
-        ],
-        [summary],
-    )
-    write_csv(
-        out / "sizing_cases.csv",
-        [
-            "sizing_lens",
-            "input_value",
-            "formula",
-            "implied_size_pct_nav",
-            "implied_notional",
-            "binding_flag",
-            "notes",
-        ],
-        sizing,
-    )
-    write_csv(
-        out / "scenario_pnl.csv",
-        [
-            "scenario",
-            "probability",
-            "price_or_return",
-            "pnl_dollars",
-            "pnl_pct_nav",
-            "time_horizon",
-            "liquidity_assumption",
-            "action_rule",
-            "notes",
-        ],
-        scenarios,
-    )
-    write_csv(
-        out / "exposure_impact.csv",
-        [
-            "exposure_type",
-            "before",
-            "incremental",
-            "after",
-            "limit",
-            "status",
-            "source",
-        ],
-        exposures,
-    )
-    write_csv(
-        out / "liquidity_exit.csv",
-        [
-            "security",
-            "price",
-            "adv_shares",
-            "adv_dollars",
-            "position_shares",
-            "position_dollars",
-            "position_pct_nav",
-            "participation_rate",
-            "days_to_exit",
-            "stressed_participation_rate",
-            "stressed_days_to_exit",
-            "notes",
-        ],
-        liquidity,
-    )
-    write_csv(
-        out / "monitoring_rules.csv",
-        ["trigger_type", "metric", "threshold", "action", "owner", "cadence", "source"],
-        monitoring,
-    )
-    write_report(out / "support_note.md", summary, sizing, scenarios, liquidity)
-
-
-def summarize_console(summary: dict[str, Any]) -> tuple[str, str]:
-    return (
-        f"Recommended size: {fmt(summary.get('recommended_size_pct_nav'))}% NAV",
-        f"Binding constraint: {summary.get('raw_binding_constraint')}",
-    )
-
-
-def has_source_basis(data: dict[str, Any]) -> bool:
-    return bool(source_basis(data))
-
-
-def row_count(rows: Iterable[dict[str, Any]]) -> int:
-    return sum(1 for _ in rows)
-
-
-def positive_size(summary: dict[str, Any]) -> bool:
-    value = fnum(summary.get("recommended_size_pct_nav"))
-    return value is not None and value > 0
diff --git a/plugins/financial-markets/skills/portfolio-risk-management/scripts/score_hedge_candidates.py b/plugins/financial-markets/skills/portfolio-risk-management/scripts/score_hedge_candidates.py
deleted file mode 100644
index 39b7594..0000000
--- a/plugins/financial-markets/skills/portfolio-risk-management/scripts/score_hedge_candidates.py
+++ /dev/null
@@ -1,395 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize a deterministic hedge candidate scorecard and basis-risk ledger."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-REQUIRED = ["hedge", "hedge_type", "risk_hedged"]
-
-SCORE_WEIGHTS = {
-    "exposure_fit_score": 1.30,
-    "thesis_preservation_score": 1.25,
-    "relationship_stability_score": 0.95,
-    "downside_behavior_score": 1.15,
-    "tenor_alignment_score": 0.80,
-    "cost_carry_score": 0.90,
-    "liquidity_score": 0.85,
-    "implementation_complexity_score": 0.60,
-    "basis_risk_score": 1.20,
-}
-
-SCORECARD_FIELDS = [
-    "rank",
-    "hedge",
-    "hedge_type",
-    "risk_hedged",
-    "target_position",
-    "composite_score",
-    "bucket",
-    "valid_score_count",
-    "implementation_status",
-    "as_of",
-    "source",
-    "readiness_status",
-    "live_pricing_status",
-    "borrow_status",
-    "option_chain_status",
-    "risk_model_status",
-    "basis_risk",
-    "basis_risk_mitigation",
-    "monitoring_trigger",
-    "warnings",
-]
-
-BASIS_FIELDS = [
-    "hedge",
-    "hedge_type",
-    "risk_hedged",
-    "basis_risk",
-    "severity",
-    "why_it_matters",
-    "mitigation",
-    "monitoring_trigger",
-    "implementation_status",
-    "as_of",
-    "source",
-    "readiness_status",
-    "live_pricing_status",
-    "borrow_status",
-    "option_chain_status",
-    "risk_model_status",
-]
-
-READINESS_FIELDS = [
-    "live_pricing_status",
-    "borrow_status",
-    "option_chain_status",
-    "risk_model_status",
-]
-
-READY_STATUSES = {"current", "available", "confirmed", "validated", "not_applicable", "n/a", "na"}
-
-CREDIT_HEDGE_WARNING = (
-    "Credit hedge construction belongs in Credit Markets; use here only as "
-    "public-equity risk context."
-)
-
-CREDIT_HEDGE_TYPE_PATTERNS = {
-    "cds",
-    "credit default swap",
-    "bond",
-    "bonds",
-    "loan",
-    "loans",
-    "bank loan",
-    "leveraged loan",
-    "high yield",
-    "investment grade",
-    "spread dv01",
-    "spread_dv01",
-    "spread hedge",
-    "credit spread",
-    "cs01",
-    "dv01",
-    "capital structure",
-    "capital-structure",
-    "distressed",
-    "recovery",
-    "covenant",
-    "credit hedge",
-    "credit instrument",
-    "credit security",
-}
-
-
-def _normalized_text(value: Any) -> str:
-    return str(value or "").strip().lower().replace("_", " ").replace("/", " ").replace("-", " ")
-
-
-def is_credit_hedge_type(value: Any) -> bool:
-    text = _normalized_text(value)
-    if not text:
-        return False
-    return any(
-        pattern.replace("_", " ").replace("-", " ") in text
-        for pattern in CREDIT_HEDGE_TYPE_PATTERNS
-    )
-
-
-def row_has_credit_hedge(row: dict[str, str]) -> bool:
-    return any(
-        is_credit_hedge_type(row.get(field))
-        for field in ["hedge_type", "instrument_type", "security_type", "asset_class"]
-    )
-
-
-def parse_score(value: Any, field: str, warnings: list[str]) -> float | None:
-    text = str(value or "").strip().replace(",", "")
-    if not text:
-        return None
-    try:
-        score = float(text)
-    except ValueError:
-        warnings.append(f"{field} invalid numeric value {value!r}")
-        return None
-    if not math.isfinite(score):
-        warnings.append(f"{field} is not finite")
-        return None
-    if score < 0:
-        warnings.append(f"{field} negative score excluded")
-        return None
-    if score <= 5:
-        return score / 5.0 * 100.0
-    if score <= 100:
-        return score
-    warnings.append(f"{field} above 100 capped at 100")
-    return 100.0
-
-
-def bucket(score: float | None) -> str:
-    if score is None:
-        return "needs_scoring_inputs"
-    if score >= 80:
-        return "primary hedge candidate"
-    if score >= 65:
-        return "usable with checks"
-    if score >= 50:
-        return "watchlist / situational"
-    return "reject or resize instead"
-
-
-def severity_from_basis(score: float | None, text: str) -> str:
-    lowered = text.lower()
-    if any(
-        term in lowered
-        for term in ["high", "severe", "correlation break", "over-hedge", "wrong tenor"]
-    ):
-        return "high"
-    if score is None:
-        return "needs_review"
-    if score >= 75:
-        return "low"
-    if score >= 50:
-        return "medium"
-    return "high"
-
-
-def valid_date(value: str) -> bool:
-    try:
-        datetime.strptime(value, "%Y-%m-%d")
-    except ValueError:
-        return False
-    return True
-
-
-def readiness_status(row: dict[str, str], warnings: list[str]) -> str:
-    as_of = str(row.get("as_of") or row.get("source_as_of") or "").strip()
-    source = str(row.get("source") or "").strip()
-    if not as_of:
-        warnings.append("missing as_of")
-    elif not valid_date(as_of):
-        warnings.append("as_of invalid date; expected YYYY-MM-DD")
-    if not source:
-        warnings.append("missing source")
-
-    missing_statuses: list[str] = []
-    weak_statuses: list[str] = []
-    for field in READINESS_FIELDS:
-        status = str(row.get(field) or "").strip().lower()
-        if not status:
-            missing_statuses.append(field)
-        elif status not in READY_STATUSES:
-            weak_statuses.append(f"{field}={status}")
-
-    if missing_statuses:
-        warnings.append("missing readiness fields: " + ", ".join(missing_statuses))
-    if weak_statuses:
-        warnings.append("non-ready readiness fields: " + ", ".join(weak_statuses))
-
-    if not as_of or not source or missing_statuses:
-        return "screen-grade"
-    if weak_statuses:
-        return "needs-targeted-checks"
-    return "implementation-data-ready"
-
-
-def load_rows(path: Path) -> list[dict[str, str]]:
-    with path.open(newline="", encoding="utf-8-sig") as handle:
-        reader = csv.DictReader(handle)
-        fields = [str(f or "").strip() for f in (reader.fieldnames or [])]
-        missing = [field for field in REQUIRED if field not in fields]
-        if missing:
-            raise ValueError("missing required columns: " + ", ".join(missing))
-        rows = [dict(row) for row in reader]
-    if not rows:
-        raise ValueError("input CSV must include at least one hedge candidate")
-    return rows
-
-
-def score_row(row: dict[str, str], index: int) -> dict[str, Any]:
-    warnings: list[str] = []
-    for field in REQUIRED:
-        if not str(row.get(field) or "").strip():
-            raise ValueError(f"row {index}: {field} is required")
-    weighted = 0.0
-    weight_total = 0.0
-    valid_count = 0
-    score_values: dict[str, float | None] = {}
-    for field, weight in SCORE_WEIGHTS.items():
-        value = parse_score(row.get(field), field, warnings)
-        score_values[field] = value
-        if value is None:
-            continue
-        weighted += value * weight
-        weight_total += weight
-        valid_count += 1
-    composite = round(weighted / weight_total, 2) if weight_total else None
-    if valid_count == 0:
-        warnings.append("no valid score fields provided")
-    if not str(row.get("basis_risk") or "").strip():
-        warnings.append("missing basis_risk")
-    if not str(row.get("implementation_status") or "").strip():
-        warnings.append("missing implementation_status")
-    readiness = readiness_status(row, warnings)
-    credit_handoff = row_has_credit_hedge(row)
-    if credit_handoff:
-        warnings.append(CREDIT_HEDGE_WARNING)
-        readiness = "route_to_credit_markets"
-
-    basis_score = score_values.get("basis_risk_score")
-    implementation_status = str(row.get("implementation_status") or "").strip()
-    if credit_handoff:
-        implementation_status = "route_to_credit_markets"
-    candidate_bucket = bucket(composite)
-    if credit_handoff:
-        candidate_bucket = "route to Credit Markets"
-    return {
-        "rank": 0,
-        "hedge": str(row.get("hedge") or "").strip(),
-        "hedge_type": str(row.get("hedge_type") or "").strip(),
-        "risk_hedged": str(row.get("risk_hedged") or "").strip(),
-        "target_position": str(row.get("target_position") or "").strip(),
-        "retained_exposure": str(row.get("retained_exposure") or "").strip(),
-        "composite_score": composite,
-        "bucket": candidate_bucket,
-        "valid_score_count": valid_count,
-        "implementation_status": implementation_status,
-        "as_of": str(row.get("as_of") or row.get("source_as_of") or "").strip(),
-        "source": str(row.get("source") or "").strip(),
-        "readiness_status": readiness,
-        "live_pricing_status": str(row.get("live_pricing_status") or "").strip(),
-        "borrow_status": str(row.get("borrow_status") or "").strip(),
-        "option_chain_status": str(row.get("option_chain_status") or "").strip(),
-        "risk_model_status": str(row.get("risk_model_status") or "").strip(),
-        "basis_risk": str(row.get("basis_risk") or "").strip(),
-        "basis_risk_mitigation": str(
-            row.get("basis_risk_mitigation") or row.get("mitigation") or ""
-        ).strip(),
-        "monitoring_trigger": str(row.get("monitoring_trigger") or "").strip(),
-        "basis_risk_severity": severity_from_basis(basis_score, str(row.get("basis_risk") or "")),
-        "warnings": "; ".join(warnings),
-    }
-
-
-def materialize(rows: list[dict[str, str]]) -> list[dict[str, Any]]:
-    scored = [score_row(row, idx) for idx, row in enumerate(rows, start=2)]
-    scored.sort(
-        key=lambda row: (
-            row["readiness_status"] == "route_to_credit_markets",
-            -1 if row["composite_score"] is None else -row["composite_score"],
-            row["hedge"],
-        )
-    )
-    for rank, row in enumerate(scored, start=1):
-        row["rank"] = rank
-    return scored
-
-
-def write_csv(path: Path, fields: list[str], rows: list[dict[str, Any]]) -> None:
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fields)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow(
-                {field: "" if row.get(field) is None else row.get(field, "") for field in fields}
-            )
-
-
-def basis_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    out = []
-    for row in rows:
-        out.append(
-            {
-                "hedge": row["hedge"],
-                "hedge_type": row["hedge_type"],
-                "risk_hedged": row["risk_hedged"],
-                "basis_risk": row["basis_risk"],
-                "severity": row["basis_risk_severity"],
-                "why_it_matters": "Hedge can fail if this mismatch dominates the target exposure.",
-                "mitigation": row["basis_risk_mitigation"],
-                "monitoring_trigger": row["monitoring_trigger"],
-                "implementation_status": row["implementation_status"],
-                "as_of": row["as_of"],
-                "source": row["source"],
-                "readiness_status": row["readiness_status"],
-                "live_pricing_status": row["live_pricing_status"],
-                "borrow_status": row["borrow_status"],
-                "option_chain_status": row["option_chain_status"],
-                "risk_model_status": row["risk_model_status"],
-            }
-        )
-    return out
-
-
-def markdown(rows: list[dict[str, Any]]) -> str:
-    lines = [
-        "# Hedge Candidate Scorecard",
-        "",
-        "> Deterministic materializer from supplied inputs only. Confirm live pricing, borrow, options, liquidity, and risk-model data before implementation.",
-        "",
-        "| Rank | Hedge | Type | Risk Hedged | Score | Bucket | Readiness | Implementation | Basis Risk | Warnings |",
-        "|---:|---|---|---|---:|---|---|---|---|---|",
-    ]
-    for row in rows:
-        score = "" if row["composite_score"] is None else f"{row['composite_score']:.2f}"
-        lines.append(
-            f"| {row['rank']} | {row['hedge']} | {row['hedge_type']} | {row['risk_hedged']} | {score} | {row['bucket']} | {row['readiness_status']} | {row['implementation_status']} | {row['basis_risk']} | {row['warnings']} |"
-        )
-    return "\n".join(lines) + "\n"
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Score supplied public-equity-investing hedge candidates."
-    )
-    parser.add_argument("input_csv", type=Path)
-    parser.add_argument("--output-dir", type=Path, default=Path("output"))
-    args = parser.parse_args()
-    try:
-        rows = materialize(load_rows(args.input_csv))
-    except Exception as exc:
-        print(f"ERROR: {exc}")
-        return 1
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    write_csv(args.output_dir / "hedge_scorecard.csv", SCORECARD_FIELDS, rows)
-    write_csv(args.output_dir / "basis_risk_ledger.csv", BASIS_FIELDS, basis_rows(rows))
-    (args.output_dir / "hedge_scorecard.json").write_text(
-        json.dumps({"rows": rows, "basis_risk_ledger": basis_rows(rows)}, indent=2) + "\n",
-        encoding="utf-8",
-    )
-    (args.output_dir / "hedge_scorecard_support_note.md").write_text(
-        markdown(rows), encoding="utf-8"
-    )
-    print(f"Wrote hedge scorecard outputs to {args.output_dir}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/public-equity-investing/SKILL.md b/plugins/financial-markets/skills/public-equity-investing/SKILL.md
deleted file mode 100644
index acdd0f2..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/SKILL.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-name: public-equity-investing
-description: Route Public Equity Investing only when explicitly named or tagged, or for an unmistakable listed-equity investor workflow tied to a public security, such as earnings investment work, a long/short thesis, public-equity valuation or model update, catalysts, or position sizing. Do not use for generic company research, reports, documents, models, valuation, or share-price questions.
----
-
-# Public Equity Investing Router
-
-## Bundled Path Resolution
-
-Derive the plugin root once from the actual filesystem location of this `SKILL.md`: it is two parent directories above this router directory. Set the shell working directory to that plugin root before the first bundled read or command, and resolve every router-owned bundled path from that root. Use only plugin-root-relative paths such as `shared/...`, `skills/...`, and `skills/public-equity-investing/internal-support/...`. Do not apply `../..` to an already resolved plugin root, resolve paths from the broader marketplace or cache root, or probe alternate relative paths.
-
-## Invocation Gate
-
-Read `shared/invocation-policy.md` before choosing any specialist. If the prompt is neither an explicit Public Equity Investing invocation nor a perfect-fit listed-equity investor mandate, do not route into this plugin.
-
-## User Context Preflight
-
-After the invocation gate passes and before substantive Public Equity Investing work, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root. Set the working directory before the first attempt; do not probe alternate relative paths.
-
-Use the returned envelope as a soft read-only preflight. Pass relevant entries from `saved_context` to the selected lead skill as handoff context. The router must not interpret saved output preferences, resolve or announce a presentation surface, or decide whether the requested work belongs in chat. Missing, malformed, unreadable, or uninitialized state must never block the requested workflow. Do not initialize, overwrite, repair, or reset state during ordinary workflow preflight. Do not inspect connectors or source readiness.
-
-When an ordinary workflow returns `next_action.id = "offer_orientation"`, complete the requested work and then append one short optional setup offer: `I can also save a couple of Public Equity Investing defaults, connect source tools, offer one optional automation, and help you pick a starter workflow. Want to do that now?`
-
-Do not append the offer for direct saved-context setup or status requests. Do not append it when `next_action` is `null`, including after onboarding is completed, deferred, or quiet. Leave other onboarding steps to the explicit `user-context` flow.
-
-Route explicit onboarding, setup, orientation, or get-started requests, including a detail-page starter such as `Help me get started`, plus explicit remember, save, update, forget, inspect, export, reset, source-setup, or automation-setup requests for Public Equity Investing context to `skills/user-context/SKILL.md` as the primary workflow.
-
-## Workflow Routing
-
-After the gate passes, select one owning research, model, event, or risk skill from the user's intent and the focused skill descriptions. The router owns admission and lead-skill selection only. After selecting the owner, load `skills/<lead-skill>/SKILL.md` from the plugin root before source gathering, analysis, connector use, deliverable intake, or any user-facing announcement about execution or packaging. The router must not continue substantive work as a substitute for the selected owner. Pass the request, routing rationale, and relevant saved context to the loaded owner without choosing or announcing format, depth, artifact architecture, or whether the request is lightweight. The selected owner applies `shared/final-deliverable-framework.md` as needed and, for a new standalone reader-facing hero artifact, reads `shared/deliverable-intake-policy.md` before source gathering or analysis to resolve any presentation decision. Support and presentation skills inherit resolved choices and do not re-prompt.
-
-## Internal Support
-
-Read `skills/public-equity-investing/internal-support/policy.md` when the selected workflow needs evidence control, generic data cleaning, rendering, style application, sector context, or provider-specific call shaping after selecting a callable connector route. Those supporting capabilities are bundled internal playbooks rather than selectable skills. Keep standalone normalization and model-audit requests with the visible `financials-normalizer` and `model-audit-tieout` workflows. For an explicitly requested internal support-only task admitted to this plugin, this router coordinates the task through the matching internal playbook.
diff --git a/plugins/financial-markets/skills/public-equity-investing/agents/openai.yaml b/plugins/financial-markets/skills/public-equity-investing/agents/openai.yaml
deleted file mode 100644
index 078e167..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Public Equity Investing Router"
-  short_description: "Routes explicit or unmistakable listed-equity mandates only"
-  default_prompt: "Use $public-equity-investing only for an explicitly requested Public Equity Investing workflow or an unmistakable listed-equity investor mandate."
-policy:
-  allow_implicit_invocation: true
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/INTERNAL.md
deleted file mode 100644
index 424e7f0..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/INTERNAL.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Daloopa Provider Guide
-
-> Internal provider guide. Load through `internal-support/policy.md` only after the current workflow selects a callable Daloopa route for an attempted source category. This is not a selectable skill.
-
-## Load Gate
-
-Do not load this guide during user-context preflight, onboarding, or unrelated source setup. A configured `.app.json` entry, preferred provider, or surfaced tool name is not proof that Daloopa is callable for the current user.
-
-## Authority And Scope
-
-Use the live Daloopa action schema as the source of truth. If it differs from this guide, follow the live schema and retry with the smallest valid call. Use Daloopa for source-backed public-company financials, KPIs, and targeted document snippets. Keep live prices, consensus, news, and non-Daloopa sources separately labeled when another route owns them.
-
-Before calling Daloopa, read `references/connector-playbook.md`. For a workbook workflow, also read `references/workbook-mode.md` before editing the workbook.
-
-## Default Sequence
-
-1. Call `discover_companies` with one ticker or clean company name and capture the returned `company_id` plus latest available period fields.
-2. Call `discover_company_series` with that `company_id`, one explicit anchor period, and one narrow metric family.
-3. Inspect returned labels, select the needed series IDs, and call `get_company_fundamentals` with explicit periods and roughly 15 series IDs or fewer per batch.
-4. Use `search_documents` only for targeted qualitative context such as guidance, strategy, risks, or performance explanations.
-5. Compute derived values from sourced inputs and preserve their provenance.
-
-Never invent company IDs, series IDs, periods, fundamental IDs, or source URLs.
-
-## Call Shape And Data Rules
-
-- `discover_companies` uses `keywords: list[str]`.
-- `discover_company_series` uses `company_id`, `keywords`, and `periods`.
-- `get_company_fundamentals` uses `company_id`, `periods`, and `series_ids`.
-- `search_documents` uses `keywords`, `company_ids`, and `periods`; its keywords use AND logic, so retry sparse searches with fewer terms.
-- Use explicit `YYYYQ#` or `YYYYFY` periods. Prefer the connector-returned latest period over an assumption based on the current date.
-- For quarterly analysis, use `value_quarter` when available. Label any conversion from year-to-date values.
-- Keep reported actuals, guidance, estimates, and derived calculations separate.
-
-## Output Budget And Recovery
-
-Prefer several small calls over broad pulls. If a call is too large, narrow the metric family, periods, peer set, or series-ID batch before retrying. If a call fails, check live parameter names and types, retry minimally, and state the specific remaining gap if the route is still blocked.
-
-## Citation Discipline
-
-Capture each returned fundamental `id` at pull time and carry it through tables, artifacts, charts, and prose. Cite every Daloopa-sourced figure using its source URL. Cite computed values through their sourced inputs. Identify Daloopa as the data source without implying that unrelated values came from Daloopa.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/connector-playbook.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/connector-playbook.md
deleted file mode 100644
index c350e80..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/connector-playbook.md
+++ /dev/null
@@ -1,220 +0,0 @@
-# Daloopa Connector Playbook
-
-Load this reference only after the owning workflow selects a callable Daloopa route. The source prompt was verified against the active Daloopa connector on 2026-04-14; the live tool schema remains authoritative if it differs.
-
-## Authority Order
-
-Use this order when instructions conflict:
-
-1. The live Daloopa action schema exposed in the current runtime.
-2. The canonical call shapes and workflow rules in this playbook.
-3. The user's latest explicit instruction when it does not conflict with connector schema, citation rules, or data integrity.
-4. The owning analytical workflow, such as comps, DCF, earnings analysis, working-capital analysis, or guidance tracking.
-
-Never call an action with missing required arguments or `params: null`. Construct the smallest valid argument object first. Do not copy parameter names from examples blindly after the live schema changes.
-
-## Scope And Source Order
-
-Use Daloopa for source-backed public-company financials, KPIs, and targeted filing or document snippets. Use another clearly labeled source for live prices, consensus, or news when Daloopa does not provide the needed data. Keep Daloopa values distinct from non-Daloopa values.
-
-## Canonical Sequence
-
-1. Discover the company.
-2. Discover relevant series IDs.
-3. Pull fundamentals for explicit periods and selected series IDs.
-4. Search documents only when qualitative context is needed.
-5. Compute derived metrics from sourced values.
-6. Present results with citations and clear assumptions.
-
-Never invent `company_id`, `series_ids`, `fundamental_id`, periods, or source URLs.
-
-## Output Budget
-
-Daloopa can return large result sets. Prefer several narrow calls over one broad call.
-
-- `discover_companies`: search one company at a time with at most one or two keywords, usually ticker and short company name.
-- `discover_company_series`: use one company, one anchor period, and one narrow metric family per call.
-- `discover_company_series`: use one to five tightly related keywords, not a full financial-statement keyword list.
-- `get_company_fundamentals`: use selected series IDs only and keep normal batches to roughly 15 series IDs or fewer.
-- `search_documents`: start with one to four keywords because keywords use AND logic.
-
-For financial statements, discover small metric families separately: revenue, profitability, balance-sheet cash and debt, then cash flow. If a call is too large, split by fewer keywords first and fewer periods second.
-
-## `discover_companies`
-
-Purpose: find the Daloopa company record and capture `company_id`.
-
-Expected shape:
-
-```python
-discover_companies(keywords: list[str])
-```
-
-Correct examples:
-
-```json
-{"keywords": ["AAPL"]}
-```
-
-```json
-{"keywords": ["AVGO", "Broadcom"]}
-```
-
-Avoid scalar `keyword`, a `ticker` field, or a combined string such as `"Broadcom Inc AVGO"`.
-
-Search rules:
-
-- For tickers, use the exact ticker symbol.
-- For company names, use the core name without legal suffixes such as Inc., Corp., Ltd., LLC, PLC, GmbH, or S.A.
-- If a result is absent or ambiguous, retry with a shorter or more distinctive company name.
-- Discover peers separately or in very small batches.
-
-Capture:
-
-- `company_id`
-- `ticker`
-- `name`
-- `latest_calendar_quarter`
-- `latest_fiscal_quarter`
-
-## `discover_company_series`
-
-Purpose: find available financial or KPI series and their IDs.
-
-Expected shape:
-
-```python
-discover_company_series(company_id: int, keywords: list[str], periods: list[str])
-```
-
-Correct example:
-
-```json
-{"company_id": 2, "keywords": ["revenue", "sales"], "periods": ["2025Q4"]}
-```
-
-Usage rules:
-
-- Call this only after `discover_companies`; do not guess `company_id`.
-- Use focused keyword lists and explicit periods.
-- Search related synonyms when needed, such as `["revenue", "sales", "net sales"]`.
-- Inspect returned labels before choosing series IDs.
-- Capture `id`, `full_series_name`, and hierarchy or row metadata when available.
-
-When building historical actuals, prefer series in this order:
-
-1. Income Statement, Balance Sheet, or Cash Flow Statement lines.
-2. Actual reported line items for the requested period.
-3. Segment detail only when the task requests segment revenue or KPIs.
-4. Guidance only when the task explicitly asks for guidance or forecast commentary.
-5. Avoid Guidance, Projected, 8-K, Reconciliation, and KPI series for historical actuals unless no direct statement line exists.
-
-If multiple series match, choose the cleanest statement hierarchy and most direct label, then pull a small sample to verify it.
-
-## `get_company_fundamentals`
-
-Purpose: retrieve values for selected periods and series IDs.
-
-Expected shape:
-
-```python
-get_company_fundamentals(company_id: int, periods: list[str], series_ids: list[int])
-```
-
-Correct example:
-
-```json
-{"company_id": 2, "periods": ["2024Q1", "2024Q2", "2024Q3", "2024Q4"], "series_ids": [12345, 67890]}
-```
-
-Usage rules:
-
-- Obtain `series_ids` from `discover_company_series` first.
-- Use explicit period strings only: `YYYYQ#` for quarters and `YYYYFY` for fiscal years.
-- Use the latest period fields returned by `discover_companies` as anchors when available.
-- For TTM, pull the last four quarters and aggregate them.
-- For YoY, pull the current period and the same period one year earlier.
-- For sequential trends, pull consecutive quarters.
-- Capture returned `id` or `fundamental_id` for each value.
-
-Expected return fields:
-
-- `id`
-- `series_id`
-- `title`
-- `value_year_to_date`
-- `value_quarter`
-- `unit`
-- `calendar_period`
-- `fiscal_period`
-
-Use `value_quarter` for quarterly analysis when available. Use `value_year_to_date` only for explicit YTD work or when quarterly values are unavailable. If cash-flow values are cumulative and quarterly values are required, subtract the prior quarter's YTD amount and label the conversion.
-
-## `search_documents`
-
-Purpose: search filings, transcripts, and company documents for qualitative context.
-
-Expected shape:
-
-```python
-search_documents(keywords: list[str], company_ids: list[int], periods: list[str])
-```
-
-Correct example:
-
-```json
-{"keywords": ["revenue", "guidance"], "company_ids": [2], "periods": ["2024Q3", "2024Q4"]}
-```
-
-Use document search for management commentary, risk factors, guidance, outlook, strategy, competition, acquisitions, disclosures, or explanations for performance. Use `company_ids`, not tickers. Keywords use AND logic; retry sparse searches with fewer or broader terms. Treat document search as beta when presenting its results and use returned document IDs for citations.
-
-## Period Rules
-
-Use explicit `YYYYQ#` and `YYYYFY` periods in connector calls.
-
-Anchor periods in this order:
-
-1. Latest-period fields returned by `discover_companies`.
-2. Periods visible in an active artifact or user-provided data.
-3. A period range supplied by the user.
-4. The current date only as a last resort, with the assumption stated.
-
-For multi-company analysis, use calendar periods for comparability. Present fiscal labels when useful.
-
-## Guidance Versus Actuals
-
-Before calculating beat, miss, or guidance accuracy:
-
-1. Create a mapping table showing the guidance period and the results period it applies to.
-2. Verify the offset.
-3. Calculate the comparison only after the mapping is clear.
-
-Quarterly guidance from quarter N usually applies to quarter N+1 actuals. Do not compare same-quarter guidance to same-quarter actuals unless the disclosure explicitly says to do so. Annual guidance from Q1, Q2, or Q3 usually applies to the current fiscal year; Q4 annual guidance usually applies to the next fiscal year unless the disclosure says otherwise.
-
-## Formatting And Tables
-
-Use readable financial formatting: `$X.Xbn` or `$X,XXXmm`, `$X.XX` per share, one decimal for percentages and multiples, signed growth rates, signed basis points, and clearly labeled share counts.
-
-For financial tables, use metrics as rows and chronological periods as columns. Place derived rows such as growth, margins, and variance below their sourced metrics unless the user requests another layout.
-
-## Citation Rules
-
-Capture source IDs when pulling data and carry them through tables, prose, charts, and artifacts.
-
-- Cite each Daloopa financial figure with `https://daloopa.com/src/{fundamental_id}`.
-- Cite document results with `https://marketplace.daloopa.com/document/{document_id}`.
-- Cite computed figures through their sourced inputs.
-- Add `Data sourced from Daloopa` to outputs that use Daloopa data.
-
-Never present uncited Daloopa figures or imply that non-Daloopa values came from Daloopa.
-
-## Recovery
-
-If a call fails:
-
-1. Check live parameter names and types.
-2. Retry with the smallest valid call.
-3. For oversized output, narrow metric families, periods, peer sets, or series-ID batches before retrying.
-4. For empty series discovery, retry with broader synonyms.
-5. For empty document search, retry with fewer keywords because keywords use AND logic.
-6. If still blocked, state the exact failed call and the smallest missing input or fallback needed.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/workbook-mode.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/workbook-mode.md
deleted file mode 100644
index 9fde988..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/daloopa-provider-guide/references/workbook-mode.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Daloopa Workbook Mode
-
-Load this reference only when a routed workflow uses Daloopa while reading or editing a workbook.
-
-## Source Order
-
-1. Inspect the active workbook first.
-2. Use existing workbook data when it is already present, source-backed, and current enough for the task.
-3. Use Daloopa when the workbook lacks the data, the user asks for fresh data, or the task requires provider-backed sourcing.
-4. Keep non-Daloopa values separately labeled.
-
-## Workbook Rules
-
-- Never overwrite analyst-created formulas, assumptions, notes, or formatting without asking.
-- Put imported source data on a clearly named raw-data tab such as `Daloopa_Data` unless the user requests another layout.
-- Put analyst assumptions on an `Inputs` or `Assumptions` tab.
-- Keep formulas on model or output tabs, not in the raw-data area.
-- Preserve source IDs, source URLs, or citation columns when importing Daloopa values.
-- Preserve the workbook's existing formatting unless the user asks for a redesign.
-- After edits, summarize changed tabs and ranges.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md
deleted file mode 100644
index 988ef61..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md
+++ /dev/null
@@ -1,113 +0,0 @@
----
-name: dashboard-builder
-description: Use when rendering, validating, or adapting standardized responsive Public Equity Investing HTML dashboards from structured analysis produced by visible Public Equity Investing skills. This capability owns the optional dashboard shell, module schema, and HTML export; it does not own underlying investment analysis or bespoke HTML design.
----
-
-# Dashboard Builder
-
-> Internal support playbook. Load through `internal-support/policy.md`; this renderer is bundled with the visible router rather than exposed as a skill entrypoint.
-
-## Deliverable Intake
-
-When another skill owns the analysis, inherit its resolved deliverable preferences and do not re-prompt before rendering. Only when this skill independently owns a new substantive standalone dashboard should it, before source gathering, analysis, or rendering, load `../../../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices.
-
-Build standardized, responsive, source-visible Public Equity Investing dashboards from typed listed-equity analysis payloads. Follow `../../../../shared/html-artifact-standard.md` for overall HTML quality. Use this capability when the user asks for a standardized dashboard, reusable dashboard template, structured payload-driven render, or when another Public Equity Investing skill explicitly selects this standardized rendering path. Do not load it merely because a producer skill is creating a polished bespoke HTML report.
-
-## Boundary
-
-- This skill owns the dashboard shell, module contract, responsive layout, validation, and HTML rendering.
-- Visible producer skills may create bespoke HTML artifacts directly under `shared/html-artifact-standard.md`; they do not need to map every report into this schema.
-- Credit Markets dashboards, credit-security dashboards, recovery dashboards, covenant dashboards, and distressed debt dashboards belong in the Credit Markets plugin. Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-- The primary Public Equity Investing workflow owns the finance logic. For example, `earnings-preview` owns the pre-print bar and KPIs; `earnings-deep-dive` owns beat/miss and EPS quality; `long-short-pitch` owns trade expression and variant perception.
-- Do not duplicate finance reasoning inside the renderer. Convert the primary skill's final analysis into a `public_equity_investing_dashboard.v1` payload. The JSON payload is an internal handoff contract, not the final user-facing artifact.
-- Use generic issuer identity tiles by default. Do not require logos; use sourced or user-provided logos only when available and safe. The ticker badge should use a sourced or reasonable brand color when available, but prefer a darker brand shade so the white ticker text remains readable.
-
-## Workflow
-
-1. Select the primary analysis skill first unless the user supplied a finished dashboard payload.
-2. Load `references/module-contract.md` when choosing modules or mapping a producer skill into tabs.
-3. Use `references/mobile-qa.md` before finalizing a dashboard or changing layout behavior.
-4. Create or receive a JSON payload with `kind: public_equity_investing_dashboard.v1`, `mode`, `layout`, issuer metadata, `metadata`, `hero`, `snapshot`, sections/tabs, modules, sources, workbook `model_citations`/`model_citations_path` when applicable, and explicit missing evidence. Keep this JSON as support/audit material; the rendered HTML is the user-facing artifact.
-   - Production dashboards must set `metadata.payload_stage: "production"` and `metadata.citation_policy: "strict"`.
-   - Production metadata must include `freeze_time`, `source_posture`, `readiness_label`, `readiness_posture`, and `decision_context`.
-   - Draft/support payloads may use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"`, but gaps must remain visible and the payload must not be presented as PM-ready.
-   - Assign stable source IDs in top-level `sources` (`S1`, `S2`, or more descriptive IDs), then cite every material number, estimate, quote, date-sensitive claim, and assumption inline using `citations`, `source_id/source_ids`, or bracketed markers such as `[S1]` in text.
-5. Validate the payload:
-
-```bash
-python skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py payload.json --profile production
-```
-
-6. Render a standalone HTML dashboard:
-
-```bash
-python skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py payload.json output/index.html --profile production
-```
-
-7. Run a structural smoke pass: no unresolved placeholders, valid tab ids, supported modules only, source ledger present, and mobile-safe tables.
-8. Run the chart-readiness smoke pass: financial trend, EPS actual/estimate, and equity price/event charts must have enough complete renderer-ready rows to display. If a chart does not have enough complete source-backed rows, omit the chart module and add a `missing_evidence` module that names the unavailable series.
-9. Run the citation smoke pass: no unresolved citation chips, no numeric material without inline citation support, hero `dek` and `callout` preserve `hero.citations`, and hover/focus previews work for citation chips.
-
-## Producer Pack Index
-
-Producer skills own the investment analysis and choose the source-backed content. `dashboard-builder` owns the shared shell, module rendering, validation, responsive behavior, and export only when its standardized dashboard path is selected. When a producer retains a local `references/DASHBOARD_PACK.md` and elects this path, load that pack before mapping analysis into `public_equity_investing_dashboard.v1`.
-
-Typed producer packs:
-
-- `company-tearsheet/references/DASHBOARD_PACK.md`
-- `comps-valuation/references/DASHBOARD_PACK.md`
-- `deck-report-qc/references/DASHBOARD_PACK.md`
-- `earnings-deep-dive/references/DASHBOARD_PACK.md`
-- `earnings-preview/references/DASHBOARD_PACK.md`
-- `economic-impact-report/references/DASHBOARD_PACK.md`
-- `event-driven-analyzer/references/DASHBOARD_PACK.md`
-- `idea-generation/references/DASHBOARD_PACK.md`
-- `initiating-coverage/references/DASHBOARD_PACK.md`
-- `long-short-pitch/references/DASHBOARD_PACK.md`
-- `meeting-prep/references/DASHBOARD_PACK.md`
-- `memo-builder/references/DASHBOARD_PACK.md`
-- `equity-model-update/references/DASHBOARD_PACK.md`
-- `portfolio-risk-management/references/DASHBOARD_PACK.md`
-- `scenario-sensitivity-generator/references/DASHBOARD_PACK.md`
-- `thesis-tracker/references/DASHBOARD_PACK.md`
-
-If a workflow has no typed producer pack, use `references/module-contract.md`, keep the producer skill as analysis owner, and include `missing_evidence` for any unsupported module rather than inventing dashboard-only numbers.
-
-Model-output dashboard maps:
-
-- `comps-valuation/references/workbook/dashboard-map.md`
-- `dcf-model-builder/references/dashboard-map.md`
-- `model-audit-tieout/references/dashboard-map.md`
-- `equity-model-update/references/dashboard-map.md`
-- `three-statement-model-builder/references/dashboard-map.md`
-
-Load the relevant model-output map whenever a dashboard cites workbook cells, model audit findings, formula exceptions, source-to-cell provenance, or safe workbook-update results.
-
-## Output Rules
-
-- Keep enough analytical depth to satisfy the task, but do not add sections solely to fill a dashboard module inventory. Short dashboards are explicit-only.
-- Use `layout: "single_page"` for PM diligence dashboards unless the user asks for tab-only navigation. It keeps all sections visible on one page and renders a sticky table of contents for section jumping.
-- Every material number, quote, estimate, source timestamp, date-sensitive claim, and assumption must be cited inline where it appears, not only in the bottom source ledger. Use `citations: ["S1"]`, `source_id: "S1"`, `source_ids: ["S1", "S2"]`, or bracketed text markers such as `Q1 revenue was $8.1M [S1]`.
-- Prefer subtle numeric citation links for actual metric values. When a sourced value is numeric, the rendered number itself should link to the source ledger/source URL and show the hover/focus preview; avoid placing a separate citation chip beside every metric.
-- When every line in a section depends on the same source set, use a compact section-level source note rather than repeating identical chips after each sentence. Keep point-of-use citations for specific numbers, quotes, dates, and claims that need their own source.
-- The bottom source ledger remains mandatory, but it is not sufficient by itself. The reader should be able to audit a number or claim from the module where they see it.
-- Workbook-backed dashboards may include `model_citations` or `model_citations_path` from DCF, three-statement, comps, or equity-model-update workflows. These records are rendered as source-ledger rows and citation popovers with workbook, sheet, range, value, and formula details. Keep the citation JSON and any Markdown `support_note.md` sidecar as support material; do not expose them as the lead artifact.
-- Citation links/chips must support click-through to the source ledger or source URL and hover/focus previews with source title, type/status, date, and excerpt/pinpoint when available. Citation links should inherit normal text color rather than using default bright-blue hyperlink styling.
-- Preserve GAAP/non-GAAP, period, units, scale, estimate `as_of`, and source posture labels from the producer skill.
-- The quarter/period belongs in the hero headline, dek, or metadata. Do not spend a snapshot/highlight tile on the quarter when it is already in the headline.
-- Snapshot/highlight tiles must hero the investor question, not merely the largest absolute numbers. Prefer surprise %, growth acceleration, clean EPS beat, guide change, unit growth, backlog, margin path, capex guide, or other name-specific KPIs over generic reported values. If the stock-moving point is a growth rate or surprise %, make that the tile value and put the absolute dollar value in the detail.
-- Use generic issuer identity tiles by default. Treat logos as optional decoration, not a dependency. Populate `issuer.accent_color`, `issuer.brand_color`, `issuer.brand_dark_color`, `issuer.identity_color`, or `issuer.brand_colors` when known; the renderer will choose a dark, white-text-readable ticker badge color from those inputs.
-- Keep PM dashboards information dense by default: avoid decorative whitespace, keep section spacing compact, and use dense table mode for analyst-facing KPI, guide, beat/miss, transcript, and read-through modules.
-- Keep typography consistent across modules. Use the shared dashboard font scale and fixed responsive breakpoints rather than ad hoc viewport-scaled heading sizes.
-- Include `market_events` when recent news, market structure, macro, regulatory, litigation, product, management, peer, or forward-catalyst context could affect the investment decision. The dashboard renders the event table; the producer skill owns event selection, sourcing, and impact analysis.
-- For earnings-preview and earnings-deep-dive dashboards, include the earnings visualization pack only when complete source-backed data exists:
-  - `financial_trend_chart` for the last several reported quarters with revenue, gross profit, net income, and the best source-backed profitability margin line for the issuer and quarter. Default to net margin only when it is a fair recurring-profitability proxy. Use operating margin, adjusted operating margin, EBITDA margin, FCF margin, or another explicitly labeled margin when net income is distorted by below-the-line gains/losses, tax, FX, mark-to-market, equity-investment gains, restructuring, impairments, litigation, asset sales, or other non-recurring items.
-  - `eps_actual_vs_estimate_chart` for the past five quarters of estimated EPS versus actual EPS, or fewer only when the visible module states the available window.
-  - `equity_price_event_chart` only when the price tape is substantive: source-backed daily data needs at least 10 distinct price points over at least 7 calendar days, hourly data needs at least 24 time-stamped price points over at least 6 hours, and minute/intraday data needs at least 60 time-stamped price points over at least 45 minutes. A sparse reaction tape, quote-card trio, or a few article-reported closes is not enough; use `market_events` plus `missing_evidence` instead.
-  If any required series is missing, stale, not parseable, not comparable by period/basis, or too thin for the chart's minimum window, omit the chart and list the missing series in `missing_evidence` rather than filling blanks. QA validates enough chart-ready rows survive renderer field requirements; strict payloads with incomplete chart modules fail validation.
-- For `financial_trend_chart`, the producer skill should set `data.margin_metric`, `data.margin_label`, and `data.margin_rationale` when it selects a line other than net margin. The renderer supports this handoff but does not decide finance quality on its own.
-- Chart axes must be readable as scales, not mystery thresholds. Positive-only bar and EPS charts should start at zero, show multiple gridlines/ticks, and avoid negative lower bounds created only by padding.
-- Add charts only when they reduce interpretation time. Prefer compact native charts for small comparable metric sets such as segment growth, revenue mix, guidance deltas, exposure, or catalyst probability. Do not chart every table.
-- Hero `dek` and `callout` honor `hero.citations`. Use them for source-backed masthead claims, including numeric callouts that should render as subtle source links.
-- Render as one standalone local HTML file unless the user asks for a web app or deployment.
-- Keep JavaScript limited to local UI behavior such as tabs. Do not fetch remote data from rendered dashboards.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/comps-valuation-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/comps-valuation-sample.json
deleted file mode 100644
index bfdbf55..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/comps-valuation-sample.json
+++ /dev/null
@@ -1,207 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "comps_analysis",
-  "layout": "single_page",
-  "title": "ACME Software: public comps dashboard",
-  "subtitle": "Synthetic strict-mode example for the Public Equity Investing dashboard pack.",
-  "issuer": {
-    "ticker": "ACME",
-    "name": "ACME Software",
-    "sector": "Software",
-    "brand_dark_color": "#1f4b7a"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic provider export plus company filing example",
-    "citation_policy": "strict",
-    "decision_context": "Whether ACME deserves a premium to vertical software peers.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Comps analysis",
-    "headline": "ACME screens at a premium to slower-growth vertical software peers",
-    "dek": "The dashboard preserves peer logic, selected-multiple rationale, and source gaps.",
-    "callout_label": "Core Debate",
-    "callout": "The premium only works if ACME's retention and margin path remain above the peer median.",
-    "citations": [
-      "S1",
-      "S2"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Comps Posture",
-      "value": "Screen-grade",
-      "detail": "Selected peer set requires live market refresh before decision use.",
-      "status": "watch",
-      "citations": [
-        "S2"
-      ]
-    },
-    {
-      "label": "Selected Range",
-      "value": "Eight to ten times NTM revenue",
-      "detail": "Illustrative range based on the synthetic provider export.",
-      "status": "watch",
-      "citations": [
-        "S2"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "PM read",
-            "stance": "Watch",
-            "summary": "ACME merits a modest premium only if retention and margin evidence survive a live data refresh.",
-            "thesis_change": "Peer set looks usable, but market data freshness is the gating item.",
-            "next_catalyst": "Refresh provider export before using the valuation range.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        },
-        {
-          "type": "cards",
-          "data": {
-            "items": [
-              {
-                "title": "Peer logic",
-                "body": "Closest peers share vertical software exposure, recurring revenue, and similar enterprise buyer cycles.",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "title": "Main caveat",
-                "body": "Live share price, net debt, and consensus estimates need refresh before the dashboard is decision-grade.",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "valuation",
-      "label": "Valuation",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Company",
-              "Ticker",
-              "Business fit",
-              "Revenue multiple",
-              "Growth context",
-              "Read"
-            ],
-            "rows": [
-              {
-                "Company": "ACME Software",
-                "Ticker": "ACME",
-                "Business fit": "Target issuer",
-                "Revenue multiple": "Nine times",
-                "Growth context": "Above peer median",
-                "Read": "Premium requires retention support",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              },
-              {
-                "Company": "PeerCo",
-                "Ticker": "PEER",
-                "Business fit": "Vertical software peer",
-                "Revenue multiple": "Seven times",
-                "Growth context": "Near peer median",
-                "Read": "Useful anchor but lower growth",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "scenario_map",
-          "data": {
-            "cases": [
-              {
-                "label": "Downside",
-                "status": "bear",
-                "summary": "Premium compresses if retention fades or margin expansion stalls.",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              },
-              {
-                "label": "Base",
-                "status": "base",
-                "summary": "ACME holds a modest premium to slower-growth peers.",
-                "citations": [
-                  "S2"
-                ]
-              },
-              {
-                "label": "Upside",
-                "status": "bull",
-                "summary": "Premium expands if retention and margin evidence both improve.",
-                "citations": [
-                  "S1"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "refresh-gaps",
-      "label": "Refresh Gaps",
-      "modules": [
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Live price, share count, net debt, and consensus export are required before decision use.",
-              "Peer set should be rechecked against business-model and liquidity filters."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "ACME FY2026 Form 10-K excerpt",
-      "url": "https://example.com/acme-10k",
-      "as_of": "2026-03-01",
-      "type": "filing",
-      "status": "synthetic example",
-      "excerpt": "Issuer operating profile and retention discussion."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic public comps provider export",
-      "url": "https://example.com/provider-export",
-      "as_of": "2026-05-18",
-      "type": "market data",
-      "status": "synthetic example",
-      "excerpt": "Peer trading multiples and market-data placeholders."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/deck-report-qc-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/deck-report-qc-sample.json
deleted file mode 100644
index d3aaf88..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/deck-report-qc-sample.json
+++ /dev/null
@@ -1,166 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "deck_report_qc",
-  "layout": "single_page",
-  "title": "Research Deck QC Dashboard",
-  "subtitle": "Synthetic strict-mode example for deck/report quality control.",
-  "issuer": {
-    "name": "Public Equity Investing QC Pack",
-    "sector": "Cross-sector",
-    "brand_dark_color": "#4b5563"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic deck extraction plus model and source support examples",
-    "citation_policy": "strict",
-    "decision_context": "Whether the deck can move to senior review.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Deck/report QC",
-    "headline": "The deck needs targeted fixes before circulation",
-    "dek": "The dashboard prioritizes issue severity, tie-out gaps, and remediation sequence.",
-    "callout_label": "QC Verdict",
-    "callout": "Source coverage and repeated-number tie-out need work before senior review.",
-    "citations": [
-      "S1",
-      "S2"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Posture",
-      "value": "Needs targeted fixes",
-      "detail": "Synthetic extraction found source and tie-out issues.",
-      "status": "risk",
-      "citations": [
-        "S1"
-      ]
-    },
-    {
-      "label": "Top Blocker",
-      "value": "Source coverage",
-      "detail": "Several data-heavy pages need source/date support.",
-      "status": "risk",
-      "citations": [
-        "S1",
-        "S2"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "qc-verdict",
-      "label": "Verdict",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "QC posture",
-            "stance": "Risk",
-            "summary": "The deck is not ready for circulation until source coverage and repeated-number tie-out are resolved.",
-            "next_catalyst": "Run targeted remediation and review updated pages.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "issue-log",
-      "label": "Issue Log",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Section",
-              "Severity",
-              "Issue",
-              "Suggested fix",
-              "Evidence"
-            ],
-            "rows": [
-              {
-                "Section": "Executive summary",
-                "Severity": "High",
-                "Issue": "Repeated metric lacks consistent source support",
-                "Suggested fix": "Tie summary metric to model output and source footnote",
-                "Evidence": "Synthetic extraction and model support disagree",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              },
-              {
-                "Section": "Valuation page",
-                "Severity": "Medium",
-                "Issue": "Axis unit is ambiguous",
-                "Suggested fix": "Add unit and period label to chart",
-                "Evidence": "Synthetic visual QC note",
-                "citations": [
-                  "S1"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "remediation",
-      "label": "Remediation",
-      "modules": [
-        {
-          "type": "timeline",
-          "data": {
-            "events": [
-              {
-                "date": "First pass",
-                "event": "Resolve source footnotes and repeated-number tie-out",
-                "impact": "Unlocks senior-review readiness",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Underlying chart data, source footnotes, and model support files are required for full circulation review.",
-              "Visual review is still required for image-only pages."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic deck extraction output",
-      "url": "https://example.com/qc-extraction",
-      "as_of": "2026-05-18",
-      "type": "extraction",
-      "status": "synthetic example",
-      "excerpt": "First-pass deck text, chart, and issue extraction."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic model support file",
-      "url": "https://example.com/model-support",
-      "as_of": "2026-05-18",
-      "type": "model support",
-      "status": "synthetic example",
-      "excerpt": "Repeated metric and valuation support fields."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-incomplete-chart-support-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-incomplete-chart-support-sample.json
deleted file mode 100644
index 6839520..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-incomplete-chart-support-sample.json
+++ /dev/null
@@ -1,38 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "ExampleCo: Draft Incomplete Chart",
-  "issuer": {
-    "ticker": "EXCO"
-  },
-  "metadata": {
-    "payload_stage": "draft",
-    "citation_policy": "warn",
-    "readiness_label": "Draft support only",
-    "readiness_posture": "working_draft"
-  },
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "financial_trend_chart",
-          "data": {
-            "periods": [
-              {
-                "period": "Q1 FY2026",
-                "revenue": "100",
-                "gross_profit": "",
-                "net_income": "20",
-                "net_margin": "20%"
-              }
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": []
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-support-source-gap-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-support-source-gap-sample.json
deleted file mode 100644
index 48ef6be..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-support-source-gap-sample.json
+++ /dev/null
@@ -1,29 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "title": "ExampleCo: Draft Support Payload",
-  "issuer": {
-    "ticker": "EXCO"
-  },
-  "metadata": {
-    "payload_stage": "support",
-    "citation_policy": "warn",
-    "readiness_label": "Draft support only",
-    "readiness_posture": "working_draft"
-  },
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "stance": "Draft",
-            "summary": "Revenue was $100M but this draft intentionally lacks source support."
-          }
-        }
-      ]
-    }
-  ],
-  "sources": []
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/earnings-preview-minimal.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/earnings-preview-minimal.json
deleted file mode 100644
index 0f8729f..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/earnings-preview-minimal.json
+++ /dev/null
@@ -1,126 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "ExampleCo: Pre-Earnings Dashboard",
-  "subtitle": "Minimal fixture for renderer validation.",
-  "issuer": {
-    "ticker": "EXCO",
-    "name": "ExampleCo",
-    "exchange": "NYSE",
-    "sector": "Example sector",
-    "accent_color": "#245f5a"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-15 09:00 ET",
-    "source_posture": "Example fixture",
-    "citation_policy": "strict",
-    "decision_context": "Is the bar low enough into the print?",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Pre-earnings dashboard",
-    "headline": "ExampleCo: Q1 pre-print dashboard",
-    "dek": "Minimal renderer fixture.",
-    "callout_label": "Core Debate",
-    "callout": "Whether KPI stabilization is enough to support guidance.",
-    "citations": [
-      "S1"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Event",
-      "value": "May 20, 2026",
-      "detail": "Example date.",
-      "status": "good",
-      "citations": [
-        "S1"
-      ]
-    },
-    {
-      "label": "Bar",
-      "value": "Consensus pending",
-      "detail": "Terminal-only field.",
-      "status": "gap"
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "title": "PM View",
-          "eyebrow": "Overview",
-          "data": {
-            "stance": "Example stance.",
-            "summary": "This is a fixture, not investment analysis.",
-            "thesis_change": "Unchanged",
-            "estimate_revision": "Not applicable",
-            "stock_skew": "Not applicable",
-            "next_catalyst": "Example earnings",
-            "citations": [
-              "S1"
-            ],
-            "key_points": [
-              "Renderer supports decision boxes.",
-              "Missing data remains visible."
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "kpis",
-      "label": "KPIs",
-      "modules": [
-        {
-          "type": "table",
-          "title": "KPI Board",
-          "eyebrow": "Fixture",
-          "data": {
-            "columns": [
-              {
-                "key": "metric",
-                "label": "Metric"
-              },
-              {
-                "key": "bar",
-                "label": "Bar"
-              },
-              {
-                "key": "read",
-                "label": "Read"
-              }
-            ],
-            "rows": [
-              {
-                "metric": "Revenue",
-                "bar": "Not provided",
-                "read": "Keep visible as a gap.",
-                "citations": [
-                  "S1"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Fixture source",
-      "type": "Example source",
-      "status": "Reviewed fixture",
-      "note": "Example only.",
-      "excerpt": "Minimal payload source used for renderer validation.",
-      "as_of": "2026-05-15"
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/financial-trend-incomplete-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/financial-trend-incomplete-sample.json
deleted file mode 100644
index 3378242..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/financial-trend-incomplete-sample.json
+++ /dev/null
@@ -1,78 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "chart_readiness_test",
-  "layout": "single_page",
-  "title": "Incomplete Financial Trend Fixture",
-  "subtitle": "Synthetic strict-mode fixture that should fail chart-readiness validation.",
-  "issuer": {
-    "ticker": "MISS",
-    "name": "Missing Series Co.",
-    "sector": "Testing",
-    "brand_dark_color": "#7f1d1d"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic incomplete chart data",
-    "citation_policy": "strict",
-    "decision_context": "Incomplete chart modules should not render silently.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Chart QA",
-    "headline": "Incomplete chart fixture",
-    "dek": "This fixture intentionally has incomplete chart rows.",
-    "callout_label": "Expected Validation",
-    "callout": "Strict validation should fail.",
-    "citations": [
-      "S1"
-    ]
-  },
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "financial_trend_chart",
-          "data": {
-            "periods": [
-              {
-                "period": "Q1 FY2026",
-                "revenue": "100",
-                "gross_profit": "60",
-                "net_income": "20",
-                "net_margin": "20%",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "period": "Q2 FY2026",
-                "revenue": "110",
-                "gross_profit": "",
-                "net_income": "22",
-                "net_margin": "20%",
-                "citations": [
-                  "S1"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic incomplete chart source",
-      "url": "https://example.com/incomplete-chart",
-      "as_of": "2026-05-18",
-      "type": "test source",
-      "status": "synthetic example",
-      "excerpt": "Includes one complete period and one incomplete period."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/hero-citations-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/hero-citations-sample.json
deleted file mode 100644
index 993ac49..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/hero-citations-sample.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "hero_citation_test",
-  "layout": "single_page",
-  "title": "Hero Citation Test Dashboard",
-  "subtitle": "Synthetic strict-mode dashboard for hero citation rendering.",
-  "issuer": {
-    "ticker": "HERO",
-    "name": "Hero Citation Co.",
-    "sector": "Testing",
-    "brand_dark_color": "#1f4b7a"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic source ledger for renderer regression coverage",
-    "citation_policy": "strict",
-    "decision_context": "Hero citations should remain visible.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Renderer QA",
-    "headline": "Hero citations remain visible",
-    "dek": "The hero dek should append a source chip when citations are provided.",
-    "callout_label": "Numeric Callout",
-    "callout": "Revenue grew 24.5% in the cited period.",
-    "citations": [
-      "S1"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Citation State",
-      "value": "Visible",
-      "detail": "Hero citations should render in the masthead.",
-      "status": "good",
-      "citations": [
-        "S1"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "QA read",
-            "stance": "Good",
-            "summary": "Hero citation rendering is the behavior under test.",
-            "citations": [
-              "S1"
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "No missing evidence; this module is included so sample coverage preserves the standard dashboard QA shape."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic hero citation source",
-      "url": "https://example.com/hero-citation",
-      "as_of": "2026-05-18",
-      "type": "test source",
-      "status": "synthetic example",
-      "excerpt": "Supports hero dek and callout citation rendering."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/idea-generation-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/idea-generation-sample.json
deleted file mode 100644
index f734047..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/idea-generation-sample.json
+++ /dev/null
@@ -1,180 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "idea_generation",
-  "layout": "single_page",
-  "title": "Public Equity Investing idea funnel dashboard",
-  "subtitle": "Synthetic strict-mode example for candidate triage.",
-  "issuer": {
-    "name": "Multi-name screen",
-    "sector": "Cross-sector",
-    "brand_dark_color": "#4b5563"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic screen plus filing/news examples",
-    "citation_policy": "strict",
-    "decision_context": "Which names deserve immediate research follow-up.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Idea generation",
-    "headline": "The screen surfaces a small number of research-worthy candidates",
-    "dek": "The dashboard separates candidates, watchlist items, and rejected false positives.",
-    "callout_label": "Screen Purpose",
-    "callout": "Prioritize follow-up, not final trade recommendations.",
-    "citations": [
-      "S1"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Top Candidate",
-      "value": "ACME Software",
-      "detail": "Best combination of catalyst path and valuation setup in the synthetic screen.",
-      "status": "good",
-      "citations": [
-        "S1",
-        "S2"
-      ]
-    },
-    {
-      "label": "Readiness",
-      "value": "Triage only",
-      "detail": "No candidate is implementation-ready without live market refresh.",
-      "status": "watch",
-      "citations": [
-        "S1"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "screen-summary",
-      "label": "Summary",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "PM triage",
-            "stance": "Watch",
-            "summary": "The screen produces research candidates, not recommendations; ACME Software should move to comps and earnings work first.",
-            "next_catalyst": "Refresh market data and validate the catalyst path.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "candidate-board",
-      "label": "Candidates",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Candidate",
-              "Bucket",
-              "Why now",
-              "First rejection risk",
-              "Next workflow"
-            ],
-            "rows": [
-              {
-                "Candidate": "ACME Software",
-                "Bucket": "Immediate research candidate",
-                "Why now": "Valuation compression plus near-term product catalyst.",
-                "First rejection risk": "Consensus refresh may already embed the catalyst.",
-                "Next workflow": "Comps analysis and earnings preview",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              },
-              {
-                "Candidate": "Beta Retail",
-                "Bucket": "Watchlist",
-                "Why now": "Margin inflection is possible but evidence is thin.",
-                "First rejection risk": "Consumer demand data may remain weak.",
-                "Next workflow": "Thesis tracker",
-                "citations": [
-                  "S1",
-                  "S3"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "next-actions",
-      "label": "Next Actions",
-      "modules": [
-        {
-          "type": "question_list",
-          "data": {
-            "questions": [
-              {
-                "question": "Does live consensus already price the catalyst?",
-                "why": "It determines whether the idea is variant or merely visible.",
-                "citations": [
-                  "S2"
-                ]
-              },
-              {
-                "question": "Which rejected names failed because of data quality rather than thesis quality?",
-                "why": "It controls what gets parked on the watchlist.",
-                "citations": [
-                  "S1"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Live market data, liquidity, borrow, options, and consensus fields need refresh before any trade pitch.",
-              "Rejected candidates need source-backed rejection notes before archival."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic public equity screen export",
-      "url": "https://example.com/idea-screen",
-      "as_of": "2026-05-18",
-      "type": "screen",
-      "status": "synthetic example",
-      "excerpt": "Candidate scores and triage fields."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic ACME catalyst note",
-      "url": "https://example.com/acme-catalyst",
-      "as_of": "2026-05-18",
-      "type": "research note",
-      "status": "synthetic example",
-      "excerpt": "Product catalyst and consensus caveat."
-    },
-    {
-      "id": "S3",
-      "title": "Synthetic retail macro note",
-      "url": "https://example.com/retail-macro",
-      "as_of": "2026-05-18",
-      "type": "macro note",
-      "status": "synthetic example",
-      "excerpt": "Consumer demand risk context."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/initiating-coverage-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/initiating-coverage-sample.json
deleted file mode 100644
index 50e3699..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/initiating-coverage-sample.json
+++ /dev/null
@@ -1,220 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "initiating_coverage",
-  "layout": "single_page",
-  "title": "ACME Infrastructure: initiation dashboard",
-  "subtitle": "Synthetic strict-mode example for a thesis-led initiation.",
-  "issuer": {
-    "ticker": "AINF",
-    "name": "ACME Infrastructure",
-    "sector": "Infrastructure",
-    "brand_dark_color": "#335c3b"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic filings, model output, and market-data examples",
-    "citation_policy": "strict",
-    "decision_context": "Whether capacity additions support a new long-only research posture.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Initiating coverage",
-    "headline": "Capacity growth can compound if contract renewals stay disciplined",
-    "dek": "The dashboard maps thesis, valuation method, catalysts, and evidence gaps.",
-    "callout_label": "Variant View",
-    "callout": "The market may be underweighting renewal quality relative to headline capacity additions.",
-    "citations": [
-      "S1",
-      "S2"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Research Posture",
-      "value": "Constructive watch",
-      "detail": "Needs model refresh before a rating-style conclusion.",
-      "status": "watch",
-      "citations": [
-        "S2"
-      ]
-    },
-    {
-      "label": "Key Catalyst",
-      "value": "Investor day",
-      "detail": "Management should update renewal and capacity commentary.",
-      "status": "good",
-      "citations": [
-        "S3"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "view",
-      "label": "View",
-      "modules": [
-        {
-          "type": "executive_summary",
-          "data": {
-            "headline": "Initiation frame",
-            "body": "The initiation thesis is constructive but not decision-grade until renewal quality, capex cadence, and valuation support are refreshed.",
-            "net_read": "Build the model and track renewal disclosure before upgrading the view.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        },
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "Decision",
-            "stance": "Watch",
-            "summary": "Thesis is plausible; valuation and renewal evidence still gate a publishable recommendation.",
-            "next_catalyst": "Investor day disclosure.",
-            "citations": [
-              "S2",
-              "S3"
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "thesis",
-      "label": "Thesis",
-      "modules": [
-        {
-          "type": "cards",
-          "data": {
-            "items": [
-              {
-                "title": "Thesis pillar",
-                "body": "Capacity additions matter only if renewal spreads remain disciplined.",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "title": "Falsifier",
-                "body": "Capex acceleration without renewal improvement would weaken the long-only case.",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "scenario_map",
-          "data": {
-            "cases": [
-              {
-                "label": "Bear",
-                "status": "bear",
-                "summary": "Renewals fade and capacity growth dilutes returns.",
-                "citations": [
-                  "S1",
-                  "S2"
-                ]
-              },
-              {
-                "label": "Base",
-                "status": "base",
-                "summary": "Renewals remain stable while capacity growth supports steady compounding.",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "label": "Bull",
-                "status": "bull",
-                "summary": "Renewals improve and capacity comes online ahead of plan.",
-                "citations": [
-                  "S3"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "valuation-evidence",
-      "label": "Valuation & Evidence",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Section",
-              "Current support",
-              "Open item",
-              "PM read"
-            ],
-            "rows": [
-              {
-                "Section": "Model",
-                "Current support": "Synthetic operating model output",
-                "Open item": "Refresh capex and renewal inputs",
-                "PM read": "Model support is incomplete",
-                "citations": [
-                  "S2"
-                ]
-              },
-              {
-                "Section": "Catalyst",
-                "Current support": "Investor day date from synthetic event source",
-                "Open item": "Confirm agenda and webcast timing",
-                "PM read": "Useful near-term checkpoint",
-                "citations": [
-                  "S3"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Live market data, consensus estimates, and final model sensitivities are required before publication-ready output.",
-              "Source conflicts should be resolved before any rating-style language is used."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "ACME Infrastructure synthetic annual filing",
-      "url": "https://example.com/ainf-filing",
-      "as_of": "2026-03-15",
-      "type": "filing",
-      "status": "synthetic example",
-      "excerpt": "Capacity and renewal discussion."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic initiation model output",
-      "url": "https://example.com/ainf-model-output",
-      "as_of": "2026-05-18",
-      "type": "model output",
-      "status": "synthetic example",
-      "excerpt": "Forecast and valuation placeholders."
-    },
-    {
-      "id": "S3",
-      "title": "Synthetic investor day calendar item",
-      "url": "https://example.com/ainf-investor-day",
-      "as_of": "2026-05-18",
-      "type": "event source",
-      "status": "synthetic example",
-      "excerpt": "Upcoming investor day reference."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-hedge-mode-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-hedge-mode-sample.json
deleted file mode 100644
index 23bdc49..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-hedge-mode-sample.json
+++ /dev/null
@@ -1,194 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "portfolio_risk_hedge_design",
-  "layout": "single_page",
-  "title": "ACME Hedge Finder Dashboard",
-  "subtitle": "Synthetic strict-mode example for public-equity hedge selection.",
-  "issuer": {
-    "ticker": "ACME",
-    "name": "ACME Software",
-    "sector": "Software",
-    "brand_dark_color": "#1f4b7a"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic thesis, portfolio, and hedge-candidate examples",
-    "citation_policy": "strict",
-    "decision_context": "Whether the unwanted market and growth-factor exposure should be hedged or managed by a smaller equity size.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Hedge finder",
-    "headline": "Hedge beta without hedging away the stock thesis",
-    "dek": "The dashboard separates retained exposure, basis risk, and readiness gaps.",
-    "callout_label": "Senior View",
-    "callout": "Use a liquid equity-index hedge only if it preserves the idiosyncratic product thesis; route credit hedges to Credit Markets.",
-    "citations": [
-      "S1",
-      "S2"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Recommendation",
-      "value": "Partial index hedge",
-      "detail": "Preserves product-specific upside while reducing broad market drawdown risk.",
-      "status": "watch",
-      "citations": [
-        "S1",
-        "S2"
-      ]
-    },
-    {
-      "label": "Readiness",
-      "value": "Needs checks",
-      "detail": "Live pricing, risk model, and option-chain inputs must be refreshed before implementation.",
-      "status": "risk",
-      "citations": [
-        "S2"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "hedge-view",
-      "label": "Decision",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "Hedge read",
-            "stance": "Watch",
-            "summary": "Prefer a partial index or sector hedge over a credit instrument; size down if basis risk is worse than the exposure.",
-            "stock_skew": "Retained exposure is the product catalyst and estimate-revision path.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "candidate-set",
-      "label": "Candidates",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Candidate",
-              "Mechanism",
-              "Basis risk",
-              "Readiness"
-            ],
-            "rows": [
-              {
-                "Candidate": "Index hedge",
-                "Mechanism": "Reduces broad market beta while retaining issuer-specific thesis.",
-                "Basis risk": "May under-hedge software factor drawdown.",
-                "Readiness": "Needs live sizing checks",
-                "citations": [
-                  "S2"
-                ]
-              },
-              {
-                "Candidate": "Peer short basket",
-                "Mechanism": "Targets sector and factor exposure more directly.",
-                "Basis risk": "Can introduce independent short squeeze risk.",
-                "Readiness": "Needs borrow checks",
-                "citations": [
-                  "S2"
-                ]
-              },
-              {
-                "Candidate": "CDS hedge",
-                "Mechanism": "Credit-risk signal only in this plugin.",
-                "Basis risk": "Credit hedge construction routes to Credit Markets.",
-                "Readiness": "route_to_credit_markets",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "scenario_map",
-          "data": {
-            "cases": [
-              {
-                "label": "Thesis right",
-                "status": "bull",
-                "summary": "Equity alpha remains if the hedge does not remove product-specific upside.",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "label": "Hedge fails",
-                "status": "bear",
-                "summary": "Proxy breaks if software factor sells off more than the broad index.",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "monitoring",
-      "label": "Monitoring",
-      "modules": [
-        {
-          "type": "timeline",
-          "data": {
-            "events": [
-              {
-                "date": "Next review",
-                "event": "Refresh hedge inputs",
-                "impact": "Determines whether to resize, roll, remove, or size down instead.",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Live pricing, correlations, borrow, option-chain depth, and risk-model exposure are required before calling the hedge implementation-ready.",
-              "Credit hedge candidates require a Credit Markets handoff rather than local implementation."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic ACME thesis summary",
-      "url": "https://example.com/acme-thesis",
-      "as_of": "2026-05-18",
-      "type": "user thesis",
-      "status": "synthetic example",
-      "excerpt": "Thesis and desired retained exposure."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic hedge-candidate snapshot",
-      "url": "https://example.com/acme-hedges",
-      "as_of": "2026-05-18",
-      "type": "risk data",
-      "status": "synthetic example",
-      "excerpt": "Candidate hedge, readiness, and basis-risk placeholders."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-sizing-mode-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-sizing-mode-sample.json
deleted file mode 100644
index b22dc3d..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/portfolio-sizing-mode-sample.json
+++ /dev/null
@@ -1,185 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "portfolio_risk_position_sizing",
-  "layout": "single_page",
-  "title": "ACME Position Sizing Dashboard",
-  "subtitle": "Synthetic strict-mode example for sizing and risk constraints.",
-  "issuer": {
-    "ticker": "ACME",
-    "name": "ACME Software",
-    "sector": "Software",
-    "brand_dark_color": "#1f4b7a"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic thesis, portfolio, and market-data examples",
-    "citation_policy": "strict",
-    "decision_context": "Whether the position size should be constrained by downside loss or liquidity.",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "pm_ready"
-  },
-  "hero": {
-    "eyebrow": "Risk position sizing",
-    "headline": "Size is capped by liquidity and downside-loss constraints",
-    "dek": "The dashboard separates the PM action from the support bundle.",
-    "callout_label": "Binding Constraint",
-    "callout": "Liquidity exit, not conviction, is the binding sizing constraint in the synthetic example.",
-    "citations": [
-      "S1",
-      "S2"
-    ]
-  },
-  "snapshot": [
-    {
-      "label": "Action",
-      "value": "Start small",
-      "detail": "Use a starter size until live liquidity and downside inputs are refreshed.",
-      "status": "watch",
-      "citations": [
-        "S1",
-        "S2"
-      ]
-    },
-    {
-      "label": "Constraint",
-      "value": "Liquidity",
-      "detail": "Synthetic ADV support indicates exit capacity is the tightest check.",
-      "status": "risk",
-      "citations": [
-        "S2"
-      ]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "sizing-decision",
-      "label": "Decision",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "Sizing read",
-            "stance": "Watch",
-            "summary": "Use a starter position until liquidity, volatility, and downside support are refreshed.",
-            "stock_skew": "Upside is plausible but exit capacity gates the size.",
-            "citations": [
-              "S1",
-              "S2"
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "constraints",
-      "label": "Constraints",
-      "modules": [
-        {
-          "type": "table",
-          "data": {
-            "columns": [
-              "Constraint",
-              "Current read",
-              "Sizing implication",
-              "Open item"
-            ],
-            "rows": [
-              {
-                "Constraint": "Downside loss",
-                "Current read": "Needs refreshed target and downside case",
-                "Sizing implication": "Caps starter size",
-                "Open item": "Refresh target/downside support",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "Constraint": "Liquidity exit",
-                "Current read": "Most restrictive based on synthetic ADV support",
-                "Sizing implication": "Binding constraint",
-                "Open item": "Refresh ADV and participation assumption",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "scenario_map",
-          "data": {
-            "cases": [
-              {
-                "label": "Base",
-                "status": "base",
-                "summary": "Position works if product catalyst and retention evidence hold.",
-                "citations": [
-                  "S1"
-                ]
-              },
-              {
-                "label": "Stress",
-                "status": "bear",
-                "summary": "Liquidity stress forces slower exit and smaller size.",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "monitoring",
-      "label": "Monitoring",
-      "modules": [
-        {
-          "type": "timeline",
-          "data": {
-            "events": [
-              {
-                "date": "Next review",
-                "event": "Refresh market data and risk inputs",
-                "impact": "Determines whether the starter size can be increased",
-                "citations": [
-                  "S2"
-                ]
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Live NAV, limits, price, ADV, volatility, borrow, and downside inputs are required before decision-grade sizing.",
-              "Factor and portfolio exposure outputs are unavailable in this synthetic example."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic ACME thesis summary",
-      "url": "https://example.com/acme-thesis",
-      "as_of": "2026-05-18",
-      "type": "user thesis",
-      "status": "synthetic example",
-      "excerpt": "Thesis, target, downside, and catalyst assumptions."
-    },
-    {
-      "id": "S2",
-      "title": "Synthetic liquidity and risk snapshot",
-      "url": "https://example.com/acme-risk",
-      "as_of": "2026-05-18",
-      "type": "risk data",
-      "status": "synthetic example",
-      "excerpt": "ADV, volatility, and exposure placeholders."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-complete-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-complete-sample.json
deleted file mode 100644
index 0adefbd..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-complete-sample.json
+++ /dev/null
@@ -1,75 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "ExampleCo: Production Dashboard",
-  "issuer": {
-    "ticker": "EXCO",
-    "name": "ExampleCo",
-    "exchange": "NYSE",
-    "sector": "Example sector",
-    "accent_color": "#245f5a"
-  },
-  "metadata": {
-    "payload_stage": "production",
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic issuer release reviewed for validator fixture.",
-    "readiness_label": "PM-ready fixture",
-    "readiness_posture": "pm_ready",
-    "citation_policy": "strict",
-    "decision_context": "Whether the pre-print setup is ready for a PM read."
-  },
-  "hero": {
-    "eyebrow": "Production dashboard",
-    "headline": "ExampleCo: source-visible PM setup",
-    "dek": "This fixture carries the complete production metadata contract.",
-    "callout_label": "Core Debate",
-    "callout": "Whether the setup is actionable for review.",
-    "citations": ["S1"]
-  },
-  "snapshot": [
-    {
-      "label": "Event",
-      "value": "May 20, 2026",
-      "detail": "Issuer release fixture.",
-      "status": "good",
-      "citations": ["S1"]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "title": "PM View",
-          "data": {
-            "stance": "Ready for review",
-            "summary": "Production metadata, source ledger, and point-of-use citations are present.",
-            "citations": ["S1"]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "title": "Missing Evidence",
-          "data": {
-            "items": [
-              "No missing evidence in this synthetic validator fixture."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "ExampleCo release fixture",
-      "type": "Synthetic source",
-      "status": "Reviewed fixture",
-      "as_of": "2026-05-18",
-      "excerpt": "Source-visible production validation fixture."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-missing-source-fails-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-missing-source-fails-sample.json
deleted file mode 100644
index 4017f80..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-missing-source-fails-sample.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "ExampleCo: Missing Source Fixture",
-  "issuer": {
-    "ticker": "EXCO",
-    "name": "ExampleCo"
-  },
-  "metadata": {
-    "payload_stage": "production",
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Source ledger intentionally omitted for negative validation.",
-    "readiness_label": "PM-ready fixture",
-    "readiness_posture": "pm_ready",
-    "citation_policy": "strict",
-    "decision_context": "Production dashboards must not pass without sources."
-  },
-  "hero": {
-    "headline": "ExampleCo: missing source ledger",
-    "dek": "This should fail production validation."
-  },
-  "snapshot": [
-    {
-      "label": "Status",
-      "value": "Blocked",
-      "detail": "Source ledger missing."
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "stance": "Blocked",
-            "summary": "Production dashboards require a source ledger."
-          }
-        }
-      ]
-    }
-  ],
-  "sources": []
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-unresolved-citation-fails-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-unresolved-citation-fails-sample.json
deleted file mode 100644
index fb117fe..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-unresolved-citation-fails-sample.json
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "ExampleCo: Bad Citation Fixture",
-  "issuer": {
-    "ticker": "EXCO",
-    "name": "ExampleCo"
-  },
-  "metadata": {
-    "payload_stage": "production",
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic source ledger with an intentionally unresolved citation.",
-    "readiness_label": "PM-ready fixture",
-    "readiness_posture": "pm_ready",
-    "citation_policy": "strict",
-    "decision_context": "Production dashboards must not pass unresolved citations."
-  },
-  "hero": {
-    "headline": "ExampleCo: unresolved citation",
-    "dek": "This should fail because it cites [S99]."
-  },
-  "snapshot": [
-    {
-      "label": "Status",
-      "value": "Blocked",
-      "detail": "Unknown citation is present.",
-      "citations": ["S99"]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "overview",
-      "label": "Overview",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "stance": "Blocked",
-            "summary": "The summary cites an unknown source [S99]."
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Known source",
-      "type": "Synthetic source",
-      "status": "Reviewed fixture",
-      "as_of": "2026-05-18"
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/scenario-sensitivity-sample.json b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/scenario-sensitivity-sample.json
deleted file mode 100644
index c629f09..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/scenario-sensitivity-sample.json
+++ /dev/null
@@ -1,202 +0,0 @@
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "scenario_sensitivity",
-  "layout": "single_page",
-  "title": "ACME Scenario Sensitivity Dashboard",
-  "subtitle": "Synthetic strict-mode example for public-equity scenario skew.",
-  "issuer": {
-    "ticker": "ACME",
-    "name": "ACME Software"
-  },
-  "metadata": {
-    "freeze_time": "2026-05-18 09:00 ET",
-    "source_posture": "Synthetic user-provided scenario inputs",
-    "citation_policy": "strict",
-    "decision_context": "Does probability-weighted upside clear the hurdle after downside and evidence quality?",
-    "payload_stage": "production",
-    "readiness_label": "Production fixture",
-    "readiness_posture": "senior_review_ready"
-  },
-  "hero": {
-    "eyebrow": "Scenario sensitivity",
-    "headline": "Expected return clears the hurdle, but downside skew still governs action",
-    "dek": "The dashboard keeps raw tables as support artifacts and surfaces the PM decision layer.",
-    "callout_label": "PM Action",
-    "callout": "Hold / wait for proof until probability support and model tie-out are refreshed.",
-    "citations": ["S1"]
-  },
-  "snapshot": [
-    {
-      "label": "Current price",
-      "value": "$100",
-      "detail": "Synthetic spot anchor.",
-      "status": "neutral",
-      "citations": ["S1"]
-    },
-    {
-      "label": "Probability-weighted value",
-      "value": "$116",
-      "detail": "Weighted by supplied bull/base/bear probabilities.",
-      "status": "watch",
-      "citations": ["S1"]
-    },
-    {
-      "label": "Expected return vs hurdle",
-      "value": "+4%",
-      "detail": "Expected return less 12% hurdle.",
-      "status": "watch",
-      "citations": ["S1"]
-    },
-    {
-      "label": "Downside/upside ratio",
-      "value": "0.45x",
-      "detail": "Downside is acceptable but not trivial.",
-      "status": "risk",
-      "citations": ["S1"]
-    },
-    {
-      "label": "Break-even probability",
-      "value": "31%",
-      "detail": "Probability required to justify spot.",
-      "status": "neutral",
-      "citations": ["S1"]
-    },
-    {
-      "label": "Skew label",
-      "value": "optical upside / needs proof",
-      "detail": "Upside is visible but source posture caps conviction.",
-      "status": "watch",
-      "citations": ["S1"]
-    },
-    {
-      "label": "PM action",
-      "value": "hold / wait for proof",
-      "detail": "Model tie-out and probability support are gating items.",
-      "status": "neutral",
-      "citations": ["S1"]
-    }
-  ],
-  "tabs": [
-    {
-      "id": "pm-decision",
-      "label": "PM Decision",
-      "modules": [
-        {
-          "type": "decision_box",
-          "data": {
-            "label": "Scenario read",
-            "stance": "Hold / wait for proof",
-            "summary": "Probability-weighted value is $116 versus $100 spot, but the action remains proof-gated.",
-            "stock_skew": "Optical upside / needs proof.",
-            "citations": ["S1"]
-          }
-        },
-        {
-          "type": "metric_tiles",
-          "data": {
-            "items": [
-              {
-                "label": "Expected return vs hurdle",
-                "value": "+4%",
-                "detail": "Synthetic expected return less 12% hurdle.",
-                "citations": ["S1"]
-              },
-              {
-                "label": "Break-even probability",
-                "value": "31%",
-                "detail": "Probability needed to justify spot.",
-                "citations": ["S1"]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "scenario-map",
-      "label": "Scenario Map",
-      "modules": [
-        {
-          "type": "scenario_map",
-          "data": {
-            "cases": [
-              {
-                "label": "Bull",
-                "status": "bull",
-                "summary": "Bull value is $140 on estimate upside and multiple support.",
-                "citations": ["S1"]
-              },
-              {
-                "label": "Base",
-                "status": "base",
-                "summary": "Base value is $118 with moderate estimate revision support.",
-                "citations": ["S1"]
-              },
-              {
-                "label": "Bear",
-                "status": "bear",
-                "summary": "Bear value is $82 if growth decelerates and multiple support fades.",
-                "citations": ["S1"]
-              }
-            ]
-          }
-        },
-        {
-          "type": "table",
-          "data": {
-            "columns": ["Scenario", "Value", "Return", "Skew", "Action"],
-            "rows": [
-              {
-                "Scenario": "Probability weighted",
-                "Value": "$116",
-                "Return": "+16%",
-                "Skew": "optical upside / needs proof",
-                "Action": "hold / wait for proof",
-                "citations": ["S1"]
-              }
-            ]
-          }
-        }
-      ]
-    },
-    {
-      "id": "evidence-and-qa",
-      "label": "Evidence & QA",
-      "modules": [
-        {
-          "type": "source_list",
-          "data": {
-            "sources": [
-              {
-                "id": "S1",
-                "title": "Synthetic scenario packet",
-                "as_of": "2026-05-18",
-                "type": "user/model input",
-                "status": "synthetic example",
-                "excerpt": "Synthetic spot price, scenario values, probabilities, and hurdle inputs."
-              }
-            ]
-          }
-        },
-        {
-          "type": "missing_evidence",
-          "data": {
-            "items": [
-              "Refresh current price, probability support, model tie-out, and source freshness before sizing or portfolio action."
-            ]
-          }
-        }
-      ]
-    }
-  ],
-  "sources": [
-    {
-      "id": "S1",
-      "title": "Synthetic scenario packet",
-      "as_of": "2026-05-18",
-      "type": "user/model input",
-      "status": "synthetic example",
-      "excerpt": "Synthetic spot price, scenario values, probabilities, and hurdle inputs."
-    }
-  ]
-}
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/earnings-dashboard-feedback-map.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/earnings-dashboard-feedback-map.md
deleted file mode 100644
index 2f24be7..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/earnings-dashboard-feedback-map.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Earnings Dashboard Feedback Map
-
-Use this when converting user feedback on a pre- or post-earnings dashboard into reusable skill changes.
-
-## Where Feedback Lives
-
-- `dashboard-builder` owns reusable presentation capabilities: single-page vs tabbed layout, sticky table of contents, responsive tables, issuer identity tiles, module rendering, module validation, and source/missing-evidence visibility.
-- `earnings-deep-dive` owns post-print analytical depth: executive summary, beat/miss versus consensus, EPS quality and normalization, guidance deep dive, quarterly key metrics, growth trajectory, transcript Q&A quality, read-throughs, catalysts, contradictions, and filing/transcript/interview evidence.
-- `earnings-preview` owns pre-print analytical depth: expectation bar, consensus/guide/whisper posture, last-quarter KPI baseline, quarterly key metrics, growth trajectory, guide credibility, peer/sector read-throughs, scenarios, and call-prep questions.
-- `sector-context-overlay` should be used by the producer skill to pick nuanced company-specific KPIs, read-through relationships, and sector accounting conventions. It should not render dashboards.
-- `financial-source-of-truth` owns evidence conflicts, source hierarchy, stale data flags, and precise absence labels when sources disagree or a value cannot be verified.
-
-## Feedback Structure
-
-Structure future dashboard feedback as:
-
-1. `Analysis requirement`: what the producer skill must research or normalize.
-2. `Visual/module requirement`: what the dashboard-builder must render.
-3. `Coverage rule`: whether the requirement applies to pre-earnings, post-earnings, all PM dashboards, or only a sector.
-4. `Evidence standard`: official filing/release, transcript, consensus source, price/market data, alternative data, management interview, or analyst-derived estimate.
-5. `Quality gate`: what should fail, warn, or be called out as missing evidence.
-
-## Cross-Skill Rules
-
-- Put the quarter once in the hero/title area; do not duplicate it in a highlight tile.
-- Highlight tiles must be PM-selective, not mechanically populated. Use the metric that changed the debate: revenue surprise %, normalized EPS beat, guide raise/cut, growth acceleration, margin inflection, backlog, capex guide, customer metrics, churn, NIM, reserves, production, ARR, or sector-specific KPI.
-- When a growth rate, surprise %, or acceleration is the investor signal, use it as the highlighted value and place the absolute dollar amount in supporting detail. Do not default to the largest reported absolute number.
-- Financial trend charts should use net margin only when net income is a fair recurring-profitability proxy. If EPS quality is distorted by below-the-line gains/losses, tax, FX, mark-to-market, equity-investment gains, restructuring, impairments, litigation, asset sales, or other one-time items, the producer skill should select operating margin or another source-backed profitability metric and pass `margin_metric`, `margin_label`, and `margin_rationale` to `dashboard-builder`.
-- Positive-only chart scales should start at zero and show multiple readable tick/grid lines. Negative axis labels should only appear when the underlying data is negative, not because the renderer padded an all-positive scale.
-- Company-specific quarterly key metrics belong in both pre-earnings and post-earnings dashboards. Pre-earnings uses the baseline and bar; post-earnings uses actuals, growth, consensus/guide delta, and PM read.
-- Post-earnings dashboards should include a dense executive summary, granular beat/miss, EPS quality bridge, guidance deep dive, transcript Q&A map, read-through table, catalyst timeline, and missing-evidence list.
-- Transcript modules should capture analyst, firm, topic, management response, short direct quote where available, and response quality. If the response deflects, say so.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/mobile-qa.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/mobile-qa.md
deleted file mode 100644
index ab12541..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/mobile-qa.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Mobile And Device QA
-
-Use this before finalizing a dashboard or changing shared layout assets.
-
-## Required Checks
-
-- Dashboard must remain readable at desktop, tablet, and phone widths.
-- Tables must not require precision horizontal scrolling on phones. The shared CSS converts table rows into key/value cards below 700px.
-- Touch targets for tabs and buttons should be at least 44px high.
-- Sticky table-of-contents links in `single_page` layout should also be at least 44px high and horizontally scrollable on phones.
-- Do not rely on hover-only controls.
-- Citation chips must also work on keyboard focus and click/tap. Hover previews are additive; click should jump to the source ledger or open the source URL.
-- Do not use remote fonts, remote logo fetches, or remote JavaScript for local dashboards.
-- Generic issuer identity tiles must work when no logo exists.
-- Text should wrap before shrinking. Do not use viewport-width font sizing for dashboard body text.
-- Source and missing-evidence modules must remain visible on mobile.
-- Inline citation chips, numeric citation links, section source notes, and source IDs must wrap without causing horizontal overflow.
-- Single-page dashboards must keep all sections in the DOM; navigation should jump to anchors rather than hiding content.
-- The dashboard should open from `file://` without a dev server.
-
-## Smoke Commands
-
-```bash
-python skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py payload.json --profile production
-python skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py payload.json output/index.html --profile production
-python -m py_compile shared/dashboard/*.py skills/public-equity-investing/internal-support/dashboard-builder/scripts/*.py
-```
-
-Then scan the rendered HTML for unresolved authoring text:
-
-```bash
-rg -n "TODO|FIXME|PLACEHOLDER|\\[TOKEN\\]|undefined" output/index.html
-```
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/module-contract.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/module-contract.md
deleted file mode 100644
index c2dc250..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/references/module-contract.md
+++ /dev/null
@@ -1,341 +0,0 @@
-# Dashboard Module Contract
-
-Use this when mapping a Public Equity Investing skill output into a dashboard payload.
-
-## Payload Shape
-
-```json
-{
-  "kind": "public_equity_investing_dashboard.v1",
-  "mode": "earnings_preview",
-  "layout": "single_page",
-  "title": "Issuer: dashboard title",
-  "subtitle": "Optional supporting copy",
-  "issuer": {
-    "ticker": "HD",
-    "name": "The Home Depot",
-    "exchange": "NYSE",
-    "sector": "Retail",
-    "accent_color": "#f26a21",
-    "brand_dark_color": "#9a3412",
-    "brand_colors": ["#f26a21", "#9a3412"]
-  },
-  "metadata": {
-    "payload_stage": "production",
-    "freeze_time": "2026-05-15 09:00 ET",
-    "source_posture": "Company IR first, then public fallback",
-    "readiness_label": "PM-ready",
-    "readiness_posture": "pm_ready",
-    "citation_policy": "strict",
-    "decision_context": "The core debate shown in the hero"
-  },
-  "hero": {
-    "eyebrow": "Pre-earnings dashboard",
-    "headline": "The Home Depot: Q1 FY2026 pre-print",
-    "dek": "Short PM-facing setup sentence.",
-    "callout_label": "Core Debate",
-    "callout": "The most important question."
-  },
-  "snapshot": [
-    {"label": "Event", "value": "May 19, 2026", "detail": "Q1 call at 9:00 a.m. ET", "status": "good"}
-  ],
-  "tabs": [
-    {"id": "overview", "label": "Overview", "modules": []}
-  ],
-  "sources": [
-    {"id": "S1", "title": "Issuer Q1 2026 10-Q", "url": "https://example.com/10q", "as_of": "2026-05-15", "type": "SEC filing", "excerpt": "Relevant supporting excerpt."}
-  ],
-  "model_citations_path": "model_citations.json",
-  "qa": {"warnings": [], "hard_failures": []}
-}
-```
-
-## Inline Citation Contract
-
-Public Equity Investing dashboards are source-visible at the point of use. The source ledger is required, but it is not enough by itself.
-
-- Give every source a stable top-level `sources[].id` such as `S1`, `S2`, `Filing-Q1`, or `Transcript-Q1`.
-- Cite every material number, estimate, quote, date-sensitive claim, and assumption inline where it appears.
-- Supported citation forms:
-  - string markers: `"Q1 revenue was $8.1M [S1]"`
-  - field citations: `{"value": "$8.1M", "citations": ["S1"]}`
-  - row/module citations: `{"metric": "Revenue", "current": "$8.1M", "citations": ["S1"]}`
-  - source ID aliases: `source_id: "S1"` or `source_ids: ["S1", "S2"]`
-  - external-only fallback: `{"value": "June CPI", "source_url": "https://...", "source_title": "BLS CPI schedule", "as_of": "2026 schedule"}`
-- Citation chips click through to the source ledger for known source IDs. External-only fallback chips open the source URL in a new window.
-- Numeric values should usually render as the citation target themselves: the number keeps normal text color, gets a subtle underline/background affordance, links to the source ledger or source URL, and exposes the same hover/focus preview.
-- Citation chips are still allowed for non-numeric claims, but keep them visually quiet and small. If a whole section or card uses the same source set, prefer a compact source note at the bottom of that section/card instead of repeating identical chips after every sentence.
-- Citation chips and numeric citation links expose a hover/focus preview using source title, type/status, date/as-of, and excerpt/pinpoint/note when available.
-- Workbook-backed dashboards may include `model_citations` or `model_citations_path` from model builders. Each record should have `id`, `workbook_path`, `sheet`, `cell` or `range`, optional `value`, optional `formula`, `line_item`, `source_id`, and `evidence_label`. Modules should cite the model-output record ID, for example `{"value": "$48.25", "citations": ["model-output:dcf-value-per-share"]}`. The renderer treats these records as source ledger entries and shows workbook/sheet/cell details in chips and popovers.
-- Production payloads must set `metadata.payload_stage: "production"` and `metadata.citation_policy: "strict"` so validation hard-fails missing source ledgers, unresolved citation IDs, and numeric material without citation support.
-- Production metadata must include `freeze_time`, `source_posture`, `readiness_label`, `readiness_posture`, and `decision_context`. Use production readiness postures such as `pm_ready`, `client_ready`, `committee_ready`, `senior_review_ready`, `external`, or `publication_ready` only when those gates are actually satisfied.
-- Draft/support payloads may set `metadata.payload_stage: "draft"` or `"support"` and `metadata.citation_policy: "warn"`, but the rendered artifact must visibly retain gaps and cannot be described as PM-ready, client-ready, committee-ready, or publication-ready.
-- Use `missing_evidence` for unsupported values. Do not create an uncited number and then bury the source gap at the bottom.
-
-## Supported Modules
-
-## Layout Modes
-
-- `tabs` is the backward-compatible layout. It renders tab buttons and one visible panel at a time.
-- `single_page` renders every tab as an anchored section and adds a sticky table of contents. Use this for PM diligence dashboards when the user wants to scan the full work product and jump between sections.
-- Public Equity Investing dashboards should default to analyst-dense spacing. Use white space for hierarchy and scanning, not as decoration.
-- Issuer identity tiles must work with 1-4 character tickers and should remain readable without crowding the box. Do not use a logo as a dependency. When available, provide brand colors in `issuer.accent_color`, `issuer.brand_color`, `issuer.brand_dark_color`, `issuer.identity_color`, or `issuer.brand_colors`; the ticker badge should use a darker brand shade so white ticker text remains legible.
-
-## Snapshot / Highlight Rules
-
-- Put quarter/period in the hero headline, dek, or metadata. Do not duplicate it as a highlight tile unless the user explicitly asks for a period tile.
-- Highlight tiles should answer "what should the PM remember first?" Prefer surprise percentages, growth acceleration/deceleration, normalized EPS, guide deltas, margin inflections, backlog, unit growth, capex guide, or the company-specific KPI that changed the debate. When the investor signal is a rate or surprise, make that the primary value and put the absolute dollar value in supporting detail.
-- For EPS, label GAAP, adjusted, operating, or clean/recurring basis. If GAAP is distorted, show the quality-adjusted read in the summary and beat/miss table rather than heroing the distorted headline alone.
-- For capex, use the annual guide/range when that is the investor issue; use quarterly capex only when quarterly cash burn is the issue.
-
-## Chart Rules
-
-- Use charts when they shorten diligence time: segment growth, revenue mix, guide delta, margin trend, exposure concentration, or catalyst probability.
-- Pair charts with the underlying table when precision matters. The chart should help scanning; the table should preserve exact values.
-- Do not chart every module. If a table is already small and self-explanatory, keep it as a table.
-
-### `decision_box`
-
-Use for the PM bottom line.
-
-Data keys:
-
-- `label`
-- `stance`
-- `summary`
-- `thesis_change`
-- `estimate_revision`
-- `stock_skew`
-- `next_catalyst`
-- `key_points`
-
-### `metric_tiles`
-
-Use for event, price, guide, valuation, spread, probability, liquidity, catalyst, or data-quality tiles.
-
-Data keys:
-
-- `items`: array of `{label, value, detail, status}`
-- Each item may also include `citations`, `source_id`, `source_ids`, `source_url`, `source_title`, `excerpt`, or bracketed source markers in `value` / `detail`.
-
-### `highlight_tiles`
-
-Alias of `metric_tiles` for the top PM highlights when a producer wants to distinguish investment highlights from routine KPI tiles.
-
-Data keys:
-
-- `items`: array of `{label, value, detail, status}`
-
-### `executive_summary`
-
-Use for a dense PM-style paragraph that synthesizes the print, quality of beat, guidance, new disclosures, call-only datapoints, contradictions, and watch items.
-
-Data keys:
-
-- `headline`
-- `body`
-- `bullets`
-- `net_read`
-
-### `key_metrics`
-
-Use for quarterly key metrics. This should be company-specific, not just generic financials. Include absolute values and growth/trajectory where relevant.
-
-Data keys:
-
-- `rows`: array of `{metric, current, growth, compare, trajectory, read}` by default
-- `columns`: optional custom column list
-- `density`: optional; use `dense` for PM tables
-- Cite each row or cell with `citations`, `source_id/source_ids`, or inline source markers.
-
-### `growth_trajectory`
-
-Use for acceleration/deceleration across the metrics that actually drive the name.
-
-Data keys:
-
-- `rows`: array of `{metric, prior, current, trajectory, read}` by default
-- `columns`: optional custom column list
-
-### `cards`
-
-Use for debates, drivers, risks, read-throughs, thesis pillars, evidence deltas, or source notes.
-
-Data keys:
-
-- `items`: array of `{eyebrow, title, body, status, bullets}`
-
-### `table`
-
-Use for KPI boards, expectation bars, beat/miss, guide bridges, EPS-quality screens, model updates, risks, or source posture.
-
-Data keys:
-
-- `columns`: array of strings or `{key, label}`
-- `rows`: array of objects keyed to columns
-- `mobile_label`: optional table description
-- Cite cells with mapping values (`{"value": "$8.1M", "citations": ["S1"]}`), bracketed markers, or row-level `citations`.
-
-### `scenario_map`
-
-Use for bull/base/bear, probability trees, payoff trees, downside/recovery, or valuation cases.
-
-Data keys:
-
-- `cases`: array of `{type, title, headline, bullets}`
-
-### `question_list`
-
-Use for call prep, management-meeting prep, diligence requests, or monitoring questions.
-
-Data keys:
-
-- `questions`: array of `{question, why, listen_for}`
-
-### `transcript_qa`
-
-Use for post-earnings transcript and Q&A maps. Prefer this over `question_list` when a transcript exists and the user needs analyst, topic, management response, direct quote, and answer quality in one dense table.
-
-Data keys:
-
-- `rows`: array of `{number, analyst, topic, response, quote, quality}` by default
-- `columns`: optional custom column list
-
-Quote rule: keep direct quotes short, source-tagged, and only when transcript access supports them. If quotes are unavailable, state `transcript not provided` or use paraphrase with precise source posture.
-
-### `read_throughs`
-
-Use for in-depth cross-company and industry read-throughs.
-
-Data keys:
-
-- `rows`: array of `{name, relationship, parent_said, read_through, implication, confidence}` by default
-- `columns`: optional custom column list
-
-### `market_events`
-
-Use for major news coverage, market events, regulatory items, macro shocks, product launches, management changes, litigation, capital allocation, upcoming catalysts, and other external context that can change the investor decision.
-
-This module is presentation-only. The producer skill decides which events are material, over what lookback window, and what the impact is. Prefer a three-window scan: last quarter, last twelve months, and forward-looking anticipated events. Every event needs a cited source or an explicit missing-evidence label.
-
-Data keys:
-
-- `events`: array of `{date, event, type, impact, investor_read, source_title, source_url, as_of, citations}` by default
-- `rows`: accepted as an alias for `events`
-- `mobile_label`: optional table description
-
-Event selection rules:
-
-- Include only events that plausibly affect estimates, multiple, risk, positioning, industry read-through, or catalyst path.
-- Separate company-specific events from sector, macro, regulatory, legal, geopolitical, rate/FX, commodity, and peer events.
-- Include upcoming events when they are dated or have a well-labeled window. Do not invent exact dates.
-- Cite the source for the event and label source posture, especially for public web fallback, analyst inference, or terminal-only gaps.
-- Put the citation on the event row, not only in the source column, so the date/window, event, impact, and investor read each carry a visible chip.
-
-### `bar_chart`
-
-Use for compact native charting without external JavaScript. Good for small sets of comparable metrics.
-
-Data keys:
-
-- `items`: array of `{label, value, bar_value, display, detail}`
-- `bar_value`: numeric value used for bar scaling
-- `display`: optional value shown at the right of the bar
-- `detail`: optional subtitle under the label
-
-### `financial_trend_chart`
-
-Use for earnings-preview and earnings-deep-dive dashboards when source-backed quarterly financial history is available. It renders grouped bars for revenue, gross profit, and net income by quarter, with a selected profitability-margin line overlay. Omit the module when any required metric is missing, stale, non-comparable, not parseable, or on an inconsistent basis. Strict QA requires at least two chart-ready rows after renderer field requirements are applied.
-
-Data keys:
-
-- `periods`: array of `{period, revenue, gross_profit, net_income, net_margin, operating_margin, adjusted_operating_margin, ebitda_margin, fcf_margin, source, as_of}`. Only the selected margin field is required, but include net margin too when source-backed and useful for the table or EPS-quality narrative.
-- `revenue`, `gross_profit`, and `net_income` must be numeric and use the same units/scale.
-- `margin_metric`: optional line metric key. Default is `net_margin`. Set to `operating_margin`, `adjusted_operating_margin`, `ebitda_margin`, `fcf_margin`, or another explicit row key when that better reflects recurring profitability.
-- `margin_label`: optional display label such as `Operating Margin` or `Adjusted Operating Margin`.
-- `margin_rationale`: optional note explaining why this line metric was selected, especially when net margin is distorted by below-the-line gains/losses, tax, FX, equity-investment marks, restructuring, impairments, litigation, asset sales, or other non-recurring items.
-- The selected margin may be a decimal ratio such as `0.245` or a percent string such as `24.5%`.
-- `mobile_label`: optional table description.
-
-Producer selection rule:
-
-- Default to net margin only when it is a fair recurring-profitability proxy.
-- Prefer operating margin when net income is distorted but operating income remains comparable.
-- Prefer adjusted operating margin, EBITDA margin, contribution margin, or FCF margin when those are the issuer's actual investor KPI and the basis is clearly sourced.
-- If the margin choice is ambiguous, use the line that best answers the investor question and state the rationale in `margin_rationale`; do not silently chart a distorted margin.
-
-Renderer scale behavior:
-
-- Positive-only bar and EPS scales start at zero and use readable gridlines/ticks rather than padded negative lower bounds.
-- The right-axis line scale should be labeled with multiple percentage ticks, not only top/bottom endpoints.
-
-### `eps_actual_vs_estimate_chart`
-
-Use for earnings dashboards when estimated EPS and actual EPS are available for the past five quarters on the same basis. If fewer quarters are available, use the chart only when the module title or note states the actual window. Omit the module when estimate timestamps, EPS basis, actual EPS, or parseable values are missing. Strict QA requires at least two chart-ready rows after renderer field requirements are applied.
-
-Data keys:
-
-- `periods`: array of `{period, estimated_eps, actual_eps, surprise, basis, source, as_of}`.
-- `estimated_eps` and `actual_eps` must be numeric and on a consistent GAAP, adjusted, operating, or clean EPS basis.
-- `surprise` is optional; when omitted, the renderer displays `actual_eps - estimated_eps`.
-- `mobile_label`: optional table description.
-
-### `equity_price_event_chart`
-
-Use only when sourced equity-price history can be paired with material events that plausibly affected the equity and the price tape is substantive enough to visualize. This module renders a price line with numbered event markers and the event table directly underneath. Prefer this over a generic news list when the user needs to see how events map to price action, but omit it when the available market data is only a few closes, article-reported price points, or a sparse quote-card sample. Strict QA fails thin price tapes rather than letting them render.
-
-Minimum price-tape gates:
-
-- Daily price data: at least 10 distinct chart-ready price rows spanning at least 7 calendar days.
-- Hourly price data: at least 24 distinct time-stamped chart-ready price rows spanning at least 6 hours.
-- Minute or intraday price data: at least 60 distinct time-stamped chart-ready price rows spanning at least 45 minutes.
-
-Data keys:
-
-- `prices`: array of `{date, price}` or `{timestamp, price}` rows, with direct citations/source IDs on rows or a module-level price source.
-- `price_granularity`, `price_frequency`, or `price_interval`: optional but recommended; accepted families are `daily`, `hourly`, and `minute`/`intraday`. When omitted, QA infers the cadence from timestamps and still applies the relevant minimums.
-- `events`: array of `{date, event, type, impact, investor_read, source_title, source_url, as_of}`.
-- `date` should match a price point where possible; otherwise markers are distributed across the visible price window.
-- Include only sourced company, sector, macro, regulatory, product, management, legal, peer, or forward-catalyst events that affected estimates, multiple, risk, positioning, or catalyst path.
-
-### `timeline`
-
-Use for catalysts, post-print next checks, regulatory paths, event windows, or thesis-monitoring cadence.
-
-Data keys:
-
-- `events`: array of `{date, title, detail}`
-
-### `text_block`
-
-Use for compact explanatory copy where a table or cards would be overkill.
-
-Data keys:
-
-- `body`
-- `bullets`
-
-### `missing_evidence`
-
-Use for terminal-only fields, unavailable transcript, stale estimates, missing options chain, no source timestamp, or model inputs still needed.
-
-Data keys:
-
-- `items`: array of strings or `{item, needed}`
-
-### `source_list`
-
-Usually added from top-level `sources`; use directly only when a tab needs a separate source ledger.
-
-Data keys:
-
-- `sources`: array of `{id, title, url, as_of, note, type, status, excerpt, pinpoint}`
-
-## Producer Guidance
-
-- Start with the primary skill's output contract; do not invent dashboard-only numbers.
-- Map existing sections into modules rather than changing the analysis structure to fit a visual.
-- Keep unavailable but important modules visible with precise absence labels.
-- Do not create empty chart shells. If the financial trend, EPS actual/estimate, or equity event data is incomplete, omit the chart module and disclose the missing series in `missing_evidence`; strict QA fails chart modules that would be filtered to empty by the renderer.
-- Put source limitations in both the relevant module and `missing_evidence`.
-- Hero `dek` and `callout` honor `hero.citations`; use those citations for source-backed masthead claims and numeric callouts.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py
deleted file mode 100644
index aec9b19..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env python3
-"""Render a standalone Public Equity Investing dashboard HTML file."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[5]
-sys.path.insert(0, str(PLUGIN_ROOT))
-
-from shared.dashboard.qa import load_payload, validate_payload  # noqa: E402
-from shared.dashboard.renderer import render_dashboard  # noqa: E402
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("payload", help="Path to dashboard payload JSON.")
-    parser.add_argument("output", help="Path to write standalone HTML.")
-    parser.add_argument(
-        "--profile",
-        choices=["draft", "production"],
-        default="production",
-        help="Validation profile. Rendering defaults to production because HTML is the user-facing artifact.",
-    )
-    parser.add_argument("--qa-json", help="Optional path to write validation report JSON.")
-    args = parser.parse_args()
-
-    payload = load_payload(args.payload)
-    report = validate_payload(payload, profile=args.profile)
-    if args.qa_json:
-        qa_path = Path(args.qa_json)
-        qa_path.parent.mkdir(parents=True, exist_ok=True)
-        qa_path.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
-    if report["hard_failures"]:
-        for failure in report["hard_failures"]:
-            print(f"failure: {failure}", file=sys.stderr)
-        return 1
-
-    html = render_dashboard(payload, validate=False)
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    output_path.write_text(html, encoding="utf-8")
-    for warning in report["warnings"]:
-        print(f"warning: {warning}", file=sys.stderr)
-    print(str(output_path))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py
deleted file mode 100644
index d3b5c33..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env python3
-"""Validate a Public Equity Investing dashboard payload."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from pathlib import Path
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[5]
-sys.path.insert(0, str(PLUGIN_ROOT))
-
-from shared.dashboard.qa import load_payload, validate_payload  # noqa: E402
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("payload", help="Path to dashboard payload JSON.")
-    parser.add_argument(
-        "--profile",
-        choices=["draft", "production"],
-        default="production",
-        help="Validation profile. Production hard-fails missing metadata, sources, readiness, and citations.",
-    )
-    parser.add_argument("--json", action="store_true", help="Print full validation JSON.")
-    args = parser.parse_args()
-
-    payload = load_payload(args.payload)
-    report = validate_payload(payload, profile=args.profile)
-    if args.json:
-        print(json.dumps(report, indent=2))
-    else:
-        print(f"status={report['status']}")
-        for warning in report["warnings"]:
-            print(f"warning: {warning}")
-        for failure in report["hard_failures"]:
-            print(f"failure: {failure}")
-    return 1 if report["hard_failures"] else 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/INTERNAL.md
deleted file mode 100644
index c8946bb..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/INTERNAL.md
+++ /dev/null
@@ -1,72 +0,0 @@
----
-name: excel-data-cleaner
-description: Use when cleaning messy public-equity-investing tables for CSV/XLSX/model inputs. Do not use for unrelated corporate cleanup.
----
-
-# Excel Data Cleaner
-
-> Internal support playbook. Load through `internal-support/policy.md`; this data-cleaning capability is bundled with the visible router rather than exposed as a skill entrypoint.
-
-## Deliverable Intake
-
-When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new substantive standalone cleanup deliverable should it, before source gathering, analysis, or rendering, load `../../../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved preferences.
-
-## Purpose
-
-Load `shared/equity-research-support-standard.md` and `shared/support-layer-routing-contract.md` before substantial source, data, QA, or style work.
-
-Clean messy Public Equity Investing tables into analyst-grade CSV/XLSX/model inputs. Preserve decision-critical detail, trace transformations, and produce outputs an equity research, event-driven, ETF/index, portfolio/risk, or PM analyst can inspect.
-
-Use for public-company financials, earnings tables, market/security data, portfolio/risk, ETF/index, security master, consensus/provider exports, and event-driven datasets. Do not use for corporate FP&A cleanup, GL reconciliation, private-company QoE, formula audit, or Credit Markets instrument analysis. If the table is clearly bonds, loans, CDS, covenants, recovery, restructuring, or debt-security selection, route to Credit Markets after preserving the source posture.
-
-## Embedded Support Routing
-
-This is an embedded service under an owning workflow unless the user explicitly asks for standalone table/workbook cleanup. Preserve the `owning_workflow` internally, such as `financials-normalizer`, `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `portfolio-risk-management`, `event-driven-analyzer`, `thesis-tracker`, `meeting-prep`, or `dashboard-builder`.
-
-For substantial embedded work, preserve `decision_impact`, `readiness_effect`, `artifact_role`, and `hidden_unless_requested` in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own the investment conclusion; state in natural language how duplicate rows, malformed fiscal periods, stale timestamps, broken identifiers, missing units, or ambiguous table grain changes model readiness, dashboard reliability, valuation support, sizing confidence, or circulation readiness. Cleaned workbooks, CSV, JSON, profiles, logs, and manifests are secondary/support artifacts when invoked by an owning workflow.
-
-## Reference Router
-- `cleaning-standards.md`: transformation policy and destructive-decision guardrails.
-- `domain-playbook.md`: domain keys, validations, units, and formatting.
-- `workbook-output-spec.md`: workbook tabs, formatting, and QA.
-- `examples.md`: common messy-data patterns.
-- `profile_tabular_data.py`: profile CSV/XLSX/XLS before editing.
-- `clean_tabular_data.py`: deterministic first-pass cleaner/workbook materializer.
-
-## Rules
-- User instructions win unless they would destroy integrity.
-- Preserve raw data, IDs, timestamps, units, currencies, fiscal labels, notes, sources, and exceptions.
-- Infer row grain before cleaning: issuer metric, security, instrument, position, estimate, event, period, line item, or source record.
-- Do not invent values, mappings, categories, signs, currencies, or formulas.
-- Log assumptions, transformations, dropped rows/columns, duplicate decisions, and parsing uncertainty.
-- Ask before fuzzy merges, conflicting duplicate merges, source overwrites, or business-critical imputations.
-
-## Workflow
-1. Classify objective, domain, grain, must-preserve fields, output type, and destructive choices.
-2. For non-trivial files, profile first:
-
-```bash
-python scripts/profile_tabular_data.py input.xlsx --output profile.json
-```
-
-3. Define a cleaning spec: headers, type conversions, duplicate policy, missing-value policy, category mapping, validations, assumptions.
-4. Clean conservatively: remove only clear structural noise, standardize headers, parse obvious types, preserve IDs as text, and flag uncertain duplicates/outliers.
-5. For deterministic first pass:
-
-```bash
-python scripts/clean_tabular_data.py input.xlsx --output cleaned.xlsx --domain auto --dedupe exact
-```
-
-6. Review and refine with analyst judgment; the script is a helper, not a substitute for review.
-
-## Dependencies
-Scripts require `pandas`, `openpyxl`, and `xlrd` from `scripts/requirements.txt`. They lazy-load after argparse, so `--help` works without installs. If packages are missing, install requirements or disclose that deterministic cleaning could not run.
-
-## Output Contract
-Default workbook tabs: `Cover`, `clean_data`, `raw_source`, `summary`, `data_dictionary`, `quality_checks`, `assumptions_audit`.
-
-The `Cover` tab is the first-tab dashboard: input file, workbook mode, sheet/row/column counts, inferred domain/grain where available, quality issue counts, fatal/warning count, cleaning policies, raw-source preservation, workbook map, and downstream-use limitations.
-
-Domain lenses: issuer financials, markets/security data, portfolio/risk, ETF/index data, consensus/provider exports, and event-driven data. Preserve the identifiers, periods, units, source timestamps, and metric definitions that make each domain auditable. Credit tables may be profiled only as `credit_markets_handoff` / equity-risk signal support.
-
-Final response: what changed, what was preserved, high/medium/low issues, user-directed vs inferred assumptions, and deliverable path or cleaned table.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/cleaning-standards.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/cleaning-standards.md
deleted file mode 100644
index cbb13f5..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/cleaning-standards.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Cleaning Standards
-
-Use this reference when the cleaning choice affects model inputs, investment conclusions, or auditability.
-
-## Decision Priority
-1. User-provided rules, definitions, mappings, and output format.
-2. Source semantics visible in sheet names, formulas, notes, labels, and data structure.
-3. Public Equity Investing domain conventions from `domain-playbook.md`.
-4. Conservative general defaults.
-
-When rules conflict, preserve the source value and record the conflict in `assumptions_audit` unless the user explicitly decides.
-
-## Non-Negotiables
-- Preserve raw data and original-to-clean mappings.
-- Prefer reversible changes: trim text, normalize obvious blanks, parse clear numbers/dates, rename headers with mappings.
-- Do not silently fuzzy-merge entities, impute missing values, delete outliers, change signs, collapse categories, convert currencies, or aggregate detail rows.
-- Treat identifiers as text: CUSIP, ISIN, account, SKU, zip, employee ID, phone-like fields.
-- Keep units/currencies explicit; if mixed, preserve or add unit/currency fields.
-
-## Structural Rules
-- Detect headers as the first mostly non-empty label row followed by detail data; beware titles, metadata, footnotes, and multi-row headers.
-- Combine multi-row headers only when levels form meaningful labels; preserve the header path in the dictionary.
-- Remove fully empty rows/columns from `clean_data`; preserve them in `raw_source`.
-- Flag subtotals/section rows. Remove from detail only when obvious and log the rule.
-- Fill down merged/group labels only when clearly hierarchical and needed for analysis-ready rows.
-
-## Header Rules
-- Use clear, unique labels suitable for Excel tables, formulas, pivots, and imports.
-- Keep standard market abbreviations: ARR, MRR, EBITDA, COGS, CAC, SKU, SLA, API, ID.
-- Disambiguate duplicate headers with source context, not arbitrary suffixes when avoidable.
-- Preserve both display names and machine names when the user needs import-ready output.
-
-## Type Rules
-- Normalize blank tokens: `""`, `-`, `--`, `n/a`, `na`, `null`, `none`, `not available`, `#n/a`.
-- Parse obvious accounting numbers: `(1,234.50)` -> `-1234.50`.
-- Do not parse magnitude suffixes such as `$1.2m` unless explicitly supported; otherwise flag.
-- Treat `12%`, `12`, `0.12`, and `12 bps` as different until context proves the convention.
-- Use real dates for actual dates; preserve fiscal periods such as `Q1 FY26`, `Apr-26`, or `2026-04` as periods when day precision is not real.
-- Normalize booleans only for truly boolean fields; do not collapse business statuses with distinct meanings.
-
-## Missing, Duplicate, And Exception Rules
-- Missingness severity:
-  - **Critical:** key/date/amount/owner/status/domain-required field missing.
-  - **Analytical:** optional dimension missing but row usable.
-  - **Cosmetic:** display field or note missing.
-- Duplicate policy:
-  - remove exact duplicates only when all values match and source identity is not needed;
-  - keep and flag same-key rows with conflicting values;
-  - merge only with a supplied or unambiguous business rule.
-- Outliers are often the point. Flag unusual values rather than deleting them.
-
-## Common Public Equity Investing Keys
-- Issuer financials: issuer + metric + period + scenario/status + currency + units.
-- Markets/risk: portfolio/account + security ID/ticker + date + metric.
-- ETF/index: index or ETF + constituent ticker/security ID + effective/as-of date + weight + source.
-- Credit Markets handoff / equity-risk signal: issuer + instrument ID/CUSIP/ISIN + maturity + metric + pricing date, only to preserve source posture or route out.
-- Consensus/provider: provider + ticker + metric + fiscal period + estimate date + actual/estimate flag.
-- Event-driven: target/acquirer + security ID/ticker + deal/event date + metric.
-
-## Formula And Workbook Handling
-- Preserve formulas unless asked to convert to values.
-- Do not break named ranges, external links, pivots, charts, or formulas without warning.
-- If extracting from formula-driven reports, state whether displayed values or formulas were used.
-
-## Final QA
-Before delivery, confirm raw source is preserved, row grain is coherent, headers are unique, types/formats are defensible, IDs remain text, duplicates are documented, quality checks are populated, assumptions are logged, and the workbook opens cleanly.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/domain-playbook.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/domain-playbook.md
deleted file mode 100644
index 0a8cc04..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/domain-playbook.md
+++ /dev/null
@@ -1,162 +0,0 @@
-# Public Equity Investing Domain Playbook
-
-Use this reference to adapt cleaning, validation, and formatting to the user's Public Equity Investing context. This skill can clean arbitrary tables when explicitly invoked, but its default routing and examples should stay focused on public equity, ETF/index, event-driven, market-data, portfolio/risk, consensus, and issuer-reporting workflows.
-
-## Domain Inference Signals
-
-Infer domain from user language, sheet names, headers, values, and expected output.
-
-- Financial statements / issuer reporting: filing, release, supplement, segment, revenue, COGS, EBITDA, EPS, FCF, capex, debt, cash, share count, fiscal year, quarter, month, guidance, actual, estimate, forecast.
-- Investing / markets: ticker, CUSIP, ISIN, SEDOL, security, issuer, portfolio, NAV, return, yield, spread, basis points, benchmark, price, holdings, exposure, market value.
-- Credit Markets handoff / equity-risk signal: bond, loan, coupon, maturity, rating, spread, yield, debt, liquidity, covenant, collateral, priority, recovery, restructuring, exchange offer, tender. Use this only to route out or preserve equity-risk evidence.
-- Consensus / provider exports: provider, estimate date, consensus, actual, revision, mean, median, high, low, fiscal period, metric, broker, timestamp.
-- Portfolio / risk: position, weight, exposure, P&L, gross, net, factor, beta, VaR, sector, geography, benchmark, hedge tag.
-- Event-driven / special situations: deal, consideration, unaffected price, spread, probability, closing date, break price, regulatory approval, tender, merger, spin, index event.
-
-If multiple domains are present, clean to the most downstream purpose. Example: a provider export used in an earnings model should preserve provider metadata, fiscal periods, actual/estimate labels, and source timestamps even if the raw table looks like a generic spreadsheet.
-
-## Financial Statements / Issuer Reporting
-
-### Preserve
-
-- entity, issuer, segment, geography, product line, account/metric name.
-- period, fiscal year, quarter, month, actual/estimate/forecast/guidance labels.
-- currency, units, source, source date, fiscal calendar, sign convention.
-- reported vs adjusted vs non-GAAP labels.
-
-### Cleaning judgment
-
-- Do not flip signs unless the source sign convention is explicit.
-- Do not combine actual, estimate, forecast, and guidance without a scenario/status column.
-- Treat period strings such as `FY26 Q1`, `Q1 FY26`, and `2026-Q1` as labels unless fiscal calendar is known.
-- Preserve rollup rows separately from detail rows when the table mixes both.
-
-### Checks
-
-- Missing amount where metric/period exists.
-- Multiple currencies without currency column.
-- Period labels inconsistent.
-- Duplicate issuer + metric + period + scenario rows.
-- Totals embedded in detail data.
-
-## Investing / Markets
-
-### Preserve
-
-- ticker, CUSIP, ISIN, SEDOL, issuer/security name, portfolio/account, broker/custodian.
-- trade date, settle date, pricing date, benchmark, metric units, currency.
-- basis points vs percent vs decimal return.
-
-### Cleaning judgment
-
-- Treat security identifiers as text.
-- Do not infer ticker-to-company mappings without a trusted source.
-- Do not convert return units unless unit labels are clear.
-- Do not aggregate positions across accounts, currencies, share classes, or dates unless requested.
-
-### Checks
-
-- Missing security ID/ticker/date.
-- Mixed currencies or return units.
-- Duplicate portfolio + security + date rows with conflicting values.
-- Negative prices, impossible yields, stale pricing dates.
-
-## Credit Markets handoff / equity-risk signal
-
-### Preserve for handoff
-
-- issuer, borrower/obligor, security, tranche, CUSIP/ISIN, coupon, maturity, seniority, collateral, guarantor, rating.
-- price, yield, spread, OID, call protection, recovery assumption, debt amount, liquidity metric, covenant definition.
-- source document, pricing timestamp, filing period, and legal document reference.
-
-These fields are not local analysis ownership. Preserve them so Credit Markets can continue the work, or keep only the equity read-through if the downstream decision is common equity.
-
-### Cleaning judgment
-
-- Keep legal/covenant terms as text and route interpretation to Credit Markets.
-- Do not merge debt instruments by issuer alone; tranche and maturity matter for handoff integrity.
-- Preserve restricted vs unrestricted cash and secured vs unsecured debt labels when they affect common-equity risk.
-
-### Checks
-
-- Missing maturity, coupon, debt balance, price/yield/spread, or source timestamp where material to equity downside or Credit Markets handoff.
-- Mixed price formats, yield formats, or spread units.
-- Duplicate instrument IDs with conflicting economics.
-- Covenant or liquidity metric missing definition.
-- Add `route_to_credit_markets` when the requested output is credit analysis, debt-security selection, covenant analysis, recovery, or restructuring.
-
-## Consensus / Provider Exports
-
-### Preserve
-
-- provider, estimate date, retrieval timestamp, broker/count metadata, mean/median/high/low, actual/estimate flag.
-- fiscal period, calendarized period if available, metric definition, currency, units.
-
-### Cleaning judgment
-
-- Do not blend providers without labeling provider and as-of date.
-- Keep actuals and estimates separate.
-- Do not silently overwrite consensus with company guidance or analyst assumptions.
-
-### Checks
-
-- Missing provider, estimate date, fiscal period, or metric definition.
-- Actual and estimate rows mixed without status.
-- Multiple estimate dates in one table without a clear latest flag.
-
-## Portfolio / Risk
-
-### Preserve
-
-- portfolio/account, position date, security ID, issuer, strategy, sector, region, benchmark, hedge tag.
-- market value, exposure, weight, P&L, return, beta, factor exposure, VaR, liquidity bucket.
-
-### Cleaning judgment
-
-- Do not net long and short rows unless the user requests net exposure.
-- Preserve currency, fund/account, and position-date grain.
-- Do not roll up by ticker if multiple share classes, currencies, or instruments exist.
-
-### Checks
-
-- Weights do not sum where expected.
-- Missing price/date/security ID.
-- Long/short sign convention unclear.
-- Mixed currencies without FX treatment.
-
-## Event-Driven / Special Situations
-
-### Preserve
-
-- target/acquirer/issuer, security, consideration, unaffected price, current price, spread, probability, expected close, break price.
-- regulatory, shareholder, court, financing, or tender milestones.
-- source date and exact document/source behind deal terms.
-
-### Cleaning judgment
-
-- Do not calculate spread or probability-weighted return unless terms, price, and timing are clear.
-- Preserve cash vs stock vs mixed consideration labels.
-- Label stale unaffected prices or old deal-spread snapshots.
-
-### Checks
-
-- Missing current price, consideration, closing date, probability, or break price in a return table.
-- Mixed date formats for milestones.
-- Deal terms unsupported by a primary source.
-
-
-## ETF / Index Data
-
-### Preserve
-
-- index or ETF name, methodology source, constituent ticker/security ID, weight, float adjustment, rebalance date, effective date, sector/industry, country, share class, and source timestamp.
-
-### Cleaning judgment
-
-- Do not infer index membership or weights without an index/ETF sponsor source or user-provided export.
-- Preserve additions/deletions, pro forma weights, and current weights separately.
-
-### Checks
-
-- Missing as-of/effective date, methodology source, ticker/security ID, or constituent weight.
-- Mixed index families or ETF share classes without a source field.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/examples.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/examples.md
deleted file mode 100644
index c0a94c8..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/examples.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Public Equity Investing Examples
-
-## Example 1: Issuer financial supplement with messy headers
-
-User: "Clean this earnings supplement export and make it model-input ready."
-
-Expected behavior:
-
-- Infer financial statement / issuer reporting domain.
-- Preserve issuer, segment, metric, fiscal period, actual/estimate/guidance labels, currency, units, and source date.
-- Remove report title rows and obvious subtotals from `clean_data`, preserving them in `raw_source` and logging the action.
-- Standardize period labels but do not infer a fiscal calendar if not provided.
-- Format amounts with thousands separators; preserve currency and unit columns if mixed.
-- Create checks for duplicate issuer + metric + period + scenario rows, missing amounts, missing periods, and totals embedded in detail data.
-
-## Example 2: Consensus/provider estimate export
-
-User: "Clean this consensus export before I refresh the model."
-
-Expected behavior:
-
-- Infer consensus / provider export domain.
-- Preserve provider, estimate date, broker/count metadata, actual/estimate flag, fiscal period, metric definition, currency, and units.
-- Keep actuals and estimates separate; do not overwrite one with the other.
-- Flag missing estimate dates, mixed providers, duplicate metric + period rows, and stale snapshots.
-- Output a clean table plus data dictionary, quality checks, and assumptions audit.
-
-## Example 3: Credit Markets handoff preservation / equity-risk signal sheet
-
-User: "Profile this bond trading sheet, preserve the fields, and route the credit note to Credit Markets."
-
-Expected behavior:
-
-- Infer `credit_markets_handoff` domain and route credit-note ownership to Credit Markets.
-- Preserve issuer, instrument ID, tranche, coupon, maturity, seniority, price, yield, spread, rating, and pricing timestamp.
-- Treat CUSIPs/ISINs as text.
-- Do not merge instruments by issuer alone.
-- Flag missing maturities, mixed spread units, negative/impossible yields, duplicate CUSIPs with conflicting economics, and missing source timestamps; do not interpret the credit economics locally.
-
-## Example 4: Portfolio holdings / risk export
-
-User: "Clean this holdings file so I can use it for exposure and hedge analysis."
-
-Expected behavior:
-
-- Infer portfolio / risk domain.
-- Preserve portfolio/account, position date, security ID, issuer, long/short sign convention, market value, exposure, weight, benchmark, and hedge tags.
-- Do not net long and short rows unless explicitly requested.
-- Flag missing security IDs, mixed currencies, unclear signs, stale prices, and weights that do not sum where expected.
-
-## Example 5: Event-driven deal tracker
-
-User: "Clean this merger arb tracker and make the spread fields reliable."
-
-Expected behavior:
-
-- Infer event-driven / special situations domain.
-- Preserve target, acquirer, consideration, unaffected price, current price, spread, probability, expected close, break price, and regulatory milestones.
-- Do not calculate spread or probability-weighted return unless deal terms, price, and timing are clear.
-- Flag stale price dates, unsupported deal terms, mixed consideration types, and missing break-price assumptions.
-
-## Example 6: Explicit user instructions
-
-User: "Clean the attached CSV. Keep only rows where Status = Active, convert dates to yyyy-mm-dd, do not remove duplicates, and output snake_case headers."
-
-Expected behavior:
-
-- Follow the explicit filter, date, duplicate, and header instructions.
-- Preserve raw source.
-- Log that inactive rows were filtered per user instruction.
-- Do not apply exact duplicate removal even if duplicates exist; flag duplicates in quality checks if useful.
-- Use snake_case headers in `clean_data`.
-
-- Add `route_to_credit_markets` when the requested output is a credit note, covenant review, recovery analysis, or debt-security decision.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/workbook-output-spec.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/workbook-output-spec.md
deleted file mode 100644
index 69604de..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/references/workbook-output-spec.md
+++ /dev/null
@@ -1,169 +0,0 @@
-# Workbook Output Specification
-
-Use this reference when producing an Excel deliverable.
-
-## Default Workbook Tabs
-
-### `Cover`
-
-The first visible tab when producing a polished `.xlsx` deliverable. Required by `shared/workbook-artifact-standard.md`.
-
-Recommended content:
-
-- input file and workbook mode;
-- source sheets cleaned;
-- clean row and column counts;
-- inferred domain/grain where available;
-- quality issue count and fatal/warning count;
-- major cleaning actions, dedupe policy, header policy, subtotal policy;
-- raw-source preservation status;
-- workbook map for clean, raw, dictionary, quality, and audit tabs;
-- downstream-use limitations, source gaps, unsupported connector/provider gaps, and Credit Markets handoff flags that should remain visible.
-
-### `clean_data`
-
-The primary analysis-ready table.
-
-Requirements:
-
-- One row per intended grain.
-- Unique, clear headers.
-- Excel table with filters enabled.
-- Freeze top row.
-- Real dates/numbers/percentages where safe.
-- IDs preserved as text.
-- No decorative title rows, blank bands, or report-only subtotal rows unless intentionally preserved.
-- Add flags for uncertain records rather than hiding them.
-
-### `raw_source`
-
-A copy of the unmodified source data.
-
-Requirements:
-
-- Keep original order and values.
-- If multiple input sheets exist, use `raw_source_<sheet>` or preserve all raw sheets with clear names.
-- Do not apply cleaning transformations here except minimal formatting for readability.
-
-### `data_dictionary`
-
-A field-level reference for reviewers and downstream users.
-
-Recommended columns:
-
-- `source_sheet`
-- `original_field`
-- `clean_field`
-- `inferred_type`
-- `excel_format`
-- `null_count`
-- `unique_count`
-- `example_values`
-- `cleaning_notes`
-- `business_notes`
-
-### `quality_checks`
-
-A reviewer-oriented issue log.
-
-Recommended columns:
-
-- `severity`: fatal, warning, info.
-- `issue_type`: missing_required_field, duplicate_key, mixed_type, outlier, invalid_date, subtotal_row, etc.
-- `field`
-- `affected_rows`
-- `affected_count`
-- `description`
-- `recommended_action`
-
-Severity guidance:
-
-- **fatal:** prevents reliable use for the stated objective.
-- **warning:** usable, but reviewer should inspect or resolve.
-- **info:** notable cleaning action or low-risk observation.
-
-### `assumptions_audit`
-
-A transformation and assumption log.
-
-Recommended columns:
-
-- `step`
-- `action`
-- `basis`
-- `affected_sheet`
-- `affected_field`
-- `affected_rows_or_count`
-- `risk_level`
-- `notes`
-
-Include both user-directed and inferred assumptions.
-
-### `summary` optional
-
-Use when the user wants a polished deliverable or the data has non-obvious risks.
-
-Recommended content:
-
-- dataset overview.
-- key cleaning actions.
-- top quality risks.
-- recommended next steps.
-
-Do not add analytical conclusions unless requested or naturally part of the task.
-
-## Naming and Formatting
-
-### Sheet names
-
-Use lowercase with underscores, max 31 characters, unique:
-
-- `clean_data`
-- `raw_source`
-- `data_dictionary`
-- `quality_checks`
-- `assumptions_audit`
-- `summary`
-
-### Header formatting
-
-- Bold header row.
-- Filters enabled.
-- Freeze top row.
-- Wrap header text only when needed.
-- Use concise labels.
-
-### Column widths
-
-- Use width based on content with reasonable minimum/maximum.
-- IDs and codes: 12-24.
-- Names/descriptions/notes: 24-60.
-- Amounts/dates/status: 12-18.
-
-### Number formats
-
-- Integer counts: `#,##0`.
-- Amounts where currency is known and uniform: `$#,##0` or `$#,##0.00` depending on precision.
-- Amounts with multiple currencies: numeric format plus separate currency column; avoid a single currency symbol.
-- Percentages: `0.0%` or `0.00%` depending on domain.
-- Dates: `yyyy-mm-dd`.
-- Month periods: `yyyy-mm` or `mmm-yy` if the value is truly a date bucket.
-
-### Conditional indicators
-
-Use flags and issue sheets instead of relying only on colors. Color formatting can help, but the workbook must remain understandable without it.
-
-## Deliverable QA
-
-Before delivering:
-
-- Open or inspect the workbook after writing.
-- Confirm all expected sheets are present.
-- Confirm table dimensions and headers are plausible.
-- Confirm raw source is preserved.
-- Confirm formulas, if any, are not broken.
-- Confirm quality checks and audit sheets are populated or explicitly state when no issues were found.
-- Confirm file size is reasonable and workbook is not corrupted.
-
-
-If a workbook includes credit-instrument data, the Cover and `quality_checks` tabs should mark `route_to_credit_markets` unless the credit data is only an equity-risk signal such as refinancing stress or solvency pressure.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/clean_tabular_data.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/clean_tabular_data.py
deleted file mode 100755
index 98d24a5..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/clean_tabular_data.py
+++ /dev/null
@@ -1,1000 +0,0 @@
-#!/usr/bin/env python3
-"""Conservative first-pass cleaner for messy CSV/XLSX data.
-
-Creates an audit-ready workbook with cleaned data, raw source, data dictionary,
-quality checks, and assumptions/audit sheets. This is a helper for the skill's
-analyst workflow, not a replacement for human/AI review of ambiguous business rules.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import re
-import sys
-from collections import defaultdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Iterable
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from workbook_formatting import autosize_and_table, safe_excel_sheet_name
-
-BLANK_TOKENS = {
-    "",
-    "-",
-    "--",
-    "n/a",
-    "na",
-    "null",
-    "none",
-    "not available",
-    "#n/a",
-    "nan",
-}
-TOTAL_RE = re.compile(r"\b(grand\s+total|subtotal|sub-total|total)\b", re.I)
-ID_HINT_RE = re.compile(
-    r"\b(id|code|sku|zip|postal|phone|cusip|isin|sedol|ticker|account\s*#|invoice\s*#|po\s*#|employee)\b",
-    re.I,
-)
-CURRENCY_RE = re.compile(r"[$€£¥]|\b(usd|eur|gbp|jpy|cad|aud|chf|cny)\b", re.I)
-PERCENT_RE = re.compile(r"%|\bbps\b|\bbasis\s+points\b", re.I)
-NUMERIC_RE = re.compile(
-    r"^\(?[+-]?\s*[$€£¥]?\s*\d{1,3}(?:,\d{3})*(?:\.\d+)?\s*\)?$|^\(?[+-]?\s*[$€£¥]?\s*\d+(?:\.\d+)?\s*\)?$"
-)
-
-DOMAIN_REQUIRED_HINTS: dict[str, list[str]] = {
-    "financial_statement_reporting": [
-        "revenue",
-        "ebitda",
-        "eps",
-        "fiscal",
-        "segment",
-        "currency",
-        "period",
-    ],
-    "investing_markets": [
-        "ticker",
-        "security",
-        "cusip",
-        "isin",
-        "date",
-        "price",
-        "return",
-        "currency",
-    ],
-    "credit_markets_handoff": [
-        "issuer",
-        "debt",
-        "maturity",
-        "coupon",
-        "yield",
-        "spread",
-        "rating",
-        "covenant",
-    ],
-    "portfolio_risk": [
-        "portfolio",
-        "position",
-        "weight",
-        "exposure",
-        "p&l",
-        "benchmark",
-        "factor",
-    ],
-    "consensus_provider_export": [
-        "consensus",
-        "estimate",
-        "provider",
-        "actual",
-        "forecast",
-        "revision",
-    ],
-    "event_driven": [
-        "deal",
-        "consideration",
-        "spread",
-        "probability",
-        "closing",
-        "regulatory",
-        "break",
-    ],
-}
-
-
-def ensure_dependencies() -> None:
-    """Load runtime-only dependencies after argparse handles --help."""
-    global pd
-    try:
-        import openpyxl  # noqa: F401
-        import pandas as pandas
-    except ImportError as exc:
-        print(
-            "ERROR: pandas and openpyxl are required to clean tabular data.",
-            file=sys.stderr,
-        )
-        print(
-            "Install with: python -m pip install -r scripts/requirements.txt",
-            file=sys.stderr,
-        )
-        raise SystemExit(2) from exc
-    pd = pandas
-
-
-def norm_cell(value: Any) -> str:
-    if value is None:
-        return ""
-    if isinstance(value, float) and math.isnan(value):
-        return ""
-    return re.sub(r"\s+", " ", str(value).replace("\u00a0", " ").strip())
-
-
-def add_cover_sheet(
-    path: Path,
-    *,
-    input_path: Path,
-    summary_rows: list[dict[str, Any]],
-    quality_checks: list[dict[str, Any]],
-    audit_rows: list[dict[str, Any]],
-    dedupe_policy: str,
-    header_style: str,
-    remove_subtotals: bool,
-) -> None:
-    """Insert a first-tab dashboard cover after pandas materializes the workbook."""
-    from openpyxl import load_workbook
-    from openpyxl.styles import Font, PatternFill
-
-    wb = load_workbook(path)
-    if "Cover" in wb.sheetnames:
-        del wb["Cover"]
-    ws = wb.create_sheet("Cover", 0)
-    header_fill = PatternFill("solid", fgColor="1F4E78")
-    header_font = Font(color="FFFFFF", bold=True)
-    rows = [
-        {
-            "section": "Header",
-            "metric": "Input file",
-            "value": str(input_path),
-            "notes": "Raw source tabs are preserved behind this cover.",
-        },
-        {
-            "section": "Status",
-            "metric": "Workbook mode",
-            "value": "cleaned_data_export",
-            "notes": "Deterministic first-pass cleaner; reviewer judgment is still required.",
-        },
-        {
-            "section": "Dataset overview",
-            "metric": "Source sheets cleaned",
-            "value": len(summary_rows),
-            "notes": "See summary for per-sheet row/column counts.",
-        },
-        {
-            "section": "Dataset overview",
-            "metric": "Clean rows / columns",
-            "value": f"{sum(int(r.get('clean_rows', 0) or 0) for r in summary_rows)} / {sum(int(r.get('clean_columns', 0) or 0) for r in summary_rows)}",
-            "notes": "Aggregate across all cleaned sheets.",
-        },
-        {
-            "section": "Quality dashboard",
-            "metric": "Quality issues",
-            "value": len(quality_checks),
-            "notes": "See quality_checks before using the workbook for modeling.",
-        },
-        {
-            "section": "Quality dashboard",
-            "metric": "Fatal / warning issues",
-            "value": sum(
-                1
-                for item in quality_checks
-                if str(item.get("severity", "")).lower() in {"fatal", "warning"}
-            ),
-            "notes": "Fatal issues should block downstream use.",
-        },
-        {
-            "section": "Cleaning actions",
-            "metric": "Audit log rows",
-            "value": len(audit_rows),
-            "notes": "Every inferred transformation should be visible in assumptions_audit.",
-        },
-        {
-            "section": "Cleaning actions",
-            "metric": "Dedupe / header / subtotal policy",
-            "value": f"{dedupe_policy} / {header_style} / {'remove' if remove_subtotals else 'preserve'}",
-            "notes": "Confirm this matches the intended downstream use.",
-        },
-        {
-            "section": "Source posture",
-            "metric": "Raw source preservation",
-            "value": "Preserved",
-            "notes": "Raw sheets should remain unmodified except readability formatting.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "clean_data sheets",
-            "value": "Analysis-ready output",
-            "notes": "Use only after reviewing quality_checks and assumptions_audit.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "raw_source sheets",
-            "value": "Original inputs",
-            "notes": "Use for audit trail and tie-out.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "summary / data_dictionary / quality_checks / assumptions_audit",
-            "value": "Reviewer dashboard and audit trail",
-            "notes": "These tabs keep source gaps and cleaning assumptions visible.",
-        },
-    ]
-    ws.append(["section", "metric", "value", "notes"])
-    for cell in ws[1]:
-        cell.fill = header_fill
-        cell.font = header_font
-    for row in rows:
-        ws.append([row["section"], row["metric"], row["value"], row["notes"]])
-    ws.freeze_panes = "A2"
-    for col, width in {"A": 22, "B": 34, "C": 44, "D": 82}.items():
-        ws.column_dimensions[col].width = width
-    wb.save(path)
-
-
-def is_blank(value: Any) -> bool:
-    return norm_cell(value).lower() in BLANK_TOKENS
-
-
-def clean_text_value(value: Any) -> Any:
-    text = norm_cell(value)
-    if text.lower() in BLANK_TOKENS:
-        return None
-    return text
-
-
-def map_dataframe(df: pd.DataFrame, func):
-    mapper = getattr(df, "map", None)
-    if mapper is not None:
-        return mapper(func)
-    return df.applymap(func)
-
-
-def snake_case(text: str) -> str:
-    text = norm_cell(text).lower()
-    text = re.sub(r"[^a-z0-9]+", "_", text)
-    text = re.sub(r"_+", "_", text).strip("_")
-    return text or "column"
-
-
-def display_header(text: str) -> str:
-    text = norm_cell(text)
-    text = re.sub(r"[_\-]+", " ", text)
-    text = re.sub(r"\s+", " ", text).strip()
-    if not text:
-        return "Column"
-    keep_upper = {
-        "id",
-        "sku",
-        "arr",
-        "mrr",
-        "acv",
-        "tcv",
-        "cogs",
-        "ebitda",
-        "api",
-        "sla",
-        "po",
-        "gl",
-        "fy",
-    }
-    words = []
-    for word in text.split(" "):
-        stripped = re.sub(r"[^A-Za-z0-9]", "", word).lower()
-        if stripped in keep_upper:
-            words.append(stripped.upper())
-        else:
-            words.append(word[:1].upper() + word[1:].lower() if word else word)
-    return " ".join(words)
-
-
-def unique_headers(
-    raw_headers: Iterable[Any], style: str
-) -> tuple[list[str], list[dict[str, Any]]]:
-    seen: dict[str, int] = defaultdict(int)
-    headers: list[str] = []
-    notes: list[dict[str, Any]] = []
-    for idx, raw in enumerate(raw_headers, start=1):
-        original = norm_cell(raw)
-        if not original:
-            original = f"column_{idx}"
-            notes.append({"issue": "blank_header", "column_index": idx, "cleaned": original})
-        base = snake_case(original) if style == "snake" else display_header(original)
-        key = base.lower()
-        seen[key] += 1
-        cleaned = (
-            base
-            if seen[key] == 1
-            else f"{base}_{seen[key]}"
-            if style == "snake"
-            else f"{base} {seen[key]}"
-        )
-        if seen[key] > 1:
-            notes.append(
-                {
-                    "issue": "duplicate_header",
-                    "column_index": idx,
-                    "original": original,
-                    "cleaned": cleaned,
-                }
-            )
-        headers.append(cleaned)
-    return headers, notes
-
-
-def cell_has_letters(text: str) -> bool:
-    return bool(re.search(r"[A-Za-z]", text))
-
-
-def looks_numeric(text: str) -> bool:
-    if not text:
-        return False
-    return bool(NUMERIC_RE.match(text.replace("%", "")))
-
-
-def header_score(df: pd.DataFrame, row_idx: int) -> float:
-    row = [norm_cell(v) for v in df.iloc[row_idx].tolist()]
-    nonempty = [v for v in row if v]
-    if len(nonempty) < 2:
-        return -1000.0
-    unique_ratio = len({v.lower() for v in nonempty}) / max(len(nonempty), 1)
-    text_ratio = sum(cell_has_letters(v) for v in nonempty) / max(len(nonempty), 1)
-    numeric_ratio = sum(looks_numeric(v) for v in nonempty) / max(len(nonempty), 1)
-    next_ratio = 0.0
-    if row_idx + 1 < len(df):
-        next_vals = [norm_cell(v) for v in df.iloc[row_idx + 1].tolist()]
-        next_ratio = sum(bool(v) for v in next_vals) / max(len(next_vals), 1)
-    total_penalty = 2.0 if any(TOTAL_RE.search(v) for v in nonempty) else 0.0
-    return (
-        (len(nonempty) * 0.15)
-        + (unique_ratio * 2.0)
-        + (text_ratio * 3.0)
-        + (next_ratio * 1.5)
-        - (numeric_ratio * 2.0)
-        - total_penalty
-    )
-
-
-def detect_header_row(df: pd.DataFrame) -> int:
-    if df.empty:
-        return 0
-    scores = [(header_score(df, i), i) for i in range(min(len(df), 30))]
-    scores.sort(reverse=True)
-    return scores[0][1]
-
-
-def read_input(path: Path, sheet: str | None = None) -> dict[str, pd.DataFrame]:
-    ext = path.suffix.lower()
-    if ext in {".xlsx", ".xlsm", ".xls"}:
-        data = pd.read_excel(
-            path,
-            sheet_name=sheet if sheet else None,
-            header=None,
-            dtype=object,
-            keep_default_na=False,
-        )
-        if isinstance(data, pd.DataFrame):
-            return {sheet or "sheet1": data}
-        return {str(k): v for k, v in data.items()}
-    if ext in {".csv", ".tsv", ".txt"}:
-        sep = "\t" if ext == ".tsv" else None
-        try:
-            df = pd.read_csv(
-                path,
-                header=None,
-                dtype=object,
-                keep_default_na=False,
-                sep=sep,
-                engine="python",
-                encoding="utf-8-sig",
-            )
-        except pd.errors.ParserError:
-            df = pd.read_csv(
-                path,
-                header=None,
-                dtype=object,
-                keep_default_na=False,
-                sep=sep,
-                engine="python",
-                encoding="utf-8-sig",
-                on_bad_lines="warn",
-            )
-        return {"csv": df}
-    raise ValueError(f"unsupported input type: {ext}")
-
-
-def parse_number(value: Any) -> float | None:
-    s = norm_cell(value)
-    if not s or s.lower() in BLANK_TOKENS:
-        return None
-    s = CURRENCY_RE.sub("", s)
-    s = s.replace(",", "").replace("%", "").strip()
-    neg = s.startswith("(") and s.endswith(")")
-    s = s.strip("() ")
-    try:
-        number = float(s)
-        return -number if neg else number
-    except ValueError:
-        return None
-
-
-def parse_percent(value: Any) -> float | None:
-    s = norm_cell(value).lower()
-    if not s or s in BLANK_TOKENS:
-        return None
-    if "bps" in s or "basis" in s:
-        n = parse_number(s.replace("basis points", "").replace("bps", ""))
-        return None if n is None else n / 10000.0
-    n = parse_number(s)
-    if n is None:
-        return None
-    if "%" in s:
-        return n / 100.0
-    # Leave ambiguous bare numbers as-is only if they already look like decimal percentages.
-    return n if abs(n) <= 1 else None
-
-
-def parse_bool(value: Any) -> bool | None:
-    s = norm_cell(value).lower()
-    if s in {"true", "yes", "y", "1"}:
-        return True
-    if s in {"false", "no", "n", "0"}:
-        return False
-    return None
-
-
-def parse_dates(series: pd.Series) -> pd.Series:
-    return pd.to_datetime(series, errors="coerce")
-
-
-def infer_column_type(header: str, series: pd.Series) -> dict[str, Any]:
-    values = [norm_cell(v) for v in series.tolist()]
-    nonblank = [v for v in values if v.lower() not in BLANK_TOKENS]
-    sample_n = max(min(len(nonblank), 1000), 1)
-    if not nonblank:
-        return {"type": "empty", "confidence": 1.0, "format": "general"}
-
-    header_id_like = bool(ID_HINT_RE.search(header))
-    leading_zero = any(re.match(r"^0\d+", v) for v in nonblank[:1000])
-    bool_ratio = sum(parse_bool(v) is not None for v in nonblank[:1000]) / sample_n
-    pct_ratio = sum(parse_percent(v) is not None for v in nonblank[:1000]) / sample_n
-    num_ratio = sum(parse_number(v) is not None for v in nonblank[:1000]) / sample_n
-    currency_ratio = sum(bool(CURRENCY_RE.search(v)) for v in nonblank[:1000]) / sample_n
-    date_candidates = [v for v in nonblank[:1000] if not looks_numeric(v)]
-    if date_candidates:
-        date_ratio = parse_dates(pd.Series(date_candidates)).notna().sum() / len(date_candidates)
-    else:
-        date_ratio = 0.0
-
-    if header_id_like or leading_zero:
-        return {"type": "text_identifier", "confidence": 0.90, "format": "@"}
-    if bool_ratio >= 0.90:
-        return {"type": "boolean", "confidence": bool_ratio, "format": "general"}
-    if pct_ratio >= 0.80:
-        return {"type": "percent", "confidence": pct_ratio, "format": "0.0%"}
-    if currency_ratio >= 0.40 and num_ratio >= 0.80:
-        return {
-            "type": "currency_amount",
-            "confidence": min(0.95, (currency_ratio + num_ratio) / 2),
-            "format": "#,##0.00",
-        }
-    if num_ratio >= 0.88:
-        decimals = any(
-            (parse_number(v) is not None and abs(parse_number(v) - round(parse_number(v))) > 1e-9)
-            for v in nonblank[:1000]
-        )
-        return {
-            "type": "number",
-            "confidence": num_ratio,
-            "format": "#,##0.00" if decimals else "#,##0",
-        }
-    if date_ratio >= 0.85:
-        return {
-            "type": "date_or_datetime",
-            "confidence": date_ratio,
-            "format": "yyyy-mm-dd",
-        }
-    if 0.30 <= num_ratio < 0.88 or 0.30 <= date_ratio < 0.85:
-        return {
-            "type": "mixed",
-            "confidence": max(num_ratio, date_ratio),
-            "format": "general",
-        }
-    return {"type": "text", "confidence": 0.70, "format": "general"}
-
-
-def convert_column(series: pd.Series, col_type: str) -> pd.Series:
-    cleaned = series.map(clean_text_value)
-    if col_type in {"text", "text_identifier", "mixed", "empty"}:
-        return cleaned
-    if col_type == "boolean":
-        return cleaned.map(lambda v: parse_bool(v) if v is not None else None)
-    if col_type == "percent":
-        return cleaned.map(lambda v: parse_percent(v) if v is not None else None)
-    if col_type in {"number", "currency_amount"}:
-        return cleaned.map(lambda v: parse_number(v) if v is not None else None)
-    if col_type == "date_or_datetime":
-        return parse_dates(cleaned)
-    return cleaned
-
-
-def likely_subtotal_mask(df: pd.DataFrame) -> pd.Series:
-    if df.empty:
-        return pd.Series([], dtype=bool)
-
-    def row_has_total(row: pd.Series) -> bool:
-        vals = [norm_cell(v) for v in row.tolist()[:5]]
-        return any(TOTAL_RE.search(v) for v in vals if v)
-
-    return df.apply(row_has_total, axis=1)
-
-
-def infer_domain(sheet_name: str, headers: list[str], override: str) -> str:
-    if override and override != "auto":
-        return override
-    text = " ".join([sheet_name] + headers).lower()
-    scores: dict[str, int] = {}
-    for domain, hints in DOMAIN_REQUIRED_HINTS.items():
-        scores[domain] = sum(1 for h in hints if re.search(r"\b" + re.escape(h) + r"\b", text))
-    best, score = max(scores.items(), key=lambda x: x[1])
-    return best if score else "general"
-
-
-def required_field_checks(
-    domain: str, clean_df: pd.DataFrame, sheet_name: str
-) -> list[dict[str, Any]]:
-    checks: list[dict[str, Any]] = []
-    if domain == "credit_markets_handoff":
-        return [
-            {
-                "severity": "warning",
-                "issue_type": "route_to_credit_markets",
-                "sheet": sheet_name,
-                "field": "domain",
-                "affected_count": len(clean_df),
-                "description": "Credit instrument table detected; route bonds/loans/CDS/covenants/recovery/debt-security analysis to Credit Markets unless this is only equity-risk context.",
-                "recommended_action": "preserve source columns and hand off to Credit Markets for credit analysis; keep only equity-risk signal use in Public Equity Investing.",
-            }
-        ]
-    hints = DOMAIN_REQUIRED_HINTS.get(domain, [])
-    lower_cols = {c.lower(): c for c in clean_df.columns}
-    for hint in hints:
-        matches = [actual for lower, actual in lower_cols.items() if hint in lower]
-        for col in matches[:2]:
-            missing = int(clean_df[col].isna().sum())
-            if missing:
-                checks.append(
-                    {
-                        "severity": "warning",
-                        "issue_type": "missing_domain_field_values",
-                        "sheet": sheet_name,
-                        "field": col,
-                        "affected_count": missing,
-                        "description": f"{missing} rows are missing values in a domain-relevant field.",
-                        "recommended_action": "review whether these rows are incomplete or need enrichment from the source system.",
-                    }
-                )
-    return checks
-
-
-def apply_header_row(
-    sheet_name: str,
-    raw_df: pd.DataFrame,
-    args: argparse.Namespace,
-    audit: list[dict[str, Any]],
-    checks: list[dict[str, Any]],
-) -> pd.DataFrame:
-    header_idx = args.header_row - 1 if args.header_row else detect_header_row(raw_df)
-    raw_headers = raw_df.iloc[header_idx].tolist() if len(raw_df) else []
-    headers, header_notes = unique_headers(raw_headers, args.header_style)
-    data = raw_df.iloc[header_idx + 1 :].copy() if len(raw_df) else pd.DataFrame()
-    if len(headers) == data.shape[1]:
-        data.columns = headers
-    audit.append(
-        {
-            "step": "header_detection",
-            "action": f"used row {header_idx + 1} as header",
-            "basis": "user-specified header row"
-            if args.header_row
-            else "automatic header inference",
-            "affected_sheet": sheet_name,
-            "affected_field": "all",
-            "affected_rows_or_count": 1,
-            "risk_level": "low" if args.header_row else "medium",
-            "notes": "verify if the source has multi-row headers or report titles.",
-        }
-    )
-    for note in header_notes:
-        checks.append(
-            {
-                "severity": "warning",
-                "issue_type": note["issue"],
-                "sheet": sheet_name,
-                "field": note.get("cleaned"),
-                "affected_count": 1,
-                "description": str(note),
-                "recommended_action": "review header mapping in data_dictionary.",
-            }
-        )
-    return data
-
-
-def remove_empty_structure(
-    sheet_name: str,
-    data: pd.DataFrame,
-    audit: list[dict[str, Any]],
-) -> pd.DataFrame:
-    before_rows, before_cols = data.shape
-    data = data.dropna(how="all")
-    data = data.loc[:, [not all(is_blank(v) for v in data[col].tolist()) for col in data.columns]]
-    data = map_dataframe(data, clean_text_value)
-    data = data.dropna(how="all")
-    after_rows, after_cols = data.shape
-    if before_rows != after_rows or before_cols != after_cols:
-        audit.append(
-            {
-                "step": "structural_cleanup",
-                "action": "removed fully empty rows/columns from clean output",
-                "basis": "safe structural cleanup; raw_source remains preserved",
-                "affected_sheet": sheet_name,
-                "affected_field": "all",
-                "affected_rows_or_count": f"rows {before_rows}->{after_rows}, columns {before_cols}->{after_cols}",
-                "risk_level": "low",
-                "notes": "only fully empty rows/columns were removed.",
-            }
-        )
-    return data
-
-
-def handle_subtotals(
-    sheet_name: str,
-    data: pd.DataFrame,
-    args: argparse.Namespace,
-    checks: list[dict[str, Any]],
-    audit: list[dict[str, Any]],
-) -> pd.DataFrame:
-    subtotal_mask = likely_subtotal_mask(data)
-    subtotal_count = int(subtotal_mask.sum())
-    if subtotal_count:
-        checks.append(
-            {
-                "severity": "warning",
-                "issue_type": "possible_total_or_subtotal_rows",
-                "sheet": sheet_name,
-                "field": "row",
-                "affected_count": subtotal_count,
-                "description": "rows contain labels such as total or subtotal.",
-                "recommended_action": "remove from detail data only if they are report subtotal rows, not real records.",
-            }
-        )
-        if args.remove_subtotals:
-            data = data.loc[~subtotal_mask].copy()
-            audit.append(
-                {
-                    "step": "subtotal_handling",
-                    "action": "removed possible total/subtotal rows",
-                    "basis": "--remove-subtotals option",
-                    "affected_sheet": sheet_name,
-                    "affected_field": "row",
-                    "affected_rows_or_count": subtotal_count,
-                    "risk_level": "medium",
-                    "notes": "verify these were not legitimate records.",
-                }
-            )
-    return data
-
-
-def infer_and_convert_columns(
-    sheet_name: str,
-    data: pd.DataFrame,
-    checks: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
-    dictionary: list[dict[str, Any]] = []
-    for original_col in list(data.columns):
-        info = infer_column_type(original_col, data[original_col])
-        converted = convert_column(data[original_col], info["type"])
-        data[original_col] = converted
-        non_null = data[original_col].dropna()
-        examples = [str(v) for v in non_null.head(5).tolist()]
-        dictionary.append(
-            {
-                "source_sheet": sheet_name,
-                "original_field": original_col,
-                "clean_field": original_col,
-                "inferred_type": info["type"],
-                "excel_format": info["format"],
-                "null_count": int(data[original_col].isna().sum()),
-                "unique_count": int(non_null.nunique(dropna=True)) if len(non_null) else 0,
-                "example_values": ", ".join(examples),
-                "cleaning_notes": f"confidence={round(float(info['confidence']), 3)}",
-                "business_notes": "",
-            }
-        )
-        if info["type"] == "mixed":
-            checks.append(
-                {
-                    "severity": "warning",
-                    "issue_type": "mixed_type_column",
-                    "sheet": sheet_name,
-                    "field": original_col,
-                    "affected_count": int(len(non_null)),
-                    "description": "column contains mixed values that were left mostly as text.",
-                    "recommended_action": "review source values and provide a conversion rule if this field should be numeric/date.",
-                }
-            )
-    return dictionary
-
-
-def handle_duplicates(
-    sheet_name: str,
-    data: pd.DataFrame,
-    args: argparse.Namespace,
-    checks: list[dict[str, Any]],
-    audit: list[dict[str, Any]],
-) -> pd.DataFrame:
-    if args.dedupe == "exact" and not data.empty:
-        before = len(data)
-        data = data.drop_duplicates(keep="first")
-        removed = before - len(data)
-        if removed:
-            audit.append(
-                {
-                    "step": "duplicate_handling",
-                    "action": "removed exact duplicate rows",
-                    "basis": "--dedupe exact; all cleaned values matched",
-                    "affected_sheet": sheet_name,
-                    "affected_field": "all",
-                    "affected_rows_or_count": removed,
-                    "risk_level": "low",
-                    "notes": "potential non-exact duplicates are not merged by this script.",
-                }
-            )
-            checks.append(
-                {
-                    "severity": "info",
-                    "issue_type": "exact_duplicate_rows_removed",
-                    "sheet": sheet_name,
-                    "field": "all",
-                    "affected_count": removed,
-                    "description": "exact duplicates were removed from clean output.",
-                    "recommended_action": "review if source row identity matters.",
-                }
-            )
-    return data
-
-
-def clean_sheet(
-    sheet_name: str, raw_df: pd.DataFrame, args: argparse.Namespace
-) -> tuple[pd.DataFrame, list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], str]:
-    audit: list[dict[str, Any]] = []
-    checks: list[dict[str, Any]] = []
-
-    data = apply_header_row(sheet_name, raw_df, args, audit, checks)
-    data = remove_empty_structure(sheet_name, data, audit)
-    domain = infer_domain(sheet_name, list(data.columns), args.domain)
-    data = handle_subtotals(sheet_name, data, args, checks, audit)
-    dictionary = infer_and_convert_columns(sheet_name, data, checks)
-    data = handle_duplicates(sheet_name, data, args, checks, audit)
-    checks.extend(required_field_checks(domain, data, sheet_name))
-    return data.reset_index(drop=True), dictionary, checks, audit, domain
-
-
-def write_support_manifest(
-    output_path: Path, *, standalone: bool, quality_checks: list[dict[str, Any]]
-) -> None:
-    output_dir = output_path.resolve().parent
-    run_log_path = output_dir / "run_log.json"
-    manifest_path = output_dir / "manifest.json"
-    primary = str(output_path.resolve()) if standalone else None
-    output_manifest = [
-        {
-            "key": "cleaned_workbook",
-            "path": str(output_path.resolve()),
-            "required": True,
-            "written": output_path.exists(),
-            "artifact_role": "primary_human_deliverable" if standalone else "support_artifact",
-            "hidden_unless_requested": not standalone,
-            "description": "Cleaned workbook for Public Equity Investing table support.",
-        },
-        {
-            "key": "run_log",
-            "path": str(run_log_path),
-            "required": True,
-            "written": True,
-            "artifact_role": "support_artifact",
-            "hidden_unless_requested": True,
-            "description": "Cleaner run log.",
-        },
-        {
-            "key": "manifest",
-            "path": str(manifest_path),
-            "required": True,
-            "written": True,
-            "artifact_role": "support_artifact",
-            "hidden_unless_requested": True,
-            "description": "Cleaner manifest.",
-        },
-    ]
-    run_log = {
-        "status": "completed",
-        "model_status": "screen-grade" if quality_checks else "senior-review-ready",
-        "artifact_level": "standalone_support_request"
-        if standalone
-        else "embedded_support_artifact",
-        "workbook_mode": "cleaned_workbook",
-        "primary_human_deliverable": primary,
-        "support_artifacts_user_visible_default": False,
-        "warnings": [
-            row.get("description", "")
-            for row in quality_checks
-            if row.get("severity") in {"warning", "medium", "high"}
-        ],
-        "hard_failures": [
-            row.get("description", "")
-            for row in quality_checks
-            if row.get("severity") in {"fatal", "critical"}
-        ],
-        "output_manifest": output_manifest,
-        "final_response_guidance": {
-            "lead_with": "cleaned_workbook" if standalone else "owning_workflow_hero_artifact",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-    }
-    run_log_path.write_text(json.dumps(run_log, indent=2) + "\n", encoding="utf-8")
-    manifest_path.write_text(
-        json.dumps(
-            {
-                "outputs": output_manifest,
-                "primary_human_deliverable": primary,
-                "support_artifacts_user_visible_default": False,
-                "final_response_guidance": run_log["final_response_guidance"],
-            },
-            indent=2,
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Conservatively clean CSV/XLSX data into an audit-ready workbook."
-    )
-    parser.add_argument("input", help="input .xlsx, .xls, .csv, .tsv, or .txt file")
-    parser.add_argument("--output", "-o", default="cleaned.xlsx", help="output .xlsx path")
-    parser.add_argument("--sheet", help="optional Excel sheet name to clean")
-    parser.add_argument(
-        "--domain",
-        default="auto",
-        help="auto, financial_statement_reporting, investing_markets, credit_markets_handoff, portfolio_risk, consensus_provider_export, event_driven, or general",
-    )
-    parser.add_argument(
-        "--dedupe",
-        choices=["none", "exact"],
-        default="exact",
-        help="duplicate policy for clean output",
-    )
-    parser.add_argument("--header-row", type=int, help="1-based header row override")
-    parser.add_argument(
-        "--header-style",
-        choices=["display", "snake"],
-        default="display",
-        help="cleaned header style",
-    )
-    parser.add_argument(
-        "--remove-subtotals",
-        action="store_true",
-        help="remove rows that appear to be totals/subtotals",
-    )
-    parser.add_argument(
-        "--standalone-cleaned-workbook",
-        action="store_true",
-        help="Mark the cleaned workbook as the explicit standalone cleanup deliverable.",
-    )
-    args = parser.parse_args()
-    ensure_dependencies()
-
-    input_path = Path(args.input)
-    output_path = Path(args.output)
-    tables = read_input(input_path, args.sheet)
-
-    all_dictionary: list[dict[str, Any]] = []
-    all_checks: list[dict[str, Any]] = []
-    all_audit: list[dict[str, Any]] = []
-    summary_rows: list[dict[str, Any]] = []
-    sheet_formats: dict[str, dict[str, str]] = {}
-    used_sheet_names: set[str] = set()
-
-    with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
-        for source_name, raw_df in tables.items():
-            clean_df, dictionary, checks, audit, domain = clean_sheet(source_name, raw_df, args)
-            clean_sheet_name = safe_excel_sheet_name(
-                "clean_data" if len(tables) == 1 else f"clean_{source_name}",
-                used_sheet_names,
-            )
-            raw_sheet_name = safe_excel_sheet_name(
-                "raw_source" if len(tables) == 1 else f"raw_{source_name}",
-                used_sheet_names,
-            )
-            clean_df.to_excel(writer, sheet_name=clean_sheet_name, index=False)
-            raw_df.to_excel(writer, sheet_name=raw_sheet_name, index=False, header=False)
-            fmt_map = {
-                row["clean_field"]: row["excel_format"]
-                for row in dictionary
-                if row.get("excel_format") not in {None, "general"}
-            }
-            sheet_formats[clean_sheet_name] = fmt_map
-            all_dictionary.extend(dictionary)
-            all_checks.extend(checks)
-            all_audit.extend(audit)
-            summary_rows.append(
-                {
-                    "source_sheet": source_name,
-                    "clean_sheet": clean_sheet_name,
-                    "raw_sheet": raw_sheet_name,
-                    "inferred_domain": domain,
-                    "clean_rows": len(clean_df),
-                    "clean_columns": len(clean_df.columns),
-                    "quality_issue_count": len(checks),
-                }
-            )
-
-        pd.DataFrame(summary_rows).to_excel(writer, sheet_name="summary", index=False)
-        pd.DataFrame(all_dictionary or [{"note": "no columns profiled"}]).to_excel(
-            writer, sheet_name="data_dictionary", index=False
-        )
-        pd.DataFrame(
-            all_checks
-            or [
-                {
-                    "severity": "info",
-                    "issue_type": "no_issues_logged",
-                    "description": "no quality issues were logged by the deterministic cleaner",
-                }
-            ]
-        ).to_excel(writer, sheet_name="quality_checks", index=False)
-        all_audit.append(
-            {
-                "step": "workbook_creation",
-                "action": "created cleaned workbook",
-                "basis": "deterministic first-pass cleaning script",
-                "affected_sheet": "all",
-                "affected_field": "all",
-                "affected_rows_or_count": len(tables),
-                "risk_level": "low",
-                "notes": f"generated_at={datetime.now(timezone.utc).isoformat()}; input={input_path}",
-            }
-        )
-        pd.DataFrame(all_audit).to_excel(writer, sheet_name="assumptions_audit", index=False)
-
-    add_cover_sheet(
-        output_path,
-        input_path=input_path,
-        summary_rows=summary_rows,
-        quality_checks=all_checks,
-        audit_rows=all_audit,
-        dedupe_policy=args.dedupe,
-        header_style=args.header_style,
-        remove_subtotals=args.remove_subtotals,
-    )
-    autosize_and_table(output_path, sheet_formats)
-    write_support_manifest(
-        output_path, standalone=args.standalone_cleaned_workbook, quality_checks=all_checks
-    )
-    print(f"wrote cleaned workbook to {output_path}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/profile_tabular_data.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/profile_tabular_data.py
deleted file mode 100755
index b5b787d..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/profile_tabular_data.py
+++ /dev/null
@@ -1,524 +0,0 @@
-#!/usr/bin/env python3
-"""Profile messy CSV/XLSX data before analyst-grade cleaning.
-
-Outputs JSON with header suggestions, type guesses, domain hints, and quality issues.
-This script is intentionally conservative: it profiles and flags; it does not mutate data.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import re
-import sys
-from collections import defaultdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Iterable
-
-BLANK_TOKENS = {"", "-", "--", "n/a", "na", "null", "none", "not available", "#n/a", "nan"}
-TOTAL_RE = re.compile(r"\b(grand\s+total|subtotal|sub-total|total)\b", re.I)
-ID_HINT_RE = re.compile(
-    r"\b(id|code|sku|zip|postal|phone|cusip|isin|sedol|ticker|account\s*#|invoice\s*#|po\s*#|employee)\b",
-    re.I,
-)
-CURRENCY_RE = re.compile(r"[$€£¥]|\b(usd|eur|gbp|jpy|cad|aud|chf|cny)\b", re.I)
-PERCENT_RE = re.compile(r"%|\bbps\b|\bbasis\s+points\b", re.I)
-NUMERIC_RE = re.compile(
-    r"^\(?[+-]?\s*[$€£¥]?\s*\d{1,3}(?:,\d{3})*(?:\.\d+)?\s*\)?$|^\(?[+-]?\s*[$€£¥]?\s*\d+(?:\.\d+)?\s*\)?$"
-)
-
-DOMAIN_KEYWORDS: dict[str, list[str]] = {
-    "financial_statement_reporting": [
-        "actual",
-        "forecast",
-        "guidance",
-        "segment",
-        "revenue",
-        "gross margin",
-        "opex",
-        "capex",
-        "ebitda",
-        "eps",
-        "fiscal",
-        "quarter",
-        "annual",
-        "cash flow",
-        "balance sheet",
-        "share count",
-    ],
-    "investing_markets": [
-        "ticker",
-        "cusip",
-        "isin",
-        "sedol",
-        "security",
-        "portfolio",
-        "nav",
-        "return",
-        "yield",
-        "spread",
-        "benchmark",
-        "price",
-        "holdings",
-        "exposure",
-        "market value",
-        "bps",
-    ],
-    "credit_markets_handoff": [
-        "issuer",
-        "bond",
-        "loan",
-        "coupon",
-        "maturity",
-        "spread",
-        "yield",
-        "rating",
-        "covenant",
-        "liquidity",
-        "debt",
-        "recovery",
-        "priority",
-        "collateral",
-    ],
-    "portfolio_risk": [
-        "position",
-        "weight",
-        "exposure",
-        "p&l",
-        "benchmark",
-        "factor",
-        "beta",
-        "var",
-        "sector",
-        "gross",
-        "net exposure",
-        "hedge",
-    ],
-    "consensus_provider_export": [
-        "consensus",
-        "estimate",
-        "provider",
-        "street",
-        "actual",
-        "revision",
-        "mean",
-        "median",
-        "high",
-        "low",
-        "fiscal period",
-    ],
-    "event_driven_special_situations": [
-        "deal",
-        "consideration",
-        "spread",
-        "probability",
-        "closing",
-        "regulatory",
-        "approval",
-        "break price",
-        "unaffected",
-        "tender",
-        "merger",
-    ],
-}
-
-
-def ensure_dependencies() -> None:
-    """Load pandas only when profiling runs; argparse --help should not require deps."""
-    global pd
-    try:
-        import pandas as pandas
-    except ImportError as exc:
-        print("ERROR: pandas is required to profile tabular data.", file=sys.stderr)
-        print("Install with: python -m pip install -r scripts/requirements.txt", file=sys.stderr)
-        raise SystemExit(2) from exc
-    pd = pandas
-
-
-def norm_cell(value: Any) -> str:
-    if value is None:
-        return ""
-    if isinstance(value, float) and math.isnan(value):
-        return ""
-    return re.sub(r"\s+", " ", str(value).replace("\u00a0", " ").strip())
-
-
-def is_blank(value: Any) -> bool:
-    return norm_cell(value).lower() in BLANK_TOKENS
-
-
-def map_dataframe(df: pd.DataFrame, func):
-    mapper = getattr(df, "map", None)
-    if mapper is not None:
-        return mapper(func)
-    return df.applymap(func)
-
-
-def safe_sheet_name(name: Any) -> str:
-    text = norm_cell(name) or "sheet"
-    return text[:80]
-
-
-def read_input(path: Path, sheet: str | None = None) -> dict[str, pd.DataFrame]:
-    ext = path.suffix.lower()
-    if ext in {".xlsx", ".xlsm", ".xls"}:
-        data = pd.read_excel(
-            path,
-            sheet_name=sheet if sheet else None,
-            header=None,
-            dtype=object,
-            keep_default_na=False,
-        )
-        if isinstance(data, pd.DataFrame):
-            return {sheet or "sheet1": data}
-        return {safe_sheet_name(k): v for k, v in data.items()}
-    if ext in {".csv", ".tsv", ".txt"}:
-        sep = "\t" if ext == ".tsv" else None
-        try:
-            df = pd.read_csv(
-                path,
-                header=None,
-                dtype=object,
-                keep_default_na=False,
-                sep=sep,
-                engine="python",
-                encoding="utf-8-sig",
-            )
-        except pd.errors.ParserError:
-            df = pd.read_csv(
-                path,
-                header=None,
-                dtype=object,
-                keep_default_na=False,
-                sep=sep,
-                engine="python",
-                encoding="utf-8-sig",
-                on_bad_lines="warn",
-            )
-        return {"csv": df}
-    raise ValueError(f"unsupported input type: {ext}")
-
-
-def cell_has_letters(text: str) -> bool:
-    return bool(re.search(r"[A-Za-z]", text))
-
-
-def looks_numeric(text: str) -> bool:
-    if not text:
-        return False
-    return bool(NUMERIC_RE.match(text.replace("%", "")))
-
-
-def header_score(df: pd.DataFrame, row_idx: int) -> float:
-    row = [norm_cell(v) for v in df.iloc[row_idx].tolist()]
-    nonempty = [v for v in row if v]
-    if len(nonempty) < 2:
-        return -1000.0
-    unique_ratio = len({v.lower() for v in nonempty}) / max(len(nonempty), 1)
-    text_ratio = sum(cell_has_letters(v) for v in nonempty) / max(len(nonempty), 1)
-    numeric_ratio = sum(looks_numeric(v) for v in nonempty) / max(len(nonempty), 1)
-    next_ratio = 0.0
-    if row_idx + 1 < len(df):
-        next_vals = [norm_cell(v) for v in df.iloc[row_idx + 1].tolist()]
-        next_ratio = sum(bool(v) for v in next_vals) / max(len(next_vals), 1)
-    total_penalty = 2.0 if any(TOTAL_RE.search(v) for v in nonempty) else 0.0
-    title_penalty = 1.5 if row_idx > 0 and len(nonempty) == 2 else 0.0
-    return (
-        (len(nonempty) * 0.15)
-        + (unique_ratio * 2.0)
-        + (text_ratio * 3.0)
-        + (next_ratio * 1.5)
-        - (numeric_ratio * 2.0)
-        - total_penalty
-        - title_penalty
-    )
-
-
-def detect_header_row(df: pd.DataFrame) -> int:
-    max_scan = min(len(df), 30)
-    if max_scan == 0:
-        return 0
-    scores = [(header_score(df, i), i) for i in range(max_scan)]
-    scores.sort(reverse=True)
-    best_score, best_idx = scores[0]
-    return best_idx if best_score > -999 else 0
-
-
-def unique_headers(raw_headers: Iterable[Any]) -> tuple[list[str], list[dict[str, Any]]]:
-    seen: dict[str, int] = defaultdict(int)
-    headers: list[str] = []
-    notes: list[dict[str, Any]] = []
-    for idx, raw in enumerate(raw_headers, start=1):
-        base = norm_cell(raw)
-        if not base:
-            base = f"column_{idx}"
-            notes.append({"column_index": idx, "issue": "blank_header", "cleaned": base})
-        cleaned = re.sub(r"\s+", " ", base)
-        key = cleaned.lower()
-        seen[key] += 1
-        if seen[key] > 1:
-            cleaned = f"{cleaned}_{seen[key]}"
-            notes.append(
-                {
-                    "column_index": idx,
-                    "issue": "duplicate_header",
-                    "cleaned": cleaned,
-                    "original": base,
-                }
-            )
-        headers.append(cleaned)
-    return headers, notes
-
-
-def parse_number(text: str) -> float | None:
-    s = norm_cell(text)
-    if not s or s.lower() in BLANK_TOKENS:
-        return None
-    s = CURRENCY_RE.sub("", s)
-    s = s.replace(",", "").replace("%", "").strip()
-    neg = s.startswith("(") and s.endswith(")")
-    s = s.strip("() ")
-    try:
-        value = float(s)
-        return -value if neg else value
-    except ValueError:
-        return None
-
-
-def parse_bool(text: str) -> bool | None:
-    s = norm_cell(text).lower()
-    if s in {"true", "yes", "y", "1"}:
-        return True
-    if s in {"false", "no", "n", "0"}:
-        return False
-    return None
-
-
-def parse_date_ratio(values: list[str]) -> float:
-    candidates = [v for v in values if v and not looks_numeric(v)]
-    if not candidates:
-        return 0.0
-    parsed = pd.to_datetime(pd.Series(candidates[:1000]), errors="coerce")
-    return float(parsed.notna().sum()) / max(len(candidates[:1000]), 1)
-
-
-def infer_type(header: str, values: list[str]) -> dict[str, Any]:
-    nonblank = [v for v in values if v and v.lower() not in BLANK_TOKENS]
-    sample = nonblank[:5]
-    if not nonblank:
-        return {"type": "empty", "confidence": 1.0, "sample_values": []}
-
-    header_id_like = bool(ID_HINT_RE.search(header))
-    leading_zero = any(re.match(r"^0\d+", v) for v in nonblank[:1000])
-    bool_ratio = sum(parse_bool(v) is not None for v in nonblank[:1000]) / max(
-        min(len(nonblank), 1000), 1
-    )
-    num_ratio = sum(parse_number(v) is not None for v in nonblank[:1000]) / max(
-        min(len(nonblank), 1000), 1
-    )
-    pct_ratio = sum(bool(PERCENT_RE.search(v)) for v in nonblank[:1000]) / max(
-        min(len(nonblank), 1000), 1
-    )
-    currency_ratio = sum(bool(CURRENCY_RE.search(v)) for v in nonblank[:1000]) / max(
-        min(len(nonblank), 1000), 1
-    )
-    date_ratio = parse_date_ratio(nonblank)
-
-    if header_id_like or leading_zero:
-        guess = "text_identifier"
-        conf = 0.90 if header_id_like else 0.75
-    elif bool_ratio >= 0.90:
-        guess = "boolean"
-        conf = bool_ratio
-    elif pct_ratio >= 0.60:
-        guess = "percent_or_bps"
-        conf = pct_ratio
-    elif currency_ratio >= 0.40 and num_ratio >= 0.70:
-        guess = "currency_amount"
-        conf = min(0.95, (currency_ratio + num_ratio) / 2)
-    elif num_ratio >= 0.85:
-        guess = "number"
-        conf = num_ratio
-    elif date_ratio >= 0.80:
-        guess = "date_or_datetime"
-        conf = date_ratio
-    elif 0.30 <= num_ratio < 0.85 or 0.30 <= date_ratio < 0.80:
-        guess = "mixed"
-        conf = max(num_ratio, date_ratio)
-    else:
-        guess = "text"
-        conf = 0.70
-
-    return {
-        "type": guess,
-        "confidence": round(float(conf), 3),
-        "sample_values": sample,
-        "numeric_parse_ratio": round(float(num_ratio), 3),
-        "date_parse_ratio": round(float(date_ratio), 3),
-        "currency_signal_ratio": round(float(currency_ratio), 3),
-        "percent_signal_ratio": round(float(pct_ratio), 3),
-        "boolean_parse_ratio": round(float(bool_ratio), 3),
-    }
-
-
-def profile_column(header: str, series: pd.Series, row_count: int) -> dict[str, Any]:
-    values = [norm_cell(v) for v in series.tolist()]
-    missing = sum(v.lower() in BLANK_TOKENS for v in values)
-    nonblank = [v for v in values if v.lower() not in BLANK_TOKENS]
-    type_info = infer_type(header, values)
-    return {
-        "field": header,
-        "missing_count": int(missing),
-        "missing_pct": round(missing / max(row_count, 1), 4),
-        "unique_count": int(len(set(nonblank))),
-        **type_info,
-    }
-
-
-def duplicate_row_count(df: pd.DataFrame) -> int:
-    if df.empty:
-        return 0
-    normalized = map_dataframe(df, lambda v: norm_cell(v).lower())
-    return int(normalized.duplicated().sum())
-
-
-def likely_total_rows(df: pd.DataFrame) -> list[int]:
-    rows: list[int] = []
-    for idx, row in df.iterrows():
-        vals = [norm_cell(v) for v in row.tolist()[:5]]
-        if any(TOTAL_RE.search(v) for v in vals if v):
-            rows.append(int(idx) + 1)  # 1-based within cleaned data segment, not original sheet
-    return rows[:100]
-
-
-def infer_domain(headers: list[str], sheet_name: str, data_values: Iterable[str]) -> dict[str, Any]:
-    text = " ".join([sheet_name] + headers + list(data_values)[:200]).lower()
-    scores: dict[str, int] = {}
-    matched: dict[str, list[str]] = {}
-    for domain, keywords in DOMAIN_KEYWORDS.items():
-        hits = [kw for kw in keywords if re.search(r"\b" + re.escape(kw.lower()) + r"\b", text)]
-        if hits:
-            scores[domain] = len(hits)
-            matched[domain] = hits[:12]
-    if not scores:
-        return {"best_guess": "general_tabular", "scores": {}, "matched_keywords": {}}
-    best = max(scores.items(), key=lambda x: x[1])[0]
-    result = {"best_guess": best, "scores": scores, "matched_keywords": matched}
-    if best == "credit_markets_handoff":
-        result["routing"] = "route_to_credit_markets"
-        result["routing_note"] = (
-            "Credit instrument tables belong in Credit Markets unless used only as public-equity risk context."
-        )
-    return result
-
-
-def profile_sheet(sheet_name: str, df: pd.DataFrame) -> dict[str, Any]:
-    header_idx = detect_header_row(df)
-    raw_headers = df.iloc[header_idx].tolist() if len(df) else []
-    headers, header_notes = unique_headers(raw_headers)
-    data = df.iloc[header_idx + 1 :].copy() if len(df) else pd.DataFrame()
-    if len(headers) == data.shape[1]:
-        data.columns = headers
-    row_count = int(len(data))
-    col_count = int(data.shape[1])
-
-    empty_rows = (
-        int(sum(all(is_blank(v) for v in row) for row in data.values.tolist())) if row_count else 0
-    )
-    empty_cols = (
-        int(sum(all(is_blank(v) for v in data.iloc[:, i].tolist()) for i in range(data.shape[1])))
-        if col_count
-        else 0
-    )
-    col_profiles = [profile_column(col, data[col], row_count) for col in data.columns]
-    all_sample_values: list[str] = []
-    for col in data.columns[:10]:
-        all_sample_values.extend(
-            [norm_cell(v) for v in data[col].head(20).tolist() if norm_cell(v)]
-        )
-
-    issues: list[dict[str, Any]] = []
-    for note in header_notes:
-        issues.append({"severity": "warning", "issue_type": note["issue"], "details": note})
-    if empty_rows:
-        issues.append(
-            {"severity": "info", "issue_type": "fully_empty_rows", "affected_count": empty_rows}
-        )
-    if empty_cols:
-        issues.append(
-            {"severity": "info", "issue_type": "fully_empty_columns", "affected_count": empty_cols}
-        )
-    dupes = duplicate_row_count(data)
-    if dupes:
-        issues.append(
-            {"severity": "warning", "issue_type": "exact_duplicate_rows", "affected_count": dupes}
-        )
-    subtotal_rows = likely_total_rows(data)
-    if subtotal_rows:
-        issues.append(
-            {
-                "severity": "warning",
-                "issue_type": "possible_total_or_subtotal_rows",
-                "example_row_numbers_in_data_segment": subtotal_rows[:20],
-                "affected_count_at_least": len(subtotal_rows),
-            }
-        )
-    for cp in col_profiles:
-        if cp["missing_pct"] >= 0.80 and cp["type"] != "empty":
-            issues.append(
-                {
-                    "severity": "info",
-                    "issue_type": "high_missingness",
-                    "field": cp["field"],
-                    "missing_pct": cp["missing_pct"],
-                }
-            )
-        if cp["type"] == "mixed":
-            issues.append(
-                {
-                    "severity": "warning",
-                    "issue_type": "mixed_type_column",
-                    "field": cp["field"],
-                    "numeric_parse_ratio": cp.get("numeric_parse_ratio"),
-                    "date_parse_ratio": cp.get("date_parse_ratio"),
-                }
-            )
-
-    return {
-        "sheet_name": sheet_name,
-        "source_dimensions": {"rows": int(df.shape[0]), "columns": int(df.shape[1])},
-        "detected_header_row_1_based": int(header_idx + 1),
-        "data_dimensions_after_header": {"rows": row_count, "columns": col_count},
-        "domain_hint": infer_domain(headers, sheet_name, all_sample_values),
-        "columns": col_profiles,
-        "quality_issues": issues,
-    }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Profile messy CSV/XLSX data and emit JSON diagnostics."
-    )
-    parser.add_argument("input", help="input .xlsx, .xls, .csv, .tsv, or .txt file")
-    parser.add_argument("--output", "-o", default="profile.json", help="output JSON path")
-    parser.add_argument("--sheet", help="optional Excel sheet name to profile")
-    args = parser.parse_args()
-    ensure_dependencies()
-
-    path = Path(args.input)
-    tables = read_input(path, args.sheet)
-    profile = {
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "input_file": str(path),
-        "sheets_profiled": len(tables),
-        "sheets": [profile_sheet(name, df) for name, df in tables.items()],
-    }
-    out = Path(args.output)
-    out.write_text(json.dumps(profile, indent=2, ensure_ascii=False), encoding="utf-8")
-    print(f"wrote profile to {out}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/requirements.txt b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/requirements.txt
deleted file mode 100644
index 48852f4..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-pandas>=2.0.0
-openpyxl>=3.0.10
-xlrd>=2.0.1
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/workbook_formatting.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/workbook_formatting.py
deleted file mode 100644
index e3dfa1e..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/workbook_formatting.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""Workbook naming and formatting helpers for excel-data-cleaner."""
-
-from __future__ import annotations
-
-import re
-from pathlib import Path
-
-
-def safe_excel_sheet_name(base: str, used: set[str]) -> str:
-    name = re.sub(r"[\\/*?:\[\]]", "_", base).strip() or "sheet"
-    name = name[:31]
-    original = name
-    i = 2
-    while name in used:
-        suffix = f"_{i}"
-        name = (original[: 31 - len(suffix)] + suffix)[:31]
-        i += 1
-    used.add(name)
-    return name
-
-
-def autosize_and_table(path: Path, sheet_formats: dict[str, dict[str, str]]) -> None:
-    """Apply light Excel presentation polish after pandas writes the workbook."""
-    from openpyxl import load_workbook
-    from openpyxl.utils import get_column_letter
-
-    wb = load_workbook(path)
-    for ws in wb.worksheets:
-        ws.freeze_panes = "A2"
-        max_row = ws.max_row
-        max_col = ws.max_column
-        if max_row >= 1 and max_col >= 1:
-            for cell in ws[1]:
-                cell.style = "Headline 4"
-            if headers_are_table_ready(ws, max_row, max_col):
-                add_table(ws, max_col, max_row)
-        apply_column_formats(ws, sheet_formats.get(ws.title, {}), max_row, max_col)
-        autosize_columns(ws, max_row, max_col, get_column_letter)
-    wb.save(path)
-
-
-def headers_are_table_ready(ws, max_row: int, max_col: int) -> bool:
-    return max_row >= 2 and all(
-        ws.cell(1, col_idx).value not in (None, "") for col_idx in range(1, max_col + 1)
-    )
-
-
-def add_table(ws, max_col: int, max_row: int) -> None:
-    from openpyxl.utils import get_column_letter
-    from openpyxl.worksheet.table import Table, TableStyleInfo
-
-    ref = f"A1:{get_column_letter(max_col)}{max_row}"
-    table_name = re.sub(r"[^A-Za-z0-9_]", "_", f"tbl_{ws.title}")[:240]
-    if not table_name or table_name[0].isdigit():
-        table_name = f"tbl_{table_name}"
-    table = Table(displayName=table_name, ref=ref)
-    table.tableStyleInfo = TableStyleInfo(
-        name="TableStyleMedium2",
-        showFirstColumn=False,
-        showLastColumn=False,
-        showRowStripes=True,
-        showColumnStripes=False,
-    )
-    try:
-        ws.add_table(table)
-    except ValueError:
-        pass
-
-
-def apply_column_formats(
-    ws,
-    formats: dict[str, str],
-    max_row: int,
-    max_col: int,
-) -> None:
-    header_to_col = {str(ws.cell(1, col_idx).value): col_idx for col_idx in range(1, max_col + 1)}
-    for header, fmt in formats.items():
-        col_idx = header_to_col.get(header)
-        if col_idx:
-            for row_idx in range(2, max_row + 1):
-                ws.cell(row_idx, col_idx).number_format = fmt
-
-
-def autosize_columns(ws, max_row: int, max_col: int, get_column_letter) -> None:
-    for col_idx in range(1, max_col + 1):
-        col_letter = get_column_letter(col_idx)
-        max_len = 10
-        for row_idx in range(1, min(max_row, 200) + 1):
-            value = ws.cell(row_idx, col_idx).value
-            if value is not None:
-                max_len = max(max_len, min(len(str(value)), 60))
-        ws.column_dimensions[col_letter].width = min(max(max_len + 2, 10), 64)
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/INTERNAL.md
deleted file mode 100644
index 67209c8..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/INTERNAL.md
+++ /dev/null
@@ -1,122 +0,0 @@
----
-name: financial-source-of-truth
-description: Use when enforcing Public Equity Investing source discipline. Do not use as the primary memo, model, earnings, or pitch owner.
----
-
-# Financial Source Of Truth
-
-> Internal support playbook. Load through `internal-support/policy.md`; this evidence-control capability is bundled with the visible router rather than exposed as a skill entrypoint.
-
-## Deliverable Intake
-
-When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new substantive standalone source-review deliverable should it, before source gathering, analysis, or rendering, load `../../../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved preferences.
-
-## Purpose
-
-Load `shared/equity-research-support-standard.md` and `shared/support-layer-routing-contract.md` before substantial source, data, QA, or style work.
-
-
-Act as the evidence-control layer for Public Equity Investing analysis: public-equity research notes, PM memos, sell-side notes, earnings work, models, ETF/index diligence, public-company diligence, risk workflows, and decks. This skill ranks sources, checks freshness, resolves conflicts, labels facts versus assumptions, and creates citation-ready evidence ledgers for downstream equity workflows.
-
-This is not the primary valuation, earnings, event, risk, deck, style, pitch, or memo skill. Use it before or alongside those skills when evidence quality affects the public-equity decision.
-
-## Embedded Support Routing
-
-This is an embedded service under an owning workflow unless the user explicitly asks for a standalone source-of-truth pass. Preserve the `owning_workflow` internally, such as `memo-builder`, `earnings-preview`, `earnings-deep-dive`, `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `long-short-pitch`, `economic-impact-report`, `thesis-tracker`, `meeting-prep`, `deck-report-qc`, or `dashboard-builder`.
-
-For substantial embedded work, preserve `decision_impact`, `readiness_effect`, `artifact_role`, and `hidden_unless_requested` in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own recommendations; instead, show in natural language how stale, missing, conflicted, or weak evidence changes confidence, valuation support, rating/target support, sizing, hedge posture, watchlist status, or circulation readiness. Evidence ledgers, CSV, JSON, Markdown, and logs are secondary/support artifacts unless the user explicitly asks for them.
-
-## Public Equity Investing Boundary
-
-Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis. This skill may track debt, liquidity, rating, maturity, CDS, or spread facts only as evidence for a listed-equity decision, and must label them as equity-risk context rather than local credit research.
-
-
-Private-market workflows such as CIM teardowns, QoE bridges, LBO sponsor models, private credit underwriting, or deal diligence are non-local to this plugin. If the user provides those materials, label them as user-provided evidence and avoid advertising unavailable downstream handoffs.
-
-## Core Rule
-
-Never let an assumption, issuer claim, management statement, stale source, inference, or estimate masquerade as a verified fact.
-
-Practicality rule: do not stop at policy language. Even when evidence is incomplete, return a usable source posture that separates supported facts, assumptions, limitations, conflicts, source hierarchy, and next actions. Be concrete about what is known and what is missing, but never invent precision to make the answer feel complete.
-
-Use the canonical evidence labels in `references/fact-assumption-labeling.md`, including `fact_source_reported`, `fact_provider_standardized`, `derived_calculation`, `issuer_management_claim`, `analyst_interpretation`, `assumption_user_provided`, `assumption_inferred`, `estimate_consensus`, `stale_source`, `contradicted_source`, `missing_required_source`, and `unknown`.
-
-## Workflow
-
-1. **Define decision context.** Identify the artifact, decision, and minimum evidence required.
-2. **Build source inventory.** Assign source IDs and capture owner, date, period, as-of date, access date, type, and intended use.
-3. **Apply hierarchy.** Use `references/evidence-hierarchy.md`; prefer primary, filed, executed, audited, or source-nearest evidence over summaries and marketing materials.
-4. **Check staleness.** Use `references/staleness-and-conflicts.md`; missing as-of dates are quality issues, especially for market-sensitive data.
-5. **Resolve conflicts.** Do not average conflicting sources without explanation. Prefer the source most authoritative for the exact claim or escalate the conflict.
-6. **Label claims.** Mark facts, assumptions, estimates, inferences, issuer/management claims, stale sources, contradicted sources, missing required sources, and unknowns.
-7. **Protect EPS quality.** For EPS claims, identify GAAP versus adjusted/operating basis, consensus basis, reconciliation source, below-the-line items, tax/share-count effects, and whether the claim is recurring or one-time.
-8. **Attach citations.** Cite load-bearing metrics, dates, quotes, market data, debt terms, valuation inputs, guidance, consensus, and key claims.
-9. **Apply connector honesty.** Use live/vendor/connector data only when the runtime provides it; otherwise request the user export and label the gap `missing_required_source`.
-9. **State evidence posture.** Use decision-grade, research-grade, preliminary, assumption-led, or not supportable.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: source hierarchy, freshness/currentness, conflict resolution, assumption labeling, and evidence register. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-When embedded in a broader workflow, "lead" means lead for source discipline only; the owning workflow remains the investment-artifact owner.
-
-
-## Minimum Useful Answer
-
-When the user asks for source discipline, source review, evidence quality, or a source-of-truth pass, include these components unless the task is explicitly narrower:
-
-1. `Evidence posture`: decision-grade, research-grade, preliminary, assumption-led, or not supportable.
-2. `Supported facts`: what current evidence actually supports, with source IDs or native citations.
-3. `Assumptions and inferences`: what is assumed, inferred, estimated, or user-provided.
-4. `Limitations and conflicts`: stale data, missing as-of dates, contradictory sources, weak issuer claims, and unsupported precision.
-5. `Source hierarchy`: which sources should govern the disputed or load-bearing claims.
-6. `Next actions`: exact source requests or tie-outs needed to upgrade the work.
-
-If no adequate source is available, still produce the framework above with `not sourced`, `unknown`, or `missing_required_source` labels. The output should help the user continue the analysis rather than simply saying more sources are needed.
-
-## Output Contract
-
-Default to a full source posture for source-discipline requests. Use a compact evidence note only when the user explicitly asks for a short answer or one disputed claim.
-
-- Evidence posture block.
-- Source inventory.
-- Evidence ledger.
-- Assumption register.
-- Conflict register.
-- Evidence request list.
-- Compact evidence note for explicitly short or single-claim answers.
-
-For ledger formatting and examples, load `references/citation-and-ledger-format.md`. For practical response patterns under partial, conflicting, or weak source context, load `references/source-discipline-output-patterns.md`.
-
-## Optional Validation
-
-When a task produces or receives a CSV/JSON evidence ledger, run:
-
-```bash
-python scripts/validate_evidence_ledger.py path/to/evidence_ledger.csv
-```
-
-The script catches missing claim labels, source IDs, freshness labels, as-of dates for market-sensitive claims, and high-impact assumptions. It is a QA aid, not a substitute for judgment.
-
-## Local Handoffs
-
-Use this skill as source discipline for:
-
-- `comps-valuation`: peer rationale, EV bridge support, market-data timestamps, and definition consistency.
-- `dcf-model-builder` and `three-statement-model-builder`: sourced facts versus forecast assumptions.
-- `equity-model-update`: reported actuals, guidance, consensus, model changes, and analyst/user assumptions.
-- `earnings-preview` and `earnings-deep-dive`: consensus, guidance, release, filing, transcript, quote support, and EPS-quality / reconciliation evidence.
-- `economic-impact-report`, `event-driven-analyzer`, `long-short-pitch`, and `memo-builder`: evidence posture, source conflicts, and assumptions.
-- `deck-report-qc`: citations, footnotes, units, dates, and repeated metrics that support the exact deck narrative.
-
-## Reference Map
-
-- `references/evidence-hierarchy.md`: source hierarchy by workflow.
-- `references/fact-assumption-labeling.md`: label definitions and edge cases.
-- `references/staleness-and-conflicts.md`: freshness thresholds and conflict handling.
-- `references/citation-and-ledger-format.md`: evidence posture, source inventory, ledger, assumption, and conflict formats.
-- `references/source-discipline-output-patterns.md`: concise output patterns that balance source rigor with actionable next steps.
-
-## Final QA
-
-Confirm the evidence posture is stated or obvious, material numbers and quotes are cited or labeled, source/as-of dates are visible, issuer and management claims are not treated as verified facts, EPS surprise is not treated as recurring without a source-backed quality screen, conflicts are reconciled or flagged, stale data is not buried, and evidence requests are specific enough to act on.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/citation-and-ledger-format.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/citation-and-ledger-format.md
deleted file mode 100644
index 33b5638..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/citation-and-ledger-format.md
+++ /dev/null
@@ -1,147 +0,0 @@
-# Citation and Evidence Ledger Format
-
-Use this reference to make source support auditable across markdown, spreadsheets, documents, and slide outputs.
-
-## Native citations first
-
-If the active environment provides native citations from web, files, callable connectors, or internal search, use those citations exactly as required by that environment. Do not imply connector access when it is not callable, and do not replace native citations with generic source IDs in the final user-facing answer.
-
-Use source IDs such as S1, S2, and S3 as an internal and appendix-friendly shorthand, especially when building a source inventory or evidence ledger.
-
-## What requires citation
-
-Cite or label the following:
-
-- Every hard number that affects analysis: revenue, EBITDA, FCF, growth, margin, leverage, valuation, market price, consensus, ownership, ETF/index weight, borrow, short interest, option data, CDS/spread signals, ARR, churn, net debt, share count, EPS, NAV.
-- Every direct quote or paraphrased management statement.
-- Every issuer, management, broker, or company-presentation claim extracted from an investor deck, earnings call, conference deck, sell-side note, or other non-filed source.
-- Every consensus, market data, rating, forecast, or third-party estimate.
-- Credit terms only when they support a common-equity risk view: maturity, coupon, covenant headline, lien/priority signal, guarantee, amortization, or call protection. Route credit-security analysis to Credit Markets.
-- Every macro release or policy fact that drives the view.
-- Every evidence finding that supports a red flag, kill criterion, recommendation, or investment action.
-
-## What does not always require citation
-
-Do not overload short outputs with citations for generic reasoning. The following can usually be uncited unless the specific claim is disputed or decision-critical:
-
-- Basic finance formulas.
-- Your own clearly labeled inference.
-- A scenario assumption explicitly chosen by the user.
-- Formatting comments or writing-quality feedback.
-- An evidence request derived from a missing source.
-
-## Citation placement
-
-Place the citation immediately after the supported claim.
-
-Good:
-
-```markdown
-Revenue grew 12 percent year over year in Q2, while gross margin declined 180 bps. [S1]
-```
-
-Bad:
-
-```markdown
-Revenue grew 12 percent year over year and margins declined, showing cost pressure and weak execution. [S1]
-```
-
-The bad example over-cites the interpretation. Rewrite as:
-
-```markdown
-Revenue grew 12 percent year over year and gross margin declined 180 bps. [S1] The margin decline suggests cost pressure, but the cause needs support from management commentary or cost detail.
-```
-
-## Source inventory template
-
-```markdown
-| ID | Source | Source owner | Type | Date / as-of | Period | Tier | Used for | Freshness | Limits |
-|---|---|---|---|---|---|---|---|---|---|
-| S1 | [title or file name] | [company / vendor / user / court / regulator / broker] | [filing / model / transcript / vendor / agreement / presentation] | [date] | [period] | [tier] | [metric/claim] | [label] | [caveat] |
-```
-
-## Evidence ledger template
-
-```markdown
-| Claim / metric | Label | Source ID(s) | Exact support | Caveat or conflict | Decision impact |
-|---|---|---|---|---|---|
-| [claim] | [`fact_source_reported` / `issuer_management_claim` / `assumption_inferred` / `estimate_consensus`] | [S1] | [what the source actually supports] | [limits] | [why it matters] |
-```
-
-## Assumption register template
-
-```markdown
-| Assumption | Type | Basis | Evidence support | Sensitivity | Owner | Status |
-|---|---|---|---|---|---|---|
-| [assumption] | [operating / valuation / credit / macro / timing / legal] | [why chosen] | [direct / indirect / none / missing_required_source] | [what changes if wrong] | [user / analyst / management] | [accepted / needs evidence] |
-```
-
-## Evidence requests template
-
-```markdown
-| Priority | Request | Why it matters | Current evidence | Required support | Owner |
-|---:|---|---|---|---|---|
-| 1 | [specific request] | [decision impact] | [current source/claim] | [document/data needed] | [company / management / vendor / user] |
-```
-
-## Spreadsheet and model source notes
-
-When producing or reviewing a workbook:
-
-1. Add a first visible `Cover` or dashboard tab that summarizes source posture, stale/missing evidence, unsupported assumptions, and where source support lives.
-2. Add a `Sources` or `Evidence Ledger` tab when the model is decision-grade.
-3. Add source comments or notes to key input cells when feasible.
-4. Do not cite formulas as sources. Cite the inputs behind the formulas.
-5. Mark assumptions in a different section from historical sourced values.
-6. Include as-of dates for market data, consensus, rates, spreads, and prices.
-7. Include a checks section that flags missing source notes, stale inputs, and hardcoded formulas that should be linked.
-
-Suggested source note format for a model input:
-
-```text
-Source: S3, FY2025 10-K, Note 12, filed 2026-02-20. As-of: FY2025. Used for debt maturity schedule.
-```
-
-## Slides and decks
-
-When producing or reviewing slides:
-
-1. Keep source footnotes short but specific.
-2. Make repeated metrics tie to the same source and definition across the deck.
-3. If a slide uses a derived number, cite the source for inputs and name the calculation.
-4. Flag any slide where title claim is stronger than the evidence.
-5. Put detailed source inventory in appendix when footnotes would overwhelm the slide.
-
-## Output labels
-
-Use labels inline when useful:
-
-- **Verified fact:** directly supported by appropriate evidence.
-- **Issuer / management claim:** from investor deck, earnings call, conference deck, broker note, or company presentation.
-- **Assumption:** chosen input; not proven by evidence.
-- **Inference:** analyst conclusion from evidence.
-- **Estimate:** calculated or approximated.
-- **Stale:** may be superseded.
-- **Conflict:** sources disagree.
-
-## Compact evidence note
-
-Use this for fast answers:
-
-```markdown
-Evidence note: [verified / preliminary / assumption-led]. Primary support: [sources]. Key caveat: [stale/conflicting/missing item].
-```
-
-## Practical evidence posture block
-
-Use this when the user needs a source-of-truth answer, not only a ledger:
-
-```markdown
-Evidence posture: [decision-grade / research-grade / preliminary / assumption-led / not supportable]
-Supported facts: [what the evidence actually supports]
-Assumptions / inferences: [what is assumed, inferred, estimated, or user-provided]
-Limitations / conflicts: [what is stale, missing, contradictory, or too precise]
-Next actions: [specific source request or tie-out that upgrades the work]
-```
-
-This block is the default fallback when source context is partial. It should be concrete enough to guide the next analyst step and conservative enough to avoid invented precision.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/evidence-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/evidence-hierarchy.md
deleted file mode 100644
index f995f82..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/evidence-hierarchy.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Evidence Hierarchy for Public Equity Investing
-
-Use the hierarchy that fits the claim. The best source is the source closest to the claim, not always the newest or longest document.
-
-## General ranking rules
-
-1. Prefer primary sources over secondary summaries.
-2. Prefer filed, audited, executed, court-filed, regulator-published, or system-of-record sources over presentations, emails, and verbal claims.
-3. Prefer current-period sources over older sources unless the user requests historical analysis.
-4. Prefer source-specific documents over broad marketing materials. For covenant terms requested as credit work, route to Credit Markets. For actual revenue, use filings or earnings releases. For management claims, use the transcript, investor presentation, or meeting note and label it as a claim.
-5. Do not rely on a lower-tier source when a higher-tier source is available and directly addresses the claim.
-6. When the highest-tier source is unavailable, use lower-tier sources but label the evidence posture.
-
-## Public-company equity and valuation work
-
-Use this for comps, DCF, three-statement models, earnings, initiating coverage, investment memos, public-company model updates, long/short pitches, and economic-impact reports.
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | Filed annual reports and audited financial statements: 10-K, 20-F, annual report, audited notes | Historical audited financials, segment data, risks, accounting policies, debt notes | Best source for annual historical financials; may be stale for current events |
-| 1 | Filed current or quarterly reports: 10-Q, 6-K, 8-K, prospectus, S-1, F-1, proxy, merger proxy | Quarterly financials, current events, acquisitions, management changes, securities issuance, governance | Use current-event filings for event-specific facts |
-| 2 | Earnings release, supplemental, or company-posted investor presentation | KPI detail, non-GAAP reconciliations, management framing, guidance | Company controlled; reconcile with filings when filings are available |
-| 2 | Earnings call transcript, investor day transcript, or company-hosted call | Management quotes, guidance explanation, Q&A, qualitative claims | Quote carefully; distinguish statement from your interpretation |
-| 3 | Reliable market data or financial data vendor | Price, volume, consensus, estimates, multiples, financial data pulls | Always include vendor and as-of timestamp/date |
-| 3 | Sell-side research or third-party expert reports | Consensus debate, variant views, market narrative | Secondary source; do not treat as proof of company facts |
-| 4 | News media, blogs, social, unsourced web summaries | Event awareness, sentiment, early signals | Use only with caveats and corroborate before using in decisions |
-| 5 | User-provided unsourced assumptions | Scenarios, placeholders, what-if analysis | Label as assumption and show sensitivity |
-
-### EPS quality source order
-
-Use this when an output discusses EPS surprise, operating EPS, recurring EPS, or an ex-gain/ex-loss bridge.
-
-1. Filed statements and notes for GAAP EPS, net income, tax rate, share count, and below-the-line items.
-2. Earnings release or filed exhibit for adjusted EPS and company non-GAAP reconciliation.
-3. Supplemental deck or prepared remarks for management explanation of one-time or recurring items.
-4. Transcript for qualitative explanation and Q&A pushback only; do not use it as the primary source for filed EPS.
-5. Consensus/provider export for estimate basis and as-of timestamp.
-6. Analyst-derived bridge only when the formula, source inputs, confidence, and remaining gaps are shown.
-
-
-## Public-equity market, ownership, ETF/index, borrow, and options data
-
-Use this for listed-equity prices, market cap, float, ownership, ETF/index exposure, borrow, options, factor exposure, and liquidity evidence.
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | Exchange, issuer, transfer agent, index provider, ETF sponsor, official options/short-interest source, or user-provided risk/portfolio export | share count, float, index membership, ETF holdings, benchmark weight, borrow/short-interest support when available, option-chain source, portfolio exposure | Capture as-of timestamp and methodology; do not infer access if source is unavailable |
-| 2 | Callable market-data / consensus / risk-model connector or user-provided vendor export | price, volume, ADV, market cap, EV, consensus, ownership, estimates, factor exposure, borrow, options | Use only if available in runtime or supplied as export; cite provider and timestamp |
-| 3 | Broker/sell-side research, expert notes, or third-party reports | debate framing, estimate rationale, positioning color, source leads | Treat as opinion or secondary support, not proof of issuer facts |
-| 4 | Public web or news fallback | event awareness or preliminary checks | Label as fallback and request higher-quality export when decision-critical |
-
-## Credit Markets handoff / equity-risk signal sources
-
-Use this section only to source equity-risk signals such as refinancing pressure, maturity walls, liquidity stress, ratings pressure, CDS/spread warning signals, or solvency pressure. If the requested output is Credit Markets research, capital-structure analysis, distressed-security work, covenant review, recovery analysis, or credit committee support, route to Credit Markets.
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | Executed credit agreement, indenture, note purchase agreement, intercreditor agreement, security agreement | Equity-risk context for maturity, liquidity, or covenant pressure | Route covenant math and debt-security analysis to Credit Markets |
-| 1 | Filed financial statements, debt notes, liquidity disclosures, exchange offers, tender documents, prospectus supplements | Debt balances, maturities, liquidity, issuance, exchange economics, securities terms | Tie debt values to period and filing |
-| 1 | Court filings, plan documents, disclosure statements, DIP orders, cash collateral orders | Equity-risk context for restructuring timeline or solvency pressure | Route recovery waterfall and claim-priority analysis to Credit Markets |
-| 2 | Rating agency reports, trustee notices, creditor presentations if public or user-provided | Ratings pressure, liquidity context, refinancing risk as equity evidence | Secondary; verify legal terms in Credit Markets when credit work is requested |
-| 3 | Callable market-data source or user-provided vendor export | CDS/spread/yield warning signal and issuance context for equity downside | Timestamp market data; do not imply vendor access if unavailable |
-| 4 | News, broker notes, creditor presentations, docket summaries | Early warning signals, stakeholder arguments, market sentiment | Corroborate before underwriting or trading conclusion |
-
-## Macro, rates, FX, and commodities as public-equity context
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | Central bank releases, government statistical agencies, treasury/finance ministry data, regulator releases | Policy rates, inflation, labor data, GDP, auction results, debt issuance, official reserves | Record release time, revision status, and period |
-| 1 | Exchange, clearinghouse, index provider, issuer, or official curve source | Index levels, official rates, settlement data, contract specs relevant to equity analysis | Use exact identifiers when possible; route bond-term work to Credit Markets |
-| 2 | Market data vendor or terminal pull | Prices, yields, curves, spreads, vol, FX, commodities | Timestamp and define units/conventions |
-| 3 | Bank strategy research, economist notes, market commentary | Interpretation, scenarios, positioning | Treat as opinion, not official data |
-| 4 | News and social media | Event detection, color, early reactions | Confirm with primary or market data before conclusion |
-
-## Public REITs and listed real assets
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | REIT filings, audited statements, supplemental packages, investor presentations, property-level disclosures | NOI, same-store NOI, occupancy, AFFO/FFO, leverage, maturity schedule, capex, tenant exposure | Reconcile supplements to filings where available |
-| 1 | Public debt documents, prospectuses, rating reports, secured debt disclosures | Equity-risk context for leverage, maturities, liquidity, and solvency risk | Route legal term, covenant, and recovery work to Credit Markets |
-| 2 | Market data vendors, public records, comparable sale/lease data | Cap rates, rent comps, supply pipeline, NAV assumptions, local market context | Capture geography, asset type, and date |
-| 3 | Broker research, industry publications, news | Market color, rent assumptions, transaction context | Use only as context unless corroborated |
-
-## Event-driven and special situations
-
-| Tier | Source type | Best used for | Notes |
-|---:|---|---|---|
-| 1 | Merger agreement, proxy, tender offer document, court/regulator filing, company press release | Definitive terms, consideration, timing, conditions, break fees, regulatory process | Use documents over press summaries |
-| 1 | Antitrust/court/agency records, docket filings, exchange notices | Milestones, approvals, hearings, remedies, index/event mechanics | Capture filing date and proceeding status |
-| 2 | Company presentations, management calls, investor day materials | Management framing, synergy targets, strategic rationale | Label as company claim unless independently supported |
-| 3 | News, sell-side research, event-data providers | Probability framing, market expectations, timeline color | Cite date and avoid treating as definitive |
-
-## Source override rule
-
-If a lower-tier source is more directly relevant to the exact claim than a higher-tier source, explain the override. Example: an investor presentation may be a better source for management's stated long-term target, while the 10-K is a better source for historical audited revenue.
-
-## Unsupported materiality rule
-
-If a claim affects valuation, recommendation, risk sizing, common-equity risk view, or investment action and lacks adequate support, do not bury it in prose. Surface it as an evidence gap or evidence request.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/fact-assumption-labeling.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/fact-assumption-labeling.md
deleted file mode 100644
index 172cca6..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/fact-assumption-labeling.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# Fact, Assumption, Claim, and Inference Labeling
-
-Use this guide to keep public-equity-investing analysis honest about what is known, what is asserted, what is assumed, and what is inferred.
-
-## Canonical label definitions
-
-Use these machine-readable labels across source, normalization, audit, model update, and tearsheet outputs.
-
-| Label | Definition | Example | Required treatment |
-|---|---|---|---|
-| `fact_source_reported` | Directly supported by a source document or connected system | FY2025 revenue from audited filing | Cite source and use normally |
-| `fact_provider_standardized` | Provider-standardized data sourced from a trusted market/financial data vendor | Consensus EPS from provider export | Cite provider, timestamp, and limits |
-| `derived_calculation` | Calculated from cited inputs | LTM revenue from four quarters | Show formula and cite inputs |
-| `issuer_management_claim` | Asserted by issuer, company, or management | Investor presentation says AI attach rates improved retention | Attribute, caveat, and test |
-| `management_adjusted` | Management-defined adjusted metric or add-back | Adjusted EBITDA per release | Cite definition and avoid treating as verified economics |
-| `analyst_adjusted` | Analyst normalization or pro forma adjustment | Excluding one-time restructuring cost | State method, support, and inclusion decision |
-| `analyst_interpretation` | Analyst conclusion drawn from evidence | Margin decline likely reflects mix pressure | Cite underlying facts and state confidence |
-| `assumption_user_provided` | Explicit user assumption | User-provided terminal growth rate | Preserve and label |
-| `assumption_inferred` | Agent/analyst assumption inferred from incomplete evidence | Estimated KPI split | Keep low confidence and request evidence |
-| `estimate_consensus` | Consensus/provider forecast or estimate | Street FY2026 EPS | Cite provider/as-of date |
-| `stale_source` | Source may be superseded for current decision | Old consensus pull | Flag freshness and replace if material |
-| `contradicted_source` | Source conflicts with another material source | Filing and deck disagree | Preserve conflict and escalate |
-| `missing_required_source` | Required fact or metric not available | Segment KPI not disclosed | Convert to evidence request |
-| `unknown` | Evidence type cannot be determined | Unlabeled copied table | Do not use for decision-grade work |
-
-Legacy prose labels such as "verified fact", "issuer/management claim", "assumption", and "estimate" are accepted by the validator as aliases, but new outputs should use the canonical labels above.
-
-## Labeling standard by work product
-
-### DCF and three-statement model
-
-- Label historical values as sourced facts when tied to filings, releases, or audited statements.
-- Label forecast drivers as assumptions unless explicitly supported by company guidance, disclosed backlog, or a cited operating driver.
-- Label WACC, terminal growth, exit multiple, discount rate, and scenario cases as assumptions or estimates.
-
-### Comps and valuation
-
-- Label market prices, share counts, net debt, estimates, and multiples with timestamp/date and source.
-- Label peer selection rationale separately from objective facts.
-- Label adjusted EBITDA, non-GAAP, or calendarization comparability issues as estimates if definitions differ.
-
-### Earnings and equity model update
-
-- Label reported results, consensus, guidance, transcript statements, and analyst interpretation separately.
-- Do not treat a management explanation as verified causality without supporting data.
-- Separate company guidance from Street estimates and internal forecast assumptions.
-- For EPS, separate reported GAAP EPS, management-adjusted EPS, analyst-adjusted recurring EPS, below-the-line items, tax/share-count effects, and consensus basis. A headline GAAP EPS beat is not a recurring earnings fact unless the bridge supports it.
-
-### Credit Markets handoff / equity-risk signals
-
-- Route covenant definitions, debt terms, trading levels, spreads, prices, yields, recovery values, and distressed-security conclusions to Credit Markets when the output is credit analysis.
-- In Public Equity Investing, label CDS/spreads, ratings, maturity walls, refinancing pressure, liquidity runway, and covenant headlines only as equity-risk context.
-- Label equity downside cases built from credit signals as analyst interpretation, estimate, or assumption unless directly supported by source documents.
-
-### Event-driven and special situations
-
-- Label definitive transaction terms only when tied to agreements, proxies, tender documents, or regulatory/court filings.
-- Label probability, timing, remedy, and spread assumptions as assumptions unless explicitly source-backed.
-- Separate company statements, press reports, and market-implied conclusions.
-
-### Investment memo and long/short pitch
-
-- Use labels to separate what supports the recommendation from what still needs evidence.
-- In the executive summary, avoid conclusions that require unsourced assumptions unless clearly framed as conditional.
-- Label variant perception and disconfirming evidence explicitly.
-
-## Language standards
-
-Use stronger language only when evidence supports it.
-
-| Evidence strength | Suitable language | Avoid |
-|---|---|---|
-| Source fact | "reported", "filed", "contracted", "audited", "per source" | "appears" if no uncertainty exists |
-| Provider fact | "according to", "reported by", "as stated in" | "verified" without primary support |
-| Issuer / management claim | "company states", "management says", "issuer presentation claims" | "the company has" unless supported |
-| Assumption | "we assume", "case assumes", "if" | "will", "should", "is expected" without source |
-| Analyst interpretation | "suggests", "indicates", "is consistent with" | "proves", "confirms" |
-| Missing / unknown | "not yet supported", "requires evidence" | filler or false precision |
-
-## Assumption escalation rule
-
-Escalate an assumption into a visible caveat when any of these are true:
-
-- It changes valuation by more than a modest amount.
-- It changes buy/sell/hold/pass, rating/target support, sizing, or common-equity risk view.
-- It changes liquidity runway, solvency risk, refinancing pressure, or equity impairment risk.
-- It is unsupported by current evidence.
-- It contradicts management, market, historical, or peer evidence.
-- It is a heroic assumption relative to peer or historical benchmarks.
-
-## Claims ledger template
-
-```markdown
-| Issuer / management claim | Label | Evidence support | Test | Evidence request | Risk if false |
-|---|---|---|---|---|---|
-| [claim] | [`issuer_management_claim` / `analyst_interpretation`] | [source ID or none] | [how to test] | [request] | [impact] |
-```
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/source-discipline-output-patterns.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/source-discipline-output-patterns.md
deleted file mode 100644
index de497f9..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/source-discipline-output-patterns.md
+++ /dev/null
@@ -1,100 +0,0 @@
-# Source Discipline Output Patterns
-
-Use these patterns when source review needs to be useful, not just compliant.
-
-## Core response shape
-
-Start with a posture block:
-
-```markdown
-Evidence posture: [decision-grade / research-grade / preliminary / assumption-led / not supportable]
-What is usable now: [one sentence]
-What is not yet supportable: [one sentence]
-Highest-priority next action: [specific source request or tie-out]
-```
-
-Then use the full table set by default:
-
-1. `Supported facts`
-2. `Assumptions / inferences`
-3. `Limitations / conflicts`
-4. `Next actions`
-
-This keeps the output actionable without overstating support. Use fewer tables only when the user explicitly asks for a short answer or one disputed claim.
-
-## Sparse-source pattern
-
-Use this when the user provides a claim, note, model, deck, or ask but not the underlying sources.
-
-```markdown
-Evidence posture: assumption-led.
-
-| Item | Current treatment | Why | What would upgrade it |
-|---|---|---|---|
-| [metric/claim] | `assumption_user_provided` / `unknown` | User provided it, but no source is attached | [filing/vendor export/transcript/model tab/date needed] |
-```
-
-Do not say the work cannot proceed. Provide a usable framework and mark unsupported items clearly.
-
-## Conflict pattern
-
-Use this when two sources disagree.
-
-```markdown
-Evidence posture: preliminary due to unresolved conflict.
-
-| Claim | Source A | Source B | Preferred source | Treatment | Next action |
-|---|---|---|---|---|---|
-| [claim] | [S1 + exact support] | [S2 + exact support] | [S1/S2/unresolved] | [use preferred / show both / sensitize] | [specific tie-out] |
-```
-
-Do not average conflicting numbers unless the method is explicit and appropriate.
-
-## Unsupported precision pattern
-
-Use this when the requested output contains exact figures that are not backed by exact support.
-
-```markdown
-Evidence posture: not supportable at the stated precision.
-
-Supported directionally: [what the evidence supports]
-Unsupported precision: [exact number/range/date that lacks support]
-Safer wording: [rewrite without false precision]
-Required support: [source, page/tab, provider timestamp, or calculation bridge]
-```
-
-## Practical next-action language
-
-Good next actions are specific:
-
-- "Provide the latest 10-Q debt note and the model EV bridge tab to support net debt."
-- "Provide consensus export with provider name and estimate-as-of date."
-- "Tie the investor-deck adjusted EBITDA bridge to the earnings release reconciliation."
-- "Confirm whether the market price is current as of the valuation date or a stale placeholder."
-
-Avoid generic next actions:
-
-- "Need more sources."
-- "Verify data."
-- "Add citations."
-- "Check assumptions."
-
-## Language calibration
-
-| Evidence state | Use this language | Avoid |
-|---|---|---|
-| Supported fact | "reported", "filed", "per S1" | "appears" |
-| User-provided input | "user-provided assumption" | "verified" |
-| Issuer claim | "management states" | "the company has proven" |
-| Weak support | "directionally supported" | exact unsupported decimals |
-| Conflict | "unresolved conflict" | burying one source |
-| Missing source | "not yet supportable" | false certainty |
-
-
-## Unsupported Connector Or Provider Access
-
-When a requested Bloomberg, FactSet, S&P Capital IQ, CapIQ, LSEG, Refinitiv, Daloopa, PitchBook, Morningstar, broker, email, collaboration-app, or internal data connector is not callable in the runtime, do not pretend to have pulled it. Use user-provided exports if available, request the specific export, label the missing evidence as `missing_required_source`, and keep the evidence posture preliminary or screen-grade.
-
-## Credit Markets Handoff Pattern
-
-If the user asks for a public-credit memo, bond/loan/CDS analysis, covenant review, recovery waterfall, restructuring view, spread/yield relative value, or debt-security selection, route to Credit Markets. If credit data is present inside a public-equity packet, retain only the equity read-through: refinancing stress, solvency warning, maturity-wall risk, CDS/spread signal, or liquidity pressure that could affect common-equity downside.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/staleness-and-conflicts.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/staleness-and-conflicts.md
deleted file mode 100644
index b3f40b6..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/references/staleness-and-conflicts.md
+++ /dev/null
@@ -1,98 +0,0 @@
-# Staleness and Source Conflict Handling
-
-Use this guide to decide when a source is too old, incomplete, or contradicted to support a Public Equity Investing conclusion.
-
-## Required freshness fields
-
-For each material source, record:
-
-- Document date
-- Period covered
-- As-of date
-- Access date
-- Version or draft status if available
-- Whether the source may have been superseded
-
-If one of these fields is missing and it matters to the decision, mark freshness as unknown.
-
-## Freshness thresholds by data type
-
-These thresholds are defaults. Tighten them when markets are volatile, the issuer is balance-sheet-stressed, an event is live, or the user asks for current information.
-
-| Data type | Freshness expectation | Flag as stale when |
-|---|---|---|
-| Public-company historical annual financials | Latest filed annual period | New annual filing or restatement likely supersedes it |
-| Public-company quarterly financials | Latest filed quarter or latest earnings release for the relevant period | New quarter has reported, filing/release supersedes it, or period is ambiguous |
-| Earnings release and transcript | Exact reported quarter | New guidance, 10-Q/6-K, 8-K, investor day, or later management update supersedes it |
-| Market prices, FX, yields, spreads, commodities | Timestamp or current date | No timestamp, old quote, volatile market, or output implies live precision |
-| Consensus estimates | Vendor and date | No date, changed after earnings/guidance, or source does not identify estimate set |
-| Company investor presentation or supplemental | Date of pack and period referenced | Newer filing, release, supplemental, or investor day supersedes it |
-| Non-filed management data or conference deck | Specific date, event, and period | Later company disclosure, filing, or transcript contradicts or supersedes it |
-| Credit ratings and outlooks | Agency, date, issuer/instrument | Later rating action, watch/outlook change, or debt instrument mismatch |
-| Debt balances and liquidity | Latest reporting period or issuer disclosure | Quarter-end data likely changed materially or new issuance/refinancing occurred |
-| Covenants, indentures, and legal terms | Current executed agreement, filed agreement, or current user-provided draft | Summary only, outdated draft, amendment likely supersedes it |
-| Distressed/restructuring process data | Latest court docket, company filing, or proceeding milestone | New court filing, plan update, order, or hearing result supersedes it |
-| Macro releases | Official release date, revision status | Revised data, newer release, preliminary estimate superseded by final |
-
-## Staleness labels
-
-Use these labels in evidence ledgers:
-
-| Label | Meaning |
-|---|---|
-| current | Fresh enough for the stated use |
-| current but volatile | Fresh now, but likely to change quickly; timestamp required |
-| stale but usable for history | Old but appropriate for historical context |
-| potentially superseded | There may be newer source material; avoid definitive conclusions |
-| stale for decision | Too old for the decision; use only as context |
-| unknown freshness | Missing date/as-of/version information |
-
-## Conflict handling workflow
-
-1. Identify the exact conflict.
-   - Metric mismatch: revenue, EBITDA, leverage, FCF, debt balance, CDS/spread signal, price, valuation range, consensus, short interest, ownership, ETF/index weight.
-   - Definition mismatch: adjusted EBITDA, ARR, net debt, same-store NOI, active customer, organic growth.
-   - Timing mismatch: LTM period, fiscal year, quarter, as-of date, spot price.
-   - Scope mismatch: consolidated versus segment, continuing ops versus total, restricted group versus issuer.
-   - Source type mismatch: filed number versus company presentation versus broker summary.
-
-2. Classify materiality.
-   - High: changes recommendation, valuation range, common-equity risk view, risk sizing, rating/target support, or investment action.
-   - Medium: changes framing but not decision.
-   - Low: immaterial or disclosure-only.
-
-3. Select preferred source.
-   - Prefer the source that is most authoritative for that claim.
-   - Prefer the source with the most precise scope, period, and definition.
-   - Prefer current executed/filed/audited/source-system data over summaries.
-   - If using a lower-tier source, explain why.
-
-4. Document treatment.
-   - Resolved: explain reconciliation and use corrected figure.
-   - Partially resolved: use preferred figure and flag caveat.
-   - Unresolved: present both figures and convert to evidence request.
-   - Sensitivity: run both versions if the conflict affects value, credit, or risk sizing.
-
-## Conflict register template
-
-```markdown
-| Issue | Source A | Source B | Difference | Materiality | Preferred source | Treatment | Evidence request |
-|---|---|---|---|---|---|---|---|
-| [metric/claim] | [S1] | [S2] | [variance] | [high/medium/low] | [source ID/unresolved] | [resolved/partial/unresolved/sensitivity] | [specific request] |
-```
-
-## Examples of correct treatment
-
-- If an investor deck claims 30 percent EBITDA growth but the latest filing shows flat adjusted EBITDA, label the deck statement as an issuer/management claim, use the filing as preferred evidence for reported performance, and request the bridge.
-- If consensus revenue differs across two vendors, cite both, state date and vendor, and avoid false precision. If the delta matters, run sensitivity.
-- If covenant or recovery evidence is needed for a credit-security conclusion, route to Credit Markets. If it is used only for equity downside, cite the highest-quality available source and label it equity-risk context.
-- If a market price is from yesterday and the user asks for current valuation, either retrieve current data or state that the price is stale for live valuation.
-
-## Do-not-do list
-
-- Do not average conflicting numbers without explaining source, definition, and timing differences.
-- Do not use a company presentation as proof of adjusted EBITDA without reconciliation.
-- Do not treat management guidance as achieved performance.
-- Do not cite a transcript quote as proof that the strategy will work.
-- Do not use stale market data in a conclusion that implies live pricing.
-- Do not hide unresolved conflicts in footnotes if they affect the decision.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/scripts/validate_evidence_ledger.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/scripts/validate_evidence_ledger.py
deleted file mode 100755
index cc568a3..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/financial-source-of-truth/scripts/validate_evidence_ledger.py
+++ /dev/null
@@ -1,298 +0,0 @@
-#!/usr/bin/env python3
-"""
-Validate a Public Equity Investing evidence ledger in CSV or JSON format.
-
-The script checks for required fields, accepted evidence labels, freshness labels,
-missing source IDs, missing as-of dates for market-sensitive claims, and unsupported
-high-impact assumptions.
-
-Usage:
-  python scripts/validate_evidence_ledger.py evidence_ledger.csv
-  python scripts/validate_evidence_ledger.py evidence_ledger.json
-
-JSON may be either a list of row objects or an object with a top-level "rows" list.
-"""
-
-from __future__ import annotations
-
-import csv
-import json
-import re
-import sys
-from pathlib import Path
-from typing import Any, Iterable
-
-CANONICAL_LABELS = {
-    "fact_source_reported",
-    "fact_provider_standardized",
-    "derived_calculation",
-    "issuer_management_claim",
-    "management_adjusted",
-    "analyst_adjusted",
-    "analyst_interpretation",
-    "assumption_user_provided",
-    "assumption_inferred",
-    "estimate_consensus",
-    "stale_source",
-    "contradicted_source",
-    "missing_required_source",
-    "unknown",
-}
-
-LABEL_ALIASES = {
-    "verified fact": "fact_source_reported",
-    "reported fact": "fact_source_reported",
-    "issuer claim": "issuer_management_claim",
-    "issuer / management claim": "issuer_management_claim",
-    "issuer/management claim": "issuer_management_claim",
-    "management statement": "issuer_management_claim",
-    "management claim": "issuer_management_claim",
-    "assumption": "assumption_inferred",
-    "user assumption": "assumption_user_provided",
-    "inference": "analyst_interpretation",
-    "estimate": "assumption_inferred",
-    "consensus estimate": "estimate_consensus",
-    "pro forma adjustment": "analyst_adjusted",
-    "stale item": "stale_source",
-    "contradicted item": "contradicted_source",
-    "unsupported": "missing_required_source",
-}
-
-ACCEPTED_FRESHNESS = {
-    "current",
-    "current but volatile",
-    "stale but usable for history",
-    "potentially superseded",
-    "stale for decision",
-    "unknown freshness",
-    "unknown",
-    "n/a",
-    "na",
-    "",
-}
-
-REQUIRED_CANONICAL = ["claim", "label", "support"]
-
-FIELD_ALIASES = {
-    "claim": [
-        "claim",
-        "claim / metric",
-        "metric",
-        "issuer / management claim",
-        "management claim",
-        "issue",
-    ],
-    "label": ["label", "evidence label", "type", "status"],
-    "source": ["source", "source id", "source ids", "source id(s)", "sources"],
-    "support": ["support", "exact support", "evidence support", "basis"],
-    "freshness": ["freshness", "staleness", "freshness label"],
-    "as_of": ["as of", "as-of", "as-of date", "date / as-of", "date"],
-    "impact": ["impact", "decision impact", "materiality"],
-    "caveat": ["caveat", "caveat / conflict", "conflict", "notes"],
-}
-
-MARKET_SENSITIVE_TERMS = re.compile(
-    r"\b(price|yield|spread|fx|foreign exchange|rate|rates|curve|vol|volatility|"
-    r"market cap|enterprise value|ev/|multiple|consensus|rating|cap rate|rent roll|"
-    r"liquidity|covenant|headroom|debt balance|borrowing base)\b",
-    re.IGNORECASE,
-)
-
-VAGUE_SUPPORT_VALUES = {
-    "source",
-    "filing",
-    "company filing",
-    "company report",
-    "provider",
-    "vendor",
-    "transcript",
-    "presentation",
-    "website",
-    "model",
-    "deck",
-    "memo",
-    "n/a",
-    "na",
-}
-
-
-def normalize_key(key: str) -> str:
-    return re.sub(r"\s+", " ", key.strip().lower())
-
-
-def normalize_label(label: str) -> str:
-    key = normalize_key(label).replace("_", " ")
-    if label in CANONICAL_LABELS:
-        return label
-    return LABEL_ALIASES.get(key, label)
-
-
-def canonicalize_row(row: dict[str, Any]) -> dict[str, str]:
-    normalized = {
-        normalize_key(str(k)): "" if v is None else str(v).strip() for k, v in row.items()
-    }
-    out: dict[str, str] = {}
-    for canonical, aliases in FIELD_ALIASES.items():
-        for alias in aliases:
-            if alias in normalized:
-                out[canonical] = normalized[alias]
-                break
-        else:
-            out[canonical] = ""
-    return out
-
-
-def load_rows(path: Path) -> tuple[list[dict[str, str]], list[str]]:
-    warnings: list[str] = []
-    suffix = path.suffix.lower()
-    if suffix == ".csv":
-        with path.open(newline="", encoding="utf-8-sig") as f:
-            reader = csv.DictReader(f)
-            if not reader.fieldnames:
-                raise ValueError("CSV has no header row")
-            raw_rows = list(reader)
-    elif suffix == ".json":
-        data = json.loads(path.read_text(encoding="utf-8"))
-        if isinstance(data, dict) and isinstance(data.get("rows"), list):
-            raw_rows = data["rows"]
-        elif isinstance(data, list):
-            raw_rows = data
-        else:
-            raise ValueError(
-                "JSON must be a list of objects or an object with a top-level 'rows' list"
-            )
-        if not all(isinstance(r, dict) for r in raw_rows):
-            raise ValueError("Each JSON row must be an object")
-    else:
-        raise ValueError("Only .csv and .json ledgers are supported")
-
-    rows = [canonicalize_row(r) for r in raw_rows]
-    if not rows:
-        warnings.append("Ledger contains no rows")
-    return rows, warnings
-
-
-def validate_rows(rows: Iterable[dict[str, str]]) -> tuple[list[str], list[str]]:
-    errors: list[str] = []
-    warnings: list[str] = []
-
-    for idx, row in enumerate(rows, start=1):
-        prefix = f"row {idx}"
-        for field in REQUIRED_CANONICAL:
-            if not row.get(field):
-                errors.append(f"{prefix}: missing required field '{field}'")
-
-        raw_label = row.get("label", "")
-        label = normalize_label(raw_label.lower())
-        if label and label not in CANONICAL_LABELS:
-            warnings.append(f"{prefix}: unrecognized evidence label '{row.get('label')}'")
-
-        freshness = row.get("freshness", "").lower()
-        if freshness not in ACCEPTED_FRESHNESS:
-            warnings.append(f"{prefix}: unrecognized freshness label '{row.get('freshness')}'")
-
-        claim = row.get("claim", "")
-        source = row.get("source", "")
-        as_of = row.get("as_of", "")
-        impact = row.get("impact", "").lower()
-        support = row.get("support", "")
-
-        source_required = {
-            "fact_source_reported",
-            "fact_provider_standardized",
-            "derived_calculation",
-            "issuer_management_claim",
-            "management_adjusted",
-            "analyst_adjusted",
-            "estimate_consensus",
-        }
-        if label in source_required and not source:
-            errors.append(f"{prefix}: '{label}' requires a source ID")
-
-        support_terms = [term for term in re.split(r"\s+", support.strip()) if term]
-        if label in source_required and support.lower() in VAGUE_SUPPORT_VALUES:
-            warnings.append(
-                f"{prefix}: source-backed claim needs exact support, not generic support '{support}'"
-            )
-        elif label in source_required and 0 < len(support_terms) < 3:
-            warnings.append(
-                f"{prefix}: source-backed claim support may be too vague; add page, table, quote, provider timestamp, or calculation bridge"
-            )
-
-        if label in {"assumption_user_provided", "assumption_inferred"} and source and not support:
-            warnings.append(
-                f"{prefix}: assumption has a source ID but no basis/support explanation"
-            )
-
-        if label == "issuer_management_claim" and not row.get("caveat"):
-            warnings.append(
-                f"{prefix}: issuer claim should include caveat, test, or evidence request"
-            )
-
-        if label == "analyst_interpretation" and not source:
-            warnings.append(
-                f"{prefix}: analyst interpretation should cite underlying facts or state that no supporting source is available"
-            )
-
-        if MARKET_SENSITIVE_TERMS.search(claim) and not as_of:
-            warnings.append(f"{prefix}: market-sensitive claim may need an as-of date")
-
-        judgment_labels = {
-            "assumption_user_provided",
-            "assumption_inferred",
-            "analyst_interpretation",
-        }
-        impact_terms = ["high", "critical", "decision", "valuation", "credit"]
-        if label in judgment_labels and any(term in impact for term in impact_terms):
-            warnings.append(f"{prefix}: high-impact {label} should be sensitized or escalated")
-
-    return errors, warnings
-
-
-def main(argv: list[str]) -> int:
-    if any(arg in {"-h", "--help"} for arg in argv[1:]):
-        print(
-            "Usage: python validate_evidence_ledger.py <evidence_ledger.csv|evidence_ledger.json>"
-        )
-        return 0
-    if len(argv) != 2:
-        print(
-            "Usage: python validate_evidence_ledger.py <evidence_ledger.csv|evidence_ledger.json>"
-        )
-        return 2
-
-    path = Path(argv[1])
-    if not path.exists():
-        print(f"Error: file not found: {path}")
-        return 2
-
-    try:
-        rows, load_warnings = load_rows(path)
-        errors, warnings = validate_rows(rows)
-        warnings = load_warnings + warnings
-    except Exception as exc:  # noqa: BLE001
-        print(f"Error: {exc}")
-        return 2
-
-    print(f"Validated {len(rows)} ledger row(s).")
-    if errors:
-        print("\nErrors:")
-        for item in errors:
-            print(f"- {item}")
-    if warnings:
-        print("\nWarnings:")
-        for item in warnings:
-            print(f"- {item}")
-
-    if errors:
-        print("\nResult: FAIL")
-        return 1
-    if warnings:
-        print("\nResult: PASS WITH WARNINGS")
-        return 0
-    print("\nResult: PASS")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main(sys.argv))
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/policy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/policy.md
deleted file mode 100644
index bc5b53f..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/policy.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Internal Support Policy
-
-## Purpose
-
-This file is the Public Equity Investing adapter layer for bundled support capabilities. Visible skills own listed-equity decisions and user-facing workflows. Internal playbooks provide evidence control, data preparation,
-rendering, style application, and sector context without expanding the selectable skill catalogue. Model QA remains a visible workflow.
-
-## Internal Capability Registry
-
-These capabilities are packaged with this plugin but are not registered `SKILL.md` entrypoints:
-
-| Capability label | Internal playbook | Plugin-specific obligation |
-| --- | --- | --- |
-| `dashboard-builder` | `internal-support/dashboard-builder/INTERNAL.md` | Render optional standardized dashboards with citation visibility and readiness posture. |
-| `financial-source-of-truth` | `internal-support/financial-source-of-truth/INTERNAL.md` | Apply public-company source hierarchy, market-data freshness, and evidence labels. |
-| `excel-data-cleaner` | `internal-support/excel-data-cleaner/INTERNAL.md` | Preserve ticker, period, unit, timestamp, and equity-workflow identifiers. |
-| `style-guide-adapter` | `internal-support/style-guide-adapter/INTERNAL.md` | Restyle without changing facts, formulas, citations, or investment conclusions. |
-| `sector-context-overlay` | `internal-support/sector-context-overlay/INTERNAL.md` | Supply sector lens only under an owning listed-equity workflow. |
-| `daloopa-provider-guide` | `internal-support/daloopa-provider-guide/INTERNAL.md` | Shape bounded, explicit-period Daloopa calls and preserve provider citations. |
-| `quartr-provider-guide` | `internal-support/quartr-provider-guide/INTERNAL.md` | Shape bounded Quartr filing, document, event, and standardized-financial calls with provenance. |
-
-`financials-normalizer`, `model-audit-tieout`, `deck-report-qc`, and `scenario-sensitivity-generator` remain visible in this release because normalization, model review, circulation review, and scenario interpretation can each be explicit user-facing public-equity jobs.
-
-## Routing Rule
-
-After the plugin invocation gate passes, a visible owning workflow may load an internal playbook whenever its support lane is needed. Existing documents may continue to use the capability labels above; resolve each label to its `INTERNAL.md` file rather than asking the user to select a separate skill.
-
-For an explicit support-only request within Public Equity Investing, such as cleaning an equity workbook, applying a style guide, checking sources, or rendering an already-defined dashboard, the root router coordinates the request and loads the matching internal playbook.
-Use `standalone_support_request` as the owning workflow where the playbook requires one, and preserve its support-artifact/readiness rules.
-
-Provider guides are narrower: load one only after an ordinary workflow attempts a semantic source category, selects Quartr or Daloopa as the concrete route, and confirms that route is callable in the current runtime. Do not load provider guides during preflight, onboarding, broad setup, or merely because `.app.json` declares a dependency.
-
-## Customization Boundary
-
-This policy and the referenced internal playbooks are plugin-owned adapters:
-customized plugin distributions may change them to reflect team evidence,
-style, readiness, workbook, or dashboard requirements. Reusable support engines must consume these Public Equity-specific obligations rather than silently replacing them with generic behavior.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/INTERNAL.md
deleted file mode 100644
index 4e06d3f..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/INTERNAL.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Quartr Provider Guide
-
-> Internal provider guide. Load through `internal-support/policy.md` only after the current workflow selects a callable Quartr route for an attempted source category. This is not a selectable skill.
-
-## Load Gate
-
-Do not load this guide during user-context preflight, onboarding, or unrelated source setup. A configured `.app.json` entry, preferred provider, or surfaced tool name is not proof that Quartr is callable for the current user.
-
-## Authority And Scope
-
-Use the live Quartr action schema as the source of truth. If it differs from this guide, follow the live schema and retry with the smallest valid call. When using a generic connector runner, place action arguments inside top-level `params`. Use Quartr before web search for public-company filings, annual and quarterly reports, earnings releases, presentations, transcripts, events, management commentary, and standardized actual financials.
-
-Before calling Quartr, read `references/connector-playbook.md`. For a workbook workflow, also read `references/workbook-mode.md` before editing the workbook.
-
-## Default Sequence
-
-1. Call `search_companies` with one ticker or clean company name.
-2. Validate the company match before using `companyId`. Do not accept a fuzzy top result when ticker or normalized company name does not match.
-3. Use `get_financials` for standardized actuals, one `financialType` at a time.
-4. Use `list_documents` or `list_events` to identify bounded source material.
-5. Prefer `search_documents`, `get_document_summary`, or `get_event_summary` before broad document reads. Use targeted `read_document` calls only when needed.
-6. Compute derived values from sourced inputs and preserve their provenance.
-
-Never invent company IDs, document IDs, event IDs, source URLs, filing dates, or page references.
-
-## Call Shape And Data Rules
-
-- `search_companies` uses `query`.
-- `get_financials` uses `companyId`, ISO `startDate` and `endDate` when needed, `periodType`, and one `financialType`: `incomeStatement`, `balanceSheet`, or `cashFlowStatement`.
-- `list_documents` uses `companyId`, narrow `documentTypes`, and a small `limit`, usually 5-10.
-- `search_documents` uses a short `query`, `documentTypes`, and `filter: "companyId:=<id>"` when restricting results to one company.
-- Use ISO dates for Quartr inputs. Label non-calendar fiscal periods from returned event and document metadata.
-- Keep reported actuals, guidance, estimates, and derived calculations separate.
-
-## Output Budget And Recovery
-
-Prefer several small calls over broad pulls. If `get_financials` is too large, retain one `financialType` and split the date range to one year per call. If a document read is too large, use a summary or targeted search, then read fewer pages. If company search returns weak matches, retry with ticker or a shorter distinctive name rather than using an unrelated result.
-
-## Citation Discipline
-
-Carry returned `referenceUrl`, `reportUrl`, document URL, event URL, and page reference fields into tables, artifacts, charts, and prose. Cite computed values through their sourced inputs. Identify Quartr as the data source without implying that unrelated values came from Quartr.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/connector-playbook.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/connector-playbook.md
deleted file mode 100644
index be9b751..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/connector-playbook.md
+++ /dev/null
@@ -1,252 +0,0 @@
-# Quartr Connector Playbook
-
-Load this reference only after the owning workflow selects a callable Quartr route. The source prompt was verified against the active Quartr connector on 2026-04-15; the live tool schema remains authoritative if it differs.
-
-## Authority Order
-
-Use this order when instructions conflict:
-
-1. The live Quartr action schema exposed in the current runtime.
-2. The canonical call shapes and workflow rules in this playbook.
-3. The user's latest explicit instruction when it does not conflict with connector schema, citation rules, or data integrity.
-4. The owning analytical workflow, such as comps, DCF, earnings analysis, working-capital analysis, or guidance tracking.
-
-Never call an action with missing required arguments or `params: null`. When using a generic connector runner, pass action arguments inside top-level `params`, not `input`, `arguments`, or top-level action fields. Do not copy parameter names from examples blindly after the live schema changes.
-
-## Scope And Source Order
-
-Prefer Quartr over web search for public-company filings, annual reports, quarterly reports, earnings releases, presentations, transcripts, event summaries, management commentary, and standardized financial statements. Use another clearly labeled source only for data outside Quartr's coverage. Keep Quartr values distinct from non-Quartr values.
-
-## Canonical Sequence
-
-1. Search for the company.
-2. Validate the match before using `companyId`.
-3. Pull standardized actual financials one statement type at a time.
-4. List documents or events to identify bounded source material.
-5. Use document search, summaries, or targeted reads for segment detail, KPIs, guidance, strategy, risks, and qualitative context.
-6. Compute derived metrics from sourced values.
-7. Present results with provenance and clear assumptions.
-
-Never invent `companyId`, `documentId`, `eventId`, source URLs, filing dates, or source page references.
-
-## Output Budget
-
-Quartr responses can be large because financial values include source metadata and document reads include full text.
-
-- `search_companies`: search one company at a time.
-- `get_financials`: always specify one `financialType`.
-- `get_financials`: probe availability with one statement and one annual period first.
-- `get_financials`: pull multi-year models one statement type at a time; split oversized responses into one year per call.
-- `list_documents`: keep `limit` small, usually 5-10.
-- `search_documents`: use short targeted queries, usually one to five words.
-- `read_document`: use summaries or search before broad reads; use small `maxPages` values.
-
-Do not repeatedly list connectors once Quartr actions are already known. Do not fall back to web search for filing values after Quartr has located the relevant filing.
-
-## `search_companies`
-
-Purpose: find the Quartr company record and capture `companyId`.
-
-Expected shape:
-
-```python
-search_companies(query: str)
-```
-
-Correct examples:
-
-```json
-{"query": "AAPL"}
-```
-
-```json
-{"query": "Apple"}
-```
-
-Avoid `keywords`, a `ticker` field, or a combined query such as `"Broadcom Inc AVGO"`.
-
-Match rules:
-
-- Search with ticker when known; otherwise use a clean company name without legal suffixes.
-- Do not search a peer set in one call.
-- Prefer active companies for current financial analysis.
-- If the result is fuzzy, retry with ticker, a shorter distinctive name, or an alternate spelling.
-- A ticker match or normalized company-name match is strong. Shared industry words alone are weak.
-- If no exact or highly likely match appears, do not use the top result.
-
-Capture:
-
-- `id` as `companyId`
-- `ticker`
-- `name`
-- `country`
-- `gics`
-- `status`
-- company `url`
-
-## `get_company`
-
-Purpose: retrieve profile metadata or sanity-check a known company.
-
-Expected shape:
-
-```python
-get_company(companyId: number)
-```
-
-Use this when profile metadata, industry context, company URLs, or an additional match check would improve the analysis.
-
-## `get_financials`
-
-Purpose: retrieve standardized income-statement, balance-sheet, or cash-flow values.
-
-Expected shape:
-
-```python
-get_financials(companyId: number, startDate?: str, endDate?: str, periodType?: str, financialType?: str)
-```
-
-Valid `periodType` values:
-
-- `quarterly`
-- `halfYear`
-- `yearly`
-- `periodical`
-
-Valid `financialType` values:
-
-- `incomeStatement`
-- `balanceSheet`
-- `cashFlowStatement`
-
-Correct example:
-
-```json
-{"companyId": 4742, "startDate": "2024-01-01", "endDate": "2025-12-31", "periodType": "yearly", "financialType": "incomeStatement"}
-```
-
-Usage rules:
-
-- Obtain `companyId` from `search_companies`; do not guess it.
-- Use ISO dates.
-- Use `yearly` for annual historical actuals and `quarterly` for trend or earnings-update work.
-- Pull each statement separately.
-- For broad historical models, probe with one statement and one year first.
-- Treat `null` values as missing, not zero.
-
-Capture returned event, currency, statement type, report URL, source page, and reference URL metadata when available.
-
-## `list_documents`
-
-Purpose: identify company filings, releases, slides, transcripts, letters, and reports.
-
-Expected shape:
-
-```python
-list_documents(companyId: number, documentTypes?: list[str], startDate?: str, endDate?: str, limit?: number)
-```
-
-Common `documentTypes`:
-
-- `annual_report_10k`
-- `quarterly_report_10q`
-- `earnings_release_8k`
-- `slide`
-- `transcript`
-- `annual_report_20f`
-- `annual_report_40f`
-- `press_release`
-- `shareholder_letter`
-- `sustainability_esg_report`
-
-Use narrow document-type filters and small limits. Capture `id` as `documentId`, URL, title, document type, filing type, filing date, and `eventId`.
-
-## `search_documents`
-
-Purpose: search reports, slides, and transcripts for targeted qualitative or KPI detail.
-
-Expected shape:
-
-```python
-search_documents(query: str, documentTypes: list[str], limit?: number, startDate?: str, endDate?: str, filingTypes?: list[str], filter?: str)
-```
-
-Correct example:
-
-```json
-{"query": "revenue segments", "documentTypes": ["reports"], "filingTypes": ["10-K"], "filter": "companyId:=4742", "limit": 5}
-```
-
-Use `documentTypes: ["reports"]` for filings, `["transcripts"]` for earnings-call commentary, and `["slides"]` for presentations. Use filing types only with reports. Use `filter: "companyId:=<id>"` for company-specific results. Keep queries short and retry empty searches with fewer words or synonyms.
-
-Capture document, event, company, page, URL, and snippet fields.
-
-## Document And Event Detail
-
-Use these shapes:
-
-```python
-read_document(documentId: number, maxPages?: number)
-get_document_summary(documentId: number)
-list_events(companyId?: number, eventTypes?: list[str], limit?: number, startDate?: str, endDate?: str, order?: str)
-get_event(eventId: number)
-get_event_summary(eventId: number)
-list_related_companies(companyId: number)
-```
-
-Prefer summaries or document search before `read_document`. Keep document reads narrow. Use event tools for earnings calls, investor days, guidance events, conferences, AGMs, M&A announcements, and transcript-driven qualitative analysis. Use related companies for peer discovery, then verify peers before using them in comps.
-
-## Date And Period Rules
-
-Use ISO `YYYY-MM-DD` dates in Quartr inputs. Anchor periods in this order:
-
-1. User-specified dates or years.
-2. Filing dates and event dates returned by Quartr.
-3. Periods visible in an active artifact or user-provided data.
-4. A period range supplied by the user.
-5. The current date only as a last resort, with the assumption stated.
-
-For non-calendar fiscal years, use a broad date range that covers the filings and label fiscal periods using returned event and document metadata.
-
-## Guidance Versus Actuals
-
-Before calculating beat, miss, or guidance accuracy:
-
-1. Find the relevant earnings call or guidance event.
-2. Retrieve the guidance language through an event summary, transcript search, or targeted read.
-3. Create a mapping table showing the guidance period and the results period it applies to.
-4. Verify the offset.
-5. Calculate the comparison only after the mapping is clear.
-
-Quarterly guidance from quarter N usually applies to quarter N+1 actuals. Do not compare same-quarter guidance to same-quarter actuals unless the disclosure explicitly says to do so. Annual guidance from Q1, Q2, or Q3 usually applies to the current fiscal year; Q4 annual guidance usually applies to the next fiscal year unless the disclosure says otherwise.
-
-## Formatting And Tables
-
-Use readable financial formatting: `$X.Xbn` or `$X,XXXmm`, `$X.XX` per share, one decimal for percentages and multiples, signed growth rates, signed basis points, and clearly labeled share counts.
-
-For financial tables, use metrics as rows and chronological periods as columns. Place derived rows such as growth, margins, and variance below their sourced metrics unless the user requests another layout.
-
-## Citation Rules
-
-Carry provenance through tables, prose, charts, and artifacts.
-
-- Prefer returned `referenceUrl` for standardized financial values.
-- Otherwise cite `reportUrl`, document URL, event URL, or company URL.
-- For document snippets, cite the document URL and include page number when available.
-- Cite computed figures through their sourced inputs.
-- Add `Data sourced from Quartr` to outputs that use Quartr data.
-
-Never imply that non-Quartr values came from Quartr.
-
-## Recovery
-
-If a call fails:
-
-1. Check live parameter names and types.
-2. Retry with the smallest valid call.
-3. For weak company matches, retry with ticker or a shorter distinctive name.
-4. For oversized financial pulls, retain one `financialType` and split to one year per call.
-5. For oversized document reads, use a summary or search, then read fewer pages.
-6. For empty document search, retry with fewer words, synonyms, broader document types, or no filing-type filter.
-7. If Quartr lacks the company, filing, or line item, state the gap and use another source only for the missing data.
-8. If still blocked, state the exact failed call and the smallest missing input or fallback needed.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/workbook-mode.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/workbook-mode.md
deleted file mode 100644
index bccbc88..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/quartr-provider-guide/references/workbook-mode.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Quartr Workbook Mode
-
-Load this reference only when a routed workflow uses Quartr while reading or editing a workbook.
-
-## Source Order
-
-1. Inspect the active workbook first.
-2. Use existing workbook data when it is already present, source-backed, and current enough for the task.
-3. Prefer Quartr over web search for filings, reports, transcripts, presentations, management commentary, and standardized actual financials.
-4. Keep non-Quartr values separately labeled.
-
-## Workbook Rules
-
-- Never overwrite analyst-created formulas, assumptions, notes, or formatting without asking.
-- Put imported source data on a clearly named raw-data tab such as `Quartr_Data` unless the user requests another layout.
-- Put analyst assumptions on an `Inputs` or `Assumptions` tab.
-- Keep formulas on model or output tabs, not in the raw-data area.
-- Add a `Sources` tab or source section with item, value, units, period or as-of date, source URL, source name, reference, notes, and access date where useful.
-- Preserve `referenceUrl` for standardized values and document URLs for filings, slides, transcripts, and reports.
-- Preserve the workbook's existing formatting unless the user asks for a redesign.
-- After edits, summarize changed tabs and ranges.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/INTERNAL.md
deleted file mode 100644
index 62592c0..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/INTERNAL.md
+++ /dev/null
@@ -1,102 +0,0 @@
----
-name: sector-context-overlay
-description: Use when public-equity-investing work needs sector-specific KPIs, modeling rules, valuation conventions, or red-flag checks. Do not use as the primary owner for earnings, models, memos, pitches, equity events, or risk artifacts.
----
-
-# Sector Context Overlay
-
-> Internal support playbook. Load through `internal-support/policy.md`; this context overlay is bundled with the visible router rather than exposed as a skill entrypoint.
-
-## Deliverable Intake
-
-This overlay does not own a hero deliverable. Inherit the owning workflow's resolved deliverable preferences and do not call `request_user_input` independently.
-
-Use this as a Public Equity Investing context layer, not as a standalone artifact engine. It preserves sector-specific judgment while keeping the primary deliverable owned by the relevant local skill. It must never fail just because a prompt is sparse, cross-sector, or attached to a stale legacy sector label.
-
-## Use When
-
-Use when a public issuer, security, sector screen, model, earnings note, investment memo, long/short pitch, catalyst review, credit analysis, or risk output needs one of the supported sector lenses.
-
-Supported sector lenses:
-
-- `banks`
-- `biotech-pharma`
-- `consumer-internet-marketplaces`
-- `exchanges-market-infrastructure`
-- `insurance`
-- `oil-gas-ep`
-- `reits`
-- `saas-subscription-software`
-
-## Do Not Use For
-
-Do not use as the primary owner for DCFs, three-statement models, comps, earnings previews, earnings deep dives, memos, long/short pitches, event math, credit recovery, catalyst calendars, hedge work, or deck/report QC. Route those artifacts to the owning skill and load this skill only for sector context.
-
-Do not force a sector lens when the issuer belongs to an unsupported sector. Use the core Public Equity Investing skill and state that no local sector overlay is installed.
-
-When sector labels conflict with business-model evidence, business-model economics win. Example: Snowflake, Rapid7, Cloudflare, and cybersecurity/data-platform prompts use `saas-subscription-software`, not a stale legacy REIT/biotech label.
-
-## Workflow
-
-1. Identify the primary artifact owner first: `earnings-preview`, `earnings-deep-dive`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `equity-model-update`, `company-tearsheet`, `initiating-coverage`, `memo-builder`, `long-short-pitch`, `event-driven-analyzer`, Credit Markets, `portfolio-risk-management`, or `deck-report-qc`.
-2. Identify the applicable sector lens using `references/sector-index.md`.
-3. If the issuer/sector is ambiguous, cross-sector, or legacy-labeled, run `scripts/resolve_sector_lens.py --prompt "<user request>"` and follow its primary lens, confidence, secondary lenses, and reference-file list.
-4. Load only the relevant sector folder under `references/<sector>/`.
-5. Load `archetypes.md` first when classification, peer set, or business-model diagnosis matters.
-6. Load `kpi-cheat-sheet.md` for sector metric definitions, KPI tables, or measurement framework.
-7. Load `modeling-rules.md`, `valuation-rules.md`, or `model-architecture.md` only for valuation, forecast, model update, sensitivity, security underwriting, or downside work.
-8. Load `source-hierarchy.md` when sources conflict, provenance is thin, or diligence/source questions matter.
-9. Load `references/pm-judgment-heuristics.md` and `references/research-output-overlay-contract.md` for substantial outputs.
-9. Load `red-flags.md` before finalizing recommendations, trade conclusions, downside cases, or circulation-ready outputs.
-10. Load `output-overlays.md` when adapting sector judgment into the owning skill's deliverable.
-
-## Cross-Sector Guardrail
-
-- If one supported sector clearly drives economics, use it as primary and mention secondary lenses only where they affect KPIs or risk.
-- If two supported sectors both matter, choose the lens that drives valuation and forecast mechanics; add a short `Secondary Sector Checks` section.
-- If no supported sector fits, do not improvise a fake overlay. Use the owning skill and state `No local sector overlay installed for this issuer`.
-- Every routed overlay should still produce usable sector KPI guidance: selected lens, issuer archetype, first questions, KPI set, modeling/valuation conventions, red flags, and missing sources.
-
-## Handoff Contract
-
-Keep the primary deliverable with the owning skill:
-
-- Research and earnings: `company-tearsheet`, `earnings-preview`, `earnings-deep-dive`, `initiating-coverage`, `meeting-prep`.
-- Models and valuation: `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `equity-model-update`, `scenario-sensitivity-generator`.
-- Written investment artifacts: `memo-builder`, `long-short-pitch`, `idea-generation`, `thesis-tracker`.
-- Event and risk: `catalyst-calendar`, `event-driven-analyzer`, `portfolio-risk-management`. Use Credit Markets for credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-- Source, data, and circulation: `financial-source-of-truth`, `financials-normalizer`, `excel-data-cleaner`, `model-audit-tieout`, `deck-report-qc`, `style-guide-adapter`.
-
-## Output Contract
-
-Any output using this overlay should state:
-
-- The primary owning skill.
-- The selected sector lens and issuer archetype.
-- The sector KPIs or modeling conventions that changed the analysis.
-- The sector-specific valuation, forecast, downside, or source adjustments applied.
-- The main red flags and diligence/source gaps.
-- The sector reference files loaded, when useful for auditability.
-- The mandate lens, PM debate, KPI hierarchy, valuation implications, benchmark/positioning relevance, disconfirmers, dashboard modules to add, and source gaps.
-- Any secondary lenses considered and why they were not primary.
-
-## Legacy Skill Name Mapping
-
-If a user explicitly asks for a retired sector skill name, route it here:
-
-- `bank-sector-analysis` -> `sector-context-overlay` with `banks`.
-- `biotech-pharma-sector-analysis` -> `sector-context-overlay` with `biotech-pharma`.
-- `consumer-internet-marketplaces-sector-analysis` -> `sector-context-overlay` with `consumer-internet-marketplaces`.
-- `exchanges-market-infrastructure-sector-analysis` -> `sector-context-overlay` with `exchanges-market-infrastructure`.
-- `insurance-sector-analysis` -> `sector-context-overlay` with `insurance`.
-- `oil-gas-ep-sector-analysis` -> `sector-context-overlay` with `oil-gas-ep`.
-- `reits-sector-analysis` -> `sector-context-overlay` with `reits`.
-- `saas-subscription-software-sector-analysis` -> `sector-context-overlay` with `saas-subscription-software`.
-
-## Public Equity PM Judgment Layer
-
-For substantial sector overlays, load `shared/pm-judgment-heuristics.md` and `references/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-This skill should inject sector judgment into the owning skill, not produce a standalone sector explainer.
-
-Required overlay fields: `sector_overlay.selected_lens`, `issuer_archetype`, `mandate_lens`, `pm_debate`, `kpi_set`, `valuation_implications`, `dashboard_modules_to_add`, `source_gaps`, `red_flags`, and `secondary_lenses`.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/archetypes.md
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--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/archetypes.md
+++ /dev/null
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-# Archetypes And Classification
-
-Deferred reference for `Bank Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the bank
-
-Classify the company before analyzing it. Do not compare banks mechanically across different business models.
-
-## Archetypes
-
-### Money-center / universal bank
-- Core engines: broad deposit franchise, commercial banking, cards, markets, wealth, treasury services.
-- Primary watch items: core NII versus market-sensitive revenues, capital return capacity, stress capital buffer, investment-banking and trading normalization, wholesale funding access.
-- Common trap: mixing stable spread earnings with volatile capital-markets revenue and calling the result a clean core trend.
-
-### Regional commercial / retail bank
-- Core engines: spread income, commercial and consumer lending, branch-based deposits, treasury management, some wealth.
-- Primary watch items: deposit mix and pricing, CRE concentration, securities marks, office exposure, core expense flexibility, M&A appetite.
-- Common trap: treating deposit growth or NIM expansion as durable without testing beta, remix, and attrition.
-
-### Community bank
-- Core engines: local commercial lending, residential mortgage, small-business relationships, low-cost local deposits.
-- Primary watch items: concentration risk, local economic exposure, succession/management quality, core deposit durability, expense burden.
-- Common trap: over-trusting headline capital ratios without testing liquidity, concentration, and franchise depth.
-
-### Custody / trust / transaction bank
-- Core engines: servicing, custody, payments, treasury services, sweep balances, some spread income on operational deposits.
-- Primary watch items: fee durability, operating deposit stickiness, rate sensitivity of noninterest-bearing balances, operational risk, regulatory/compliance spend.
-- Common trap: applying standard regional-bank loan-credit frameworks to a fee-heavy franchise.
-
-### Card / consumer lender with bank funding
-- Core engines: card yields, revolving balances, interchange, late fees, retail deposits, unsecured consumer lending.
-- Primary watch items: net charge-offs, delinquencies, payment rates, marketing intensity, customer acquisition quality, reserve sufficiency, funding costs.
-- Common trap: misreading a late-cycle yield tailwind as true earnings quality while credit is quietly deteriorating.
-
-### CRE-heavy / construction / specialty commercial lender
-- Core engines: relationship lending, construction, multifamily, office, sponsor finance, niche vertical lending.
-- Primary watch items: concentration limits, criticized/classified migration, refinancing walls, covenant quality, collateral marks, deposit concentration.
-- Common trap: relying on benign current nonperformers while ignoring maturity walls and borrower equity erosion.
-
-### Mortgage / servicing-heavy bank
-- Core engines: mortgage originations, servicing income, spread banking, warehouse or correspondent channels.
-- Primary watch items: gain-on-sale margins, servicing asset valuation, hedge effectiveness, prepayment assumptions, warehouse funding, rate sensitivity.
-- Common trap: missing the difference between cyclical origination economics and more stable servicing income.
-
-### Online / direct bank / brokered-deposit-heavy lender
-- Core engines: high-yield deposits, online acquisition, concentrated lending niches, securities carry.
-- Primary watch items: deposit price competition, uninsured and non-relationship funding, customer acquisition efficiency, liquidity contingency, mark-to-market risk.
-- Common trap: mistaking fast growth for franchise quality when the liability side is rented rather than owned.
-
-### Non-U.S. / IFRS bank
-- Core engines: vary widely.
-- Primary watch items: local capital and liquidity regime, structural hedge, IFRS 9 staging, sovereign exposure, MREL/TLAC, currency mismatches.
-- Common trap: translating local metrics into U.S. language too loosely and missing accounting or regulatory differences.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/kpi-cheat-sheet.md
deleted file mode 100644
index f165d2f..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Bank Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Bank KPI Map
-
-## 1) Franchise and funding quality
-### Metrics to track
-- deposit growth by type
-- noninterest-bearing deposit mix
-- loan-to-deposit ratio
-- cumulative and incremental deposit beta
-- cost of deposits and cost of interest-bearing liabilities
-- uninsured deposit share
-- wholesale funding share
-- average versus period-end deposits
-
-## 2) Asset mix, repricing, and net interest income
-### Metrics to track
-- NII and NIM
-- ex-markets or adjusted NII where relevant
-- asset sensitivity / liability sensitivity tables
-- earning-asset mix
-- deposit beta and remix
-- securities yield versus duration
-- loan yield, deposit cost, funding cost, and spread by major segment
-
-## 3) Credit quality and reserve adequacy
-### Metrics to track
-- nonperforming assets / loans
-- nonaccruals
-- criticized / classified assets if disclosed
-- 30+/60+/90+ delinquencies
-- net charge-offs and annualized NCO rate
-- provision expense
-- ACL / loans and ACL / NCOs
-- coverage of nonaccruals and NPLs
-- modified / troubled loans, deferrals, or other watchlist indicators
-
-## 4) Securities book, AOCI, and hidden balance-sheet pressure
-### Metrics to track
-- AFS and HTM securities balances
-- unrealized gains/losses by book
-- AOCI impact on tangible book and regulatory capital
-- duration, yield, and repricing profile where disclosed
-- cash-flow hedge marks and hedge effectiveness where relevant
-- tangible common equity and capital ratios with and without AOCI
-- securities losses required to reposition the balance sheet
-
-## 5) Capital as both constraint and catalyst
-### Metrics to track
-- CET1 ratio and CET1 dollars
-- standardized and advanced CET1 where relevant
-- leverage ratio / SLR
-- total capital ratio
-- stress capital buffer or local equivalent
-- RWA growth and RWA density
-- TCE, tangible book value per share, and ROTCE
-- payout ratio, dividend capacity, and buyback capacity
-
-## 6) Liquidity, contingency funding, and confidence risk
-### Metrics to track
-- HQLA / liquid assets if disclosed
-- LCR and NSFR where relevant
-- cash and reserve balances
-- available borrowing capacity
-- encumbered versus unencumbered assets
-- uninsured and concentrated deposits
-- liquidity coverage of uninsured deposits or short-term liabilities
-
-## 7) Fees, expenses, and operating leverage
-### Metrics to track
-- noninterest income by recurring versus market-sensitive fee pool
-- wealth, custody, card, payments, mortgage, IB, markets, and servicing fee drivers where relevant
-- efficiency ratio and adjusted expense ratio
-- compensation, technology, branch, regulatory remediation, FDIC/special assessments, litigation, and restructuring costs
-- operating leverage excluding volatile fee income and one-time items
-
-## 8) Public-equity security context and Credit Markets handoff
-### Metrics to track
-- normalized EPS, ROTCE, tangible book value per share, and P/TBV for equity
-- preferred/AT1/T2, holdco/opco debt, rating triggers, maturities, and recovery path only as Credit Markets handoff fields
-- instrument-level yield/spread, price, call economics, and downside recovery assumptions route to Credit Markets; retain only common-equity confidence, capital, and funding read-through
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` bank sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for banks
-
-Build models around average balances, rates, credit costs, and capital. Do not force industrial-company templates onto a bank.
-
-## Required model structure
-Forecast at minimum:
-- average earning assets by major portfolio
-- asset yields by portfolio
-- average deposits by type and deposit costs by type
-- wholesale funding balances and costs
-- fee revenue by major line
-- operating expenses
-- provision for credit losses
-- taxes
-- RWAs, CET1 dollars, CET1 ratio, leverage ratio
-- dividends, buybacks, and share count where relevant
-- tangible book value per share and ROTCE
-
-## Mandatory decomposition
-- Separate volume from rate for both assets and liabilities.
-- Model provision from a credit view, not as a plug to hit EPS.
-- Keep average-balance and period-end-balance concepts distinct.
-- Carry securities marks, OCI, and capital consequences where relevant.
-- Reflect acquisition accretion, hedges, and structural balance-sheet changes explicitly.
-
-## Mandatory sensitivities
-Run explicit sensitivities on:
-- rate path and curve shape
-- deposit beta and remix
-- loan growth versus deposit growth
-- credit costs / NCOs / cost of risk
-- office CRE stress or other concentrated portfolio stress
-- RWA inflation
-- securities marks / liquidity stress where relevant
-- payout and buyback assumptions
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/modeling-rules.md
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-# Bank Modeling Rules
-
-Deferred reference for `Bank Sector Analysis`. Load this only for bank valuation, forecast, model-update, sensitivity, public-equity security context, or downside work.
-
-## Core Lens
-
-Analyze a bank as a leveraged portfolio of financial assets funded by confidence-sensitive liabilities inside a regulatory wrapper. Do not start with EBITDA, EV, or industrial DCF templates. Start with funding, asset repricing, credit, capital, liquidity, and the exact security's claim in the stack.
-
-## Required Sequence
-
-1. **Franchise and funding quality.** Split deposits by type, cost, relationship quality, uninsured share, concentration, seasonality, and marginal funding source. Purchased liquidity is not the same as franchise growth.
-2. **Asset mix and NII.** Build NII from average balances, asset yields, deposit/funding costs, fixed/floating mix, floors, repricing lag, hedge roll-off, purchase accounting accretion, and securities reinvestment.
-3. **Credit quality and reserves.** Underwrite by portfolio, vintage, collateral, geography, borrower type, criticized/classified migration, delinquencies, NCOs, ACL/ECL coverage, and reserve assumptions.
-4. **Securities, AOCI, and duration.** Reconcile AFS/HTM marks, duration, runoff, hedges, pledgability, encumbrance, TCE/TBV impact, and whether liquidity stress could force uneconomic sales.
-5. **Capital constraints.** Identify the binding regime: CET1, leverage, SLR, SCB, G-SIB surcharge, TLAC/MREL, local ring-fence, or rating pressure. Bridge capital through earnings, OCI, provisions, RWAs, dividends, buybacks, and M&A.
-6. **Liquidity and confidence risk.** Map cash, reserves, unencumbered securities, central bank/FHLB capacity, secured funding, debt maturities, deposit runoff, collateral calls, and contingency funding credibility.
-7. **Fees and expenses.** Separate recurring fees from market-dependent income; adjust efficiency ratios for business mix; identify regulatory remediation, technology, branch, litigation, FDIC, and underinvestment risks.
-8. **Public-equity security context and Credit Markets handoff.** For equity, specify the issuer entity and how funding, capital, and resolution risk affect common-equity value. Preferred/AT1/T2, holdco debt, and opco debt require Credit Markets for instrument-level economics, triggers, subordination, resolution path, or recovery.
-
-## Model Guardrails
-
-- Use bank-native valuation: P/TBV, normalized EPS, ROTCE versus COE, residual income, and DDM where appropriate.
-- Use residual income or DDM before forcing FCFF/EV methods.
-- For equity, tie value to tangible book compounding, normalized ROTCE, through-cycle credit costs, capital return, and deposit franchise durability.
-- Route preferreds, AT1, T2, hybrids, and debt-security analysis to Credit Markets; retain only common-equity read-through such as capital access, dilution, funding cost, and confidence risk.
-- In downside, test deposit beta/outflow, wholesale funding substitution, NII compression, credit normalization, CRE stress, securities marks, capital buffers, liquidity coverage, and regulatory capital return restrictions.
-
-## Common Misreads
-
-- Treating lagged deposit repricing benefit as durable NII.
-- Celebrating reserve releases without checking forward credit risk.
-- Ignoring HTM marks because they are outside reported earnings.
-- Using one capital ratio as proof of excess capital.
-- Treating reported liquidity as usable without haircutting encumbrance and monetization friction.
-- Applying generic peer multiples without normalizing ROTCE, credit risk, funding quality, capital, and business mix.
-
-## Deeper References
-
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: driver decomposition, model tabs, and sensitivities.
-- `red-flags.md`: rejection tests and failure modes.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Bank Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-Focus on what the market is mispricing about normalized ROTCE, capital return, funding durability, or credit costs. Force a bull/base/bear on NII, credit, and capital.
-
-## Event-driven
-Prioritize catalysts: M&A, divestitures, consent orders, stress-test outcomes, capital-return changes, reserve releases/builds, management changes, asset sales, recapitalizations, regulatory settlements.
-
-## Macro
-Emphasize rate sensitivity, deposit migration, curve shape, unemployment, property-market stress, sovereign or FX transmission, and policy/regulatory regime changes.
-
-## Quant / systematic
-Standardize the variables that revise fastest and matter most: NII guidance changes, deposit cost progression, beta, reserve trajectory, NCO trend, capital buffer, tangible book growth, efficiency ratio trend, and securities-mark pressure.
-
-## Credit Markets handoff / equity-risk debt context
-Focus on legal entity, ranking, collateral, encumbrance, resolution dynamics, deposit preference, refinance risk, and downside recovery under regulatory intervention.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-Translate “growth” into capital-efficient profitable balance-sheet expansion, durable low-cost funding, fee-pool share gains, branchless acquisition efficiency, cross-sell density, and technology-enabled operating leverage without weakening controls.
-## Output overlays when this skill is used with common finance tasks
-
-## With an earnings deep dive
-Always add:
-- NII/NIM bridge
-- deposit and funding table
-- credit scorecard
-- capital and payout bridge
-- liquidity / securities / AOCI snapshot
-- next-quarter watch items and what changed in guidance
-
-## With an investment memo
-Always include:
-- bank archetype and why the market is likely framing it wrong or right
-- core thesis by funding, NII, credit, capital, and catalyst
-- variant perception
-- disconfirming evidence
-- security-specific recommendation with stack position and risk/reward
-
-## With a financial model
-Always produce:
-- average-balance driver tree
-- rate and beta assumptions by portfolio
-- credit-cost framework
-- RWA and capital schedule
-- TBVPS and ROTCE output
-- explicit rate, credit, and capital sensitivities
-
-## When debt or structured credit work is requested, route to Credit Markets
-Always include:
-- legal entity and structural subordination map
-- recovery and encumbrance assessment
-- liquidity runway versus near-term uses
-- regulatory intervention path
-- downside case tied to capital and confidence stress
-
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-
-## Default writing style
-
-Write like a veteran bank analyst:
-- lead with the actual driver of value or risk
-- separate facts, interpretation, and variant view
-- quantify balance-sheet and capital impacts whenever possible
-- keep the tone skeptical, precise, and aware of accounting optics
-- prefer “what matters, why it matters, what would change the view” over generic management-summary prose
-
-When unsure, prioritize franchise durability, funding quality, and capital resilience over short-term EPS optics.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `Bank Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-Treat these as high-priority warning signals:
-- loan growth meaningfully outpacing core deposit growth
-- margin upside driven by risky asset mix, temporary accretion, or lagged liability repricing that is about to reverse
-- heavy reliance on brokered, promotional, or concentrated uninsured funding
-- reserve releases despite worsening leading indicators
-- large office or other concentrated exposures with stale appraisals and limited disclosure
-- capital ratio comfort that disappears once OCI, RWA inflation, or stress losses are incorporated
-- buybacks, M&A, or dividend promises made from a thin true capital cushion
-- management guidance that highlights NII but avoids deposit migration, hedges, or funding mix
-- “strong liquidity” language unsupported by monetizable asset quality
-- holdco debt or preferreds treated casually without entity-level analysis
-- efficiency improvements coming from under-spending on controls, remediation, or franchise investment
-- fast balance-sheet growth at an online or niche lender without evidence of durable funding
-## Never do this
-
-- Do not lead with EBITDA, EV/EBITDA, or generic FCF language.
-- Do not compare banks on P/TBV without comparing normalized ROTCE and tail risk.
-- Do not celebrate NIM expansion without decomposing yield, beta, remix, hedges, and credit quality.
-- Do not treat reserve releases as earnings quality by default.
-- Do not discuss capital without asking what the binding constraint is.
-- Do not write on bank debt or preferreds without naming the issuer, entity, and ranking.
-- Do not compare efficiency ratios across very different bank archetypes without adjustment.
-- Do not assume reported regulatory capital equals economic capital strength.
-- Do not let period-end funding or capital snapshots substitute for average-balance earnings analysis.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Bank Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order unless the task requires otherwise:
-1. regulatory filings and statutory disclosures
-2. annual and quarterly reports, earnings supplements, and investor-day materials
-3. earnings transcript and management Q&A
-4. regulator actions, consent orders, capital disclosures, Pillar 3, call reports, FR Y-9C, UBPR or local equivalents
-5. rating-agency and sell-side framing
-6. virtual data room materials: loan tapes, deposit studies, hedge books, ALCO materials, liquidity decks, top depositor lists, classified-loan migration, regulatory correspondence
-
-## Reconciliation rules
-- Reconcile company-adjusted metrics back to reported and regulatory numbers.
-- Prefer average-balance disclosures to reverse-engineer NII.
-- Prefer period-end disclosures for capital and liquidity snapshots.
-- If management emphasizes one ratio, find the ratio it is trying to distract from.
-## Bank-specific diligence requests
-
-When the task involves diligence, request or prioritize the following if available:
-- deposit tape by customer type, size bucket, insured status, geography, and rate paid
-- top depositor list and concentration analysis
-- loan tape with vintage, risk rating, geography, collateral type, maturity, coupon, and underwriting metrics
-- criticized / classified migration by quarter
-- office and CRE detail including refinance schedule and updated collateral values
-- securities book by AFS/HTM, duration, unrealized marks, and pledgability
-- ALM / rate-sensitivity deck and hedge inventory
-- liquidity contingency plan and stress assumptions
-- capital policy, buffer targets, and payout framework
-- regulator correspondence, enforcement actions, audit findings, and remediation status
-- legal-entity chart and debt maturity schedule
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/banks/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` bank sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use bank-specific valuation, not generic corporate valuation
-
-## Equity valuation hierarchy
-Use these lenses first:
-1. price / tangible book value
-2. price / earnings on normalized EPS
-3. ROTCE relative to cost of equity
-4. excess return / residual income
-5. dividend discount where the franchise is mature and payout is meaningful
-
-### Valuation rules
-- A higher P/TBV is justified by sustainable excess ROTCE, funding quality, fee durability, capital optionality, and lower tail risk.
-- Two banks with similar current EPS can deserve very different multiples if one is over-earning on rates or under-reserving on credit.
-- Tangible book matters most when capital is real and not flattered by accounting or hidden liquidity stress.
-- Use sum-of-the-parts only when business lines truly deserve different capital intensity and multiples.
-
-## Debt valuation hierarchy
-- spread versus expected loss and recovery
-- distance to regulatory minima and intervention triggers
-- refinancing path and call incentives
-- entity-level structural protection or subordination
-- liquidity runway and access to secured or unsecured markets
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the biotech or pharma company before analyzing it
-
-Classify the company first. Do not compare everything in the sector on one multiple or one catalyst framework.
-
-## Archetypes
-
-### Discovery or preclinical platform biotech
-- Core engines: target discovery, platform validation, partner interest, preclinical differentiation, option value, and financing access.
-- Primary watch items: biological plausibility, translation risk, breadth versus depth, quality of early datasets, partner economics, and burn versus time to human proof of concept.
-- Common trap: paying for a platform multiple before any human evidence exists or counting optionality multiple times across similar assets.
-
-### Single-asset or tightly concentrated clinical-stage biotech
-- Core engines: one or two readouts, one regulatory path, and one financing clock.
-- Primary watch items: protocol quality, endpoint appropriateness, control-arm relevance, data maturity, label breadth, and cash runway through the next catalyst.
-- Common trap: treating management confidence or early uncontrolled data as if the asset were already validated.
-
-### Multi-asset clinical-stage biotech
-- Core engines: diversified catalyst set, platform validation across assets, capital allocation, and partnership flexibility.
-- Primary watch items: whether the assets are truly independent, whether the organization can execute multiple studies, and whether platform value is being supported by human evidence.
-- Common trap: describing the company as diversified when multiple programs are exposed to the same scientific bet or manufacturing bottleneck.
-
-### Cell therapy, gene therapy, or other advanced-modality developer
-- Core engines: transformative efficacy, durability, manufacturing execution, treatment-center activation, and reimbursement viability.
-- Primary watch items: safety, durability of response, comparability, vein-to-vein time or treatment logistics, vector or starting-material supply, and COGS.
-- Common trap: modeling clinical efficacy upside without pricing in CMC, capacity, site-of-care, and payer-friction risk.
-
-### Commercial rare-disease or specialty biotech
-- Core engines: patient finding, diagnosis, treatment initiation, persistence, label expansion, and premium pricing defended by meaningful benefit.
-- Primary watch items: true prevalence, testing funnel, new prescriber growth, refill or compliance trends, gross-to-net, and competitive encroachment.
-- Common trap: annualizing early launch demand before testing how much came from pent-up patients, channel loading, or easy-to-find patients.
-
-### Specialty pharma or in-licensed branded-asset portfolio company
-- Core engines: business development, lifecycle management, price-volume mix, channel execution, and SG&A leverage.
-- Primary watch items: gross-to-net, payer rebates, copay support, inventory, tender timing, formulation or line-extension economics, and debt or royalty burdens.
-- Common trap: mistaking price-led growth or acquisition-led growth for durable organic franchise strength.
-
-### Large-cap diversified branded pharma
-- Core engines: mature-franchise defense, pipeline replacement, global market access, manufacturing reliability, M&A, and capital allocation.
-- Primary watch items: patent cliff timing, new-product ramp, lifecycle management, business-development economics, litigation, tax, and FCF conversion.
-- Common trap: valuing near-term earnings too highly while ignoring the post-LOE earnings hole.
-
-### Generic or biosimilar-exposed pharma company
-- Core engines: cost position, manufacturing reliability, tender wins, launch cadence, litigation, and working-capital discipline.
-- Primary watch items: price erosion, customer concentration, regulatory compliance, plant quality, and mix shift into more defendable products.
-- Common trap: using innovative-biopharma valuation logic on a business governed mostly by procurement and cost.
-
-### Royalty, milestone, or structured-life-sciences finance vehicle
-- Core engines: diversified contractual cash flows, low operating cost, partner credit quality, and duration of royalty streams.
-- Primary watch items: concentration, asset expiry, step-down schedules, underlying product durability, and legal seniority of claims.
-- Common trap: treating a high-yielding royalty stream as if it has no patent-cliff or label-risk exposure.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Biotech and pharma cheat sheet
-
-Use these as anchors, not as substitutes for reading the actual disclosure. Definitions, endpoint construction, and revenue treatment vary materially by issuer.
-
-- IND: investigational new drug application that permits clinical testing to begin.
-- NDA / BLA / sNDA / sBLA: the filing form matters because small molecules, biologics, and line extensions face different comparability, exclusivity, and competition frameworks.
-- Phase 1 / 2 / 3: useful labels, but the real question is what has actually been learned, how controlled the evidence is, and whether the next study is registrational.
-- Primary endpoint: the pre-specified outcome the study is designed to win on. This matters more than the best slide in a deck.
-- Secondary endpoints: helpful only after confirming multiplicity handling and whether they are supportive or exploratory.
-- ORR / CR / PR / DOR: response metrics often matter in oncology and some rare diseases, but they are not interchangeable with survival or long-term functional benefit.
-- PFS / OS / event-free survival / relapse-free survival: time-to-event endpoints that demand close attention to censoring, follow-up duration, control-arm strength, and hazard-ratio interpretation.
-- Surrogate endpoint: a marker used instead of direct clinical benefit. Valuable when validated; dangerous when weakly linked to real outcomes.
-- p-value: tells you about statistical compatibility with the null under the specified test, not clinical importance, durability, or commercial value.
-- Hazard ratio: directionally useful, but only meaningful with context on absolute benefit, medians, tails, and follow-up maturity.
-- Biomarker-enriched population: may improve hit rate and label value, but also changes the commercial denominator and testing burden.
-- PDUFA date: a review target date, not a guaranteed approval date.
-- AdCom: an advisory committee meeting that can shape perception and labeling expectations even when the vote is nonbinding.
-- CRL: complete response letter. A CRL is not one thing; it can signal clinical, statistical, manufacturing, facility-inspection, comparability, labeling, or post-marketing issues with very different repair times.
-- Label: the actual economic asset. Approval without a commercially useful label can destroy the modeled value.
-- REMS / black box / post-marketing requirement: constraints that can narrow adoption, raise cost, or preserve uncertainty.
-- Gross-to-net: the difference between gross sales and recognized net product revenue after rebates, chargebacks, distribution fees, returns, and other deductions.
-- WAC / list price / net price: never treat list price as realized economics.
-- Peak sales: not a fact. It is an assumption that must be built from eligible patients, diagnosis rate, treatment rate, share, persistence, geography, and price net of gross-to-net.
-- LOE: loss of exclusivity. This is often the most important date in mature pharma.
-- Orange Book / Purple Book: tools for mapping patent and exclusivity timing, but not substitutes for reading the full IP and competition picture.
-- Royalty interest / synthetic royalty / revenue interest financing: can look equity-like in good times and debt-like in bad times.
-- Cash runway: the number of quarters until the company likely needs financing under realistic spend assumptions, not management's most optimistic presentation.
-
-## Clinical, Launch, And Financing Metrics
-
-## 1) Disease, mechanism, modality, and true unit of value
-### Metrics to track
-- prevalence, incidence, diagnosed population, and treated population
-- biomarker-testing rate and positivity rate where relevant
-- patient starts, new prescriptions, total prescriptions, doses, or infusions
-- persistence, refill rates, discontinuations, and compliance where relevant
-- share within indicated line of therapy
-- partner sales and royalty-bearing sales where applicable
-
-## 2) Evidence quality: trial design, endpoint discipline, and data interpretation
-### Metrics to track
-- enrollment size, screen-failure rate, dropout rate, and exposure duration
-- primary and key secondary endpoint results
-- p-values, confidence intervals, hazard ratios, and absolute benefit
-- response rate, complete response rate, duration of response, progression-free survival, overall survival, or other relevant endpoint metrics
-- discontinuations, dose modifications, serious adverse events, and treatment-related deaths
-- durability metrics and follow-up time
-
-## 3) Regulatory path, filing risk, and label economics
-### Metrics to track
-- data readout date, filing date, filing acceptance date, AdCom date, and PDUFA date
-- expedited-program status and what it really changes
-- likely label breadth and line-of-therapy scope
-- post-marketing commitment or confirmatory-study burden
-- facility inspection status or manufacturing readiness indicators where disclosed
-- approval timing by geography
-
-## 4) Commercial opportunity, patient flow, and market-access reality
-### Metrics to track
-- treated-patient growth and patient-start trends
-- script trends, vials or units, refill rates, and persistence
-- list price, net price, and gross-to-net percentage
-- payer coverage, prior-authorization rate, and time to reimbursement where disclosed
-- specialty-pharmacy or channel inventory days where relevant
-- market share by line of therapy or segment
-
-## 5) Manufacturing, supply chain, and CMC execution
-### Metrics to track
-- batch yield, capacity, throughput, and lot-failure indicators where available
-- inventory on hand and inventory turns for commercial products
-- COGS as a percentage of net sales
-- site-activation count for advanced therapies where relevant
-- time from order to treatment or release where relevant
-- history of shortages, recalls, or inspection-related disclosures
-
-## 6) IP, regulatory exclusivity, lifecycle management, and LOE
-### Metrics to track
-- key patent-expiry dates and exclusivity-expiry dates
-- first expected generic or biosimilar entry date
-- revenue exposed to LOE in the next 1, 3, and 5 years
-- share of sales from new launches versus mature products
-- royalty step-down dates and rates
-- contribution from lifecycle extensions and follow-on assets
-
-## 7) Financial quality: cash burn, revenue quality, and P&L interpretation
-### Metrics to track
-- ending cash, short-term investments, and net cash
-- quarterly operating cash burn and normalized burn
-- product revenue growth, royalty growth, and collaboration or milestone mix
-- gross-to-net percentage and gross margin
-- R&D, SG&A, and launch-investment trajectory
-- cash runway in quarters under base and downside spend assumptions
-- diluted shares outstanding and in-the-money converts or warrants
-
-## 8) Capital structure, financing path, and public-equity security context and Credit Markets handoff
-### Metrics to track
-- cash plus investments versus debt and royalty obligations
-- net leverage for profitable issuers
-- quarters of runway to next major catalyst and to required financing
-- convert conversion prices, maturities, and potential dilution
-- royalty burden as a share of product or partner revenue
-- minimum liquidity and covenant headroom where relevant
-- debt maturity ladder and interest coverage for mature issuers
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` biotech-pharma sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for biotech and pharma
-
-## Required model structure
-- Asset-level schedule listing modality, indication, stage, key catalyst dates, filing path, probability of success, launch timing, geography, exclusivity window, and patent-cliff timing.
-- Commercial forecast by patient flow: eligible patients, diagnosis, testing, treatment rate, share, persistence, units or doses, list price, gross-to-net, and net revenue.
-- Separate schedules for product revenue, collaboration revenue, milestone revenue, royalty revenue, and one-time items.
-- R&D schedule by program and development stage where possible, with explicit registrational-trial and post-marketing spend.
-- SG&A build tied to launch stage, field-force needs, DTC or KOL spend where relevant, and geography.
-- Manufacturing and gross-margin assumptions tied to modality and scale.
-- Cash-flow and financing schedule including ATM usage, convert dilution, debt service, and royalty obligations.
-- For mature pharma, explicit LOE schedule and replacement pipeline bridge.
-
-## Mandatory decomposition
-- Separate science risk from regulatory risk, and regulatory risk from commercial risk.
-- Separate base business from pipeline optionality.
-- Separate gross sales from net sales.
-- Separate real product demand from inventory and channel movement.
-- Separate recurring royalties from milestones and upfronts.
-- Separate current-label value from label-expansion value.
-- Separate patent and exclusivity duration from terminal value.
-
-## Mandatory sensitivities
-- probability of success by asset and stage
-- readout, filing, and launch timing
-- label breadth and line-of-therapy assumptions
-- patient identification, treatment rate, and persistence
-- gross-to-net and realized net price
-- manufacturing yield, capacity, and COGS for complex modalities
-- timing of generic or biosimilar entry and erosion rate
-- burn rate, financing timing, and dilution
-- valuation multiple or discount rate appropriate to maturity and risk
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/modeling-rules.md
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-# Biotech And Pharma Modeling Rules
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load only when the task needs sector modeling detail.
-
-## Core Frame
-
-Underwrite biotech/pharma as an option tree across science, evidence, regulation, label, access, manufacturing, IP, financing, and security claims. Do not start with EBITDA, simple revenue growth, or management words like de-risked, best-in-class, platform, or blockbuster.
-
-Start with the disease and mechanism, then ask whether the data prove a clinically meaningful benefit in the likely label population, whether regulators can accept it, whether patients can reach and stay on therapy, whether manufacturing can scale, how long exclusivity lasts, and whether cash runway reaches the next value-inflecting event.
-
-Treat a beat as low quality when driven by milestones, upfront-license revenue, stocking, channel fill, one-time government orders, PRV or asset-sale gains, tax noise, or deferred trial spend rather than cleaner product demand or improved pipeline probability.
-
-## Analysis Sequence
-
-### 1) Disease, mechanism, and unit of value
-- Define disease state, standard of care, line of therapy, unmet need, and real commercial denominator: diagnosed, biomarker-positive, eligible, referred, treated, or persistent patients.
-- Identify modality and delivery burden: small molecule, biologic, ADC, cell/gene therapy, RNA, radiopharma, vaccine, combo, hospital, office, specialty pharmacy, or site-of-care dependent.
-- Map mechanism to disease biology; target novelty is not evidence of clinical relevance.
-- Build value from patient starts, treated patients, persistence, infusions, doses, scripts, vials, royalties, or partner sales, not TAM adjectives.
-- For diversified pharma, separate mature franchises, launch assets, and pipeline options before combining them.
-
-### 2) Evidence quality and endpoint discipline
-- Read study design before valuation: randomization, blinding, control, inclusion/exclusion, line of therapy, geography, stratification, powering, endpoint hierarchy, and follow-up maturity.
-- Separate biological plausibility, statistical evidence, clinical relevance, regulatory acceptability, and commercial attractiveness.
-- Haircut uncontrolled, small, post-hoc, immature, crossover-heavy, investigator-assessed, or subgroup-sliced datasets unless the design supports the claim.
-- Safety matters economically: grade 3+ AEs, SAEs, discontinuations, dose reductions, treatment deaths, boxed warnings, monitoring burden, and real-world tolerability.
-- Hazard ratios need medians, tails, absolute benefit, confidence intervals, and follow-up context; statistical significance can still be commercially weak.
-
-### 3) Regulatory path and label economics
-- Map NDA/BLA/supplemental, accelerated or traditional approval, orphan/RMAT/Breakthrough/Priority status, filing acceptance, AdCom, inspection, PDUFA, and post-marketing commitments.
-- Treat designations as process aids, not proof of approval.
-- Underwrite label scenarios: indication wording, line of therapy, biomarker restriction, dose, combination language, REMS, monitoring, confirmatory-study burden, and geography.
-- Pressure-test CMC and facility-inspection risk, especially for biologics, sterile products, and advanced modalities.
-- Approval is an input; label breadth, restrictions, reimbursement, and confirmatory obligations determine value.
-
-### 4) Launch, access, and commercial quality
-- Model patient flow: prevalence, diagnosis, testing, referral, eligibility, site readiness, initiation, persistence, retreatment, geography, and share.
-- Use realized net price. Bridge WAC/list price to gross sales, rebates, chargebacks, copay assistance, returns, distribution fees, and net product revenue.
-- Identify payer channel and friction: commercial, Part B/D, Medicaid, buy-and-bill, specialty pharmacy, tender, prior auth, step edits, and out-of-pocket burden.
-- Early launch can be inflated by pent-up demand, easiest-to-find patients, stocking, or inventory; persistence and patient starts are cleaner than list-price-driven growth.
-- For rare disease and advanced therapy, test patient finding, newborn screening, advocacy reach, treatment-center capacity, cold chain, and release timing.
-
-### 5) Manufacturing, supply chain, and CMC
-- Classify manufacturing complexity: conventional, biologic, sterile, personalized, short shelf-life, cold-chain, outsourced, single-source, or quality-system dependent.
-- For cell/gene therapy, track vein-to-vein time, batch failures, release testing, comparability, vector/raw-material supply, throughput, and center logistics.
-- For biologics and radiopharma, test scale-up, yield, fill-finish, distribution, decay, capacity, and process consistency.
-- Commercial supply issues, warning letters, recalls, shortages, and inventory swings can drive both revenue quality and regulatory downside.
-
-### 6) IP, exclusivity, lifecycle, and LOE
-- Map composition, formulation, method-of-use, manufacturing, device, salt/polymorph patents, plus Orange Book/Purple Book and regulatory exclusivity.
-- Build LOE by product, market, and competition type: generic, biosimilar, branded follow-on, or therapeutic substitution.
-- Separate legal moat, regulatory exclusivity, manufacturing barrier, brand/physician habit, and lifecycle innovation.
-- Lifecycle extensions soften LOE only when they change clinical or access value; secondary patents alone deserve caution.
-- For royalty streams, map royalty step-downs, expiry mechanics, tail periods, and revenue exposure to competition.
-
-### 7) Financial quality and runway
-- Split product, collaboration, milestone, royalty, manufacturing, and one-time revenue.
-- For commercial assets, model gross-to-net, gross margin, launch SG&A, R&D, and operating profit by product or franchise.
-- For development-stage biotech, reconcile cash to normalized burn, trial footprint, working-capital noise, upfront receipts, capex, financing, and runway in quarters.
-- Lower R&D can be optical if caused by cancelled programs or delayed trials; deal revenue can mask weak recurring demand.
-
-### 8) Capital structure and security-specific risk
-- Map cash, restricted cash, debt, converts, warrants, royalty financings, synthetic royalties, term loans, minimum-cash covenants, liens, and pledged assets.
-- Test whether runway reaches the next catalyst under realistic spend and whether a miss forces dilution, covenant pressure, royalty monetization, licensing, or asset sale.
-- For debt or royalty claims, route claim-seniority, collateral-quality, and recovery underwriting to Credit Markets; retain downside cash generation, expiry, competition, and label-change effects only as equity dilution/runway risk.
-- For large pharma, include patent cliffs, M&A appetite, shareholder returns, litigation, FX/tax noise, and maturity profile.
-
-## Deferred Modeling Layers
-
-Load deeper files only when needed:
-- `valuation-rules.md`: valuation hierarchy, rNPV/SOTP, and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: model structure, driver decomposition, and sensitivities.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-- Focus on the real asset-level debate: evidence quality, probability-weighted label value, launch trajectory, gross-to-net risk, and the shape of the patent cliff versus replacement pipeline.
-
-## Event-driven
-- Focus on the catalyst tree: readout setup, endpoint win conditions, AdCom optics, PDUFA risk, CRL repair time, M&A optionality, financing overhang, and what the market already expects.
-
-## Macro
-- Focus on drug-pricing policy, reimbursement pressure, biotech financing windows, rates and risk appetite, FDA pace and enforcement tone, M&A appetite, and how those change the sector's cost of capital and multiple structure.
-
-## Quant / systematic
-- Standardize around catalyst calendar, estimate revisions, launch trajectory, gross-to-net trends, burn and dilution, balance-sheet strength, volatility around binary events, and LOE exposure for mature pharma.
-
-## Credit Markets handoff / structured-finance context
-- Focus on runway to value inflection, collateral quality, royalty encumbrance, covenant tightness, minimum liquidity, debt maturity versus catalyst timing, and downside cash generation if adoption or approval slips.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-- Focus on platform repeatability, number of shots on goal, commercial-infrastructure leverage, ability to extend into adjacent indications, and whether reinvestment is compounding value rather than simply extending runway.
-## Output overlays for common tasks
-
-## For a post-earnings deep dive
-- Lead with what changed in the asset-level probability tree, not just with EPS or revenue versus consensus.
-- Separate product demand, gross-to-net, pipeline progress, manufacturing readiness, and financing runway.
-- Call out whether quarter strength came from recurring product economics or from collaboration and milestone noise.
-- End with the real debate: what bulls think got de-risked and what bears think remains fragile.
-
-## For a pre-earnings preview
-- Focus on the next 1 to 4 catalysts: readouts, filings, AdComs, PDUFAs, launch inflections, financing risk, and LOE-sensitive franchise performance.
-- Identify which reported metrics matter most for that name: patient starts, scripts, gross-to-net, inventory, R&D timing, or pure runway.
-- State clearly whether the setup is about numbers, catalyst framing, financing, or business-development optionality.
-
-## For an investment memo
-- Make the core debate about evidence quality, regulatory and label risk, and commercial durability.
-- Use a clear bull, base, and bear case around label, launch, LOE timing, and financing path.
-- Show where value sits today: cash, base business, lead asset, follow-on assets, and optionality.
-- Be explicit about what must happen next for the thesis to work.
-
-## For a financial model
-- Build bottom-up patient-flow and probability-adjusted asset schedules instead of relying on a single revenue CAGR.
-- Link catalysts to spend, financing, and valuation changes.
-- Model gross-to-net, persistence, capacity, and LOE explicitly.
-- Include scenario cases for failed readout, delayed approval, narrow label, weak reimbursement, and faster-than-expected erosion.
-
-## When debt or structured work is requested, route to Credit Markets
-- Prioritize liquidity runway, debt or royalty seniority, covenant headroom, and catalyst timing relative to maturities.
-- Test downside under readout failure, CRL, delayed launch, reimbursement friction, and erosion of the underlying royalty stream.
-- Treat IP value and pipeline optionality cautiously in recovery unless there is a proven commercial product or clearly monetizable royalty asset.
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-## Style rules for using this sector pack
-
-- Write like a veteran buy-side biotech or pharma analyst, not like a generic healthcare summarizer.
-- Always say what stage the asset is in, what the endpoint was, how many patients were studied, and what the comparison standard is.
-- Separate facts, management framing, and your inference.
-- Distinguish biological plausibility, statistical significance, regulatory acceptability, and commercial attractiveness instead of blending them together.
-- Treat time as a value driver: months of delay matter for runway, dilution, and NPV.
-- Distinguish gross sales, net sales, patient starts, persistence, and inventory movement instead of calling everything demand.
-- When the company is diversified, present mature franchises, launch assets, and pipeline options separately before combining them.
-- Treat the label and the access funnel as part of the asset, not as afterthoughts.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-- Management spotlights a favorable subgroup after the primary endpoint missed.
-- The dataset is statistically positive but clinically weak or commercially narrow.
-- Follow-up is short, censoring is heavy, or the control arm underperformed versus current standard of care.
-- Management uses platform language to imply replication before any second asset shows real human proof.
-- A rare-disease launch looks strong because of pent-up demand and easy-to-find patients, but persistence or new-patient flow weakens quickly.
-- Gross-to-net rises while reported net sales still grow, masking worsening pricing power.
-- Collaboration or milestone revenue hides weak product demand or heavy cash burn.
-- Manufacturing readiness lags the clinical narrative, especially for advanced modalities.
-- A product appears approvable, but the likely label, REMS, or monitoring burden sharply lowers real uptake.
-- Patent protection is weaker than the narrative suggests because secondary patents dominate the estate.
-- The company sells future economics through royalty monetization to avoid an equity raise, reducing long-term upside and adding senior claims.
-- Cash runway does not comfortably reach the next real inflection point.
-- The company depends on one partner, one wholesaler channel, one supplier, or one trial readout more than the headline story implies.
-- A large-cap pharma company buys revenue to bridge a patent cliff, but the deal economics or integration burden make replacement value poor.
-- Analysts compare ORR, PFS, or launch growth across names without normalizing for line, label, follow-up, or gross-to-net.
-## Never do this
-
-- Never treat orphan designation, Breakthrough Therapy, or Priority Review as proof that the asset will work or the label will be broad.
-- Never equate statistical significance with clinical relevance or commercial value.
-- Never annualize one quarter of milestone revenue, stocking, or government orders into durable product economics.
-- Never compare response rates or survival curves across trials without checking population and design.
-- Never use list price as if it were realized price.
-- Never assume approval automatically means smooth reimbursement or rapid uptake.
-- Never ignore CMC and inspection risk for biologics, cell therapy, gene therapy, or sterile manufacturing.
-- Never assume patents and regulatory exclusivities are the same thing.
-- Never value a cash-box biotech without explicitly modeling burn, dilution, and time to financing.
-- Never forget that debt, converts, royalties, and liens change downside even when the science is attractive.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Biotech Pharma Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order:
-
-1. 10-K, 10-Q, annual report, earnings release, 8-K top-line data disclosure, and official investor presentation
-2. protocol, statistical analysis plan, and ClinicalTrials.gov record when available
-3. peer-reviewed papers, major medical-meeting abstracts and presentations, and KOL-relevant scientific disclosures
-4. FDA label, approval letter, briefing documents, AdCom materials, inspection or manufacturing disclosures when public, and other official regulator documents
-5. Orange Book, Purple Book, patent-litigation or settlement disclosures, and exclusivity references
-6. management commentary on pricing, reimbursement, channel inventory, patient-finding, and manufacturing
-7. peer disclosures for market-share, pricing, endpoint, and label comparability checks
-
-## Reconciliation rules
-- Protocol and regulator-facing documents outrank slide-deck spin.
-- Pre-specified endpoints outrank post-hoc analyses.
-- Net sales outrank script anecdotes when gross-to-net is moving.
-- Script data, patient-start data, and inventory data must be reconciled before concluding that demand accelerated.
-- Trial comparisons across companies are only valid after aligning population, line of therapy, endpoint definition, follow-up time, control-arm strength, and geography.
-- If management stops disclosing a launch or patient metric, treat the absence as information.
-- If the company mixes product sales with milestones, royalties, and collaboration revenue, analyze each separately before recombining.
-## Biotech- and pharma-specific diligence requests
-
-Request these when deeper underwriting is needed:
-
-- patient-flow bridge from prevalence to treated patients by geography and line of therapy
-- protocol, endpoint hierarchy, powering assumptions, multiplicity plan, stratification, and interim-analysis rules
-- detailed efficacy tables and safety tables including discontinuations and dose modifications
-- subgroup and biomarker analysis with clear separation of pre-specified versus exploratory work
-- manufacturing map including sites, suppliers, tech transfer, release testing, capacity, and backup plans
-- gross-to-net bridge by rebate, chargeback, copay, return, distribution fee, and channel mix
-- wholesaler and specialty-pharmacy inventory levels where relevant
-- payer-coverage status, coding, reimbursement timing, and prior-authorization friction
-- exclusivity and patent timeline with litigation status and likely entrant timing
-- collaboration economics including milestones, royalties, opt-ins, co-funding, and geographic splits
-- runway analysis under base, delayed-catalyst, and failed-readout cases
-- convert terms, royalty-financing terms, lien package, and covenant definitions for credit work
-- launch cohort data: patient starts, persistency, refill behavior, and prescriber breadth
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/biotech-pharma/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` biotech-pharma sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use life-sciences valuation, not generic healthcare shortcuts
-
-## Equity valuation hierarchy
-
-1. Risk-adjusted NPV or probability-weighted sum-of-the-parts for clinical-stage and pipeline-driven companies.
-2. Hybrid sum-of-the-parts for companies with both a commercial base business and meaningful pipeline optionality.
-3. EV / sales or EV / gross profit for early commercial companies where margins are not yet stable but launch quality matters.
-4. DCF, FCF yield, or earnings-based frameworks for mature diversified pharma once LOE and pipeline replacement are modeled explicitly.
-5. Yield and DCF analysis for royalty and milestone-finance vehicles after mapping duration and expiry.
-
-### Valuation rules
-- Value the label and the actual treated-patient funnel, not a generic TAM slide.
-- Use higher probability weights only when data are controlled, clinically meaningful, manufacturable, and regulatorily clean.
-- Peak-sales assumptions must be built from patient flow, access, persistence, share, net price, and geography, not from management aspiration.
-- For diversified pharma, split cash-cow franchises, launch assets, business-development dependence, and patent-cliff exposures rather than applying one multiple to all earnings.
-- Revenue multiples are less useful when gross-to-net is moving, launch demand is channel-loaded, or the company mixes product revenue with collaboration revenue.
-- Royalty streams should be valued on durability and legal priority, not just on current yield.
-
-## Credit valuation hierarchy
-
-1. Liquidity runway and access to financing.
-2. Asset quality and durability of cash-generating products or royalties.
-3. Timing mismatch between debt maturities and major catalysts.
-4. Royalty, lien, and covenant structure.
-5. Recovery realism under a failed readout, CRL, or faster-than-expected erosion event.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the marketplace before analyzing it
-
-Classify the company first. Do not compare all consumer-internet platforms with one template.
-
-## Archetypes
-
-### Horizontal third-party ecommerce marketplace
-- Core engines: buyer frequency, seller breadth, listing quality, conversion, ads attach, trust, and take-rate discipline.
-- Primary watch items: active buyers, active sellers, GMS per buyer, focus-category strength, promoted-listing penetration, and seller economics.
-- Common trap: treating GMV growth as durable when it came mainly from AOV inflation, a temporary category spike, or a seller-fee reset.
-
-### Vertical specialist marketplace
-- Core engines: category authority, high-intent traffic, better curation, trusted supply, and premium monetization in a niche.
-- Primary watch items: category liquidity, repeat rate, authentic supply, seller concentration, and whether niche leadership truly creates pricing power.
-- Common trap: assigning horizontal-marketplace multiples to a niche category whose TAM or liquidity ceiling is lower.
-
-### Managed local-services or labor marketplace
-- Core engines: local density, service quality, worker or provider supply, conversion, scheduling efficiency, and trust.
-- Primary watch items: geographic density, fill rate, cancellation rate, provider utilization, incentives, and customer rebooking.
-- Common trap: valuing it like a pure software or classifieds model when the service layer introduces real operating complexity.
-
-### Mobility, delivery, or local-commerce marketplace
-- Core engines: monthly active consumers, order or trip frequency, cross-category use, membership penetration, dispatch efficiency, and logistics cost.
-- Primary watch items: frequency, marketplace GOV, contribution profit, average delivery time, Dasher or courier supply, merchant density, and ad monetization.
-- Common trap: reading GOV growth as value creation without checking whether incentives, insurance, and logistics intensity are rising just as fast.
-
-### Travel marketplace or accommodations platform
-- Core engines: room nights or nights booked, ADR, supply depth, global demand mix, cancellation behavior, and merchant-versus-agency mix.
-- Primary watch items: room nights, gross bookings or GBV, take rate, cross-border mix, app penetration, and unearned-fee dynamics.
-- Common trap: confusing ADR and FX tailwinds for true transaction growth, or confusing merchant-mix shift for real take-rate improvement.
-
-### Classifieds or listings marketplace
-- Core engines: audience scale, listing density, lead conversion, and monetization of seller or advertiser demand rather than direct transaction take rate.
-- Primary watch items: listing growth, paying dealers or agents, ARPA, lead quality, renewal rates, and off-platform conversion.
-- Common trap: applying GMV frameworks to a business that monetizes listings and leads rather than closed transactions.
-
-### Recommerce or resale marketplace
-- Core engines: inventory intake, authentication or trust, buyer engagement, seller acquisition, and category-specific liquidity.
-- Primary watch items: seller acquisition cost, supply quality, return rates, authentication throughput, and contribution margins after reverse logistics.
-- Common trap: calling the model asset-light when handling, verification, and returns introduce meaningful working-capital and operational friction.
-
-### Hybrid marketplace plus ads, payments, or fintech platform
-- Core engines: core commerce liquidity plus monetization through ads, payments, lending, or merchant services.
-- Primary watch items: unique active buyers, payment penetration, ad load, checkout and fulfillment attach, and whether ancillary products deepen the moat.
-- Common trap: giving the high multiple of a scaled fintech or ad platform to a company whose core marketplace still drives most of the economics.
-
-### Hybrid first-party plus third-party platform
-- Core engines: traffic and ecosystem advantages, marketplace mix shift, ads, and optionality from fulfillment and private-label adjacency.
-- Primary watch items: gross versus net revenue mix, first-party inventory risk, seller-services profit pools, and segment contribution margins.
-- Common trap: valuing the whole company like a pure marketplace when material first-party, logistics, or inventory exposure changes the model.
-
-### Reacceleration, turnaround, or distressed marketplace
-- Core engines: direct traffic durability, existing buyer reactivation, seller quality, cost reset, and stabilization of liquidity.
-- Primary watch items: cohort retention, repeat buyers, paid-marketing dependency, negative operating leverage, and balance-sheet runway.
-- Common trap: assuming one quarter of margin improvement proves the marketplace is healthy when liquidity may still be deteriorating.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Consumer internet and marketplace KPI cheat sheet
-
-Use these as interpretation anchors, not as universal truths. Definitions vary materially by issuer.
-
-- GMV / GMS / GOV / gross bookings / GBV: total transaction value flowing through the platform. Always normalize for taxes, shipping, tips, refunds, cancellations, and off-platform activity before comparing companies.
-- Take rate / revenue take rate / net revenue margin: revenue as a percentage of GMV or GOV. Always break into transaction fees, ads, subscriptions, payments, fulfillment, financing, or other services.
-- Active buyers / active users / active consumers / active bookers / MAPCs: demand-side counts. Never compare a monthly metric to a trailing-twelve-month metric without adjustment.
-- Active sellers / merchants / hosts / couriers / listings supply: supply-side counts. Volume counts are not enough; test liquidity and quality of supply.
-- Orders / trips / room nights / items sold / bookings: the transaction unit that maps the network's activity into volume growth.
-- Order frequency / purchase frequency / booking frequency / repeat rate: measures repeat demand quality.
-- AOV / ADR / basket size / average gross booking per transaction: the ticket-size component of GMV growth.
-- Cohort retention / reactivation / resurrection: the durability of demand after initial acquisition.
-- Supply retention / merchant retention / host retention: the durability of the seller or partner base.
-- Contribution profit / variable contribution: a better lens than GAAP gross margin for managed marketplaces with logistics or support intensity.
-- CAC / payback / LTV: useful only after separating paid acquisition from direct traffic, brand, SEO, and referrals.
-- Ads penetration / promoted listings / sponsored placement mix: monetization layers that can be attractive but can also mask weaker core demand.
-- Payments, fulfillment, insurance, or financing attach: ancillary layers that can improve monetization but change risk and capital intensity.
-- Refunds / chargebacks / fraud / cancellations / disputes: quality-control metrics that often worsen before headline KPIs show strain.
-- Working-capital float: customer funds, unearned revenue, seller payables, and timing balances that can flatter cash flow.
-
-## Marketplace Liquidity And Unit-Economics Metrics
-
-## 1) Transaction architecture and the true unit of account
-### Metrics to track
-- GMV / GMS / GOV / gross bookings / GBV
-- orders, trips, room nights, or items sold
-- active buyers or active consumers
-- active sellers, merchants, hosts, or listings
-- AOV, ADR, or average booking per order
-- take rate by revenue component
-
-## 2) Liquidity, density, and matching quality
-### Metrics to track
-- fill rate or successful-match rate
-- sell-through or booking conversion
-- average delivery or response time
-- active listings per active buyer where relevant
-- orders or transactions per active seller or merchant
-- cancellation, refund, or defect rates
-
-## 3) Demand acquisition, cohorts, and repeat behavior
-### Metrics to track
-- active buyers, MAUs, MAPCs, or active bookers
-- new, repeat, and reactivated user counts
-- order frequency or booking frequency
-- GMS or GMV per active buyer
-- retention by cohort and mature-cohort curves
-- paid versus direct traffic mix where available
-
-## 4) Supply quality, incentives, and partner economics
-### Metrics to track
-- active sellers, merchants, hosts, or providers
-- listings, selection, or available inventory
-- seller or merchant retention and churn
-- take rate paid by the supply side
-- supply acquisition cost
-- average earnings or utilization where relevant
-
-## 5) GMV growth decomposition and quality
-### Metrics to track
-- GMV or gross-booking growth
-- active-buyer or active-consumer growth
-- order, trip, or room-night growth
-- average order value or ADR
-- category and geographic growth mix
-- organic versus acquired growth
-
-## 6) Take rate, ad load, and monetization layers
-### Metrics to track
-- revenue take rate or net revenue margin
-- core transaction take rate
-- ads as a percentage of revenue or GMV
-- payments or checkout penetration
-- membership or subscription penetration
-- merchant-versus-agency or gross-versus-net mix where relevant
-
-## 7) Contribution economics, fulfillment, and trust and safety
-### Metrics to track
-- contribution profit and contribution margin where disclosed
-- variable cost per order, trip, or booking
-- payment-processing costs
-- promotions and incentives as a percentage of GOV or revenue
-- support, refund, or trust-and-safety costs
-- on-time, defect, and customer-satisfaction metrics where disclosed
-
-## 8) Competitive position, network effects, and platform power
-### Metrics to track
-- direct versus paid traffic mix
-- cross-category attach or multi-product usage
-- app penetration and repeat usage
-- seller or merchant concentration
-- active supply overlap where disclosed
-- customer acquisition cost by channel
-
-## 9) Cash flow quality, working capital, and balance-sheet reality
-### Metrics to track
-- operating cash flow and free cash flow
-- restricted cash and customer funds
-- seller, merchant, or host payable balances
-- unearned revenue or deferred fees where relevant
-- capex and lease obligations
-- SBC and diluted-share growth
-
-## 10) Capital structure and public-equity security context and Credit Markets handoff
-### Metrics to track
-- gross and net leverage
-- liquidity and restricted cash
-- debt and convert maturity schedule
-- fixed-cost base and cash-burn sensitivity
-- normalized free cash flow under slower growth
-- potential dilution from SBC and converts
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` consumer-internet-marketplaces sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for consumer internet and marketplaces
-
-## Required model structure
-- GMV or gross-bookings bridge built from active demand, frequency, ticket size, and mix.
-- Revenue split into core transaction revenue, ads, subscriptions or memberships, payments or fintech, shipping or fulfillment, first-party or other, where relevant.
-- Take-rate schedule by revenue component.
-- Cohort-informed demand build where data allows.
-- Supply-side schedule including active sellers, merchants, hosts, or providers and liquidity assumptions where relevant.
-- Contribution-margin schedule capturing incentives, payment costs, support, trust and safety, insurance, logistics, and fulfillment costs.
-- Opex schedule by sales and marketing, product or engineering, G and A, and regional investments.
-- SBC and diluted-share-count schedule.
-- Cash-flow schedule that explicitly captures working-capital balances, restricted cash, customer funds, and capex.
-
-## Mandatory decomposition
-- Separate growth from active-demand growth, frequency growth, and ticket-size growth.
-- Separate take-rate changes from core-transaction growth.
-- Separate ads, subscriptions, and fintech from the core marketplace.
-- Separate direct-margin improvement from promotional cuts or timing benefits.
-- Separate organic growth from M and A, divestitures, and FX.
-- Separate gross and net recognition effects when mix can distort reported revenue.
-
-## Archetype-specific driver trees
-
-### Ecommerce marketplace
-- active buyers x orders per buyer x AOV = GMS or GMV
-- GMS x transaction take rate plus ads plus payments or services = revenue
-
-### Travel marketplace
-- room nights or nights booked x ADR = gross bookings or GBV
-- gross bookings x effective take rate, adjusted for merchant or agency mix and timing = revenue
-
-### Delivery or mobility marketplace
-- monthly active consumers x orders or trips per consumer x average basket or gross booking per trip = GOV or gross bookings
-- GOV x net revenue margin plus ads and memberships = revenue
-- revenue less variable fulfillment and incentives = contribution profit
-
-### Classifieds or listings marketplace
-- paying listers or dealers x ARPA plus lead or media products = revenue
-- do not force a GMV framework onto a listings model unless transactions are truly monetized
-
-## Mandatory sensitivities
-- active-buyer or active-consumer growth
-- order, trip, or booking frequency
-- AOV or ADR
-- supply density or seller growth
-- core transaction take rate
-- ads or ancillary-service attach
-- promotions and incentive intensity
-- contribution margin and logistics cost
-- working-capital support and restricted-cash assumptions
-- valuation multiple on revenue, gross profit, contribution profit, EBITDA, or FCF as appropriate
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/modeling-rules.md
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-# Marketplace Modeling Rules
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load only when the task needs sector modeling detail.
-
-## Core Frame
-
-Underwrite consumer internet and marketplace issuers as transaction networks. Value depends on repeat demand, scarce-side supply, local/category liquidity, trust, monetization quality, contribution economics, cash conversion, and security claims, not reported revenue growth alone.
-
-Start with what is being matched, booked, delivered, or sold; who pays; whether the scarce side is demand or supply; how GMV/GMS/GOV/bookings convert to revenue and cash; whether cohorts repeat without subsidies; whether take rate rises for durable reasons; and whether working-capital float or restricted cash flatters liquidity.
-
-Treat a beat as low quality when driven by temporary incentive cuts, ticket inflation, travel normalization, gross-versus-net accounting, agency/merchant mix, favorable FX, one-time cancellations/refunds behavior, or ad load that raises revenue while core liquidity weakens.
-
-## Analysis Sequence
-
-### 1) Transaction architecture and unit of account
-- Identify the core unit: order, item, room night, trip, booking, service hour, lead, listing, or transaction.
-- Bridge GMV/GMS/GOV/gross bookings to recognized revenue, contribution profit, operating cash flow, and security value.
-- Split monetization into transaction fees, seller/buyer fees, ads, memberships, payments, financing, shipping, insurance, fulfillment, and other services.
-- Determine who pays each fee, what is mandatory versus optional, and whether revenue recognition timing or cancellation windows distort growth.
-- Do not compare gross and net revenue models without normalizing pass-throughs, taxes, tips, shipping, refunds, and incentives.
-
-### 2) Liquidity, density, and match quality
-- Analyze liquidity by geography, category, cohort, and use case; total network scale can hide weak local density.
-- Determine whether buyers get selection, availability, speed, and trust, and whether sellers/providers earn enough volume to remain active.
-- Track fill rate, conversion, sell-through, response time, utilization, cancellations, refunds, defects, and successful-match rate where available.
-- Growth purchased in immature markets deserves a lower quality score than growth from better conversion and mature-market repeat behavior.
-- Multi-homing and easy disintermediation weaken network effects even when headline scale looks large.
-
-### 3) Demand acquisition, cohorts, and repeat behavior
-- Build demand from new users, reactivated users, repeat users, frequency, AOV/ADR, category expansion, and geography.
-- Separate direct traffic, SEO, app, referrals, brand, and paid acquisition; rising paid mix can signal weaker organic pull.
-- Cohorts should be cut by acquisition channel, geography, category, and first-order month where possible.
-- Frequency gains and category expansion usually matter more than one-time ticket-size spikes.
-- Reactivation is attractive only when reactivated cohorts keep spending after incentives normalize.
-
-### 4) Supply quality and partner economics
-- Identify the supply-side actor: seller, merchant, host, courier, driver, provider, lister, or professional service partner.
-- Test onboarding friction, time to first transaction, supply retention, concentration, multi-homing, utilization, and average earnings.
-- Separate supply count growth from supply productivity; more listings with lower utilization can signal weaker liquidity.
-- Fee increases and ad monetization are durable only if supply ROI and retention remain healthy.
-- Normalize incentives, fee rebates, marketing credits, and support costs needed to recruit or retain supply.
-
-### 5) GMV growth and take-rate quality
-- Decompose GMV/bookings into active-demand growth, frequency, volume units, AOV/ADR, category mix, geography, FX, inflation, acquisitions, and divestitures.
-- Separate core transaction take rate from ads, subscriptions, payments, fulfillment, financing, and other ancillary layers.
-- Rising take rate is good only if liquidity, repeat behavior, trust, and supply economics remain healthy.
-- Ads are high quality when they follow buyer intent and seller ROI; excessive ad load can tax sellers and degrade conversion.
-- For OTAs, merchant-versus-agency mix can change revenue and cash timing without a true demand change.
-
-### 6) Contribution economics, trust, and fulfillment
-- Build contribution profit after promotions, payment processing, support, refunds, chargebacks, insurance, logistics, trust and safety, and merchant/driver support.
-- Distinguish order acquisition from order fulfillment and from fixed platform cost.
-- Density, routing, ML matching, and better fraud controls should lower variable cost per transaction; complexity can offset scale.
-- Asset-light narratives are weak if refunds, fraud, insurance, support, compliance, or logistics intensity are structurally rising.
-- Margin improvement from lower incentives is less durable than improvement from liquidity, automation, and cost-per-order leverage.
-
-### 7) Cash flow, working capital, and balance sheet
-- Reconcile FCF with customer funds, restricted cash, unearned revenue, seller/merchant/host payables, payment reserves, accrued incentives, leases, and SBC dilution.
-- Stable working-capital float can be real but is not the same as high incremental margins; it reverses when GMV slows.
-- Customer custodial balances and restricted cash are not discretionary liquidity.
-- Review convert maturities, capped calls, warehouse facilities, regulated entities, insurance reserves, guarantees, and legal contingencies.
-- For debt instruments, route underwriting to Credit Markets; retain downside cash generation, fixed-cost flexibility, working-capital reversal, and marketplace liquidity only as common-equity risk context.
-
-### 8) Moat and security value
-- Determine whether the moat comes from local liquidity, brand, trust, unique supply, logistics, data, habit, direct traffic, or regulatory position.
-- Test dependence on app stores, search, social platforms, paid acquisition, and third-party logistics or payments.
-- Category expansion creates value only when user overlap, supply leverage, and monetization logic are real.
-- Equity rerates need proof of stronger cohorts, liquidity, take-rate durability, contribution margin, and moat depth.
-- A good marketplace can still be a bad security if value depends on multiple expansion, vendor float, dilution, or fragile legal/regulatory assumptions.
-
-## Deferred Modeling Layers
-
-Load deeper files only when needed:
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: model structure, driver decomposition, and sensitivities.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-- Focus on cohort durability, liquidity quality, take-rate sustainability, ad and ancillary monetization quality, margin durability, and whether the moat is getting stronger or weaker.
-
-## Event-driven
-- Focus on beat-and-raise quality, fee or pricing changes, regulatory decisions, M and A, strategic pivots into new categories, activist angles, convert overhangs, and whether margin moves came from structural improvements or transient incentive changes.
-
-## Macro
-- Focus on consumer-discretionary sensitivity, travel trends, labor availability, fuel and insurance costs where relevant, FX, cross-border demand, ad spend sensitivity, and whether the platform is skewed to discretionary or nondiscretionary categories.
-
-## Quant or systematic
-- Standardize around active-demand growth, order or booking growth, GMV growth, take-rate trend, contribution-margin trend, marketing efficiency, free-cash-flow conversion, dilution, and estimate-revision direction. Be careful with definitional changes.
-
-## Credit Markets handoff / structured-finance context
-- Focus on downside cash generation, fixed-cost flexibility, working-capital reversal risk, regulated-entity constraints, legal exposures, debt maturities, convert overhang, and whether the platform could self-fund through a downturn.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-- Focus on category expansion, geographic density, new-supply onboarding, cross-category behavior, ad and fintech attach, and whether the platform can expand the lifetime value of both sides without destroying economics.
-## Output overlays for common tasks
-
-## For a post-earnings deep dive
-- Lead with the GMV bridge: active demand, frequency, ticket size, and take rate.
-- State clearly whether the quarter was driven by better liquidity, better cohorts, higher monetization, or accounting and mix noise.
-- Separate the core marketplace from ads, subscriptions, payments, logistics, or fintech.
-- Call out any mix shifts, incentive changes, or metric-definition changes that distort comparability.
-- End with the debate: why bulls think the network is getting stronger and why bears think monetization is outrunning liquidity.
-
-## For a pre-earnings preview
-- Focus on the setup around demand growth, supply density, take-rate cadence, promotions, category mix, travel or seasonal timing, and contribution-margin expectations.
-- Identify which metrics the market is most likely to key on for that name.
-- State whether the quarter is more likely to hinge on liquidity, ad monetization, fulfillment efficiency, or working-capital optics.
-
-## For an investment memo
-- Make the core debate about network strength, not just about revenue growth.
-- State the scarce side of the marketplace and why that matters.
-- Include a clean bull, base, and bear case around cohorts, liquidity, take-rate sustainability, and contribution-margin progression.
-- Be explicit about what portion of value belongs to the core marketplace versus ancillary layers.
-
-## For a financial model
-- Build a bottom-up GMV or gross-bookings model tied to the actual transaction architecture.
-- Model take-rate components separately.
-- Explicitly capture incentives, logistics, trust and safety, and working-capital dynamics.
-- Include scenario cases for cohort strength, supply density, take-rate changes, and contribution margin.
-
-## When debt or structured work is requested, route to Credit Markets
-- Prioritize downside liquidity, working-capital reversal risk, regulatory liabilities, fixed-cost flexibility, and normalised cash generation.
-- Treat customer funds and restricted balances carefully.
-- Evaluate whether the company can continue investing enough to protect liquidity without relying on external capital.
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-## Style rules for using this sector pack
-
-- Write like a veteran internet and marketplace analyst, not like a generic digital-business summarizer.
-- Define the company's own KPIs before using them in an argument.
-- Separate facts, management framing, and your inference.
-- Be explicit about which side of the network is scarce and which side is monetized.
-- Distinguish demand quality, supply quality, and monetization quality instead of rolling them into one word like engagement.
-- When the company is hybrid, present the marketplace, ads, payments, logistics, and first-party pieces separately before combining them.
-- Treat the business as a network and unit-economics problem, not just a revenue-growth story.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-- GMV grows while active buyers, orders, or room nights stagnate because ticket size or FX did the work.
-- Revenue grows faster than GMV due mainly to fee increases or ad monetization while core liquidity weakens.
-- Supply counts rise but utilization or sell-through falls, signaling worse liquidity.
-- Margins improve because incentives were cut, only to be followed by weaker growth later.
-- Paid marketing rises while management emphasizes strong user growth without acknowledging worsening organic demand.
-- Travel-platform revenue improves because of merchant-mix shift or cash timing, not because demand strengthened.
-- Delivery or mobility GOV growth looks strong while contribution margin stalls due to insurance, labor, or routing costs.
-- Ads or fintech become the story because the core marketplace is maturing or weakening.
-- GMV or GOV metrics change definitions and are treated as comparable to prior periods.
-- Working-capital float is mistaken for clean, repeatable free cash flow.
-- Hybrid first-party and third-party models are valued like pure asset-light marketplaces.
-- Fraud, counterfeits, returns, refunds, or chargebacks are understated in the debate.
-- Regulatory exposure around labor classification, travel regulation, consumer protection, competition, or privacy is dismissed as noise.
-- One category, one geography, or one large merchant drives an outsized share of growth and risk.
-## Never do this
-
-- Never value all marketplaces on EV / GMV.
-- Never compare monthly active users to trailing-twelve-month active buyers without adjustment.
-- Never assume GMV growth means share gains before normalizing for price, mix, and FX.
-- Never treat ad revenue as proof of marketplace health if order frequency, conversion, or seller ROI are worsening.
-- Never ignore multi-homing, off-platform leakage, or disintermediation risk.
-- Never treat working-capital float as the same thing as structurally high margins.
-- Never let a management narrative about network effects substitute for proof of local density and repeat behavior.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Consumer Internet Marketplaces Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order:
-
-1. 10-K, 10-Q, annual report, earnings release, and official shareholder letter
-2. KPI appendices, investor-day materials, and formal metric definitions
-3. revenue-recognition, customer-funds, seller-payable, and legal-reserve footnotes
-4. management commentary on pricing, incentives, liquidity, new categories, and regulatory matters
-5. trusted third-party app, traffic, transaction, or credit-card data only after reconciling with company definitions
-6. peer filings for normalization and definition checks
-
-## Reconciliation rules
-- Never compare GMV, GMS, GOV, GBV, or gross bookings across companies until taxes, shipping, tips, refunds, cancellations, and off-platform activity are normalized.
-- Never compare active-buyer metrics until the measurement window is normalized.
-- Always separate gross versus net revenue recognition before reading relative growth or take rate.
-- If a company has ads, payments, fintech, or first-party businesses, analyze those pieces separately before recombining.
-- Normalize for FX, leap years, pandemic or post-pandemic distortions, large event timing, and acquisitions or divestitures.
-- If management stops disclosing a KPI, treat the absence as information.
-- If the business is two-sided locally, prioritize local-density evidence over national aggregates.
-## Consumer internet and marketplace-specific diligence requests
-
-Request these when deeper underwriting is needed:
-
-- cohort tables by acquisition month, channel, category, and geography
-- repeat-rate and order-frequency curves for mature cohorts
-- reactivation behavior and first-year value of reactivated cohorts
-- active buyers or consumers, active sellers or merchants, and liquidity metrics by geography and category
-- supply retention, time to first transaction, and supply-side incentive history
-- conversion, fill rate, cancellation, refund, and defect metrics by category and region
-- take-rate bridge by core transaction, ads, subscriptions, payments, shipping, and other services
-- paid marketing mix, direct traffic mix, CAC by channel, and payback by cohort
-- promoted-listing or ads penetration and merchant ROI evidence
-- membership penetration, cross-category behavior, and attach rates to new services
-- trust-and-safety metrics including fraud, counterfeit, chargeback, dispute, and insurance-loss trends
-- contribution margin by geography or category if available
-- customer-funds, seller-payables, and restricted-cash schedules
-- legal and regulatory matters by geography, especially labor, travel, tax, privacy, and platform obligations
-- debt maturities, convert terms, and any warehouse, receivable, or guarantee obligations
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/consumer-internet-marketplaces/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` consumer-internet-marketplaces sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use marketplace valuation, not generic internet shortcuts
-
-## Equity valuation hierarchy
-
-1. EV / next-twelve-month revenue for high-growth platforms where monetization is still deepening and margins are not yet mature.
-2. EV / next-twelve-month gross profit when pass-through costs, payment costs, or logistics costs make revenue less comparable.
-3. EV / contribution profit for managed marketplaces where contribution margin best captures local density and unit-economics improvement.
-4. EV / EBITDA or FCF yield for more mature, scaled platforms with clearer cash-generation quality.
-5. Sum-of-the-parts for hybrids with distinct marketplace, ads, payments, fintech, logistics, or first-party retail components.
-
-### Valuation rules
-- Higher multiples belong to dense, habit-forming platforms with strong repeat behavior, improving take-rate quality, high direct traffic, and a credible path to cash generation.
-- Penalize models with high multi-homing, incentive dependence, weak cohort retention, heavy logistics intensity, or regulatory overhang.
-- Avoid mechanical EV / GMV comparisons across different models. GMV is useful as a scale and liquidity metric, not as a universal valuation base.
-- Use gross profit or contribution-profit lenses when revenue contains meaningful pass-through elements.
-- Treat ad and fintech layers as valuable only after confirming they do not mask deterioration in core transaction liquidity.
-
-## Credit valuation hierarchy
-
-1. Downside-stable contribution profit and normalized free cash flow.
-2. Working-capital resilience if GMV growth slows or reverses.
-3. Legal, regulatory, and labor exposure.
-4. Liquidity, debt maturity profile, and access to capital.
-5. Recovery value of the platform, brand, and any regulated or cash-generative subsidiaries.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the company
-
-Classify the company before analyzing it. Do not compare all exchange and market-infrastructure businesses mechanically.
-
-## Archetypes
-
-### Listed derivatives exchange plus central counterparty
-- Core engines: benchmark contract ADV, open interest, product mix, clearing economics, margin efficiencies, and market-data monetization.
-- Primary watch items: benchmark-product concentration, rate or commodity volatility sensitivity, average rate per contract, collateral balances, member concentration, regulatory changes, and cross-margin value.
-- Common trap: extrapolating crisis-period activity without testing whether higher volume came from durable hedging adoption or temporary panic.
-
-### Cash equities and options exchange network
-- Core engines: matched-share volume, options volume, proprietary products, market share, connectivity, access fees, and data.
-- Primary watch items: rebates and liquidity payments, proprietary-index options mix, market-center share shifts, exchange-routing dynamics, and regulatory pressure on fees or market structure.
-- Common trap: focusing on gross revenue or share volume without measuring net capture after rebates and pass-through items.
-
-### Diversified exchange, index, data, listings, and workflow conglomerate
-- Core engines: transaction revenue in markets businesses plus recurring revenue from data, indices, listings, software, and workflow tools.
-- Primary watch items: recurring versus transactional mix, cross-sell, acquisition integration, debt load, data pricing power, capital-markets sensitivity in listings, and software retention.
-- Common trap: valuing the entire company on one exchange multiple or one software multiple instead of separating the transaction business from the recurring franchise.
-
-### Electronic fixed-income, rates, FX, or repo venue
-- Core engines: ADNV or ADV, protocol mix, market share, fixed fees, variable fees, automation, and client workflow integration.
-- Primary watch items: FPM, protocol shifts, all-to-all adoption, portfolio-trading mix, compression, international growth, and product-channel mix.
-- Common trap: assuming all volume growth is equally valuable when the fastest-growing protocols may earn lower fees per million.
-
-### Clearing, post-trade, and collateral utility
-- Core engines: cleared notional, member and client penetration, margin balances, eligible-collateral breadth, netting benefits, and settlement or post-trade workflow revenue.
-- Primary watch items: default waterfall design, resource sufficiency, concentration of clearing members, collateral policy, regulatory capital treatment, and operational resilience.
-- Common trap: treating the business like a pure software or exchange franchise and underweighting systemic-risk, legal, and operational-continuity issues.
-
-### Listings, index, and market-data franchise
-- Core engines: annual issuer fees, index licensing, benchmark use, assets linked to products, pricing power, and subscription renewals.
-- Primary watch items: AUM and net-flow sensitivity, benchmark competition, client concentration, data redistribution pricing, and capital-markets issuance cycles.
-- Common trap: labeling it fully cyclical like an exchange or fully recurring like software without decomposing the real exposure.
-
-### Hybrid company with multiple archetypes
-If the company spans more than one archetype, split it before concluding anything. A hybrid business with a strong recurring-data segment can deserve a different valuation and downside case than its more cyclical transaction segment would suggest on its own.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Exchange and market infrastructure KPI cheat sheet
-
-Use these terms precisely and only after checking company definitions.
-
-- ADV: average daily volume. Usually contracts, shares, or trades. Always confirm whether the company reports contracts, notional, or shares.
-- ADNV: average daily notional value. Common in repo, FX, fixed income, and cash equities.
-- Open interest: end-period outstanding derivatives contracts. Use it to judge franchise stickiness and position inventory, not just trading heat.
-- Average rate per contract or RPC: clearing and transaction fees divided by contracts traded. Use only within the company’s own definition.
-- Net capture: net revenue earned per share or per notional traded after rebates or direct trading costs. Gross volume with weak capture can be low quality.
-- FPM: fees per million notional traded. Common in electronic fixed income, rates, FX, repo, and other OTC or RFQ-style venues.
-- Liquidity payments or rebates: payments to market makers or order-flow providers that reduce net revenue quality.
-- Recurring revenue: data, connectivity, index licensing, annual listings, workflow, technology, and subscription-like revenue. Verify contract structure and renewal quality.
-- ARR: annualized recurring revenue. Useful for recurring businesses, but definitions vary and are not safely comparable across companies without adjustment.
-- Initial margin / performance bond: collateral posted to cover potential future exposure at a CCP.
-- Variation margin: mark-to-market cash exchanged as positions move.
-- Default fund: mutualized resources contributed by clearing members and sometimes supplemented by CCP capital.
-- Eligible collateral: the assets a clearinghouse will accept. This matters for customer behavior, funding efficiency, and CCP attractiveness.
-- Market share: only use after checking the denominator. Shares based on company-defined TRACE, matched shares, or notional can overstate or understate the economic position.
-- New-product mix: the share of economics from recently launched contracts, data products, auction protocols, or workflow tools. This is how secular growth often shows up before the P&L fully reflects it.
-
-## Exchange KPI Map
-
-## 1) Business mix, economic model, and regulatory perimeter
-### Metrics to track
-- revenue by segment and by recurring versus transactional classification
-- net revenue versus gross revenue where relevant
-- EBITDA or operating income by segment
-- percentage of revenue from top products, top geographies, and top business lines
-- debt at holdco versus regulated subsidiary level
-
-## 2) Volume quality, product mix, and franchise durability
-### Metrics to track
-- ADV or ADNV by product and asset class
-- open interest or outstanding positions where relevant
-- benchmark-franchise concentration
-- volume by customer channel or protocol where disclosed
-- share of activity from proprietary products versus fungible products
-
-## 3) Monetization: rpc, net capture, fpm, and fee quality
-### Metrics to track
-- average rate per contract or RPC
-- net capture per share or per notional
-- FPM by asset class or protocol
-- liquidity payments and rebate intensity
-- royalty or licensing fees as a percentage of product revenue
-
-## 4) Recurring revenue: data, connectivity, index, listings, and workflow
-### Metrics to track
-- recurring revenue as a percentage of total revenue
-- ARR or annualized subscription metrics where defined
-- data and connectivity growth
-- index-linked assets or benchmark-linked product growth where disclosed
-- annual listings or issuer-services revenue versus transaction listings revenue
-
-## 5) Clearing, collateral, rate sensitivity, and balance economics
-### Metrics to track
-- initial margin and variation-margin flows where disclosed
-- default fund size and contribution framework
-- cash and non-cash collateral balances
-- interest income or spread earned on margin-related balances
-- member concentration and client-clearing penetration
-
-## 6) Competitive position, market structure, and switching costs
-### Metrics to track
-- market share by product and region
-- customer count or member count where disclosed
-- top-product concentration
-- share of revenue from proprietary or benchmark products
-- connectivity, subscription, or workflow attachment metrics where disclosed
-
-## 7) Technology, resilience, cyber, and operating leverage
-### Metrics to track
-- adjusted operating margin and incremental margin
-- technology and capex spend
-- capitalized software as a share of development spend where disclosed
-- incident history, fines, or remediation costs
-- employee mix in technology and product if discussed
-
-## 8) Growth drivers: new products, secular electronification, and cross-sell
-### Metrics to track
-- revenue from new products or new protocols where disclosed
-- adoption metrics by channel or asset class
-- organic revenue growth versus acquisition contribution
-- international growth versus domestic growth
-- cross-sell or multi-product client adoption measures where disclosed
-
-## 9) Capital allocation, balance sheet, and public-equity security context and Credit Markets handoff
-### Metrics to track
-- gross and net leverage
-- debt maturity ladder
-- free cash flow conversion
-- buyback and dividend payout profile
-- holdco cash and subsidiary cash availability
-
-## 10) Guidance quality, normalization risk, and scenario analysis
-### Metrics to track
-- volume and revenue sensitivity to volatility proxies
-- rate sensitivity on margin or customer-cash economics
-- recurring revenue growth under lower activity assumptions
-- segment-level downside and upside scenarios
-- normalized EPS, EBITDA, and free cash flow
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` exchanges-market-infrastructure sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for exchanges and market infrastructure
-
-## Required model structure
-
-Build segment-first models, not one-line CAGR models.
-
-At minimum, separate:
-1. transaction revenue by major business line
-2. recurring revenue by major business line
-3. collateral- or rate-sensitive economics
-4. expense base, technology investment, and capital allocation
-
-## Mandatory decomposition
-
-- Listed derivatives: volume by product family times average rate per contract, with separate assumptions for mix and open-interest durability.
-- Cash equities and options: matched shares or contracts times net capture, explicitly modeling liquidity payments and any proprietary-product mix.
-- Electronic trading venues: ADNV or ADV by asset class and channel times FPM, plus fixed fees by asset class where relevant.
-- Data, connectivity, index, and listings: price times customer base, linked assets, issuer base, or subscription units depending on the business.
-- Clearing and collateral economics: collateral balances times earned spread or fee logic, with clear rate assumptions.
-
-## Mandatory sensitivities
-
-Always include:
-- volume sensitivity in key product lines
-- product-mix or protocol-mix sensitivity
-- net capture or FPM sensitivity
-- rate sensitivity on collateral or balance-related economics
-- recurring growth sensitivity
-- expense or investment sensitivity
-- downside case for regulatory or fee-pressure effects where material
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/modeling-rules.md
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-# Exchange And FMI Modeling Rules
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only for valuation, forecast, model-update, sensitivity, public-equity security context, or downside work.
-
-## Core Lens
-
-Analyze an exchange or financial-market-infrastructure company as a regulated network, a monetization stack, and a critical-risk utility. Do not start with generic fintech, asset-manager, or software templates. Start with the exact revenue mix, benchmark or proprietary product strength, volume/mix/price/rate exposure, collateral economics, regulatory perimeter, and failure mode.
-
-Raw activity is not enough. Low-yield contracts, compression, portfolio trading, rebated cash-equity flow, collateral spread, and recurring data or workflow revenue each deserve different durability and multiple treatment.
-
-## Required Sequence
-
-1. **Business mix and perimeter.** Split transaction, clearing, data, connectivity, index, listings, workflow/software, and pass-through lines. Map holdco, exchange, CCP, CSD/settlement, data, broker/intermediary, and regulator exposures.
-2. **Volume quality and franchise.** Decompose ADV/ADNV by product, asset class, protocol, customer channel, geography, and open interest. Separate benchmark/proprietary franchises from fungible or rebated activity.
-3. **Monetization quality.** Bridge volume to net revenue using RPC, net capture, FPM, rebates, liquidity payments, direct costs, royalties, and mix. Treat reported peer capture metrics as non-comparable until definitions are reconciled.
-4. **Recurring revenue.** Separate data, connectivity, index, annual listings, workflow, and subscription-like streams from transaction-adjacent revenue tied to AUM, issuance, redistribution, terminal counts, or market activity.
-5. **Clearing and collateral.** Distinguish initial margin, variation margin, default fund, eligible collateral, client clearing, cross-margining, netting, and collateral investment policy. Test whether margin cash or spread income is durable, rate-sensitive, passed through, or trapped.
-6. **Market structure and moat.** Identify whether the moat is liquidity, benchmark status, regulation, data, workflow integration, colocation/connectivity, clearing efficiency, or capital relief. Check open access, interoperability, fee scrutiny, and rationality of share gains.
-7. **Technology and resilience.** Underwrite uptime, major outages, data latency, cyber history, cloud migrations, capitalized software, resiliency spend, and whether operating leverage reflects scale or underinvestment.
-8. **Growth and M&A.** Separate structural electronification, new products, new channels, automation, all-to-all, cross-sell, and workflow embedding from volatility, issuance, rates, or acquired growth.
-9. **Capital and security stack.** Map debt by entity and upstreaming capacity. Test leverage, buybacks, dividends, acquisition policy, regulated-subsidiary ring-fencing, indirect CCP support expectations, and event risk for equity, debt, and hybrids.
-10. **Normalization and scenarios.** Build calm, volatile, rate-cut, rate-rise, listings/issuance recovery, fee-pressure, outage/cyber, and regulatory scenarios. Value through-cycle volume, capture, recurring growth, collateral income, and required resilience investment.
-
-## Model Guardrails
-
-- Use segment-first models, not one-line revenue CAGRs.
-- Model transaction lines from activity times monetization, with separate mix and rebate assumptions.
-- Model clearing/collateral lines from balances, eligible collateral, default resources, investment policy, and earned spread or fees.
-- Model recurring lines from price, contracts, linked assets, issuer count, subscription units, or workflow penetration, not generic ARR.
-- Apply SOTP for hybrids and security-level analysis when economics are ring-fenced.
-- Do not capitalize peak volatility, crisis volume, or high-rate collateral income as recurring revenue.
-
-## Common Misreads
-
-- Treating all volume as equal.
-- Confusing gross share gain with profitable net capture.
-- Missing fee schedule, rebate tier, protocol mix, or contract-value changes.
-- Comparing company-defined ARR, market share, or FPM mechanically.
-- Ignoring default-waterfall design, member concentration, margin methodology, recovery tools, or mutualization risk in a CCP.
-- Underweighting outages, data integrity, cyber incidents, cloud migrations, and regulatory remediation because the business looks asset-light.
-
-## Deeper References
-
-- `valuation-rules.md`: valuation hierarchy, public-equity security context and Credit Markets handoff, and Credit Markets handoff rules.
-- `model-architecture.md`: driver decomposition, model tabs, and sensitivities.
-- `red-flags.md`: rejection tests and failure modes.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-Focus on what is normalized versus temporary, what deserves premium multiple support, and where consensus is misreading the mix between recurring and cyclical earnings.
-
-## Event-driven
-Prioritize earnings surprises driven by volume, capture, or rate-sensitive balances; M&A and integration; fee changes; regulatory decisions; lawsuits; product launches; and outage or cyber events.
-
-## Macro
-Treat the name as an expression of volatility, rate path, issuance cycle, passive flows, energy or commodity activity, and funding conditions. The key is identifying which macro variable matters most for that specific franchise.
-
-## Quant / systematic
-Normalize for regime effects. Raw volume, earnings momentum, and margin expansion can be misleading unless adjusted for volatility, turnover, rates, and one-time event periods. Use product-mix and regime controls where possible.
-
-## Credit Markets handoff / special-situation credit context
-Focus on holdco leverage, structural subordination, acquisition debt, contingent legal and cyber exposures, technology underinvestment, and any path where regulatory intervention or operational failure impairs cash flow.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-Spend more time on secular electronification, workflow embedding, data monetization, cross-sell, index and analytics expansion, and the quality of new product launches. Do not let a growth narrative override careful normalization of event-driven revenues.
-## Output overlays for common tasks
-
-## For a post-earnings deep dive
-Lead with:
-1. what changed in volume, mix, capture, and recurring growth
-2. what portion of the quarter was structural versus volatility-driven
-3. what changed in clearing, collateral, and rate-sensitive economics
-4. whether competitive position improved or simply benefited from the tape
-5. whether guidance implies normalization or continued favorable conditions
-
-## For a pre-earnings preview
-Focus on:
-1. current monthly volume trends and product mix
-2. expected volatility and funding backdrop
-3. listings and issuance environment where relevant
-4. likely direction of capture, FPM, or rebates
-5. any regulatory, outage, or product-launch catalyst around the print
-
-## For an investment memo
-State explicitly:
-1. the company archetype
-2. the exact revenue mix between recurring, transactional, and rate-sensitive economics
-3. the moat source
-4. the normalized earnings base
-5. the key debate, what the market is misunderstanding, and what would falsify the thesis
-
-## For a financial model
-The model must show:
-1. segment-level revenue drivers
-2. product-level or channel-level activity assumptions
-3. monetization assumptions by line
-4. explicit rate and volatility sensitivities
-5. capital allocation and security-level implications
-
-## When debt or structured work is requested, route to Credit Markets
-Prioritize:
-1. entity structure and upstreaming
-2. downside free cash flow after required investment
-3. event risk from operational and regulatory failure
-4. concentration and earnings cyclicality
-5. whether regulated or ring-fenced subsidiaries limit creditor access to value
-
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-
-## Style rules for using this sector pack
-
-- Always say what exact business you are analyzing before offering an opinion.
-- Prefer net economics over gross activity.
-- Normalize by regime; do not let one exceptional quarter define the franchise.
-- Use the company’s own metric definitions, then translate them into a comparable framework only after the definitions are confirmed.
-- Call out what is benchmark, what is fungible, and what is recurring.
-- Separate true moat from temporary stress-driven demand.
-- For security-level underwriting, map the entity and the regulation before talking about downside protection.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/red-flags.md
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-# Exchange Red Flags And Failure Modes
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Activity and Fee Traps
-
-- Treating all ADV/ADNV as equal or annualizing peak volatility and crisis turnover.
-- Ignoring rebates, liquidity payments, maker-taker economics, royalty fees, regulatory pass-throughs, or direct clearing costs.
-- Assuming RPC, FPM, or net capture holds when mix shifts toward low-yield contracts, compression, portfolio trading, or automated flow.
-- Missing fee-schedule changes, volume tiers, liquidity incentives, contract-value changes, or proprietary-versus-fungible product mix.
-- Confusing gross share gain with profitable share gain.
-
-## Clearing and Default Waterfall Traps
-
-- Valuing a clearing-heavy franchise without mapping margin methodology, initial margin, variation margin, default fund, CCP skin-in-the-game, assessment powers, recovery tools, and loss-allocation sequence.
-- Ignoring clearing-member concentration, client-clearing exposure, wrong-way risk, stressed liquidity needs, settlement-bank dependence, or operational capacity during default management.
-- Treating guaranty-fund resources as parent economics or assuming member mutualization protects shareholders without reputational, litigation, or regulatory support risk.
-- Missing collateral eligibility changes, procyclical margin calls, cross-margining changes, or recovery-and-resolution rules that alter customer behavior.
-
-## Collateral, Rate, and Balance Traps
-
-- Treating collateral spread, treasury income, or margin-cash interest as recurring fee revenue without rate-cycle and pass-through sensitivity.
-- Ignoring whether collateral economics accrue to the parent, a regulated subsidiary, members, or customers.
-- Over-crediting earnings lifted by high rates, wider spreads, or volatility-inflated margin balances.
-
-## Operational, Cyber, and Regulatory Traps
-
-- Underweighting outages, data latency, failed migrations, cyber incidents, resilience underinvestment, or data-integrity failures because the company looks asset-light.
-- Assuming market-data or index pricing power is unlimited despite regulatory review, customer consolidation, redistribution disputes, substitution, or benchmark-transition risk.
-- Missing product-specific rules: position limits, transaction taxes, clearing mandates, tick-size reforms, open-access/interoperability, market-structure reviews, benchmark governance, and market-data fee litigation.
-- Ignoring structural subordination between the analyzed security and regulated or ring-fenced cash flows.
-
-## Never Do This
-
-- Do not compare exchanges on headline volume growth without normalizing product mix, contract value, fee tier, rebates, and net capture.
-- Do not capitalize peak volatility, collateral income, or issuance/listings bursts as normal recurring revenue.
-- Do not call market-data, index, or workflow revenue "SaaS-like" without checking churn, contract duration, price regulation, customer concentration, and activity sensitivity.
-- Do not underwrite CCP economics without a stress-liquidity, default-management, and mutualization analysis.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Exchanges Market Infrastructure Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use this source order whenever possible:
-1. latest earnings release and earnings presentation for current management framing
-2. annual report or 10-K or 20-F for segment definitions, accounting, legal structure, and risks
-3. monthly or quarterly volume and capture reports for current activity and mix
-4. clearinghouse rulebooks and risk disclosures for margin, collateral, eligible assets, and default waterfalls
-5. fee schedules and product documentation for monetization details
-6. only then use third-party market-share summaries, and always reconcile company-defined denominators first
-
-## Reconciliation rules
-
-- Never compare ARR, market share, ADV, ADNV, RPC, net capture, or FPM across companies without checking definitions.
-- Confirm whether volume is contract-based, notional-based, share-based, single-counted, or double-counted.
-- Strip out pass-through items, rebates, liquidity payments, royalty fees, and direct trading costs before drawing conclusions about economics.
-- If management reports preliminary capture or fee metrics, treat them as subject to change until the quarter closes.
-- If the company changed methodology because of acquisitions, restatements, or product regrouping, rebuild comparability yourself.
-## Exchange and market infrastructure-specific diligence requests
-
-Request or reconstruct the following whenever possible:
-- segment revenue bridge between volume, price, recurring revenue, and balance-related economics
-- product-level concentration map showing what actually drives EBITDA
-- monthly activity history by product and by capture metric
-- clearing and collateral economics, including who owns the spread and how balances are invested
-- member and client concentration, especially for CCPs and institutional platforms
-- regulatory matters, fee litigation, and material market-structure debates
-- technology roadmap, outage history, cyber posture, and cloud-migration status
-- acquisition scorecard for synergy realization and cross-sell credibility
-- entity-level debt map and any restrictions on upstreaming cash
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/exchanges-market-infrastructure/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` exchanges-market-infrastructure sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use exchange and market infrastructure-specific valuation, not generic financial-company shortcuts
-
-## Equity valuation hierarchy
-
-### For listed derivatives and clearing-heavy franchises
-- Use EV to EBITDA, P/E, and free-cash-flow yield on normalized rather than peak activity.
-- Cross-check with a scenario-weighted view of volatility, rates, and collateral economics.
-- Give credit for benchmark products and clearing moats only if concentration risk and fee durability are understood.
-
-### For diversified exchange and recurring-data groups
-- Use sum-of-the-parts where warranted: transaction businesses, data and connectivity, indices, listings, and workflow or software.
-- Higher-multiple recurring segments deserve separate treatment only if retention, pricing power, and margin durability are real.
-- Do not award software multiples to recurring lines that still depend heavily on markets activity or capital-markets issuance.
-
-### For electronic trading venues
-- Use EV to EBITDA, P/E, and revenue-multiple cross-checks, but anchor them to normalized FPM and share rather than just volume growth.
-- Distinguish between structurally stronger network effects and cyclical revenue from high-volatility periods.
-
-### Valuation rules
-- Never capitalize peak crisis or event-driven volume without a reversion case.
-- Separate recurring revenue from transactional and rate-sensitive revenue explicitly.
-- Normalize collateral-related earnings and interest income instead of blindly annualizing them.
-- Apply premium multiples only to businesses with durable pricing power, benchmark products, or high-retention recurring revenue.
-- Penalize businesses where share is bought through rebates or where economics are concentrated in one product set that may be vulnerable.
-
-## Credit valuation hierarchy
-
-- Start with entity-level cash flow, not consolidated optics.
-- Prioritize free cash flow after required resiliency, compliance, and technology investment.
-- Underwrite operational and regulatory event risk explicitly.
-- Treat systemic-importance status as both a support and a complication: it can stabilize the franchise while also increasing supervisory burden and consequence of failure.
-- For subordinated or structurally junior securities, be conservative about upstreaming and ring-fenced subsidiary economics.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `Insurance Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the insurer
-
-Classify the company before analyzing it. Do not compare insurers mechanically across different liability structures.
-
-## Archetypes
-
-### Personal-lines P&C writer
-- Core engines: pricing, exposure growth, frequency and severity, claims handling, direct or agent distribution, investment income.
-- Primary watch items: rate adequacy, repair and medical inflation, homeowner catastrophe exposure, retention, advertising efficiency, reinsurance.
-- Common trap: treating premium growth and a better reported combined ratio as durable when current accident-year margin is still below adequate price.
-
-### Commercial multiline P&C writer
-- Core engines: commercial property, casualty, workers compensation, packages, surety, broker distribution.
-- Primary watch items: rate versus loss-cost trend, casualty social inflation, reserve picks, broker retention, cat and large-loss exposure.
-- Common trap: letting prior-year reserve releases hide weak current accident-year casualty economics.
-
-### Specialty / E&S writer
-- Core engines: niche underwriting, broker relationships, disciplined capacity, tailored terms, higher attachment points.
-- Primary watch items: renewal pricing, submission quality, limit profile, large-loss frequency, reinsurance availability, hard-versus-soft market position.
-- Common trap: capitalizing peak hard-market returns as if they are permanent.
-
-### Global reinsurer / Bermuda cat writer
-- Core engines: property catastrophe, casualty, specialty, retrocession, life and health reinsurance.
-- Primary watch items: renewal pricing, attachment points, aggregate covers, trapped capital, PMLs, reserve shocks, collateral, retro counterparties.
-- Common trap: annualizing a cat-light year or ignoring retrocession counterparty risk.
-
-### Life and retirement / spread-based annuity writer
-- Core engines: spread income on the general account, fee income on riders, portfolio management, product manufacturing, reinsurance.
-- Primary watch items: new-money yields, cost of funds, credited-rate resets, surrender behavior, alternative asset mix, ALM, statutory capital, cash remittances.
-- Common trap: mistaking temporary spread expansion or alternative-income strength for durable distributable earnings.
-
-### Protection-oriented life / disability / group benefits writer
-- Core engines: mortality and morbidity underwriting, persistency, employer relationships, group benefits, individual protection.
-- Primary watch items: claim incidence and duration, pricing adequacy, disability recoveries, reinsurance, expense discipline.
-- Common trap: ignoring assumption drift because GAAP earnings appear smooth.
-
-### Multiline / composite insurer
-- Core engines: mix of P&C, life, retirement, and sometimes asset-management-adjacent economics.
-- Primary watch items: conglomerate discount, capital allocation, reserve complexity, inter-segment capital fungibility, disclosure quality.
-- Common trap: netting a good spread business against weak casualty or vice versa and calling the group diversified.
-
-### Monoline / title / mortgage / financial guaranty / runoff insurer
-- Core engines: specialized underwriting, housing or credit-cycle exposure, runoff reserve releases, investment portfolio leverage.
-- Primary watch items: claim emergence lag, concentration, litigation, capital, investment risk, regulatory constraints.
-- Common trap: comparing to broad P&C peers on standard combined-ratio frameworks without adjusting for cycle and claim timing.
-
-### Non-U.S. / IFRS 17 / Solvency II insurer
-- Core engines: vary widely.
-- Primary watch items: contractual service margin, insurance service result versus finance result, solvency ratio, contract boundary, volatility adjustment, local remittance rules.
-- Common trap: translating IFRS 17 insurance revenue or CSM release directly into U.S. GAAP premium intuition.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Insurance Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Insurance KPI Map
-
-## 1) Liability promise, business mix, and unit economics
-### Metrics to track
-- gross written premium, net written premium, net earned premium, or insurance revenue
-- retention ratio and ceded ratio
-- renewal retention and rate change
-- policies in force, account values, deposits, or premiums by product
-- short-tail versus long-tail mix
-- catastrophe-exposed values or geographic exposure where relevant
-- product mix by line, duration, and distribution channel
-
-## 2) Underwriting or liability-margin quality
-### Metrics to track
-- combined ratio, loss ratio, and expense ratio
-- current accident-year combined ratio ex-cat and ex-prior-year development
-- catastrophe and large-loss load
-- renewal retention and rate change
-- new-business margin, spread, or fee mix where disclosed
-- net investment spread, base yield, cost of funds, or credited rate
-- lapses, surrenders, mortality, morbidity, and disability incidence
-- underwriting income, adjusted operating income, and core EPS
-
-## 3) Reserve adequacy and adverse-development risk
-### Metrics to track
-- prior-year reserve development in dollars and points of combined ratio
-- carried reserve to paid and incurred indications if available
-- IBNR as a share of reserves
-- case reserve adequacy and claim closure trends
-- reserve leverage to surplus or equity
-- adverse development history by line and accident year
-- ceded reserve recoverables and collectability
-
-## 4) Reinsurance, retrocession, and tail-risk transfer
-### Metrics to track
-- gross versus net written and earned premium
-- ceded ratio and retention ratio
-- catastrophe PML relative to earnings and capital
-- attachment points, reinstatement premiums, and aggregate cover terms
-- reserve cover or ADC size where relevant
-- recoverables by counterparty and collateral type
-- funds-withheld or modco balances
-
-## 5) Investment portfolio and asset-liability management
-### Metrics to track
-- net investment income and base yield
-- new-money rate or reinvestment yield
-- variable investment income or alternative-investment contribution
-- cost of funds, credited rate, or net investment spread
-- fixed income quality mix and structured-product share
-- commercial real estate, private credit, CLO, and alternative allocations
-- duration gap and key-rate sensitivity
-- unrealized losses, AOCI, and derivative collateral exposure
-
-## 6) Capital, solvency, ratings, and holding-company liquidity
-### Metrics to track
-- RBC or local solvency ratio
-- total adjusted capital, statutory surplus, or eligible own funds
-- rating-agency capital buffer if disclosed
-- financial leverage and fixed-charge coverage
-- holdco cash and liquid assets
-- statutory dividends upstreamed and upstream capacity
-- PML or stress loss relative to capital
-- share repurchases, ordinary dividends, and special capital actions
-
-## 7) Earnings quality, accounting, and normalization
-### Metrics to track
-- normalized ROE or ROA
-- underwriting income and ex-cat or ex-PYD underwriting margin
-- core spread income and fee income
-- contractual service margin and insurance service result where relevant
-- adjusted book value, tangible book, or statutory capital growth
-- cash remittances and capital-return capacity
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` insurance sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for insurers
-
-Build models around premiums, exposures, reserves, and capital for P&C, or around spread-earning assets, liabilities, and capital for life. Do not force industrial-company templates onto an insurer.
-
-## Required model structure
-Forecast at minimum:
-- business mix by line or product
-- gross and net written or earned premium, or sales, deposits, and account values
-- rate, exposure, retention, and loss-trend assumptions
-- current accident-year loss ratio, cat load, prior-year development, and expense ratio for P&C
-- spread-earning assets, base yield, cost of funds, fee income, lapses or surrenders, and benefit ratios for life and annuity
-- net investment income and alternative or variable investment contribution
-- taxes and interest at holdco where relevant
-- book value, adjusted book value, or statutory capital
-- RBC or local solvency ratio, holdco liquidity, and capital return
-
-## Mandatory decomposition
-- separate price from exposure or volume
-- separate gross from net and retained from ceded
-- separate current accident-year from prior-year reserve development
-- separate catastrophe or large loss from attritional results
-- separate recurring base investment yield from variable or mark-sensitive income
-- keep GAAP or IFRS, statutory, and holdco-cash concepts distinct
-
-## Mandatory sensitivities
-Run explicit sensitivities on:
-- rate path and reinvestment yield
-- loss trend, social inflation, and claim severity
-- reserve strengthening or weaker development
-- catastrophe burden or a PML event
-- reinsurance cost and retention
-- lapse, surrender, or policyholder behavior
-- asset credit losses or spread widening
-- rating downgrade or capital-action constraints
-- buyback and dividend assumptions
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/modeling-rules.md
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-# Insurance Modeling Rules
-
-Deferred reference for `Insurance Sector Analysis`. Load this only for valuation, forecast, model-update, sensitivity, public-equity security context, or downside work.
-
-## Core Lens
-
-Analyze an insurer as a liability writer, investment portfolio, and regulatory/rating-capital structure. Do not start with EBITDA, EV, or generic revenue-growth templates. Start with the promise sold, pricing duration, retained versus ceded risk, reserve adequacy, float investment, solvency/rating constraints, holdco liquidity, and the exact security's claim behind policyholders and regulated opcos.
-
-Reported EPS can be low quality when driven by reserve releases, cat luck, alternative marks, hedge gains, tax items, capital releases, or one-time reinsurance transactions.
-
-## Required Sequence
-
-1. **Liability promise and unit economics.** Classify true risk-bearing versus distribution/fee business; short-tail, long-tail, catastrophe, spread, protection, runoff, or fee-like exposure; and the clean denominator: earned premium, insurance revenue, account values, deposits, policies, exposures, or sums insured.
-2. **Underwriting or liability margin.** For P&C/reinsurance, build price, exposure, retention, current accident-year attritional loss, cat/large-loss load, and expense ratio. For life/retirement/protection, build spread-earning assets, base yield, credited rate, fee income, mortality/morbidity, hedging, lapses, surrenders, and expenses.
-3. **Reserve adequacy.** Underwrite accident year, line, paid/incurred development, case reserving, IBNR, inflation, social inflation, mass tort, environmental, professional, medical, property-cat reopening, claim closure, discounting, and reinsurance recoverables.
-4. **Reinsurance and tail transfer.** Map quota share, XOL, aggregate, cat, stop-loss, ADC, funds-withheld, modco, retrocession, affiliated reinsurance, collateral, counterparty concentration, trapped capital, reinstatement features, and whether protection is economic or optical.
-5. **Investment portfolio and ALM.** Separate base yield from variable/mark-sensitive income. Map rating/NAIC mix, duration, liquidity, CRE, structured products, CLOs, private credit, alternatives, AOCI, derivative collateral, and whether asset risk is subsidizing weak underwriting.
-6. **Capital, solvency, and holdco liquidity.** Identify the binding regime: RBC, statutory surplus, Solvency II, SST, Bermuda capital, rating-agency capital, leverage, liquidity, or remittance limits. Trace capital through underwriting, spread, reserve changes, cats, OCI, taxes, dividends upstreamed, debt, and reinsurance.
-7. **Accounting and normalization.** Bridge reported net income to core economic earnings. Separate underwriting, prior-year development, cat load, investment spread, variable income, gains/losses, hedges, taxes, capital actions, statutory cash generation, GAAP/IFRS, IFRS 17 CSM/service result, and market-risk benefits.
-8. **Public-equity security context and Credit Markets handoff.** For equity, anchor on normalized ROE/book compounding and capital return. Route surplus notes, hybrids, preferreds, debt, issuer-entity ranking, coupon flexibility, structural subordination, recovery path, and rating-trigger analysis to Credit Markets.
-
-## Model Guardrails
-
-- Use insurance-native valuation: P/BV or P/TBV adjusted for ROE, normalized operating EPS, embedded value, SOTP, DDM, residual income, and capital adequacy.
-- Do not compare written premium, deposits, account values, and IFRS 17 insurance revenue as if they are the same.
-- For P&C, separate current accident-year, prior-year development, cat/large loss, attritional loss, expense ratio, and reinsurance effects.
-- For life/annuity, separate spread, fee, hedge, market-sensitive accounting, policyholder behavior, statutory capital strain, and distributable cash.
-- Treat holdco liquidity separately from opco surplus; upstream capacity often matters more than headline solvency.
-- Scenario cat events, reserve strengthening, rate/reinvestment shifts, spread widening, lapse/surrender stress, reinsurance repricing, downgrade, and buyback/dividend constraints.
-
-## Common Misreads
-
-- Treating premium or deposit growth as value creation without rate adequacy and capital strain.
-- Accepting favorable prior-year development as recurring earnings.
-- Viewing reserve strengthening as clean-up without testing pricing, capital, ratings, and franchise damage.
-- Treating RBC or a solvency ratio as the only binding constraint.
-- Ignoring CRE, structured/private assets, AOCI, collateral calls, and forced-sale liquidity risk.
-- Calling a security "safe" because the insurer is well capitalized without mapping issuer, ranking, policyholder priority, and remittance path.
-
-## Deeper References
-
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: driver decomposition, model tabs, and sensitivities.
-- `red-flags.md`: rejection tests and failure modes.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Insurance Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-Focus on what the market is mispricing about normalized combined ratio, reserve adequacy, cycle position, spread durability, or capital return.
-
-## Event-driven
-Prioritize catalysts: reserve charges, catastrophe events, rating actions, reinsurance or ADC transactions, asset-intensive reinsurance deals, M&A, demutualizations, spin-offs, buyback changes, litigation, or regulatory findings.
-
-## Macro
-Emphasize rates, credit spreads, inflation, labor and medical trend, housing activity, catastrophe seasons, and regulatory capital changes.
-
-## Quant / systematic
-Standardize the variables that revise fastest and matter most: current accident-year margin, rate versus loss trend, reserve movement, catastrophe load, base yield, alternative-income contribution, solvency ratio, and capital-return capacity.
-
-## Credit Markets handoff / equity-risk debt context
-Focus on opco-versus-holdco map, policyholder priority, remittance capacity, collateral, reinsurance recoverable and counterparty risk, and recovery under reserve, cat, and asset-spread stress.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-Translate growth into capital-efficient growth written above the cost of capital. Focus on pricing power, underwriting selection, digital acquisition efficiency, capital-light fee or spread growth, and compounding of surplus rather than raw premium or deposit growth.
-## Output overlays when this skill is used with common finance tasks
-
-## With an earnings deep dive
-Always add:
-- underwriting or spread bridge
-- reserve and prior-year-development scorecard
-- reinsurance and tail-risk snapshot
-- investment and ALM review
-- capital, ratings, and holdco-liquidity table
-- next-quarter or next-renewal watch items
-
-## With an investment memo
-Always include:
-- insurer archetype and why the market is likely framing it wrong or right
-- core thesis by underwriting, reserves, investments, capital, and catalyst
-- variant perception
-- disconfirming evidence
-- security-specific recommendation with stack position and risk or reward
-
-## With a financial model
-Always produce:
-- current accident-year or spread driver tree
-- reserve and capital schedules
-- reinsurance and ALM assumptions
-- book value, adjusted book value, and RBC or solvency outputs
-- explicit catastrophe, reserve, and rate sensitivities
-
-## When debt or structured credit work is requested, route to Credit Markets
-Always include:
-- legal-entity and claim-priority map
-- remittance and holdco-liquidity analysis
-- capital and ratings stress path
-- recovery discussion tied to policyholder seniority
-
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-
-## Default writing style
-
-Write like a veteran insurance analyst:
-- lead with the actual driver of underwriting, reserve, asset, or capital risk
-- separate reported results from normalized economics
-- distinguish current-period performance from prior-period release or mark noise
-- quantify reserve, capital, and ALM consequences whenever possible
-- keep the tone skeptical, precise, and alert to accounting and solvency optics
-
-When unsure, prioritize reserve adequacy, asset quality, rating resilience, and holdco cash over short-term EPS optics.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `Insurance Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-Treat these as high-priority warning signals:
-- premium growth outpacing rate adequacy or capital growth
-- favorable prior-year development masking weak current accident-year underwriting
-- casualty reserve releases despite worsening social inflation or claim severity
-- catastrophe-exposed writer with thin retrocession or optimistic PML assumptions
-- life or annuity earnings propped up by illiquid asset reach, alternative marks, or lagged crediting resets
-- holdco buybacks or dividends funded from thin liquid resources or unsustainably high remittances
-- aggressive affiliated reinsurance or reserve financing used to manufacture capital optics
-- large CRE, lower-rated structured, or private-asset exposures with weak disclosure
-- ratings pressure ignored because RBC looks optically high
-- management steering attention to adjusted earnings while avoiding reserve or capital disclosures
-- conglomerate complexity used to bury a weak business line
-- runoff or ADC benefits mistaken for true franchise improvement
-## Never do this
-
-- Do not lead with EBITDA, EV/EBITDA, or generic revenue-growth language.
-- Do not compare combined ratios without normalizing for catastrophe load, prior-year development, mix, and reserve quality.
-- Do not accept reserve releases as high-quality earnings by default.
-- Do not analyze life insurers without separating spread, fee, hedge, and capital generation.
-- Do not discuss debt or preferreds without mapping the exact issuing entity and structural subordination.
-- Do not treat RBC or solvency ratios as the only capital constraint.
-- Do not compare premiums, deposits, and IFRS 17 insurance revenue as if they are the same thing.
-- Do not annualize a cat-light year or a temporarily favorable spread environment.
-- Do not ignore reinsurance recoverable and counterparty risk.
-- Do not let reported EPS substitute for reserve, asset, and capital analysis.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Insurance Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order unless the task requires otherwise:
-1. statutory statements, annual statements, solvency or RBC disclosures, actuarial opinions, regulator filings
-2. annual and quarterly reports, earnings supplements, and investor-day materials
-3. reserve triangles, cat or PML disclosures, reinsurance-program summaries, earnings transcripts
-4. rating-agency commentary and local solvency disclosures
-5. virtual data room materials: loss triangles, bordereaux, treaty summaries, asset tapes, ALM or hedge decks, ORSA-style stress materials, actuarial memos
-
-## Reconciliation rules
-- Reconcile gross written, net written, net earned, and insurance revenue. They are not interchangeable.
-- Reconcile GAAP or IFRS earnings to statutory capital and holdco cash generation.
-- Reconcile adjusted operating metrics back to reserve movements, catastrophe load, and investment marks.
-- For life insurers, separate premiums from deposits and account-value flows.
-- If management emphasizes book-value growth, check whether the asset book, reserve position, and rating capital actually support it.
-## Insurance-specific diligence requests
-
-When the task involves diligence, request or prioritize the following if available:
-- reserve triangles by line, accident year, paid, incurred, case, and large-loss detail
-- actuarial reserve reviews, carried-versus-indicated reserve analysis, and adverse-development commentary
-- rate change, retention, submission, and exposure data by line and geography
-- catastrophe exposure by peril, geography, attachment point, and modeled PML or tail return periods
-- reinsurance treaties, attachments, covers, counterparty concentrations, and collateral terms
-- investment holdings tape by asset class, rating, duration, liquidity, CRE, private credit, structured, and affiliated exposure
-- ALM, liquidity, and hedge deck including collateral stress
-- statutory entity chart, dividend restrictions, RBC or solvency by entity, and holdco debt maturity schedule
-- policyholder behavior studies: lapse, surrender, mortality, morbidity, disability incidence, and duration
-- rating-agency correspondence and capital framework
-- affiliated reinsurance, funds withheld, modco, reserve financing, and side-letter summary if relevant
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/insurance/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` insurance sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use insurance-specific valuation, not generic corporate valuation
-
-## Equity valuation hierarchy
-
-### P&C / specialty / reinsurance
-Use these lenses first:
-1. price / tangible book or clean book
-2. price / earnings on normalized EPS
-3. normalized ROE relative to cost of equity
-4. underwriting franchise value plus investment leverage
-
-### Life / retirement / annuity
-Use these lenses first:
-1. price / adjusted book value or clean book
-2. price / adjusted operating EPS or distributable EPS
-3. value of remittances or free cash generation
-4. appraisal value or embedded value where relevant and credible
-
-### Valuation rules
-- Higher multiples are justified by reserve quality, underwriting moat, capital-light growth, rating resilience, cleaner asset books, and better cash-remittance visibility.
-- Two insurers with similar current EPS can deserve radically different multiples if one is under-reserved, overexposed to asset risk, or capital constrained.
-- For life insurers, do not confuse mark-driven book volatility with true franchise impairment, but do not ignore capital and liquidity consequences either.
-- Use sum-of-the-parts only when segments truly have different capital intensity and investors can realistically access that value.
-
-## Debt valuation hierarchy
-- spread versus expected loss and recovery
-- distance to rating or solvency impairment
-- holdco liquidity and remittance capacity
-- structural subordination to policyholders and opcos
-- catastrophe, reserve, and asset-spread stress path
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the E&P company
-
-Classify the company before analyzing it. Do not compare all E&Ps mechanically.
-
-## Archetypes
-
-### Oil-weighted shale consolidator
-- Core engines: short-cycle development, repeatable manufacturing, high well cadence, scale benefits, M&A optionality.
-- Primary watch items: inventory tiering, well costs, type-curve durability, parent-child interference, oil differentials, corporate capital allocation.
-- Common trap: paying a premium for "scale" when the real story is shrinking tier-one inventory.
-
-### Gas-weighted Appalachia or Haynesville producer
-- Core engines: dry-gas reservoir quality, firm transport portfolio, basis management, LNG and seasonal demand leverage, lower lifting costs.
-- Primary watch items: basis and transport, gathering contracts, hedge policy, methane and emissions intensity, maintenance capital versus strip.
-- Common trap: underwriting on Henry Hub alone and ignoring basis, transportation expense, and seasonal cash-flow volatility.
-
-### Liquids-rich multi-basin unconventional producer
-- Core engines: commodity diversification, basin optionality, capital flexibility, mix optimization.
-- Primary watch items: oil versus gas versus NGL margin, basin competition for capital, corporate overhead, non-operated exposure.
-- Common trap: using blended BOE metrics that hide a low-quality molecule mix.
-
-### Offshore or deepwater independent
-- Core engines: large long-cycle projects, lower base decline after start-up, higher upfront development, project concentration.
-- Primary watch items: project timing, uptime, working-interest structure, geopolitical and fiscal terms, decommissioning, concentration risk.
-- Common trap: applying shale-style quarter-to-quarter heuristics to a project business with lumpy start-ups and lower but less flexible decline.
-
-### International conventional independent
-- Core engines: conventional reservoir life, PSC or royalty/tax complexity, country exposure, infrastructure access.
-- Primary watch items: fiscal regime, repatriation, local pricing, lifting cost stability, geopolitical risk, contract renewal.
-- Common trap: comparing headline multiples directly with U.S. onshore names without adjusting for sovereign and fiscal complexity.
-
-### Mature conventional / decline-managed consolidator
-- Core engines: low-decline production, acquired PDP cash flow, optimization of legacy fields, cash harvesting.
-- Primary watch items: abandonment liability, reserve revisions, workover burden, water handling, field-life extension assumptions.
-- Common trap: mistaking lower headline capex for structural superiority when value is being harvested from a declining asset base.
-
-### Small-cap exploration / appraisal / emerging-basin story
-- Core engines: resource delineation, appraisal success, development funding, strategic optionality.
-- Primary watch items: funding runway, risked NAV, drilling catalysts, farm-outs, regulatory approvals, convertibility from contingent resource to reserves.
-- Common trap: treating unrisked resource numbers like proved value.
-
-### Non-operated-heavy E&P
-- Core engines: acreage position, partner-operated development, lean overhead, optionality across operators.
-- Primary watch items: operator timing control, cost inflation pass-through, schedule uncertainty, partner quality, cash-call burden.
-- Common trap: modeling volume ramps as if management controls the development schedule.
-
-Pure mineral and royalty companies are not the main target of this skill. Use only if the underwriting question is still mostly about operator-like reserve and basin exposure rather than pure royalty economics.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## E&P KPI cheat sheet
-
-Use these definitions consistently and do not let management hide behind boe shorthand.
-
-- Production: reported sales volumes or production volumes by commodity. Always separate oil, gas, and NGLs before recombining.
-- BOE: barrel-of-oil-equivalent. Useful for scale, dangerous for value. A 6:1 gas conversion is not an economic equivalency.
-- WI / NRI: working interest and net revenue interest. WI drives capex and operating exposure; NRI drives realized revenue.
-- Realized price: what the company actually receives after benchmark move, location basis, quality, transport, marketing, and mix.
-- Differential: the gap between benchmark price and realized wellhead or sales price.
-- LOE: lease operating expense. Lifting cost. Must be separated from GPT and production taxes.
-- GPT: gathering, processing, and transportation. Often the key hidden cost line, especially for gas and NGL names.
-- DD&A: depletion, depreciation, and amortization. Useful, but not a substitute for reserve quality or full-cycle economics.
-- PDP / PDNP / PUD: proved developed producing, proved developed non-producing, and proved undeveloped reserves.
-- Reserve replacement ratio: net reserve additions divided by production. Distinguish total from organic.
-- Production replacement: whether drilling and acquisitions are replacing decline in actual output, not just reserve bookings.
-- Reserve life index: reserves divided by current production. Useful only after checking commodity mix and PUD quality.
-- Standardized measure: GAAP discounted future net cash flow of proved reserves using SEC-style assumptions. Helpful for comparability, not fair value.
-- PV-10: pre-tax present value of future net revenues from proved reserves discounted at 10%. Useful, but often excludes taxes and hedges and depends on the price deck.
-- F&D / FD&A cost: finding and development, or finding, development, and acquisition cost per BOE added.
-- Recycle ratio: margin per BOE divided by FD&A cost per BOE. Can be informative, but only if both numerator and denominator are clean.
-- Maintenance capital: capital required to hold production approximately flat, after allowing for natural decline.
-
-Interpretation rules:
-- BOE growth is not valuable if the mix shifts toward lower-margin gas or NGL and differentials worsen.
-- Production growth is not valuable if reserve quality, inventory duration, or free cash flow deteriorates.
-- A high reserve replacement ratio is not automatically bullish if it came from acquisitions, price revisions, or low-quality PUDs.
-- A low LOE number can be misleading if GPT, workover, water handling, or corporate costs sit elsewhere.
-- PV-10 and standardized measure are tools, not answers. They are highly sensitive to price deck, development timing, reserve category, and tax treatment.
-- Cheap EV / EBITDA or EV / cash flow can be a trap if the company has steep decline, shallow inventory, heavy transport commitments, or a looming maturity wall.
-
-## E&P Operating Metric Map
-
-## 1) Molecule, basin, and economic unit of account
-### Metrics to track
-- production by commodity and basin
-- oil mix, gas mix, NGL mix
-- WI and NRI by core asset where available
-- realized prices by commodity
-- benchmark-to-realized differential
-- operated versus non-operated mix
-- regional production concentration
-
-## 2) Reserve quality, inventory depth, and development runway
-### Metrics to track
-- proved reserves by commodity
-- reserve replacement ratio and organic reserve replacement ratio
-- PDP, PDNP, and PUD mix
-- reserve life index
-- net reserve additions by source
-- proved undeveloped conversion and aging
-- location count by tier and by basin where disclosed
-- type-curve assumptions, EURs, lateral lengths, and spacing
-
-## 3) Production durability, decline, and maintenance capital
-### Metrics to track
-- production growth by commodity and basin
-- new wells turned in line, completion cadence, and rig count where relevant
-- base decline and corporate decline estimates
-- maintenance capital estimate
-- capex per incremental BOE or per incremental barrel of oil
-- inventory years at current pace and maintenance pace
-- DUC inventory and completion timing where relevant
-
-## 4) Realized pricing, differentials, hedges, and transportation exposure
-### Metrics to track
-- realized price by commodity
-- benchmark price by commodity
-- cash hedge settlement and mark-to-market effect
-- hedged volume as a percentage of expected production
-- hedge tenor and strike structure
-- GPT cost per BOE or per Mcfe
-- firm transport commitments, reservation charges, and MVCs
-- basis exposure and geographic price realization quality
-
-## 5) Cost structure and capital efficiency
-### Metrics to track
-- LOE per BOE
-- GPT per BOE or per Mcfe
-- production taxes per BOE
-- cash G&A per BOE
-- D&C cost per well, per lateral foot, or per location
-- corporate break-even and asset-level break-even
-- FD&A or F&D cost per BOE
-- recycle ratio
-- DD&A per BOE
-
-## 6) Balance sheet, liquidity, and capital allocation
-### Metrics to track
-- gross debt, net debt, and liquidity
-- debt maturity schedule
-- secured versus unsecured mix
-- net debt / EBITDAX and interest coverage
-- borrowing-base utilization where relevant
-- shareholder returns versus capex versus debt paydown
-- asset-retirement obligations and plugging liabilities
-- fixed transport or midstream commitments
-
-## 7) Earnings quality, reserve accounting, and normalization
-### Metrics to track
-- adjusted EBITDAX or cash flow
-- hedge settlement and unrealized derivative impact
-- reserve revisions from price versus other factors
-- acquisition and divestiture contribution
-- standardized measure and PV-10 disclosures where available
-- per-share production, reserve, and free-cash-flow metrics
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` oil-gas-ep sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for E&P companies
-
-Build E&P models from production by commodity and basin upward. Do not force a generic revenue-growth model onto a depleting asset business.
-
-## Required model structure
-Forecast at minimum:
-- production by commodity and by major basin or asset
-- benchmark price deck and realized-price bridge by commodity
-- hedge volumes, strike structure, and cash settlements
-- LOE, GPT, production taxes, cash G&A, and interest
-- capex split into drilling, completions, facilities, land, and other where available
-- maintenance versus growth capital framing
-- reserve roll-forward or inventory roll-forward logic
-- free cash flow, leverage, and liquidity
-- NAV and downside case by security
-
-## Mandatory decomposition
-- separate oil, gas, and NGL economics
-- separate benchmark price from differentials and hedge effects
-- separate PDP decline from new-well contribution
-- separate maintenance capital from growth capital
-- separate organic reserve additions from acquired reserve additions
-- separate PDP value from undeveloped inventory value
-- separate operating control from non-operated exposure
-
-## Mandatory sensitivities
-Run explicit sensitivities on:
-- WTI or Brent
-- Henry Hub and relevant basis differentials
-- NGL realizations
-- D&C and service cost inflation or deflation
-- type-curve or EUR assumptions
-- drilling cadence and maintenance capital
-- hedge roll-off
-- transport and gathering cost burden
-- exit multiple or NAV discount where used
-- refinancing cost and liquidity availability
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/modeling-rules.md
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-# E&P Modeling Guardrails
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this when the task needs forecast, valuation, downside, credit, or security-specific modeling detail.
-
-## Reserve-First Posture
-
-Treat an E&P issuer as a depleting reserve base, commodity/differential exposure machine, decline-curve business, capital-allocation engine, and reserve-backed balance sheet. Do not lead with generic revenue growth, simple EBITDA margin, or blended BOE heuristics.
-
-Start with molecule, basin, asset quality, reserve credibility, development control, realized pricing, cost/capex intensity, and the claim position of the security being analyzed. Treat reported EPS as lower quality when it is driven by commodity price moves, unrealized hedge marks, price-led reserve revisions, ceiling-test accounting, gains on sale, acquisition accounting, or tax noise.
-
-## Analysis Path
-
-1. Molecule and basin: separate oil, gas, and NGL volumes and margins; map basin, play, bench, asset cluster, working interest, NRI, operated exposure, and benchmark-to-realized price bridge.
-2. Reserves and inventory: bridge proved reserves through production, extensions, discoveries, revisions, purchases, and sales; split PDP, PDNP, and PUD; test PUD funding, aging, and development timing; risk inventory by tier, type curve, lateral length, spacing, and parent-child interference.
-3. Production and maintenance capital: bridge volumes through base decline, wells turned in line, downtime, acquisitions, divestitures, and timing; estimate PDP and corporate decline; distinguish maintenance capital from growth capital and acquisition carryover.
-4. Realized pricing and obligations: build realized price by commodity from benchmark, basis, quality, transport, marketing, and hedge settlements; map hedge tenor, strike, volume coverage, and counterparty; treat firm transport, gathering, MVCs, and processing bottlenecks as quasi-fixed constraints.
-5. Costs and capital efficiency: separate LOE, GPT, production taxes, cash G&A, interest, D&C, facilities, workover, and water-handling costs; compare half-cycle and full-cycle economics; clean FD&A and recycle ratio before using them; build corporate break-even, not only wellhead break-even.
-6. Balance sheet and allocation: map debt, maturity wall, secured versus unsecured status, RBL terms, borrowing-base sensitivity, covenant room, liquidity after letters of credit and working capital, ARO, transport commitments, dividends, buybacks, M&A, and debt paydown.
-7. Earnings quality and reserve accounting: separate price from volume, cost, and reserve-quality change; distinguish cash hedge settlements from derivative marks; split price revisions from geology or performance revisions; reconcile standardized measure/PV-10 to management NAV language.
-8. Public-equity security context and Credit Markets handoff: for equity, anchor on strip and mid-cycle NAV, inventory duration, maintenance capex, and per-share value creation. Route unsecured-debt PDP cushion, maturity runway, secured-debt leakage, secured/RBL collateral, reserve-engineer assumptions, redetermination risk, and debt hedge requirements to Credit Markets.
-
-## Model Must Show
-
-- Commodity-separated production, realized prices, differentials, hedge settlements, and unhedged exposure.
-- Reserve bridge, PDP/PUD mix, inventory duration, decline rates, and maintenance versus growth capex.
-- LOE, GPT, production taxes, D&C/facilities cost, cash G&A, interest, ARO, and transport commitments as separate drivers.
-- NAV or valuation bridge with price deck, decline assumption, reserve category, development timing, tax treatment, and fixed obligations.
-- Sensitivities for WTI, Brent, Henry Hub, basin basis, NGL price, well cost, decline, downtime, borrowing base, and abandonment cost.
-
-## Do Not
-
-- Use blended BOE growth as a value proxy without molecule and margin mix.
-- Treat reserve replacement from acquisitions, price revisions, or low-quality PUDs as drill-bit success.
-- Use EV/EBITDAX or FCF yield without decline, inventory duration, maintenance capital, transport commitments, and maturity-wall context.
-- Assume undeveloped inventory protects creditors unless it is financeable, economic, and likely to be developed.
-
-## Deferred Modeling Layers
-
-Load deeper files only when the task needs that layer:
-
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: required model structure, driver decomposition, and sensitivities.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-Focus on what the market is mispricing about reserve quality, inventory duration, maintenance capital, capital allocation, or balance-sheet resilience.
-
-## Event-driven
-Prioritize catalysts: M&A, acreage swaps, asset sales, reserve report surprises, spacing updates, borrowing-base redeterminations, debt exchanges, activist pressure, major discoveries, project start-ups, or regulatory and pipeline events.
-
-## Macro
-Emphasize commodity curves, OPEC+ and supply response, LNG demand, basis and takeaway, service-cost inflation, interest rates, and how those macro variables interact with company-specific hedging and capital allocation.
-
-## Quant / systematic
-Standardize the most predictive variables: oil mix, realized-price quality, reserve replacement, inventory duration, maintenance-capital burden, margin per BOE, leverage, hedge coverage, and free-cash-flow yield.
-
-## Credit Markets handoff / equity-risk debt context
-Focus on PDP value, collateral leakage, borrowing-base risk, transport and other fixed commitments, covenant headroom, maturity wall, and recovery under downside decks.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-Translate growth into per-share reserve value, per-share production value, and sustainable free cash flow. Favor companies that can grow without exhausting core inventory or levering the balance sheet.
-## Output overlays when this skill is used with common finance tasks
-
-## With an earnings deep dive
-Always add:
-- production bridge by commodity and basin
-- reserve and production replacement bridge
-- realized-price and differential table
-- hedge and transport exposure summary
-- maintenance-capital versus growth-capital framing
-- leverage, liquidity, and next 12 to 24 month catalyst table
-
-## With an investment memo
-Always include:
-- e&p archetype and basin classification
-- thesis by inventory, price realization, cost position, and balance sheet
-- variant perception on reserve quality, maintenance capital, or commodity sensitivity
-- downside NAV and downside liquidity case
-- disconfirming evidence
-- security-specific recommendation with claim-position discussion
-
-## With a financial model
-Always produce:
-- production schedules by commodity and basin
-- realized-price and hedge schedules
-- LOE, GPT, production-tax, and G&A schedules
-- capex schedule split between maintenance and growth logic
-- reserve or inventory roll-forward logic
-- free-cash-flow and leverage outputs
-- NAV and commodity sensitivity tables
-
-## When debt or structured credit work is requested, route to Credit Markets
-Always include:
-- PDP reserve-value framing and downside collateral discussion
-- maturity, liquidity, and refinancing path
-- downside strip and basis stress
-- transport and fixed-obligation burden
-- abandonment-liability and collateral-leakage discussion
-
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-
-## Default writing style
-
-Write like a veteran upstream analyst:
-- lead with rock quality, reserve quality, and inventory duration, not headline production growth
-- separate molecules before combining them
-- separate benchmark prices from realized prices
-- separate PDP durability from undeveloped promise
-- translate management optimism into maintenance capital, reserve replacement, and per-share value consequences
-- be skeptical of acreage maps, type curves, and BOE metrics until they reconcile to cash flow and reserve quality
-
-When unsure, prioritize reserve quality, inventory depth, realized-price quality, maintenance-capital burden, and balance-sheet resilience over one quarter of strong commodity prices or management volume guidance.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-Treat these as high-priority warning signals:
-- reserve replacement sustained only by acquisitions rather than organic drilling
-- reserve growth driven mainly by price revisions or looser booking assumptions
-- high PUD proportion with weak funding visibility or stretched development schedule
-- management highlighting location count while avoiding value per location or inventory quality
-- production growth that requires materially outspending cash flow with no clear per-share value creation
-- steep decline masked by acquisitions or unusually high completion cadence
-- strong benchmark prices but weak realized pricing because of basis, transport, or quality issues
-- low LOE used as the hero metric while GPT, water, or corporate costs rise elsewhere
-- cheap EV / cash-flow multiple on a short-duration or high-obligation asset base
-- type curves or EURs inflated by longer laterals, wider spacing assumptions, or immature data sets
-- non-operated exposure framed as capital flexibility even though schedule control is weak
-- large transport or MVC commitments that become fixed-charge pain in a downturn
-- hedges rolling off just as leverage rises or capex commitments remain high
-- abandonment liabilities or remediation burden treated as distant rather than real
-- M&A presented as inventory extension without clear cost-of-acquired-inventory math
-- one commodity or one basin concentration ignored because BOE reporting looks diversified
-## Never do this
-
-- Do not lead with revenue growth, EPS growth, or generic EBITDA margin.
-- Do not compare producers on blended BOE metrics without decomposing commodity mix.
-- Do not accept reserve replacement without identifying the source of the additions.
-- Do not annualize favorable spot prices or hedge gains.
-- Do not use EV / flowing barrel or EV / proved reserves as standalone valuation methods.
-- Do not underwrite a gas producer on Henry Hub without basis and transport analysis.
-- Do not confuse lower capex with lower maintenance capital unless decline has been proven.
-- Do not treat PUD value as equivalent to PDP value.
-- Do not assume acreage count equals inventory quality.
-- Do not underwrite credit without downside reserve value and liquidity analysis.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `Oil Gas E&P Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order unless the task requires otherwise:
-1. annual reports, 10-Ks, 10-Qs, reserve disclosures, reserve engineer reports, hedge tables, debt schedules, and basin maps
-2. earnings presentations, conference-call commentary, investor days, and supplemental operating statistics
-3. credit agreements, bond documents, rating materials, and midstream or transport disclosures
-4. VDR engineering reports, A&D marketing materials, well-level data, lease maps, and internal reserve cases
-5. basin data, state filings, commodity and basis data, service-cost benchmarks, and third-party engineering or operating comparisons
-
-## Reconciliation rules
-- Reconcile production volumes to sales volumes when they differ materially.
-- Reconcile benchmark prices to realized prices commodity by commodity.
-- Reconcile reserve additions into organic, acquired, price-driven, and technical buckets.
-- Reconcile capital spending to wells turned in line, locations developed, and maintenance-capital needs.
-- Reconcile corporate BOE metrics back to molecule-specific economics.
-- Reconcile NAV or PV discussions to the specific price deck and tax treatment used.
-- Reconcile hedged volumes and settlement values to forecast production and liquidity needs.
-## E&P-specific diligence requests
-
-When the task involves diligence, request or prioritize the following if available:
-- full reserve report with engineer assumptions, by basin, commodity, and reserve category
-- reserve roll-forward and detail on revisions due to price versus other factors
-- inventory deck by location tier, lateral length, spacing, and development timing
-- type curves, EUR assumptions, spacing studies, parent-child and cube-development analysis
-- well-level production history, turn-in-line dates, and operating downtime history
-- capex budget by drill, complete, facilities, land, and infrastructure category
-- operated versus non-operated asset split and partner schedule control
-- acreage maps, HBP status, lease expirations, royalty burdens, and title issues
-- gathering, processing, transport, and marketing contracts, including MVCs and reservation charges
-- hedge book by commodity, tenor, and instrument type
-- debt documents, borrowing-base calculations, collateral package, and covenant summary
-- abandonment, plugging, water handling, environmental, and reclamation obligations
-- acquisition or divestiture models and synergy cases if M&A is part of the story
-- for international assets: PSC, concession, tax, and repatriation terms
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/oil-gas-ep/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` oil-gas-ep sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use E&P-specific valuation, not generic corporate valuation
-
-## Equity valuation hierarchy
-
-### Large-cap or diversified independents
-Use these lenses first:
-1. NAV based on PDP value plus risk-adjusted undeveloped inventory
-2. EV / EBITDAX or EV / cash flow under a clearly stated price deck
-3. free-cash-flow yield under strip and under a normalized mid-cycle deck
-4. EV / proved reserves and EV / flowing production only as secondary cross-checks
-
-### Shale producers with visible short-cycle inventory
-Use these lenses first:
-1. NAV with explicit inventory tiering and maintenance-capital logic
-2. free-cash-flow yield and cash return potential
-3. EV / EBITDAX or EV / DACF adjusted for hedges and differentials
-4. value per high-quality location or inventory year only as a sense-check, never a primary method
-
-### Gas-weighted producers
-Use these lenses first:
-1. NAV under explicit Henry Hub plus basis and transport assumptions
-2. free-cash-flow yield at strip and downside winter or shoulder-season cases
-3. EV / cash flow after normalizing for basis, transport, and hedge effects
-4. optionality value only after proving solvency and transport adequacy
-
-### Offshore, international, or pre-development stories
-Use these lenses first:
-1. risked NAV or project-level NPV
-2. funding-gap and dilution analysis
-3. downside liquidity runway and contingent-value framing
-
-### Valuation rules
-- Compare only within similar commodity mix, decline profile, inventory depth, balance sheet, and transport burden.
-- A low EV / cash-flow multiple can be deserved if the company has short inventory life, poor realized pricing, heavy transport commitments, or weak reserve quality.
-- A premium multiple can be deserved if the company has deep tier-one inventory, strong realized pricing, low maintenance capital, and superior capital-allocation discipline.
-- Never use EV / flowing barrel or EV / BOE of reserves by itself.
-- Do not capitalize one quarter of unusually strong commodity prices.
-- Do not use PV-10 or standardized measure mechanically without adjusting for taxes, hedges, undeveloped timing, and the difference between SEC pricing and investor price expectations.
-
-## Debt valuation hierarchy
-- PDP reserve value and downside reserve value
-- claim priority and collateral position
-- liquidity runway and maturity wall
-- hedging, transport, and fixed-charge burden
-- reserve revision risk and borrowing-base sensitivity
-- abandonment and environmental liability burden
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/pm-judgment-heuristics.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/pm-judgment-heuristics.md
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-# Sector PM Judgment Heuristics
-
-Use this reference to adapt sector context for public-equity PM decision support. Sector context should sharpen the investment decision, not become a generic sector primer.
-
-## Taste Rules
-
-- Identify the sector-specific PM debate before listing KPIs.
-- Name the KPI hierarchy: primary driver, secondary driver, and common false signal.
-- State what is priced in and which sector convention the market is likely using.
-- Distinguish durable signal from one-quarter noise.
-- Identify valuation convention and the metric that would force multiple expansion or compression.
-- Flag source gaps and unsupported precision.
-- Force falsifiers: what evidence would break the sector thesis or change sizing?
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `REIT Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the REIT
-
-Classify the company before analyzing it. Do not compare all REITs mechanically.
-
-## Archetypes
-
-### Net lease / triple-net REIT
-- Core engines: contractual rent escalators, tenant credit, rent coverage, lease duration, capital recycling.
-- Primary watch items: tenant concentration, lease-maturity walls, re-leasing residual value, CPI versus fixed escalators, sale-leaseback underwriting, acquisition spreads versus cost of capital.
-- Common trap: calling the cash flow bond-like without underwriting unit-level tenant health, rent coverage, and real-estate residual value.
-
-### Shopping center / open-air retail REIT
-- Core engines: grocer or necessity-anchor health, small-shop leasing, occupancy, tenant sales, mark-to-market rents, recoveries.
-- Primary watch items: anchor renewal risk, co-tenancy, bad debt, leasing spreads, redevelopment yield.
-- Common trap: focusing on headline occupancy without tenant sales productivity, occupancy cost, or weak inline leasing economics.
-
-### Mall / outlet REIT
-- Core engines: tenant sales per square foot, occupancy, releasing spreads, mixed-use redevelopment, foot traffic.
-- Primary watch items: department-store box exposure, occupancy cost, redevelopment capex, tenant bankruptcies.
-- Common trap: treating high sales per square foot or stable occupancy as enough without measuring how much capex and redevelopment spend are required to sustain it.
-
-### Industrial / logistics REIT
-- Core engines: occupancy, cash rent change, net effective rent change, retention, development, market-rent mark-to-market.
-- Primary watch items: supply deliveries, tenant demand, lease duration, land bank, development yields, customer concentration.
-- Common trap: annualizing peak rent spreads after supply turns or ignoring development risk.
-
-### Apartment / residential REIT
-- Core engines: same-store revenue growth from occupancy, rate, concessions, and bad debt; expense control; development delivery.
-- Primary watch items: supply wave, affordability, resident turnover, concessions, bad debt, local regulation, taxes and insurance, blended lease rates.
-- Common trap: trusting average rent growth without separating occupancy, concessions, bad debt, and expense inflation.
-
-### Self-storage REIT
-- Core engines: street rates versus in-place rates, occupancy, move-in and move-out cadence, rate optimization, brand and digital demand capture.
-- Primary watch items: occupancy trajectory, realized rent per occupied square foot, marketing spend, new supply, acquisition integration.
-- Common trap: missing that near-term revenue can rise or fall because management is trading off occupancy versus price.
-
-### Healthcare REIT
-- Core engines: varies by subsector.
-- Triple-net MOB, SNF, hospital, lab style: rent coverage, tenant or operator health, lease duration, reimbursement sensitivity.
-- SHOP or RIDEA seniors housing style: occupancy, RevPOR, labor cost, margin, local supply, acuity.
-- Primary watch items: operator concentration, reimbursement, labor, capex, asset obsolescence, government-policy sensitivity.
-- Common trap: analyzing all healthcare REITs like net lease when a large share of value may sit in operating assets.
-
-### Lodging / hotel REIT
-- Core engines: RevPAR, ADR, occupancy, food and beverage or ancillary revenue, hotel EBITDA margin, asset management, capex.
-- Primary watch items: group versus transient mix, market mix, renovation schedule, brand fees, labor, insurance, cyclicality.
-- Common trap: using landlord-style NOI logic on what is effectively a daily mark-to-market operating business with heavy recurring capex.
-
-### Data center / digital infrastructure REIT
-- Core engines: signed bookings, commencements, backlog, power availability, interconnection, rent escalators, development pipeline.
-- Primary watch items: power procurement, customer concentration, lease-commencement lag, capital intensity, competition, technology obsolescence.
-- Common trap: treating backlog as current earnings without modeling the lag, capital spend, and customer concentration.
-
-### Tower / communications infrastructure REIT
-- Core engines: organic tenant billings growth, colocations, amendment activity, churn, escalators, international FX, ground-lease economics.
-- Primary watch items: carrier spending cycles, churn from consolidations, contractual escalators, emerging-technology impact, zoning moat.
-- Common trap: comparing tower REITs directly to traditional landlord REITs on simple occupancy or cap-rate logic.
-
-### Office / lab / challenged-asset REIT
-- Core engines: lease rollover, tenant credit, amenity and capex intensity, market vacancy, redevelopment or conversion potential.
-- Primary watch items: near-term rollover, TI/LC burden, downtime, obsolescence, lender posture, tax reassessment.
-- Common trap: accepting a low multiple or high dividend as enough without testing liquidation value and refinancing risk.
-
-### Specialty / gaming / experiential REIT
-- Core engines: tenant or operator coverage, master leases, escalators, regional demand, transaction underwriting.
-- Primary watch items: operator concentration, asset fungibility, regulatory risk, financing receivables, rent coverage, acquisition yields.
-- Common trap: treating long lease duration as safety when asset use is narrow or tenant or operator concentration is high.
-
-### Mortgage REIT
-Not covered by this skill. Mortgage REITs are financing spread vehicles, not property-cash-flow landlords. Treat mREIT support as future or non-local unless a dedicated real estate credit or mREIT workflow is explicitly available.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `REIT Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Property Cash-Flow Metric Map
-
-## 1) Property type, lease model, and economic unit of account
-### Metrics to track
-- same-store NOI or equivalent operating measure
-- occupancy, leased rate, retention, and average remaining lease term
-- cash rent change, GAAP rent change, or blended lease-rate growth
-- tenant concentration and top-tenant exposure
-- property mix by sector, market, and asset quality
-- proportion of contractual escalators fixed versus CPI-linked
-- percentage of revenue from operating assets versus contractual rent
-
-## 2) Portfolio quality, market selection, and asset-level moat
-### Metrics to track
-- same-store pool size and composition
-- top markets and concentration by NOI
-- tenant concentration by annualized base rent or NOI
-- weighted average lease term, expiration schedule, and percent of NOI rolling by year
-- sales per square foot, occupancy cost, or tenant coverage where relevant
-- same-store occupancy and leased-to-occupied spread
-- new supply as a percent of market inventory where relevant
-- asset age, redevelopment pipeline, and major capex needs
-
-## 3) Leasing, occupancy, same-store NOI, and embedded growth
-### Metrics to track
-- same-store NOI growth, revenue growth, and expense growth
-- cash leasing spreads and GAAP leasing spreads
-- leased rate, occupied rate, and retention
-- bad debt, concessions, and free-rent burn-off
-- rent per square foot, rent per occupied square foot, or rate per unit
-- TI/LC burden and downtime where relevant
-- RevPAR, ADR, Revenue Per Occupied Room, or hotel EBITDA margin where relevant
-- signed-not-open backlog, commencements, or organic tenant billings where relevant
-
-## 4) External growth, development, redevelopment, and capital allocation
-### Metrics to track
-- acquisition cap rate or initial cash yield
-- development yield on cost, stabilization timeline, and prelease percentage
-- disposition cap rate and gains
-- retained cash after dividend
-- acquisition and development spend versus funding sources
-- equity issuance, ATM usage, forward equity, JV capital, and debt financing
-- percentage of AFFO growth coming from same-store versus external sources
-
-## 5) Balance sheet, leverage, liquidity, and cost of capital
-### Metrics to track
-- net debt to EBITDAre or adjusted EBITDAre
-- fixed-charge coverage and interest coverage
-- weighted average interest rate and weighted average term to maturity
-- fixed versus floating debt mix
-- secured debt as a percent of gross assets
-- unencumbered NOI and unsecured debt coverage
-- available liquidity, revolver capacity, and near-term maturity ladder
-- debt to enterprise value or debt to gross assets where relevant
-
-## 6) FFO, AFFO, dividend safety, and earnings-quality normalization
-### Metrics to track
-- Nareit FFO per share and growth
-- AFFO or CAD per share and growth
-- dividend payout as a percent of AFFO or CAD
-- recurring capex plus TI/LC as a percent of NOI or revenue
-- straight-line rent as a percent of revenue
-- same-store NOI contribution versus acquisition or development contribution
-- retained cash after dividends
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` reits sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for REITs
-
-Build REIT models from property cash flows upward. Do not force a generic corporate revenue-growth model onto a landlord.
-
-## Required model structure
-Forecast at minimum:
-- same-store revenue, expenses, and NOI
-- occupancy, lease rates, concessions, bad debt, or operating KPIs appropriate to the sector
-- acquisition, disposition, development, and redevelopment activity
-- recurring capex, TI/LC, and maintenance spend
-- G&A and any fee income or JV income
-- debt balances, interest rate, and maturity or refinancing assumptions
-- preferred dividends or non-controlling interests where relevant
-- Nareit FFO, AFFO or CAD, and dividend payout
-- net asset value bridge and leverage metrics
-
-## Mandatory decomposition
-- separate same-store from non-same-store
-- separate occupancy from pricing
-- separate cash rent change from GAAP rent change
-- separate contractual escalators from mark-to-market lease rollover
-- separate development contribution from acquisition contribution
-- separate recurring capex and leasing costs from one-time redevelopment or value-add capex
-- separate organic NOI from noncash adjustments and gains on sale
-
-## Mandatory sensitivities
-Run explicit sensitivities on:
-- occupancy
-- leasing spreads or mark-to-market rent capture
-- same-store expense growth
-- development yield and lease-up timing
-- acquisition cap rate versus funding cost
-- exit cap rate and NAV
-- refinancing rate and debt availability
-- tenant default, bad debt, or rent-coverage deterioration
-- dividend payout policy and retained cash
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/modeling-rules.md
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-# REIT Modeling Guardrails
-
-Deferred reference for `REIT Sector Analysis`. Load this when the task needs forecast, valuation, downside, credit, or security-specific modeling detail.
-
-## Property-Cash-Flow Posture
-
-Treat a REIT as a portfolio of property cash flows, a liability/capital-market structure, and an external-growth engine that only works when underwriting discipline and cost of capital align. Do not lead with GAAP EPS, generic revenue growth, or corporate margin templates.
-
-Start with property type, lease model, supply/demand cycle, cash NOI, same-store growth, embedded rent mark-to-market, recurring leakage from NOI to AFFO, capital access, dividend safety, and where the security sits in the stack. GAAP net income is usually noisy because depreciation, straight-line rent, gains on sale, lease intangibles, JV accounting, and financing items distort cash earnings power.
-
-## Analysis Path
-
-1. Property type and lease model: identify the economic unit of account, whether cash flow is contractual or operating, and how quickly leases, occupancy, street rates, RevPAR, SHOP rates, data-center commencements, or tower billings reset.
-2. Portfolio quality: map market, asset quality, age, competitive set, replacement cost, supply pipeline, tenant/operator quality, and asset-level moat; separate resilient assets from obsolete or capex-heavy assets.
-3. Leasing and same-store NOI: bridge revenue through occupancy, cash rent spreads, commencements, free-rent burn-off, bad debt, recoveries, percentage rent, lease termination income, and expense inflation; separate cash leasing spreads from GAAP spreads.
-4. Capex and embedded growth: haircut rent mark-to-market for downtime, TI/LC, commissions, free rent, and asset quality; split defensive redevelopment from true value creation; identify recurring building capex and turn costs before AFFO.
-5. External growth: break growth into same-store, acquisitions, development, redevelopment, dispositions, JVs, and capital markets; test acquisition/development yields against fully loaded marginal cost of capital, not stale hurdle rates.
-6. Balance sheet and liquidity: map debt maturity, fixed/floating mix, secured/unsecured status, covenant room, rating access, unencumbered asset pool, JV/preferred exposure, revolver capacity, forward equity, and asset-sale optionality.
-7. FFO/AFFO and dividend quality: bridge GAAP net income to Nareit FFO and company AFFO/CAD; normalize gains, straight-line rent, lease-intangible amortization, transaction costs, one-time recoveries, noncash financing items, recurring capex, TI/LC, and leasing commissions.
-8. Public-equity security context and Credit Markets handoff: for equity, anchor on asset quality, AFFO durability, NAV, external-growth runway, leverage, and dividend. Route preferred/debt issuing entity, claim priority, maturity, coverage, covenants, collateral, and stressed recovery analysis to Credit Markets.
-
-## Model Must Show
-
-- Same-store NOI bridge, occupancy, leasing spreads, rent roll, lease expirations, property mix, and market exposure.
-- NOI-to-FFO-to-AFFO bridge with recurring capex, TI/LC, straight-line rent, lease intangibles, and nonrecurring items separated.
-- NAV or implied-cap-rate bridge with property-level NOI, cap rates by segment, debt/preferreds/JVs, and development value.
-- Debt maturity ladder, fixed/floating exposure, secured debt, unencumbered NOI, fixed-charge coverage, liquidity, and refinancing assumptions.
-- Sensitivities for cap rates, occupancy, cash leasing spreads, expense inflation, TI/LC, development yield, rates, refinancing access, and dividend payout.
-
-## Do Not
-
-- Use GAAP EPS, EBITDA margin, or generic DCF shortcuts as the lead valuation frame.
-- Treat same-store NOI as clean without occupancy, rent spread, bad-debt, recovery, and expense detail.
-- Call external growth accretive without marginal cost of capital and funding-source math.
-- Underwrite dividend safety only from management payout ratios without AFFO normalization and balance-sheet context.
-
-## Deferred Modeling Layers
-
-Load deeper files only when the task needs that layer:
-
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: required model structure, driver decomposition, and sensitivities.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `REIT Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-Focus on what the market is mispricing about same-store growth durability, mark-to-market rent, capex burden, balance-sheet flexibility, or private-market NAV.
-
-## Event-driven
-Prioritize catalysts: asset sales, take-private or activist setups, capital raises, dividend changes, refinancing events, tenant bankruptcies, large redevelopments, spin-offs, portfolio simplification, rating actions, and M&A.
-
-## Macro
-Emphasize rates, cap rates, financing spreads, construction costs, supply delivery, labor and insurance inflation, consumer demand, business formation, and property-sector cycle position.
-
-## Quant / systematic
-Standardize the fastest-moving and most predictive variables: same-store NOI, occupancy, leasing spreads, rent coverage, retained AFFO, leverage, maturity wall, and valuation versus NAV or historical multiples.
-
-## Credit Markets handoff / equity-risk debt context
-Focus on collateral quality, unsecured versus secured claim position, unencumbered pool, refinancing risk, covenant headroom, tenant stress, and recovery under downside cap-rate and occupancy assumptions.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-Translate growth into per-share AFFO and NAV growth, not just gross asset growth. Favor REITs with real internal growth, disciplined development, and capital access that does not depend on serial issuance at fragile valuations.
-## Output overlays when this skill is used with common finance tasks
-
-## With an earnings deep dive
-Always add:
-- same-store NOI bridge
-- leasing and occupancy dashboard
-- rent-roll or rollover summary
-- balance-sheet and maturity-ladder table
-- AFFO and dividend-quality bridge
-- next 12 to 24 month catalysts by sector cycle and financing need
-
-## With an investment memo
-Always include:
-- REIT archetype and sector-cycle position
-- core thesis by assets, leases, balance sheet, valuation, and catalyst
-- variant perception
-- private-market NAV view and public-equity multiple view
-- disconfirming evidence
-- security-specific recommendation with stack position and downside case
-
-## With a financial model
-Always produce:
-- same-store revenue, expense, and NOI schedules
-- acquisition, disposition, and development schedules
-- recurring capex and TI/LC schedules
-- debt and interest schedule with maturity assumptions
-- FFO, AFFO, and dividend outputs
-- NAV and cap-rate sensitivities
-
-## When debt or structured credit work is requested, route to Credit Markets
-Always include:
-- unencumbered-asset and unsecured-debt coverage discussion
-- maturity or refinancing path
-- NOI and asset-value downside stress
-- recovery discussion tied to claim position and collateral leakage
-
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-
-## Default writing style
-
-Write like a veteran REIT analyst:
-- lead with the property cash-flow driver, not the accounting metric
-- separate same-store from external growth
-- separate contractual rent from true cash economics
-- quantify recurring cash leakage between NOI and AFFO
-- tie valuation to asset quality, balance sheet, and cycle position
-- be skeptical of "accretion," "stability," and "yield" language unless proven with cash flow and asset values
-
-When unsure, prioritize rent-roll quality, balance-sheet resilience, recurring capex burden, and downside NAV over headline dividend yield or one quarter of FFO upside.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/red-flags.md
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-# Red Flags And Failure Modes
-
-Deferred reference for `REIT Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-Treat these as high-priority warning signals:
-- AFFO defined too generously relative to real recurring capex or TI/LC
-- same-store NOI growth driven by easy bad-debt compares, recoveries, or one-time fees
-- leverage defended with optimistic asset sales or serial equity issuance
-- near-term maturity wall with weak unsecured-market access
-- capex-heavy sector valued on price/FFO without cash-leakage adjustment
-- occupancy looks stable but leased-to-occupied gap, concessions, or bad debt is worsening
-- tenant concentration or operator concentration hidden by master leases or aggregate reporting
-- development pipeline sized for a better capital-market environment than exists today
-- acquisition growth justified by "accretion" without a real spread over fully loaded cost of capital
-- office or challenged-asset REIT using buybacks or dividend support to distract from rollover or obsolescence
-- hotel or SHOP margin gains that are actually cycle-driven and capex-intensive
-- straight-line rent or above-market lease amortization inflating perceived cash coverage
-- management talking mostly about dividend yield while avoiding NAV, leverage, or recurring capex
-- secured-debt creep eroding unsecured-bondholder protection
-- asset marks or stated NAV relying on pre-rate-shock cap rates while debt still sits at higher refinancing costs
-## Never do this
-
-- Do not lead with P/E or generic EPS growth.
-- Do not compare REITs across sectors on price/FFO without adjusting for capex, lease model, and balance sheet.
-- Do not assume same-store NOI definitions are standardized.
-- Do not accept AFFO as comparable across issuers without rebuilding it.
-- Do not use occupancy alone as proof of pricing power or portfolio quality.
-- Do not treat acquisition accretion as value creation unless the capital source and underwriting spread are clear.
-- Do not discuss the dividend without discussing leverage, maturities, and retained cash.
-- Do not analyze office or other challenged sectors without TI/LC and downtime.
-- Do not analyze healthcare, hotel, or operating-style assets as if they were pure triple-net landlords.
-- Do not analyze preferred or debt securities without mapping the claim position and unencumbered asset coverage.
-- Do not confuse mortgage REITs with equity REITs.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/source-hierarchy.md
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-# Source Hierarchy And Diligence
-
-Deferred reference for `REIT Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order unless the task requires otherwise:
-1. annual reports, 10-Ks, 10-Qs, supplements, rent rolls, debt schedules, property-level statistics
-2. earnings supplements, investor days, and supplemental KPI definitions
-3. unsecured bond indentures, rating reports, and covenant packages
-4. asset-level diligence materials, market studies, appraisals, JV agreements, and development budgets
-5. tenant or operator credit materials, sales reports, coverage schedules, CMBS servicer data, and third-party market data
-
-## Reconciliation rules
-- Reconcile GAAP revenue and net income to cash NOI, FFO, and AFFO.
-- Reconcile same-store definitions across periods and across peers; same-store pools are not automatically comparable.
-- Reconcile leased versus occupied metrics.
-- Reconcile acquisition cap rates and development yields to stabilized cash returns, not just stated day-one yields.
-- Reconcile dividend safety to AFFO and to liquidity or refinancing needs.
-- If management emphasizes NAV, verify the cap-rate assumptions against actual private-market evidence and sector fundamentals.
-## REIT-specific diligence requests
-
-When the task involves diligence, request or prioritize the following if available:
-- full rent roll with tenant, industry, lease start and end, rent, square footage, escalators, options, security deposits, and concessions
-- top-tenant schedule and operator coverage or EBITDAR coverage where relevant
-- same-store bridge showing occupancy, pricing, bad debt, recoveries, and expense drivers
-- TI/LC history and forward budget by asset and market
-- recurring maintenance capex by asset type
-- development and redevelopment pipeline with total cost, spend to date, yield on cost, preleasing, and stabilization timing
-- debt maturity ladder, secured versus unsecured breakdown, covenant summary, and hedging schedule
-- JV structure, promote or fee arrangements, and distribution waterfalls
-- asset appraisal or broker-opinion packages, market studies, and supply-pipeline data
-- tenant sales, occupancy cost, foot traffic, or operator KPIs where relevant
-- hotel renovation schedules, property-improvement plans, brand agreements, and management contracts where relevant
-- data-center power or capacity pipeline, commencements backlog, and customer concentration
-- tower master lease and churn-exposure details
-- healthcare operator financials, reimbursement mix, labor, and licensure issues
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/reits/valuation-rules.md
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-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` reits sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use REIT-specific valuation, not generic corporate valuation
-
-## Equity valuation hierarchy
-
-### Net lease, industrial, apartments, shopping center, self-storage, towers, and many high-quality equity REITs
-Use these lenses first:
-1. price / AFFO or price / cash earnings
-2. price / Nareit FFO
-3. premium or discount to NAV
-4. implied cap rate versus private-market value and growth
-
-### Hotel, office, mall, healthcare operating, and capex-heavy or transition situations
-Use these lenses first:
-1. NAV or downside NAV
-2. EV / EBITDAre or property cash flow
-3. AFFO only after heavy capex normalization
-4. dividend yield only as a secondary check, never as the thesis
-
-### Data center and specialty growth REITs
-Use these lenses first:
-1. AFFO or FFO plus backlog- or development-adjusted growth
-2. NAV adjusted for development pipeline and capital intensity
-3. private-market replacement cost and power or capacity constraints where relevant
-
-### Valuation rules
-- Compare multiples only within similar lease models, capex burdens, and growth profiles.
-- A low FFO multiple can be deserved if rollover risk, TI/LC burden, capex, or refinancing stress is high.
-- A premium multiple can be deserved if the REIT has durable embedded rent growth, scarce development runway, superior balance-sheet flexibility, and low recurring cash leakage.
-- Never let the headline dividend yield substitute for NAV, leverage, or AFFO quality.
-- When using NAV, be explicit about cap rates, mark-to-market rents, stabilized occupancy, G&A, recurring capex, and debt or preferred claims.
-- Price/FFO is useful; price/AFFO is often better; neither is sufficient without a NAV cross-check.
-
-## Debt valuation hierarchy
-- asset coverage and downside NAV
-- unsecured versus secured position and recovery
-- maturity and refinancing risk
-- liquidity and covenant headroom
-- tenant or operator stress path and NOI resilience
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/research-output-overlay-contract.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/research-output-overlay-contract.md
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-# Research Output Overlay Contract
-
-Use this PM handoff block when sector context is injected into another Public Equity Investing skill.
-
-Required fields:
-
-- `sector_overlay.selected_lens`
-- `issuer_archetype`
-- `mandate_lens`
-- `pm_debate`
-- `kpi_set`
-- `valuation_implications`
-- `dashboard_modules_to_add`
-- `source_gaps`
-- `red_flags`
-- `secondary_lenses`
-
-The owning skill keeps the artifact. This overlay supplies sector-specific judgment, KPI hierarchy, valuation conventions, red flags, and missing evidence.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/archetypes.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/archetypes.md
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-# Archetypes And Classification
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## First classify the software company before analyzing it
-
-Classify the company first. Do not compare all recurring-software businesses with one template.
-
-## Archetypes
-
-### Horizontal enterprise workflow / system-of-record suite
-- Core engines: mission-critical workflows, seat growth, edition upgrades, module attach, price realization, and platform consolidation.
-- Primary watch items: seat expansion, cross-sell, enterprise standardization, renewal quality, service dependency, and competitive bundling.
-- Common trap: treating revenue growth as purely new-logo driven when most value is actually in multi-product expansion inside the base.
-
-### Vertical SaaS
-- Core engines: deep domain workflow fit, low churn from embedded operations, pricing power through mission-critical functionality, and workflow expansion.
-- Primary watch items: end-market health, customer concentration by vertical, implementation complexity, payments or fintech attach, and whether the company is truly software or partially labor-enabled services.
-- Common trap: overpaying for low churn without testing whether growth is capped by the vertical’s size or by weak upsell paths.
-
-### Dev tools / collaboration / seat-based platform
-- Core engines: user growth, paid-seat conversion, enterprise expansion, adjacent product attach, community or ecosystem pull, and workflow standardization.
-- Primary watch items: seat optimization, free-to-paid conversion, cohort maturity, AI seat attach, open-source substitution, and cross-functional expansion beyond technical users.
-- Common trap: assuming seat growth is durable without testing whether a prior hiring boom or migration wave pulled demand forward.
-
-### Cybersecurity / observability / infrastructure software with usage or hybrid pricing
-- Core engines: workload growth, data-ingestion growth, node or asset expansion, module attach, security incidents that drive urgency, and pricing architecture.
-- Primary watch items: committed versus overage revenue, cloud cost burden, customer optimization, multi-product penetration, and how elastic usage is under macro pressure.
-- Common trap: reading a strong quarter as structural acceleration when revenue was heavily influenced by bursty consumption or a one-time incident environment.
-
-### Data platform / cloud platform with consumption pricing
-- Core engines: customer workloads, storage and compute consumption, committed capacity, overages, platform breadth, and data/AI workload growth.
-- Primary watch items: visibility versus volatility, RPO quality, workload concentration, large-customer behavior, and whether revenue is governed by consumption timing rather than contract size.
-- Common trap: applying classic seat-based SaaS frameworks to a model where product revenue depends on usage behavior rather than straight-line subscription recognition.
-
-### Hybrid seat plus usage platform
-- Core engines: stable seat base plus elastic usage layers, module expansion, enterprise standardization, and AI or automation usage monetization.
-- Primary watch items: which portion of growth is recurring base versus variable workload, whether usage is incremental or cannibalistic, and how blended margins evolve.
-- Common trap: using one valuation multiple on a business whose subscription and consumption components deserve different durability assumptions.
-
-### SMB / mid-market PLG platform
-- Core engines: self-serve acquisition, trial conversion, expansion within small accounts, ecosystem distribution, and low-friction onboarding.
-- Primary watch items: logo churn, ARPU expansion, sales-assisted upmarket motion, CAC inflation, and macro sensitivity of smaller customers.
-- Common trap: over-focusing on customer-count growth without testing payback, retention, and whether larger-customer penetration is actually improving.
-
-### Hybrid license / maintenance / subscription transition story
-- Core engines: maintenance base durability, cloud migration, subscription mix shift, upsell into cloud products, and margin improvement from delivery-model change.
-- Primary watch items: cannibalization, migration pacing, deferred-revenue and RPO comparability, sales-comp redesign, and the real shape of the installed-base conversion funnel.
-- Common trap: giving cloud mix credit twice by ignoring that some growth is merely migration of legacy revenue into a new bucket.
-
-### Multi-product roll-up / platform consolidator
-- Core engines: acquisition integration, cross-sell, pricing standardization, back-office leverage, and rationalization of overlapping products.
-- Primary watch items: organic versus acquired ARR growth, customer overlap, integration execution, churn of acquired cohorts, and whether platform rhetoric is supported by real attach rates.
-- Common trap: valuing acquired growth at the same multiple as clean organic platform growth.
-
-### Distressed or reacceleration story
-- Core engines: installed-base durability, path to stable growth, operating-cost reset, product relevance, and balance-sheet runway.
-- Primary watch items: churn inflection, renewal calendar, covenant headroom, sales-force productivity, leadership credibility, and whether AI or product repositioning is real or promotional.
-- Common trap: treating a single quarter of stabilized billings or margin as proof of a durable turnaround.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/kpi-cheat-sheet.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/kpi-cheat-sheet.md
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-# KPI Cheat Sheet
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## SaaS KPI cheat sheet
-
-Use these as interpretation anchors, not as universal truths. Definitions vary materially by issuer.
-
-- ARR: annualized recurring revenue run-rate of the installed base. Always test what is included, when it is measured, whether usage overages are embedded, and whether services, term licenses, or pilot contracts are excluded.
-- ACV: annualized contract value. Useful for large-deal tracking and sales productivity, but only after confirming whether it is gross or net of services, one-time fees, and channel economics.
-- MRR: monthly recurring revenue. More useful in SMB and PLG businesses than in large-enterprise multi-year contract models.
-- Dollar-based net retention / NRR / DBNRR: existing-customer revenue retention including expansion, price, contraction, and churn. Never read it without gross retention.
-- Gross retention / gross revenue retention: installed-base durability before upsell. Often more informative than NRR when optimization or churn risk is rising.
-- Logo retention: customer-count retention. Most useful in SMB and PLG; less useful than revenue retention in enterprise models.
-- Billings / calculated billings: revenue plus change in deferred revenue. Helpful but noisy because invoice timing, contract duration, annual prepay mix, acquisitions, and FX can distort a single quarter.
-- Deferred revenue: invoiced but not yet recognized revenue. Best interpreted in annual-upfront enterprise models; less informative in monthly or consumption-heavy models.
-- RPO / cRPO: total and current remaining performance obligations. Better backlog indicators than deferred revenue for enterprise models, but quality depends on contract duration, cancellation terms, and usage mechanics.
-- Rule of 40 / Rule of 50: shorthand for growth plus margin. Only useful after checking whether the margin is real and how much dilution is being used to generate it.
-- Magic Number / CAC payback: useful for GTM efficiency, but only after adjusting for acquisitions, capitalization of commissions, channel-heavy motions, and unusual seasonality.
-- Large-customer counts: customers above a disclosed ARR or ACV threshold. Valuable for monitoring enterprise penetration if the company keeps definitions consistent.
-
-## Recurring Software Metric Map
-
-## 1) Monetization architecture and the true unit of account
-### Metrics to track
-- paid seats or paid users
-- ARPU or ARPA
-- committed ARR or committed usage
-- overage revenue as a percentage of total revenue
-- multi-product customer count or attach rate
-- AI attach rate where disclosed
-
-## 2) Installed-base quality: ARR, customers, and retention durability
-### Metrics to track
-- ARR growth and organic ARR growth
-- customer count by disclosed thresholds
-- customers above $100k, $500k, or $1m ARR or ACV where disclosed
-- gross retention and logo retention
-- NRR / DBNRR
-- customer concentration
-
-## 3) Expansion behavior and cohort quality
-### Metrics to track
-- NRR by customer bucket if disclosed
-- cohort ARR or cohort revenue curves
-- products per customer or attach rate
-- seat growth versus price / ARPU growth
-- usage growth versus committed-capacity growth
-- expansion contribution to total ARR growth
-
-## 4) Bookings, billings, RPO, duration, and revenue recognition
-### Metrics to track
-- billings or calculated billings
-- current and noncurrent deferred revenue
-- total RPO and cRPO
-- implied duration from RPO mix where possible
-- annual upfront billing mix
-- revenue-conversion rate from beginning backlog
-
-## 5) Go-to-market quality and sales efficiency
-### Metrics to track
-- S&M as a percentage of revenue
-- Magic Number or CAC payback where relevant
-- net new ARR per sales rep or per quota carrier if available
-- enterprise pipeline coverage and win rates
-- partner-sourced or channel-sourced mix
-- average contract value and large-deal count
-
-## 6) Gross-margin architecture and the true cost to serve
-### Metrics to track
-- subscription gross margin
-- professional-services gross margin
-- total gross margin
-- infrastructure and hosting cost as a percentage of revenue
-- AI serving cost or inference cost where disclosed
-- services mix and support intensity
-
-## 7) Operating leverage, cash conversion, and dilution quality
-### Metrics to track
-- GAAP and non-GAAP operating margin
-- operating cash flow margin and free cash flow margin
-- SBC as a percentage of revenue
-- diluted share-count growth
-- contract-acquisition-cost asset growth and amortization
-- capex as a percentage of revenue
-
-## 8) Product moat, competition, and AI transition risk
-### Metrics to track
-- module attach or suite penetration
-- % of customers on higher-value editions
-- developer / partner ecosystem indicators
-- product-adoption or usage intensity metrics
-- AI-specific adoption metrics only when tied to paid usage or paid seats
-
-## 9) Guidance quality, linearity, and revision risk
-### Metrics to track
-- implied next-quarter and full-year revenue growth
-- implied margin progression
-- cRPO growth versus revenue growth
-- large-deal count or large-customer growth trends
-- guidance changes to billings, ARR, or retention where disclosed
-
-## 10) Capital structure and public-equity security context and Credit Markets handoff
-### Metrics to track
-- gross and net leverage
-- cash runway and liquidity
-- recurring-revenue leverage where relevant
-- interest coverage or cash interest burden
-- convert maturity schedule and potential dilution
-- acquisition capacity
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/model-architecture.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/model-architecture.md
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-# Model Architecture
-
-Deferred reference for `sector-context-overlay` saas-subscription-software sector lens. Load only when the task needs a model build, model update, driver decomposition, or sensitivity design.
-
-## Modeling rules for SaaS and subscription software
-
-## Required model structure
-- Revenue split into subscription or SaaS, usage-based product revenue where applicable, professional services, and other.
-- Customer or ARR bridge showing beginning ARR, new-logo ARR, expansion, price, contraction, churn, acquisition, FX, and ending ARR when possible.
-- Separate drivers for seats, ARPU or price, usage or workload growth, and product attach.
-- Backlog schedule linking deferred revenue, RPO or cRPO, billings, and revenue recognition.
-- Gross-margin build by subscription, services, and major infrastructure or AI-cost drivers.
-- Opex schedule by S&M, R&D, and G&A with explicit headcount or productivity assumptions when available.
-- SBC and diluted-share-count schedule.
-- Cash-flow schedule that captures deferred-revenue dynamics, capitalized commissions, capex, leases, and taxes.
-
-## Mandatory decomposition
-- Separate growth from new logos, expansion, price, and M&A.
-- Separate seat-based growth from usage-based growth.
-- Separate retention quality from expansion quality.
-- Separate AI monetization from core product growth.
-- Separate reported free cash flow from normalized free cash flow.
-
-## Mandatory sensitivities
-- gross retention
-- NRR / DBNRR
-- new-logo adds or new ARR
-- contract-duration / billing-cadence assumptions
-- subscription gross margin and AI/infrastructure cost
-- sales-efficiency / Magic Number or payback assumptions
-- SBC and diluted-share-count growth
-- valuation multiple on revenue, gross profit, or FCF as appropriate
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/modeling-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/modeling-rules.md
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-# SaaS Modeling Guardrails
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this when the task needs forecast, valuation, downside, credit, or security-specific modeling detail.
-
-## Recurring-Revenue Posture
-
-Treat a SaaS issuer as a recurring gross-profit stream whose value depends on retention, expansion, monetization architecture, product durability, and cash efficiency. Do not lead with generic technology shortcuts or headline revenue growth alone.
-
-Start with what the customer buys, how seats/usage/transactions/data volumes convert into ARR, ACV, billings, RPO, revenue, and gross profit, how much growth comes from existing customers versus new logos, whether AI increases wallet share or cost to serve, and where the security sits in the capital structure. Treat beats as lower quality when they come from duration pull-forward, invoice timing, one-time mega-deals, services mix, reseller pass-through, acquisition noise, non-durable usage spikes, or aggressive FCF add-backs.
-
-## Analysis Path
-
-1. Monetization architecture: classify seat, usage, transaction, asset, API, bundled, or hybrid pricing; separate commitments from overages, prepaid credits, true-ups, onboarding, services, and channel pass-through.
-2. Installed-base quality: verify ARR definition and measurement timing; split customer size, geography, product, channel, and end market; compare ARR growth, large-customer growth, gross retention, logo retention, NRR/DBNRR, and concentration.
-3. Expansion behavior: decompose expansion into seat growth, usage, price, edition upgrades, module attach, cross-sell, and AI add-ons; test whether mature cohorts keep compounding or flatten after onboarding.
-4. Billings, RPO, and revenue recognition: reconcile revenue, calculated billings, deferred revenue, RPO/cRPO, contract duration, billing cadence, renewal seasonality, cancellation terms, services deferrals, acquisitions, and FX.
-5. Go-to-market quality: separate new-logo and expansion selling; review sales capacity, quota attainment, ramp time, pipeline coverage, win rates, partner productivity, Magic Number, CAC payback, and whether leverage is real or underinvestment.
-6. Gross-margin architecture: isolate subscription, services, support, cloud, data-transfer, storage, partner revenue share, payment processing, data/content licensing, and AI inference costs; test whether AI is margin accretive, neutral, or dilutive at current pricing.
-7. Cash conversion and dilution: reconcile GAAP margin, non-GAAP margin, operating cash flow, FCF, annual prepay, deferred revenue, restructuring, tax timing, SBC, share count, buybacks, capitalized commissions, internal-use software, and lease commitments.
-8. Moat and AI risk: decide whether the product is system of record, system of action, point tool, infrastructure layer, or bundled feature; test workflow embedment, data lock-in, compliance burden, ecosystem, open-source risk, suite bundling, hyperscaler pressure, and AI commoditization.
-9. Guidance and revision risk: compare revenue, margin, ARR, cRPO, billings, retention, and FCF guide to seasonality, backlog conversion, renewal calendar, large-deal timing, quarter-end linearity, FX, and management conservatism.
-10. Public-equity security context and Credit Markets handoff: for equity, define rerating drivers, multiple support, dilution, and refinancing read-through. Route debt/convert instrument underwriting, covenant definitions, capped-call mechanics, and debt-security refinancing analysis to Credit Markets.
-
-## Model Must Show
-
-- Revenue driver tree by seat, usage, transaction, module, edition, price, AI attach, services, and channel economics.
-- ARR, customer thresholds, gross retention, NRR/DBNRR, churn, expansion, cohort behavior, and customer concentration.
-- Billings, deferred revenue, RPO/cRPO, contract duration, revenue recognition, and backlog conversion.
-- Subscription gross margin, services gross margin, cloud/AI cost, S&M efficiency, CAC payback, operating leverage, SBC, dilution, OCF, and FCF.
-- Sensitivities for retention, usage optimization, large-deal timing, AI cost, gross margin, sales productivity, cRPO conversion, multiple compression, and convert refinancing.
-
-## Do Not
-
-- Treat ARR, billings, cRPO, or Rule of 40 as clean without definitions, duration, usage, acquisitions, FX, and cash-quality checks.
-- Read NRR without gross retention and logo retention.
-- Value all recurring software on one multiple when seat-based, consumption, PLG, vertical SaaS, and transition stories have different durability.
-- Treat FCF as high quality when it is mainly annual prepay, deferred-revenue growth, restructuring, SBC, or underinvestment.
-
-## Deferred Modeling Layers
-
-Load deeper files only when the task needs that layer:
-
-- `valuation-rules.md`: valuation hierarchy and public-equity security context; route Credit Markets handoff rules to Credit Markets.
-- `model-architecture.md`: required model structure, driver decomposition, and sensitivities.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/output-overlays.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/output-overlays.md
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-# Output Overlays And Handoffs
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Adapt the lens to the investor mandate
-
-## Long-short fundamental
-- Focus on retention durability, cohort behavior, cRPO quality, GTM efficiency, multiple durability, and whether AI changes the slope of growth or only the story.
-
-## Event-driven
-- Focus on beat-and-raise quality, backlog-to-revenue conversion, CFO or CRO changes, pricing or packaging shifts, activist angles, strategic M&A, convert overhangs, and whether AI announcements translate into paid demand.
-
-## Macro
-- Focus on IT-budget sensitivity, seat optimization risk, usage elasticity, enterprise spending cycles, FX, public-cloud cost trends, and hiring conditions that influence seat growth.
-
-## Quant / systematic
-- Standardize around revenue growth, cRPO growth, billings or calculated billings where relevant, retention trends, large-customer count growth, gross-margin trend, FCF conversion, dilution, and estimate-revision direction.
-
-## Credit Markets handoff / structured-finance context
-- Focus on contract durability, annual-prepay mix, customer concentration, issuer behavior, acquisition appetite, covenant definitions, working-capital support from deferred revenue, liquidity runway, and downside cash generation if growth slows sharply.
-
-Credit Markets owns debt-security, covenant-package, recovery-waterfall, and credit-instrument analysis. In Public Equity Investing, use this section only to translate those risks into common-equity downside, valuation, catalyst, sizing, hedge, or re-underwrite implications.
-
-## Growth-oriented investor
-- Focus on installed-base expansion, product adjacency, attach-rate trajectory, platform depth, TAM expansion, AI monetization realism, and whether growth is compounding with acceptable dilution.
-## Output overlays for common tasks
-
-## For a post-earnings deep dive
-- Lead with the driver tree: new logos, expansion, retention, billings / cRPO, margin, and FCF quality.
-- Explicitly separate seat-based and usage-based components.
-- Call out any metric-definition changes, duration effects, acquisition effects, or AI-monetization noise.
-- Show what changed in the quarter that matters for the next four quarters, not just what beat consensus.
-- End with the debate: why bulls think durability improved and why bears think quality weakened.
-
-## For a pre-earnings preview
-- Focus on setup around guide, cRPO or billings expectations, deal linearity, consumption trends, hiring and seat trends, and cloud-cost or AI-margin risk.
-- Identify which metrics the market is most likely to key on for that name.
-- State whether the quarter is more likely to hinge on demand, conversion of backlog, margin, or AI narrative.
-
-## For an investment memo
-- Make the core debate about durability of the installed base and the right valuation framework.
-- Include a clean bull, base, and bear case around retention, expansion, GTM efficiency, and margin path.
-- State clearly whether the moat is workflow, data, ecosystem, switching cost, or distribution.
-- Separate real platform expansion from revenue complexity and accounting noise.
-
-## For a financial model
-- Build a bottom-up revenue bridge that reflects the company’s actual pricing architecture.
-- Link backlog metrics to revenue only in a way consistent with the company’s billing cadence and model.
-- Model subscription gross margin, services drag, AI cost burden, and dilution explicitly.
-- Include scenario cases for retention, new-logo adds, duration changes, and AI monetization.
-
-## When debt or structured work is requested, route to Credit Markets
-- Prioritize renewal durability, customer concentration, liquidity, debt maturities, covenants, and normalized cash generation under slower growth.
-- Treat deferred revenue as helpful working capital but not as a substitute for liquidity.
-- Evaluate whether the company could keep servicing debt if growth fell sharply and margin expansion stalled.
-Do not turn the sector overlay into a debt-security recommendation. Route instrument-level yield/spread, claim priority, covenant enforcement, recovery waterfall, and restructuring work to Credit Markets.
-## Style rules for using this sector pack
-
-- Write like a veteran buy-side software analyst, not like a generic technology summarizer.
-- Always define the company’s own metrics before using them in an argument.
-- Separate facts, management framing, and your inference.
-- Be explicit about what is contractual visibility and what is behavioral visibility.
-- Distinguish seat growth, price, usage, cross-sell, and AI uplift instead of rolling them into one word like expansion.
-- When the company is hybrid, present the subscription and consumption pieces separately before combining them.
-- Treat software as a gross-profit and cash-flow business, not just a revenue-growth story.
-
-## Mandate Overlay Sections
-
-### Long-only
-
-Focus on thesis durability, benchmark-relative risk, liquidity, downside capture, add/trim path, and whether the sector KPI path supports a core or satellite position.
-
-### Long/short
-
-Focus on variant wedge, catalyst timing, shortability/borrow where relevant, factor crowding, pair/hedge logic, and cover or exit triggers.
-
-### Sell-side / initiation
-
-Focus on rating/target-price debate, estimate revision bridge, Street debate, sector valuation convention, and risks to rating or target.
-
-### ETF / index
-
-Focus on index membership, constituent weight, liquidity, float, passive ownership, factor/sector exposure, rebalance risk, and flow-vs-ADV where relevant.
-
-### Public equity diligence
-
-Focus on business quality, governance, accounting quality, capital allocation, management credibility, source gaps, and next diligence source.
-
-### Model / valuation
-
-Focus on the sector driver that changes estimates, multiple, risk premium, or terminal value. Identify the metric that would force the model to move.
-
-### Earnings / catalyst
-
-Focus on which sector KPI changes the forward estimate path, narrative, multiple, position size, downside, liquidity, or event probability.
-
-### Source discipline
-
-State which sector claims are sourced, stale, inferred, or missing. Flag unsupported precision and use `missing_evidence` for material gaps.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/red-flags.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/red-flags.md
deleted file mode 100644
index c05631b..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/red-flags.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Red Flags And Failure Modes
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Red flags and common traps
-
-- ARR grows while revenue and large-customer metrics do not, because the ARR definition changed.
-- NRR stays healthy while gross retention weakens and new customer acquisition slows.
-- Billings or cRPO outperforms mainly because contract durations lengthened.
-- Deferred revenue weakens due to billing-cadence changes, but analysts misread it as demand deterioration.
-- Management emphasizes AI ACV, AI ARR, seats, or tokens without showing durable paid usage and margin impact.
-- Usage revenue spikes because of one customer, one incident, or one project rather than broad adoption.
-- Professional services rise too quickly, suggesting product complexity or weak self-serve implementation.
-- Free cash flow improves due to layoffs, tax timing, vendor stretch, or annual prepay mix rather than structural leverage.
-- SBC remains extremely high while management celebrates non-GAAP margin.
-- Large-deal strength masks weak broad-market demand and poorer logo retention.
-- Partner-led growth accelerates, but the company gives up pricing control or margin.
-- The company stops disclosing NRR, billings, or seat counts just as those metrics become harder to defend.
-- A cloud-transition story gets multiple expansion credit even though the mix shift is largely migration of legacy maintenance revenue.
-- Acquired growth is described as platform cross-sell before enough time has passed to prove it.
-- Analysts value a consumption business on seat-based SaaS heuristics or vice versa.
-## Never do this
-
-- Never annualize one quarter of strong calculated billings into a durable demand thesis.
-- Never quote NRR without also asking what gross retention and customer concentration look like.
-- Never treat all backlog metrics as equally predictive across seat-based, monthly-billed, and consumption models.
-- Never let non-GAAP margin or free cash flow stand in for unit economics without checking SBC and deferred-revenue support.
-- Never compare software companies mechanically when one is seat-based, another is usage-based, and another is services-assisted.
-- Never assume AI monetization is accretive before checking pricing, attach, and inference cost.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/source-hierarchy.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/source-hierarchy.md
deleted file mode 100644
index 4d40db0..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/source-hierarchy.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Source Hierarchy And Diligence
-
-Deferred reference for `SaaS Subscription Software Sector Analysis`. Load this only when the task needs this level of sector detail.
-
-## Source hierarchy and reconciliation rules
-
-Use sources in this order:
-
-1. 10-K, 10-Q, annual report, earnings release, and official shareholder letter
-2. earnings deck, KPI appendix, investor-day materials, and metric definitions
-3. revenue-recognition footnotes, deferred-revenue and RPO disclosures, and SBC / cash-flow footnotes
-4. management commentary on pricing, AI monetization, customer behavior, and GTM changes
-5. pricing pages, product packaging disclosures, and channel / ecosystem details when available
-6. peer filings for benchmarking and definition checks
-
-## Reconciliation rules
-- Reconcile revenue, ARR, billings, deferred revenue, and RPO before drawing conclusions.
-- Never assume one company’s NRR, ARR, billings, or large-customer definition is comparable with another’s.
-- When acquisitions are meaningful, separate organic growth from acquired growth everywhere.
-- Normalize for FX, fiscal-calendar quirks, 53-week years, billing-cadence changes, and revenue-recognition shifts.
-- If management stops disclosing a KPI, treat the absence as information rather than quietly substituting a weaker proxy.
-- If a company has both subscription and consumption components, analyze them separately before recombining.
-## SaaS-specific diligence requests
-
-Request these when deeper underwriting is needed:
-
-- ARR bridge by new, expansion, price, contraction, churn, acquisition, and FX
-- Gross retention and NRR by customer size, cohort, geography, and product family
-- Renewal calendar and upcoming concentration of large renewals
-- Seat counts, seat growth, ARPU bridge, and seat-optimization history for seat-based models
-- Committed usage, overage mix, rollover behavior, and usage concentration for consumption models
-- Product attach and multi-product penetration by cohort
-- Sales rep productivity, quota attainment, ramp curves, pipeline coverage, and channel contribution
-- Contract-duration mix, annual-upfront versus monthly billing mix, and RPO aging
-- Subscription gross-margin bridge including cloud, support, partner share, and AI inference costs
-- Professional-services economics and whether services are required to land or retain customers
-- Top-customer concentration and any exposure to one platform, vertical, or macro factor
-- Capitalized commissions and renewal-commission policy
-- AI monetization detail: pricing, attach, cost to serve, upsell behavior, and cannibalization risk
-- Organic-versus-acquired growth bridge for roll-up stories
-- Debt maturities, convert terms, and covenant definitions for credit work
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/valuation-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/valuation-rules.md
deleted file mode 100644
index 787c97b..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/saas-subscription-software/valuation-rules.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Valuation Rules
-
-Deferred reference for `sector-context-overlay` saas-subscription-software sector lens. Load only when the task needs valuation, public-equity security context and Credit Markets handoff, or Credit Markets handoff rules.
-
-## Use recurring-software valuation, not generic tech shortcuts
-
-## Equity valuation hierarchy
-
-1. EV / next-twelve-month revenue for high-growth companies where revenue durability and scale advantages matter more than near-term margins.
-2. EV / next-twelve-month gross profit when comparing businesses with very different gross-margin structures, especially consumption or AI-heavy models.
-3. EV / free cash flow or FCF yield for mature companies with more stable growth and higher visibility.
-4. Sum-of-the-parts for hybrid businesses with distinct subscription, usage, services, or legacy-license components.
-
-### Valuation rules
-- Higher multiples belong to businesses with strong gross retention, healthy NRR, durable multi-product expansion, efficient GTM, high incremental margins, and manageable dilution.
-- Penalize companies whose growth is duration-driven, acquisition-driven, or overly dependent on one volatile usage vector.
-- Normalize for constant currency, acquisitions, divestitures, and metric-definition changes before comparing peers.
-- Revenue multiples are less useful when the company has structurally lower gross margin, heavy services mix, or AI economics that meaningfully depress contribution margin.
-- FCF-based valuation is only credible when the cash flow is not being flattered by billing structure, restructuring, or aggressive SBC.
-
-## Credit valuation hierarchy
-
-1. Recurring-revenue durability and gross retention.
-2. Free cash flow conversion after realistic cash costs.
-3. Net leverage, liquidity runway, and debt maturity profile.
-4. Covenant quality, issuer behavior, and acquisition appetite.
-5. Recovery value based on software asset quality, customer concentration, and renewal durability.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/sector-index.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/sector-index.md
deleted file mode 100644
index e4cc301..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/references/sector-index.md
+++ /dev/null
@@ -1,103 +0,0 @@
-# Sector Index
-
-Use this index to select one primary sector lens before loading deeper references. If more than one sector could apply, pick the economics that drive the investment case and state the ambiguity.
-
-If a prompt contains a stale legacy skill label or a misleading sector label, do not follow the label blindly. Classify from issuer economics, KPIs, products, revenue model, and risk drivers. Use `scripts/resolve_sector_lens.py` when the prompt is cross-sector, sparse, or ambiguous.
-
-## Cross-Sector Decision Rules
-
-- Business-model clues beat stale labels. A consumption data platform with product revenue, RPO, net retention, AI workload margin, and large-customer growth is `saas-subscription-software`, even if a surrounding filename or legacy label says REIT.
-- Security-underwriting objective matters, but it does not override issuer economics. A credit view on leveraged cybersecurity software still uses `saas-subscription-software` for KPIs, with Credit Markets owning credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt security analysis.
-- Real-asset wrappers matter only when landlord cash-flow economics dominate. Data center landlords are `reits`; data platforms using cloud infrastructure are `saas-subscription-software`.
-- If two lenses genuinely matter, select the lens that drives forecast and valuation mechanics, then include `Secondary Sector Checks` for the other lens.
-- If no supported lens fits, say no local overlay is installed and route back to the owning Public Equity Investing skill.
-
-## Minimum Overlay Output
-
-Every sector-context output should include selected lens, issuer archetype, first questions, KPI set, modeling or valuation conventions that matter, red flags, and missing sources. This minimum output applies even when the prompt is sparse.
-
-## Banks
-
-Reference folder: `references/banks/`
-
-Use when the issuer is primarily a regulated bank, thrift, BHC, custody/trust bank, card lender, CRE-heavy lender, online bank, or deposit-funded specialty lender.
-
-Do not use for insurers, asset managers, exchanges, card networks, payment processors, brokers, or generic specialty finance unless the entity is fundamentally being underwritten as a bank.
-
-First questions: deposits, funding confidence, liquidity, asset quality, capital, NII sensitivity, AOCI/TBV, and regulatory constraints.
-
-## Biotech Pharma
-
-Reference folder: `references/biotech-pharma/`
-
-Use when the case depends on clinical assets, commercial drugs, regulatory catalysts, launch curves, loss of exclusivity, BD economics, royalties, manufacturing, or cash runway.
-
-Do not use for medtech, CRO/CDMO, healthcare services, tools, diagnostics, or distributors unless drug-asset economics are the primary underwriting driver.
-
-First questions: asset/platform archetype, probability of technical/regulatory success, label, launch curve, gross-to-net, LOE, royalty/milestone burden, and financing runway.
-
-## Consumer Internet Marketplaces
-
-Reference folder: `references/consumer-internet-marketplaces/`
-
-Use when marketplace or platform economics drive the case: ecommerce marketplaces, classifieds, resale, travel, delivery, mobility, local services, labor marketplaces, or hybrid ad/services platforms.
-
-Do not use for pure SaaS, payment processors, first-party retail, media, or fintech unless marketplace matching and transaction-network economics are primary.
-
-First questions: scarce side, liquidity, take rate, GMV/GOV/GMS/bookings bridge, cohort quality, incentives, fraud/trust, and contribution margin.
-
-## Exchanges Market Infrastructure
-
-Reference folder: `references/exchanges-market-infrastructure/`
-
-Use when the issuer is an exchange, CCP, cash-equity/options network, derivatives venue, electronic FI/FX/repo venue, listings/index/data franchise, or integrated market infrastructure group.
-
-Do not use for brokers, investment banks, asset managers, card networks, payment processors, or fintech software unless FMI economics dominate.
-
-First questions: volume/open-interest regime, RPC/net capture, data/index quality, clearing/default waterfall, collateral spread, outage/cyber risk, and regulatory constraints.
-
-## Insurance
-
-Reference folder: `references/insurance/`
-
-Use when the company is primarily a risk-bearing insurer or reinsurer and the case depends on underwriting, reserves, reinsurance, investment spread, capital, ratings, or holdco/opco structure.
-
-Do not use for brokers, asset managers, healthcare services, mortgage REITs, or services companies unless insurance reserving and solvency economics dominate.
-
-First questions: liability duration, distribution, reserve adequacy, accident-year margin, cat load, investment income, ALM, capital regime, and dividend capacity.
-
-## Oil Gas E&P
-
-Reference folder: `references/oil-gas-ep/`
-
-Use when the issuer is an independent upstream producer, oil- or gas-weighted E&P, shale producer, offshore/conventional producer, or diversified independent dominated by upstream economics.
-
-Do not use for refiners, midstream, oilfield services, integrated majors dominated by downstream/chemicals, or mineral/royalty vehicles unless upstream reserve economics dominate.
-
-First questions: basin, commodity mix, PDP/PUD, decline curve, inventory depth, realized pricing, hedges, differentials, maintenance capex, RBL risk, and abandonment liabilities.
-
-## REITs
-
-Reference folder: `references/reits/`
-
-Use when the issuer is primarily an equity REIT or listed landlord across net lease, industrial, apartments, retail, office, storage, healthcare, lodging, gaming, data centers, towers, or specialty property sectors.
-
-Do not use for mortgage REITs, homebuilders, brokers, asset managers, or real-estate service businesses unless landlord cash-flow economics dominate.
-
-First questions: property type, same-store NOI, leasing spreads, occupancy, FFO/AFFO, recurring capex, NAV, dividend sustainability, debt maturity wall, and cost of capital.
-
-## SaaS Subscription Software
-
-Reference folder: `references/saas-subscription-software/`
-
-Use when the issuer is primarily recurring software: enterprise apps, SMB apps, workflow software, collaboration, dev tools, cybersecurity, data platforms, vertical SaaS, or license-to-cloud/consumption hybrids.
-
-Do not use for semiconductors, IT services, marketplaces, ad tech, hardware, or payments unless recurring software economics are primary.
-
-First questions: ARR/revenue bridge, billings/RPO, GRR/NRR, churn, expansion, CAC payback, gross margin, SBC, cash conversion, consumption exposure, AI cost/monetization, and rule of 40.
-
-## PM Overlay Minimum Output
-
-Every sector overlay should state selected sector lens, issuer archetype, mandate lens, core PM debate, KPI hierarchy, valuation sensitivity, benchmark/sector exposure relevance, source posture, thesis-break checks, source gaps, and secondary lenses considered.
-
-For ETF/index work, distinguish company fundamentals, sector exposure, factor/rate/commodity sensitivity, index mechanics, liquidity, passive ownership, constituent concentration, and rebalance/flow relevance.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/scripts/resolve_sector_lens.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/scripts/resolve_sector_lens.py
deleted file mode 100644
index 8993824..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/sector-context-overlay/scripts/resolve_sector_lens.py
+++ /dev/null
@@ -1,495 +0,0 @@
-#!/usr/bin/env python3
-"""Resolve a public-equity-investing sector overlay from prompt text.
-
-The helper is intentionally simple and dependency-free. It gives the agent a
-deterministic routing guardrail for ambiguous or cross-sector prompts; it does
-not replace investment judgment.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-from pathlib import Path
-from typing import Any
-
-REFERENCE_FILES = {
-    "always": ["archetypes.md", "kpi-cheat-sheet.md", "red-flags.md", "output-overlays.md"],
-    "modeling": ["modeling-rules.md", "model-architecture.md"],
-    "valuation": ["valuation-rules.md"],
-    "source": ["source-hierarchy.md"],
-}
-
-SECTOR_RULES: dict[str, dict[str, Any]] = {
-    "banks": {
-        "folder": "references/banks/",
-        "strong": [
-            "bank",
-            "bancorp",
-            "bank holding company",
-            "thrift",
-            "deposit",
-            "deposits",
-            "loan loss",
-            "nii",
-            "net interest income",
-            "nim",
-            "aoci",
-            "cet1",
-            "tier 1",
-            "tangible book",
-            "p/tbv",
-        ],
-        "weak": ["asset quality", "funding", "credit costs", "commercial real estate", "cre"],
-        "first_questions": "deposits, funding confidence, asset quality, capital, NII sensitivity, AOCI/TBV",
-    },
-    "biotech-pharma": {
-        "folder": "references/biotech-pharma/",
-        "strong": [
-            "biotech",
-            "pharma",
-            "clinical",
-            "phase 1",
-            "phase 2",
-            "phase 3",
-            "pdufa",
-            "fda",
-            "drug",
-            "label",
-            "loe",
-            "loss of exclusivity",
-            "pipeline",
-            "rnpv",
-            "royalty",
-        ],
-        "weak": [
-            "trial",
-            "launch curve",
-            "gross-to-net",
-            "manufacturing",
-            "cash runway",
-            "milestone",
-        ],
-        "first_questions": "asset archetype, probability of success, label, launch curve, LOE, runway",
-    },
-    "consumer-internet-marketplaces": {
-        "folder": "references/consumer-internet-marketplaces/",
-        "strong": [
-            "marketplace",
-            "gmv",
-            "gov",
-            "gms",
-            "take rate",
-            "bookings",
-            "resale",
-            "classifieds",
-            "delivery",
-            "mobility",
-            "travel platform",
-            "rideshare",
-            "airbnb",
-            "uber",
-            "doordash",
-            "etsy",
-            "mercadolibre",
-            "incentives",
-        ],
-        "weak": ["consumer internet", "liquidity", "cohort", "fraud", "trust", "churn"],
-        "first_questions": "scarce side, liquidity, GMV/bookings bridge, take rate, incentives, cohort quality",
-    },
-    "exchanges-market-infrastructure": {
-        "folder": "references/exchanges-market-infrastructure/",
-        "strong": [
-            "exchange",
-            "clearing house",
-            "ccp",
-            "default waterfall",
-            "open interest",
-            "rpc",
-            "net capture",
-            "listings",
-            "index data",
-            "derivatives venue",
-            "options exchange",
-        ],
-        "weak": ["market data", "collateral spread", "trading volume", "outage", "cyber"],
-        "first_questions": "volume/open interest, RPC, data/index quality, clearing risk, collateral spread",
-    },
-    "insurance": {
-        "folder": "references/insurance/",
-        "strong": [
-            "insurance",
-            "insurer",
-            "reinsurer",
-            "combined ratio",
-            "loss ratio",
-            "reserves",
-            "accident year",
-            "catastrophe",
-            "cat load",
-            "reinsurance",
-            "statutory capital",
-        ],
-        "weak": ["premium", "underwriting", "alm", "rbc", "ratings", "holdco", "opco"],
-        "first_questions": "reserves, accident-year margin, cat load, reinsurance, ALM, capital regime",
-    },
-    "oil-gas-ep": {
-        "folder": "references/oil-gas-ep/",
-        "strong": [
-            "e&p",
-            "exploration and production",
-            "upstream",
-            "pdp",
-            "pud",
-            "shale",
-            "basin",
-            "decline curve",
-            "rbl",
-            "wt i",
-            "wti",
-            "henry hub",
-            "proved reserves",
-        ],
-        "weak": ["production", "hedges", "differentials", "abandonment", "maintenance capex"],
-        "first_questions": "basin, commodity mix, reserves, decline curve, inventory, hedges, RBL risk",
-    },
-    "reits": {
-        "folder": "references/reits/",
-        "strong": [
-            "reit",
-            "ffo",
-            "affo",
-            "same-store noi",
-            "leasing spread",
-            "occupancy",
-            "nav",
-            "cap rate",
-            "landlord",
-            "rent roll",
-            "net lease",
-            "industrial reit",
-            "apartment reit",
-            "office reit",
-            "retail reit",
-            "data center reit",
-            "tower reit",
-            "digital realty",
-            "equinix",
-        ],
-        "weak": ["property", "tenant", "lease", "dividend sustainability", "debt maturity wall"],
-        "first_questions": "property type, same-store NOI, occupancy, leasing spreads, FFO/AFFO, NAV",
-    },
-    "saas-subscription-software": {
-        "folder": "references/saas-subscription-software/",
-        "strong": [
-            "saas",
-            "software",
-            "arr",
-            "rpo",
-            "remaining performance obligations",
-            "billings",
-            "net retention",
-            "gross retention",
-            "nrr",
-            "grr",
-            "subscription",
-            "cloud software",
-            "consumption data platform",
-            "cybersecurity saas",
-            "snowflake",
-            "rapid7",
-            "cloudflare",
-            "datadog",
-            "crowdstrike",
-            "zscaler",
-            "okta",
-            "mongodb",
-            "servicenow",
-            "salesforce",
-        ],
-        "weak": [
-            "churn",
-            "cac payback",
-            "rule of 40",
-            "sbc",
-            "ai workloads",
-            "large-customer growth",
-        ],
-        "first_questions": "ARR/revenue bridge, billings/RPO, retention, churn, CAC payback, SBC, FCF quality",
-    },
-}
-
-
-MANDATE_RULES = {
-    "long_only": [
-        "long-only",
-        "long only",
-        "benchmark",
-        "active weight",
-        "core holding",
-        "portfolio weight",
-    ],
-    "long_short": [
-        "long/short",
-        "hedge fund",
-        "short",
-        "pair trade",
-        "borrow",
-        "cover",
-        "gross",
-        "net exposure",
-    ],
-    "sell_side": [
-        "sell-side",
-        "sell side",
-        "rating",
-        "target price",
-        "initiation",
-        "coverage",
-        "client note",
-    ],
-    "etf_index": [
-        "etf",
-        "index",
-        "constituent",
-        "rebalance",
-        "russell",
-        "s&p",
-        "msci",
-        "passive flow",
-        "float",
-    ],
-    "diligence": [
-        "diligence",
-        "underwrite",
-        "source packet",
-        "governance",
-        "accounting quality",
-        "management credibility",
-    ],
-    "model": ["model", "forecast", "dcf", "three-statement", "comps", "valuation"],
-    "earnings": ["earnings", "print", "quarter", "guidance", "transcript"],
-    "event": ["event", "catalyst", "merger", "spin", "tender", "lockup", "offering"],
-}
-
-MANDATE_REQUIREMENTS = {
-    "long_only": [
-        "benchmark-relative risk",
-        "portfolio role",
-        "downside capture",
-        "add/trim discipline",
-    ],
-    "long_short": ["variant wedge", "catalyst timing", "borrow/crowding", "cover or exit triggers"],
-    "sell_side": [
-        "rating/target implication",
-        "estimate revision bridge",
-        "Street debate",
-        "risk-to-rating",
-    ],
-    "etf_index": [
-        "index methodology",
-        "constituent weight",
-        "float/liquidity",
-        "passive flow relevance",
-    ],
-    "diligence": ["business quality", "governance", "accounting quality", "next source needed"],
-    "model": ["driver hierarchy", "valuation convention", "model line affected", "sensitivity"],
-    "earnings": ["estimate path", "KPI debate", "revision risk", "next falsifier"],
-    "event": ["event path", "probability", "payoff", "liquidity/exit risk"],
-}
-
-MODE_KEYWORDS = {
-    "modeling": [
-        "model",
-        "forecast",
-        "three-statement",
-        "dcf",
-        "sensitivity",
-        "scenario",
-        "underwrite",
-    ],
-    "valuation": ["valuation", "multiple", "comps", "price target", "nav", "sotp", "rn pv", "rnpv"],
-    "source": ["source", "provenance", "conflict", "citation", "filing", "transcript", "deck"],
-}
-
-
-def normalize(text: str) -> str:
-    text = text.lower()
-    text = re.sub(r"[^a-z0-9%&./+ -]+", " ", text)
-    return re.sub(r"\s+", " ", text).strip()
-
-
-def phrase_hits(text: str, phrases: list[str]) -> list[str]:
-    hits = []
-    padded = f" {text} "
-    for phrase in phrases:
-        needle = normalize(phrase)
-        if not needle:
-            continue
-        if " " in needle or "/" in needle or "&" in needle:
-            if needle in text:
-                hits.append(phrase)
-        elif re.search(rf"(?<![a-z0-9]){re.escape(needle)}(?![a-z0-9])", padded):
-            hits.append(phrase)
-    return hits
-
-
-def score_prompt(prompt: str) -> dict[str, dict[str, Any]]:
-    text = normalize(prompt)
-    scores: dict[str, dict[str, Any]] = {}
-    for sector, rules in SECTOR_RULES.items():
-        strong_hits = phrase_hits(text, list(rules["strong"]))
-        weak_hits = phrase_hits(text, list(rules["weak"]))
-        score = (3 * len(strong_hits)) + len(weak_hits)
-        scores[sector] = {
-            "score": score,
-            "strong_hits": strong_hits,
-            "weak_hits": weak_hits,
-            "folder": rules["folder"],
-            "first_questions": rules["first_questions"],
-        }
-    return scores
-
-
-def selected_reference_files(prompt: str) -> list[str]:
-    text = normalize(prompt)
-    files = list(REFERENCE_FILES["always"])
-    for mode, keywords in MODE_KEYWORDS.items():
-        if phrase_hits(text, keywords):
-            files.extend(REFERENCE_FILES[mode])
-    return list(dict.fromkeys(files))
-
-
-def detect_mandates(prompt: str) -> list[str]:
-    text = normalize(prompt)
-    mandates: list[str] = []
-    for mandate, keywords in MANDATE_RULES.items():
-        if phrase_hits(text, keywords):
-            mandates.append(mandate)
-    return mandates or ["public_equity_diligence"]
-
-
-def mandate_output_requirements(mandates: list[str]) -> dict[str, list[str]]:
-    return {mandate: MANDATE_REQUIREMENTS.get(mandate, []) for mandate in mandates}
-
-
-def resolve(prompt: str) -> dict[str, Any]:
-    mandates = detect_mandates(prompt)
-    scores = score_prompt(prompt)
-    ranked = sorted(scores.items(), key=lambda item: item[1]["score"], reverse=True)
-    primary, primary_data = ranked[0]
-    secondary = [{"sector": sector, **data} for sector, data in ranked[1:4] if data["score"] > 0]
-    top_score = int(primary_data["score"])
-    second_score = int(secondary[0]["score"]) if secondary else 0
-
-    if top_score == 0:
-        confidence = "unsupported"
-        primary_sector = None
-    elif top_score >= 6 and top_score >= (second_score * 2):
-        confidence = "high"
-        primary_sector = primary
-    elif top_score >= 4 and top_score > second_score:
-        confidence = "medium"
-        primary_sector = primary
-    else:
-        confidence = "ambiguous"
-        primary_sector = primary
-
-    notes: list[str] = []
-    if confidence == "unsupported":
-        notes.append(
-            "No local sector overlay matched; use the primary Public Equity Investing skill without a sector overlay."
-        )
-    elif confidence == "ambiguous":
-        notes.append(
-            "Multiple sector lenses matched; choose the economics that drive the investment case and cite secondary lenses."
-        )
-    elif secondary:
-        notes.append("Secondary sector clues exist; state why the primary lens controls.")
-
-    return {
-        "primary_sector": primary_sector,
-        "confidence": confidence,
-        "selected_folder": primary_data["folder"] if primary_sector else None,
-        "selected_reference_files": selected_reference_files(prompt) if primary_sector else [],
-        "first_questions": primary_data["first_questions"] if primary_sector else None,
-        "mandate_lenses": mandates,
-        "mandate_output_requirements": mandate_output_requirements(mandates),
-        "secondary_lenses": secondary,
-        "all_scores": scores,
-        "routing_notes": notes,
-    }
-
-
-def markdown(result: dict[str, Any]) -> str:
-    if not result["primary_sector"]:
-        return "\n".join(
-            [
-                "# Sector Lens Resolution",
-                "",
-                "Primary sector: **unsupported**",
-                "Confidence: **unsupported**",
-                "",
-                "Use the owning Public Equity Investing skill without a local sector overlay.",
-            ]
-        )
-
-    lines = [
-        "# Sector Lens Resolution",
-        "",
-        f"Primary sector: **{result['primary_sector']}**",
-        f"Confidence: **{result['confidence']}**",
-        f"Reference folder: `{result['selected_folder']}`",
-        "Reference files: " + ", ".join(f"`{name}`" for name in result["selected_reference_files"]),
-        "",
-        f"First questions: {result['first_questions']}",
-        "Mandate lenses: " + ", ".join(f"`{name}`" for name in result.get("mandate_lenses", [])),
-    ]
-    if result["secondary_lenses"]:
-        lines.append("")
-        lines.append("Secondary lenses:")
-        for item in result["secondary_lenses"]:
-            lines.append(
-                f"- `{item['sector']}` score {item['score']}: {', '.join(item['strong_hits'] + item['weak_hits'])}"
-            )
-    if result["routing_notes"]:
-        lines.append("")
-        lines.extend(f"- {note}" for note in result["routing_notes"])
-    return "\n".join(lines)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Resolve the Public Equity Investing sector-context-overlay lens."
-    )
-    source = parser.add_mutually_exclusive_group(required=True)
-    source.add_argument("--prompt", help="Prompt or issuer description to classify.")
-    source.add_argument(
-        "--prompt-file", type=Path, help="Text file containing the prompt or issuer description."
-    )
-    parser.add_argument("--json-out", type=Path, help="Optional JSON output path.")
-    parser.add_argument("--markdown-out", type=Path, help="Optional Markdown output path.")
-    return parser.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    prompt = (
-        args.prompt if args.prompt is not None else args.prompt_file.read_text(encoding="utf-8")
-    )
-    result = resolve(prompt)
-
-    payload = json.dumps(result, indent=2)
-    print(payload)
-    if args.json_out:
-        args.json_out.parent.mkdir(parents=True, exist_ok=True)
-        args.json_out.write_text(payload + "\n", encoding="utf-8")
-    if args.markdown_out:
-        args.markdown_out.parent.mkdir(parents=True, exist_ok=True)
-        args.markdown_out.write_text(markdown(result) + "\n", encoding="utf-8")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/INTERNAL.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/INTERNAL.md
deleted file mode 100644
index 3edab57..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/INTERNAL.md
+++ /dev/null
@@ -1,155 +0,0 @@
----
-name: style-guide-adapter
-description: Use when extracting or QCing Public Equity Investing artifact style. Do not use as the primary file-editing engine.
----
-
-# Style Guide Adapter
-
-> Internal support playbook. Load through `internal-support/policy.md`; this style capability is bundled with the visible router rather than exposed as a skill entrypoint.
-
-## Deliverable Intake
-
-When invoked as support for an owning workflow, inherit its resolved deliverable preferences and do not re-prompt. Only when this skill independently owns a new substantive standalone style deliverable should it, before source gathering, analysis, or rendering, load `../../../../shared/deliverable-intake-policy.md` and perform its adaptive `request_user_input` preflight for materially unresolved preferences.
-
-## Overview
-
-Load `shared/equity-research-support-standard.md` and `shared/support-layer-routing-contract.md` before substantial source, data, QA, or style work.
-
-
-Adapt finance artifacts to the target institution's visual, structural, and writing style using explicit style sources first and inferred precedent patterns second. Treat style adaptation as non-destructive by default: preserve facts, numbers, formulas, citations, and source links unless the user explicitly asks to change them.
-
-This is a shared-core Public Equity Investing skill. It can be used on its own or composed with builder/QC skills such as `comps-valuation`, `dcf-model-builder`, `three-statement-model-builder`, `equity-model-update`, `memo-builder`, `long-short-pitch`, `deck-report-qc`, `financial-source-of-truth`, and spreadsheet/modeling skills.
-
-The shipped deterministic helper extracts Office style metadata only; it is not a style-application engine. `scripts/extract_office_style.py` reads `.pptx`, `.docx`, and `.xlsx` metadata and emits a style profile aid. It does not rewrite, reformat, restyle, or save edited Office artifacts.
-
-## Embedded Support Routing
-
-This is an embedded service under an owning workflow unless the user explicitly asks for standalone style extraction, style QC, or style adaptation. Preserve the `owning_workflow` internally, such as `memo-builder`, `long-short-pitch`, `initiating-coverage`, `deck-report-qc`, `dashboard-builder`, `meeting-prep`, `earnings-preview`, `earnings-deep-dive`, `dcf-model-builder`, `three-statement-model-builder`, or `comps-valuation`.
-
-For substantial embedded work, preserve `decision_impact`, `readiness_effect`, `artifact_role`, and `hidden_unless_requested` in internal context or support artifacts. Do not print those internal field names in the owning workflow's user-facing artifact. Do not own the investment conclusion; state in natural language how style choices affect clarity, auditability, source posture, compliance/circulation readiness, or reader trust. Style profiles, JSON, Markdown, logs, and manifests are secondary/support artifacts when invoked by an owning workflow, and style is not evidence.
-
-## Core decision tree
-
-1. **Identify the artifact and mode**
-   - **extract style**: build a style profile from precedents, templates, examples, or instructions.
-   - **apply style**: produce a concrete change plan and, only when an editing tool is available, apply safe style edits through that artifact-specific tool.
-   - **create in style**: produce a new artifact using the target style profile.
-   - **style QC**: compare an artifact against a target style and list fixes.
-2. **Gather style sources** using the hierarchy below. If sources are missing, proceed with a conservative finance-default style and list what would improve fidelity.
-3. **Build a style profile** covering visual system, layout grammar, writing voice, exhibit conventions, citation/footnote norms, and artifact-specific rules.
-4. **Apply only style-safe changes by default and only through the correct artifact surface.** Do not delete, overwrite, or materially change content, data, formulas, source links, speaker notes, hidden sheets, comments, tracked changes, or version history unless explicitly requested.
-5. **Run style QC** and provide a change log, unresolved ambiguities, and confidence level.
-
-## Source hierarchy
-
-Style is not evidence: precedent decks and memos can define tone, structure, and formatting, but they are not factual support for prices, consensus, estimates, financials, ratings, targets, or investment claims. Use `financial-source-of-truth` for substance.
-
-
-Use the highest-quality applicable source. For any content, number, market data, or factual claim, delegate to `financial-source-of-truth` rules rather than treating style precedents as factual evidence.
-For detailed source and non-deletion rules, use [references/source-and-safety.md](references/source-and-safety.md).
-
-1. **User's explicit instructions in the current task**.
-2. **Official style guide / brand book / writing guide / compliance template** supplied by the user or available through callable runtime apps/connectors. If not callable, request the file/export and label the style source gap.
-3. **Approved template or master file**: PowerPoint template, Word template, spreadsheet template, memo shell, model shell, or institutional boilerplate.
-4. **Current artifact being edited**: existing master slides, named styles, workbook themes, defined names, chart templates, footnote patterns, and repeated internal conventions.
-5. **Final / sent / approved precedents** from the same institution, client, team, deal type, committee, or publication series.
-6. **Draft or adjacent precedents** from the same institution or similar artifact type.
-7. **User-provided description of desired style**.
-8. **Public website / brand materials / web search** only when no private style source exists or the user asks for public-brand matching.
-9. **Generic institutional finance conventions** only as a fallback.
-
-Resolve conflicts as follows: explicit user instruction beats all; official guide beats precedent; current client style beats generic firm style; final approved precedents beat drafts; repeated patterns beat one-off quirks; recent examples beat stale examples when they are equally authoritative.
-
-## Stale-style and conflict checks
-
-Before applying a style, check whether sources are likely current:
-
-- Prefer files named or labeled current, latest, final, approved, sent, board-ready, client-ready, vfinal, or template.
-- Treat files named draft, old, archive, deprecated, backup, prior-year, or sample as lower-confidence unless the user says otherwise.
-- Use document content, metadata, and user context to assess freshness; do not rely only on file modified date.
-- If two sources conflict materially, state the conflict and use the highest-priority source. Do not blend incompatible styles unless asked.
-- If only one weak sample exists, label extracted rules as inferred, not definitive.
-
-## Fact, assumption, and inference labeling
-
-Use these labels in summaries, change logs, and style profiles when relevant:
-
-- **style fact**: directly observed from a guide, template, or repeated precedent pattern.
-- **style inference**: inferred from limited examples or repeated but undocumented behavior.
-- **style assumption**: chosen because no style source was available.
-- **content fact**: sourced number, quote, date, metric, or claim.
-- **content assumption**: unsourced or user-supplied assumption used in the artifact.
-
-Do not label everything. Label only rules or assumptions that affect meaningful style, compliance, or interpretation.
-
-## Style profile structure
-
-Create or update a style profile before applying changes. Use the detailed extraction guidance in [references/style-extraction-playbook.md](references/style-extraction-playbook.md). Include only fields that are supported by sources or useful for the task.
-
-Minimum style profile:
-
-- **source basis**: files, links, pages/slides/tabs, or user instructions used; source priority and confidence.
-- **visual system**: colors, fonts, typography hierarchy, spacing, grid, margins, logo/branding, icons, imagery, and accessibility constraints.
-- **layout grammar**: title style, subtitle/kicker style, page/slide structure, section dividers, exhibit placement, chart/table positioning, callout boxes, footers, page numbers, confidential labels, and appendix conventions.
-- **exhibit conventions**: chart types, table style, unit placement, decimal precision, period labeling, currency conventions, negative numbers, variance colors, footnote/source placement, and annotation density.
-- **writing voice**: sentence length, tone, level of assertiveness, headline style, so-what structure, bullet grammar, tense, jargon tolerance, and caveat style.
-- **artifact-specific rules**: deck, memo, spreadsheet/model, research note, PM memo, sell-side note, ETF/index diligence note, client equity report, or committee-pack requirements. Credit memo style belongs in Credit Markets unless the artifact is only a public-equity read-through.
-- **do-not-change rules**: content, data, formulas, citations, hidden tabs, notes, comments, tracked changes, source links, or legal/compliance language that must remain intact.
-
-## Non-destructive editing rules
-
-- Work on a copy unless the environment provides explicit safe edit/versioning behavior.
-- Preserve all formulas, linked data, named ranges, cell comments, hidden sheets, footnotes, citations, speaker notes, alt text, tracked changes, embedded objects, and source URLs unless explicitly asked to modify them.
-- Do not delete slides, sections, rows, columns, sheets, notes, comments, exhibits, citations, or appendix content unless the user explicitly asks.
-- Do not overwrite firm/client templates, masters, or original precedents. Use them as references or create a derived copy.
-- Keep the same factual content unless asked to rewrite. When tightening language, preserve meaning and mark substantive edits separately from style edits.
-- If a requested style change would reduce readability, accessibility, auditability, or factual clarity, flag the tradeoff and apply the safer variant.
-
-## Artifact-specific routing
-
-Use artifact-specific tools/skills for actual file edits. If those tools are not available, provide a style profile, exact edit checklist, and before/after text examples rather than implying that the extractor changed the file.
-
-- **PowerPoint / slides**: use the presentation/deck editing tool when available. Preserve master slides, layout IDs, notes, charts, embedded Excel links, footers, logos, and page numbers. Prefer native theme/layout changes over manual per-shape hacks when possible. See [references/artifact-application-rules.md](references/artifact-application-rules.md).
-- **Word / memos / reports**: use the document editing tool when available. Preserve styles, headings, bookmarks, comments, tracked changes, footnotes/endnotes, citations, tables of contents, and cross-references.
-- **Excel / Sheets / models**: use the spreadsheet/model-builder tool when available. Preserve formulas, formats tied to model semantics, named ranges, validations, comments, hidden tabs, grouping, and source tabs. Never hardcode formulas or paste values over formulas for style reasons.
-- **Text-only outputs**: adapt voice, structure, headings, bullets, caveats, and source presentation without inventing visual styling.
-
-For uploaded Office files, optionally run `scripts/extract_office_style.py` to extract theme colors, fonts, style names, slide counts, and workbook/document style metadata before deeper visual review. The helper defaults to JSON for downstream tooling; use `--format markdown` only when the user explicitly wants a style-profile support note.
-
-## Applying writing style
-
-When the task involves prose, use [references/writing-style-adaptation.md](references/writing-style-adaptation.md). Preserve meaning and evidence posture.
-
-Common finance writing defaults when no stronger style is available:
-
-- Use title headlines with a clear so-what, not generic labels.
-- Lead with the implication, then supporting data.
-- Prefer concise bullets and parallel structure.
-- Use active voice and avoid marketing adjectives unless the source language uses them.
-- Separate facts, assumptions, management claims, and analyst judgment.
-- Keep caveats crisp and decision-useful.
-
-## Output requirements
-
-Choose the format that matches the user request. For complete templates, use [references/output-templates.md](references/output-templates.md).
-
-Always provide, at minimum:
-
-1. **What was used**: style sources and confidence.
-2. **What changed**: concise change log separating visual/layout edits from writing/content edits.
-3. **What was preserved**: data, formulas, citations, source links, notes, comments, hidden sheets, or other sensitive elements.
-4. **Open issues**: missing sources, conflicts, stale-style concerns, or style assumptions.
-5. **Next best input** if fidelity is limited: template, final approved example, brand guide, master deck, memo precedent, or client-specific sample.
-
-If delivering an edited artifact through an artifact-specific tool, also provide the output file and a short QA summary. If only advising or using the bundled extractor, provide a style profile, recommended edits, and sample before/after transformations.
-
-## Quality bar
-
-Before finalizing, verify:
-
-- Style choices are traceable to the selected source hierarchy.
-- The artifact still says the same thing unless substantive edits were requested.
-- Numbers, signs, units, footnotes, formulas, and source citations were not altered accidentally.
-- The output is internally consistent across pages/slides/tabs: titles, fonts, colors, spacing, chart/table treatment, footers, and source lines.
-- Style assumptions are visible and reasonable.
-- The result looks/sounds like the target institution without copying confidential text beyond what the user supplied or has access to.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/assets/icon.svg b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/assets/icon.svg
deleted file mode 100644
index 9549bd8..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/assets/icon.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" role="img" aria-label="style guide adapter icon">
-  <rect width="64" height="64" rx="14" fill="#111827"/>
-  <rect x="13" y="14" width="38" height="36" rx="4" fill="#F9FAFB"/>
-  <rect x="18" y="20" width="20" height="4" rx="2" fill="#2563EB"/>
-  <rect x="18" y="29" width="28" height="3" rx="1.5" fill="#9CA3AF"/>
-  <rect x="18" y="36" width="22" height="3" rx="1.5" fill="#9CA3AF"/>
-  <circle cx="44" cy="43" r="7" fill="#F59E0B"/>
-  <path d="M42 43.5l2.1 2.1 4-5" fill="none" stroke="#111827" stroke-width="2.3" stroke-linecap="round" stroke-linejoin="round"/>
-</svg>
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/artifact-application-rules.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/artifact-application-rules.md
deleted file mode 100644
index b8beb22..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/artifact-application-rules.md
+++ /dev/null
@@ -1,84 +0,0 @@
-# Artifact Application Rules
-
-Use this guide when applying a style profile to a real artifact through an artifact-specific editing surface. The bundled `extract_office_style.py` script is metadata-only and does not apply edits. Preserve the artifact's substance unless the user explicitly requests rewriting, restructuring, or content deletion.
-
-## PowerPoint and slide decks
-
-### Preserve by default
-
-- master slides, layouts, placeholders, slide size, and theme unless changing them is the requested style action
-- speaker notes, comments, hidden slides, section structure, slide IDs, page numbers, confidentiality markers, logos, source lines
-- embedded Excel objects, chart data, links, alt text, images, icons, and crop settings
-- all factual claims, numbers, signs, units, dates, citations, and footnotes
-
-### Prefer these style-safe edits
-
-- apply theme colors and fonts where compatible with the master
-- align titles, subtitles, page numbers, source lines, and footers to the profile
-- normalize chart/table formatting without changing chart data
-- improve spacing, alignment, object size consistency, and typography hierarchy
-- standardize section dividers, appendix labels, page titles, and callouts
-- replace manual formatting with master/layout styles when doing so preserves content
-
-### Avoid unless requested
-
-- deleting slides, notes, appendices, footnotes, or exhibits
-- changing chart data, categories, series order, or formulas
-- flattening editable charts into images
-- breaking embedded links or template placeholders
-- overfitting to one precedent slide that conflicts with the current master
-
-## Word documents, memos, and reports
-
-### Preserve by default
-
-- headings, bookmarks, cross-references, table of contents, comments, tracked changes, footnotes/endnotes, citations, links, and document properties
-- defined styles and numbering schemes unless replacing them is the explicit task
-- substantive legal, compliance, disclosure, or risk language
-
-### Prefer these style-safe edits
-
-- map paragraphs to the nearest target named style
-- standardize heading hierarchy, table styling, caption/source formatting, footnote style, and appendix treatment
-- tighten prose for voice and consistency while preserving meaning
-- add a style profile or style notes if the user wants reusable guidance
-
-### Avoid unless requested
-
-- accepting/rejecting tracked changes
-- deleting comments, footnotes, appendix sections, tables, or cited material
-- changing definitions, legal terms, financial claims, or recommendations under the guise of tone
-
-## Excel, Google Sheets, and financial models
-
-### Preserve by default
-
-- formulas, named ranges, source links, data validations, conditional formatting logic, comments/notes, hidden rows/columns/sheets, grouping, macros/VBA, protected ranges, pivots, charts, and external data connections
-- color semantics that indicate inputs, formulas, linked cells, outputs, checks, historicals, forecasts, and errors
-
-### Prefer these style-safe edits
-
-- apply tab colors, headers, number formats, borders, freeze panes, print settings, and navigation rows consistent with the style profile
-- normalize input/output/check formatting while preserving model semantics
-- improve readability of summaries, dashboards, bridges, and charts
-- add style flags or a change log rather than silently changing sensitive workbook structure
-
-### Avoid unless requested
-
-- hardcoding formulas, pasting values over formulas, deleting hidden tabs, renaming named ranges, changing signs, moving source tabs, modifying macro logic, or overwriting linked data
-- changing financial-model color conventions if the workbook already uses a clear institutional standard
-
-## Text-only / chat / email outputs
-
-When no editable file is being produced, adapt:
-
-- subject/title format
-- opening structure and level of directness
-- bullet style, punctuation, and bold lead-ins
-- recommendation, caveat, and ask language
-- source/citation presentation
-
-Do not invent logos, exact colors, fonts, or proprietary style rules without evidence. Provide a concise style assumption note when using defaults.
-
-
-For Public Equity Investing, preserve rating/stance language, price targets, estimates, citations, caveats, source posture, and confidence labels. Style edits must not make an assumption look sourced or an issuer claim look verified.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/output-templates.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/output-templates.md
deleted file mode 100644
index bef333d..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/output-templates.md
+++ /dev/null
@@ -1,125 +0,0 @@
-# Output Templates
-
-Use these templates when producing user-facing outputs from the style-guide-adapter skill.
-
-## Style profile
-
-```markdown
-# Style profile: [target institution / client / artifact]
-
-## Source basis
-| Source | Type | Priority | Freshness | Relevance | Confidence |
-|---|---|---:|---|---|---:|
-
-## Style rules
-### Visual system
-- Colors:
-- Fonts / type hierarchy:
-- Spacing / grid:
-- Logos / marks / imagery:
-
-### Layout grammar
-- Titles:
-- Footers / sources:
-- Section dividers:
-- Exhibit placement:
-- Appendix treatment:
-
-### Charts and tables
-- Chart styling:
-- Table styling:
-- Units / decimals / number format:
-- Scenario / variance color logic:
-- Footnotes / sources:
-
-### Writing voice
-- Tone:
-- Headline style:
-- Bullet style:
-- Caveat / risk style:
-- Citation style:
-
-## Do-not-change rules
-- [Sensitive content/data/formulas/citations/notes to preserve]
-
-## Assumptions and open questions
-- [Style assumption] [basis] [confidence]
-- [Conflict / missing input] [recommended resolution]
-```
-
-## Restyle change log
-
-```markdown
-## Style adaptation summary
-
-**Style sources used:** [sources and confidence]
-**Mode:** [extract / apply via artifact tool / create in style / QC]
-**Artifact:** [deck / memo / spreadsheet / text]
-
-### Changes made
-| Area | Change | Basis | Confidence |
-|---|---|---|---:|
-| Visual/layout |  |  |  |
-| Charts/tables |  |  |  |
-| Writing/voice |  |  |  |
-| Footnotes/sources |  |  |  |
-
-### Preserved
-- Data/formulas:
-- Citations/source links:
-- Notes/comments/tracked changes:
-- Hidden sheets/slides/appendix:
-- Other sensitive elements:
-
-### Open issues
-- [Missing source, conflict, or style assumption]
-```
-
-## Style QC report
-
-```markdown
-# Style QC report: [artifact]
-
-## Overall assessment
-- **Match level:** [high / medium / low]
-- **Primary gap:** [one-sentence diagnosis]
-- **Highest-priority fixes:** [top 3]
-
-## Findings
-| Priority | Location | Issue | Target rule | Recommended fix | Risk if ignored |
-|---|---|---|---|---|---|
-| High |  |  |  |  |  |
-
-## Source and evidence notes
-- [Style facts and inferences]
-- [Conflicts or stale-source concerns]
-```
-
-## No-context fallback response
-
-When the user provides no style source, do not stop. Use this structure:
-
-```markdown
-I can create a conservative institutional finance style profile and edit plan now. If an artifact-editing tool is available for this file type, I can apply safe style edits there. Fidelity will improve if you provide a final approved precedent, template, or style guide.
-
-Default style assumptions:
-- clean executive layout, high contrast, restrained palette
-- concise so-what titles and parallel bullets
-- clear source lines, units, and footnotes
-- no deletion or overwrite of data, formulas, notes, citations, or hidden content
-
-Next best source to provide: [one targeted ask]
-```
-
-Then proceed with the task using generic institutional finance conventions.
-
-## Source conflict note
-
-```markdown
-I found a style conflict: [source A] uses [rule], while [source B] uses [rule]. I followed [selected source] because [hierarchy reason]. I left [conflicting item] unchanged / applied [safe compromise] where the conflict was unresolved.
-```
-
-
-## Public Equity Style QA Addendum
-
-Include `source posture preserved`, `numbers/citations preserved`, `caveats preserved`, `confidence labels preserved`, and `substantive edits separated from style edits` in the change log for equity research artifacts.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/source-and-safety.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/source-and-safety.md
deleted file mode 100644
index 46dec11..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/source-and-safety.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Source and Safety Rules
-
-Use this reference when deciding what evidence to rely on and how to protect user artifacts.
-
-## Style-source hierarchy
-
-1. Current user instruction.
-2. Official style guide, brand book, writing guide, compliance guide.
-3. Approved template, master slide, document template, workbook template, memo shell.
-4. Current artifact's existing master/theme/styles/formula semantics.
-5. Final approved precedents from same institution/client/team/artifact type.
-6. Draft or adjacent precedents.
-7. User description.
-8. Public brand material or web search.
-9. Generic finance conventions.
-
-## Data-source hierarchy
-
-When style adaptation touches facts, numbers, market data, financials, claims, or sources, use the financial source hierarchy instead of style sources:
-
-1. User-provided source files and explicit assumptions in the current task.
-2. Connected apps and authoritative internal systems available to the user.
-3. Company filings, official releases, transcripts, investor presentations, and source documents.
-4. Callable licensed market-data / consensus / financial-data connectors or user-provided provider exports. Do not imply live access when unavailable.
-5. Reputable public sources and web search.
-6. Clearly labeled assumptions.
-
-A precedent deck or memo can show formatting style, but it is not authoritative evidence for updating facts unless it is itself the source document for the current task.
-
-## Non-deletion default
-
-Unless the user explicitly requests deletion or replacement, do not remove:
-
-- slides, pages, paragraphs, rows, columns, sheets, sections, appendices
-- formulas, linked cells, named ranges, pivots, macros, external connections
-- citations, footnotes, source lines, hyperlinks, comments, notes, tracked changes
-- hidden slides/sheets, speaker notes, bookmarks, cross-references
-- legal, regulatory, compliance, or disclosure language
-
-When cleanup is needed, prefer moving to an appendix, flagging for review, or creating a marked-up copy rather than deleting.
-
-## Safe assumptions
-
-If style information is incomplete, make the smallest reasonable assumption:
-
-- use current artifact conventions before importing external style
-- preserve existing color semantics in financial models
-- preserve official master/footer/source structures
-- default to high contrast and readable typography
-- use conservative finance prose over promotional prose
-- state assumptions only where they matter
-
-## Unsafe assumptions
-
-Do not assume:
-
-- a public logo/website palette is the same as the internal client deliverable style
-- one slide/page defines the entire house style
-- a draft file reflects final style
-- visible formatting is intentional rather than a manual one-off
-- a style adaptation permits changing facts, numbers, formulas, disclosures, or recommendations
-
-
-## Style Is Not Evidence
-
-Precedent artifacts can define voice, layout, and formatting. They cannot validate facts, numbers, recommendations, market data, estimates, or source claims unless they are also the controlling source for the current task.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/style-extraction-playbook.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/style-extraction-playbook.md
deleted file mode 100644
index 31ac1c8..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/style-extraction-playbook.md
+++ /dev/null
@@ -1,131 +0,0 @@
-# Style Extraction Playbook
-
-Use this guide when turning a style guide, precedent, template, or current artifact into a reusable style profile.
-
-## 1. Classify each source
-
-Record each source as one of:
-
-- **official guide**: brand book, style guide, compliance-approved writing guide, legal/disclosure guide.
-- **template/master**: PowerPoint template, Word template, spreadsheet/model shell, memo shell, research report shell.
-- **final precedent**: sent, board-ready, client-ready, committee-approved, published, or final artifact.
-- **draft precedent**: working version or partially styled sample.
-- **current artifact**: the deck/doc/model being edited.
-- **verbal instruction**: user-provided preference or target style description.
-- **public brand source**: website, annual report, investor presentation, or public style reference.
-- **industry default**: generic finance style used only when no better source exists.
-
-For each source, capture: artifact type, audience, date/period, author/team/client, final-vs-draft status, source confidence, and any obvious limitations.
-
-## 2. Extract visual system
-
-Look for rules that appear in official guides, masters, templates, or repeated precedent patterns.
-
-### Colors
-
-Capture:
-
-- primary brand colors and secondary/accent colors
-- neutral palette, background colors, divider colors, table fills, and gridlines
-- semantic colors for positive/negative/neutral, actual/forecast, downside/base/upside, primary/secondary series
-- colors to avoid or use sparingly
-- accessibility concerns, especially low contrast or red/green-only encoding
-
-Prefer exact RGB/hex values from theme XML, style guides, or templates. If colors are inferred from screenshots or images, label them as approximate.
-
-### Typography
-
-Capture:
-
-- font family for title, subtitle, body, labels, footnotes, tables, chart labels, callouts, and appendix text
-- relative size hierarchy and weight, not just exact point sizes
-- capitalization rules: title case, sentence case, all caps, small caps
-- line spacing, bullet indentation, paragraph spacing, and dense-vs-airy layout preference
-- treatment of numbers, tickers, company names, and acronyms
-
-Do not embed or share font files. If the font is unavailable, select a reasonable fallback and label it.
-
-### Layout grammar
-
-Capture:
-
-- page/slide size and orientation
-- margin grid, column structure, title block, and content zones
-- footer structure: source line, page number, confidential label, logo, date, author, client name
-- section divider style and appendix style
-- title/subtitle/callout placement
-- exhibit density: number of charts/tables per page, white space norms, and annotation style
-- use of icons, logos, imagery, and watermarks
-
-### Chart and table style
-
-Capture:
-
-- preferred chart types and when they are used
-- axis, label, legend, gridline, and data-label conventions
-- number formatting: decimals, percentages, basis points, multiples, currency, K/M/B notation
-- table header style, subtotal/total rows, zebra striping, border weight, and variance columns
-- footnote markers, source placement, and chart annotations
-- color mapping for scenarios, time periods, benchmarks, and comparables
-
-## 3. Extract writing style
-
-Capture:
-
-- headline style: descriptive label vs full so-what sentence
-- tone: neutral, assertive, analytical, promotional, skeptical, legalistic, investor-oriented, executive-facing
-- default structure: recommendation-first, issue-analysis-recommendation, thesis-evidence-risk, or summary-details-appendix
-- bullet style: fragments vs complete sentences, punctuation, parallelism, indentation, and bold lead-ins
-- treatment of caveats, risk language, assumptions, and management claims
-- citation style: inline links, footnotes, endnotes, source lines, appendix source tables, or cell comments
-- audience-specific language: banker, investor, CFO, board, lender, committee, public research, client pitch
-
-## 4. Identify rules versus quirks
-
-Classify each finding:
-
-- **rule**: explicitly stated or repeated consistently across high-priority sources.
-- **pattern**: repeated in precedents but not written down.
-- **quirk**: appears once or conflicts with higher-priority style.
-- **artifact constraint**: caused by the current file, tool, template, or source data rather than a style preference.
-
-Apply rules and patterns. Avoid copying quirks unless the user specifically wants to match that exact artifact.
-
-## 5. Build a style profile
-
-Use this compact structure:
-
-```markdown
-# Style profile: [institution/client/artifact]
-
-## Source basis
-| Source | Type | Priority | Freshness | Relevance | Notes |
-|---|---:|---:|---:|---:|---|
-
-## Core style rules
-- Visual identity:
-- Layout grammar:
-- Chart/table conventions:
-- Writing voice:
-- Citation/source style:
-- Do-not-change rules:
-
-## Artifact-specific application
-- Deck:
-- Memo/report:
-- Spreadsheet/model:
-
-## Inferred assumptions
-- [Assumption] [why chosen] [confidence]
-
-## Conflicts/open questions
-- [Conflict or missing input] [recommended resolution]
-```
-
-## 6. Office metadata extraction
-
-For Office files, use `scripts/extract_office_style.py` when available to extract theme fonts, theme colors, named styles, slide layout names, workbook sheet names, and style metadata. Treat this as a supplement, not a substitute for visual review.
-
-- Theme XML is reliable for available palette and fonts, but it may not reflect manual overrides.
-- Named styles can show intent, but not all content uses them consistently.
-- Rendered review is still required for final visual fidelity, especially for charts, images, complex tables, and manually positioned objects.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/writing-style-adaptation.md b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/writing-style-adaptation.md
deleted file mode 100644
index 45f27c3..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/references/writing-style-adaptation.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# Writing Style Adaptation
-
-Use this guide when adapting the voice of buy-side PM memos, sell-side research notes, public-equity decks, earnings notes, ETF/index diligence notes, committee packs, emails, board materials, or client equity deliverables.
-
-## Preserve meaning before tone
-
-A style rewrite must not alter the decision, claim, caveat, number, source, or degree of confidence unless the user requests substantive editing. If a sentence contains a factual claim or recommendation, rewrite only after identifying what must remain unchanged.
-
-## Finance voice dimensions
-
-Capture and apply the target style across these dimensions:
-
-| Dimension | Options to infer |
-|---|---|
-| Headline style | descriptive label, so-what sentence, recommendation-first, question-led, issue-led |
-| Tone | neutral, skeptical, commercial, board-level, banker-polished, research-analytical, risk-aware |
-| Evidence posture | source-forward, assumption-forward, management-claim separated, citation-light, footnote-heavy |
-| Density | executive concise, MD-style dense, memo narrative, appendix-heavy, slide-fragmented |
-| Assertion level | tentative, balanced, decisive, investment-view-forward, compliance-constrained |
-| Bullet grammar | fragments, complete sentences, bold lead-ins, em dash structure, no punctuation, full punctuation |
-| Caveat style | inline caveat, risk section, assumptions box, footnote, appendix limitation |
-
-## Common target styles
-
-### Public-equity deck or research report
-
-- Make page titles action-oriented and client-relevant.
-- Use crisp bullets, parallel phrasing, and commercial implications.
-- Use qualifiers carefully: avoid overstating if diligence is incomplete.
-- Keep detail in exhibits/footnotes rather than long prose blocks.
-- Prefer terms like supports, implies, highlights, suggests, indicates, creates opportunity, and positions when evidence supports them.
-
-### Public-equity investment memo
-
-- Lead with recommendation, thesis, key risks, diligence status, and returns/downside implications.
-- Distinguish base case, downside, and diligence-to-date.
-- Be skeptical of unsupported company, management, market, or consensus claims.
-- Use concise but explicit risk language.
-
-### Credit Markets handoff
-
-- Route credit memos, credit instruments, creditworthiness, restructuring, distressed, recovery, spreads, yields, covenants, and debt-security analysis to Credit Markets.
-- In this plugin, preserve only the credit read-through that supports a listed-equity decision, and do not style-adapt a public-credit memo as if it were locally owned.
-
-### Public equity research
-
-- Separate reported facts, consensus/estimate changes, thesis implications, and variant view.
-- Use precise catalyst, risk, and valuation language.
-- Avoid investment advice overstatement when compliance posture is unknown.
-
-### Board-style or committee research note
-
-- Lead with investment implication and decision needed.
-- Use plain-language explanations tied to forecast, variance, KPI, valuation, risk, and monitoring actions.
-- Keep financial detail scannable and action-oriented.
-
-## Rewrite protocol
-
-1. Identify source claims, numbers, and recommendations that cannot change.
-2. Infer the target voice from the strongest style source.
-3. Rewrite titles, bullets, paragraphs, and callouts using the target voice.
-4. Compare the rewrite against the original meaning.
-5. Flag any substantive changes separately from tone/formatting changes.
-
-## Before/after format
-
-For advice or QC tasks, use:
-
-| Location | Original | Styled version | Change type | Rationale |
-|---|---|---|---|---|
-
-Change type should be one of: tone, clarity, title, structure, caveat, citation, formatting, or substantive. Do not hide substantive edits under tone.
diff --git a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/scripts/extract_office_style.py b/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/scripts/extract_office_style.py
deleted file mode 100755
index 299bf81..0000000
--- a/plugins/financial-markets/skills/public-equity-investing/internal-support/style-guide-adapter/scripts/extract_office_style.py
+++ /dev/null
@@ -1,462 +0,0 @@
-#!/usr/bin/env python3
-"""Extract lightweight style metadata from Office files.
-
-Supports .pptx, .docx, and .xlsx files without modifying them. The output is a
-style reconnaissance aid, not a replacement for rendered visual review.
-
-Usage:
-  python scripts/extract_office_style.py path/to/file.pptx --format markdown
-  python scripts/extract_office_style.py deck.pptx memo.docx model.xlsx --format json --out style.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-import zipfile
-from collections import Counter
-from pathlib import Path
-from typing import Any, Iterable
-from xml.etree import ElementTree as ET
-
-NS = {
-    "a": "http://schemas.openxmlformats.org/drawingml/2006/main",
-    "p": "http://schemas.openxmlformats.org/presentationml/2006/main",
-    "r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships",
-    "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main",
-    "x": "http://schemas.openxmlformats.org/spreadsheetml/2006/main",
-}
-
-
-def _read_xml(zf: zipfile.ZipFile, name: str) -> ET.Element | None:
-    try:
-        with zf.open(name) as fh:
-            return ET.parse(fh).getroot()
-    except (KeyError, ET.ParseError, zipfile.BadZipFile):
-        return None
-
-
-def _local(tag: str) -> str:
-    return tag.rsplit("}", 1)[-1]
-
-
-def _attr(el: ET.Element, name: str) -> str | None:
-    value = el.attrib.get(name)
-    if value is not None:
-        return value
-    for key, val in el.attrib.items():
-        if key.endswith("}" + name):
-            return val
-    return None
-
-
-def _count_members(zf: zipfile.ZipFile, prefix: str, pattern: str) -> int:
-    rx = re.compile(pattern)
-    return sum(1 for n in zf.namelist() if n.startswith(prefix) and rx.search(n))
-
-
-def _unique_sorted(values: Iterable[str], limit: int = 30) -> list[str]:
-    clean = [v for v in values if v]
-    return sorted(set(clean))[:limit]
-
-
-def _top(counter: Counter, limit: int = 12) -> list[dict[str, Any]]:
-    return [{"value": k, "count": v} for k, v in counter.most_common(limit) if k]
-
-
-def _theme_summary(zf: zipfile.ZipFile, candidates: list[str]) -> dict[str, Any]:
-    root = None
-    source = None
-    for name in candidates:
-        root = _read_xml(zf, name)
-        if root is not None:
-            source = name
-            break
-    if root is None:
-        return {}
-
-    theme: dict[str, Any] = {"source": source}
-    theme_name = _attr(root, "name")
-    if theme_name:
-        theme["theme_name"] = theme_name
-
-    colors: dict[str, str] = {}
-    clr_scheme = root.find(".//a:clrScheme", NS)
-    if clr_scheme is not None:
-        for child in list(clr_scheme):
-            slot = _local(child.tag)
-            rgb = child.find(".//a:srgbClr", NS)
-            sysclr = child.find(".//a:sysClr", NS)
-            if rgb is not None and _attr(rgb, "val"):
-                colors[slot] = "#" + _attr(rgb, "val").upper()
-            elif sysclr is not None:
-                last = _attr(sysclr, "lastClr")
-                val = _attr(sysclr, "val")
-                colors[slot] = ("#" + last.upper()) if last else f"system:{val}"
-    if colors:
-        theme["colors"] = colors
-
-    fonts: dict[str, str] = {}
-    for slot, xpath in [
-        ("major_latin", ".//a:majorFont/a:latin"),
-        ("minor_latin", ".//a:minorFont/a:latin"),
-    ]:
-        el = root.find(xpath, NS)
-        if el is not None and _attr(el, "typeface"):
-            fonts[slot] = _attr(el, "typeface")
-    if fonts:
-        theme["fonts"] = fonts
-
-    return theme
-
-
-def _drawing_text_style_counts(root: ET.Element) -> dict[str, Any]:
-    fonts: Counter = Counter()
-    sizes: Counter = Counter()
-    colors: Counter = Counter()
-
-    for rpr in root.findall(".//a:rPr", NS):
-        sz = _attr(rpr, "sz")
-        if sz and sz.isdigit():
-            sizes[str(int(sz) / 100)] += 1
-        latin = rpr.find("a:latin", NS)
-        if latin is not None and _attr(latin, "typeface"):
-            fonts[_attr(latin, "typeface")] += 1
-        srgb = rpr.find(".//a:solidFill/a:srgbClr", NS)
-        if srgb is not None and _attr(srgb, "val"):
-            colors["#" + _attr(srgb, "val").upper()] += 1
-
-    return {
-        "observed_text_fonts": _top(fonts),
-        "observed_text_sizes_pt": _top(sizes),
-        "observed_text_colors": _top(colors),
-    }
-
-
-def _pptx_summary(path: Path, zf: zipfile.ZipFile) -> dict[str, Any]:
-    summary: dict[str, Any] = {
-        "type": "pptx",
-        "slide_count": _count_members(zf, "ppt/slides/", r"slide\d+\.xml$"),
-        "layout_count": _count_members(zf, "ppt/slideLayouts/", r"slideLayout\d+\.xml$"),
-        "master_count": _count_members(zf, "ppt/slideMasters/", r"slideMaster\d+\.xml$"),
-    }
-    theme = _theme_summary(zf, ["ppt/theme/theme1.xml"])
-    if theme:
-        summary["theme"] = theme
-
-    presentation = _read_xml(zf, "ppt/presentation.xml")
-    if presentation is not None:
-        sld_sz = presentation.find("p:sldSz", NS)
-        if sld_sz is not None:
-            summary["slide_size_emu"] = {
-                "cx": _attr(sld_sz, "cx"),
-                "cy": _attr(sld_sz, "cy"),
-                "type": _attr(sld_sz, "type"),
-            }
-
-    layout_names: list[str] = []
-    for name in zf.namelist():
-        if name.startswith("ppt/slideLayouts/slideLayout") and name.endswith(".xml"):
-            root = _read_xml(zf, name)
-            if root is None:
-                continue
-            csld = root.find("p:cSld", NS)
-            if csld is not None and _attr(csld, "name"):
-                layout_names.append(_attr(csld, "name"))
-    if layout_names:
-        summary["layout_names"] = _unique_sorted(layout_names)
-
-    font_counts: Counter = Counter()
-    size_counts: Counter = Counter()
-    color_counts: Counter = Counter()
-    fill_counts: Counter = Counter()
-    line_counts: Counter = Counter()
-
-    for name in zf.namelist():
-        if not (
-            name.startswith(
-                ("ppt/slides/slide", "ppt/slideMasters/slideMaster", "ppt/slideLayouts/slideLayout")
-            )
-        ):
-            continue
-        if not name.endswith(".xml"):
-            continue
-        root = _read_xml(zf, name)
-        if root is None:
-            continue
-        counts = _drawing_text_style_counts(root)
-        for item in counts["observed_text_fonts"]:
-            font_counts[item["value"]] += item["count"]
-        for item in counts["observed_text_sizes_pt"]:
-            size_counts[item["value"]] += item["count"]
-        for item in counts["observed_text_colors"]:
-            color_counts[item["value"]] += item["count"]
-        for srgb in root.findall(".//a:solidFill/a:srgbClr", NS):
-            if _attr(srgb, "val"):
-                fill_counts["#" + _attr(srgb, "val").upper()] += 1
-        for srgb in root.findall(".//a:ln//a:srgbClr", NS):
-            if _attr(srgb, "val"):
-                line_counts["#" + _attr(srgb, "val").upper()] += 1
-
-    summary["observed"] = {
-        "text_fonts": _top(font_counts),
-        "text_sizes_pt": _top(size_counts),
-        "text_colors": _top(color_counts),
-        "fill_colors": _top(fill_counts),
-        "line_colors": _top(line_counts),
-    }
-    return summary
-
-
-def _docx_summary(path: Path, zf: zipfile.ZipFile) -> dict[str, Any]:
-    summary: dict[str, Any] = {"type": "docx"}
-    theme = _theme_summary(zf, ["word/theme/theme1.xml"])
-    if theme:
-        summary["theme"] = theme
-
-    styles = _read_xml(zf, "word/styles.xml")
-    style_rows: list[dict[str, Any]] = []
-    if styles is not None:
-        for st in styles.findall("w:style", NS):
-            row: dict[str, Any] = {
-                "type": _attr(st, "type"),
-                "style_id": _attr(st, "styleId"),
-            }
-            name_el = st.find("w:name", NS)
-            if name_el is not None:
-                row["name"] = _attr(name_el, "val")
-            based_on = st.find("w:basedOn", NS)
-            if based_on is not None:
-                row["based_on"] = _attr(based_on, "val")
-            rfonts = st.find(".//w:rFonts", NS)
-            if rfonts is not None:
-                row["font"] = (
-                    _attr(rfonts, "ascii") or _attr(rfonts, "hAnsi") or _attr(rfonts, "cs")
-                )
-            sz = st.find(".//w:sz", NS)
-            if sz is not None and _attr(sz, "val") and _attr(sz, "val").isdigit():
-                row["size_pt"] = int(_attr(sz, "val")) / 2
-            color = st.find(".//w:color", NS)
-            if color is not None and _attr(color, "val") and _attr(color, "val") != "auto":
-                row["color"] = "#" + _attr(color, "val").upper()
-            if row.get("name") or row.get("style_id"):
-                style_rows.append(row)
-    summary["styles"] = style_rows[:80]
-
-    document = _read_xml(zf, "word/document.xml")
-    if document is not None:
-        para_styles: Counter = Counter()
-        table_count = len(document.findall(".//w:tbl", NS))
-        for pstyle in document.findall(".//w:pStyle", NS):
-            if _attr(pstyle, "val"):
-                para_styles[_attr(pstyle, "val")] += 1
-        summary["document_usage"] = {
-            "paragraph_style_counts": _top(para_styles),
-            "table_count": table_count,
-        }
-    return summary
-
-
-def _xlsx_summary(path: Path, zf: zipfile.ZipFile) -> dict[str, Any]:
-    summary: dict[str, Any] = {"type": "xlsx"}
-    theme = _theme_summary(zf, ["xl/theme/theme1.xml"])
-    if theme:
-        summary["theme"] = theme
-
-    workbook = _read_xml(zf, "xl/workbook.xml")
-    if workbook is not None:
-        sheets = []
-        for sh in workbook.findall(".//x:sheet", NS):
-            row = {"name": _attr(sh, "name"), "sheet_id": _attr(sh, "sheetId")}
-            if row["name"]:
-                sheets.append(row)
-        summary["sheets"] = sheets
-
-    styles = _read_xml(zf, "xl/styles.xml")
-    if styles is not None:
-        fonts = []
-        for font in styles.findall("x:fonts/x:font", NS):
-            row: dict[str, Any] = {}
-            name_el = font.find("x:name", NS)
-            sz_el = font.find("x:sz", NS)
-            color_el = font.find("x:color", NS)
-            if name_el is not None:
-                row["name"] = _attr(name_el, "val")
-            if sz_el is not None:
-                row["size_pt"] = _attr(sz_el, "val")
-            if color_el is not None:
-                if _attr(color_el, "rgb"):
-                    row["color"] = "#" + _attr(color_el, "rgb")[-6:].upper()
-                elif _attr(color_el, "theme"):
-                    row["theme_color"] = _attr(color_el, "theme")
-            if row:
-                fonts.append(row)
-
-        fills = []
-        for fill in styles.findall("x:fills/x:fill", NS):
-            fg = fill.find(".//x:fgColor", NS)
-            if fg is not None:
-                if _attr(fg, "rgb"):
-                    fills.append("#" + _attr(fg, "rgb")[-6:].upper())
-                elif _attr(fg, "theme"):
-                    fills.append("theme:" + _attr(fg, "theme"))
-
-        cell_styles = []
-        for st in styles.findall("x:cellStyles/x:cellStyle", NS):
-            row = {"name": _attr(st, "name"), "builtin_id": _attr(st, "builtinId")}
-            if row["name"]:
-                cell_styles.append(row)
-
-        num_fmts = []
-        for nf in styles.findall("x:numFmts/x:numFmt", NS):
-            fmt = _attr(nf, "formatCode")
-            if fmt:
-                num_fmts.append(fmt)
-
-        summary["styles"] = {
-            "fonts": fonts[:60],
-            "fills": _unique_sorted(fills, limit=40),
-            "cell_styles": cell_styles[:80],
-            "custom_number_formats": num_fmts[:80],
-        }
-    return summary
-
-
-def inspect_file(path: Path) -> dict[str, Any]:
-    result: dict[str, Any] = {
-        "file": str(path),
-        "name": path.name,
-        "extension": path.suffix.lower(),
-    }
-    if not path.exists():
-        result["error"] = "file not found"
-        return result
-    if path.suffix.lower() not in {".pptx", ".docx", ".xlsx"}:
-        result["error"] = "unsupported file type"
-        return result
-    try:
-        with zipfile.ZipFile(path) as zf:
-            if path.suffix.lower() == ".pptx":
-                result.update(_pptx_summary(path, zf))
-            elif path.suffix.lower() == ".docx":
-                result.update(_docx_summary(path, zf))
-            else:
-                result.update(_xlsx_summary(path, zf))
-    except zipfile.BadZipFile:
-        result["error"] = "not a valid Office zip container"
-    return result
-
-
-def _md_table(rows: list[dict[str, Any]], columns: list[str]) -> str:
-    if not rows:
-        return ""
-    out = ["| " + " | ".join(columns) + " |", "|" + "|".join(["---"] * len(columns)) + "|"]
-    for row in rows:
-        out.append("| " + " | ".join(str(row.get(c, "")) for c in columns) + " |")
-    return "\n".join(out)
-
-
-def to_markdown(results: list[dict[str, Any]]) -> str:
-    lines: list[str] = ["# Office style extraction summary", ""]
-    for res in results:
-        lines.append(f"## {res.get('name', res.get('file'))}")
-        if res.get("error"):
-            lines.append(f"- Error: {res['error']}")
-            lines.append("")
-            continue
-        lines.append(f"- Type: {res.get('type')}")
-        for key in ["slide_count", "layout_count", "master_count"]:
-            if key in res:
-                lines.append(f"- {key.replace('_', ' ').title()}: {res[key]}")
-        if "slide_size_emu" in res:
-            lines.append(f"- Slide size EMU: {res['slide_size_emu']}")
-        theme = res.get("theme", {})
-        if theme:
-            lines.append("\n### Theme")
-            if theme.get("theme_name"):
-                lines.append(f"- Theme name: {theme['theme_name']}")
-            if theme.get("fonts"):
-                lines.append(f"- Fonts: {theme['fonts']}")
-            if theme.get("colors"):
-                lines.append("- Colors:")
-                for slot, color in theme["colors"].items():
-                    lines.append(f"  - {slot}: {color}")
-        if res.get("layout_names"):
-            lines.append("\n### Layout names")
-            for name in res["layout_names"][:30]:
-                lines.append(f"- {name}")
-        if res.get("sheets"):
-            lines.append("\n### Sheets")
-            lines.append(_md_table(res["sheets"], ["name", "sheet_id"]))
-        if res.get("observed"):
-            lines.append("\n### Observed style usage")
-            for label, rows in res["observed"].items():
-                if rows:
-                    lines.append(f"\n**{label.replace('_', ' ').title()}**")
-                    lines.append(_md_table(rows, ["value", "count"]))
-        if res.get("document_usage"):
-            usage = res["document_usage"]
-            lines.append("\n### Document usage")
-            lines.append(f"- Table count: {usage.get('table_count', 0)}")
-            if usage.get("paragraph_style_counts"):
-                lines.append(_md_table(usage["paragraph_style_counts"], ["value", "count"]))
-        if res.get("styles"):
-            styles = res["styles"]
-            lines.append("\n### Styles")
-            if isinstance(styles, list):
-                lines.append(
-                    _md_table(styles[:40], ["type", "style_id", "name", "font", "size_pt", "color"])
-                )
-            elif isinstance(styles, dict):
-                if styles.get("fonts"):
-                    lines.append("\n**Fonts**")
-                    lines.append(
-                        _md_table(styles["fonts"][:30], ["name", "size_pt", "color", "theme_color"])
-                    )
-                if styles.get("cell_styles"):
-                    lines.append("\n**Cell styles**")
-                    lines.append(_md_table(styles["cell_styles"][:40], ["name", "builtin_id"]))
-                if styles.get("fills"):
-                    lines.append("\n**Fills**")
-                    for fill in styles["fills"][:30]:
-                        lines.append(f"- {fill}")
-                if styles.get("custom_number_formats"):
-                    lines.append("\n**Custom number formats**")
-                    for fmt in styles["custom_number_formats"][:30]:
-                        lines.append(f"- `{fmt}`")
-        lines.append("")
-    lines.append(
-        "Note: This extraction is metadata-only. Confirm visual layout, charts, images, and manual overrides by rendering or inspecting the artifact before final delivery."
-    )
-    return "\n".join(lines)
-
-
-def main(argv: list[str] | None = None) -> int:
-    parser = argparse.ArgumentParser(
-        description="Extract style metadata from .pptx, .docx, and .xlsx files without modifying them."
-    )
-    parser.add_argument("files", nargs="+", help="Office files to inspect")
-    parser.add_argument(
-        "--format", choices=["markdown", "json"], default="json", help="Output format"
-    )
-    parser.add_argument("--out", help="Optional output file")
-    args = parser.parse_args(argv)
-
-    results = [inspect_file(Path(p)) for p in args.files]
-    if args.format == "json":
-        output = json.dumps(results, indent=2)
-    else:
-        output = to_markdown(results)
-
-    if args.out:
-        Path(args.out).write_text(output, encoding="utf-8")
-    else:
-        print(output)
-
-    return 1 if any("error" in r for r in results) else 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/SKILL.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/SKILL.md
deleted file mode 100644
index fe4a7fd..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/SKILL.md
+++ /dev/null
@@ -1,153 +0,0 @@
----
-name: scenario-sensitivity-generator
-description: Use when turning a public-equity base case, model, thesis, event, or catalyst into scenario skew, sensitivity, breakpoint, and PM action-threshold analysis. Do not use for first-pass model builds, credit-security valuation, or generic planning.
----
-
-# Scenario & Sensitivity Generator
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-## Internal Support
-
-When this workflow needs rendering, evidence/data preparation, style, or sector context, route support through the visible `public-equity-investing`
-router and its bundled internal playbooks. Route workbook or model QA through the visible `model-audit-tieout` workflow.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is a polished standalone HTML scenario report, with an XLSX workbook taking precedence when reusable model sensitivities or calculations are central. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. For a substantive reusable scenario analysis, explicit HTML scenario report, or sourced discrete-event success/delay/break overlay, the default resolves the presentation surface to a polished standalone HTML scenario report unless the user requests an alternate surface, a quick/no-file answer, workbook/model output, or a standardized dashboard. In interactive runs, ask only remaining material choices such as depth, audience/use, or focus; in non-interactive runs, default to the HTML scenario report and `Full working analysis` while disclosing those assumptions outside the artifact. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-## Purpose
-
-Turn an existing Public Equity Investing base case into scenario and sensitivity outputs that show what changes the investment view. This skill owns grids, breakpoints, probability cases, thesis triggers, source posture, and interpretation after a model, earnings setup, equity-risk credit-signal handoff, event case, macro view, or thesis tracker already exists.
-
-The default lens is a public-equity investor deciding whether to add, press, hold, trim, exit, hedge, wait for proof, re-underwrite, or change rating/target. The output should tell a buy-side PM, long/short analyst, sell-side equity researcher, or ETF/index diligence user what the stock already discounts, what scenario is underwriteable, what is merely optical, what breaks first, and what evidence would change the action.
-
-For a concrete dated corporate event, this skill may directly produce a success/delay/break, implied-probability, or breakpoint overlay when the user specifically requests scenario sensitivity and the relevant deal terms, current-price anchor, timing, and downside convention are available or can be verified. This is a scenario overlay, not a replacement for full event underwriting.
-
-## Do Not Use
-
-- First-pass DCF, 3-statement, comps, credit, earnings, full event-underwriting, or memo builds.
-- Raw source cleanup, source hierarchy, or evidence conflict resolution.
-- Final memo, pitch, hedge, sizing, or deck QC ownership.
-- Generic FP&A planning or target-setting exercises.
-
-Use the relevant local owner first, then return here for the scenario layer.
-
-## Routing
-
-Use downstream of `equity-model-update`, `dcf-model-builder`, `three-statement-model-builder`, `comps-valuation`, `earnings-preview`, `earnings-deep-dive`, Credit Markets read-throughs, `event-driven-analyzer`, `economic-impact-report`, or `thesis-tracker`.
-
-Use `financial-source-of-truth`, `financials-normalizer`, or `excel-data-cleaner` first if inputs are not clean or sourced. Use `model-audit-tieout` before treating workbook scenarios as final. See `references/p0-integration.md` for ownership boundaries.
-
-Use `event-driven-analyzer` when the work requires a full event fact pack, process or regulatory/legal underwriting, trade expression, execution inputs, or ongoing event monitoring. When the request is explicitly for scenario skew on a named event and verified terms and pricing can anchor the cases, this skill may own the focused overlay while making any missing event diligence visible.
-
-## Workflow
-
-1. **Confirm base case and decision.** Identify security/issuer/model/event/thesis, decision type, and base-case source. If the base case is not model-validated, label outputs screen-grade or illustrative. For event overlays, verify offer or terminal terms, current price and as-of timestamp, key dates, contractual protections where relevant, and the chosen break/downside convention before presenting expected value. Use primary transaction documents and the freshest accessible market-data source for load-bearing inputs before relying on press reporting or older price observations; a focused overlay reduces analytical scope, not source-quality requirements.
-2. **Select scenario mode.** Choose price-target, valuation, EPS/estimate revision, KPI driver, equity-liquidity downside, Credit Markets read-through sensitivity, event probability tree, macro factor, or thesis trigger table.
-3. **Materialize tables when useful.** Use the deterministic helper for repeatable shells or populated calculations; if inputs are missing, it emits input-required rows instead of inventing values. Treat JSON, CSV, Markdown, run logs, and manifests as support artifacts unless the user explicitly asks for them.
-4. **Interpret investor impact.** State the driver that matters most, what breaks first, whether upside is underwriteable, where the thesis depends on multiple expansion, estimate revisions, event probability, financing, or macro conditions, and which PM action would change. For event probability cases, state the analyst probability assumptions, hurdle, break-value convention, market-implied probability, and entry or maximum-break-probability threshold.
-5. **Hand off cleanly.** Send model mechanics back to model builders, final narrative to `memo-builder` or `long-short-pitch`, exposure/sizing and hedge construction to the appropriate `portfolio-risk-management` mode, monitoring thresholds to `thesis-tracker`, and circulated decks/reports to `deck-report-qc`.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: base-driver ownership, scenario design, sensitivity math, output tables, and QA. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Deterministic Materializer
-
-Run from this skill folder:
-
-```bash
-python3 scripts/materialize_public_equity_sensitivities.py --tables all --format json
-```
-
-With an assumptions file:
-
-```bash
-python3 scripts/materialize_public_equity_sensitivities.py --input assumptions.json --tables price_target_scenario,valuation_sensitivity --format json
-```
-
-Canonical table names:
-
-- `price_target_scenario`
-- `valuation_sensitivity`
-- `eps_revision_sensitivity`
-- `kpi_driver_sensitivity`
-- `equity_liquidity_downside`
-- `event_probability_tree`
-- `macro_factor_sensitivity`
-- `thesis_trigger_table`
-
-Probability-weighted rows require complete probabilities that sum to 100%. Deterministic rows include `source_id`, `source_posture`, and `as_of_date` so downstream artifacts can distinguish model outputs from illustrative math. Load `references/materializer-schema.md` for the full input/output contract.
-
-When `--output` or `--run-log` is used, the helper also writes `run_log.json` and `manifest.json` with `status`, `model_status`, warnings, hard failures, source basis, output paths, and output manifest. These deterministic tables are support artifacts unless a downstream workbook or dashboard turns them into the human-facing deliverable. Invalid table names or unsupported CSV multi-table requests must return non-zero with a failed run log when a log path is available.
-
-Readiness is conservative: missing input JSON, missing base case, missing current price, or invalid probabilities must cap `model_status` at `not-decision-ready`. Complete math with missing source/as-of posture is `screen-grade`, not senior-review-ready. Only source-backed current price, valid scenario values, valid probabilities, and visible source posture can produce `senior-review-ready`.
-
-If scenario outputs are inserted into or packaged as an XLSX workbook by a downstream tool, require a first visible `Cover` or scenario dashboard summarizing base case, cases tested, driver deltas, output range, source posture, validation status, warnings/hard failures, and the workbook/tab map.
-
-## Output Modes
-
-- **Full scenario report:** default route for substantive reusable scenario analysis, an explicit HTML scenario report, or a sourced discrete-event success/delay/break overlay. Produce a polished standalone HTML scenario report following `../../shared/html-artifact-standard.md`; let the decision and primary scenario object determine the layout rather than forcing a fixed dashboard module inventory.
-- **Standardized dashboard:** use only when the user explicitly asks for a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render. For that path, use `references/DASHBOARD_PACK.md`: `scenario-sensitivity-generator` owns the scenario math, stock-price anchoring, expected-return/skew interpretation, action thresholds, source posture, and missing-evidence calls; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV/run-log support files behind the HTML dashboard unless explicitly requested.
-- **Explicit quick PM read:** use only when the user explicitly asks for quick, short, summary, brief, one-page, or TL;DR. Include decision posture, current-price/base-case anchor, scenario range, binding breakpoint, primary missing evidence, and what changes the action.
-- **Workbook/model overlay:** preserve or extend the owning workbook/model when scenario outputs are an input to an existing model package rather than a new standalone report.
-
-## Artifact Contract
-
-Every final answer or handoff should include:
-
-- decision context and base-case source;
-- table names used;
-- driver changes, timing, absolute outputs, and deltas;
-- source labels and as-of dates for market-sensitive inputs;
-- whether outputs are model-validated, source-derived, user-provided, or illustrative;
-- threshold, trigger, or breakpoint interpretation;
-- expected return versus hurdle, downside/upside ratio, break-even probability, and skew label where applicable;
-- explicit PM action rules for add, press, hold, trim, exit, hedge, wait for proof, or re-underwrite;
-- what would change the investment view;
-- recommended next workflow or user data request.
-
-## HTML Guidance
-
-For a substantive standalone HTML scenario report, load `../../shared/html-artifact-standard.md` and apply these scenario-specific requirements:
-
-- Give each first-read element a distinct job: lead with the investment conclusion and posture; then show the current-price/base-case anchor, primary scenario tree or sensitivity object, market-implied hurdle or breakpoint, and what must be verified next. Do not repeat the same verdict through a hero, posture card, metric-tile row, and second decision panel.
-- For a sourced discrete-event overlay, organize the report around verified terms and as-of price, success/delay/break cases, expected value versus hurdle, implied completion probability, break-value convention, timing sensitivity, regulatory or evidence gates, and conditional action rules. If wider event diligence is missing, label the output as a focused scenario overlay or screen-grade report and route full underwriting to `event-driven-analyzer`.
-- Require agreement-verified ticking-fee or contingent-consideration mechanics and an exact assumed close or break date before featuring precise timing-adjusted payoff or hurdle-probability calculations. If either is missing, show timing math only as a clearly provisional sensitivity or omit it in favor of the evidence needed to calculate it.
-- When scenario probabilities are illustrative rather than independently underwritten, lead with market-implied probabilities and required-probability or entry-price breakpoints. Keep any sample probability-weighted value secondary, explicitly illustrative, and out of the headline investment case.
-- Distinguish `Reported` or `Source-derived` facts from `Analyst assumption`, `Derived calculation`, and `PM judgment`. Scenario probabilities, hurdle rates, break-value capture, terminal-value adjustments, and unvalidated timing assumptions must never appear to be sourced from a filing or market-data citation.
-- Use action language appropriate to position status and evidence: when no holding or executed trade is provided, prefer `Conditional Action Rules`, `wait for proof`, `research`, or `re-underwrite` over language that implies an active position.
-- Cite load-bearing terms, prices, dates, and calculations near use while keeping the page readable. Do not fragment tickers, dates, prices, percentages, scenario labels, or table cells into repetitive citation links; prefer a compact nearby source note when adjacent values share the same basis.
-- Visually inspect local HTML via local headless-browser screenshots, not the in-app Browser plugin, and iterate on hierarchy, density, clipping, citation rendering, whitespace, and whether the scenario decision is immediately usable.
-
-## Reference Map
-
-- `references/p0-integration.md`: local ownership and handoffs.
-- `references/public-equity-investing-sensitivity-taxonomy.md`: table selection and interpretation rules.
-- `references/materializer-schema.md`: deterministic script contract.
-- `references/planning-mode-router.md`: Public Equity Investing mode selection.
-- `references/scenario-overlay-contract.md`: workbook/cross-skill overlay schema.
-- `references/target-backsolve-rubric.md`: feasibility labels for target backsolves.
-- `references/output-templates.md`: trigger tables, QA checks, and reusable outputs.
-
-## Quality Bar
-
-Keep scenarios tied to a concrete security, issuer, model, event, or thesis. Label unsourced assumptions and stale data. Distinguish price-target math from investable risk/reward. Do not rebuild the base model, hide weak assumptions behind clean tables, or present probability-weighted outputs without source posture and downside. For discrete event overlays, require primary-document terms and fresh market-data anchoring when accessible, clear success/delay/break definitions, break-value methodology, expected return versus hurdle, market-implied probability where calculable, and the evidence gate that changes the conditional action. Treat timing-adjusted precision and probability-weighted values as provisional or secondary when their mechanics or probabilities have not been independently supported.
-
-## Equity Valuation PM Standard
-
-Load `shared/equity-valuation-pm-standard.md` and `shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, what changes target, rating, sizing, hedge, trim, exit, or watchlist status, and what evidence is missing.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instrument underwriting, or distressed claim valuation.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/agents/openai.yaml b/plugins/financial-markets/skills/scenario-sensitivity-generator/agents/openai.yaml
deleted file mode 100644
index 34b4979..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: "Scenario & Sensitivity Generator"
-  short_description: "Build Public Equity Investing scenario skew, valuation sensitivities, event trees, equity-liquidity downside read-throughs, and PM action thresholds."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/research_action_register_template.csv b/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/research_action_register_template.csv
deleted file mode 100644
index 7420bc2..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/research_action_register_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-action_or_decision,trigger,expected_price_value_or_risk_impact,source_or_owner,timing,reversibility,dependencies,risks,status
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/scenario_overlay_template.csv b/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/scenario_overlay_template.csv
deleted file mode 100644
index c47723c..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/scenario_overlay_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-version,scenario,setup_type,module,driver,baseline_value,scenario_value,delta_type,start_period_or_event,end_period_or_event,rationale,evidence_label,source_id,impacted_output,investment_implication,caveat
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/sensitivity_matrix_template.csv b/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/sensitivity_matrix_template.csv
deleted file mode 100644
index 42dcfdf..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/sensitivity_matrix_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-sensitivity_name,setup_type,driver_1,driver_1_value,driver_2,driver_2_value,output_metric,output_value,delta_vs_base,implied_price_or_value,threshold_or_breakpoint,source_posture,caveat
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/target_backsolve_template.csv b/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/target_backsolve_template.csv
deleted file mode 100644
index f54a7d9..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/target_backsolve_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-target_metric,target_period,target_value,locked_constraints,allowed_lever,required_lever_value,required_path,feasibility_label,what_must_be_true,what_would_disconfirm,next_workflow,caveat
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/trigger_metrics_template.csv b/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/trigger_metrics_template.csv
deleted file mode 100644
index 960f849..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/assets/trigger_metrics_template.csv
+++ /dev/null
@@ -1 +0,0 @@
-trigger,threshold,source_or_monitoring_cadence,likely_interpretation,action_implication,next_workflow,decision_deadline
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/DASHBOARD_PACK.md
deleted file mode 100644
index 92dc4c2..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Scenario Sensitivity Dashboard Pack
-
-Use this pack only when the user explicitly requests a standardized dashboard, reusable dashboard template, PM cockpit, or structured payload-driven render for a public-equity scenario pack, valuation sensitivity, earnings revision case, event probability tree, macro-factor read-through, liquidity downside case, or thesis trigger table. An ordinary substantial scenario sensitivity package or sourced event scenario overlay should be a polished standalone HTML scenario report following `../../../shared/html-artifact-standard.md`.
-
-## Producer Role
-
-`scenario-sensitivity-generator` owns the scenario math, base-case source, current-price anchoring, driver deltas, expected-return/skew interpretation, action thresholds, source posture, model-readiness caveats, and missing evidence. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `scenario_sensitivity`
-- `layout`: `single_page` for PM decision packs unless the user explicitly requests tabs
-- `hero.callout`: the decision hinge, such as "Does the upside clear the hurdle after downside and evidence quality?"
-- `snapshot`: current price, base case, probability-weighted value, expected return versus hurdle, downside/upside ratio, break-even probability, skew label, and recommended PM action
-- `sources`: model output, consensus/export, company filing, market-data snapshot, event/source document, or explicitly labeled user assumption
-- Source posture and Missing evidence must be visible in the dashboard, not buried in the support files.
-- On this explicitly selected dashboard path, the HTML dashboard/report is the human deliverable. JSON, Markdown, CSV, run logs, and manifests are support artifacts unless explicitly requested.
-
-## Tabs And Modules
-
-1. `pm-decision`
-   - `decision_box`: action posture, underwriteable versus optical upside, what is priced in, what changes the action, and top missing evidence
-   - `metric_tiles`: expected return versus hurdle, downside/upside ratio, break-even probability, skew label, current price, and source/readiness posture
-2. `scenario-map`
-   - `scenario_map`: bull/base/bear or custom cases with price/value output, probability, return, evidence quality, and falsifiers
-   - `table`: probability-weighted value, scenario values, implied returns, and action rules
-3. `sensitivity-drivers`
-   - `table`: valuation, EPS/revision, KPI, macro-factor, event, or liquidity-driver sensitivities with source labels and as-of dates
-   - `bar_chart`: compact expected-return, value, or driver-impact chart only when complete source-backed rows exist
-4. `thresholds`
-   - `question_list` or `table`: add, press, hold, trim, exit, hedge, wait-for-proof, and re-underwrite thresholds tied to model lines, source IDs, and dates
-   - `timeline`: data releases, earnings, event dates, or review checkpoints that change the scenario tree
-5. `evidence-and-qa`
-   - `source_list`: source ledger, model/source posture, and freshness labels
-   - `missing_evidence`: missing current price, stale consensus, unsupported probabilities, weak source basis, absent model tie-out, or unavailable market data
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- For discrete-event dashboards, use primary transaction documents and the freshest accessible market-data source for load-bearing offer terms, timing mechanics, and current-price inputs before relying on press reporting or older price observations.
-- Cite every current price, target/value output, probability, hurdle return, multiple, EPS/EBITDA/FCF assumption, KPI driver, factor sensitivity, liquidity input, and event date.
-- Label whether each value is model-validated, source-derived, user-provided, consensus-derived, market-data-derived, or illustrative.
-- Probability-weighted cases must disclose probability source, probability sum, and whether the output is valid. If probabilities are incomplete or do not sum to 100%, show the issue in `missing_evidence` and do not present the probability-weighted value as decision-ready.
-- When probabilities are illustrative rather than independently underwritten, lead with implied-probability and required-probability breakpoints; keep sample probability-weighted value secondary and explicitly illustrative.
-- Require agreement-verified timing-consideration mechanics and an exact assumed resolution date before featuring precise timing-adjusted expected-return or hurdle-probability outputs; otherwise label those outputs provisional.
-- Source posture and missing evidence must be visible; do not hide weak assumptions behind clean tables.
-- Empty, unsourced, or current-price-missing scenario outputs must use draft/support readiness and must not claim senior-review-ready status.
-- Keep citation rendering readable: do not fragment tickers, dates, prices, percentages, scenario labels, or every derived table cell into repetitive citation links, and do not present an analyst assumption as an external evidentiary source.
-
-## Do Not
-
-- Do not rebuild a DCF, three-statement model, comps workbook, earnings model, event analysis, memo, hedge, or sizing workflow inside this dashboard.
-- Do not make raw JSON, Markdown notes, CSV sidecars, run logs, or manifests the lead user-facing artifact unless explicitly requested.
-- Do not present credit-security valuation, recovery waterfalls, covenant-package work, spread/yield relative value, CDS hedges, bond comps, or loan comps as Public Equity outputs; route those to Credit Markets.
-- Do not call upside underwriteable if it misses the hurdle, depends on unsupported probabilities, lacks current-price anchoring, or has downside that overwhelms the expected return.
-- Do not create a standalone sector dashboard. Sector context belongs inside the owning valuation or scenario dashboard.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm the dashboard states current price, base-case source, expected return versus hurdle, downside/upside ratio, break-even probability, skew label, and action thresholds where applicable.
-- Confirm every chart has enough complete, source-backed rows; otherwise omit the chart and add `missing_evidence`.
-- Confirm analyst probabilities, hurdle rates, break-value capture, and derived calculations are visibly distinguished from sourced facts.
-- Confirm citation rendering remains readable and the recommendation is not repeated across redundant first-read modules.
-- Confirm JSON/Markdown/CSV/run-log artifacts remain support files behind the HTML dashboard/report unless the user explicitly requests them.
-- Confirm any credit, rates, FX, commodity, or macro input terminates in common-equity value, estimate, risk/reward, or portfolio-action implications.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/materializer-schema.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/materializer-schema.md
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-# Public Equity Investing Materializer Schema
-
-`scripts/materialize_public_equity_sensitivities.py` accepts an optional JSON file. If no file is provided, it emits table shells with input-required notes.
-
-## Top-Level Shape
-
-```json
-{
-  "currency": "$",
-  "source": {
-    "source_id": "equity_model_update_v3",
-    "source_posture": "model_validated",
-    "as_of_date": "2026-05-12"
-  },
-  "base": {},
-  "axes": {},
-  "cases": [],
-  "triggers": []
-}
-```
-
-## `source` Fields
-
-The materializer carries source fields into every deterministic output row. Use these to keep scenario math traceable when outputs feed a memo, pitch, thesis tracker, or model update.
-
-| field | meaning |
-|---|---|
-| `source_id` | model, note, source package, filing, transcript, or assumption set used |
-| `source_posture` | `model_validated`, `source_derived`, `user_provided`, `analyst_assumption`, `illustrative`, or similar |
-| `as_of_date` | date/version for market-sensitive or model-sensitive inputs |
-
-The same fields may be supplied at the top level, inside `source`, inside `base`, or on individual case/trigger rows. Row-level fields override base/top-level fields.
-
-## `base` Fields
-
-| field | meaning |
-|---|---|
-| `share_price` | current or anchor share price |
-| `base_price_target` | base-case target price |
-| `upside_price_target` | upside-case target price |
-| `downside_price_target` | downside-case target price |
-| `upside_probability` | probability for upside case |
-| `base_probability` | probability for base case |
-| `downside_probability` | probability for downside case |
-| `shares_outstanding` | diluted shares or units for per-share valuation |
-| `net_debt` | debt less cash and cash-like items |
-| `ebitda` | EBITDA denominator for EV/EBITDA sensitivity |
-| `eps` | EPS denominator for P/E sensitivity |
-| `ntm_eps` | NTM EPS fallback |
-| `pe_multiple` | base P/E multiple |
-| `multiple` | generic base multiple fallback |
-| `revenue` | revenue base for KPI sensitivity |
-| `ebit_margin` | EBIT margin for operating sensitivity |
-| `ebitda_margin` | EBITDA margin fallback |
-| `incremental_margin` | optional flow-through margin |
-| `cash` | unrestricted cash |
-| `revolver_availability` | available liquidity source |
-| `minimum_liquidity` | liquidity floor |
-| `fcf` | free cash flow |
-| `free_cash_flow` | FCF fallback |
-| `maturities_12m` | next-12-month maturities or debt uses |
-| `debt` | total debt for leverage |
-| `success_price` | event success value |
-| `deal_price` | event success fallback |
-| `fail_price` | event fail value |
-| `unaffected_price` | unaffected or pre-event price |
-| `rate_sensitivity_pct_per_100bps` | price impact from 100 bps rate move |
-| `spread_sensitivity_pct_per_100bps` | price impact from 100 bps spread move |
-
-## `axes` Fields
-
-| field | default |
-|---|---|
-| `valuation_multiples` | `[8.0, 10.0, 12.0]` |
-| `eps_revisions` | `[-0.10, 0.00, 0.10]` |
-| `multiple_changes` | `[-0.10, 0.00, 0.10]` |
-| `revenue_growth_shocks` | `[-0.05, 0.00, 0.05]` |
-| `margin_shock_bps` | `[-200, 0, 200]` |
-| `event_probabilities` | `[0.25, 0.50, 0.75]` |
-| `rate_changes_bps` | `[-100, 0, 100]` |
-| `spread_changes_bps` | `[-100, 0, 100]` |
-
-## `cases`
-
-Optional case rows override the default bull/base/bear rows:
-
-```json
-{
-  "scenario": "upside",
-  "price_target": 150.0,
-  "probability": 0.25,
-  "rationale": "margin recovery and multiple normalization",
-  "implication": "add if estimate revision confirms",
-  "source_id": "analyst_case_v2",
-  "source_posture": "analyst_assumption",
-  "as_of_date": "2026-05-12"
-}
-```
-
-For `price_target_scenario`, probabilities must be complete for every case and sum to 100%. If any case is missing a probability, any probability is outside `0.0` to `1.0`, or the distribution does not sum to 100%, the probability-weighted row is marked `input required` and the output includes `probability_check` plus `probability_sum`.
-
-## `triggers`
-
-Optional trigger rows populate `thesis_trigger_table`:
-
-```json
-{
-  "trigger": "FY2 EPS cut",
-  "threshold": "-5%",
-  "monitoring_cadence": "post-print",
-  "implication": "reassess target multiple",
-  "next_step": "equity-model-update",
-  "source_or_owner": "earnings model",
-  "source_id": "earnings_preview_v1",
-  "source_posture": "source_derived",
-  "as_of_date": "2026-05-12"
-}
-```
-
-## Output
-
-Each table emits:
-
-```json
-{
-  "name": "valuation_sensitivity",
-  "description": "Implied price by valuation multiple.",
-  "columns": ["..."],
-  "rows": [{"...": "..."}],
-  "notes": ["..."]
-}
-```
-
-JSON output is the default because these tables usually feed another skill, workbook script, or dashboard renderer. Markdown output is an explicit support-note format for chat or memo drafting when requested. CSV output supports one table at a time. Standard output rows include `source_id`, `source_posture`, and `as_of_date`.
-
-Run logs also include readiness metadata:
-
-- `not-decision-ready`: no input JSON, missing base case, missing current price, or invalid/missing probabilities.
-- `screen-grade`: calculations can be shown, but source/as-of posture is missing or illustrative.
-- `senior-review-ready`: current price, case values, probability validation, and source/as-of posture are complete enough for senior review.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/output-templates.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/output-templates.md
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-# Output Templates and QA
-
-## Public Equity Investing Trigger Table
-
-For thesis, earnings, valuation, equity-liquidity read-through, event, or macro-factor scenarios, include:
-
-| trigger | threshold | source / monitoring cadence | likely interpretation | action implication | next workflow | deadline |
-|---|---:|---|---|---|---|---|
-
-Default trigger categories:
-- EPS, EBITDA, revenue, FCF, or guidance revision.
-- Sector KPI threshold such as ARR, NRR, NIM, loss ratio, NOI, occupancy, production, take rate, or volume.
-- Valuation support such as target multiple, discount to NAV, DCF-implied price, expected return versus hurdle, or break-even probability.
-- Liquidity, maturity wall, refinancing, dilution, or common-equity downside threshold.
-- Event probability, approval, court, vote, regulatory, financing, or timing milestone.
-- Macro factor such as rates, FX, commodity, credit-spread signal, inflation, or policy move, only as a common-equity value or earnings read-through.
-
-Do not stop at "watch this." State what changes if the threshold is hit.
-
-## Scenario Interpretation Table
-
-| scenario | driver change | output impact | source posture | what would change the view | recommended next step |
-|---|---|---:|---|---|---|
-
-Use this when the user needs a PM-ready synthesis rather than only raw matrices.
-
-## Public Equity Investing QA Checklist
-
-Before finalizing, confirm:
-- scenario labels match the actual assumptions used;
-- base case source is named;
-- market-sensitive inputs have as-of dates or are labeled stale/missing;
-- each case changes the intended drivers only;
-- valuation, EPS, KPI, equity-liquidity, event, or macro assumptions are not blended without labels;
-- outputs show absolute values and deltas where useful;
-- probability-weighted values disclose probability source or assumption status;
-- liquidity or downside cases show what breaks first for common equity;
-- thesis triggers include thresholds and next actions;
-- final language does not turn illustrative math into a recommendation.
-
-## Handoff Outputs
-
-When handing off to another skill, include:
-- scenario overlay table;
-- table names used;
-- base-case source and as-of date;
-- exact driver changes and timing;
-- output values and deltas;
-- trigger metrics and what changes the view;
-- model-readiness caveats;
-- workbook-QA requirement if Excel was changed or delivered.
-
-## PM Action Threshold Template
-
-Include action thresholds for `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `wait for proof`, and `re-underwrite`. Thresholds must connect to a model line, price/value output, source, and date.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/p0-integration.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/p0-integration.md
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-# P0 Integration
-
-## Ownership Matrix
-
-| Area | This skill owns | Adjacent skill owns |
-|---|---|---|
-| Source data confidence | Uses caveats and reconciled inputs | `financial-source-of-truth` and `financials-normalizer` own source hierarchy, evidence labels, normalization, and exception registers |
-| Equity model update | Scenario overlay and sensitivity outputs | `equity-model-update` owns actuals, guidance, consensus, transcript, KPI, and estimate-change refreshes |
-| Integrated model mechanics | Scenario overlays and target outputs | `three-statement-model-builder` owns IS/BS/CF architecture |
-| Valuation mechanics | Sensitivity ranges and valuation implications | `dcf-model-builder`, `comps-valuation` own valuation construction |
-| Earnings implications | Scenario effects on print setup or post-print analysis | `earnings-preview` and `earnings-deep-dive` own earnings narrative and source review |
-| Equity liquidity downside | Common-equity liquidity, maturity, covenant-pressure, recovery read-through, and refinancing sensitivities | Credit Markets owns capital-structure, debt-security, covenant-package, recovery, and public-credit-instrument research |
-| Event-driven probability | Probability-weighted outcome tables | `event-driven-analyzer` owns event facts, process risk, approvals, documents, and catalyst analysis |
-| Macro factor translation | Sensitivity table from supplied factor assumptions | `economic-impact-report` owns broader macro transmission and cross-asset analysis |
-| Position/risk sizing | Scenario P&L inputs and thesis triggers | `portfolio-risk-management` owns sizing, risk limits, and hedge selection through its selected mode |
-| Memo/deck output | Scenario conclusions and handoff blocks | `memo-builder`, `long-short-pitch`, and `deck-report-qc` own final narrative and circulation QA |
-| Workbook QA | Scenario logic checks and assumptions | `model-audit-tieout` owns final workbook-level audit |
-
-## Boundary With Model Builders
-
-This skill may add scenario overlays, sensitivity tables, target backsolve tables, thesis triggers, and decision outputs to an existing model. It should not rebuild the integrated model unless the user explicitly asks for a model rebuild and no better routing is available.
-
-Use `three-statement-model-builder` when:
-- there is no integrated model;
-- the balance sheet or cash flow does not tie;
-- the forecast lacks working capital, capex, debt, tax, or retained earnings logic;
-- scenarios require statement linkage the current model does not have;
-- the user asks to build or rebuild the operating model itself.
-
-Use `dcf-model-builder` or `comps-valuation` when valuation calculations need to be constructed rather than sensitized.
-
-This skill should hand model builders a scenario overlay table, not a separate shadow model.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/planning-mode-router.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/planning-mode-router.md
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-# Public Equity Investing Mode Router
-
-Choose the right mode before building outputs.
-
-| Mode | Use when | Output should include | Avoid |
-|---|---|---|---|
-| Price-target scenario | The user needs bull/base/bear value, skew, or probability-weighted target | scenario values, return vs current price, probability if supplied, thesis implication | implying the weighted target is a recommendation by itself |
-| Valuation sensitivity | The user needs DCF, multiple, SOTP, or reverse-valuation ranges | selected valuation metric, assumption axis, implied price/value, caveats | rebuilding a full DCF or comps model inside this skill |
-| Estimate / KPI sensitivity | The user needs EPS, EBITDA, revenue, margin, guidance, or KPI driver impact | driver change, output change, estimate or price implication, source caveat | generic revenue growth shocks when a sector KPI is available |
-| Equity liquidity downside | The user needs common-equity liquidity, maturity, refinancing, CDS/spread signal, covenant-pressure, or recovery read-through downside | cash/liquidity headroom, maturity pressure, common-equity recovery read-through, spread-signal sensitivity, breach point | credit-security valuation, covenant-package analysis, recovery waterfall, or credit memo drafting; route those to Credit Markets |
-| Event probability tree | The user needs success/fail/delay math for a catalyst | probability range, success/fail values, expected value, timing/downside caveat | treating process probability as a sourced fact without support |
-| Macro factor sensitivity | Rates, FX, commodities, policy, inflation, or spreads drive the view | factor move, sensitivity source, price/value impact, next monitoring signal | broad macro essay without security-level translation |
-| Thesis trigger table | The user needs monitoring thresholds or add/trim/hedge/exit markers | trigger, threshold, implication, next step, source/owner | vague "continue to monitor" language |
-
-Combine modes when needed, but name the role of each exhibit so the pack does not become a pile of unrelated tables.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/public-equity-investing-sensitivity-taxonomy.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/public-equity-investing-sensitivity-taxonomy.md
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-# Public Equity Investing Sensitivity Taxonomy
-
-Use this reference when selecting sensitivity exhibits. Keep `SKILL.md` lean; load this file only when the user needs a full scenario pack or when choosing among multiple table types.
-
-## 1. `price_target_scenario`
-
-Purpose: compare bull/base/bear or custom price-target cases.
-
-Use when:
-- the user needs upside/downside/skew around a common stock, ADR, listed equity, ETF/index constituent, or equity-linked expression used by a public-equity investor;
-- an initiation, long/short pitch, memo, or PM update needs probability-weighted value;
-- the debate is whether expected return compensates for downside.
-
-Minimum inputs:
-- current or anchor share price;
-- scenario price targets;
-- probabilities if probability-weighted value is requested.
-
-Interpretation:
-- Do not treat a probability-weighted target as a recommendation by itself.
-- Call out whether the case depends on multiple expansion, EPS revisions, event success, or macro easing.
-
-## 2. `valuation_sensitivity`
-
-Purpose: translate valuation assumptions into implied price or value.
-
-Use when:
-- target price depends on EV/EBITDA, P/E, P/B, P/TBV, P/FFO, NAV, or SOTP assumptions;
-- public comps or a DCF output need a market-implied cross-check;
-- the question is "what multiple is required to justify the current price?"
-
-Minimum inputs:
-- EBITDA and net debt plus shares for EV/EBITDA;
-- EPS and P/E multiple for P/E;
-- relevant sector metric for sector-specific overlays.
-
-Interpretation:
-- Separate mechanically implied price from underwriteable price.
-- Use `comps-valuation`, or `dcf-model-builder` when the valuation mechanics themselves need to be built.
-
-## 3. `eps_revision_sensitivity`
-
-Purpose: test how estimate revisions and multiple changes interact.
-
-Use when:
-- pre-earnings or post-earnings work depends on EPS, EBITDA, FCF, or revenue revision risk;
-- a stock's move depends on both numbers and multiple;
-- market reaction to a print is likely to be nonlinear.
-
-Minimum inputs:
-- EPS or EBITDA base;
-- base multiple;
-- revision and multiple-change ranges.
-
-Interpretation:
-- Distinguish clean estimate revisions from low-quality beats.
-- Pair with `earnings-preview`, `earnings-deep-dive`, or `equity-model-update` when source data needs updating.
-
-## 4. `kpi_driver_sensitivity`
-
-Purpose: connect sector drivers to financial output.
-
-Use when:
-- a sector KPI drives the thesis more than broad revenue growth;
-- the user asks what a change in NIM, ARR, NRR, GMV, take rate, NOI, occupancy, loss ratio, production, or volume means;
-- the base model can be approximated with revenue and margin flow-through.
-
-Minimum inputs:
-- revenue or KPI base;
-- margin, incremental margin, or flow-through assumption;
-- shock ranges.
-
-Interpretation:
-- Name the actual sector KPI in the final answer.
-- Do not pretend the table is model-validated if it is a high-level flow-through approximation.
-
-## 5. `equity_liquidity_downside`
-
-Purpose: translate downside into liquidity and balance-sheet pressure.
-
-Use when:
-- a public issuer has maturity wall, refinancing, covenant-pressure, liquidity, or common-equity recovery read-through risk;
-- downside survives on EBITDA but fails on cash;
-- Credit Markets output needs to be summarized as a common-equity stress table.
-
-Minimum inputs:
-- cash, revolver or other liquidity, minimum liquidity threshold;
-- FCF or cash burn;
-- next-12-month maturities;
-- debt and EBITDA for leverage.
-
-Interpretation:
-- Show absolute liquidity and headroom, not only deltas.
-- Route deeper credit analysis to Credit Markets.
-
-## 6. `event_probability_tree`
-
-Purpose: model probability-weighted outcomes for special situations.
-
-Use when:
-- M&A, tender, CVR, litigation, regulatory, spin, activism, restructuring, index inclusion, or liability-management outcomes drive value;
-- the debate is probability, downside, timing, or spread.
-
-Minimum inputs:
-- success price;
-- fail price;
-- probability range;
-- anchor price.
-
-Interpretation:
-- Treat timing, borrow, financing, legal/regulatory risk, and liquidity as separate caveats.
-- Route event facts and process analysis to `event-driven-analyzer`.
-
-## 7. `macro_factor_sensitivity`
-
-Purpose: test public-equity exposure to rates, credit-spread signals, FX, commodities, inflation, or policy variables.
-
-Use when:
-- rate or credit-spread-signal moves affect valuation, common-equity downside, multiples, or FCF;
-- FX or commodity variables are the key swing factor;
-- a macro shock needs a quick translation into security impact.
-
-Minimum inputs:
-- factor sensitivity per unit move;
-- anchor price or model output;
-- factor move range.
-
-Interpretation:
-- Keep factor sensitivity source-labeled and tied to a stock, sector, estimate, valuation, or portfolio-action implication.
-- Route broader macro chain-of-impact analysis to `economic-impact-report`.
-
-## 8. `thesis_trigger_table`
-
-Purpose: convert scenarios into concrete monitoring and decision rules.
-
-Use when:
-- a thesis needs confirm/disconfirm markers;
-- a PM needs add/trim/hedge/exit levels;
-- a memo needs exact watch items and what would change the view.
-
-Minimum inputs:
-- trigger;
-- threshold;
-- implication;
-- next action or next skill.
-
-Interpretation:
-- Do not use vague "monitor" language without a threshold.
-- Route persistent monitoring to `thesis-tracker`.
-
-## Credit Markets Boundary
-
-Use `equity_liquidity_downside` only when liquidity, maturity, or refinancing risk changes common-equity value, dilution risk, or solvency optionality. Route credit-security valuation, recovery waterfall, covenant-package analysis, spread/yield relative value, CDS, bond comps, and loan comps to Credit Markets.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/scenario-overlay-contract.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/scenario-overlay-contract.md
deleted file mode 100644
index d9fdf33..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/scenario-overlay-contract.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Scenario Overlay Contract
-
-When scenarios need to feed a workbook, memo, deck/report, or another skill, produce a scenario overlay table with these fields.
-
-| field | purpose |
-|---|---|
-| version | model, note, or source version |
-| scenario | bull, base, bear, stress, success, fail, delay, credit stress, macro shock |
-| setup_type | equity, credit, distressed, event-driven, macro, earnings, valuation, thesis |
-| module | revenue, EPS, EBITDA, FCF, KPI, valuation, multiple, liquidity, recovery, event, macro |
-| driver | exact driver being changed |
-| baseline_value | current base-case value |
-| scenario_value | proposed case value |
-| delta_type | absolute, percentage change, bps change, replacement, timing shift, probability, floor, cap |
-| start_period_or_event | first affected period or event milestone |
-| end_period_or_event | last affected period or milestone |
-| rationale | why the change belongs in the case |
-| evidence_label | sourced, derived, user_provided, analyst_assumption, placeholder |
-| source_id | source reference or missing |
-| impacted_output | price target, EV, EPS, EBITDA, FCF, spread, liquidity, recovery, return, thesis status |
-| investment_implication | add, trim, hedge, avoid, monitor, update model, escalate credit, no conclusion |
-| caveat | limitation, missing source, stale input, or model-readiness issue |
-
-## Handoff Rules
-
-- Use one row per changed driver per scenario.
-- Do not hide formula changes inside scenario cases.
-- Keep actual periods locked unless explicitly modeling pro forma or restated history.
-- If the overlay contains placeholders, label them and keep the scenario status as not model-validated.
-- Do not represent an overlay as a complete model rebuild.
-
-## Equity Scenario Skew Contract
-
-Every public-equity scenario package should show expected return versus hurdle, downside/upside ratio, break-even probability when calculable, kill/add/trim/exit thresholds, skew label, current-price anchoring, source posture, and whether upside is underwriteable or merely optical. Use Credit Markets for credit-security stress, recovery waterfall, covenant-package, spread/yield, or debt valuation outputs.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/target-backsolve-rubric.md b/plugins/financial-markets/skills/scenario-sensitivity-generator/references/target-backsolve-rubric.md
deleted file mode 100644
index 539df49..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/references/target-backsolve-rubric.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Target Backsolve Rubric
-
-Use this when the user asks what must be true for a price target, return hurdle, valuation, spread, liquidity threshold, recovery value, or thesis trigger to work.
-
-## Feasibility Labels
-
-For every target backsolve, classify the result:
-
-- `mathematically possible and underwriteable`
-- `mathematically possible but stretched`
-- `mathematically possible but dependent on multiple expansion`
-- `mathematically possible but dependent on aggressive estimate revisions`
-- `mathematically possible but dependent on event probability`
-- `mathematically possible but dependent on financing or liquidity access`
-- `mathematically possible but inconsistent with historical performance`
-- `mathematically possible but violates another constraint`
-- `not mathematically possible within allowed levers`
-- `not enough information`
-
-## Feasibility Tests
-
-Always test feasibility against:
-- historical best observed performance;
-- consensus and guidance path;
-- sector KPI bounds;
-- valuation multiple support;
-- balance sheet, liquidity, maturity, and covenant constraints;
-- event timing and probability support;
-- macro factor sensitivity;
-- downside support and risk/reward skew;
-- source quality and as-of dates.
-
-## Backsolve Output
-
-Show:
-- target metric;
-- time horizon;
-- locked constraints;
-- allowed levers;
-- required path;
-- feasibility label;
-- what must be true;
-- what would disconfirm the path;
-- next workflow or data request.
-
-Never present a solved target path as investable without underwriteability and evidence posture.
diff --git a/plugins/financial-markets/skills/scenario-sensitivity-generator/scripts/materialize_public_equity_sensitivities.py b/plugins/financial-markets/skills/scenario-sensitivity-generator/scripts/materialize_public_equity_sensitivities.py
deleted file mode 100755
index 9a57390..0000000
--- a/plugins/financial-markets/skills/scenario-sensitivity-generator/scripts/materialize_public_equity_sensitivities.py
+++ /dev/null
@@ -1,1388 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize standard Public Equity Investing sensitivity tables.
-
-The script is dependency-free and intentionally conservative. If required
-inputs are missing, it emits "input required" rows instead of inventing values.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-DEFAULT_AXES = {
-    "valuation_multiples": [8.0, 10.0, 12.0],
-    "eps_revisions": [-0.10, 0.0, 0.10],
-    "multiple_changes": [-0.10, 0.0, 0.10],
-    "revenue_growth_shocks": [-0.05, 0.0, 0.05],
-    "margin_shock_bps": [-200, 0, 200],
-    "event_probabilities": [0.25, 0.50, 0.75],
-    "rate_changes_bps": [-100, 0, 100],
-    "spread_changes_bps": [-100, 0, 100],
-}
-
-TABLE_ORDER = [
-    "price_target_scenario",
-    "valuation_sensitivity",
-    "eps_revision_sensitivity",
-    "kpi_driver_sensitivity",
-    "equity_liquidity_downside",
-    "event_probability_tree",
-    "macro_factor_sensitivity",
-    "thesis_trigger_table",
-]
-
-SOURCE_COLUMNS = ["source_id", "source_posture", "as_of_date"]
-PROBABILITY_TOLERANCE = 0.0001
-
-
-def load_payload(path: str | None) -> dict[str, Any]:
-    if not path:
-        return {"base": {}, "axes": {}, "triggers": [], "cases": [], "_input_was_provided": False}
-    with open(path, "r", encoding="utf-8") as handle:
-        payload = json.load(handle)
-    payload.setdefault("base", {})
-    payload.setdefault("axes", {})
-    payload.setdefault("triggers", [])
-    payload.setdefault("cases", [])
-    payload["_input_was_provided"] = True
-    return payload
-
-
-def axis(payload: dict[str, Any], name: str) -> list[Any]:
-    values = payload.get("axes", {}).get(name, DEFAULT_AXES[name])
-    if not isinstance(values, list) or not values:
-        return DEFAULT_AXES[name]
-    return values
-
-
-def number(base: dict[str, Any], key: str, default: float | None = None) -> float | None:
-    value = base.get(key, default)
-    if value is None or value == "":
-        return None
-    try:
-        numeric = float(value)
-    except (TypeError, ValueError):
-        return None
-    if math.isnan(numeric) or math.isinf(numeric):
-        return None
-    return numeric
-
-
-def numeric_value(value: Any) -> float | None:
-    if value is None or value == "":
-        return None
-    try:
-        numeric = float(value)
-    except (TypeError, ValueError):
-        return None
-    if math.isnan(numeric) or math.isinf(numeric):
-        return None
-    return numeric
-
-
-def probability_value(value: Any) -> float | None:
-    numeric = numeric_value(value)
-    if numeric is None:
-        return None
-    if numeric < 0 or numeric > 1:
-        return None
-    return numeric
-
-
-def source_context(payload: dict[str, Any], item: dict[str, Any] | None = None) -> dict[str, str]:
-    base = payload.get("base", {})
-    source = payload.get("source", {})
-    metadata = payload.get("metadata", {})
-    if not isinstance(source, dict):
-        source = {}
-    if not isinstance(metadata, dict):
-        metadata = {}
-    item = item or {}
-
-    def pick(*values: Any, default: str) -> str:
-        for value in values:
-            if value not in (None, ""):
-                return str(value)
-        return default
-
-    return {
-        "source_id": pick(
-            item.get("source_id"),
-            item.get("source"),
-            base.get("source_id"),
-            source.get("source_id"),
-            metadata.get("source_id"),
-            payload.get("source_id"),
-            default="missing",
-        ),
-        "source_posture": pick(
-            item.get("source_posture"),
-            base.get("source_posture"),
-            source.get("source_posture"),
-            metadata.get("source_posture"),
-            payload.get("source_posture"),
-            default="user_supplied"
-            if metadata.get("source_name") and metadata.get("as_of")
-            else "unsourced_or_illustrative",
-        ),
-        "as_of_date": pick(
-            item.get("as_of_date"),
-            base.get("as_of_date"),
-            source.get("as_of_date"),
-            metadata.get("as_of"),
-            payload.get("as_of_date"),
-            default="missing",
-        ),
-    }
-
-
-def with_source(
-    row: dict[str, Any], payload: dict[str, Any], item: dict[str, Any] | None = None
-) -> dict[str, Any]:
-    row.update(source_context(payload, item))
-    return row
-
-
-def validate_probability_distribution(cases: list[dict[str, Any]]) -> dict[str, Any]:
-    values: list[float] = []
-    missing: list[str] = []
-    invalid: list[str] = []
-
-    for index, case in enumerate(cases):
-        label = str(case.get("scenario", f"case_{index + 1}"))
-        raw = case.get("probability")
-        if raw in (None, ""):
-            missing.append(label)
-            continue
-        parsed = probability_value(raw)
-        if parsed is None:
-            invalid.append(label)
-            continue
-        values.append(parsed)
-
-    total = sum(values)
-    if missing:
-        return {
-            "ok": False,
-            "status": "missing probabilities: " + ", ".join(missing),
-            "sum": total if values else None,
-        }
-    if invalid:
-        return {
-            "ok": False,
-            "status": "invalid probabilities outside 0.0-1.0: " + ", ".join(invalid),
-            "sum": total if values else None,
-        }
-    if not values:
-        return {"ok": False, "status": "missing probabilities", "sum": None}
-    if abs(total - 1.0) > PROBABILITY_TOLERANCE:
-        return {
-            "ok": False,
-            "status": f"probabilities must sum to 100%; supplied sum is {total * 100:.1f}%",
-            "sum": total,
-        }
-    return {"ok": True, "status": "ok", "sum": total}
-
-
-def fmt(value: Any, digits: int = 1) -> str:
-    if value is None:
-        return "input required"
-    if isinstance(value, str):
-        return value
-    try:
-        numeric = float(value)
-    except (TypeError, ValueError):
-        return str(value)
-    if math.isnan(numeric) or math.isinf(numeric):
-        return "n/a"
-    return f"{numeric:.{digits}f}"
-
-
-def fmt_pct(value: Any, digits: int = 1) -> str:
-    if value is None:
-        return "input required"
-    try:
-        return f"{float(value) * 100:.{digits}f}%"
-    except (TypeError, ValueError):
-        return str(value)
-
-
-def fmt_bps(value: Any) -> str:
-    if value is None:
-        return "input required"
-    try:
-        return f"{float(value):.0f} bps"
-    except (TypeError, ValueError):
-        return str(value)
-
-
-def fmt_multiple(value: Any, digits: int = 1) -> str:
-    if value is None:
-        return "input required"
-    try:
-        return f"{float(value):.{digits}f}x"
-    except (TypeError, ValueError):
-        return str(value)
-
-
-def return_pct(value: float | None, base_price: float | None) -> float | None:
-    if value is None or base_price is None or base_price == 0:
-        return None
-    return value / base_price - 1
-
-
-def fmt_ratio(value: float | None) -> str:
-    if value is None:
-        return "input required"
-    return f"{value:.2f}x"
-
-
-def break_even_probability(
-    upside_price: float | None, downside_price: float | None, share_price: float | None
-) -> float | None:
-    if upside_price is None or downside_price is None or share_price is None:
-        return None
-    spread = upside_price - downside_price
-    if spread == 0:
-        return None
-    probability = (share_price - downside_price) / spread
-    if probability < 0 or probability > 1:
-        return None
-    return probability
-
-
-def skew_label(
-    expected_return: float | None,
-    required_return: float | None,
-    downside_upside_ratio: float | None,
-) -> str:
-    if expected_return is None:
-        return "input required"
-    hurdle = required_return if required_return is not None else 0.10
-    if expected_return >= hurdle and (
-        downside_upside_ratio is None or downside_upside_ratio <= 0.75
-    ):
-        return "underwriteable upside"
-    if expected_return > 0:
-        return "optical upside / needs proof"
-    if expected_return < 0:
-        return "negative skew"
-    return "balanced / watchlist"
-
-
-def action_rule(
-    return_value: float | None, required_return: float | None, downside_upside_ratio: float | None
-) -> str:
-    if return_value is None:
-        return "input required"
-    hurdle = required_return if required_return is not None else 0.10
-    if return_value >= hurdle and (downside_upside_ratio is None or downside_upside_ratio <= 0.75):
-        return "add / press if evidence confirms"
-    if return_value >= 0:
-        return "hold / wait for proof"
-    if return_value <= -0.15:
-        return "trim / exit unless thesis is re-underwritten"
-    return "watchlist / re-underwrite"
-
-
-def table(
-    name: str,
-    description: str,
-    columns: list[str],
-    rows: list[dict[str, Any]],
-    notes: list[str] | None = None,
-) -> dict[str, Any]:
-    return {
-        "name": name,
-        "description": description,
-        "columns": columns,
-        "rows": rows,
-        "notes": notes or [],
-    }
-
-
-def build_price_target_scenario(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    share_price = number(base, "share_price")
-    required_return = number(base, "required_return", number(base, "hurdle_return", 0.10))
-    cases = payload.get("cases") or [
-        {
-            "scenario": "upside",
-            "price_target": base.get("upside_price_target"),
-            "probability": base.get("upside_probability"),
-            "rationale": "upside case",
-        },
-        {
-            "scenario": "base",
-            "price_target": base.get("base_price_target"),
-            "probability": base.get("base_probability"),
-            "rationale": "base case",
-        },
-        {
-            "scenario": "downside",
-            "price_target": base.get("downside_price_target"),
-            "probability": base.get("downside_probability"),
-            "rationale": "downside case",
-        },
-    ]
-    probability_check = validate_probability_distribution(cases)
-    prepared_cases = []
-    probability_weighted = 0.0
-    targets_complete = True
-    returns = []
-    for case in cases:
-        target = number(case, "price_target")
-        probability = probability_value(case.get("probability"))
-        return_value = return_pct(target, share_price)
-        if target is None:
-            targets_complete = False
-        if return_value is not None:
-            returns.append(return_value)
-        if target is not None and probability is not None and probability_check["ok"]:
-            probability_weighted += target * probability
-        prepared_cases.append((case, target, probability, return_value))
-    positive_upside = max([ret for ret in returns if ret > 0], default=None)
-    downside_abs = abs(min([ret for ret in returns if ret < 0], default=0.0)) if returns else None
-    downside_upside_ratio = (
-        downside_abs / positive_upside
-        if positive_upside not in (None, 0) and downside_abs is not None
-        else None
-    )
-    target_values = [target for _, target, _, _ in prepared_cases if target is not None]
-    break_even = break_even_probability(
-        max(target_values) if target_values else None,
-        min(target_values) if target_values else None,
-        share_price,
-    )
-    rows = []
-    for case, target, probability, return_value in prepared_cases:
-        rows.append(
-            with_source(
-                {
-                    "scenario": case.get("scenario", "case"),
-                    "price_target": fmt(target),
-                    "return_vs_share_price": fmt_pct(return_value),
-                    "required_return": fmt_pct(required_return),
-                    "expected_return_vs_hurdle": fmt_pct(
-                        return_value - required_return
-                        if return_value is not None and required_return is not None
-                        else None
-                    ),
-                    "downside_upside_ratio": fmt_ratio(downside_upside_ratio),
-                    "break_even_probability": fmt_pct(break_even),
-                    "skew_label": skew_label(return_value, required_return, downside_upside_ratio),
-                    "action_rule": action_rule(
-                        return_value, required_return, downside_upside_ratio
-                    ),
-                    "probability": fmt_pct(probability),
-                    "probability_check": probability_check["status"],
-                    "probability_sum": fmt_pct(probability_check["sum"]),
-                    "rationale": case.get("rationale", ""),
-                    "implication": case.get("implication", ""),
-                },
-                payload,
-                case,
-            )
-        )
-    can_weight = probability_check["ok"] and targets_complete
-    weighted_return = return_pct(probability_weighted if can_weight else None, share_price)
-    rows.append(
-        with_source(
-            {
-                "scenario": "probability_weighted",
-                "price_target": fmt(probability_weighted if can_weight else None),
-                "return_vs_share_price": fmt_pct(weighted_return),
-                "required_return": fmt_pct(required_return),
-                "expected_return_vs_hurdle": fmt_pct(
-                    weighted_return - required_return
-                    if weighted_return is not None and required_return is not None
-                    else None
-                ),
-                "downside_upside_ratio": fmt_ratio(downside_upside_ratio),
-                "break_even_probability": fmt_pct(break_even),
-                "skew_label": skew_label(weighted_return, required_return, downside_upside_ratio),
-                "action_rule": action_rule(weighted_return, required_return, downside_upside_ratio),
-                "probability": "n/a",
-                "probability_check": probability_check["status"],
-                "probability_sum": fmt_pct(probability_check["sum"]),
-                "rationale": "weighted by supplied case probabilities",
-                "implication": "compare expected return to hurdle and downside/upside skew"
-                if can_weight
-                else "input required: complete case targets and probabilities summing to 100%",
-            },
-            payload,
-        )
-    )
-    return table(
-        "price_target_scenario",
-        "Bull/base/bear price-target, expected return, break-even probability, and skew table.",
-        [
-            "scenario",
-            "price_target",
-            "return_vs_share_price",
-            "required_return",
-            "expected_return_vs_hurdle",
-            "downside_upside_ratio",
-            "break_even_probability",
-            "skew_label",
-            "action_rule",
-            "probability",
-            "probability_check",
-            "probability_sum",
-            "rationale",
-            "implication",
-        ]
-        + SOURCE_COLUMNS,
-        rows,
-        [
-            "Requires share_price and complete case price targets for calculated return output.",
-            "Probability-weighted output requires every case probability to be present and sum to 100%.",
-            "Skew labels distinguish underwriteable upside from merely optical upside.",
-        ],
-    )
-
-
-def build_valuation_sensitivity(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    ebitda = number(base, "ebitda")
-    eps = number(base, "eps", number(base, "ntm_eps"))
-    net_debt = number(base, "net_debt", 0.0)
-    shares = number(base, "shares_outstanding")
-    rows = []
-    for multiple in axis(payload, "valuation_multiples"):
-        ev_method_price = None
-        if ebitda is not None and net_debt is not None and shares not in (None, 0):
-            ev_method_price = (ebitda * float(multiple) - net_debt) / shares
-        pe_method_price = eps * float(multiple) if eps is not None else None
-        rows.append(
-            with_source(
-                {
-                    "multiple": fmt_multiple(multiple),
-                    "ev_ebitda_implied_price": fmt(ev_method_price),
-                    "pe_implied_price": fmt(pe_method_price),
-                    "method_note": "EV/EBITDA requires ebitda, net_debt, shares; P/E requires eps",
-                },
-                payload,
-            )
-        )
-    return table(
-        "valuation_sensitivity",
-        "Implied price by valuation multiple.",
-        ["multiple", "ev_ebitda_implied_price", "pe_implied_price", "method_note"] + SOURCE_COLUMNS,
-        rows,
-    )
-
-
-def build_eps_revision_sensitivity(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    eps = number(base, "eps", number(base, "ntm_eps"))
-    base_multiple = number(base, "pe_multiple", number(base, "multiple"))
-    rows = []
-    for eps_revision in axis(payload, "eps_revisions"):
-        for multiple_change in axis(payload, "multiple_changes"):
-            revised_eps = eps * (1 + float(eps_revision)) if eps is not None else None
-            revised_multiple = (
-                base_multiple * (1 + float(multiple_change)) if base_multiple is not None else None
-            )
-            implied_price = (
-                revised_eps * revised_multiple
-                if revised_eps is not None and revised_multiple is not None
-                else None
-            )
-            rows.append(
-                with_source(
-                    {
-                        "eps_revision": fmt_pct(eps_revision),
-                        "multiple_change": fmt_pct(multiple_change),
-                        "revised_eps": fmt(revised_eps, 2),
-                        "revised_multiple": fmt_multiple(revised_multiple),
-                        "implied_price": fmt(implied_price),
-                    },
-                    payload,
-                )
-            )
-    return table(
-        "eps_revision_sensitivity",
-        "Price sensitivity to EPS revisions and multiple change.",
-        ["eps_revision", "multiple_change", "revised_eps", "revised_multiple", "implied_price"]
-        + SOURCE_COLUMNS,
-        rows,
-        ["Requires eps and pe_multiple or multiple."],
-    )
-
-
-def build_kpi_driver_sensitivity(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    revenue = number(base, "revenue")
-    margin = number(base, "ebit_margin", number(base, "ebitda_margin"))
-    flowthrough = number(base, "incremental_margin", margin)
-    rows = []
-    for revenue_shock in axis(payload, "revenue_growth_shocks"):
-        for margin_bps in axis(payload, "margin_shock_bps"):
-            revenue_delta = revenue * float(revenue_shock) if revenue is not None else None
-            margin_delta = float(margin_bps) / 10000.0
-            profit_impact = None
-            if revenue is not None and margin is not None:
-                base_profit = revenue * margin
-                scenario_revenue = revenue * (1 + float(revenue_shock))
-                scenario_margin = margin + margin_delta
-                profit_impact = scenario_revenue * scenario_margin - base_profit
-            rows.append(
-                with_source(
-                    {
-                        "revenue_shock": fmt_pct(revenue_shock),
-                        "margin_shock": fmt_bps(margin_bps),
-                        "revenue_delta": fmt(revenue_delta),
-                        "profit_impact": fmt(profit_impact),
-                        "flowthrough_note": "uses margin shock on scenario revenue; incremental margin available separately"
-                        if flowthrough is not None
-                        else "input required",
-                    },
-                    payload,
-                )
-            )
-    return table(
-        "kpi_driver_sensitivity",
-        "Operating KPI sensitivity to revenue and margin shocks.",
-        ["revenue_shock", "margin_shock", "revenue_delta", "profit_impact", "flowthrough_note"]
-        + SOURCE_COLUMNS,
-        rows,
-        [
-            "Adapt driver names to the relevant sector KPI: NIM, ARR, NRR, NOI, production, loss ratio, take rate, or other key KPI."
-        ],
-    )
-
-
-def build_equity_liquidity_downside(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    cash = number(base, "cash")
-    revolver = number(base, "revolver_availability")
-    minimum_liquidity = number(base, "minimum_liquidity")
-    fcf = number(base, "fcf", number(base, "free_cash_flow"))
-    maturities = number(base, "maturities_12m")
-    debt = number(base, "debt")
-    ebitda = number(base, "ebitda")
-    rows = []
-    for revenue_shock in axis(payload, "revenue_growth_shocks"):
-        stressed_fcf = fcf * (1 + float(revenue_shock)) if fcf is not None else None
-        liquidity = None
-        if any(value is not None for value in [cash, revolver, stressed_fcf, maturities]):
-            liquidity = (
-                (cash or 0.0) + (revolver or 0.0) + (stressed_fcf or 0.0) - (maturities or 0.0)
-            )
-        liquidity_headroom = (
-            liquidity - minimum_liquidity
-            if liquidity is not None and minimum_liquidity is not None
-            else None
-        )
-        leverage = debt / ebitda if debt is not None and ebitda not in (None, 0) else None
-        rows.append(
-            with_source(
-                {
-                    "stress_case": f"fcf shock {fmt_pct(revenue_shock)}",
-                    "stressed_fcf": fmt(stressed_fcf),
-                    "liquidity_after_12m_maturities": fmt(liquidity),
-                    "minimum_liquidity": fmt(minimum_liquidity),
-                    "liquidity_headroom": fmt(liquidity_headroom),
-                    "debt_to_ebitda": fmt_multiple(leverage),
-                    "implication": "liquidity breach"
-                    if liquidity_headroom is not None and liquidity_headroom < 0
-                    else ("liquidity ok" if liquidity_headroom is not None else "input required"),
-                },
-                payload,
-            )
-        )
-    return table(
-        "equity_liquidity_downside",
-        "Common-equity liquidity downside stress; route credit-security valuation to Credit Markets.",
-        [
-            "stress_case",
-            "stressed_fcf",
-            "liquidity_after_12m_maturities",
-            "minimum_liquidity",
-            "liquidity_headroom",
-            "debt_to_ebitda",
-            "implication",
-        ]
-        + SOURCE_COLUMNS,
-        rows,
-    )
-
-
-def build_event_probability_tree(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    share_price = number(base, "share_price", number(base, "unaffected_price"))
-    success_price = number(base, "success_price", number(base, "deal_price"))
-    fail_price = number(base, "fail_price", number(base, "downside_price_target"))
-    rows = []
-    for probability in axis(payload, "event_probabilities"):
-        parsed_probability = probability_value(probability)
-        probability_check = (
-            "ok"
-            if parsed_probability is not None
-            else "invalid probability: use decimal values between 0% and 100%"
-        )
-        expected_price = None
-        if success_price is not None and fail_price is not None and parsed_probability is not None:
-            expected_price = (
-                parsed_probability * success_price + (1 - parsed_probability) * fail_price
-            )
-        rows.append(
-            with_source(
-                {
-                    "success_probability": fmt_pct(parsed_probability),
-                    "probability_check": probability_check,
-                    "success_price": fmt(success_price),
-                    "fail_price": fmt(fail_price),
-                    "expected_price": fmt(expected_price),
-                    "expected_return": fmt_pct(return_pct(expected_price, share_price)),
-                    "break_even_probability": fmt_pct(
-                        break_even_probability(success_price, fail_price, share_price)
-                    ),
-                    "skew_label": skew_label(
-                        return_pct(expected_price, share_price),
-                        number(base, "required_return", number(base, "hurdle_return", 0.10)),
-                        None,
-                    ),
-                    "action_rule": action_rule(
-                        return_pct(expected_price, share_price),
-                        number(base, "required_return", number(base, "hurdle_return", 0.10)),
-                        None,
-                    ),
-                    "interpretation": "compare expected return to hurdle, timing, borrow, liquidity, downside gap, and exit plan",
-                },
-                payload,
-            )
-        )
-    return table(
-        "event_probability_tree",
-        "Probability-weighted event outcome table.",
-        [
-            "success_probability",
-            "probability_check",
-            "success_price",
-            "fail_price",
-            "expected_price",
-            "expected_return",
-            "break_even_probability",
-            "skew_label",
-            "action_rule",
-            "interpretation",
-        ]
-        + SOURCE_COLUMNS,
-        rows,
-        [
-            "Requires success_price and fail_price for calculated expected value.",
-            "Each event probability is a standalone success-probability case and must be a decimal between 0.0 and 1.0.",
-        ],
-    )
-
-
-def build_macro_factor_sensitivity(payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload["base"]
-    share_price = number(base, "share_price")
-    rate_sensitivity = number(base, "rate_sensitivity_pct_per_100bps")
-    spread_sensitivity = number(base, "spread_sensitivity_pct_per_100bps")
-    rows = []
-    for rate_bps in axis(payload, "rate_changes_bps"):
-        pct_impact = (
-            rate_sensitivity * (float(rate_bps) / 100.0) if rate_sensitivity is not None else None
-        )
-        price_impact = (
-            share_price * pct_impact if share_price is not None and pct_impact is not None else None
-        )
-        rows.append(
-            with_source(
-                {
-                    "factor": "rates",
-                    "factor_move": fmt_bps(rate_bps),
-                    "price_impact_pct": fmt_pct(pct_impact),
-                    "price_impact": fmt(price_impact),
-                    "caveat": "requires rate_sensitivity_pct_per_100bps",
-                },
-                payload,
-            )
-        )
-    for spread_bps in axis(payload, "spread_changes_bps"):
-        pct_impact = (
-            spread_sensitivity * (float(spread_bps) / 100.0)
-            if spread_sensitivity is not None
-            else None
-        )
-        price_impact = (
-            share_price * pct_impact if share_price is not None and pct_impact is not None else None
-        )
-        rows.append(
-            with_source(
-                {
-                    "factor": "credit_spread",
-                    "factor_move": fmt_bps(spread_bps),
-                    "price_impact_pct": fmt_pct(pct_impact),
-                    "price_impact": fmt(price_impact),
-                    "caveat": "requires spread_sensitivity_pct_per_100bps",
-                },
-                payload,
-            )
-        )
-    return table(
-        "macro_factor_sensitivity",
-        "Macro/rate/spread sensitivity table.",
-        ["factor", "factor_move", "price_impact_pct", "price_impact", "caveat"] + SOURCE_COLUMNS,
-        rows,
-    )
-
-
-def build_thesis_trigger_table(payload: dict[str, Any]) -> dict[str, Any]:
-    triggers = payload.get("triggers") or []
-    rows = []
-    if not triggers:
-        triggers = [
-            {
-                "trigger": "estimate revision",
-                "threshold": "input required",
-                "implication": "confirm or disconfirm thesis",
-                "next_step": "equity-model-update",
-            },
-            {
-                "trigger": "valuation support",
-                "threshold": "input required",
-                "implication": "reassess risk/reward",
-                "next_step": "memo-builder",
-            },
-            {
-                "trigger": "downside / stop case",
-                "threshold": "input required",
-                "implication": "review sizing or hedge",
-                "next_step": "portfolio-risk-management",
-            },
-        ]
-    for item in triggers:
-        rows.append(
-            with_source(
-                {
-                    "trigger": item.get("trigger", ""),
-                    "threshold": item.get("threshold", "input required"),
-                    "monitoring_cadence": item.get("monitoring_cadence", item.get("cadence", "")),
-                    "implication": item.get("implication", ""),
-                    "next_step": item.get("next_step", ""),
-                    "source_or_owner": item.get("source_or_owner", item.get("owner", "")),
-                },
-                payload,
-                item,
-            )
-        )
-    return table(
-        "thesis_trigger_table",
-        "Confirm/disconfirm triggers and next actions.",
-        [
-            "trigger",
-            "threshold",
-            "monitoring_cadence",
-            "implication",
-            "next_step",
-            "source_or_owner",
-        ]
-        + SOURCE_COLUMNS,
-        rows,
-    )
-
-
-BUILDERS = {
-    "price_target_scenario": build_price_target_scenario,
-    "valuation_sensitivity": build_valuation_sensitivity,
-    "eps_revision_sensitivity": build_eps_revision_sensitivity,
-    "kpi_driver_sensitivity": build_kpi_driver_sensitivity,
-    "equity_liquidity_downside": build_equity_liquidity_downside,
-    "event_probability_tree": build_event_probability_tree,
-    "macro_factor_sensitivity": build_macro_factor_sensitivity,
-    "thesis_trigger_table": build_thesis_trigger_table,
-}
-
-
-def select_tables(raw: str) -> list[str]:
-    if raw == "all":
-        return TABLE_ORDER
-    names = [part.strip() for part in raw.split(",") if part.strip()]
-    unknown = [name for name in names if name not in BUILDERS]
-    if unknown:
-        raise ValueError(f"Unknown table(s): {', '.join(unknown)}")
-    return names
-
-
-def render_markdown(tables: list[dict[str, Any]]) -> str:
-    chunks = []
-    for tbl in tables:
-        chunks.append(f"## {tbl['name']}\n")
-        chunks.append(f"{tbl['description']}\n")
-        columns = tbl["columns"]
-        chunks.append("| " + " | ".join(columns) + " |")
-        chunks.append("| " + " | ".join(["---"] * len(columns)) + " |")
-        for row in tbl["rows"]:
-            chunks.append("| " + " | ".join(str(row.get(col, "")) for col in columns) + " |")
-        if tbl["notes"]:
-            chunks.append("")
-            for note in tbl["notes"]:
-                chunks.append(f"- {note}")
-        chunks.append("")
-    return "\n".join(chunks).strip() + "\n"
-
-
-def primary_source_id(payload: dict[str, Any]) -> str:
-    source = payload.get("source", {})
-    base = payload.get("base", {})
-    metadata = payload.get("metadata", {})
-    if not isinstance(source, dict):
-        source = {}
-    if not isinstance(metadata, dict):
-        metadata = {}
-    for value in [
-        source.get("source_id"),
-        base.get("source_id"),
-        metadata.get("source_id"),
-        payload.get("source_id"),
-    ]:
-        if value not in (None, "", "missing"):
-            return str(value)
-    return "S1"
-
-
-def source_ledger(payload: dict[str, Any]) -> list[dict[str, str]]:
-    source = payload.get("source", {})
-    base = payload.get("base", {})
-    metadata = payload.get("metadata", {})
-    if not isinstance(source, dict):
-        source = {}
-    if not isinstance(metadata, dict):
-        metadata = {}
-    sid = primary_source_id(payload)
-    source_title = (
-        source.get("title")
-        or source.get("source_name")
-        or base.get("source_name")
-        or metadata.get("source_name")
-    )
-    source_as_of = (
-        source.get("as_of_date")
-        or base.get("as_of_date")
-        or metadata.get("as_of")
-        or payload.get("as_of_date")
-    )
-    source_status = (
-        source.get("source_posture")
-        or base.get("source_posture")
-        or metadata.get("source_posture")
-        or payload.get("source_posture")
-        or ("user_supplied" if source_title and source_as_of else "unsourced_or_illustrative")
-    )
-    return [
-        {
-            "id": sid,
-            "title": str(source_title or "Scenario sensitivity input package"),
-            "as_of": str(source_as_of or "Not provided"),
-            "type": str(source.get("source_type") or "user/model input"),
-            "status": str(source_status),
-            "excerpt": "Scenario, price, probability, hurdle, and source-posture inputs used by the sensitivity materializer.",
-        }
-    ]
-
-
-def first_table(tables: list[dict[str, Any]], name: str) -> dict[str, Any] | None:
-    for tbl in tables:
-        if tbl.get("name") == name:
-            return tbl
-    return tables[0] if tables else None
-
-
-def row_by_scenario(table_obj: dict[str, Any] | None, scenario: str) -> dict[str, Any]:
-    if not table_obj:
-        return {}
-    for row in table_obj.get("rows", []):
-        if str(row.get("scenario", "")).lower() == scenario:
-            return row
-    return {}
-
-
-def visible_missing_evidence(tables: list[dict[str, Any]], payload: dict[str, Any]) -> list[str]:
-    issues: list[str] = []
-    text_blob = json.dumps(tables).lower()
-    if "input required" in text_blob:
-        issues.append(
-            "Some scenario values are input required; do not treat the probability-weighted value as decision-ready until completed."
-        )
-    if "probabilities must sum" in text_blob or "missing probabilities" in text_blob:
-        issues.append("Scenario probabilities are missing, invalid, or do not sum to 100%.")
-    source = source_ledger(payload)[0]
-    if source["as_of"].lower() in {"not provided", "missing", ""}:
-        issues.append(
-            "Source as-of date is missing; refresh current price, consensus/model inputs, and probability support."
-        )
-    if not issues:
-        issues.append(
-            "Validate current price, probability source, model tie-out, and source freshness before sizing or portfolio action."
-        )
-    return issues
-
-
-def classify_readiness(tables: list[dict[str, Any]], payload: dict[str, Any]) -> dict[str, Any]:
-    base = payload.get("base", {})
-    if not isinstance(base, dict):
-        base = {}
-    missing_required: list[str] = []
-    warnings: list[str] = []
-
-    if not payload.get("_input_was_provided"):
-        missing_required.append("input_json")
-    if not base:
-        missing_required.append("base_case")
-    if number(base, "share_price") is None:
-        missing_required.append("base.share_price")
-
-    price_table = first_table(tables, "price_target_scenario")
-    probability_statuses = {
-        str(row.get("probability_check", ""))
-        for row in (price_table or {}).get("rows", [])
-        if str(row.get("scenario", "")).lower() != "probability_weighted"
-    }
-    invalid_probability_statuses = [
-        status for status in probability_statuses if status and status != "ok"
-    ]
-    if invalid_probability_statuses:
-        missing_required.append("scenario.probabilities")
-        warnings.append(
-            "Scenario probabilities are missing, invalid, or do not sum to 100%; probability-weighted value is not decision-ready."
-        )
-
-    text_blob = json.dumps(tables).lower()
-    if "input required" in text_blob:
-        warnings.append("One or more scenario outputs contain input required placeholders.")
-
-    source = source_ledger(payload)[0]
-    source_missing = source["as_of"].lower() in {"not provided", "missing", ""} or source[
-        "status"
-    ].lower() in {
-        "unsourced_or_illustrative",
-        "missing",
-        "not provided",
-    }
-    if source_missing:
-        warnings.append(
-            "Source/as-of posture is missing or illustrative; cap the output below senior-review-ready."
-        )
-
-    if missing_required:
-        model_status = "not-decision-ready"
-        readiness_effect = "blocked"
-        decision_impact = "Do not use for target, rating, sizing, circulation, or PM action until required base-case and probability inputs are supplied."
-    elif source_missing:
-        model_status = "screen-grade"
-        readiness_effect = "screen_grade"
-        decision_impact = (
-            "Useful for triage, but missing source/as-of support keeps it below decision-grade."
-        )
-    else:
-        model_status = "senior-review-ready"
-        readiness_effect = "senior_review_ready"
-        decision_impact = "Scenario math, source/as-of posture, current price, and probability checks are complete enough for senior review."
-
-    return {
-        "model_status": model_status,
-        "readiness_effect": readiness_effect,
-        "decision_impact": decision_impact,
-        "missing_required_inputs": sorted(set(missing_required)),
-        "warnings": sorted(set(warnings)),
-        "source_posture": source["status"],
-        "probability_validation": "ok"
-        if not invalid_probability_statuses
-        else "; ".join(sorted(invalid_probability_statuses)),
-    }
-
-
-def build_dashboard_payload(
-    tables: list[dict[str, Any]], payload: dict[str, Any], readiness: dict[str, Any] | None = None
-) -> dict[str, Any]:
-    base = payload.get("base", {})
-    if not isinstance(base, dict):
-        base = {}
-    readiness = readiness or classify_readiness(tables, payload)
-    production_ready = readiness["model_status"] == "senior-review-ready"
-    sid = primary_source_id(payload)
-    price_table = first_table(tables, "price_target_scenario")
-    weighted = row_by_scenario(price_table, "probability_weighted")
-    rows = price_table.get("rows", []) if price_table else []
-    table_columns = price_table.get("columns", []) if price_table else []
-    cited_rows = [dict(row, source_id=sid, citations=[sid]) for row in rows]
-    scenario_cases = [
-        {
-            "label": str(row.get("scenario", "case")),
-            "status": "base"
-            if str(row.get("scenario", "")).lower() == "base"
-            else ("bear" if "down" in str(row.get("scenario", "")).lower() else "bull"),
-            "summary": f"Value {row.get('price_target', '')}; return {row.get('return_vs_share_price', '')}; action {row.get('action_rule', '')}.",
-            "citations": [sid],
-        }
-        for row in rows
-        if str(row.get("scenario", "")).lower() != "probability_weighted"
-    ]
-    snapshot = [
-        {
-            "label": "Current price",
-            "value": str(base.get("share_price", "input required")),
-            "detail": "Spot anchor for return and break-even math.",
-            "status": "neutral",
-            "citations": [sid],
-        },
-        {
-            "label": "Probability-weighted value",
-            "value": str(weighted.get("price_target", "input required")),
-            "detail": "Valid only when probabilities are complete and sum to 100%.",
-            "status": "watch",
-            "citations": [sid],
-        },
-        {
-            "label": "Expected return vs hurdle",
-            "value": str(weighted.get("expected_return_vs_hurdle", "input required")),
-            "detail": "Compares expected return to the PM hurdle.",
-            "status": "watch",
-            "citations": [sid],
-        },
-        {
-            "label": "Downside/upside ratio",
-            "value": str(weighted.get("downside_upside_ratio", "input required")),
-            "detail": "Measures whether downside overwhelms upside.",
-            "status": "risk",
-            "citations": [sid],
-        },
-        {
-            "label": "Break-even probability",
-            "value": str(weighted.get("break_even_probability", "input required")),
-            "detail": "Probability needed to justify current price.",
-            "status": "neutral",
-            "citations": [sid],
-        },
-        {
-            "label": "Skew label",
-            "value": str(weighted.get("skew_label", "input required")),
-            "detail": "PM interpretation of underwriteable versus optical upside.",
-            "status": "watch",
-            "citations": [sid],
-        },
-        {
-            "label": "PM action",
-            "value": str(weighted.get("action_rule", "input required")),
-            "detail": "Add, hold, trim, exit, wait for proof, or re-underwrite rule.",
-            "status": "neutral",
-            "citations": [sid],
-        },
-    ]
-    return {
-        "kind": "public_equity_investing_dashboard.v1",
-        "mode": "scenario_sensitivity",
-        "layout": "single_page",
-        "title": f"{base.get('ticker') or base.get('issuer') or 'Issuer'} Scenario Sensitivity Dashboard",
-        "subtitle": "Decision infrastructure for public-equity scenario skew, thresholds, and source posture.",
-        "issuer": {
-            "ticker": str(base.get("ticker") or "TBD"),
-            "name": str(base.get("issuer") or base.get("company") or "Scenario issuer"),
-        },
-        "metadata": {
-            "freeze_time": str(
-                base.get("freeze_time")
-                or datetime.now(timezone.utc).replace(microsecond=0).isoformat()
-            ),
-            "source_posture": str(source_ledger(payload)[0]["status"]),
-            "citation_policy": "strict" if production_ready else "warn",
-            "decision_context": "Does probability-weighted upside clear the hurdle after downside, evidence quality, and missing inputs?",
-            "payload_stage": "production" if production_ready else "draft",
-            "readiness_label": "Scenario sensitivity production payload"
-            if production_ready
-            else "Scenario sensitivity draft payload; missing evidence visible",
-            "readiness_posture": "senior_review_ready"
-            if production_ready
-            else readiness["readiness_effect"],
-            "model_status": readiness["model_status"],
-            "decision_impact": readiness["decision_impact"],
-            "missing_required_inputs": readiness["missing_required_inputs"],
-            "probability_validation": readiness["probability_validation"],
-        },
-        "hero": {
-            "eyebrow": "Scenario sensitivity",
-            "headline": "Scenario table anchors current price, expected return, skew, and action thresholds",
-            "dek": "Raw JSON/CSV/Markdown tables remain support artifacts; the dashboard surfaces PM decision logic.",
-            "callout_label": "PM action",
-            "callout": str(weighted.get("action_rule", "wait for proof until inputs are complete")),
-            "citations": [sid],
-        },
-        "snapshot": snapshot,
-        "tabs": [
-            {
-                "id": "pm-decision",
-                "label": "PM Decision",
-                "modules": [
-                    {
-                        "type": "decision_box",
-                        "data": {
-                            "label": "Scenario read",
-                            "stance": str(weighted.get("action_rule", "Wait for proof")),
-                            "summary": f"Probability-weighted value is {weighted.get('price_target', 'input required')} with expected return versus hurdle of {weighted.get('expected_return_vs_hurdle', 'input required')}.",
-                            "stock_skew": str(weighted.get("skew_label", "input required")),
-                            "citations": [sid],
-                        },
-                    },
-                    {"type": "metric_tiles", "data": {"items": snapshot}},
-                ],
-            },
-            {
-                "id": "scenario-map",
-                "label": "Scenario Map",
-                "modules": [
-                    {
-                        "type": "scenario_map",
-                        "data": {
-                            "cases": scenario_cases
-                            or [
-                                {
-                                    "label": "Input required",
-                                    "status": "watch",
-                                    "summary": "Add scenario rows before relying on the dashboard.",
-                                    "citations": [sid],
-                                }
-                            ]
-                        },
-                    },
-                    {"type": "table", "data": {"columns": table_columns, "rows": cited_rows}},
-                ],
-            },
-            {
-                "id": "evidence-and-qa",
-                "label": "Evidence & QA",
-                "modules": [
-                    {"type": "source_list", "data": {"sources": source_ledger(payload)}},
-                    {
-                        "type": "missing_evidence",
-                        "data": {"items": visible_missing_evidence(tables, payload)},
-                    },
-                ],
-            },
-        ],
-        "sources": source_ledger(payload),
-    }
-
-
-def write_csv(table_obj: dict[str, Any], output: str | None) -> None:
-    handle = open(output, "w", newline="", encoding="utf-8") if output else sys.stdout
-    close = output is not None
-    try:
-        writer = csv.DictWriter(handle, fieldnames=table_obj["columns"])
-        writer.writeheader()
-        writer.writerows(table_obj["rows"])
-    finally:
-        if close:
-            handle.close()
-
-
-def run_log_path(output: str | None, explicit: str | None) -> Path | None:
-    if explicit:
-        return Path(explicit)
-    if output:
-        return Path(output).resolve().parent / "run_log.json"
-    return None
-
-
-def write_run_log(
-    path: Path | None,
-    *,
-    status: str,
-    output: str | None,
-    output_format: str,
-    table_names: list[str],
-    source_basis: dict[str, Any],
-    warnings: list[str],
-    hard_failures: list[str],
-    readiness: dict[str, Any] | None = None,
-) -> None:
-    if path is None:
-        return
-    readiness = readiness or {
-        "model_status": "not-decision-ready" if hard_failures else "screen-grade",
-        "readiness_effect": "blocked" if hard_failures else "screen_grade",
-        "decision_impact": "Readiness could not be classified; treat as support only.",
-        "missing_required_inputs": [],
-        "warnings": [],
-        "source_posture": source_basis.get("source_posture")
-        if isinstance(source_basis, dict)
-        else "",
-        "probability_validation": "not_checked",
-    }
-    path.parent.mkdir(parents=True, exist_ok=True)
-    outputs = {
-        "primary": str(Path(output).resolve()) if output else "stdout",
-        "run_log": str(path),
-        "manifest": str(path.parent / "manifest.json"),
-    }
-    output_manifest = [
-        {
-            "key": key,
-            "path": artifact_path,
-            "required": key != "primary" or bool(output),
-            "written": key in {"run_log", "manifest"}
-            or (bool(output) and Path(artifact_path).exists()),
-            "description": "Scenario-sensitivity deterministic artifact.",
-            "artifact_role": "narrative_support"
-            if key == "primary" and output_format == "markdown"
-            else "support_artifact",
-            "hidden_unless_requested": key == "primary" or key in {"run_log", "manifest"},
-        }
-        for key, artifact_path in outputs.items()
-    ]
-    payload = {
-        "status": status,
-        "model_status": "not-decision-ready" if hard_failures else readiness["model_status"],
-        "readiness_effect": readiness["readiness_effect"],
-        "decision_impact": readiness["decision_impact"],
-        "missing_required_inputs": readiness["missing_required_inputs"],
-        "source_posture": readiness["source_posture"],
-        "probability_validation": readiness["probability_validation"],
-        "artifact_level": "deterministic_export",
-        "workbook_mode": f"{output_format}_export",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "tables": table_names,
-        "source_basis": [source_basis] if source_basis else [],
-        "warnings": sorted(set(warnings + list(readiness.get("warnings", [])))),
-        "hard_failures": hard_failures,
-        "outputs": outputs,
-        "primary_human_deliverable": None,
-        "support_artifacts": [artifact_path for artifact_path in outputs.values() if artifact_path],
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": {
-            "lead_with": "html_dashboard_or_workbook_when_available",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-        "output_manifest": output_manifest,
-    }
-    path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
-    (path.parent / "manifest.json").write_text(
-        json.dumps(
-            {
-                "outputs": output_manifest,
-                "primary_human_deliverable": None,
-                "support_artifacts_user_visible_default": False,
-                "final_response_guidance": payload["final_response_guidance"],
-            },
-            indent=2,
-        )
-        + "\n",
-        encoding="utf-8",
-    )
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Materialize Public Equity Investing sensitivity tables."
-    )
-    parser.add_argument("--input", help="Optional JSON input file.")
-    parser.add_argument("--tables", default="all", help="Comma-separated table names or 'all'.")
-    parser.add_argument("--format", choices=["markdown", "json", "csv"], default="json")
-    parser.add_argument("--output", help="Optional output path. CSV supports one table at a time.")
-    parser.add_argument(
-        "--dashboard-output",
-        help="Optional production dashboard payload JSON path for scenario_sensitivity.",
-    )
-    parser.add_argument(
-        "--run-log",
-        help="Optional run log path. Defaults to output directory when --output is used.",
-    )
-    args = parser.parse_args()
-
-    log_path = run_log_path(args.output, args.run_log)
-    try:
-        payload = load_payload(args.input)
-        names = select_tables(args.tables)
-        tables = [BUILDERS[name](payload) for name in names]
-        readiness = classify_readiness(tables, payload)
-    except Exception as exc:
-        write_run_log(
-            log_path,
-            status="failed",
-            output=args.output,
-            output_format=args.format,
-            table_names=[],
-            source_basis={},
-            warnings=[],
-            hard_failures=[str(exc)],
-        )
-        print(f"ERROR: {exc}", file=sys.stderr)
-        return 1
-
-    try:
-        if args.format == "json":
-            text = json.dumps({"tables": tables}, indent=2)
-            if args.output:
-                Path(args.output).write_text(text + "\n", encoding="utf-8")
-            else:
-                print(text)
-        elif args.format == "markdown":
-            text = render_markdown(tables)
-            if args.output:
-                Path(args.output).write_text(text, encoding="utf-8")
-            else:
-                print(text, end="")
-        else:
-            if len(tables) != 1:
-                failure = "CSV output supports exactly one table. Pass --tables <single_table>."
-                write_run_log(
-                    log_path,
-                    status="failed",
-                    output=args.output,
-                    output_format=args.format,
-                    table_names=names,
-                    source_basis=payload.get("source", {}),
-                    warnings=[],
-                    hard_failures=[failure],
-                )
-                print(f"ERROR: {failure}", file=sys.stderr)
-                return 1
-            write_csv(tables[0], args.output)
-        if args.dashboard_output:
-            dashboard_payload = build_dashboard_payload(tables, payload, readiness)
-            Path(args.dashboard_output).parent.mkdir(parents=True, exist_ok=True)
-            Path(args.dashboard_output).write_text(
-                json.dumps(dashboard_payload, indent=2) + "\n", encoding="utf-8"
-            )
-    except Exception as exc:
-        failure = f"could not write output: {exc}"
-        write_run_log(
-            log_path,
-            status="failed",
-            output=args.output,
-            output_format=args.format,
-            table_names=names,
-            source_basis=payload.get("source", {}),
-            warnings=[],
-            hard_failures=[failure],
-            readiness=readiness,
-        )
-        print(f"ERROR: {failure}", file=sys.stderr)
-        return 1
-    write_run_log(
-        log_path,
-        status="completed",
-        output=args.output,
-        output_format=args.format,
-        table_names=names,
-        source_basis=payload.get("source", {}),
-        warnings=[],
-        hard_failures=[],
-        readiness=readiness,
-    )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/thesis-tracker/SKILL.md b/plugins/financial-markets/skills/thesis-tracker/SKILL.md
deleted file mode 100644
index 4690cd9..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/SKILL.md
+++ /dev/null
@@ -1,128 +0,0 @@
----
-name: thesis-tracker
-description: Use when building or updating Public Equity Investing thesis trackers. Do not use for generic news summaries, trade pitches, or first-pass memos.
----
-
-# Thesis Tracker
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-### Source Resolution
-
-Load `../../shared/workflow-source-resolution.md`. Use `source_category_plan` lazily and attempt only the categories needed for this workflow: `company_filings_ir`, `earnings_transcripts_presentations`, `internal_research`, `portfolio_models_trackers`, and `market_data_estimates`.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is an XLSX thesis tracker workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-For an update to an attached or existing thesis tracker, treat a polished XLSX thesis tracker workbook as the resolved presentation surface unless the user requests another format, a quick/no-file answer, or an HTML review package. Ask only remaining material choices that change the analysis.
-
-## Purpose
-
-Maintain a falsifiable public-equity-investing thesis over time. Preserve original underwriting, append new evidence, and translate each update into company-thesis status, security-thesis readiness, KPI/estimate/catalyst movement, model/valuation impact, risk/reward, and PM action.
-
-## Non-negotiables
-
-- Preserve user data append-only by default; never delete, collapse, or rewrite prior thesis data unless explicitly asked.
-- Prefer user prompt, files, existing trackers/models/memos, and connected context before public sources. Use web/current sources only when needed and cite facts.
-- Label source type, as-of date, reliability, stale data, assumptions, judgments, and possible MNPI/restricted or redistribution-sensitive material.
-- Separate company-thesis status, security-thesis readiness, and position action; better fundamentals can coexist with worse forward risk/reward, and incomplete market data can block a stock conclusion.
-- Track confirming and disconfirming evidence. Do not treat price action as proof; decompose it into fundamentals, estimates, multiple/spread, factor/sector, positioning, liquidity, rates/macro, and options effects.
-- Convert narrative into prioritized falsifiable pillars, KPIs, thresholds, catalysts, kill criteria, and action triggers. Retain numeric weights or scores only when inherited from an existing tracker or explicitly requested; do not create scoring charts that imply false precision.
-- For a material security-thesis update, attempt to retrieve current public price and basic market context when accessible, record the as-of timestamp and source, and label any unavailable consensus, internal model, ownership, or portfolio inputs as decision-blocking gaps.
-- Identify whether each action threshold is an `Inherited threshold`, `Draft threshold for PM confirmation`, or `Approved monitoring rule`; never present analyst-created falsifiers as approved mandate rules.
-- Every material update must state impact on assumptions/model lines, valuation/downside, conviction, sizing/hedge, decision timing, and next proof point.
-- Avoid vague "monitor" language unless paired with exact metric, threshold, source, date, and action.
-
-## When To Load References
-
-- Missing/partial/conflicting context or source sensitivity: `references/intake-and-source-priority.md`.
-- Full build/update sequence: `references/workflow-core.md`.
-- Tables, fields, statuses, scoring, or spreadsheet shape: `references/thesis-schema.md`.
-- CSV/XLSX scaffold helper: `references/tracker-materializer.md`.
-- Investor mandate or sector-specific nuance: `references/investor-sector-overlays.md`.
-- Final memo/dashboard/table formats: `references/output-templates.md`.
-- Final PM judgment, compliance, drift, and red-team checks: `references/quality-guardrails.md`.
-
-## Operating Modes
-
-| Mode | Default behavior |
-|---|---|
-| Blank template | No ticker/thesis/files: create institutional shell plus minimal intake; invent no facts. |
-| Ticker-only | Build preliminary shell from objective facts; label thesis fields as inferred/requires house-view confirmation. |
-| Existing thesis | Extract original thesis, variant view, pillars, KPIs, catalysts, kill criteria, risks, and open questions. |
-| Tracker update | Append evidence, preserve prior data, update statuses, and add changelog. |
-| Post-earnings | Map release/transcript/guidance/estimate changes to pillars and decision impact. |
-| Portfolio review | Triage by deterioration, improvement with worse risk/reward, catalysts, and required decisions. |
-| Sell-side | Tie facts to rating, target price, estimates, valuation, client debate, and risks. |
-| Long/short | Emphasize variant perception, setup, crowding/borrow/squeeze, catalyst path, sizing, and hedges. |
-| Equity-risk credit signal / Credit Markets handoff | Track only credit facts that change common-equity downside, sizing, hedge, or re-underwrite status; route credit-security, covenant-package, restructuring, and recovery analysis to Credit Markets. |
-
-## Workflow Contract
-
-1. Define context: issuer/security, direction, mandate, horizon, exposure, benchmark/peers, rating/target/cost basis if supplied, and output need.
-2. Inventory sources: name/type/as-of/reliability/coverage/limitations; retrieve current price and basic public market context when accessible; show missing or stale consensus, model, portfolio, and tracker data.
-3. Preserve original underwriting: thesis, variant perception, market setup, valuation anchor, scenarios, catalysts, KPIs, risks, kill criteria, position implication, open diligence.
-4. Decompose into prioritized falsifiable pillars with baseline, expected path, confirm/warning/break thresholds, timing, model linkage, action linkage, and next proof point. Use numeric weights only when source-backed or requested, and label their origin.
-5. Append evidence ledger rows with fact, source/date, pillar, confirm/disconfirm signal, magnitude, quality, model/valuation/confidence/action impact, follow-up, and owner.
-6. Update KPIs, catalysts, estimate revisions, and market-implied setup versus house view, guidance, consensus, buyside expectations if supplied, thresholds, peers, and prior periods.
-7. Translate into model/valuation/risk-reward: near-term vs structural changes, affected model lines, fair value/target/downside/spread or recovery read-through where relevant to common equity, and hurdle clearance. If current price or valuation inputs remain unavailable, label the security thesis not decision-grade rather than inferring risk/reward.
-8. Assess company-thesis status separately from security-thesis readiness: strengthening, intact, watch, impaired, broken, changed, untested, or retired for fundamentals; ready, conditional, re-underwrite, or not decision-grade for the security call. Explain movement with evidence, not price action alone. Reconcile aggregate status to core pillars: do not label the company thesis `Watch` when an inherited core pillar is `Impaired` without an explicit evidence-supported override rationale; when multiple core pillars are `Impaired`, default aggregate company-thesis status to `Impaired`.
-9. Recommend action: add/press, hold, trim, exit/cover, upgrade/downgrade, hedge/pair, wait, re-underwrite, update model, diligence, or escalate. For every proposed hold/trim/exit threshold, show threshold origin and approval status.
-10. Red-team: strongest opposing view, evidence for it, what would make it right, what changes the recommendation, open questions, next review date, changelog.
-11. For XLSX delivery, keep the cover and key monitoring sheets readable at ordinary zoom: prefer compact decision-facing columns, move detail into the evidence ledger, freeze key identifier columns, render every sheet before delivery, and repair excessively wide or clipped tables. Keep full action-rule matrices and full diligence/gap registers on their dedicated tabs; the `Cover` tab should summarize action posture, top blockers, and next gate rather than duplicate them. Do not include a scored pillar chart unless the scoring method is inherited or explicitly requested.
-
-## Sub-agent decomposition
-
-For complex medium/large requests, use sub-agents where available; otherwise emulate the split as named workstreams. Suggested lanes: prior thesis extraction, evidence/KPI updates, valuation impact, action log, and red-team review. Keep this skill as the lead: reconcile conflicts, source labels, assumptions, open items, final QA, and the user-facing answer.
-
-
-## Default Output
-
-Unless the user explicitly asks for a short summary or one section, produce a full decision-grade thesis update:
-
-1. Thesis status and recommendation.
-2. Company-thesis status versus security-thesis readiness, including missing inputs that limit the stock call.
-3. What changed versus prior house view, consensus, and market setup.
-4. Pillar tracker with priority or inherited weight, status, latest evidence, signal, next test.
-5. KPI / estimate / catalyst update.
-6. Model, valuation, downside, and risk/reward impact.
-7. Action thresholds with origin/approval status and decision log/changelog.
-8. Red-team, kill criteria, open questions, sources/as-of dates.
-
-For tracker builds or updates, lead with a polished XLSX thesis tracker workbook when XLSX is available; use an HTML report only when the user requests it or the task is a presentation-oriented review package rather than a live tracker update. Run `python scripts/materialize_thesis_tracker.py [tracker_input.json] --output-dir output` when its structured CSV/XLSX scaffold is useful, then apply workbook formatting and render-and-inspect QA for the final workbook. The helper writes a new CSV support bundle and optional XLSX when `openpyxl` is installed; it does not edit existing trackers in place. XLSX outputs should start with a compact `Cover` tab separating company-thesis status, security-thesis readiness, position action, market-data as-of, next catalyst, evidence gaps, and action thresholds. CSV/JSON support files should not be the lead user-facing artifact unless explicitly requested.
-
-For optional dashboard handoffs requested by the user or needed for a reusable presentation view, use `references/DASHBOARD_PACK.md`. `thesis-tracker` owns thesis status, evidence ledger, KPI/catalyst monitoring, decision log, and update cadence; `dashboard-builder` owns the shell/rendering/QA. Build a `public_equity_investing_dashboard.v1` payload as an internal renderer input, and keep JSON/Markdown/CSV/run-log support files behind the HTML dashboard unless explicitly requested.
-
-## Adjacent Routing
-
-Use or recommend adjacent public-equity-investing skills rather than duplicating work when the primary need is source hierarchy, financial normalization, model tie-out/update, scenario sensitivity, earnings preview/deep dive, new pitch, hedge/sizing, event/credit/macro analysis, or memo/deck QC.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` before changing pillar status based on new evidence. Use `financials-normalizer` or `excel-data-cleaner` before tracker updates rely on messy KPI, consensus, ownership, market-data, or model-output tables. Use `equity-model-update` and `model-audit-tieout` for estimate/model changes. Use `deck-report-qc` and `style-guide-adapter` only for circulation packets. Support files stay secondary to the tracker workbook, HTML dashboard/report, or decision-grade update.
-
-## Language Standard
-
-Write like a top PM or research MD: concise, evidence-weighted, and action-oriented. Call out when the business thesis improved but the security thesis worsened because expectations rerated, and when headline results look good but evidence quality or cash conversion is poor.
-
-## Public Equity PM Judgment Layer
-
-For substantial thesis updates, load `shared/pm-judgment-heuristics.md` before finalizing. Audience modes: `long_only_pm`, `long_short_hf`, `sell_side_research`, `etf_index_diligence`, `public_equity_diligence`.
-
-Portfolio Monitoring Mode is first-class. Every update should classify the stock as `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `wait for proof`, or `re-underwrite`.
-
-Required PM judgment:
-- Separate company-thesis status from security-thesis readiness, stock action, and portfolio role.
-- Make the operating model explicit: PM owner, analyst owner, evidence owner, KPI owner, model owner, decision authority, review cadence, post-catalyst update SLA, escalation triggers, next review gate, active weight, portfolio role, status, action threshold, and append-only decision log.
-- Include benchmark weight, active weight, sector/factor exposure, ETF ownership, passive-flow sensitivity, index inclusion/deletion/rebalance risk, liquidity, crowding, and priced-in status when relevant.
-- Strengthen market setup into consensus, buyside whisper, what is priced, what is ignored, and where the PM edge sits.
-- Maintain a diligence ladder: filings/transcripts, KPI history, competitor checks, customer/channel checks if supplied, management credibility, accounting quality, ownership/flow setup, sell-side consensus, and variant debate.
diff --git a/plugins/financial-markets/skills/thesis-tracker/agents/openai.yaml b/plugins/financial-markets/skills/thesis-tracker/agents/openai.yaml
deleted file mode 100644
index 0b4ebcc..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/agents/openai.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-interface:
-  display_name: Thesis Tracker
-  short_description: PM-grade public-equity-investing thesis tracking across evidence, KPIs, catalysts, estimates, valuation, and thesis status
-  icon_small: assets/icon.svg
-  icon_large: assets/icon.svg
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/thesis-tracker/assets/blank-thesis-tracker-starter.csv b/plugins/financial-markets/skills/thesis-tracker/assets/blank-thesis-tracker-starter.csv
deleted file mode 100644
index f5e8795..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/assets/blank-thesis-tracker-starter.csv
+++ /dev/null
@@ -1,18 +0,0 @@
-section,field,value,notes
-Dashboard,Company / issuer,,
-Dashboard,Ticker / security,,
-Dashboard,Direction,,long / short / watchlist / rating
-Dashboard,Current thesis status,untested,"strengthening / intact / watch / impaired / broken / changed / untested / retired"
-Original underwriting,One-sentence thesis,,
-Original underwriting,Variant perception,,
-Original underwriting,Consensus / market setup,,
-Original underwriting,Valuation anchor,,
-Original underwriting,Kill criteria,,
-Evidence ledger,Date,,append-only
-Evidence ledger,Source,,
-Evidence ledger,Evidence fact,,
-Evidence ledger,Pillar affected,,
-Evidence ledger,Signal,,confirming / neutral / mixed / weakening / invalidating
-Decision log,Date,,append-only
-Decision log,Decision,,add / hold / trim / exit / cover / hedge / wait / re-underwrite
-Decision log,Next review,,
diff --git a/plugins/financial-markets/skills/thesis-tracker/assets/icon.svg b/plugins/financial-markets/skills/thesis-tracker/assets/icon.svg
deleted file mode 100644
index 9934699..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/assets/icon.svg
+++ /dev/null
@@ -1,9 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" role="img" aria-label="Thesis Tracker icon">
-  <rect width="64" height="64" rx="14" fill="#0B1F33"/>
-  <path d="M16 45h32" stroke="#FFFFFF" stroke-width="4" stroke-linecap="round"/>
-  <path d="M18 39l9-10 8 6 11-15" fill="none" stroke="#6EE7B7" stroke-width="4" stroke-linecap="round" stroke-linejoin="round"/>
-  <circle cx="18" cy="39" r="3" fill="#6EE7B7"/>
-  <circle cx="27" cy="29" r="3" fill="#6EE7B7"/>
-  <circle cx="35" cy="35" r="3" fill="#6EE7B7"/>
-  <circle cx="46" cy="20" r="3" fill="#6EE7B7"/>
-</svg>
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/DASHBOARD_PACK.md b/plugins/financial-markets/skills/thesis-tracker/references/DASHBOARD_PACK.md
deleted file mode 100644
index 3e6d34d..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/references/DASHBOARD_PACK.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# Thesis Tracker Dashboard Pack
-
-Use this only when the user explicitly requests an HTML dashboard/report or a reusable presentation view for a thesis tracker, monitoring update, evidence ledger, KPI/catalyst review, or portfolio thesis status package. Ordinary tracker builds and updates lead with the XLSX workbook when available.
-
-## Producer Role
-
-`thesis-tracker` owns thesis status, pillar evidence, KPI/catalyst monitoring, estimate/model change logs, decision logs, and update cadence. `dashboard-builder` owns the shared HTML shell, module rendering, responsive layout, citation behavior, and validation.
-
-## Recommended Payload
-
-- `mode`: `thesis_tracker`
-- `layout`: `single_page` for reusable tracker reviews unless the user explicitly requests tabs
-- `hero.callout`: company-thesis status, security-thesis readiness, and position action
-- `snapshot`: company-thesis status, security-thesis readiness, conviction/rating, base/bull/bear value if supportable, current price and market-data as-of when accessible, next catalyst, evidence density, open-question count
-- `sources`: tracker input/user files first, then filings/releases/transcripts, market/consensus data, model outputs, and assumptions
-- Raw JSON, Markdown notes, CSV exports, run logs, and manifests are support/audit material unless the user explicitly asks for them.
-
-## Tabs And Modules
-
-1. `thesis-status`
-   - `decision_box`: current thesis status, recommendation/action, conviction, next catalyst, and break condition
-   - `metric_tiles`: status, value/range if sourced, evidence density, source confidence, open questions, next review
-2. `pillars-evidence`
-   - `cards`: thesis pillars, supporting/refuting evidence, confidence, and what changed
-   - `table`: evidence ledger with source, date, implication, status, and owner
-3. `kpi-catalyst-monitor`
-   - `key_metrics`: KPIs, thresholds, current values, trend, and read
-   - `timeline`: catalysts, review dates, and monitoring events
-   - `market_events`: source-backed developments affecting thesis status
-4. `model-decision-log`
-   - `table`: estimate revisions, model changes, decisions, dates, owners, and rationale
-5. `open-items`
-   - `question_list`: diligence questions and next evidence checks
-   - `missing_evidence`: stale sources, missing KPIs, unresolved conflicts, possible restricted/MNPI flags, and open model fields
-
-The source tab is normally generated from top-level `sources`.
-
-## Required Evidence
-
-- Production dashboard payloads must include `metadata.payload_stage: "production"`, `mode`, `layout`, `hero`, non-empty `snapshot`, `sources`, `metadata.freeze_time`, `metadata.source_posture`, `metadata.readiness_label`, `metadata.readiness_posture`, `metadata.decision_context`, and `metadata.citation_policy: "strict"`.
-- Use `metadata.payload_stage: "draft"` or `"support"` with `metadata.citation_policy: "warn"` only for internal support payloads; final HTML/XLSX/chat handoffs must keep gaps visible and must not claim PM-ready, client-ready, committee-ready, external, or publication-ready status.
-- Cite every thesis-moving event, KPI, estimate revision, model change, catalyst date, and decision log item.
-- Label source type, as-of date, stale data, assumptions, PM judgment, possible restricted material, and missing evidence.
-- Label generated action thresholds as `Draft threshold for PM confirmation` until approved; do not present them as inherited or approved monitoring rules.
-- Reconcile the hero company-thesis status to core-pillar states; any aggregate `Watch` status above an inherited core pillar marked `Impaired` must state an explicit evidence-supported override rationale.
-- Do not include a scored pillar summary or score chart unless a scoring method is inherited from the existing tracker or explicitly requested.
-- Use `metadata.citation_policy: "strict"` for production dashboards.
-
-## Do Not
-
-- Do not overwrite an existing tracker unless the user explicitly authorizes it.
-- Do not use possible MNPI/restricted material for public-equity trading recommendations; flag it for review.
-- Do not make raw JSON, Markdown notes, CSV sidecars, run logs, or manifests the lead user-facing artifact unless explicitly requested.
-
-## QA Checks
-
-- Validate with `skills/public-equity-investing/internal-support/dashboard-builder/scripts/validate_payload.py`.
-- Confirm company-thesis status, security-thesis readiness, evidence changes, KPI/catalyst monitor, decision log, threshold origin/approval status, and open questions are visible.
-- Confirm tracker support files stay behind the dashboard or workbook hero artifact.
-
-## PM Judgment Dashboard Slot
-
-When this skill produces or hands off a `public_equity_investing_dashboard.v1` payload, surface the PM judgment layer inside existing supported modules rather than inventing a custom shell.
-
-Required dashboard content where relevant:
-- PM decision box: use `decision_box` for actionability, position action, or rating/target implication.
-- Variant wedge and what is priced in: use `text_block`, `key_metrics`, or `table`.
-- Estimate/revision bridge: use `table`, `metric_tiles`, or `financial_trend_chart` when source-backed.
-- Scenario skew and downside mechanism: use `scenario_map`.
-- Catalyst timeline and decision pressure: use `timeline` or `market_events`.
-- Disconfirmers and action rules: use `question_list`, `table`, or `text_block`.
-- Benchmark/factor/ETF exposure: use `table` or `key_metrics` when relevant and source-backed.
-- Source posture and missing evidence: include `source_list` and `missing_evidence` in all production-ready dashboards.
-
-Sector context belongs in the owning skill dashboard through hero debate, snapshot KPI, key metrics, valuation table, risk/falsifier cards, missing evidence, and source ledger. Do not create a standalone sector dashboard.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/intake-and-source-priority.md b/plugins/financial-markets/skills/thesis-tracker/references/intake-and-source-priority.md
deleted file mode 100644
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--- a/plugins/financial-markets/skills/thesis-tracker/references/intake-and-source-priority.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Intake And Source Priority
-
-Use when context is missing, partial, conflicting, stale, or source-sensitive.
-
-## Context Rules
-
-- **No context:** do not invent a company thesis. Produce blank institutional tracker tables and ask for minimum inputs.
-- **Ticker-only:** use connected/public sources for objective facts only; label thesis, mandate, cost basis, target, risk tolerance, and house view as preliminary/missing.
-- **Partial context:** extract what exists and expose gaps. Memo = thesis/pillars/catalysts/risks; model = drivers/assumptions; tracker = preserve structure and flag stale/missing fields; release/transcript = evidence only unless prior thesis is supplied; position list = triage review priority.
-- **Full context:** append a new update, preserve original underwriting and prior entries, update statuses, and add changelog.
-
-Minimum intake: issuer/ticker/security, direction, role/mandate, horizon, thesis, position/rating, source materials, output format.
-
-## Source Hierarchy
-
-1. User prompt and explicit instructions.
-2. User-provided files, tracker, model, memo, notes as house source of record unless stale/contradicted.
-3. Callable connected routes or user-provided firm-system exports.
-4. Official company/regulatory sources.
-5. Market data and consensus vendors.
-6. Earnings calls, conferences, investor days.
-7. Broker/third-party research, expert calls, alt-data with licensing/bias labels.
-8. News/web for current public facts and source discovery.
-9. Assumptions/inferences, clearly labeled.
-
-Reliability labels: high (primary/user/source file), medium (reputable transcript/vendor/news/research), low (anecdote/rumor/vague/stale), unknown (uncited/not inspectable).
-
-## Freshness
-
-Track source date, reporting period, model version, consensus timestamp, market data timestamp, prior tracker as-of, and whether post-date events are included. If a key point may have changed and no current source is available, mark stale/unavailable.
-
-## Clarification Policy
-
-Do not block on broad questions when a useful labeled output is possible. Ask only targeted blockers such as direction, append-vs-rewrite, model source of truth, or desired output format.
-
-## Preservation
-
-Append rows/sections, create version labels, keep changelog, and mark stale/superseded/duplicate/contradicted items rather than deleting. Show proposed deletions/merges before cleanup unless direct deletion is explicitly authorized.
-
-## Labels
-
-Use `Fact`, `Assumption`, `Judgment`, `Open question`, and `Sensitive` labels where source boundaries matter.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/investor-sector-overlays.md b/plugins/financial-markets/skills/thesis-tracker/references/investor-sector-overlays.md
deleted file mode 100644
index ba027cd..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/references/investor-sector-overlays.md
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-# Investor And Sector Overlays
-
-Use only decision-useful overlays; do not force every item into every output.
-
-## Investor Types
-
-| Mandate | Emphasize | Action language |
-|---|---|---|
-| Long-only | Durability, benchmark active weight, downside, opportunity cost, governance/capital allocation, best use of capital | add to active weight, maintain, trim, exit, watchlist, re-underwrite |
-| Long/short | Variant perception, catalyst/setup, revisions, valuation/multiple risk, crowding, borrow/squeeze, factor/sector beta, gross/net/liquidity | press, add, reduce gross, cover, pair, hedge, wait, monetize |
-| Sell-side | Rating/target support, estimates, valuation method, client debate, published-fact/opinion split | maintain, upgrade/downgrade, raise/lower target, revise estimates, publish |
-| Event-driven | Scenario tree, probability, timing/path dependency, legal/regulatory/shareholder/financing milestones, unaffected/break price, EV/spread | update probability/EV, hedge event risk, wait for milestone, exit |
-| Equity-risk credit signal / Credit Markets handoff | Common-equity downside from leverage, liquidity runway, maturities/refi, covenant pressure, rating pressure, CDS/spread signals, or equity-credit divergence | trim/exit/hedge/re-underwrite equity; route debt-security, recovery, covenant-package, and stack-priority work to Credit Markets |
-| Macro/rates-linked | Rates, FX, commodities, inflation, curve, central bank/fiscal/regulatory drivers, duration/convexity, factor/correlation | isolate macro beta, hedge factor, wait for print, resize |
-
-## Sector KPI And Red-Flag Map
-
-| Sector | Track | Red flags |
-|---|---|---|
-| Software/SaaS | ARR/MRR, NRR/GRR, churn/expansion, bookings/billings/RPO/cRPO, sales efficiency, gross/FCF margin, Rule of 40, pricing/usage/cloud costs | cRPO decel, NRR masked by price, sales-efficiency erosion, RPO quality, margin from underinvestment |
-| Internet/marketplaces | GMV/GTV, take rate, users, frequency, conversion, cohorts, liquidity, CAC, regulation/platform risk | take-rate pressure, traffic quality, seller churn, regulatory overhang, paid-growth dependence |
-| Semis/hardware | units, ASP, mix, backlog, book-to-bill, inventory, utilization, lead times, gross margin, capex, design wins, export controls | inventory correction, pull-forward, underutilization, order cancels, customer capex cuts |
-| Banks/lenders | NIM/NII, deposit beta, loan growth, funding mix, credit losses/reserves/NPLs, CET1, liquidity, HTM marks, fees/expenses | deposit outflows, funding spike, reserve gap, CRE concentration, capital constraints |
-| Insurance | combined/loss/expense ratio, pricing/renewal/retention, reserves, cat exposure, investment income, capital/buybacks | adverse development, underpriced risk, cat creep, social inflation, capital strain |
-| REITs/real estate | same-store NOI, occupancy, leasing spreads, AFFO/FFO, cap rates/NAV, debt maturities, tenant health, asset sales | cap-rate expansion, tenant bankruptcy, maturity wall, dividend coverage, occupancy decline |
-| Energy/E&P | production, realized prices, hedge book, capex/FCF/leverage, reserves, lifting costs, inventory depth, well productivity | inventory exhaustion, cost inflation, poor hedges, production miss, leverage creep |
-| Biotech/pharma | trial design/endpoints/safety, regulatory path, cash runway/dilution, IP/exclusivity, launch scripts/coverage, pipeline odds | endpoint ambiguity, safety imbalance, subgroup mining, cash shortfall, trial/regulatory delay |
-| Consumer/retail | comps, traffic, ticket, conversion, gross margin, markdowns, shrink, freight, inventory/sell-through, unit economics | inventory build, promotions, traffic masked by pricing, category slowdown, working-capital strain |
-| Industrials/transports | orders/backlog/book-to-bill, organic growth, price/cost, margins, supply chain/labor, capex cycle, FCF conversion | order deterioration, cancels, price/cost reversal, margin miss, channel destock |
-| Media/telecom/cable | subs, ARPU, churn, broadband adds, ads, content/sports costs, capex, leverage, FCF | churn acceleration, ARPU pressure, leverage/capex squeeze, content inflation, ad decline |
-| Exchanges/brokers/asset managers | AUM/AUA, flows, fee rate, market beta, volumes/volatility, NII, expense discipline, regulation | fee compression, flow weakness, active underperformance, volume normalization, fee caps |
-| Payments/fintech | TPV/GPV, take rate, transaction margin, active accounts, engagement, credit losses, fraud/compliance, funding cost | take-rate compression, loss normalization, regulatory scrutiny, funding pressure, cohort decay |
-
-## Cross-Sector Debate Prompts
-
-Ask whether the debate is growth, margin, multiple, balance sheet, management credibility, or regulation; whether the KPI is leading or lagging; whether evidence changes earnings power or timing; whether weakness is cyclical/company-specific/structural; what is market-implied; and what the smartest opposing investor would say.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/output-templates.md b/plugins/financial-markets/skills/thesis-tracker/references/output-templates.md
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index efac793..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/references/output-templates.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# Output Contracts
-
-Use these full structures by default; keep all decision-critical rows. Compress only when the user explicitly asks for a summary, quick read, one-pager, brief, TL;DR, or one section.
-
-## Workbook-First Tracker Update
-
-For an attached or existing thesis tracker update, the default hero deliverable is a polished XLSX workbook. The `Cover` tab must make the decision legible before the audit trail:
-
-| First-read block | Required content |
-|---|---|
-| Company thesis | Prior status, current status, what confirmed, what weakened, next fundamental proof point |
-| Security thesis readiness | `Ready`, `Conditional`, `Re-underwrite`, or `Not decision-grade`; current price and market-data as-of; valuation readiness and blocked inputs |
-| Position action | `Add`, `Hold`, `Trim`, `Exit`, `Wait for proof`, or `Re-underwrite`, with the action condition clearly stated |
-| Action-rule summary | Current action, approval posture of applicable rules, and next review gate; full triggers, provenance, and required sources remain on `Action Rules` |
-| Evidence-gap summary | Top decision blockers and their readiness consequence; the full diligence/open-question register remains on its dedicated tab |
-
-Keep the first-read pillar view compact: pillar, priority or inherited weight, prior/current status, thesis-moving evidence, next proof point, and action implication. Put detailed threshold definitions, citations, model impacts, and follow-ups into the dedicated monitoring or evidence tabs rather than expanding the pillar sheet horizontally.
-
-When an inherited core pillar is `Impaired`, an aggregate company-thesis status of `Watch` requires an explicit evidence-supported override rationale. When multiple core pillars are `Impaired`, default aggregate company-thesis status to `Impaired` unless that rationale is stated.
-
-Keep `Cover` concise: do not duplicate the full action-rule matrix or full diligence/open-question register on it.
-
-Use `Inherited threshold`, `Draft threshold for PM confirmation`, or `Approved monitoring rule` for every action trigger. Do not display a generated threshold as an approved hold, trim, or exit rule.
-
-Do not add an aggregate pillar score or scored chart unless the prior tracker contains a defined scoring methodology or the user explicitly requests one. Qualitative signal/status formatting is sufficient by default.
-
-## Default Update Memo
-
-```markdown
-# [Company / Ticker] Thesis Tracker Update
-As of: [date/time] | Direction/mandate: [long/short/rating/etc.] | Company thesis status: [current vs prior] | Security thesis readiness: [readiness] | Recommendation: [action] | Next decision point: [date/catalyst/threshold]
-
-## PM Summary
-[3-6 sentences: what changed, whether evidence confirms or weakens the company thesis, whether the security thesis is decision-grade, whether risk/reward improved or deteriorated, and what action follows.]
-
-## What Changed
-| Item | Prior house view | Consensus / market setup | New evidence | Thesis read-through |
-|---|---|---|---|---|
-
-## Pillar Tracker
-| Pillar | Weight | Prior | Current | Latest evidence | Signal | Next proof point |
-|---|---:|---|---|---|---|---|
-
-## KPI / Estimate / Catalyst Update
-[Only metrics, revisions, and events that affect the decision.]
-
-## Model, Valuation, Risk/Reward
-[Changed assumptions, target/fair-value/downside impact, estimate vs multiple read-through, hurdle clearance.]
-
-## Action Thresholds
-| Trigger | Threshold/date | Origin | Approval status | Required source | Action |
-|---|---|---|---|---|---|
-
-## Operating Model
-| Role / cadence item | Owner / value | Decision responsibility | Next date / SLA | Escalation trigger |
-|---|---|---|---|---|
-| PM owner |  | decision authority |  |  |
-| Analyst owner |  | evidence synthesis |  |  |
-| Evidence owner |  | source updates |  |  |
-| KPI owner |  | KPI thresholds |  |  |
-| Model owner |  | estimate/model changes |  |  |
-| Review cadence |  | tracker refresh |  |  |
-| Post-catalyst update SLA |  | rapid update |  |  |
-
-## Red-Team / Open Questions / Changelog
-Best opposing view: [view]. What makes it right: [evidence]. Open questions: [asks].
-| Date | Change | Source | Rationale | Owner/next step |
-|---|---|---|---|---|
-```
-
-## Blank Tracker Shell
-
-Use when no thesis is provided; invent no facts.
-
-Required sections/tables: Dashboard, Original Underwriting, Thesis Pillars, Evidence Ledger, KPI Tracker, Catalyst Calendar, Estimate Revisions, Model Changelog, Decision Log, Sources, Open Questions.
-
-Minimum fields:
-
-- Dashboard: issuer/security, direction, mandate, horizon, company-thesis status, security-thesis readiness, position action, market-data as-of, base/bull/bear value if supportable, next catalyst, recommendation, next review, and blocked inputs.
-- Operating model: PM owner, analyst owner, evidence owner, KPI owner, model owner, decision authority, review cadence, post-catalyst update SLA, escalation triggers, next review gate, and decision log owner.
-- Underwriting: thesis, variant perception, market setup, valuation anchor, catalysts, risks, kill criteria, diligence.
-- Pillars: pillar, priority or inherited weight and its origin, claim, KPI/evidence, confirm/warning/break thresholds, status, next proof point.
-- Evidence: date, source, fact, pillar, signal, magnitude, model impact, action implication, follow-up.
-- Action rules: trigger, threshold/date, threshold origin, threshold approval status, required source, action, next review.
-- Decision log: date, decision, rationale, evidence, company-thesis status, security-thesis readiness, position/rating change, next review.
-
-## PM Dashboard
-
-```markdown
-Company thesis: [status/change] | Security readiness: [readiness] | Action: [recommendation] | Conviction: [level/change] | Risk/reward: [upside/downside/skew or blocked] | Next catalyst: [event/date] | Key call: [one sentence]
-
-| Dimension | Current read | PM judgment |
-|---|---|---|
-| Fundamentals / estimates / valuation / setup / catalyst path / thesis risk |  |  |
-```
-
-## Portfolio Review
-
-Include: priority actions, names with deterioration, names with improved evidence but worse risk/reward, upcoming catalyst watchlist.
-
-## Mandate-Specific Language
-
-- Sell-side: rating/target implication, estimate changes, valuation bridge, key risks, client debate.
-- Long/short: trade read-through, positioning/setup, catalyst path, borrow/squeeze/factor risk, hedge/trim thresholds.
-- Equity-risk credit signal: liquidity, maturities, covenant pressure, CDS/spread warning signal, and common-equity downside action; route credit-security and recovery work to Credit Markets.
-
-## Tone Examples
-
-Prefer: "Thesis is playing out, but valuation rerated faster than estimates; business thesis is stronger, stock thesis is less attractive."
-
-Avoid: "Earnings were good and the stock was up. Continue to monitor."
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/quality-guardrails.md b/plugins/financial-markets/skills/thesis-tracker/references/quality-guardrails.md
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-# Quality Guardrails
-
-## PM-Grade Checklist
-
-Before delivery, answer:
-
-- What did we originally believe, what did the market believe, and what happened?
-- What is the company-thesis status, and separately is the security thesis ready for an investment decision?
-- Is aggregate company-thesis status reconciled to core-pillar statuses, with any override rationale explicitly supported by evidence?
-- Which pillar is affected, and is the signal confirmatory, disconfirmatory, mixed, irrelevant, or untested?
-- Is the change temporary, cyclical, structural, timing-related, or already priced in?
-- Does the model, valuation, downside, conviction, sizing, hedge, or decision timing change?
-- What action follows, what would make us wrong, and what is the next proof point?
-
-## Market Data And Readiness
-
-- For a material security-thesis update, attempt to obtain current public price and basic market context when accessible; capture source and as-of timestamp.
-- A missing current price, valuation model, consensus frame, position size, benchmark/active weight, or risk budget must appear as an explicit decision gap when it changes the action.
-- Do not turn a weakened company thesis into a valuation-led `trim`, `exit`, or `add` conclusion when the security-thesis inputs required for that call remain missing. Use `Re-underwrite`, `Conditional`, or `Not decision-grade` as appropriate.
-
-## Falsifiability And Drift
-
-Every tracker needs thesis, variant perception, market-implied expectation, prioritized pillars, KPIs, confirm/warning/break thresholds, catalysts, kill criteria, action thresholds, and open diligence. If missing, propose and label them. Retain numeric weights or scores only when inherited from an existing tracking method or explicitly requested; do not manufacture an aggregate score or scored chart for presentation.
-
-Every new action threshold must be labeled `Draft threshold for PM confirmation` until approved. Keep `Inherited threshold` and `Approved monitoring rule` separately labeled with source and status.
-
-An inherited core pillar marked `Impaired` should prevent aggregate `Watch` unless an explicit evidence-supported override rationale is stated. Multiple core pillars marked `Impaired` should normally result in aggregate `Impaired`.
-
-Flag thesis drift when the reason to own/short changes, failed catalysts are replaced by vague qualitative rationale, valuation support replaces the original growth/margin thesis, repeated disconfirming evidence is ignored as "long term," or downside protection becomes the new rationale for an upside position.
-
-Labels: thesis intact, thesis evolved, thesis changed, thesis broken, thesis retired.
-
-## Evidence Quality
-
-- Headline beat/miss is insufficient: parse revenue, mix, price, cost cuts, tax, buyback, FX, one-time items, KPI quality, guidance, FY2/long-term revisions, cash conversion, management credibility, and buyside expectations.
-- Management commentary earns weight when quantified, consistent, source-supported, specific, and candid. Discount vague optimism, KPI definition changes, selective disclosure, and unexplained macro blame.
-- Price action must be decomposed into fundamentals, estimates, multiple/spread/rates, sector/factor beta, positioning/crowding/short squeeze, liquidity/flows, options/vol, and macro/policy.
-
-## Red Flags
-
-KPI definition/disclosure changes, rising non-GAAP adjustments, revenue recognition changes, receivables/inventory build, weak cash conversion, capitalized cost spike, segment changes, auditor/CFO/controller departure, related-party transactions, aggressive add-backs, repeated one-time charges.
-
-## Compliance And Sensitivity
-
-Flag possible MNPI, wall-crossed or restricted-list material, expert-network sensitivity, broker redistribution limits, rumors, and uncited claims. Do not use sensitive information for public-equity trading recommendations; suggest compliance review or an external-clean version.
-
-## Adjacent Handoffs
-
-Route when primary need is pre/post-earnings analysis, detailed model update, new pitch, sizing/hedging, event scenario tree, Credit Markets handoff work, macro impact, committee memo, or deck/report QC.
-
-## Final QA
-
-Verify original thesis is preserved, evidence is appended, sources/as-of dates are visible, both sides are considered, KPIs are compared to expectations/thresholds where available, company-thesis status and security-thesis readiness are separately stated, proposed thresholds are not presented as approved rules, recommendation has timing/threshold/action, and red-team is credible.
-
-## XLSX Presentation QA
-
-For workbook deliverables:
-
-- Keep the `Cover` tab decision-facing and readable without horizontal scrolling.
-- Prefer a compact pillar sheet and move extended thresholds, citations, and model impacts to dedicated tabs.
-- Keep full action-rule and diligence/gap registers on their dedicated tabs; `Cover` should show only summarized action posture, top blockers, and next gate.
-- Freeze key identity columns and use bounded column widths; render every workbook tab and inspect it at ordinary zoom before delivery.
-- Repair clipped headers, overly wide monitoring tables, unreadable source paths, or decision-critical text that requires excessive horizontal navigation.
-- Do not include a weighted score chart unless its calculation methodology is inherited from the existing tracker or explicitly requested.
-
-## PM Monitoring Guardrails
-
-Every update should separate company thesis, stock thesis, portfolio role, and market setup. Required decision actions are `add`, `press`, `hold`, `trim`, `exit`, `hedge`, `wait for proof`, or `re-underwrite`. Include benchmark/ETF implications, liquidity, crowding, and what evidence would force a different decision when relevant.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/thesis-schema.md b/plugins/financial-markets/skills/thesis-tracker/references/thesis-schema.md
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index 8c14a66..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/references/thesis-schema.md
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-# Thesis Tracker Schema Contract
-
-Use these fields for markdown tables, CSV/XLSX bundles, databases, or structured outputs. Keep blank fields visible rather than inventing values.
-
-## Core Tables
-
-| Table | Required fields |
-|---|---|
-| Dashboard | issuer/security, direction, mandate/role, company_thesis_status/current_prior, status_override_rationale_if_applicable, security_thesis_readiness, position_action, conviction/change, position/rating, market_data_as_of, base/bull/bear value, upside/downside, risk/reward, horizon, next catalyst, key confirm signal, key concern, recommendation, next review, sources, data gaps |
-| Thesis pillars | pillar_id, pillar_name, claim, priority_or_inherited_weight, weighting_origin, current/prior status, KPI/evidence, baseline, expected path, confirm/warning/break thresholds, latest evidence, signal, evidence quality, model linkage, position linkage, next proof point, owner |
-| Evidence ledger | evidence_id, date/time, reporting period, source, source_type, citation/file, event, evidence fact, prior house expectation, consensus/market expectation, interpretation, pillar affected, signal, magnitude, quality, model impact, valuation impact, confidence impact, action implication, follow-up, owner/due date |
-| KPI tracker | KPI, pillar, source, unit, period, baseline, prior actual, current actual, house estimate, consensus, guidance, threshold, status, trend, PM comment |
-| Catalyst calendar | catalyst_id, date/window, event, pillar tested, expected outcome, market setup, upside case, downside case, required prep, actual result, status/action |
-| Estimate revisions | date, source, metric, period, prior estimate, new estimate, change, driver, thesis implication, stock reaction link |
-| Model changelog | version, date, changed assumption, old value, new value, reason/source, impact, reviewer, status |
-| Action rules | action, trigger, threshold/date, threshold_origin, threshold_approval_status, required_source, action_implication, next_review |
-| Decision log | date, decision, rationale, evidence IDs, company_thesis_status, security_thesis_readiness, position/rating change, risk/reward snapshot, PM/owner, next review |
-| Sources | source_id, name, type, date/as-of, reliability, used for, citation/link, limitation |
-| Open questions | question, related pillar/evidence, owner, due date, required source, decision impact |
-| Operating model | pm_owner, analyst_owner, evidence_owner, kpi_owner, model_owner, decision_authority, review_cadence, post_catalyst_update_sla, escalation_trigger, next_review_gate, decision_log_owner, portfolio_role, active_weight |
-
-## Status And Scoring
-
-Evidence signal: `+2 strongly confirming`, `+1 mildly confirming`, `0 neutral`, `mixed`, `-1 mildly weakening`, `-2 strongly weakening`, `break/invalidating`, `untested`.
-
-Thesis status guide:
-
-- Strengthening: core pillars confirm and risk/reward remains attractive.
-- Intact: core pillars stable with no material contradiction.
-- Watch: one core concern or several secondary warnings.
-- Impaired: core pillar under pressure or repeated KPI misses.
-- Broken: kill criterion hit or original premise failed.
-- Changed: original reason no longer governs; needs explicit approval.
-- Untested: no decisive proof point.
-- Retired: position/coverage closed.
-
-Status reconciliation: an aggregate `Watch` status alongside an inherited core pillar marked `Impaired` requires an explicit evidence-supported override rationale. Multiple core pillars marked `Impaired` default aggregate company-thesis status to `Impaired` unless an override rationale is stated.
-
-Evidence signals are qualitative discipline aids, not an additive scoring framework. Do not aggregate them into pillar scores, weighted directions, or a scored chart unless a user-supplied or inherited methodology defines that calculation. PM judgment overrides mechanical status when explained.
-
-Security-thesis readiness guide:
-
-- Ready: sufficient current market, valuation, and portfolio inputs support the stated action.
-- Conditional: the company/security interpretation is usable, but named implementation inputs remain open.
-- Re-underwrite: thesis-moving evidence requires a revised model, valuation, or action framework.
-- Not decision-grade: missing market, valuation, or portfolio inputs prevent a security conclusion.
-
-Action threshold provenance:
-
-- `Inherited threshold`: present in the supplied original underwriting or existing tracker.
-- `Draft threshold for PM confirmation`: created in the current update and not yet approved.
-- `Approved monitoring rule`: explicitly accepted by the user or governing portfolio process.
-
-## Public Equity Portfolio Monitoring Fields
-
-Add these fields where relevant:
-
-- Dashboard: `benchmark_weight`, `active_weight`, `portfolio_role`, `liquidity_score`, `crowding_read`, `index_etf_flow_exposure`, `priced_in_status`, `position_action`, `security_thesis_readiness`.
-- Thesis pillars: `portfolio_relevance`, `priced_in_status`, `diligence_source_needed`.
-- Evidence ledger: `priced_in_read`, `benchmark_or_flow_impact`, `position_action`.
-- Portfolio monitor table: ticker, status, active weight, next catalyst, thesis status, setup status, decision required, PM action, owner, due date.
-
-Evidence remains append-only. Do not overwrite prior thesis history without a dated decision-log entry.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/tracker-materializer.md b/plugins/financial-markets/skills/thesis-tracker/references/tracker-materializer.md
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index 49731dd..0000000
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-# Tracker Materializer
-
-Run `scripts/materialize_thesis_tracker.py` to create a new local CSV bundle, with optional XLSX packaging when `openpyxl` is installed. The helper scaffolds artifacts; it does not decide thesis status, provide final presentation formatting, or edit existing trackers in place.
-
-```bash
-python scripts/materialize_thesis_tracker.py [tracker_input.json] --output-dir output
-```
-
-Input JSON top-level keys are optional: `dashboard`, `thesis_pillars`, `evidence_ledger`, `kpi_tracker`, `catalyst_calendar`, `estimate_revisions`, `model_changelog`, `decision_log`, `sources`, `open_questions`.
-
-Outputs: one CSV per table, `run_log.json`, and `manifest.json`; XLSX is best-effort if dependencies exist. Optional XLSX packages start with `Cover`, a senior-review dashboard summarizing thesis status, conviction/rating, valuation frame, next catalyst, action recommendation, evidence density, source/open-question counts, and workbook map. Use a new output directory/file for updates unless the user explicitly authorizes overwriting a workbook.
-
-For a final tracker-update deliverable, use the XLSX workbook as the hero artifact when available and ensure its `Cover` distinguishes company-thesis status from security-thesis readiness and position action. Refine generated workbook layouts as needed so first-read tabs are compact and decision-facing; move detailed evidence into audit tabs. Summarize action-rule posture and top decision blockers on `Cover`, while keeping full action-rule and diligence/gap registers on dedicated tabs. Render and inspect every final workbook tab before delivery, and do not add numeric score charts unless a scoring method is inherited or explicitly requested.
diff --git a/plugins/financial-markets/skills/thesis-tracker/references/workflow-core.md b/plugins/financial-markets/skills/thesis-tracker/references/workflow-core.md
deleted file mode 100644
index a8b3a79..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/references/workflow-core.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# Core Workflow Contract
-
-Use for full thesis-tracker builds or updates.
-
-## Sequence
-
-1. **Context.** Capture issuer/security, direction, mandate, horizon, exposure, cost basis/rating/target if supplied, benchmark/peers, and output format. If missing, use labeled placeholders.
-2. **Sources/freshness.** Inventory source name, type, as-of/reporting period, reliability, coverage, and limitation. Prefer user/connected materials, then official filings/releases/transcripts, vendor market/consensus data, research/news, then assumptions. For a material stock-thesis update, attempt to retrieve current public price and basic market context when accessible; record source and timestamp, and identify missing consensus, model, position, benchmark, and risk inputs separately.
-3. **Original underwriting.** Preserve separately: one-sentence thesis, variant perception, market setup, valuation anchor, scenarios, expected return, catalysts, KPIs, kill criteria, position implication, and open diligence.
-4. **Pillars.** Convert thesis into prioritized, testable claims with baseline, expected path, confirm/warning/break thresholds, cadence, model linkage, action linkage, and next proof point. Carry forward numeric weights or scoring only when inherited from the prior tracker or requested by the user; otherwise use qualitative priority and no aggregate score chart.
-5. **Evidence ledger.** Append material facts with source/date, affected pillar, prior expectation, consensus/market expectation, interpretation, signal, magnitude, quality, model/valuation/confidence/action impact, follow-up, and owner.
-6. **KPIs/revisions/catalysts.** Compare actuals to prior period, house model, guidance, consensus, buyside expectation if supplied, thresholds, and peers. Mark unavailable data explicitly.
-7. **Model/valuation.** State changed model lines, whether change is timing/cyclical/structural, estimate implications, fair value/target/downside/spread/recovery impact, and whether risk/reward still clears the hurdle. Without current price and required valuation inputs, state that security-thesis readiness is `not decision-grade` rather than making a valuation-led action call.
-8. **Status/action.** Assign a company-thesis status of strengthening, intact, watch, impaired, broken, changed, untested, or retired separately from security-thesis readiness of ready, conditional, re-underwrite, or not decision-grade. Reconcile the aggregate status to core-pillar statuses: an inherited core pillar marked `Impaired` requires an explicit evidence-supported override rationale before aggregate status can remain `Watch`; multiple core pillars marked `Impaired` default aggregate company-thesis status to `Impaired`. Recommend add/press, hold, trim, exit/cover, upgrade/downgrade, hedge/pair, wait, re-underwrite, update model, diligence, or escalation. Classify each threshold as `Inherited threshold`, `Draft threshold for PM confirmation`, or `Approved monitoring rule`.
-9. **Red-team/log.** Include strongest opposing view, evidence for it, what changes the call, open questions, next review date, and append-only changelog.
-10. **Workbook QA.** For XLSX output, lead with a compact decision-facing cover, use detail sheets for audit trails rather than repeating every field on each first-read table, freeze key columns, and visually render every sheet before delivery. Keep full action-rule matrices and full diligence/gap registers on their dedicated tabs; show only action posture, top decision blockers, and next gate on the cover. Reduce or split tables that require excessive horizontal scrolling, and omit score charts unless the scoring basis is inherited or requested.
-
-## Operating Model And Cadence
-
-Every full tracker should name the operating model:
-
-- PM owner and decision authority;
-- analyst owner;
-- evidence owner;
-- KPI owner;
-- model owner;
-- review cadence;
-- post-catalyst update SLA;
-- escalation triggers;
-- next review gate;
-- portfolio role and active weight when supplied;
-- decision log owner and append-only changelog process.
-
-Every update should end in one of: `add`, `press`, `hold`, `trim`, `exit`, `cover`, `hedge`, `wait for proof`, `re-underwrite`, `update model`, or `escalate`.
-
-Newly proposed monitoring thresholds are draft decision aids, not approved mandate rules. Preserve inherited thresholds and approved monitoring rules distinctly, including their source and approval status.
-
-## Interpretation Rules
-
-- Evidence direction: strongly confirming, mildly confirming, neutral, mixed, mildly weakening, strongly weakening, invalidating, or untested.
-- A thesis can strengthen while the stock becomes less attractive if valuation/expectations moved faster than evidence.
-- A headline beat can weaken a thesis if leading KPIs, quality, cash conversion, or guidance disappoint.
-- Price action is a signal to decompose, not proof of thesis status.
-- Every "monitor" conclusion must specify metric, source, threshold, date, and action.
diff --git a/plugins/financial-markets/skills/thesis-tracker/scripts/materialize_thesis_tracker.py b/plugins/financial-markets/skills/thesis-tracker/scripts/materialize_thesis_tracker.py
deleted file mode 100644
index 30563c2..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/scripts/materialize_thesis_tracker.py
+++ /dev/null
@@ -1,512 +0,0 @@
-#!/usr/bin/env python3
-"""Create deterministic CSV/XLSX thesis tracker artifacts from structured inputs."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-SECTIONS: dict[str, list[str]] = {
-    "dashboard": [
-        "company_issuer",
-        "ticker_security",
-        "direction",
-        "mandate_role",
-        "current_thesis_status",
-        "conviction",
-        "position_rating",
-        "base_bull_bear_value",
-        "next_catalyst",
-        "action_recommendation",
-        "next_review_date",
-        "data_gaps",
-    ],
-    "thesis_pillars": [
-        "pillar_id",
-        "pillar_name",
-        "claim",
-        "weight",
-        "current_status",
-        "kpi_evidence",
-        "confirm_threshold",
-        "warning_threshold",
-        "break_threshold",
-        "latest_evidence",
-        "signal",
-        "model_linkage",
-        "next_proof_point",
-        "owner",
-    ],
-    "evidence_ledger": [
-        "evidence_id",
-        "date_time",
-        "source",
-        "source_type",
-        "event",
-        "evidence_fact",
-        "prior_house_expectation",
-        "consensus_market_expectation",
-        "interpretation",
-        "pillar_affected",
-        "signal_direction",
-        "evidence_quality",
-        "model_impact",
-        "valuation_impact",
-        "action_implication",
-        "follow_up",
-    ],
-    "kpi_tracker": [
-        "kpi",
-        "pillar",
-        "source",
-        "unit",
-        "period",
-        "baseline",
-        "prior_actual",
-        "current_actual",
-        "house_estimate",
-        "consensus",
-        "threshold",
-        "status",
-        "trend",
-        "comment",
-    ],
-    "catalyst_calendar": [
-        "catalyst_id",
-        "date_window",
-        "catalyst",
-        "pillar_tested",
-        "expected_outcome",
-        "market_setup",
-        "upside_case",
-        "downside_case",
-        "required_prep",
-        "actual_result",
-        "status_action",
-    ],
-    "estimate_revisions": [
-        "date",
-        "source",
-        "metric",
-        "period",
-        "prior_estimate",
-        "new_estimate",
-        "change",
-        "driver",
-        "thesis_implication",
-    ],
-    "model_changelog": [
-        "version",
-        "date",
-        "changed_assumption",
-        "old_value",
-        "new_value",
-        "reason",
-        "impact",
-        "reviewer",
-        "status",
-    ],
-    "decision_log": [
-        "date",
-        "decision",
-        "rationale",
-        "evidence_ids",
-        "thesis_status",
-        "position_rating_change",
-        "risk_reward",
-        "pm_owner",
-        "next_review",
-    ],
-    "sources": [
-        "source_id",
-        "source_name",
-        "source_type",
-        "date_as_of",
-        "reliability",
-        "used_for",
-        "citation_link",
-        "limitation",
-    ],
-    "open_questions": [
-        "question_id",
-        "question",
-        "why_it_matters",
-        "owner",
-        "due_date",
-        "status",
-        "decision_impact",
-    ],
-}
-
-
-ALIASES = {
-    "source_inventory": "sources",
-    "source_register": "sources",
-    "open_items": "open_questions",
-}
-
-
-def normalize_key(key: str) -> str:
-    return "".join(ch.lower() if ch.isalnum() else "_" for ch in key).strip("_")
-
-
-def normalize_row(row: dict[str, Any], fields: list[str]) -> dict[str, Any]:
-    normalized = {normalize_key(str(k)): v for k, v in row.items()}
-    return {field: normalized.get(field, "") for field in fields}
-
-
-def rows_for_section(
-    payload: dict[str, Any], section: str, fields: list[str]
-) -> list[dict[str, Any]]:
-    source = payload.get(section)
-    if source is None:
-        for alias, target in ALIASES.items():
-            if target == section and alias in payload:
-                source = payload[alias]
-                break
-    if source is None:
-        return [{field: "" for field in fields}]
-    if isinstance(source, dict):
-        if section == "dashboard":
-            return [{"field": k, "value": v} for k, v in source.items()] or [
-                {"field": "", "value": ""}
-            ]
-        source = [source]
-    if not isinstance(source, list):
-        return [{field: "" for field in fields}]
-    rows = [normalize_row(item, fields) for item in source if isinstance(item, dict)]
-    return rows or [{field: "" for field in fields}]
-
-
-def write_csv(path: Path, rows: list[dict[str, Any]], fields: list[str]) -> None:
-    if rows and set(rows[0].keys()) == {"field", "value"}:
-        fields = ["field", "value"]
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fields)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow({field: row.get(field, "") for field in fields})
-
-
-def row_has_data(row: dict[str, Any]) -> bool:
-    return any(value not in ("", None) for value in row.values())
-
-
-def tracker_cover_rows(
-    section_rows: dict[str, tuple[list[str], list[dict[str, Any]]]],
-) -> list[dict[str, Any]]:
-    dashboard_fields, dashboard_rows = section_rows.get("dashboard", ([], []))
-    dashboard: dict[str, Any] = {}
-    if dashboard_rows and set(dashboard_rows[0].keys()) == {"field", "value"}:
-        dashboard = {
-            normalize_key(str(row.get("field", ""))): row.get("value", "") for row in dashboard_rows
-        }
-    elif dashboard_rows:
-        dashboard = dashboard_rows[0]
-
-    def count_rows(section: str) -> int:
-        rows = section_rows.get(section, ([], []))[1]
-        return sum(1 for row in rows if row_has_data(row))
-
-    return [
-        {
-            "section": "Header",
-            "metric": "Issuer / security",
-            "value": dashboard.get("company_issuer") or dashboard.get("ticker_security", ""),
-            "notes": "Thesis tracker workbook landing page.",
-        },
-        {
-            "section": "Status",
-            "metric": "Current thesis status",
-            "value": dashboard.get("current_thesis_status", ""),
-            "notes": "Confirm whether this status is supported by the evidence ledger.",
-        },
-        {
-            "section": "Status",
-            "metric": "Conviction / position rating",
-            "value": f"{dashboard.get('conviction', '')} / {dashboard.get('position_rating', '')}",
-            "notes": "Keep rating, sizing, and evidence quality aligned.",
-        },
-        {
-            "section": "Valuation frame",
-            "metric": "Base / bull / bear value",
-            "value": dashboard.get("base_bull_bear_value", ""),
-            "notes": "Tie to model changelog or valuation artifact where available.",
-        },
-        {
-            "section": "Catalyst dashboard",
-            "metric": "Next catalyst",
-            "value": dashboard.get("next_catalyst", ""),
-            "notes": "Review catalyst_calendar and prep needs.",
-        },
-        {
-            "section": "Action dashboard",
-            "metric": "Action recommendation",
-            "value": dashboard.get("action_recommendation", ""),
-            "notes": "Should be falsifiable against pillar thresholds.",
-        },
-        {
-            "section": "Evidence posture",
-            "metric": "Thesis pillars / evidence rows",
-            "value": f"{count_rows('thesis_pillars')} / {count_rows('evidence_ledger')}",
-            "notes": "Low evidence density should keep the tracker screen-grade.",
-        },
-        {
-            "section": "Evidence posture",
-            "metric": "Sources / open questions",
-            "value": f"{count_rows('sources')} / {count_rows('open_questions')}",
-            "notes": "Open questions stay visible until resolved.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "dashboard",
-            "value": "Original input dashboard table",
-            "notes": "Cover is the senior-review view; dashboard preserves source structure.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "thesis_pillars / evidence_ledger / kpi_tracker",
-            "value": "Core proof stack",
-            "notes": "Use to validate whether the thesis is improving or breaking.",
-        },
-        {
-            "section": "Workbook map",
-            "metric": "catalyst_calendar / estimate_revisions / model_changelog",
-            "value": "Forward watchlist and model impact",
-            "notes": "Use to keep the tracker live after events.",
-        },
-    ]
-
-
-def write_xlsx(path: Path, section_rows: dict[str, tuple[list[str], list[dict[str, Any]]]]) -> None:
-    try:
-        from openpyxl import Workbook
-        from openpyxl.styles import Font, PatternFill
-    except ImportError as exc:
-        raise RuntimeError(
-            "openpyxl is required for --xlsx-out. CSV outputs were still created."
-        ) from exc
-
-    wb = Workbook()
-    default = wb.active
-    wb.remove(default)
-    header_fill = PatternFill("solid", fgColor="1F4E78")
-    header_font = Font(color="FFFFFF", bold=True)
-    cover_ws = wb.create_sheet("Cover")
-    cover_fields = ["section", "metric", "value", "notes"]
-    cover_ws.append(cover_fields)
-    for cell in cover_ws[1]:
-        cell.fill = header_fill
-        cell.font = header_font
-    for row in tracker_cover_rows(section_rows):
-        cover_ws.append([row.get(field, "") for field in cover_fields])
-    cover_ws.freeze_panes = "A2"
-    for col, width in {"A": 22, "B": 34, "C": 42, "D": 76}.items():
-        cover_ws.column_dimensions[col].width = width
-    for section, (fields, rows) in section_rows.items():
-        ws = wb.create_sheet(section[:31])
-        if rows and set(rows[0].keys()) == {"field", "value"}:
-            fields = ["field", "value"]
-        ws.append(fields)
-        for cell in ws[1]:
-            cell.fill = header_fill
-            cell.font = header_font
-        for row in rows:
-            ws.append([row.get(field, "") for field in fields])
-        ws.freeze_panes = "A2"
-    wb.save(path)
-
-
-def load_payload(path: Path | None) -> dict[str, Any]:
-    if path is None:
-        return {}
-    data = json.loads(path.read_text(encoding="utf-8"))
-    if not isinstance(data, dict):
-        raise ValueError("input JSON must be an object")
-    return data
-
-
-def output_paths(output_dir: Path, xlsx_out: Path | None) -> dict[str, str]:
-    paths = {section: str(output_dir / f"{section}.csv") for section in SECTIONS}
-    paths["run_log"] = str(output_dir / "run_log.json")
-    paths["manifest"] = str(output_dir / "manifest.json")
-    if xlsx_out:
-        paths["xlsx"] = str(xlsx_out)
-    return paths
-
-
-def output_manifest(
-    paths: dict[str, str],
-    *,
-    required_xlsx: bool = False,
-    primary_xlsx: bool = False,
-) -> list[dict[str, Any]]:
-    manifest = []
-    for key, artifact_path in paths.items():
-        is_primary = key == "xlsx" and primary_xlsx
-        is_machine = key in {"run_log", "manifest"}
-        manifest.append(
-            {
-                "key": key,
-                "path": artifact_path,
-                "required": key != "xlsx" or required_xlsx,
-                "written": key in {"run_log", "manifest"} or Path(artifact_path).exists(),
-                "description": "Thesis tracker artifact.",
-                "artifact_role": "primary_human_deliverable"
-                if is_primary
-                else ("machine_support" if is_machine else "support_artifact"),
-                "hidden_unless_requested": not is_primary,
-            }
-        )
-    return manifest
-
-
-def source_basis_from_payload(payload: dict[str, Any]) -> list[dict[str, Any]]:
-    rows = rows_for_section(payload, "sources", SECTIONS["sources"])
-    return [
-        {
-            "source_id": row.get("source_id", ""),
-            "source_name": row.get("source_name", ""),
-            "source_type": row.get("source_type", ""),
-            "as_of_date": row.get("date_as_of", ""),
-            "reliability": row.get("reliability", ""),
-            "used_for": row.get("used_for", ""),
-        }
-        for row in rows
-        if row.get("source_id") or row.get("source_name")
-    ]
-
-
-def write_run_log(output_dir: Path, run_log: dict[str, Any]) -> None:
-    output_dir.mkdir(parents=True, exist_ok=True)
-    (output_dir / "run_log.json").write_text(json.dumps(run_log, indent=2) + "\n", encoding="utf-8")
-    primary = run_log.get("primary_human_deliverable")
-    manifest = {
-        "outputs": run_log["output_manifest"],
-        "primary_human_deliverable": primary,
-        "human_deliverables": [primary] if primary else [],
-        "support_artifacts": [
-            row["path"]
-            for row in run_log["output_manifest"]
-            if row.get("artifact_role") != "primary_human_deliverable"
-        ],
-        "support_artifacts_user_visible_default": False,
-        "final_response_guidance": {
-            "lead_with": "primary_human_deliverable" if primary else "concise_status",
-            "mention_support_artifacts": "only_briefly_unless_requested",
-        },
-    }
-    (output_dir / "manifest.json").write_text(
-        json.dumps(manifest, indent=2) + "\n", encoding="utf-8"
-    )
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Materialize a public-equity-investing thesis tracker CSV bundle."
-    )
-    parser.add_argument("input_json", nargs="?", type=Path)
-    parser.add_argument("--output-dir", type=Path, default=Path("output"))
-    parser.add_argument("--xlsx-out", type=Path)
-    args = parser.parse_args()
-
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    paths = output_paths(args.output_dir, args.xlsx_out)
-    try:
-        payload = load_payload(args.input_json)
-    except Exception as exc:
-        run_log = {
-            "status": "failed",
-            "model_status": "not-decision-ready",
-            "artifact_level": "csv_tracker_bundle",
-            "workbook_mode": "csv_tracker_bundle",
-            "generated_at": datetime.now(timezone.utc).isoformat(),
-            "input_json": str(args.input_json) if args.input_json else None,
-            "source_basis": [],
-            "hard_failures": [str(exc)],
-            "warnings": [],
-            "outputs": paths,
-            "output_manifest": output_manifest(paths, required_xlsx=bool(args.xlsx_out)),
-        }
-        write_run_log(args.output_dir, run_log)
-        print(f"ERROR: {exc}")
-        return 1
-
-    section_rows: dict[str, tuple[list[str], list[dict[str, Any]]]] = {}
-    try:
-        for section, fields in SECTIONS.items():
-            rows = rows_for_section(payload, section, fields)
-            write_csv(args.output_dir / f"{section}.csv", rows, fields)
-            section_rows[section] = (fields, rows)
-    except Exception as exc:
-        run_log = {
-            "status": "failed",
-            "model_status": "not-decision-ready",
-            "artifact_level": "csv_tracker_bundle",
-            "workbook_mode": "csv_tracker_bundle",
-            "generated_at": datetime.now(timezone.utc).isoformat(),
-            "input_json": str(args.input_json) if args.input_json else None,
-            "source_basis": source_basis_from_payload(payload),
-            "hard_failures": [f"could not write output: {exc}"],
-            "warnings": [],
-            "outputs": paths,
-            "output_manifest": output_manifest(paths, required_xlsx=bool(args.xlsx_out)),
-        }
-        write_run_log(args.output_dir, run_log)
-        print(f"ERROR: could not write output: {exc}")
-        return 1
-
-    run_log = {
-        "status": "completed",
-        "model_status": "senior-review-ready",
-        "artifact_level": "csv_tracker_bundle",
-        "workbook_mode": "csv_tracker_bundle",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "artifact_mode": "csv_tracker_bundle",
-        "input_json": str(args.input_json) if args.input_json else None,
-        "sections": sorted(SECTIONS),
-        "source_basis": source_basis_from_payload(payload),
-        "hard_failures": [],
-        "xlsx_requested": bool(args.xlsx_out),
-        "xlsx_written": False,
-        "outputs": paths,
-        "warnings": [],
-    }
-    if args.xlsx_out:
-        try:
-            write_xlsx(args.xlsx_out, section_rows)
-            run_log["xlsx_written"] = True
-            run_log["xlsx_path"] = str(args.xlsx_out)
-        except Exception as exc:
-            run_log["status"] = "failed"
-            run_log["model_status"] = "not-decision-ready"
-            run_log["hard_failures"].append(str(exc))
-            run_log["warnings"].append(str(exc))
-            run_log["output_manifest"] = output_manifest(paths, required_xlsx=True)
-            write_run_log(args.output_dir, run_log)
-            print(f"ERROR: {exc}")
-            return 1
-
-    if run_log["xlsx_written"]:
-        run_log["primary_human_deliverable"] = str(args.xlsx_out)
-        run_log["artifact_mode"] = "workbook"
-        run_log["workbook_mode"] = "xlsx_tracker_workbook"
-    else:
-        run_log["primary_human_deliverable"] = None
-
-    run_log["output_manifest"] = output_manifest(
-        paths,
-        required_xlsx=bool(args.xlsx_out),
-        primary_xlsx=bool(run_log["xlsx_written"]),
-    )
-    write_run_log(args.output_dir, run_log)
-    print(f"Wrote thesis tracker CSV bundle to {args.output_dir}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/thesis-tracker/scripts/requirements.txt b/plugins/financial-markets/skills/thesis-tracker/scripts/requirements.txt
deleted file mode 100644
index 289437c..0000000
--- a/plugins/financial-markets/skills/thesis-tracker/scripts/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-openpyxl>=3.1
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/SKILL.md b/plugins/financial-markets/skills/three-statement-model-builder/SKILL.md
deleted file mode 100644
index 716f536..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/SKILL.md
+++ /dev/null
@@ -1,94 +0,0 @@
----
-name: three-statement-model-builder
-description: Use when building public-equity three-statement operating model workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits.
----
-
-# Three Statement Model Builder
-
-## Skill Configuration
-
-### User Context Preflight
-
-Before searching connectors, retrieving evidence, or drafting output, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root, and follow the returned `saved_context`, `source_category_plan`, and `next_action`. Set the working directory before the first attempt; do not probe alternate relative paths. Missing context must not block the requested workflow. Do not initialize state or run onboarding during ordinary workflow work.
-
-If `next_action.id = "offer_orientation"` and the parent router has not already handled it, complete the requested work first and append its one-line optional setup offer once.
-
-## Deliverable Intake
-
-Apply the presentation-surface precedence in `../../shared/deliverable-intake-policy.md`. This workflow's natural artifact is an XLSX three-statement model workbook. Do not choose chat-only output unless the user explicitly requests a lightweight response.
-
-Before source gathering or analysis for a new standalone reader-facing hero deliverable, load `../../shared/deliverable-intake-policy.md` and use its adaptive `request_user_input` preflight for materially unresolved format, depth, audience/use, or focus choices. Reuse resolved preferences in downstream steps; when acting only as input to an owning workflow, do not re-prompt.
-
-Own formula-first public-company operating model builds: integrated IS/BS/CF, working capital, PP&E and D&A, retained earnings, debt, revolver-style draws, cash sweep, equity-relevant liquidity/headroom metrics, scenarios, sensitivities, machine checks, and senior-review reporting.
-
-## Hard Contract
-
-- Default model-build artifact level is `banker_formula_workbook`; use the formula workbook path for normal public-equity 3-statement build, refresh, forecast, and operating-model-package requests.
-- Formula mode is executed through `scripts/build_banker_formula_workbook.py` and must be labeled `banker_formula_workbook` only when its run log, workbook inspection, and `model_citations.json` prove the formula builder ran successfully.
-- Deterministic scripts remain available for controlled computed values, smoke tests, or explicit lightweight support exports. They produce `output/model.xlsx`, `output/plan.json`, `output/run_log.json`, `output/manifest.json`, and optionally `output/support_note.md`. Legacy `output/report.md` is written only with `--write-report-md`.
-- Formula scripts produce `banker_formula_workbook.xlsx`, `banker_formula_workbook_run_log.json`, `model_citations.json`, and `manifest.json` in the selected output directory. The workbook is the hero deliverable; citation JSON, run logs, and manifests are support artifacts.
-- `output/model.xlsx` must start with a `Cover` tab that functions as a dashboard: model status, scenario outputs, revenue/EBITDA/FCF/cash, liquidity trough, leverage/headroom, source posture, warnings/hard failures, workbook map.
-- `run_log.json` and `manifest.json` are required reliability artifacts; hard failures force `not-decision-ready`.
-- Never delete, overwrite, or mutate source data unless explicitly requested. Preserve raw tabs/files and write new outputs under `output/` or a clearly named copy.
-- Material inputs must be evidence-labeled: `source_reported`, `company_provided`, `connector_sourced`, `public_filing`, `web_verified`, `management_guidance`, `analyst_estimate`, `benchmark`, `assumption`, or `placeholder`.
-- If placeholders, stale data, unsupported assumptions, or failed tie-outs remain active, mark the model no higher than `screen-grade`; hard failures mean `not-decision-ready`.
-
-## Routing
-
-Use this skill when the deliverable is a 3-statement build, refresh, rerun, operating model package, or integrated forecast rebuild. Route away when the primary ask is workbook audit/debug (`model-audit-tieout`), source hierarchy (`financial-source-of-truth`), raw financial normalization (`financials-normalizer`), spreadsheet cleanup (`excel-data-cleaner`), valuation (`dcf-model-builder`, `comps-valuation`), public estimate update (`equity-model-update`), Credit Markets research, or memo/deck polish.
-
-For complex builds, split the work into source/evidence, historical normalization, operating drivers, balance sheet and cash, QA, and executive output workstreams. If sub-agents are unavailable, emulate those workstreams and reconcile them into one plan before running scripts.
-
-## When To Invoke Support
-
-Load `shared/support-layer-routing-contract.md` when support services are needed. Use `financial-source-of-truth` for filings, releases, guidance, consensus, market data, and assumption labels; use `financials-normalizer` before building the plan from messy financials, KPI schedules, segment tables, share count, net debt, capital allocation, or provider exports; use `excel-data-cleaner` before relying on malformed workbook tables; use `model-audit-tieout` for workbook logic, external links, recalc/cache posture, and final tie-out. Support artifacts stay secondary to the formula workbook or owning dashboard/report.
-
-## Input Handling
-
-- Preferred input is a `plan.json` matching the schema.
-- No context: run `assets/plan_template.json` as illustrative, label placeholders, and cap status at `screen-grade`.
-- Partial context: preserve user facts, retain template defaults only when necessary, and label unsupported inputs.
-- Full financial package: normalize IS/BS/CF, reconcile the balance sheet, preserve source data, fill the plan, validate, run, then report checks and caveats.
-
-## Required Workflow
-
-1. Build or receive a valid `plan.json`.
-2. Validate with `python3 scripts/validate_plan.py path/to/plan.json`.
-3. Fix validation errors before running; do not silently default conclusion-changing assumptions.
-4. Default path: run `python3 scripts/build_banker_formula_workbook.py path/to/plan.json --output-dir output` and inspect `banker_formula_workbook_run_log.json`.
-5. Use `python3 scripts/run_pipeline.py path/to/plan.json` only for controlled computed values, smoke tests, explicit lightweight support exports, or when formula mode fails and the fallback is clearly labeled `deterministic_export`.
-6. Verify the relevant workbook, run log, manifest, and, in formula mode, `model_citations.json`.
-7. Read the run log; hard failures require `not-decision-ready`.
-8. Present the workbook as the hero artifact, with the `Cover` tab carrying the decision read-through, and link the normalized plan, run log, manifest, citation ledger, and optional support note as audit files.
-
-Smoke test:
-
-```bash
-python3 scripts/validate_plan.py assets/plan_template.json
-python3 scripts/build_banker_formula_workbook.py assets/plan_template.json --output-dir /private/tmp/public-equity-investing-3s-formula
-python3 scripts/run_pipeline.py assets/plan_template.json
-```
-
-## Formula Mode Guardrail
-
-Load `references/banker-formula-workbook-contract.md` before using formula mode. If the formula builder fails, the template is missing, required tabs/formulas/styles are not present, external links appear, or `model_citations.json` is absent, do not describe any fallback artifact as a banker formula workbook.
-
-## Status Labels
-
-Use exactly: `decision-grade`, `senior-review-ready`, `screen-grade`, `not-decision-ready`, `blocked`. Use `blocked` only when no reasonable run can be produced before execution.
-
-## Senior Standard
-
-Explain what drives the forecast, what must be true, where cash is consumed, what breaks first in downside, whether EBITDA converts to cash, and which assumptions deserve diligence. Balance-sheet integrity, retained earnings, cash flow linkage, debt roll-forward, PP&E roll-forward, and working-capital roll-forward are non-negotiable.
-
-## Deferred Reference Router
-
-Load `references/reference-router.md` only when deeper schema, math, QA, integration, industry, or forecast judgment guidance is needed. The router preserves the detailed reference map without loading every reference during invocation.
-
-## Equity Valuation PM Standard
-
-Load `shared/equity-valuation-pm-standard.md` and `shared/pm-judgment-heuristics.md` for substantial model, valuation, scenario, model-update, or audit work.
-
-The output must state what the current stock price implies, the variant estimate path, whether upside is driven by fundamentals, multiple expansion, mix, capital return, sentiment, or event probability, what breaks first in downside, what changes target, rating, sizing, hedge, trim, exit, or watchlist status, and what evidence is missing.
-
-Keep equity valuation as the center of gravity. Debt is allowed only as an input to common-equity value through net debt, cost of debt, leverage, liquidity, refinancing risk, or downside equity impairment. Use Credit Markets for bond comps, loan comps, CDS, spread/yield relative value, covenant-package analysis, debt-security valuation, recovery waterfall, restructuring valuation, creditworthiness, private-credit / public-credit instrument underwriting, or distressed claim valuation.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/agents/openai.yaml b/plugins/financial-markets/skills/three-statement-model-builder/agents/openai.yaml
deleted file mode 100644
index 54b590c..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/agents/openai.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-interface:
-  display_name: Three Statement Model Builder
-  short_description: Builds formula-first public-equity 3-statement operating model workbooks with IS/BS/CF, working capital, PP&E, debt, cash sweep, checks, scenarios, and QA logs.
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/assets/plan_template.json b/plugins/financial-markets/skills/three-statement-model-builder/assets/plan_template.json
deleted file mode 100644
index 30a330a..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/assets/plan_template.json
+++ /dev/null
@@ -1,530 +0,0 @@
-{
-  "meta": {
-    "company_name": "Atlas Precision Components",
-    "industry": "industrials",
-    "currency": "USD",
-    "units": "USD_mm",
-    "as_of_date": "2026-05-11",
-    "accounting_basis": "us_gaap",
-    "model_purpose": "illustrative screen-grade operating forecast",
-    "prepared_for": "senior review smoke test"
-  },
-  "source_basis": [
-    {
-      "id": "src_hist_2025",
-      "label": "illustrative audited historical statements through FY2025",
-      "source_type": "template",
-      "as_of_date": "2025-12-31",
-      "evidence_label": "source_reported",
-      "covers": ["historicals", "income_statement", "balance_sheet", "cash_flow"],
-      "confidence": "medium",
-      "notes": "template data for smoke testing; replace with actual company financials before decision use"
-    },
-    {
-      "id": "src_mgmt_plan",
-      "label": "illustrative management operating plan assumptions",
-      "source_type": "template",
-      "as_of_date": "2026-04-30",
-      "evidence_label": "company_provided",
-      "covers": ["revenue", "costs", "working_capital", "ppe", "debt", "tax"],
-      "confidence": "medium",
-      "notes": "placeholder management-plan style assumptions for deterministic pipeline smoke testing"
-    },
-    {
-      "id": "src_placeholder",
-      "label": "template placeholder assumptions",
-      "source_type": "template",
-      "as_of_date": "2026-05-11",
-      "evidence_label": "placeholder",
-      "covers": ["scenarios", "sensitivities", "covenants"],
-      "confidence": "low",
-      "notes": "active placeholders force screen-grade status until replaced with sourced assumptions"
-    }
-  ],
-  "timeline": {
-    "start_period": "FY2026",
-    "start_year": 2026,
-    "horizon_periods": 5,
-    "periodicity": "annual"
-  },
-  "historicals": {
-    "income_statement": {
-      "FY2024": {
-        "revenue": 620.0,
-        "cogs": 390.0,
-        "opex": 130.0,
-        "da": 34.0,
-        "interest": 18.0,
-        "taxes": 12.0,
-        "net_income": 36.0
-      },
-      "FY2025": {
-        "revenue": 680.0,
-        "cogs": 421.6,
-        "opex": 142.8,
-        "da": 36.0,
-        "interest": 20.0,
-        "taxes": 15.9,
-        "net_income": 43.7
-      }
-    },
-    "balance_sheet": {
-      "FY2024": {
-        "cash": 28.0,
-        "ar": 82.0,
-        "inventory": 95.0,
-        "other_current_assets": 18.0,
-        "ppe_net": 260.0,
-        "other_assets": 55.0,
-        "ap": 60.0,
-        "accrued_expenses": 44.0,
-        "deferred_revenue": 8.0,
-        "debt": 300.0,
-        "other_liabilities": 40.0,
-        "common_equity": 50.0,
-        "retained_earnings": 36.0
-      },
-      "FY2025": {
-        "cash": 35.0,
-        "ar": 90.0,
-        "inventory": 102.0,
-        "other_current_assets": 20.0,
-        "ppe_net": 284.0,
-        "other_assets": 57.0,
-        "ap": 66.0,
-        "accrued_expenses": 48.0,
-        "deferred_revenue": 9.0,
-        "debt": 305.0,
-        "other_liabilities": 42.0,
-        "common_equity": 50.0,
-        "retained_earnings": 68.0
-      }
-    },
-    "cash_flow": {
-      "FY2025": {
-        "net_income": 43.7,
-        "da": 36.0,
-        "change_nwc": 6.0,
-        "cfo": 73.7,
-        "capex": 60.0,
-        "debt_draws": 15.0,
-        "debt_repayments": 10.0,
-        "dividends": 11.7,
-        "cash_change": 7.0,
-        "ending_cash": 35.0
-      }
-    },
-    "debt_schedule": {
-      "FY2025": {
-        "beginning_debt": 300.0,
-        "draws": 15.0,
-        "repayments": 10.0,
-        "ending_debt": 305.0
-      }
-    },
-    "ppe": {
-      "FY2025": {
-        "beginning_ppe": 260.0,
-        "capex": 60.0,
-        "depreciation": 36.0,
-        "disposals": 0.0,
-        "ending_ppe": 284.0
-      }
-    },
-    "working_capital": {
-      "FY2025": {
-        "ar": 90.0,
-        "inventory": 102.0,
-        "other_current_assets": 20.0,
-        "ap": 66.0,
-        "accrued_expenses": 48.0,
-        "deferred_revenue": 9.0,
-        "nwc": 89.0
-      }
-    }
-  },
-  "revenue": {
-    "model": "segments",
-    "source_id": "src_mgmt_plan",
-    "segments": {
-      "core_components": {
-        "base_revenue": 470.0,
-        "growth_rates": {
-          "FY2026": 0.08,
-          "FY2027": 0.075,
-          "FY2028": 0.065,
-          "FY2029": 0.055,
-          "FY2030": 0.045
-        },
-        "evidence_label": "company_provided",
-        "source_id": "src_mgmt_plan"
-      },
-      "aftermarket_services": {
-        "base_revenue": 210.0,
-        "growth_rates": {
-          "FY2026": 0.115,
-          "FY2027": 0.105,
-          "FY2028": 0.095,
-          "FY2029": 0.08,
-          "FY2030": 0.065
-        },
-        "evidence_label": "company_provided",
-        "source_id": "src_mgmt_plan"
-      }
-    }
-  },
-  "costs": {
-    "source_id": "src_mgmt_plan",
-    "cogs": {
-      "method": "gross_margin",
-      "gross_margin": {
-        "FY2026": 0.385,
-        "FY2027": 0.392,
-        "FY2028": 0.398,
-        "FY2029": 0.402,
-        "FY2030": 0.405
-      },
-      "evidence_label": "company_provided",
-      "source_id": "src_mgmt_plan"
-    },
-    "opex": {
-      "method": "pct_revenue",
-      "pct_revenue": {
-        "FY2026": 0.205,
-        "FY2027": 0.200,
-        "FY2028": 0.195,
-        "FY2029": 0.190,
-        "FY2030": 0.188
-      },
-      "evidence_label": "company_provided",
-      "source_id": "src_mgmt_plan"
-    },
-    "payroll": {
-      "enabled": false
-    },
-    "other_costs": {}
-  },
-  "working_capital": {
-    "method": "days",
-    "source_id": "src_mgmt_plan",
-    "evidence_label": "company_provided",
-    "ar_days": {
-      "FY2026": 48.0,
-      "FY2027": 47.0,
-      "FY2028": 46.0,
-      "FY2029": 46.0,
-      "FY2030": 45.0
-    },
-    "inventory_days": {
-      "FY2026": 86.0,
-      "FY2027": 84.0,
-      "FY2028": 82.0,
-      "FY2029": 80.0,
-      "FY2030": 78.0
-    },
-    "ap_days": {
-      "FY2026": 57.0,
-      "FY2027": 58.0,
-      "FY2028": 59.0,
-      "FY2029": 60.0,
-      "FY2030": 60.0
-    },
-    "other_current_assets_pct_revenue": {
-      "FY2026": 0.030,
-      "FY2027": 0.030,
-      "FY2028": 0.029,
-      "FY2029": 0.029,
-      "FY2030": 0.028
-    },
-    "accrued_expenses_pct_revenue": {
-      "FY2026": 0.070,
-      "FY2027": 0.070,
-      "FY2028": 0.069,
-      "FY2029": 0.068,
-      "FY2030": 0.067
-    },
-    "deferred_revenue_pct_revenue": {
-      "FY2026": 0.014,
-      "FY2027": 0.014,
-      "FY2028": 0.014,
-      "FY2029": 0.013,
-      "FY2030": 0.013
-    }
-  },
-  "ppe": {
-    "source_id": "src_mgmt_plan",
-    "evidence_label": "company_provided",
-    "capex_method": "pct_revenue",
-    "capex_pct_revenue": {
-      "FY2026": 0.080,
-      "FY2027": 0.078,
-      "FY2028": 0.075,
-      "FY2029": 0.072,
-      "FY2030": 0.070
-    },
-    "depreciation_method": "pct_beginning_ppe",
-    "depreciation_pct_beginning_ppe": {
-      "FY2026": 0.135,
-      "FY2027": 0.135,
-      "FY2028": 0.132,
-      "FY2029": 0.130,
-      "FY2030": 0.128
-    },
-    "disposals": {}
-  },
-  "debt": {
-    "source_id": "src_mgmt_plan",
-    "evidence_label": "company_provided",
-    "beginning_debt": 305.0,
-    "beginning_revolver_drawn": 0.0,
-    "revolver_commitment": 75.0,
-    "mandatory_amortization": {
-      "FY2026": 8.0,
-      "FY2027": 10.0,
-      "FY2028": 12.0,
-      "FY2029": 14.0,
-      "FY2030": 16.0
-    },
-    "optional_draws": {},
-    "interest_rate": {
-      "FY2026": 0.075,
-      "FY2027": 0.073,
-      "FY2028": 0.071,
-      "FY2029": 0.070,
-      "FY2030": 0.070
-    },
-    "cash_sweep": {
-      "enabled": true,
-      "min_cash": 30.0,
-      "sweep_pct": 0.75
-    },
-    "covenants": {
-      "min_liquidity": 25.0,
-      "max_net_leverage": 3.75,
-      "min_interest_coverage": 2.0
-    }
-  },
-  "tax": {
-    "source_id": "src_mgmt_plan",
-    "evidence_label": "company_provided",
-    "book_tax_rate": 0.26,
-    "cash_tax_rate": 0.22,
-    "nol_balance": 0.0
-  },
-  "equity": {
-    "source_id": "src_mgmt_plan",
-    "evidence_label": "company_provided",
-    "common_equity": 50.0,
-    "dividends": {
-      "FY2026": 12.0,
-      "FY2027": 13.0,
-      "FY2028": 14.0,
-      "FY2029": 15.0,
-      "FY2030": 16.0
-    },
-    "buybacks": {},
-    "issuance": {}
-  },
-  "other_balance_sheet": {
-    "other_assets": 57.0,
-    "other_liabilities": 42.0
-  },
-  "scenarios": {
-    "base": {
-      "description": "management-plan style base case with modest operating leverage",
-      "evidence_label": "company_provided",
-      "overrides": {}
-    },
-    "downside": {
-      "description": "slower revenue, margin pressure, higher working capital drag, less debt paydown",
-      "evidence_label": "placeholder",
-      "overrides": {
-        "revenue": {
-          "segments": {
-            "core_components": {
-              "growth_rates": {
-                "FY2026": 0.035,
-                "FY2027": 0.030,
-                "FY2028": 0.025,
-                "FY2029": 0.020,
-                "FY2030": 0.020
-              }
-            },
-            "aftermarket_services": {
-              "growth_rates": {
-                "FY2026": 0.055,
-                "FY2027": 0.050,
-                "FY2028": 0.045,
-                "FY2029": 0.040,
-                "FY2030": 0.035
-              }
-            }
-          }
-        },
-        "costs": {
-          "cogs": {
-            "gross_margin": {
-              "FY2026": 0.365,
-              "FY2027": 0.365,
-              "FY2028": 0.368,
-              "FY2029": 0.370,
-              "FY2030": 0.372
-            }
-          },
-          "opex": {
-            "pct_revenue": {
-              "FY2026": 0.215,
-              "FY2027": 0.215,
-              "FY2028": 0.212,
-              "FY2029": 0.210,
-              "FY2030": 0.208
-            }
-          }
-        },
-        "working_capital": {
-          "ar_days": {
-            "FY2026": 52.0,
-            "FY2027": 53.0,
-            "FY2028": 53.0,
-            "FY2029": 52.0,
-            "FY2030": 52.0
-          },
-          "inventory_days": {
-            "FY2026": 92.0,
-            "FY2027": 92.0,
-            "FY2028": 90.0,
-            "FY2029": 90.0,
-            "FY2030": 88.0
-          },
-          "ap_days": {
-            "FY2026": 55.0,
-            "FY2027": 55.0,
-            "FY2028": 55.0,
-            "FY2029": 56.0,
-            "FY2030": 56.0
-          }
-        },
-        "ppe": {
-          "capex_pct_revenue": {
-            "FY2026": 0.082,
-            "FY2027": 0.082,
-            "FY2028": 0.080,
-            "FY2029": 0.078,
-            "FY2030": 0.076
-          }
-        },
-        "debt": {
-          "interest_rate": {
-            "FY2026": 0.083,
-            "FY2027": 0.083,
-            "FY2028": 0.082,
-            "FY2029": 0.081,
-            "FY2030": 0.080
-          },
-          "cash_sweep": {
-            "sweep_pct": 0.50
-          }
-        }
-      }
-    },
-    "upside": {
-      "description": "stronger services growth, better gross margin, modest working-capital improvement",
-      "evidence_label": "placeholder",
-      "overrides": {
-        "revenue": {
-          "segments": {
-            "core_components": {
-              "growth_rates": {
-                "FY2026": 0.105,
-                "FY2027": 0.095,
-                "FY2028": 0.080,
-                "FY2029": 0.065,
-                "FY2030": 0.055
-              }
-            },
-            "aftermarket_services": {
-              "growth_rates": {
-                "FY2026": 0.145,
-                "FY2027": 0.130,
-                "FY2028": 0.115,
-                "FY2029": 0.095,
-                "FY2030": 0.080
-              }
-            }
-          }
-        },
-        "costs": {
-          "cogs": {
-            "gross_margin": {
-              "FY2026": 0.395,
-              "FY2027": 0.405,
-              "FY2028": 0.412,
-              "FY2029": 0.417,
-              "FY2030": 0.420
-            }
-          },
-          "opex": {
-            "pct_revenue": {
-              "FY2026": 0.200,
-              "FY2027": 0.194,
-              "FY2028": 0.188,
-              "FY2029": 0.184,
-              "FY2030": 0.180
-            }
-          }
-        },
-        "working_capital": {
-          "ar_days": {
-            "FY2026": 46.0,
-            "FY2027": 45.0,
-            "FY2028": 44.0,
-            "FY2029": 44.0,
-            "FY2030": 43.0
-          },
-          "inventory_days": {
-            "FY2026": 82.0,
-            "FY2027": 80.0,
-            "FY2028": 78.0,
-            "FY2029": 76.0,
-            "FY2030": 74.0
-          },
-          "ap_days": {
-            "FY2026": 58.0,
-            "FY2027": 60.0,
-            "FY2028": 61.0,
-            "FY2029": 62.0,
-            "FY2030": 62.0
-          }
-        },
-        "ppe": {
-          "capex_pct_revenue": {
-            "FY2026": 0.078,
-            "FY2027": 0.075,
-            "FY2028": 0.072,
-            "FY2029": 0.070,
-            "FY2030": 0.068
-          }
-        },
-        "debt": {
-          "interest_rate": {
-            "FY2026": 0.072,
-            "FY2027": 0.070,
-            "FY2028": 0.069,
-            "FY2029": 0.068,
-            "FY2030": 0.068
-          },
-          "cash_sweep": {
-            "sweep_pct": 0.90
-          }
-        }
-      }
-    }
-  },
-  "sensitivities": {
-    "revenue_growth_shocks": [-0.05, 0.05],
-    "gross_margin_shocks": [-0.02, 0.02],
-    "dso_day_shocks": [5.0, -5.0],
-    "capex_pct_revenue_shocks": [0.01, -0.01],
-    "interest_rate_shocks": [0.01, -0.01]
-  }
-}
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/assets/templates/banker_formula_workbook_template.xlsx b/plugins/financial-markets/skills/three-statement-model-builder/assets/templates/banker_formula_workbook_template.xlsx
deleted file mode 100644
index 743f036..0000000
Binary files a/plugins/financial-markets/skills/three-statement-model-builder/assets/templates/banker_formula_workbook_template.xlsx and /dev/null differ
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/banker-formula-workbook-contract.md b/plugins/financial-markets/skills/three-statement-model-builder/references/banker-formula-workbook-contract.md
deleted file mode 100644
index e816856..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/banker-formula-workbook-contract.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Banker Formula Workbook Contract
-
-Formula mode is the default user-facing model-build path. It materializes `assets/templates/banker_formula_workbook_template.xlsx`, preserves the template's formulas, tabs, styles, and checks, then writes plan-derived inputs, source labels, scenario controls, and Public Equity Investing posture warnings.
-
-Run with:
-
-```bash
-python3 scripts/build_banker_formula_workbook.py path/to/plan.json --output-dir output
-```
-
-Formula-mode outputs:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-The workbook is the human deliverable. The run log, manifest, and citation ledger are support/audit artifacts. The JSON artifacts are not the user-facing deliverable unless the user asks for the audit files directly. `model_citations.json` must validate against the Public Equity Investing model-citation schema before dashboard handoff.
-
-## Truthfulness Gate
-
-Only call an artifact `banker_formula_workbook` when all of the following are true:
-
-- the formula builder ran;
-- `banker_formula_workbook.xlsx` exists;
-- required sheets are present;
-- formula count clears the model-specific inspection threshold;
-- required formula-bearing sheets are populated;
-- `Cover` is the first visible sheet;
-- named ranges or `model_citations.json` provide a stable source-to-cell anchor map;
-- styles are preserved;
-- no external workbook links are present;
-- the formula run log says `workbook_mode: "banker_formula_workbook"`;
-- `model_citations.json` exists with sheet/cell-level provenance for key outputs.
-
-If any gate fails, do not label the deterministic export or partial output as a banker formula workbook. Fall back honestly to `deterministic_export`, or mark the formula-mode run `not-decision-ready`.
-
-## Required Workbook Shape
-
-Required sheets:
-
-- `Cover`
-- `Executive Summary`
-- `Control Panel`
-- `Historical Financials`
-- `Revenue Build`
-- `Expense Build`
-- `Income Statement`
-- `Working Capital`
-- `PP&E D&A`
-- `Debt Interest`
-- `Tax`
-- `Balance Sheet`
-- `Cash Flow Statement`
-- `Scenarios`
-- `Checks`
-- `Source Notes`
-
-The `Cover` sheet must remain first and must carry public-company operating-model context: issuer/ticker when available, forecast date, source posture, placeholder warnings, model mode, final-period outputs, liquidity/debt posture, checks, and workbook map.
-
-## Source-To-Cell Provenance
-
-`model_citations.json` is the handoff from workbook generation to dashboards. Each record should include workbook path, sheet, cell or range, value when known, formula where available, scenario/case, line item, section, source ID, evidence label, source label, source date/freshness, and optional URL.
-
-Dashboards may cite model cells such as `Income Statement!I6`, `Balance Sheet!I22`, or `Checks!C6` instead of generic "model output." Keep the citation ledger as a support artifact; hero the workbook or rendered dashboard for the end user.
-
-## Limitations
-
-Formula mode preserves and populates the bundled three-statement template. It does not synthesize arbitrary new tabs, evaluate Excel formulas in Python, or guarantee every user-specific model architecture. Use formula mode by default for user-facing model builds; use deterministic mode only for controlled computed values, smoke tests, explicit lightweight support exports, or honest fallback when formula mode is unavailable.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/dashboard-map.md b/plugins/financial-markets/skills/three-statement-model-builder/references/dashboard-map.md
deleted file mode 100644
index 4c0c79a..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/dashboard-map.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Dashboard Map: Three-Statement Model Builder
-
-Use `dashboard-builder` for operating-model cockpit views across revenue, margins, cash flow, balance sheet, liquidity, scenarios, and checks.
-
-## Decision Question
-
-Is the forecast internally consistent, financeable, and useful for the public-company investment decision under base and downside cases?
-
-## Recommended Payload
-
-- `kind`: `public_equity_investing_dashboard.v1`
-- `mode`: `three_statement_model_builder`
-- `metadata.citation_policy`: `strict`
-- `model_citations_path`: `model_citations.json` from formula mode when citing workbook outputs
-- `sources`: filings, release/transcript, consensus/market data, source notes, and model citation records
-
-## Recommended Tabs And Modules
-
-1. Operating Read: `decision_box`, `metric_tiles`.
-2. Income Statement: `table`, `bar_chart`.
-3. Cash Flow And Liquidity: `table`, `scenario_map`.
-4. Balance Sheet And Debt: `table`, `cards`.
-5. Checks And Sources: `source_list`, `missing_evidence`, `table`.
-
-## Required Evidence
-
-Model-backed dashboard claims should cite workbook-cell records such as `Income Statement!I6`, `Balance Sheet!I22`, and `Checks!C6` through `model_citations_path`.
-
-## Do Not
-
-- Do not hide balance sheet or cash flow check breaks.
-- Do not present unbalanced or placeholder-driven outputs as decision-grade.
-- Do not make run logs or citation JSON the lead artifact.
-
-
-## Equity Forecast PM Modules
-
-Dashboards should surface PM decision box, current price context, estimate path, revenue/margin/EPS/FCF bridges, scenario skew, what breaks first in downside, action rules, source posture, and missing evidence.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/forecast-judgment-guide.md b/plugins/financial-markets/skills/three-statement-model-builder/references/forecast-judgment-guide.md
deleted file mode 100644
index 6d898ab..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/forecast-judgment-guide.md
+++ /dev/null
@@ -1,250 +0,0 @@
-# Forecast Judgment Guide
-
-Use this reference to make forecast assumptions CFO-grade and Portfolio Manager-grade.
-
-## Senior judgment standard
-
-A strong 3-statement model does not just project numbers. It explains the business engine, tests whether assumptions are plausible, and shows how decisions affect liquidity, profitability, and value.
-
-When setting or reviewing assumptions, ask:
-
-1. What actually drives revenue?
-2. Which costs are fixed, variable, semi-variable, or discretionary?
-3. What operating leverage is plausible?
-4. What working capital investment is required to support growth?
-5. What capex is required to sustain or expand the business?
-6. What financing constraints or covenants matter?
-7. What assumptions would a skeptical board member, lender, or PM challenge first?
-8. What is the downside path, not just the upside story?
-
-## Assumption hierarchy
-
-Prefer assumptions in this order:
-
-1. User-provided facts and source documents
-2. Historical company data after normalization
-3. Management guidance or budget, clearly labeled
-4. Contractual data such as debt terms, leases, backlog, pricing, or customer agreements
-5. Operating KPIs and unit economics
-6. External benchmarks or comparable-company logic, cited when available
-7. Modeler assumption, clearly flagged for review
-
-Do not invent facts. If a key input is missing, create a placeholder and label it as requiring review.
-
-## Historical analysis before forecasting
-
-Before forecasting, calculate historical diagnostics where data is available:
-
-- revenue growth by period
-- gross margin
-- EBITDA margin
-- operating expense ratios
-- headcount productivity, if applicable
-- DSO, DIO, DPO, and cash conversion cycle
-- capex as percent of revenue
-- D&A as percent of PP&E or capex
-- effective tax rate
-- interest rate on average debt
-- cash conversion and free cash flow conversion
-- leverage and coverage ratios
-
-Use these diagnostics to challenge forecast assumptions.
-
-## Revenue forecast judgment
-
-Choose the revenue method that fits the business model.
-
-Common methods:
-
-- price x volume
-- customers x ARPU
-- ARR / subscription base x retention x new bookings
-- GMV x take rate
-- transactions x fee per transaction
-- backlog conversion
-- same-store sales and new store openings
-- utilization x bill rate x billable headcount
-- production volume x average selling price
-- project pipeline conversion
-
-Avoid lazy revenue forecasting when driver data exists. A percentage-growth revenue row can be acceptable for a high-level model, but it should be tied to historical trend, management case, market logic, or a clear scenario narrative.
-
-Challenge revenue assumptions for:
-
-- customer concentration
-- churn or retention
-- pricing power
-- sales capacity
-- implementation lag
-- backlog quality
-- market size
-- cyclicality
-- FX exposure
-- seasonality
-- one-time revenue
-- channel constraints
-
-## Gross margin and COGS judgment
-
-Gross margin should reflect how the business earns money.
-
-Consider:
-
-- product mix
-- labor productivity
-- input costs
-- hosting / infrastructure costs
-- fulfillment / logistics
-- warranty / returns
-- utilization
-- volume leverage
-- pricing pass-through
-- inventory reserves
-- contract margin profile
-
-Do not assume margin expansion solely because revenue grows. Explain why scale benefits, mix shift, pricing, automation, or procurement improvements support the change.
-
-## Opex judgment
-
-Operating expense forecasts should reflect organizational design, not just percentages.
-
-Consider:
-
-- headcount by department
-- compensation, benefits, bonus, and payroll taxes
-- sales capacity and quota coverage
-- R&D roadmap
-- G&A infrastructure required for scale
-- public-company costs, if relevant
-- systems, software, facilities, insurance, and professional fees
-- one-time restructuring or integration costs
-
-For high-growth companies, opex often precedes revenue. For mature companies, opex may scale more slowly but still needs inflation, hiring, and capability assumptions.
-
-## Working capital judgment
-
-Working capital is often where models hide unrealistic growth.
-
-Use the right driver by account:
-
-- accounts receivable: DSO or revenue timing
-- inventory: DIO, turns, production plan, or SKU logic
-- accounts payable: DPO or COGS / inventory purchasing base
-- deferred revenue: billings, bookings, or subscription terms
-- accrued expenses: opex or payroll timing
-- other current assets / liabilities: specific operating logic or stable percentage base
-
-Challenge:
-
-- growth without receivables investment
-- inventory reduction while sales accelerate
-- payables extension beyond vendor reality
-- negative working capital assumptions without a clear business model reason
-- cash conversion cycle changes with no explanation
-
-## Capex and D&A judgment
-
-Capex should support the operating plan.
-
-Consider:
-
-- maintenance vs growth capex
-- store, facility, plant, or equipment rollout
-- software development capitalization
-- capacity utilization
-- replacement cycle
-- leasehold improvements
-- technology infrastructure
-- regulatory or safety capex
-
-D&A should follow the asset base and useful lives. Do not let D&A drift disconnected from PP&E or capex unless immaterial and clearly simplified.
-
-## Debt, interest, and liquidity judgment
-
-For debt and liquidity, model both economics and constraints.
-
-Include where relevant:
-
-- debt tranches
-- amortization
-- maturities
-- cash sweep
-- revolver draws and repayments
-- minimum cash
-- interest rates, floors, spreads, and PIK interest
-- fees
-- covenants
-- restricted cash
-- dividends or distributions
-
-Under stress, cash should not behave magically. Debt should not go negative unless explicitly allowed. Liquidity pressure should show up through cash balance, revolver usage, covenant headroom, or funding need.
-
-## Tax judgment
-
-Tax assumptions should consider:
-
-- pre-tax income base
-- losses and NOLs
-- valuation allowances
-- cash vs book taxes
-- jurisdictional mix
-- interest deductibility limits
-- deferred taxes if material
-- one-time tax items
-
-Do not blindly apply a tax rate to negative pre-tax income without checking whether tax benefit recognition is reasonable.
-
-## Scenario design
-
-Scenarios must tell economically coherent stories.
-
-### Base case
-
-The most supportable plan based on current data, management assumptions, historical performance, and reasonable execution.
-
-### Downside case
-
-A credible stress case. It should usually combine more than one pressure point, such as slower revenue, margin compression, working capital drag, and financing pressure.
-
-### Upside case
-
-A plausible outperform case. It should include the investments or constraints required to achieve it, not just better revenue and margins.
-
-### Lender case
-
-Often more conservative than management case. Focus on cash generation, covenant headroom, debt capacity, and downside protection.
-
-### Investment case
-
-Should explicitly bridge from base assumptions to the investment thesis and identify what must go right.
-
-## Assumption challenge checklist
-
-For each material assumption, ask:
-
-- Is this supported by historical data?
-- Is the magnitude plausible?
-- Is the timing plausible?
-- Does the assumption conflict with another schedule?
-- Does the model explain why this changes?
-- What happens if this assumption is wrong?
-- Is this an input, calculated output, or hidden plug?
-
-## Red flags
-
-Flag these as senior-review issues:
-
-- hockey-stick revenue with no driver
-- margin expansion without operating rationale
-- working capital release while revenue grows rapidly
-- capex too low to support growth
-- debt paydown despite weak cash generation
-- taxes that improve economics without support
-- EBITDA growth not converting to cash
-- scenario labels that do not change calculations
-- outputs moving in the wrong direction under stress
-- assumptions buried in formulas
-
-## Equity PM Forecast Overlay
-
-For Public Equity Investing, the three-statement model is an equity forecast engine. Prioritize revenue/KPI build, margin bridge, EPS revision path, FCF conversion, cash burn as common-equity risk, working-capital surprises, share count, buybacks, SBC, estimate sensitivity, and handoff to DCF/comps. Covenant and debt-stack detail is included only when it changes common-equity downside; primary covenant, maturity wall, debt-stack, spread/yield, or recovery analysis routes to Credit Markets.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/industry-playbooks.md b/plugins/financial-markets/skills/three-statement-model-builder/references/industry-playbooks.md
deleted file mode 100644
index 15b32d0..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/industry-playbooks.md
+++ /dev/null
@@ -1,274 +0,0 @@
-# Industry and Business Model Playbooks
-
-Use this reference to adapt the 3-statement model to the company's business model. Do not force a generic revenue-growth model when the economics require driver logic.
-
-## SaaS and subscription software
-
-Core drivers:
-
-- ARR / MRR
-- new bookings
-- churn / retention
-- expansion / contraction
-- customer count
-- ARPA / ARPU
-- billings and deferred revenue
-- CAC, sales capacity, and payback
-- gross margin by hosting, support, and services mix
-
-Important schedules:
-
-- ARR bridge
-- deferred revenue
-- sales and marketing productivity
-- headcount by function
-- hosting / infrastructure cost
-- capitalized software, if material
-
-Senior challenges:
-
-- Does revenue recognition align with billings?
-- Are churn and expansion assumptions credible?
-- Does sales capacity support bookings?
-- Does deferred revenue drive cash timing correctly?
-- Is CAC efficiency plausible?
-
-## Marketplace / platform
-
-Core drivers:
-
-- GMV
-- take rate
-- transaction volume
-- buyer / seller count
-- attach rates
-- payments, fulfillment, or platform costs
-- incentives, credits, or subsidies
-
-Important schedules:
-
-- GMV build
-- take-rate bridge
-- transaction economics
-- variable cost stack
-- working capital / settlement timing
-
-Senior challenges:
-
-- Does revenue reflect gross vs net presentation correctly?
-- Are incentives treated consistently?
-- Does volume growth require marketing or working capital investment?
-- Does take-rate expansion have a credible reason?
-
-## Ecommerce / retail
-
-Core drivers:
-
-- traffic
-- conversion rate
-- average order value
-- units sold
-- same-store sales
-- new store openings
-- store productivity
-- inventory turns
-- return rate
-- fulfillment and shipping costs
-
-Important schedules:
-
-- store / channel build
-- inventory and turns
-- gross margin bridge
-- fulfillment cost
-- marketing spend
-- lease / rent schedule if material
-
-Senior challenges:
-
-- Does inventory investment support sales growth?
-- Are returns and discounts reflected?
-- Do gross margin assumptions consider mix and shipping?
-- Does store opening capex match growth?
-
-## Manufacturing / industrial
-
-Core drivers:
-
-- units produced and sold
-- average selling price
-- utilization
-- backlog
-- materials cost
-- labor cost
-- overhead absorption
-- maintenance and growth capex
-- inventory and receivables cycles
-
-Important schedules:
-
-- volume and price build
-- plant / capacity schedule
-- COGS by material, labor, overhead
-- inventory schedule
-- capex and depreciation
-
-Senior challenges:
-
-- Can capacity support forecast volume?
-- Are input cost changes reflected in margins?
-- Does working capital grow with production?
-- Is maintenance capex sufficient?
-
-## Professional services / consulting
-
-Core drivers:
-
-- billable headcount
-- utilization
-- bill rate
-- project backlog
-- revenue per professional
-- subcontractor cost
-- compensation and bonus
-
-Important schedules:
-
-- headcount by level
-- utilization and bill rate
-- revenue per FTE
-- compensation model
-- AR / DSO schedule
-
-Senior challenges:
-
-- Does hiring precede revenue appropriately?
-- Are utilization and rates realistic together?
-- Does compensation scale with headcount and revenue?
-- Does cash collection lag revenue?
-
-## Consumer / media / advertising
-
-Core drivers:
-
-- users / subscribers
-- engagement
-- impressions
-- CPM / ARPU
-- churn
-- content costs
-- acquisition spend
-
-Important schedules:
-
-- user cohort or subscriber bridge
-- advertising yield
-- content / programming cost
-- marketing efficiency
-
-Senior challenges:
-
-- Are user growth and monetization assumptions compatible?
-- Is content investment sufficient to support retention?
-- Does marketing efficiency deteriorate at scale?
-
-## Healthcare / biotech
-
-Core drivers:
-
-- pipeline milestones
-- R&D spend
-- clinical trial timing
-- regulatory events
-- launch assumptions
-- reimbursement / pricing
-- manufacturing or COGS
-
-Important schedules:
-
-- R&D program spend
-- milestone timing
-- cash runway
-- financing needs
-- launch ramp if commercial
-
-Senior challenges:
-
-- Is cash runway modeled realistically?
-- Are milestone and financing assumptions explicit?
-- Are regulatory risks reflected in scenario cases?
-- Is revenue too certain before approval?
-
-## Real estate / project finance
-
-Core drivers:
-
-- occupancy
-- rent / rate
-- development cost
-- construction draw schedule
-- debt draw and amortization
-- interest capitalization
-- lease-up timing
-- operating expenses
-
-Important schedules:
-
-- development budget
-- construction draw
-- debt and interest reserve
-- lease-up
-- NOI
-- waterfall, if applicable
-
-Senior challenges:
-
-- Does debt fund construction in the right order?
-- Is interest capitalized or cash paid correctly?
-- Does lease-up timing match cash burn?
-- Are reserves and restricted cash modeled?
-
-## Financial institutions
-
-Banks, insurers, lenders, and asset managers often require specialized models. Do not force an industrial 3-statement format when regulatory capital, loan balances, deposits, premiums, claims, AUM, or fair value marks drive the business.
-
-For these companies, adapt the model around:
-
-- earning assets and liabilities
-- net interest margin or fee income
-- credit losses or claims
-- capital ratios
-- AUM / flows
-- regulatory or solvency constraints
-- cash flow presentation appropriate to the business
-
-Clearly state when a specialized financial-institution model is required.
-
-## Turnaround / distressed company
-
-Core drivers:
-
-- liquidity runway
-- weekly or monthly cash flows
-- vendor terms
-- payroll timing
-- revolver availability
-- covenant compliance
-- restructuring costs
-- asset sales
-- cash preservation measures
-
-Important schedules:
-
-- 13-week or monthly cash flow
-- liquidity bridge
-- debt and covenant schedule
-- restructuring cost schedule
-- vendor / AP aging if available
-
-Senior challenges:
-
-- Does the model show actual liquidity stress?
-- Are cash disbursements timed correctly?
-- Are covenant and borrowing-base constraints included?
-- Are turnaround savings credible and phased?
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/integrity-controls.md b/plugins/financial-markets/skills/three-statement-model-builder/references/integrity-controls.md
deleted file mode 100644
index 961d605..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/integrity-controls.md
+++ /dev/null
@@ -1,203 +0,0 @@
-# Integrity Controls and QA Reference
-
-Use this reference to prove that the 3-statement model works mechanically and behaves sensibly.
-
-## Required control philosophy
-
-A model is not complete because formulas calculate. It is complete only when the checks prove the statements tie, the scenario engine works, and the outputs behave logically under pressure.
-
-Every material control should show:
-
-- pass / fail status
-- variance amount
-- affected period
-- likely source of issue when possible
-
-## Required mechanical checks
-
-Include these checks for every 3-statement model unless clearly irrelevant.
-
-### Balance sheet balance
-
-Check total assets minus total liabilities and equity for every period. Any non-zero balance is a blocker unless immaterial due to rounding and documented.
-
-### Cash tie-out
-
-Check cash flow ending cash equals balance sheet cash in every period.
-
-### Retained earnings rollforward
-
-Beginning retained earnings plus net income minus dividends and distributions equals ending retained earnings.
-
-### Working capital tie
-
-Changes in operating balance sheet accounts reconcile to the cash flow statement.
-
-Typical checks:
-
-- AR change ties to working capital schedule
-- inventory change ties to working capital schedule
-- AP change ties to working capital schedule
-- accrued expense change ties to working capital schedule
-- deferred revenue change ties to working capital schedule, if applicable
-
-### PP&E and D&A tie
-
-Beginning PP&E plus capex minus disposals minus D&A equals ending PP&E, adjusted for gross / accumulated presentation if used.
-
-D&A on the income statement and add-back on the cash flow statement should tie to the D&A schedule.
-
-### Debt rollforward tie
-
-Beginning debt plus draws plus PIK interest minus repayments equals ending debt.
-
-Interest expense should tie to the debt schedule or interest calculation.
-
-### Tax tie
-
-Tax expense, cash taxes, deferred taxes, and NOL usage should be internally consistent if modeled.
-
-### Scenario tie
-
-The selected scenario in the control panel must match the scenario driving calculations and dashboard outputs.
-
-### Formula integrity
-
-Check for:
-
-- formulas replaced by hardcoded values
-- inconsistent formulas across periods
-- broken references
-- Excel error values
-- hardcoded assumptions inside formula regions
-- hidden rows or sheets driving outputs unexpectedly
-
-## Severity framework
-
-Use this severity ladder in the build review.
-
-| Severity | Meaning | Default action |
-| --- | --- | --- |
-| Blocker | Core output cannot be trusted; the model does not tie or a key formula chain is broken | Must fix before use |
-| High | Material logic issue or fragile behavior likely to mislead decisions | Fix immediately |
-| Medium | Local issue, missing control, or weak logic that may distort some outputs | Fix soon |
-| Low | Maintainability or transparency issue with limited decision impact | Improve when practical |
-| Question | Treatment may be intentional but requires confirmation | Flag clearly |
-
-## Pressure tests
-
-Run the lightest set of tests that materially increases confidence. For model builds, include at least scenario-switch, statement-tie, and key-driver sensitivity tests.
-
-### Base-case retest
-
-Recalculate and confirm all required checks pass in the base case.
-
-### Scenario switch test
-
-Switch each scenario and confirm the actual model engine changes, not just dashboard labels.
-
-Expected behavior:
-
-- downside should generally reduce revenue, EBITDA, FCF, liquidity, and covenant cushion
-- upside should generally improve economics unless investments absorb gains
-- lender / conservative case should stress cash and leverage more than base case
-
-### Revenue sensitivity
-
-Move a key revenue driver and confirm related outputs move correctly.
-
-Expected effects usually include:
-
-- revenue changes
-- working capital changes
-- COGS and variable opex changes if applicable
-- EBITDA changes
-- cash flow changes
-- taxes and debt paydown may change downstream
-
-### Margin sensitivity
-
-Change gross margin or operating margin and confirm EBITDA, taxes, cash flow, retained earnings, and cash change correctly.
-
-### Working capital stress
-
-Increase DSO, inventory days, or reduce DPO and confirm cash deteriorates.
-
-### Capex stress
-
-Increase capex and confirm cash flow decreases, PP&E increases, D&A may increase over time, and liquidity responds.
-
-### Debt and liquidity stress
-
-Lower cash generation and confirm revolver, cash sweep, debt paydown, interest expense, and minimum cash logic behave correctly.
-
-Debt should not go negative unless explicitly allowed. Revolver draws should respect limits if limits are modeled.
-
-### Circularity test
-
-If the model uses circularity for interest, cash sweeps, or revolver draws, confirm:
-
-- iterative calculation is enabled if required
-- circularity is intentional
-- loop converges
-- outputs are stable
-- the loop is documented
-
-If circularity is accidental, treat it as a blocker.
-
-## Control dashboard
-
-Create a `Checks` tab or checks section that includes:
-
-- statement tie checks
-- rollforward checks
-- formula integrity indicators
-- scenario status
-- key source-data tie-outs
-- major open assumptions
-- model status summary
-
-Recommended model status labels:
-
-- `Pass - no issues found in tested scope`
-- `Pass with caveats`
-- `Needs fixes`
-- `Not decision-ready`
-
-## Common blocker issues
-
-Treat these as blocker-level issues unless immaterial and documented:
-
-- balance sheet does not balance
-- ending cash does not tie
-- debt schedule does not roll
-- retained earnings does not roll
-- active Excel errors in key outputs
-- scenario switch does not drive calculations
-- cash or debt behaves impossibly under stress
-- hidden plug required to make statements tie
-- formulas overwritten inside critical formula blocks
-
-## Common high-risk issues
-
-Treat these as high severity:
-
-- material hardcoded assumptions inside formulas
-- margin expansion with no driver or rationale
-- working capital movements with wrong sign
-- debt paydown based on EBITDA instead of cash flow
-- D&A disconnected from asset base
-- tax calculation wrong under losses
-- sensitivity table references wrong cells
-- dashboard retypes outputs instead of linking to model
-- inconsistent date periods across schedules
-
-## QA sign-off requirement
-
-Before presenting the model as usable, state:
-
-- which checks passed
-- which checks failed
-- what was pressure-tested
-- what remains assumption-dependent
-- what limitations remain due to missing source data, opaque macros, external links, add-ins, or unavailable details
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/model-architecture.md b/plugins/financial-markets/skills/three-statement-model-builder/references/model-architecture.md
deleted file mode 100644
index 6ba427c..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/model-architecture.md
+++ /dev/null
@@ -1,259 +0,0 @@
-# Model Architecture Reference
-
-Use this reference when deciding how to structure a new or rebuilt 3-statement model.
-
-Current shipped behavior has two explicit paths: `scripts/build_banker_formula_workbook.py` is the default user-facing path and materializes the bundled live-formula template as `banker_formula_workbook`; the bundled deterministic pipeline produces a values-based `deterministic_export` support workbook for controlled computed values, smoke tests, explicit lightweight exports, or honest fallback. Do not claim the deterministic pipeline creates a fully linked formula workbook.
-
-## Architecture goals
-
-A senior-quality model should be:
-
-- easy to navigate
-- easy to update
-- hard to break accidentally
-- transparent about assumptions
-- explicit about scenario logic
-- auditable from dashboard back to source data
-- mechanically tied across all three statements
-
-## Default workbook tabs for manual/formula workbooks
-
-Use a lean set of tabs for small models and a more modular structure for institutional models.
-
-### Minimum viable model
-
-- `Control Panel`
-- `Assumptions`
-- `Income Statement`
-- `Balance Sheet`
-- `Cash Flow`
-- `Checks`
-- `Dashboard`
-
-### Institutional model
-
-- `README` or `Model Guide`
-- `Control Panel`
-- `Sources`
-- `Historical`
-- `Assumptions`
-- `Scenarios`
-- `Revenue Build`
-- `COGS Gross Margin`
-- `Opex Headcount`
-- `Working Capital`
-- `Capex D&A`
-- `Debt Interest`
-- `Tax`
-- `Income Statement`
-- `Balance Sheet`
-- `Cash Flow`
-- `Checks`
-- `Dashboard`
-- `Sensitivity`
-
-### When to add specialized tabs
-
-Add specialized tabs only when they clarify important logic.
-
-- `Covenants` for lender or credit models
-- `Segments` for multi-segment companies
-- `Geo` for regional reporting
-- `Customers` for concentration or retention-driven models
-- `Unit Economics` for SaaS, marketplace, ecommerce, or consumer models
-- `Inventory` for retail, manufacturing, or distribution
-- `Leases` when lease liabilities or right-of-use assets are material
-- `Equity` for option pools, preferred equity, dividends, or share count logic
-- `Restructuring` for turnaround models
-- `M&A` for acquisition, divestiture, or pro forma adjustments
-
-## Tab purpose standards
-
-### README / Model Guide
-
-Include:
-
-- model purpose
-- version / date
-- forecast horizon
-- units and currency
-- scenario definitions
-- refresh instructions
-- known limitations
-- key open questions
-
-### Control Panel
-
-Include global controls:
-
-- active scenario selector
-- forecast start date
-- units
-- currency
-- tax assumptions
-- key toggle assumptions
-- model status indicators
-- links to major outputs and checks
-
-### Sources
-
-Include source inventory:
-
-- source name
-- type of source
-- date received
-- period covered
-- reliability notes
-- major adjustments made
-- owner / provider where available
-
-### Historical
-
-Use for loaded historical statements and KPIs. Preserve source values separately from normalized values when possible.
-
-Include:
-
-- reported historical data
-- normalization adjustments
-- adjusted historical data
-- bridge between reported and adjusted figures
-- tie-outs to source totals
-
-### Assumptions
-
-Centralize model assumptions. Separate global assumptions from driver-specific assumptions.
-
-Recommended sections:
-
-- revenue assumptions
-- gross margin / COGS assumptions
-- opex assumptions
-- headcount assumptions
-- working capital assumptions
-- capex and D&A assumptions
-- debt / interest assumptions
-- tax assumptions
-- one-time or special assumptions
-
-### Scenarios
-
-Show scenario cases side-by-side. Each case should have a clear economic story, not just numbers.
-
-For each scenario, include:
-
-- case name
-- narrative purpose
-- key revenue drivers
-- margin assumptions
-- working capital assumptions
-- capex assumptions
-- debt / liquidity assumptions
-- rationale
-
-### Driver tabs
-
-Driver tabs should calculate economic reality before statement presentation.
-
-Examples:
-
-- revenue by customer, product, unit, price, volume, seat count, ARR, GMV, transactions, or projects
-- COGS by volume, margin, input cost, labor, hosting, fulfillment, or materials
-- opex by department, headcount, vendor spend, sales and marketing, R&D, G&A
-- working capital by DSO, DIO, DPO, accrued expense days, deferred revenue, or other operating balances
-- capex by maintenance, growth, project, or percentage of revenue
-- debt by tranche, amortization, revolver, interest rate, fees, and covenants
-
-## Model flow
-
-A robust model should flow as follows:
-
-1. Source data
-2. Historical normalization
-3. Assumptions and scenarios
-4. Driver schedules
-5. Statement schedules
-6. Integrated financial statements
-7. Checks
-8. Dashboard and sensitivities
-
-Avoid output tabs that calculate core assumptions. Avoid circular logic unless it is intentional, documented, and controlled.
-
-## Time structure
-
-Choose the time grain based on the decision.
-
-- Monthly: liquidity, startup, turnaround, covenant, working capital, cash burn, near-term budget
-- Quarterly: public company, portfolio monitoring, interim reporting, lender updates
-- Annual: investment screening, long-term strategic planning, valuation support
-- Mixed: monthly near-term plus annual long-term when both liquidity and strategy matter
-
-Separate historical and forecast periods. Use a visible border or row label. Do not hide the transition from actuals to forecast.
-
-## Formula conventions for manual/future formula workbooks
-
-Use consistent formulas across rows and periods.
-
-Recommended practices:
-
-- one formula pattern per row across forecast periods
-- assumptions pulled from assumption tabs or scenario tables
-- historical values linked from source or normalized historical sections
-- no unexplained hardcodes inside formulas
-- avoid merging cells in calculation areas
-- avoid volatile functions where simpler formulas work
-- avoid whole-column references in large models if they create performance issues
-- use named ranges only when they improve clarity
-- document circular references if any exist
-
-## Sign convention
-
-Pick and document one sign convention.
-
-Common convention:
-
-- revenue positive
-- expenses positive on the income statement presentation, but subtracted in EBITDA / EBIT formulas
-- assets positive
-- liabilities positive
-- operating cash inflows positive
-- cash outflows negative in the cash flow statement
-- working capital increases shown as cash outflows
-- debt draws positive and repayments negative
-
-If the user's workbook already uses a different coherent convention, preserve it and document it.
-
-## Formatting conventions
-
-Use formatting to communicate model meaning.
-
-Recommended visual logic:
-
-- inputs: visually distinct
-- formulas: normal fill
-- linked cells: optionally distinct if workbook convention supports it
-- checks: clear pass / fail and variance
-- historical periods: separated from forecast periods
-- section headers: consistent and readable
-- units: visible on each major tab
-
-Formatting should support review, not hide complexity.
-
-## Dashboard standards
-
-A dashboard should answer the senior user's likely questions quickly:
-
-- revenue growth
-- gross margin and EBITDA margin
-- cash balance and liquidity runway
-- debt and leverage
-- free cash flow
-- capex intensity
-- working capital investment
-- key scenario outputs
-- top risks and open assumptions
-
-Dashboards should link to calculated outputs, not retype them.
-
-## Public Equity Architecture Overlay
-
-The first-tab Cover should answer what changed in the equity forecast, which model lines differ from consensus, whether EBITDA converts to FCF, how share count/capital return affect EPS and value per share, and which downside driver hits common equity first. Keep debt schedules as support for interest expense, liquidity, dilution/refinancing risk, and equity impairment; route credit-security valuation and covenant-package ownership to Credit Markets.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/model-math.md b/plugins/financial-markets/skills/three-statement-model-builder/references/model-math.md
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--- a/plugins/financial-markets/skills/three-statement-model-builder/references/model-math.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Model Math
-
-## Timeline
-
-Annual models emit `FYyyyy` periods. Quarterly models emit `Qn-FYyyyy` periods. Quarterly assumptions supplied annually are converted using a period time factor of `0.25` where appropriate.
-
-## Revenue
-
-`segments`: each segment begins with `base_revenue` and compounds by period growth. Period revenue is the sum of segment revenue.
-
-`total_growth`: total revenue begins with `base_revenue` or latest historical revenue and compounds by period growth.
-
-`volume_price`: units and price compound separately. Revenue equals units times price.
-
-## Income statement
-
-- COGS = revenue x (1 - gross margin), or revenue x COGS percent.
-- Gross profit = revenue - COGS.
-- Opex = revenue x opex percent, or specified amount.
-- EBITDA = gross profit - opex.
-- D&A comes from the PP&E schedule.
-- EBIT = EBITDA - D&A.
-- Interest expense = average debt base x interest rate x period time factor.
-- EBT = EBIT - interest.
-- Book tax = positive EBT x book tax rate.
-- Cash tax = taxable income after NOL usage x cash tax rate.
-- Net income = EBT - book tax.
-- Deferred tax add-back = book tax - cash tax.
-
-## Working capital
-
-- AR = annualized revenue x AR days / 365.
-- Inventory = annualized COGS x inventory days / 365.
-- AP = annualized COGS x AP days / 365.
-- Other current assets = annualized revenue x percent of revenue.
-- Accrued expenses = annualized revenue x percent of revenue.
-- Deferred revenue = annualized revenue x percent of revenue.
-- NWC = AR + inventory + other current assets - AP - accrued expenses - deferred revenue.
-- Change in NWC = current NWC - prior NWC.
-
-A positive change in NWC is a use of cash in CFO.
-
-## PP&E and D&A
-
-- Capex = revenue x capex percent, or specified capex amount.
-- D&A = beginning PP&E x depreciation rate, or revenue x D&A percent, or specified D&A amount.
-- Ending PP&E = beginning PP&E + capex - D&A - disposals.
-
-## Cash flow
-
-- CFO = net income + D&A + deferred tax - change in NWC.
-- Cash flow from investing = -capex.
-- Cash flow from financing = debt draws - debt repayments - dividends - buybacks + issuance.
-- Ending cash = beginning cash + CFO - capex + financing flows.
-- Free cash flow in summary outputs = CFO - capex.
-
-## Debt and cash sweep
-
-The model tracks total debt and revolver availability:
-
-1. Beginning debt and beginning cash come from the latest historical balance sheet or debt plan.
-2. Scheduled draws increase debt and cash, subject to revolver availability when a revolver commitment is specified.
-3. Mandatory amortization reduces debt and cash.
-4. If cash falls below minimum cash, the model draws the revolver up to available commitment.
-5. If cash exceeds minimum cash, the model uses `sweep_pct` of excess cash to repay debt.
-6. Optional repayments pay revolver debt first, then other debt in aggregate.
-
-## Balance sheet
-
-- Cash ties to the cash flow statement.
-- AR, inventory, OCA, AP, accrued expenses, and deferred revenue come from working capital schedules.
-- PP&E comes from the PP&E schedule.
-- Debt comes from the debt schedule.
-- Common equity rolls forward with issuance less buybacks.
-- Retained earnings rolls forward with net income less dividends.
-
-## Liquidity and covenant metrics
-
-- Net debt = debt - cash.
-- Liquidity = cash + revolver availability.
-- Net leverage = net debt / EBITDA.
-- Interest coverage = EBITDA / interest expense.
-- Covenant breach flag = 1 if liquidity, leverage, or coverage fail supplied thresholds.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/output-and-review.md b/plugins/financial-markets/skills/three-statement-model-builder/references/output-and-review.md
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--- a/plugins/financial-markets/skills/three-statement-model-builder/references/output-and-review.md
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-# Output and Review Reference
-
-Use this reference to produce final outputs that meet CFO, Portfolio Manager, Managing Director, board, lender, or investment committee expectations.
-
-## Executive summary standard
-
-After building or updating a model, provide a concise but decision-oriented written summary.
-
-Include:
-
-- what was built or changed
-- model purpose and scope
-- forecast horizon and scenario set
-- key assumptions
-- key outputs
-- major sensitivities
-- checks passed / failed
-- open questions and limitations
-- recommended next steps
-
-Avoid vague language such as "looks good" or "seems fine." Use evidence.
-
-## Recommended final response structure
-
-Use this structure unless the user asks for another format.
-
-### 1. Build summary
-
-State:
-
-- workbook / model created or updated
-- scope
-- company / business model if known
-- forecast horizon and granularity
-- scenarios included
-- whether formulas and checks were added
-
-### 2. Key assumptions
-
-Summarize the most important assumptions by category:
-
-- revenue
-- gross margin / COGS
-- opex
-- working capital
-- capex and D&A
-- debt / interest / liquidity
-- tax
-- scenario-specific assumptions
-
-Label assumptions as:
-
-- source-based
-- user-provided
-- historical-trend-based
-- modeler placeholder
-- open question
-
-### 3. Model outputs
-
-Summarize the outputs that matter for the decision, such as:
-
-- revenue growth
-- EBITDA and EBITDA margin
-- net income
-- free cash flow
-- ending cash
-- debt and leverage
-- liquidity runway
-- covenant headroom
-- capital need
-- scenario downside risk
-
-### 4. Controls and QA
-
-Report:
-
-- balance sheet balance status
-- cash tie status
-- retained earnings tie status
-- debt rollforward status
-- PP&E / D&A tie status
-- working capital tie status
-- scenario switch status
-- sensitivity status
-- Excel error scan status
-
-Use pass / fail / not tested / not applicable.
-
-### 5. Senior judgment notes
-
-Call out what a CFO, Portfolio Manager, lender, or IC member would challenge first.
-
-Examples:
-
-- revenue ramp depends heavily on sales capacity expansion
-- margin improvement assumes procurement savings not yet sourced
-- working capital release is aggressive relative to historical DSO / DIO / DPO
-- capex may be too low to support planned growth
-- debt paydown depends on downside case not materializing
-- liquidity is tight even before covenant pressure
-
-### 6. Open questions and limitations
-
-List missing inputs that affect reliability. Be explicit about whether limitations reduce confidence.
-
-Common limitations:
-
-- incomplete historical financials
-- missing KPI data
-- unavailable debt agreement details
-- missing capex plan
-- unknown tax attributes
-- no source for management assumptions
-- external links, macros, add-ins, or queries not fully inspectable
-
-### 7. Recommended next steps
-
-Prioritize actions, such as:
-
-1. Replace placeholder assumptions with source data.
-2. Confirm debt terms and covenant definitions.
-3. Validate revenue drivers with sales / operations leadership.
-4. Review working capital assumptions against actual aging reports.
-5. Add sensitivities for the highest-risk drivers.
-6. Re-run QA after updates.
-
-## Sign-off statements
-
-Use one of these styles.
-
-- `This model is not decision-ready until the blocker and high-severity issues above are fixed and retested.`
-- `This model appears usable with caveats in the tested scope, subject to the limitations noted above.`
-- `No issues found in tested scope, but this is not a guarantee outside the reviewed scope, linked logic, and tested scenarios.`
-- `The model is mechanically tied in the tested scenarios, but output reliability depends on replacing the placeholder assumptions noted above.`
-
-## Findings table format
-
-When reporting issues, use this table.
-
-| # | Severity | Type | Sheet / Range | Issue | Evidence | Impact | Recommended fix |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-
-Severity options:
-
-- Blocker
-- High
-- Medium
-- Low
-- Question
-
-Issue type options:
-
-- formula error
-- hardcode / override
-- broken link
-- inconsistent formula
-- range boundary issue
-- missing control
-- statement tie failure
-- scenario issue
-- sensitivity issue
-- assumption risk
-- reasonableness concern
-- missing source data
-
-## Dashboard output standards
-
-A senior-quality dashboard should include the most decision-relevant metrics.
-
-Common dashboard sections:
-
-- scenario selector / active case
-- revenue, EBITDA, FCF, cash, debt, leverage
-- period-over-period growth and margins
-- liquidity and covenant indicators
-- key assumptions
-- downside vs base variance
-- top sensitivities
-- check status
-
-Dashboard outputs should link to the model engine, not retype values.
-
-## Model review questions
-
-Before final sign-off, answer internally:
-
-- Do the financial statements tie?
-- Are assumptions visible and editable?
-- Can a reviewer trace output back to drivers?
-- Does downside stress affect liquidity and leverage correctly?
-- Are open questions listed clearly?
-- Are unsupported assumptions labeled as placeholders?
-- Are the most important risks obvious to a senior reader?
-
-If the answer to any question is no, improve the workbook or flag the limitation.
-
-## Equity PM Review Output
-
-Review output should lead with estimate revision path, EPS/FCF sensitivity, valuation handoff, current-price relevance, downside equity impairment, and action rules. Do not lead with lender-style covenant headroom unless that is the proximate common-equity risk; if covenant mechanics are the deliverable, use Credit Markets.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/output-spec.md b/plugins/financial-markets/skills/three-statement-model-builder/references/output-spec.md
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-# Output Specification
-
-## Default generated files
-
-The default model-build path is the banker formula workbook path. It writes:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-The deterministic support pipeline remains available for explicit lightweight runs, controlled computed values, smoke tests, and honest fallback exports. It must produce:
-
-- `output/model.xlsx`
-- `output/plan.json`
-- `output/run_log.json`
-- `output/manifest.json`
-
-It may also produce:
-
-- `output/support_note.md` as an optional support note or renderer input. Legacy `output/report.md` is written only with `--write-report-md`. Do not use Markdown as the lead user-facing deliverable when the workbook is the requested artifact.
-
-## Workbook mode
-
-Two workbook modes are supported:
-
-- `banker_formula_workbook`: default live formula workbook produced only by `scripts/build_banker_formula_workbook.py`.
-- `deterministic_export`: values-based support workbook produced by `scripts/run_pipeline.py` for controlled computed values, smoke tests, explicit lightweight exports, or clearly labeled fallback.
-
-Do not call the deterministic workbook a fully linked banker model. Formula mode may be claimed only when `banker_formula_workbook_run_log.json` confirms `workbook_mode: "banker_formula_workbook"`, required sheets/formulas/styles/no-external-link checks pass, and `model_citations.json` exists.
-
-## Formula-mode files
-
-Formula mode is the default for user-facing model builds and writes:
-
-- `banker_formula_workbook.xlsx`
-- `banker_formula_workbook_run_log.json`
-- `model_citations.json`
-- `manifest.json`
-
-`model_citations.json` contains source-to-cell provenance for key operating-model outputs. Each record should include workbook path, sheet, cell/range, formula, value when known, scenario, section, line item, source ID, evidence label, source label, source date/freshness, and optional URL.
-
-If formula mode fails its truthfulness gate, do not call the fallback a banker workbook. Either fix formula mode, mark the run `not-decision-ready`, or use `deterministic_export` with the limitation stated loudly.
-
-## Deterministic support workbook sheets
-
-`model.xlsx` contains:
-
-- `Cover`: first visible PM/analyst dashboard tab required by `shared/workbook-artifact-standard.md`; includes model status, workbook mode, source posture, warnings/hard failures, base/downside/upside final-period metrics, liquidity trough, driver summary, QA posture, and workbook map.
-- `Summary`: scenario-level final-period outputs.
-- `Model`: long-format model rows for all scenarios.
-- `Sensitivities`: selected stress/sensitivity cases.
-- `Checks`: machine-computed tie-out checks.
-- `Assumptions`: source-labeled assumptions from the normalized plan.
-- `Sources`: source basis entries.
-- `Run_Log`: status and issue counts.
-
-`output/manifest.json` mirrors `run_log.json.output_manifest` and records required artifacts, paths, and whether each artifact was written.
-
-## Long-format schema
-
-The `Model` sheet uses:
-
-- `scenario`
-- `statement`
-- `section`
-- `line_item`
-- `period`
-- `value`
-- `unit`
-- `evidence_label`
-- `source_id`
-- `formula_basis`
-- `notes`
-
-Required statement groups:
-
-- `IS`
-- `BS`
-- `CF`
-- `DEBT`
-- `WORKING_CAPITAL`
-- `PPE`
-- `COVENANTS_LIQUIDITY`
-- `CHECKS`
-
-Required line items include:
-
-Income statement:
-- revenue
-- COGS
-- gross profit
-- opex
-- EBITDA
-- D&A
-- EBIT
-- interest
-- EBT
-- taxes
-- net income
-
-Balance sheet:
-- cash
-- AR
-- inventory
-- other current assets
-- PP&E
-- debt
-- AP
-- accrued expenses
-- deferred revenue
-- common equity
-- retained earnings
-- total assets
-- total liabilities and equity
-- balance check
-
-Cash flow:
-- net income
-- D&A
-- deferred tax
-- change in working capital
-- CFO
-- capex
-- debt draws
-- debt repayments
-- dividends
-- buybacks
-- issuance
-- cash change
-- ending cash
-
-Debt:
-- beginning debt
-- scheduled draws
-- required draws
-- total draws
-- mandatory repayment
-- optional repayment
-- interest
-- ending debt
-- revolver availability
-- minimum cash
-
-Working capital:
-- AR days
-- inventory days
-- AP days
-- NWC
-- change in NWC
-
-PP&E:
-- beginning PP&E
-- capex
-- depreciation
-- disposals
-- ending PP&E
-
-Checks:
-- balance sheet check
-- cash tie check
-- retained earnings roll-forward
-- debt roll-forward
-- PP&E roll-forward
-- NWC roll-forward
-
-## Run log schema
-
-`run_log.json` must contain:
-
-```json
-{
-  "status": "completed",
-  "model_status": "screen-grade",
-  "workbook_mode": "deterministic_export",
-  "artifact_level": "deterministic_export",
-  "source_basis": [],
-  "hard_failures": [],
-  "warnings": [],
-  "assumptions": {},
-  "checks": {},
-  "outputs": {},
-  "output_manifest": [],
-  "p0_handoff": {}
-}
-```
-
-`status` is `completed` when the helper ran without hard failures and `failed` when validation or execution blocks decision use.
-
-Allowed `model_status` values:
-
-- `decision-grade`
-- `senior-review-ready`
-- `screen-grade`
-- `not-decision-ready`
-- `blocked`
-
-If `hard_failures` is non-empty, `status` must be `failed` and `model_status` must be `not-decision-ready`.
-
-## Support report template
-
-When `output/support_note.md` is enabled, it is a review support note. The workbook `Cover` is the first user-facing landing page. The support note should include:
-
-1. Model status, workbook mode, artifact level, as-of date, accounting basis, and units.
-2. Scenario summary table for base/downside/upside.
-3. Base case operating read-through.
-4. QA hard failures and warnings.
-5. Sensitivity outputs.
-6. Source posture.
-7. Files produced.
-
-## Downstream P0 handoff
-
-`p0_handoff` must include:
-
-- operating forecast summary
-- base/downside/upside revenue, EBITDA, FCF, and ending cash
-- liquidity trough
-- key operating drivers
-- checks passed/failed
-- model status
-- paths to workbook, plan, run log, manifest, and optional support note/report
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/p0-integrations.md b/plugins/financial-markets/skills/three-statement-model-builder/references/p0-integrations.md
deleted file mode 100644
index 8c3361d..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/p0-integrations.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# P0 Integrations
-
-## Source and normalization
-
-Use `financial-source-of-truth` before or alongside this skill when the user provides filings, public data, connected-app data, or multiple source versions. Carry its evidence labels into `source_basis`.
-
-Use `financials-normalizer` when inputs are PDFs, filings, messy statements, VDR exports, accounting reports, or multiple tabs that need mapping into normalized IS/BS/CF.
-
-Use `excel-data-cleaner` before modeling when a workbook has broken headers, inconsistent units, date problems, hidden rows, or raw exports.
-
-## Audit and scenario expansion
-
-Use `model-audit-tieout` when the user asks to review or fix an existing model, or when formulas/hardcodes/signs/source tie-outs require a dedicated audit.
-
-Use `scenario-sensitivity-generator` when the base model is complete and the user wants deeper stress testing, breakeven analysis, severe downside, or tornado-style sensitivity outputs.
-
-## Public Equity Investing
-
-Use `equity-model-update` when updating a public-company model from earnings, guidance, filings, transcripts, consensus, or market data.
-
-Use Credit Markets when credit instruments, creditworthiness, debt stack, maturity wall, liquidity, covenant disclosure, refinancing, distressed, recovery, spreads, yields, or capital-structure analysis is the primary deliverable. Keep this model builder focused on public-company equity models.
-
-Use `dcf-model-builder`, `comps-valuation` when the primary output is intrinsic value, trading comps, SOTP, or valuation range.
-
-Use `thesis-tracker`, `earnings-preview`, or `earnings-deep-dive` when actuals, KPI monitoring, consensus changes, or thesis signals are the primary deliverable.
-
-## Memo and presentation
-
-Use `memo-builder` after the model when the user needs an IC memo, board note, lender memo, client memo, or portfolio update.
-
-Use `deck-report-qc` and `style-guide-adapter` before external circulation.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/plan-schema.md b/plugins/financial-markets/skills/three-statement-model-builder/references/plan-schema.md
deleted file mode 100644
index 2546e13..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/plan-schema.md
+++ /dev/null
@@ -1,261 +0,0 @@
-# Plan Schema: Three Statement Model Builder
-
-## Purpose
-
-`plan.json` is the single source of truth for the default banker formula workbook path and deterministic operating-model support pipeline. The user may provide it directly, or ChatGPT may create it from prompt context, files, connected apps, filings, and clearly labeled assumptions.
-
-## Top-level structure
-
-Required top-level keys:
-
-```json
-{
-  "meta": {},
-  "source_basis": [],
-  "timeline": {},
-  "historicals": {},
-  "revenue": {},
-  "costs": {},
-  "working_capital": {},
-  "ppe": {},
-  "debt": {},
-  "tax": {},
-  "equity": {},
-  "scenarios": {},
-  "sensitivities": {}
-}
-```
-
-Optional key:
-
-```json
-{"other_balance_sheet": {"other_assets": 0.0, "other_liabilities": 0.0}}
-```
-
-## Metadata
-
-Required:
-- `company_name`
-- `industry`
-- `currency`
-- `units`
-- `as_of_date`
-- `accounting_basis`: `us_gaap`, `ifrs`, `management`, `cash_basis`, or `unspecified`
-
-Recommended:
-- `model_purpose`
-- `prepared_for`
-
-## Source basis
-
-`source_basis` must be a non-empty list. Each source requires:
-- `id`
-- `label`
-- `source_type`
-- `as_of_date`
-- `evidence_label`
-- `covers`
-- `confidence`
-- `notes`
-
-Supported evidence labels:
-- `source_reported`
-- `company_provided`
-- `connector_sourced`
-- `public_filing`
-- `web_verified`
-- `management_guidance`
-- `analyst_estimate`
-- `benchmark`
-- `assumption`
-- `placeholder`
-
-`source_basis` must cover `historicals` and at least one material forecast driver such as `revenue`, `costs`, `working_capital`, `ppe`, `debt`, or `forecast`.
-
-## Timeline
-
-Required:
-- `start_year`: integer
-- `horizon_periods`: annual periods or quarterly periods depending on `periodicity`
-- `periodicity`: `annual` or `quarterly`
-
-Annual labels are emitted as `FY2026`. Quarterly labels are emitted as `Q1-FY2026`, `Q2-FY2026`, etc.
-
-## Historical financials
-
-Required historical objects:
-- `income_statement`
-- `balance_sheet`
-- `cash_flow`
-- `debt_schedule`
-- `ppe`
-- `working_capital`
-
-Minimum income statement fields per period:
-- `revenue`
-- `cogs`
-- `opex`
-- `da`
-- `interest`
-- `taxes`
-- `net_income`
-
-Minimum balance sheet fields per period:
-- `cash`
-- `ar`
-- `inventory`
-- `other_current_assets`
-- `ppe_net`
-- `other_assets`
-- `ap`
-- `accrued_expenses`
-- `deferred_revenue`
-- `debt`
-- `other_liabilities`
-- `common_equity`
-- `retained_earnings`
-
-The latest historical balance sheet must balance. If the historical balance sheet does not balance, the model is not decision-ready until corrected or explicitly explained.
-
-## Revenue
-
-Supported revenue models:
-
-### `segments`
-
-```json
-{
-  "model": "segments",
-  "source_id": "src_mgmt_plan",
-  "segments": {
-    "segment_a": {
-      "base_revenue": 100.0,
-      "growth_rates": {"FY2026": 0.08, "FY2027": 0.07},
-      "evidence_label": "company_provided",
-      "source_id": "src_mgmt_plan"
-    }
-  }
-}
-```
-
-### `total_growth`
-
-```json
-{"model": "total_growth", "base_revenue": 100.0, "growth_rates": {"FY2026": 0.05}}
-```
-
-### `volume_price`
-
-```json
-{
-  "model": "volume_price",
-  "base_units": 1000.0,
-  "base_price": 10.0,
-  "unit_growth_rates": {"FY2026": 0.04},
-  "price_growth_rates": {"FY2026": 0.03}
-}
-```
-
-## Costs
-
-Required:
-
-```json
-{
-  "cogs": {"method": "gross_margin", "gross_margin": {"FY2026": 0.40}},
-  "opex": {"method": "pct_revenue", "pct_revenue": {"FY2026": 0.20}}
-}
-```
-
-COGS methods: `gross_margin`, `pct_revenue`.
-Opex methods: `pct_revenue`, `amount`.
-
-## Working capital
-
-Current supported method: `days`.
-
-Required assumptions:
-- `ar_days`
-- `inventory_days`
-- `ap_days`
-- `other_current_assets_pct_revenue`
-- `accrued_expenses_pct_revenue`
-- `deferred_revenue_pct_revenue`
-
-## PP&E
-
-Required:
-- `capex_method`: `pct_revenue` or `amount`
-- `capex_pct_revenue` or `capex_amount`
-- `depreciation_method`: `pct_beginning_ppe`, `pct_revenue`, or `amount`
-- matching depreciation assumption map
-
-## Debt
-
-Required:
-- `beginning_debt`
-- `beginning_revolver_drawn`
-- `revolver_commitment`
-- `mandatory_amortization`
-- `optional_draws`
-- `interest_rate`
-- `cash_sweep`
-- `covenants`
-
-Cash sweep requires:
-- `enabled`
-- `min_cash`
-- `sweep_pct`
-
-Covenants may include:
-- `min_liquidity`
-- `max_net_leverage`
-- `min_interest_coverage`
-
-## Tax
-
-Required:
-- `book_tax_rate`
-- `cash_tax_rate`
-- `nol_balance`
-
-The model distinguishes book taxes from cash taxes and tracks NOL usage at a high level.
-
-## Equity
-
-Required:
-- `common_equity`
-- `dividends`
-- `buybacks`
-- `issuance`
-
-Dividends reduce retained earnings. Buybacks reduce common equity. Issuance increases common equity.
-
-## Scenarios
-
-Required scenarios:
-- `base`
-- `downside`
-- `upside`
-
-Each scenario requires:
-- `description`
-- `evidence_label`
-- `overrides`
-
-Scenarios use deep-merge overrides against the base plan. A scenario that changes labels but not outputs is a hard failure.
-
-## Sensitivities
-
-Supported lists:
-- `revenue_growth_shocks`
-- `gross_margin_shocks`
-- `dso_day_shocks`
-- `capex_pct_revenue_shocks`
-- `interest_rate_shocks`
-
-## Screen-grade vs. decision-grade
-
-A plan is screen-grade when it uses placeholders, limited evidence, or illustrative assumptions but passes mechanical checks.
-
-A plan can be decision-grade only when material historicals and drivers are sourced, reconciled, current for the purpose, and free of material hard failures or unresolved warnings.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/qa-checks.md b/plugins/financial-markets/skills/three-statement-model-builder/references/qa-checks.md
deleted file mode 100644
index f6fe3ab..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/qa-checks.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# QA Checks
-
-## Hard failures
-
-Hard failures make the model `not-decision-ready`:
-
-- Balance sheet does not balance.
-- Ending cash does not tie to the cash flow statement.
-- Retained earnings roll-forward fails.
-- Debt roll-forward fails.
-- PP&E roll-forward fails.
-- Working capital roll-forward fails.
-- Scenario switch changes labels but not model outputs.
-- `source_basis` is missing for material historicals or forecast drivers.
-- Required plan schema fields are absent or impossible.
-
-## Warnings
-
-Warnings lower confidence but may still allow a useful screen:
-
-- Aggressive revenue ramp.
-- Unsupported margin expansion.
-- Working capital release inconsistent with history.
-- Capex too low for growth.
-- Liquidity trough or covenant issue.
-- Placeholder assumptions still active.
-- Negative cash or heavy revolver dependence.
-- Historical source dates and forecast assumptions are stale or mismatched.
-
-## Status logic
-
-- Any hard failure: `not-decision-ready`.
-- Placeholder assumptions active but no hard failures: `screen-grade`.
-- No hard failures but warnings remain: `senior-review-ready`.
-- No hard failures, no warnings, and high-quality source labels: `decision-grade`.
-
-## Senior review checklist
-
-A veteran reviewer should inspect:
-
-- Whether revenue is tied to the actual business engine.
-- Whether gross margin expansion is supported by mix, pricing, utilization, procurement, or cost evidence.
-- Whether opex leverage is organizationally plausible.
-- Whether DSO, DIO, and DPO changes are supportable.
-- Whether capex supports growth and replacement needs.
-- Whether cash taxes differ materially from book taxes.
-- Whether the debt schedule, interest, revolver availability, and cash sweep behave correctly.
-- Whether downside assumptions are correlated rather than simplistic.
-- Whether the model explains what breaks first.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/references/reference-router.md b/plugins/financial-markets/skills/three-statement-model-builder/references/reference-router.md
deleted file mode 100644
index c02b69d..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/references/reference-router.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Three Statement Reference Router
-
-Load the narrowest reference needed for the current task. Keep the invoke path in `SKILL.md` compact; use these deferred docs for model nuance, implementation details, and senior review standards.
-
-## Default Formula Workbook Contract
-
-- `plan-schema.md`: input schema, source-basis requirements, examples, and decision-grade requirements.
-- `output-spec.md`: required artifacts, workbook sheets, `run_log.json`, `manifest.json`, report template, and P0 handoff shape.
-- `qa-checks.md`: hard failures, warnings, tie-out rules, status logic, and senior-review red flags.
-- `banker-formula-workbook-contract.md`: default formula workbook mode, required sheets, inspection gate, truthfulness rules, and `model_citations.json` source-to-cell handoff.
-
-## Operating Model Mechanics
-
-- `model-math.md`: IS/BS/CF formulas, line-item logic, cash sweep, working capital, PP&E, debt, and retained earnings mechanics.
-- `model-architecture.md`: preserved workbook architecture, tab structure, formula conventions, scenario layout, and formatting standards.
-- `forecast-judgment-guide.md`: CFO/PM-grade assumption logic, scenario design, unit economics, margin, capex, cash conversion, and pressure tests.
-
-## Controls And Review
-
-- `integrity-controls.md`: formula controls, sign conventions, model checks, stress tests, and sign-off procedures.
-- `output-and-review.md`: executive summary, circulation standard, caveats, diligence asks, and model-readiness communication.
-
-## Industry Adaptation
-
-- `industry-playbooks.md`: company-type and revenue-model adaptations for SaaS, consumer, industrial, energy, banks, insurers, REITs, biotech, and other public-company contexts.
-
-## Integrations
-
-- `p0-integrations.md`: coordination with Public Equity Investing and shared-core skills, including model update, DCF, comps, scenario, memo, and audit handoffs.
-
-## Loading Rule
-
-Do not load all references by default. Start with formula workbook contract docs for user-facing model builds, deterministic support docs for implementation or QA issues, operating mechanics for model construction questions, and judgment/review docs only when the user needs full diligence-quality reasoning.
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook.py
deleted file mode 100755
index cc3c7f0..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env python3
-"""Public entrypoint for the bundled formula workbook materializer."""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from runtime_loader import load_runtime_module
-
-_runtime = load_runtime_module("build_banker_formula_workbook_runtime")
-
-__all__ = [name for name in dir(_runtime) if not name.startswith("_")]
-globals().update({name: getattr(_runtime, name) for name in __all__})
-
-
-if __name__ == "__main__":
-    raise SystemExit(_runtime.main())
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook_runtime b/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook_runtime
deleted file mode 100644
index 8000c77..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/build_banker_formula_workbook_runtime
+++ /dev/null
@@ -1,967 +0,0 @@
-#!/usr/bin/env python3
-"""Materialize the bundled banker formula workbook template from plan.json.
-
-This script preserves the template's existing formulas, styles, and workbook
-structure. It only writes plan-derived inputs, source labels, warning posture,
-and scenario controls into the template.
-
-Usage:
-  python3 scripts/build_banker_formula_workbook.py assets/plan_template.json
-  python3 scripts/build_banker_formula_workbook.py plan.json --output-dir output
-"""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import json
-import re
-import sys
-import zipfile
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Tuple
-from xml.etree import ElementTree as ET
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-SKILL_ROOT = SCRIPT_DIR.parent
-PLUGIN_ROOT = SKILL_ROOT.parents[1]
-sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from validate_plan import validate  # noqa: E402
-from shared.model_citations import validate_model_citations  # noqa: E402
-from shared.workbook_inspection import inspect_formula_workbook  # noqa: E402
-
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-ET.register_namespace("", NS_MAIN)
-ET.register_namespace("r", NS_REL)
-
-REQUIRED_SHEETS = [
-    "Cover",
-    "Executive Summary",
-    "Control Panel",
-    "Historical Financials",
-    "Revenue Build",
-    "Expense Build",
-    "Income Statement",
-    "Working Capital",
-    "PP&E D&A",
-    "Debt Interest",
-    "Tax",
-    "Balance Sheet",
-    "Cash Flow Statement",
-    "Scenarios",
-    "Checks",
-    "Source Notes",
-]
-
-DEFAULT_TEMPLATE = SKILL_ROOT / "assets" / "templates" / "banker_formula_workbook_template.xlsx"
-OUTPUT_WORKBOOK = "banker_formula_workbook.xlsx"
-OUTPUT_RUN_LOG = "banker_formula_workbook_run_log.json"
-OUTPUT_MANIFEST = "manifest.json"
-OUTPUT_MODEL_CITATIONS = "model_citations.json"
-PARTIAL_CONTEXT_WARNING = "Screen-grade only; placeholder assumptions used."
-PERIOD_COLS = ["D", "E", "F", "G", "H"]
-CONTROL_HEADER_ROWS = [13, 19, 25, 31, 37, 43, 49, 55, 61, 67, 73, 79, 85, 91, 97, 103, 109, 115, 121, 127, 133, 139]
-SCENARIO_ROWS = {"base": 0, "downside": 1, "upside": 2}
-
-
-def _file_record(path: Path, role: str, description: str) -> Dict[str, Any]:
-    return {
-        "path": str(path),
-        "role": role,
-        "description": description,
-        "exists": True if path.name == OUTPUT_MANIFEST else path.exists(),
-    }
-
-
-def write_output_manifest(output_dir: Path, run_log: Dict[str, Any]) -> Dict[str, Any]:
-    manifest_path = output_dir / OUTPUT_MANIFEST
-    workbook_path = output_dir / OUTPUT_WORKBOOK
-    run_log_path = output_dir / OUTPUT_RUN_LOG
-    model_citations_path = output_dir / OUTPUT_MODEL_CITATIONS
-    hard_failures = run_log.get("hard_failures", [])
-    failed = bool(hard_failures)
-    status = run_log.get("status") or ("failed" if failed else "completed")
-    readiness_reason = (
-        "Formula workbook was written, but inspection gates failed. Treat it as a diagnostic artifact until repaired."
-        if failed
-        else ""
-    )
-    manifest = {
-        "manifest_version": "1.0",
-        "generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
-        "skill": "three-statement-model-builder",
-        "status": status,
-        "artifact_mode": "banker_formula_workbook",
-        "model_status": run_log.get("model_status"),
-        "output_dir": str(output_dir),
-        "primary_human_deliverable": str(workbook_path),
-        "first_read": {"path": str(workbook_path), "role": "primary_human_deliverable", "why": "Open the formula workbook first; support files are audit/provenance material."},
-        "human_deliverables": [_file_record(workbook_path, "human_deliverable", "banker formula workbook")],
-        "companion_deliverables": [],
-        "support_artifacts": [
-            {"path": str(run_log_path), "role": "support_artifact", "artifact_type": "json", "description": "Formula workbook run log.", "user_visible_default": False, "support_reason": "Run log is audit support for workbook generation."},
-            {"path": str(model_citations_path), "role": "support_artifact", "artifact_type": "json", "description": "Source-to-cell provenance ledger.", "user_visible_default": False, "support_reason": "Citation JSON is renderer/provenance support, not the main deliverable."},
-        ],
-        "support_artifacts_user_visible_default": False,
-        "blocked_or_partial_status": {
-            "status": "not-decision-ready" if failed else "complete",
-            "reason": readiness_reason,
-            "missing_inputs": run_log.get("missing_inputs", []),
-            "hard_failures": hard_failures,
-        },
-        "final_response_guidance": {
-            "lead_with": "workbook_diagnostic_status" if failed else "primary_human_deliverable",
-            "mention_support_artifacts": "include run log and model citations for repair" if failed else "only_briefly_unless_requested",
-        },
-        "agent_artifacts": [
-            _file_record(run_log_path, "agent_artifact", "formula workbook run log"),
-            _file_record(model_citations_path, "agent_artifact", "source-to-cell provenance ledger"),
-            _file_record(manifest_path, "agent_artifact", "agent-facing output manifest"),
-        ],
-        "hard_failure_count": len(hard_failures),
-        "warning_count": len(run_log.get("warnings", [])),
-        "discipline_note": (
-            "Do not use this workbook for target, rating, sizing, or circulation until formula inspection failures are repaired."
-            if failed
-            else "Use the workbook as the main deliverable; manifest/run_log files are agent-facing support artifacts."
-        ),
-    }
-    manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
-    return manifest
-
-
-def qname(tag: str) -> str:
-    return f"{{{NS_MAIN}}}{tag}"
-
-
-def col_to_num(col: str) -> int:
-    value = 0
-    for char in col:
-        value = value * 26 + ord(char.upper()) - 64
-    return value
-
-
-def cell_ref_parts(ref: str) -> Tuple[str, int]:
-    match = re.fullmatch(r"([A-Z]+)([0-9]+)", ref)
-    if not match:
-        raise ValueError(f"Invalid cell reference: {ref}")
-    return match.group(1), int(match.group(2))
-
-
-def cell_sort_key(ref: str) -> Tuple[int, int]:
-    col, row = cell_ref_parts(ref)
-    return row, col_to_num(col)
-
-
-def read_plan(path: Path) -> Dict[str, Any]:
-    return json.loads(path.read_text(encoding="utf-8"))
-
-
-def deep_merge(base: Any, override: Any) -> Any:
-    if isinstance(base, dict) and isinstance(override, dict):
-        merged = copy.deepcopy(base)
-        for key, value in override.items():
-            merged[key] = deep_merge(merged.get(key), value)
-        return merged
-    return copy.deepcopy(override)
-
-
-def scenario_plan(plan: Dict[str, Any], scenario: str) -> Dict[str, Any]:
-    cfg = plan.get("scenarios", {}).get(scenario, {})
-    return deep_merge(plan, cfg.get("overrides", {}))
-
-
-def period_labels(plan: Dict[str, Any], max_periods: int = 5) -> List[str]:
-    timeline = plan.get("timeline", {})
-    start_year = int(timeline.get("start_year", 2026))
-    horizon = min(int(timeline.get("horizon_periods", max_periods)), max_periods)
-    periodicity = timeline.get("periodicity", "annual")
-    if periodicity == "quarterly":
-        return [f"{start_year}Q{i + 1}E" for i in range(horizon)]
-    return [f"{start_year + i}E" for i in range(horizon)]
-
-
-def period_key_candidates(label: str) -> List[str]:
-    stripped = label.rstrip("E")
-    year_match = re.search(r"(20[0-9]{2}|19[0-9]{2})", label)
-    candidates = [label, stripped]
-    if year_match:
-        year = year_match.group(1)
-        candidates.extend([f"FY{year}", f"FY{year}E", year, f"{year}E"])
-    return list(dict.fromkeys(candidates))
-
-
-def number_for_period(value: Any, label: str, default: Optional[float] = None) -> Optional[float]:
-    if isinstance(value, (int, float)) and not isinstance(value, bool):
-        return float(value)
-    if isinstance(value, dict):
-        for key in period_key_candidates(label):
-            if key in value and isinstance(value[key], (int, float)) and not isinstance(value[key], bool):
-                return float(value[key])
-    return default
-
-
-def values_for_periods(value: Any, labels: List[str], default: Optional[float] = None) -> List[Optional[float]]:
-    return [number_for_period(value, label, default) for label in labels]
-
-
-def first_latest_period(rows: Dict[str, Any]) -> Optional[str]:
-    if not rows:
-        return None
-    return sorted(rows.keys())[-1]
-
-
-def source_with_cover(plan: Dict[str, Any], cover_options: Iterable[str]) -> Optional[Dict[str, Any]]:
-    desired = set(cover_options)
-    for source in plan.get("source_basis", []):
-        if desired & set(source.get("covers", [])):
-            return source
-    return None
-
-
-def source_for_id(plan: Dict[str, Any], source_id: Optional[str]) -> Optional[Dict[str, Any]]:
-    if not source_id:
-        return None
-    for source in plan.get("source_basis", []):
-        if isinstance(source, dict) and str(source.get("id")) == str(source_id):
-            return source
-    return None
-
-
-def source_details(plan: Dict[str, Any], *, source_id: Optional[str] = None, covers: Iterable[str] = ()) -> Dict[str, str]:
-    source = source_for_id(plan, source_id) or source_with_cover(plan, covers)
-    if not source:
-        label = str(source_id or next(iter(covers), "model-output"))
-        return {
-            "source_id": label,
-            "evidence_label": "derived",
-            "source_label": "Model output",
-            "source_date": "",
-            "freshness": "derived",
-            "url": "",
-        }
-    return {
-        "source_id": str(source.get("id") or source_id or "source"),
-        "evidence_label": str(source.get("evidence_label") or source.get("label") or ""),
-        "source_label": str(source.get("label") or source.get("title") or source.get("id") or ""),
-        "source_date": str(source.get("as_of_date") or source.get("date") or source.get("accessed_at") or ""),
-        "freshness": str(source.get("confidence") or source.get("freshness") or ""),
-        "url": str(source.get("url") or source.get("source_url") or ""),
-    }
-
-
-def citation_record(
-    plan: Dict[str, Any],
-    workbook_path: Path,
-    *,
-    record_id: str,
-    title: str,
-    sheet: str,
-    cell: str,
-    line_item: str,
-    section: str,
-    formula: str,
-    value: Any = "",
-    source_id: Optional[str] = None,
-    covers: Iterable[str] = (),
-    scenario: str = "base",
-) -> Dict[str, Any]:
-    source = source_details(plan, source_id=source_id, covers=covers)
-    short_label = f"Model: {sheet}!{cell}"
-    return {
-        "id": record_id,
-        "title": title,
-        "short_label": short_label,
-        "type": "model_cell",
-        "quality": "model_output",
-        "citation_id": record_id,
-        "workbook_path": str(workbook_path),
-        "sheet": sheet,
-        "cell_or_range": cell,
-        "cell": cell,
-        "range": cell,
-        "metric_name": title,
-        "value": "" if value is None else str(value),
-        "formula": formula,
-        "source_ids": [source["source_id"]],
-        "assumption_flag": source["evidence_label"] in {"assumption", "internal_estimate", "estimate"},
-        "tie_out_status": "model_generated",
-        "scenario": scenario,
-        "statement": "Three Statement",
-        "section": section,
-        "line_item": line_item,
-        "source_id": source["source_id"],
-        "evidence_label": source["evidence_label"],
-        "source_label": source["source_label"],
-        "source_date": source["source_date"],
-        "freshness": source["freshness"],
-        "url": source["url"],
-        "aliases": [line_item, title, short_label],
-        "notes": "Formula workbook cell provenance generated by Public Equity Investing three-statement formula mode.",
-    }
-
-
-def build_model_citations(plan: Dict[str, Any], workbook_path: Path) -> List[Dict[str, Any]]:
-    revenue_source = plan.get("revenue", {}).get("source_id")
-    cost_source = plan.get("costs", {}).get("source_id")
-    wc_source = plan.get("working_capital", {}).get("source_id")
-    debt_source = plan.get("debt", {}).get("source_id")
-    ppe_source = plan.get("ppe", {}).get("source_id")
-    tax_source = plan.get("tax", {}).get("source_id")
-    return [
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-final-revenue",
-            title="Final Forecast Revenue",
-            sheet="Income Statement",
-            cell="I6",
-            line_item="revenue",
-            section="Income Statement",
-            formula="='Expense Build'!I6",
-            source_id=revenue_source,
-            covers=["revenue"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-final-ebitda",
-            title="Final Forecast EBITDA",
-            sheet="Income Statement",
-            cell="I16",
-            line_item="ebitda",
-            section="Income Statement",
-            formula="='Expense Build'!I21",
-            source_id=cost_source,
-            covers=["costs", "margin"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-final-net-income",
-            title="Final Forecast Net Income",
-            sheet="Income Statement",
-            cell="I22",
-            line_item="net_income",
-            section="Income Statement",
-            formula="=I20-I21",
-            source_id=tax_source,
-            covers=["tax"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-ending-cash",
-            title="Ending Cash",
-            sheet="Balance Sheet",
-            cell="I6",
-            line_item="cash",
-            section="Balance Sheet",
-            formula="='Cash Flow Statement'!I23",
-            source_id=wc_source,
-            covers=["working_capital"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-ending-debt",
-            title="Ending Debt",
-            sheet="Balance Sheet",
-            cell="I22",
-            line_item="debt",
-            section="Balance Sheet",
-            formula="='Debt Interest'!I12",
-            source_id=debt_source,
-            covers=["debt", "covenants"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-free-cash-flow-conversion",
-            title="Free Cash Flow Conversion",
-            sheet="Cash Flow Statement",
-            cell="I26",
-            line_item="fcf_conversion",
-            section="Cash Flow Statement",
-            formula="=IFERROR(I25/'Income Statement'!I22,0)",
-            source_id=ppe_source,
-            covers=["ppe", "capex"],
-        ),
-        citation_record(
-            plan,
-            workbook_path,
-            record_id="model-output:three-statement-balance-check",
-            title="Balance Sheet Check",
-            sheet="Checks",
-            cell="C6",
-            line_item="balance_sheet_check",
-            section="Checks",
-            formula='=IF(ABS(B6)<0.01,"OK","FAIL")',
-            covers=["historicals", "balance_sheet", "cash_flow"],
-        ),
-    ]
-
-
-def source_row(source: Optional[Dict[str, Any]], default_note: str) -> Tuple[str, str, str, str]:
-    if not source:
-        return ("placeholder", "Low", "Missing source", default_note)
-    return (
-        str(source.get("evidence_label", "placeholder")),
-        str(source.get("confidence", "low")).title(),
-        str(source.get("label", source.get("id", "source_basis"))),
-        str(source.get("notes", default_note)),
-    )
-
-
-def has_placeholder_sources(plan: Dict[str, Any]) -> bool:
-    for source in plan.get("source_basis", []):
-        evidence = str(source.get("evidence_label", "")).lower()
-        confidence = str(source.get("confidence", "")).lower()
-        if evidence in {"placeholder", "assumption"} or confidence == "low":
-            return True
-    for section in ("revenue", "costs", "working_capital", "ppe", "debt", "tax", "equity"):
-        evidence = str(plan.get(section, {}).get("evidence_label", "")).lower()
-        if evidence == "placeholder":
-            return True
-    return False
-
-
-def revenue_growth(plan: Dict[str, Any], labels: List[str]) -> List[Optional[float]]:
-    revenue = plan.get("revenue", {})
-    model = revenue.get("model")
-    if model == "segments":
-        segments = revenue.get("segments", {})
-        total_base = sum(float(cfg.get("base_revenue", 0.0)) for cfg in segments.values() if isinstance(cfg, dict))
-        values: List[Optional[float]] = []
-        for label in labels:
-            weighted = 0.0
-            if total_base <= 0:
-                values.append(None)
-                continue
-            for cfg in segments.values():
-                if not isinstance(cfg, dict):
-                    continue
-                base = float(cfg.get("base_revenue", 0.0))
-                weighted += (base / total_base) * float(number_for_period(cfg.get("growth_rates"), label, 0.0) or 0.0)
-            values.append(weighted)
-        return values
-    if model == "total_growth":
-        return values_for_periods(revenue.get("growth_rates"), labels)
-    if model == "volume_price":
-        unit_growth = values_for_periods(revenue.get("unit_growth_rates"), labels, 0.0)
-        price_growth = values_for_periods(revenue.get("price_growth_rates"), labels, 0.0)
-        return [((1 + (u or 0.0)) * (1 + (p or 0.0)) - 1) for u, p in zip(unit_growth, price_growth)]
-    return [None for _ in labels]
-
-
-def gross_margin(plan: Dict[str, Any], labels: List[str]) -> List[Optional[float]]:
-    cogs = plan.get("costs", {}).get("cogs", {})
-    if cogs.get("method", "gross_margin") == "gross_margin":
-        return values_for_periods(cogs.get("gross_margin"), labels)
-    return [1.0 - (value or 0.0) if value is not None else None for value in values_for_periods(cogs.get("pct_revenue"), labels)]
-
-
-def opex_pct(plan: Dict[str, Any], labels: List[str], key: str) -> List[Optional[float]]:
-    opex = plan.get("costs", {}).get("opex", {})
-    split = opex.get("functional_split", {})
-    aliases = {
-        "sales_marketing": ["sales_marketing_pct_revenue", "sales_and_marketing_pct_revenue", "s&m_pct_revenue"],
-        "rd": ["rd_pct_revenue", "r_and_d_pct_revenue", "research_development_pct_revenue"],
-        "ga": ["ga_pct_revenue", "g_and_a_pct_revenue", "general_admin_pct_revenue"],
-        "other": ["other_opex_pct_revenue", "other_operating_expenses_pct_revenue"],
-    }
-    for alias in aliases[key]:
-        if alias in opex:
-            return values_for_periods(opex.get(alias), labels)
-        if isinstance(split, dict) and alias in split:
-            return values_for_periods(split.get(alias), labels)
-    if key == "ga" and opex.get("method", "pct_revenue") == "pct_revenue":
-        return values_for_periods(opex.get("pct_revenue"), labels)
-    if key in {"sales_marketing", "rd", "other"} and opex.get("method", "pct_revenue") == "pct_revenue":
-        return [0.0 for _ in labels]
-    return [None for _ in labels]
-
-
-def capex_pct(plan: Dict[str, Any], labels: List[str]) -> List[Optional[float]]:
-    ppe = plan.get("ppe", {})
-    if ppe.get("capex_method", "pct_revenue") == "pct_revenue":
-        return values_for_periods(ppe.get("capex_pct_revenue"), labels)
-    return [None for _ in labels]
-
-
-def depreciation_pct(plan: Dict[str, Any], labels: List[str]) -> List[Optional[float]]:
-    ppe = plan.get("ppe", {})
-    if ppe.get("depreciation_method", "pct_beginning_ppe") == "pct_beginning_ppe":
-        return values_for_periods(ppe.get("depreciation_pct_beginning_ppe"), labels)
-    return [None for _ in labels]
-
-
-def mandatory_repayment_pct(plan: Dict[str, Any], labels: List[str]) -> List[Optional[float]]:
-    debt = plan.get("debt", {})
-    beginning_debt = float(debt.get("beginning_debt", 0.0) or 0.0)
-    values = values_for_periods(debt.get("mandatory_amortization"), labels, 0.0)
-    pct_values: List[Optional[float]] = []
-    for value in values:
-        if value is None:
-            pct_values.append(None)
-        elif abs(value) <= 1.0:
-            pct_values.append(value)
-        elif beginning_debt > 0:
-            pct_values.append(value / beginning_debt)
-        else:
-            pct_values.append(None)
-    return pct_values
-
-
-def scalar_repeated(value: Any, labels: List[str]) -> List[Optional[float]]:
-    if isinstance(value, (int, float)) and not isinstance(value, bool):
-        return [float(value) for _ in labels]
-    return values_for_periods(value, labels)
-
-
-def segment_mix(plan: Dict[str, Any], labels: List[str]) -> List[Tuple[str, List[Optional[float]]]]:
-    revenue = plan.get("revenue", {})
-    if revenue.get("model") != "segments":
-        return []
-    segments = revenue.get("segments", {})
-    total = sum(float(cfg.get("base_revenue", 0.0)) for cfg in segments.values() if isinstance(cfg, dict))
-    output: List[Tuple[str, List[Optional[float]]]] = []
-    for name, cfg in list(segments.items())[:3]:
-        if not isinstance(cfg, dict) or total <= 0:
-            continue
-        share = float(cfg.get("base_revenue", 0.0)) / total
-        output.append((str(name).replace("_", " ").title(), [share for _ in labels]))
-    return output
-
-
-def historical_periods(plan: Dict[str, Any], max_periods: int = 3) -> List[str]:
-    periods = set()
-    for section in ("income_statement", "balance_sheet", "cash_flow"):
-        rows = plan.get("historicals", {}).get(section, {})
-        periods.update(rows.keys() if isinstance(rows, dict) else [])
-    return sorted(periods)[-max_periods:]
-
-
-def historical_inputs(plan: Dict[str, Any]) -> Dict[str, Any]:
-    periods = historical_periods(plan)
-    is_rows = plan.get("historicals", {}).get("income_statement", {})
-    bs_rows = plan.get("historicals", {}).get("balance_sheet", {})
-    cf_rows = plan.get("historicals", {}).get("cash_flow", {})
-    out: Dict[str, Any] = {"periods": periods, "rows": {}}
-    for period in periods:
-        inc = is_rows.get(period, {}) if isinstance(is_rows, dict) else {}
-        bs = bs_rows.get(period, {}) if isinstance(bs_rows, dict) else {}
-        cf = cf_rows.get(period, {}) if isinstance(cf_rows, dict) else {}
-        revenue = inc.get("revenue")
-        cogs = inc.get("cogs")
-        opex = inc.get("opex")
-        out["rows"].setdefault("revenue", []).append(revenue)
-        out["rows"].setdefault("gross_profit", []).append((revenue - cogs) if isinstance(revenue, (int, float)) and isinstance(cogs, (int, float)) else None)
-        out["rows"].setdefault("sales_marketing", []).append(0.0)
-        out["rows"].setdefault("rd", []).append(0.0)
-        out["rows"].setdefault("ga", []).append(opex if isinstance(opex, (int, float)) else None)
-        out["rows"].setdefault("other_opex", []).append(0.0)
-        out["rows"].setdefault("da", []).append(inc.get("da"))
-        out["rows"].setdefault("interest", []).append(inc.get("interest"))
-        out["rows"].setdefault("taxes", []).append(inc.get("taxes"))
-        out["rows"].setdefault("cash", []).append(bs.get("cash"))
-        out["rows"].setdefault("ar", []).append(bs.get("ar"))
-        out["rows"].setdefault("inventory", []).append(bs.get("inventory"))
-        out["rows"].setdefault("other_current_assets", []).append(bs.get("other_current_assets"))
-        out["rows"].setdefault("ppe_net", []).append(bs.get("ppe_net"))
-        out["rows"].setdefault("other_assets", []).append(bs.get("other_assets"))
-        out["rows"].setdefault("debt", []).append(bs.get("debt"))
-        out["rows"].setdefault("ap", []).append(bs.get("ap"))
-        out["rows"].setdefault("accrued_expenses", []).append(bs.get("accrued_expenses"))
-        out["rows"].setdefault("deferred_revenue", []).append(bs.get("deferred_revenue"))
-        out["rows"].setdefault("other_liabilities", []).append(bs.get("other_liabilities"))
-        common_equity = bs.get("common_equity")
-        retained_earnings = bs.get("retained_earnings")
-        out["rows"].setdefault("equity_retained", []).append(
-            (common_equity + retained_earnings)
-            if isinstance(common_equity, (int, float)) and isinstance(retained_earnings, (int, float))
-            else None
-        )
-        out["rows"].setdefault("capex", []).append(cf.get("capex"))
-        out["rows"].setdefault("change_nwc", []).append(cf.get("change_nwc"))
-    return out
-
-
-def update_series(updates: Dict[str, Any], row: int, labels: List[str], values: List[Optional[float]], cols: List[str] = PERIOD_COLS) -> None:
-    for col, value in zip(cols, values):
-        if value is not None:
-            updates[f"{col}{row}"] = value
-
-
-def build_control_panel_updates(plan: Dict[str, Any], missing_inputs: List[str], warnings: List[str]) -> Dict[str, Any]:
-    labels = period_labels(plan)
-    meta = plan.get("meta", {})
-    placeholder = has_placeholder_sources(plan)
-    status = "screen-grade" if placeholder else "template-ready"
-    updates: Dict[str, Any] = {
-        "B5": meta.get("company_name", "Company"),
-        "B6": meta.get("currency", "USD"),
-        "B7": meta.get("units", "USD_mm"),
-        "B8": "Base",
-        "B9": "banker_formula_workbook",
-        "B10": status,
-        "B11": "Template materializer output; formulas, styles, and tab architecture are preserved from the bundled workbook.",
-        "B146": PARTIAL_CONTEXT_WARNING if placeholder else "Source-backed control panel inputs; review Source Notes before circulation.",
-    }
-    for row in CONTROL_HEADER_ROWS:
-        update_series(updates, row, labels, labels)
-
-    mapping = [
-        ("revenue_growth", 14, revenue_growth),
-        ("gross_margin", 20, gross_margin),
-        ("sales_marketing_pct_revenue", 26, lambda p, l: opex_pct(p, l, "sales_marketing")),
-        ("rd_pct_revenue", 32, lambda p, l: opex_pct(p, l, "rd")),
-        ("ga_pct_revenue", 38, lambda p, l: opex_pct(p, l, "ga")),
-        ("other_opex_pct_revenue", 44, lambda p, l: opex_pct(p, l, "other")),
-        ("depreciation_pct_beginning_ppe", 50, depreciation_pct),
-        ("capex_pct_revenue", 56, capex_pct),
-        ("ar_days", 62, lambda p, l: values_for_periods(p.get("working_capital", {}).get("ar_days"), l)),
-        ("inventory_days", 68, lambda p, l: values_for_periods(p.get("working_capital", {}).get("inventory_days"), l)),
-        ("ap_days", 74, lambda p, l: values_for_periods(p.get("working_capital", {}).get("ap_days"), l)),
-        ("accrued_expenses_pct_revenue", 80, lambda p, l: values_for_periods(p.get("working_capital", {}).get("accrued_expenses_pct_revenue"), l)),
-        ("other_current_assets_pct_revenue", 86, lambda p, l: values_for_periods(p.get("working_capital", {}).get("other_current_assets_pct_revenue"), l)),
-        ("other_current_liabilities_pct_revenue", 92, lambda p, l: values_for_periods(p.get("working_capital", {}).get("deferred_revenue_pct_revenue"), l)),
-        ("book_tax_rate", 98, lambda p, l: scalar_repeated(p.get("tax", {}).get("book_tax_rate"), l)),
-        ("cash_tax_rate", 104, lambda p, l: scalar_repeated(p.get("tax", {}).get("cash_tax_rate"), l)),
-        ("cash_interest_rate", 110, lambda p, l: values_for_periods(p.get("debt", {}).get("interest_rate"), l)),
-        ("mandatory_debt_repayment_pct_beginning_debt", 116, mandatory_repayment_pct),
-        ("cash_sweep_pct_excess_cash", 122, lambda p, l: scalar_repeated(p.get("debt", {}).get("cash_sweep", {}).get("sweep_pct"), l)),
-        ("minimum_cash", 128, lambda p, l: scalar_repeated(p.get("debt", {}).get("cash_sweep", {}).get("min_cash"), l)),
-        ("diluted_shares", 134, lambda p, l: scalar_repeated(p.get("equity", {}).get("diluted_shares"), l)),
-    ]
-    for _name, base_row, extractor in mapping:
-        for scenario, offset in SCENARIO_ROWS.items():
-            values = extractor(scenario_plan(plan, scenario), labels)
-            update_series(updates, base_row + offset, labels, values)
-
-    if not isinstance(plan.get("costs", {}).get("opex", {}).get("functional_split"), dict):
-        missing_inputs.append("functional_opex_split")
-        warnings.append("No functional opex split provided; generic opex % revenue was mapped to G&A with S&M/R&D/Other set to zero.")
-    if "diluted_shares" not in plan.get("equity", {}):
-        missing_inputs.append("diluted_shares")
-    if len(labels) < int(plan.get("timeline", {}).get("horizon_periods", len(labels))):
-        warnings.append("Template supports five forecast periods; only the first five periods were materialized.")
-
-    for idx, (segment_name, values) in enumerate(segment_mix(plan, labels)):
-        row = 140 + idx
-        updates[f"C{row}"] = segment_name
-        update_series(updates, row, labels, values)
-    return updates
-
-
-def build_historical_updates(plan: Dict[str, Any], missing_inputs: List[str], warnings: List[str]) -> Dict[str, Any]:
-    hist = historical_inputs(plan)
-    periods = hist["periods"]
-    cols = ["B", "C", "D"][-len(periods) :] if periods else []
-    updates: Dict[str, Any] = {}
-    if not periods:
-        missing_inputs.append("historical_financials")
-        return updates
-
-    if len(periods) < 3:
-        warnings.append("Historical Financials tab materialized with fewer than three historical periods.")
-
-    row_map = {
-        "revenue": 5,
-        "gross_profit": 8,
-        "sales_marketing": 11,
-        "rd": 12,
-        "ga": 13,
-        "other_opex": 14,
-        "da": 19,
-        "interest": 21,
-        "taxes": 22,
-        "cash": 26,
-        "ar": 27,
-        "inventory": 28,
-        "other_current_assets": 29,
-        "ppe_net": 30,
-        "other_assets": 31,
-        "debt": 34,
-        "ap": 35,
-        "accrued_expenses": 36,
-        "deferred_revenue": 37,
-        "other_liabilities": 38,
-        "equity_retained": 39,
-        "capex": 44,
-        "change_nwc": 45,
-    }
-    for key, row in row_map.items():
-        values = hist["rows"].get(key, [])[-len(cols) :]
-        for col, value in zip(cols, values):
-            if value is not None:
-                updates[f"{col}{row}"] = value
-        updates[f"E{row}"] = "source_reported"
-        updates[f"F{row}"] = "Historical values populated from plan.json; replace template values with normalized source-backed statements before circulation."
-    missing_inputs.append("historical_functional_opex_split")
-    return updates
-
-
-def build_executive_summary_updates(plan: Dict[str, Any]) -> Dict[str, Any]:
-    labels = period_labels(plan)
-    hist = historical_periods(plan)
-    placeholder = has_placeholder_sources(plan)
-    return {
-        "B5": f"{hist[0]} - {hist[-1]}" if hist else "Historical period unavailable",
-        "B6": f"{labels[0]} - {labels[-1]}" if labels else "Forecast period unavailable",
-        "A9": "Posture warning",
-        "B9": PARTIAL_CONTEXT_WARNING if placeholder else "Source-backed template materialization; review Source Notes and Checks before circulation.",
-    }
-
-
-def build_source_notes_updates(plan: Dict[str, Any]) -> Dict[str, Any]:
-    rows = {
-        6: (source_with_cover(plan, ["historicals", "income_statement", "balance_sheet", "cash_flow"]), "Replace with audited financials, filings, management financials, trial balance, or normalized source pack."),
-        7: (source_with_cover(plan, ["revenue", "forecast"]), "Support with backlog, bookings, pipeline, market growth, pricing, retention, and management plan."),
-        8: (source_with_cover(plan, ["costs", "gross_margin", "margin"]), "Validate against gross margin bridge, cost inflation, mix, utilization, and peer benchmarks."),
-        9: (source_with_cover(plan, ["costs", "opex"]), "Tie to headcount plan, department budget, vendor spend, and standalone-cost needs."),
-        10: (source_with_cover(plan, ["working_capital"]), "Confirm DSO/DIO/DPO trends, collections, inventory reserves, supplier terms, and seasonality."),
-        11: (source_with_cover(plan, ["ppe", "capex"]), "Separate maintenance and growth capex; validate D&A/useful lives and capitalized software."),
-        12: (source_with_cover(plan, ["debt", "covenants"]), "Replace rates, amortization, maturity, covenants, liquidity, and sweep terms with credit agreement or lender model."),
-        13: (source_with_cover(plan, ["tax"]), "Validate book vs cash taxes, NOLs, jurisdictions, and interest deductibility constraints."),
-    }
-    updates: Dict[str, Any] = {}
-    for row, (source, default_note) in rows.items():
-        evidence, confidence, label, note = source_row(source, default_note)
-        updates[f"B{row}"] = evidence
-        updates[f"C{row}"] = confidence
-        updates[f"D{row}"] = label
-        updates[f"E{row}"] = note
-    return updates
-
-
-def xml_sheet_paths(xlsx_path: Path) -> Dict[str, str]:
-    with zipfile.ZipFile(xlsx_path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        rels = ET.fromstring(zf.read("xl/_rels/workbook.xml.rels"))
-        relmap = {rel.attrib["Id"]: rel.attrib["Target"] for rel in rels}
-        paths: Dict[str, str] = {}
-        sheets_el = workbook.find(qname("sheets"))
-        if sheets_el is None:
-            return paths
-        for sheet in sheets_el:
-            rid = sheet.attrib[f"{{{NS_REL}}}id"]
-            target = relmap[rid].lstrip("/")
-            path = target if target.startswith("xl/") else f"xl/{target}"
-            paths[sheet.attrib["name"]] = path
-        return paths
-
-
-def get_or_create_row(sheet_root: ET.Element, row_num: int) -> ET.Element:
-    sheet_data = sheet_root.find(qname("sheetData"))
-    if sheet_data is None:
-        sheet_data = ET.SubElement(sheet_root, qname("sheetData"))
-    for row in sheet_data.findall(qname("row")):
-        if int(row.attrib.get("r", "0")) == row_num:
-            return row
-    row = ET.Element(qname("row"), {"r": str(row_num)})
-    inserted = False
-    for idx, existing in enumerate(list(sheet_data)):
-        if existing.tag == qname("row") and int(existing.attrib.get("r", "0")) > row_num:
-            sheet_data.insert(idx, row)
-            inserted = True
-            break
-    if not inserted:
-        sheet_data.append(row)
-    return row
-
-
-def get_or_create_cell(row: ET.Element, ref: str) -> ET.Element:
-    for cell in row.findall(qname("c")):
-        if cell.attrib.get("r") == ref:
-            return cell
-    cell = ET.Element(qname("c"), {"r": ref})
-    _, new_row = cell_ref_parts(ref)
-    row.attrib["r"] = str(new_row)
-    inserted = False
-    new_col = col_to_num(cell_ref_parts(ref)[0])
-    for idx, existing in enumerate(list(row)):
-        if existing.tag == qname("c"):
-            existing_ref = existing.attrib.get("r", "A1")
-            if col_to_num(cell_ref_parts(existing_ref)[0]) > new_col:
-                row.insert(idx, cell)
-                inserted = True
-                break
-    if not inserted:
-        row.append(cell)
-    return cell
-
-
-def set_cell_value(cell: ET.Element, value: Any) -> None:
-    ref = cell.attrib.get("r", "")
-    style = cell.attrib.get("s")
-    cell.attrib.clear()
-    cell.attrib["r"] = ref
-    if style is not None:
-        cell.attrib["s"] = style
-
-    for child in list(cell):
-        cell.remove(child)
-    if isinstance(value, (int, float)) and not isinstance(value, bool):
-        v = ET.SubElement(cell, qname("v"))
-        v.text = str(value)
-    else:
-        cell.attrib["t"] = "inlineStr"
-        is_el = ET.SubElement(cell, qname("is"))
-        t = ET.SubElement(is_el, qname("t"))
-        t.text = "" if value is None else str(value)
-
-
-def patch_sheet_xml(xml_bytes: bytes, updates: Dict[str, Any]) -> bytes:
-    root = ET.fromstring(xml_bytes)
-    for ref, value in sorted(updates.items(), key=lambda item: cell_sort_key(item[0])):
-        _, row_num = cell_ref_parts(ref)
-        row = get_or_create_row(root, row_num)
-        cell = get_or_create_cell(row, ref)
-        cell.attrib["r"] = ref
-        set_cell_value(cell, value)
-    return ET.tostring(root, encoding="utf-8", xml_declaration=True)
-
-
-def materialize_workbook(template: Path, output: Path, updates_by_sheet: Dict[str, Dict[str, Any]]) -> None:
-    sheet_paths = xml_sheet_paths(template)
-    missing_sheets = [sheet for sheet in updates_by_sheet if sheet not in sheet_paths]
-    if missing_sheets:
-        raise ValueError(f"Template missing sheets required for updates: {missing_sheets}")
-
-    with zipfile.ZipFile(template, "r") as zin, zipfile.ZipFile(output, "w", zipfile.ZIP_DEFLATED) as zout:
-        for item in zin.infolist():
-            data = zin.read(item.filename)
-            for sheet_name, updates in updates_by_sheet.items():
-                if item.filename == sheet_paths[sheet_name]:
-                    data = patch_sheet_xml(data, updates)
-                    break
-            zout.writestr(item, data)
-
-
-def inspect_workbook(path: Path) -> Dict[str, Any]:
-    return inspect_formula_workbook(
-        path,
-        required_sheets=REQUIRED_SHEETS,
-        workbook_type="three_statement",
-    )
-
-def build(plan_path: Path, output_dir: Path, template: Path) -> Dict[str, Any]:
-    validation_errors = validate(str(plan_path))
-    if validation_errors:
-        raise ValueError("Plan validation failed before workbook materialization: " + "; ".join(validation_errors))
-
-    plan = read_plan(plan_path)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    output_path = output_dir / OUTPUT_WORKBOOK
-    run_log_path = output_dir / OUTPUT_RUN_LOG
-    model_citations_path = output_dir / OUTPUT_MODEL_CITATIONS
-
-    missing_inputs: List[str] = []
-    warnings: List[str] = []
-    updates_by_sheet = {
-        "Executive Summary": build_executive_summary_updates(plan),
-        "Control Panel": build_control_panel_updates(plan, missing_inputs, warnings),
-        "Historical Financials": build_historical_updates(plan, missing_inputs, warnings),
-        "Source Notes": build_source_notes_updates(plan),
-    }
-    materialize_workbook(template, output_path, updates_by_sheet)
-    inspection = inspect_workbook(output_path)
-    model_citations = build_model_citations(plan, output_path)
-    model_citations_path.write_text(
-        json.dumps({"model_citations": model_citations}, indent=2) + "\n",
-        encoding="utf-8",
-    )
-    inspection["anchor_map_path"] = str(model_citations_path)
-    inspection["anchor_map_present"] = len(model_citations) >= 5
-    citation_validation_errors = validate_model_citations({"model_citations": model_citations}, strict=True)
-
-    hard_failures = []
-    if not inspection["required_sheets_present"]:
-        hard_failures.append("banker_formula_workbook_missing_required_sheets")
-    if inspection["formula_count"] < inspection["minimum_formula_count"]:
-        hard_failures.append("banker_formula_workbook_formula_count_below_threshold")
-    if not inspection["required_formula_sheets_populated"]:
-        hard_failures.append("banker_formula_workbook_missing_required_formula_sheets")
-    if not inspection["cover_first"]:
-        hard_failures.append("banker_formula_workbook_cover_not_first_visible_sheet")
-    if not inspection.get("has_named_ranges") and not inspection.get("anchor_map_present"):
-        hard_failures.append("banker_formula_workbook_missing_named_ranges_or_anchor_map")
-    if citation_validation_errors:
-        hard_failures.append("banker_formula_workbook_model_citations_invalid")
-    if not inspection["has_styles"]:
-        hard_failures.append("banker_formula_workbook_styles_missing")
-    if not inspection["no_external_links"]:
-        hard_failures.append("banker_formula_workbook_contains_external_links")
-
-    model_status = "not-decision-ready" if hard_failures else ("screen-grade" if has_placeholder_sources(plan) else "senior-review-ready")
-    run_status = "failed" if hard_failures else "completed"
-    run_log = {
-        "status": run_status,
-        "model_status": model_status,
-        "readiness_effect": model_status.replace("-", "_"),
-        "decision_impact": (
-            "Formula inspection failed; workbook is diagnostic only and must not drive target, rating, sizing, or circulation."
-            if hard_failures
-            else "Formula workbook passed integrity gates; Excel or compatible software should recalculate on open before final circulation."
-        ),
-        "workbook_mode": "banker_formula_workbook",
-        "artifact_level": "banker_formula_workbook_template_materializer",
-        "template_path": str(template),
-        "output_paths": {
-            "banker_formula_workbook": str(output_path),
-            "banker_formula_workbook_run_log": str(run_log_path),
-            "model_citations": str(model_citations_path),
-            "manifest": str(output_dir / OUTPUT_MANIFEST),
-        },
-        "partial_context_warning": PARTIAL_CONTEXT_WARNING if has_placeholder_sources(plan) else None,
-        "missing_inputs": sorted(set(missing_inputs)),
-        "warnings": sorted(set(warnings)),
-        "hard_failures": hard_failures,
-        "workbook_inspection": inspection,
-        "model_citations_count": len(model_citations),
-        "model_citation_validation_errors": citation_validation_errors,
-        "formula_mode_limitations": [
-            "Preserves and populates the bundled formula workbook template.",
-            "Does not synthesize new tabs, formulas, or workbook architecture beyond the shipped template.",
-            "Excel or compatible spreadsheet software should recalculate formulas after opening.",
-        ],
-    }
-    run_log_path.write_text(json.dumps(run_log, indent=2), encoding="utf-8")
-    write_output_manifest(output_dir, run_log)
-    return run_log
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Materialize the 3-statement banker formula workbook template from plan.json.")
-    parser.add_argument("plan_json", type=Path)
-    parser.add_argument("--output-dir", type=Path, default=None, help="Output directory for workbook, run log, and manifest. Defaults to ./output.")
-    parser.add_argument("--template", type=Path, default=DEFAULT_TEMPLATE)
-    args = parser.parse_args()
-    output_dir = args.output_dir if args.output_dir else Path.cwd() / "output"
-
-    try:
-        run_log = build(args.plan_json, output_dir, args.template)
-    except Exception as exc:
-        print(f"banker_formula_workbook materialization FAILED: {exc}", file=sys.stderr)
-        return 1
-
-    print(json.dumps({
-        "model_status": run_log["model_status"],
-        "status": run_log["status"],
-        "workbook_mode": run_log["workbook_mode"],
-        "output_paths": run_log["output_paths"],
-        "hard_failures": run_log["hard_failures"],
-        "warnings": run_log["warnings"],
-        "missing_inputs": run_log["missing_inputs"],
-    }, indent=2))
-    return 2 if run_log["hard_failures"] else 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/run_pipeline.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/run_pipeline.py
deleted file mode 100755
index e86c70e..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/run_pipeline.py
+++ /dev/null
@@ -1,463 +0,0 @@
-#!/usr/bin/env python3
-"""Run the deterministic 3-statement model pipeline.
-
-Usage:
-  python3 scripts/run_pipeline.py path/to/plan.json [--print-report] [--no-support-note]
-
-Creates relative to the current working directory:
-  output/model.xlsx
-  output/plan.json
-  output/run_log.json
-  optional output/support_note.md support note unless --no-support-note is used
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from pathlib import Path
-from typing import Any
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-SKILL_ROOT = SCRIPT_DIR.parent
-PLUGIN_ROOT = SKILL_ROOT.parent.parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-if str(PLUGIN_ROOT) not in sys.path:
-    sys.path.insert(0, str(PLUGIN_ROOT))
-
-from skill_core import (
-    assumption_rows,
-    check_rows,
-    evaluate_hard_failures_and_warnings,
-    model_status,
-    normalize_plan,
-    p0_handoff,
-    run_scenarios,
-    run_sensitivities,
-    source_rows,
-    summarize_result,
-    summary_rows,
-    to_model_rows,
-)
-from validate_plan import validate
-from xlsx_writer import write_xlsx  # noqa: E402
-
-from shared.artifacts import (  # noqa: E402
-    artifact_paths as shared_artifact_paths,
-)
-from shared.artifacts import (
-    build_run_log,
-    write_run_log_bundle,
-)
-from shared.artifacts import (
-    output_manifest as shared_output_manifest,
-)
-from shared.workbook_artifacts import three_statement_cover_rows  # noqa: E402
-
-
-def artifact_paths(output_dir: Path) -> dict[str, str]:
-    return shared_artifact_paths(
-        output_dir,
-        {
-            "workbook": "model.xlsx",
-            "plan": "plan.json",
-            "run_log": "run_log.json",
-            "support_note": "support_note.md",
-            "legacy_report": "report.md",
-            "manifest": "manifest.json",
-        },
-    )
-
-
-def output_manifest(
-    output_dir: Path,
-    *,
-    support_note_enabled: bool,
-    legacy_report_enabled: bool,
-    written: set[str],
-) -> list[dict[str, Any]]:
-    paths = artifact_paths(output_dir)
-    optional = set()
-    if not support_note_enabled:
-        optional.add("support_note")
-    if not legacy_report_enabled:
-        optional.add("legacy_report")
-    return shared_output_manifest(
-        paths,
-        optional=optional,
-        written=written,
-        descriptions={
-            "workbook": "Deterministic long-format values workbook.",
-            "plan": "Normalized plan actually used by the pipeline.",
-            "run_log": "Status, warnings, hard failures, checks, and handoff payload.",
-            "support_note": "Markdown support note for review, renderer input, or audit.",
-            "legacy_report": "Legacy report.md copy, written only when explicitly requested.",
-            "manifest": "Machine-readable output manifest.",
-        },
-        artifact_roles={
-            "workbook": "primary_human_deliverable",
-            "support_note": "narrative_support",
-            "legacy_report": "narrative_support",
-            "plan": "machine_support",
-            "run_log": "machine_support",
-            "manifest": "machine_support",
-        },
-        hidden_unless_requested={"support_note", "legacy_report", "plan", "run_log", "manifest"},
-    )
-
-
-def write_manifest(output_dir: Path, run_log: dict[str, Any]) -> None:
-    (output_dir / "manifest.json").write_text(
-        json.dumps({"outputs": run_log.get("output_manifest", [])}, indent=2),
-        encoding="utf-8",
-    )
-
-
-def write_blocked_run_log(
-    output_dir: Path,
-    errors: list[str],
-    *,
-    support_note_enabled: bool = True,
-    legacy_report_enabled: bool = False,
-) -> dict[str, Any]:
-    output_dir.mkdir(parents=True, exist_ok=True)
-    paths = artifact_paths(output_dir)
-    run_log: dict[str, Any] = build_run_log(
-        hard_failures=[{"code": "validation_error", "message": error} for error in errors],
-        warnings=[],
-        outputs=paths,
-        output_manifest_rows=output_manifest(
-            output_dir,
-            support_note_enabled=support_note_enabled,
-            legacy_report_enabled=legacy_report_enabled,
-            written={"run_log", "manifest"},
-        ),
-        checks={"validation_errors": errors},
-        p0_handoff={
-            "model_status": "not-decision-ready",
-            "major_caveats": errors,
-            "paths": paths,
-        },
-        model_status="not-decision-ready",
-    )
-    write_run_log_bundle(output_dir, run_log)
-    return run_log
-
-
-def format_money(value: Any) -> str:
-    if value is None:
-        return "n/a"
-    if isinstance(value, str):
-        return value
-    try:
-        val = float(value)
-    except Exception:
-        return str(value)
-    if math.isinf(val) or math.isnan(val):
-        return "n/m"
-    return f"{val:,.1f}"
-
-
-def format_pct(value: Any) -> str:
-    try:
-        val = float(value)
-    except Exception:
-        return "n/a"
-    if math.isinf(val) or math.isnan(val):
-        return "n/m"
-    return f"{val * 100.0:,.1f}%"
-
-
-def render_report(
-    plan: dict[str, Any],
-    scenario_outputs: dict[str, dict[str, Any]],
-    sensitivity_rows: list[dict[str, Any]],
-    run_log: dict[str, Any],
-) -> str:
-    meta = plan.get("meta", {})
-    units = meta.get("units", "")
-    lines: list[str] = []
-    lines.append(f"# Three-Statement Operating Model - {meta.get('company_name', 'Company')}")
-    lines.append("")
-    lines.append(f"**Model status:** `{run_log['model_status']}`  ")
-    lines.append(f"**Workbook mode:** `{run_log['workbook_mode']}`  ")
-    lines.append("**Artifact level:** `deterministic_export`  ")
-    lines.append(f"**As of:** {meta.get('as_of_date', 'n/a')}  ")
-    lines.append(
-        f"**Basis:** {meta.get('accounting_basis', 'unspecified')} | {meta.get('currency', '')} | {units}"
-    )
-    lines.append("")
-    lines.append(
-        "> This export is a deterministic values workbook, not a fully linked banker formula workbook. Use the run log and QA checks before relying on it for a decision."
-    )
-    lines.append("")
-    lines.append("## Scenario summary")
-    lines.append("")
-    lines.append(
-        "| Scenario | Final Period | Revenue | EBITDA | EBITDA Margin | FCF | Ending Cash | Ending Debt | Liquidity Trough | Peak Net Leverage |"
-    )
-    lines.append("|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|")
-    for scenario in ["base", "downside", "upside"]:
-        result = scenario_outputs.get(scenario)
-        if not result:
-            continue
-        s = summarize_result(result)
-        lines.append(
-            f"| {scenario.title()} | {s['final_period']} | {format_money(s['final_revenue'])} | {format_money(s['final_ebitda'])} | {format_pct(s['final_ebitda_margin'])} | {format_money(s['final_fcf'])} | {format_money(s['ending_cash'])} | {format_money(s['ending_debt'])} | {format_money(s['liquidity_trough'])} | {format_money(s['peak_net_leverage'])}x |"
-        )
-    lines.append("")
-
-    base = scenario_outputs.get("base")
-    if base:
-        periods = base["periods"]
-        is_ = base["income_statement"]
-        cf = base["cash_flow_statement"]
-        wc = base["working_capital"]
-        debt = base["debt"]
-        lines.append("## Base case operating read-through")
-        lines.append("")
-        lines.append("| Metric | First Forecast Period | Final Forecast Period | Senior read |")
-        lines.append("|---|---:|---:|---|")
-        first = 0
-        last = len(periods) - 1
-        first_margin = (
-            is_["ebitda"][first] / is_["revenue"][first] if is_["revenue"][first] else 0.0
-        )
-        final_margin = is_["ebitda"][last] / is_["revenue"][last] if is_["revenue"][last] else 0.0
-        fcf_first = cf["cash_flow_from_operations"][first] - cf["capex"][first]
-        fcf_last = cf["cash_flow_from_operations"][last] - cf["capex"][last]
-        lines.append(
-            f"| Revenue | {format_money(is_['revenue'][first])} | {format_money(is_['revenue'][last])} | Check whether growth is supported by market, capacity, pipeline, and pricing evidence. |"
-        )
-        lines.append(
-            f"| EBITDA margin | {format_pct(first_margin)} | {format_pct(final_margin)} | Margin expansion must be tied to mix, utilization, pricing, and cost discipline, not just spreadsheet leverage. |"
-        )
-        lines.append(
-            f"| Free cash flow | {format_money(fcf_first)} | {format_money(fcf_last)} | FCF equals CFO less capex; watch working capital and reinvestment drag. |"
-        )
-        lines.append(
-            f"| Net working capital | {format_money(wc['nwc'][first])} | {format_money(wc['nwc'][last])} | DSO/DIO/DPO assumptions drive cash conversion. |"
-        )
-        lines.append(
-            f"| Debt | {format_money(debt['ending_debt'][first])} | {format_money(debt['ending_debt'][last])} | Cash sweep and required amortization determine deleveraging pace. |"
-        )
-        lines.append("")
-
-    lines.append("## QA and decision posture")
-    lines.append("")
-    hard = run_log.get("hard_failures", [])
-    warns = run_log.get("warnings", [])
-    if hard:
-        lines.append("### Hard failures")
-        for item in hard:
-            lines.append(f"- **{item.get('code')}**: {item.get('message')}")
-    else:
-        lines.append("- No hard failures were generated by the machine checks.")
-    if warns:
-        lines.append("")
-        lines.append("### Warnings")
-        for item in warns:
-            lines.append(f"- **{item.get('code')}**: {item.get('message')}")
-    else:
-        lines.append("- No warnings were generated by the senior-review heuristics.")
-    lines.append("")
-
-    if sensitivity_rows:
-        lines.append("## Sensitivity outputs")
-        lines.append("")
-        lines.append(
-            "| Case | Final Revenue | Final EBITDA | Final FCF | Ending Cash | Liquidity Trough |"
-        )
-        lines.append("|---|---:|---:|---:|---:|---:|")
-        for row in sensitivity_rows[:10]:
-            lines.append(
-                f"| {row['case']} | {format_money(row['final_revenue'])} | {format_money(row['final_ebitda'])} | {format_money(row['final_fcf'])} | {format_money(row['ending_cash'])} | {format_money(row['liquidity_trough'])} |"
-            )
-        lines.append("")
-
-    lines.append("## Source posture")
-    lines.append("")
-    sources = plan.get("source_basis", [])
-    if sources:
-        lines.append("| Source | Evidence Label | As Of | Confidence | Covers |")
-        lines.append("|---|---|---:|---|---|")
-        for src in sources:
-            covers = ", ".join(src.get("covers", []))
-            lines.append(
-                f"| {src.get('label', src.get('id', 'source'))} | {src.get('evidence_label', '')} | {src.get('as_of_date', '')} | {src.get('confidence', '')} | {covers} |"
-            )
-    else:
-        lines.append("No source basis was supplied.")
-    lines.append("")
-
-    lines.append("## Files produced")
-    lines.append("")
-    lines.append(
-        "- `output/model.xlsx` - long-format deterministic model export with scenario rows, statements, schedules, checks, assumptions, sources, and sensitivities."
-    )
-    lines.append("- `output/plan.json` - normalized plan actually used.")
-    lines.append(
-        "- `output/run_log.json` - model status, warnings, hard failures, checks, and P0 handoff."
-    )
-    lines.append("- `output/support_note.md` - support note when enabled.")
-    lines.append("")
-    return "\n".join(lines)
-
-
-def run_pipeline(
-    plan_path: Path,
-    print_report: bool,
-    write_support_note: bool,
-    write_legacy_report_md: bool,
-) -> dict[str, Any]:
-    skill_root = Path(__file__).resolve().parents[1]
-    plan = json.loads(plan_path.read_text(encoding="utf-8"))
-    normalized = normalize_plan(plan, skill_root)
-
-    output_dir = Path.cwd() / "output"
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    scenario_outputs = run_scenarios(normalized)
-    sensitivity_rows = run_sensitivities(normalized)
-    hard_failures, warnings, checks = evaluate_hard_failures_and_warnings(
-        normalized, scenario_outputs
-    )
-    status = model_status(hard_failures, warnings, normalized)
-    output_paths = artifact_paths(output_dir)
-    written = {"workbook", "plan", "run_log", "manifest"}
-    if write_support_note:
-        written.add("support_note")
-    if write_legacy_report_md:
-        written.add("legacy_report")
-
-    model_rows: list[dict[str, Any]] = []
-    for scenario in ["base", "downside", "upside"]:
-        model_rows.extend(to_model_rows(normalized, scenario_outputs[scenario]))
-
-    run_log: dict[str, Any] = build_run_log(
-        hard_failures=hard_failures,
-        warnings=warnings,
-        outputs=output_paths,
-        output_manifest_rows=output_manifest(
-            output_dir,
-            support_note_enabled=write_support_note,
-            legacy_report_enabled=write_legacy_report_md,
-            written=written,
-        ),
-        source_basis=normalized.get("source_basis", []),
-        assumptions={
-            "timeline": normalized.get("timeline", {}),
-            "accounting_basis": normalized.get("meta", {}).get("accounting_basis"),
-            "units": normalized.get("meta", {}).get("units"),
-            "cash_sweep": normalized.get("debt", {}).get("cash_sweep", {}),
-        },
-        checks=checks,
-        p0_handoff=p0_handoff(normalized, scenario_outputs, hard_failures, warnings, status),
-        model_status=status,
-        extra={
-            "primary_human_deliverable": output_paths["workbook"],
-            "support_artifacts": [
-                output_paths[key] for key in output_paths if key != "workbook" and key in written
-            ],
-        },
-    )
-
-    report = render_report(normalized, scenario_outputs, sensitivity_rows, run_log)
-
-    sheets = {
-        "Cover": three_statement_cover_rows(normalized, scenario_outputs, run_log),
-        "Summary": summary_rows(normalized, scenario_outputs),
-        "Model": model_rows,
-        "Sensitivities": sensitivity_rows,
-        "Checks": check_rows(scenario_outputs),
-        "Assumptions": assumption_rows(normalized),
-        "Sources": source_rows(normalized),
-        "Run_Log": [
-            {"field": "model_status", "value": status},
-            {"field": "workbook_mode", "value": "deterministic_export"},
-            {"field": "hard_failure_count", "value": len(hard_failures)},
-            {"field": "warning_count", "value": len(warnings)},
-        ],
-    }
-    write_xlsx(output_dir / "model.xlsx", sheets)
-    (output_dir / "plan.json").write_text(json.dumps(normalized, indent=2), encoding="utf-8")
-    write_run_log_bundle(output_dir, run_log)
-    if write_support_note:
-        (output_dir / "support_note.md").write_text(report, encoding="utf-8")
-    if write_legacy_report_md:
-        (output_dir / "report.md").write_text(report, encoding="utf-8")
-
-    if print_report:
-        print("---BEGIN THREE STATEMENT MODEL SUPPORT NOTE---")
-        print(report)
-        print("---END THREE STATEMENT MODEL SUPPORT NOTE---")
-    return run_log
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Run deterministic 3-statement model pipeline")
-    parser.add_argument("plan_json", help="Path to plan.json")
-    parser.add_argument("--print-report", action="store_true", help="Print support note to stdout")
-    parser.add_argument(
-        "--no-support-note", action="store_true", help="Do not write output/support_note.md"
-    )
-    parser.add_argument(
-        "--no-report-md", action="store_true", help="Deprecated alias for --no-support-note"
-    )
-    parser.add_argument(
-        "--write-report-md",
-        action="store_true",
-        help="Write legacy output/report.md in addition to support_note.md. Off by default.",
-    )
-    args = parser.parse_args()
-    write_support_note = not (args.no_support_note or args.no_report_md)
-
-    plan_path = Path(args.plan_json)
-    output_dir = Path.cwd() / "output"
-    if not plan_path.exists():
-        print(f"Plan not found: {plan_path}", file=sys.stderr)
-        write_blocked_run_log(
-            output_dir,
-            [f"Plan not found: {plan_path}"],
-            support_note_enabled=write_support_note,
-            legacy_report_enabled=args.write_report_md,
-        )
-        return 1
-    validation_errors = validate(str(plan_path))
-    if validation_errors:
-        write_blocked_run_log(
-            output_dir,
-            validation_errors,
-            support_note_enabled=write_support_note,
-            legacy_report_enabled=args.write_report_md,
-        )
-        print("Plan validation FAILED:", file=sys.stderr)
-        for item in validation_errors:
-            print(f"- {item}", file=sys.stderr)
-        return 1
-    try:
-        run_log = run_pipeline(
-            plan_path,
-            args.print_report,
-            write_support_note,
-            args.write_report_md,
-        )
-    except Exception as exc:
-        print(f"Pipeline failed: {exc}", file=sys.stderr)
-        write_blocked_run_log(
-            output_dir,
-            [f"Pipeline failed: {exc}"],
-            support_note_enabled=write_support_note,
-            legacy_report_enabled=args.write_report_md,
-        )
-        return 1
-    if run_log.get("hard_failures"):
-        return 2
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/runtime_loader.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/runtime_loader.py
deleted file mode 100755
index 17f9476..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/runtime_loader.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-"""Load private runtime modules that are not prompt-facing instructions."""
-
-from __future__ import annotations
-
-import hashlib
-import sys
-from importlib.machinery import SourceFileLoader
-from importlib.util import module_from_spec, spec_from_loader
-from pathlib import Path
-from types import ModuleType
-
-
-def load_runtime_module(module_name: str) -> ModuleType:
-    runtime_path = Path(__file__).resolve().parent / module_name
-    if not runtime_path.exists():
-        raise ImportError(f"runtime module not found: {runtime_path}")
-
-    digest = hashlib.sha1(str(runtime_path).encode("utf-8")).hexdigest()[:12]
-    loader_name = f"_skill_runtime_{module_name}_{digest}"
-    if loader_name in sys.modules:
-        return sys.modules[loader_name]
-
-    loader = SourceFileLoader(loader_name, str(runtime_path))
-    spec = spec_from_loader(loader.name, loader)
-    if spec is None:
-        raise ImportError(f"could not create runtime spec for {runtime_path}")
-    module = module_from_spec(spec)
-    sys.modules[loader.name] = module
-    loader.exec_module(module)
-    return module
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/skill_core.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/skill_core.py
deleted file mode 100644
index a56ea9d..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/skill_core.py
+++ /dev/null
@@ -1,1776 +0,0 @@
-"""Core deterministic 3-statement operating model engine.
-
-Design goals:
-- No network calls, no hidden randomness, standard library only.
-- Preserve source/assumption labels in outputs.
-- Produce a long-format model table suitable for deterministic export.
-- Keep workbook rendering separate from source ingestion and model judgment.
-"""
-
-from __future__ import annotations
-
-import copy
-import json
-import math
-import re
-from dataclasses import dataclass
-from datetime import date, datetime
-from pathlib import Path
-from typing import Any
-
-ALLOWED_PERIODICITIES = {"annual", "quarterly"}
-ALLOWED_SCENARIOS = ("base", "downside", "upside")
-MODEL_TOLERANCE = 0.05
-
-
-@dataclass
-class Period:
-    index: int
-    label: str
-    year: int
-    periodicity: str
-    time_factor: float
-
-
-def deep_merge(base: Any, override: Any) -> Any:
-    """Recursively merge override into base without mutating either argument."""
-    if override is None:
-        return copy.deepcopy(base)
-    if isinstance(base, dict) and isinstance(override, dict):
-        out = copy.deepcopy(base)
-        for key, value in override.items():
-            out[key] = deep_merge(out.get(key), value)
-        return out
-    return copy.deepcopy(override)
-
-
-def coalesce(*values: Any, default: Any = None) -> Any:
-    for value in values:
-        if value is not None:
-            return value
-    return default
-
-
-def is_number(value: Any) -> bool:
-    return (
-        isinstance(value, (int, float))
-        and not isinstance(value, bool)
-        and math.isfinite(float(value))
-    )
-
-
-def period_sort_key(label: str) -> tuple[int, int]:
-    text = str(label)
-    year_match = re.search(r"(19|20|21)\d{2}", text)
-    year = int(year_match.group(0)) if year_match else 0
-    q_match = re.search(r"q([1-4])", text.lower())
-    quarter = int(q_match.group(1)) if q_match else 4
-    return (year, quarter)
-
-
-def latest_period_key(mapping: dict[str, Any]) -> str | None:
-    if not isinstance(mapping, dict) or not mapping:
-        return None
-    return max(mapping.keys(), key=period_sort_key)
-
-
-def normalize_plan(plan: dict[str, Any], skill_root: Path) -> dict[str, Any]:
-    """Apply non-conclusion-changing defaults and canonical metadata."""
-    normalized = copy.deepcopy(plan)
-    normalized.setdefault("meta", {})
-    normalized["meta"].setdefault("currency", "USD")
-    normalized["meta"].setdefault("units", "USD_mm")
-    normalized["meta"].setdefault("accounting_basis", "unspecified")
-    normalized.setdefault("source_basis", [])
-    normalized.setdefault(
-        "scenarios",
-        {
-            "base": {"overrides": {}},
-            "downside": {"overrides": {}},
-            "upside": {"overrides": {}},
-        },
-    )
-    normalized.setdefault("sensitivities", {})
-    normalized.setdefault("other_balance_sheet", {})
-    normalized["other_balance_sheet"].setdefault(
-        "other_assets", last_bs_value(normalized, "other_assets", 0.0)
-    )
-    normalized["other_balance_sheet"].setdefault(
-        "other_liabilities", last_bs_value(normalized, "other_liabilities", 0.0)
-    )
-    normalized["workbook_mode"] = "deterministic_export"
-    normalized["artifact_level"] = "deterministic_export"
-    return normalized
-
-
-def build_timeline(start_year: int, horizon_periods: int, periodicity: str) -> list[Period]:
-    if periodicity not in ALLOWED_PERIODICITIES:
-        raise ValueError(f"Unsupported periodicity: {periodicity}")
-    if horizon_periods <= 0:
-        raise ValueError("horizon_periods must be positive")
-
-    periods: list[Period] = []
-    if periodicity == "annual":
-        for i in range(horizon_periods):
-            year = start_year + i
-            periods.append(Period(i, f"FY{year}", year, periodicity, 1.0))
-    else:
-        total_quarters = horizon_periods
-        for i in range(total_quarters):
-            year = start_year + (i // 4)
-            quarter = (i % 4) + 1
-            periods.append(Period(i, f"Q{quarter}-FY{year}", year, periodicity, 0.25))
-    return periods
-
-
-def get_assumption(value: Any, period: Period, default: float = 0.0) -> float:
-    """Fetch scalar or period map value with prior-period fallback."""
-    if value is None:
-        return float(default)
-    if is_number(value):
-        return float(value)
-    if not isinstance(value, dict) or not value:
-        return float(default)
-
-    exact_keys = [period.label, f"FY{period.year}", str(period.year)]
-    for key in exact_keys:
-        if key in value and value[key] is not None:
-            return float(value[key])
-
-    keyed: list[tuple[tuple[int, int], str]] = []
-    for key in value.keys():
-        keyed.append((period_sort_key(str(key)), str(key)))
-    keyed.sort()
-    target = period_sort_key(period.label)
-    prior = [key for key_sort, key in keyed if key_sort <= target]
-    if prior:
-        return float(value[prior[-1]])
-    return float(value[keyed[0][1]])
-
-
-def periodicize_annual_rate(annual_rate: float, time_factor: float) -> float:
-    if time_factor >= 1.0:
-        return annual_rate
-    if annual_rate <= -0.99:
-        return -0.99
-    return (1.0 + annual_rate) ** time_factor - 1.0
-
-
-def last_bs(plan: dict[str, Any]) -> dict[str, Any]:
-    bs = plan.get("historicals", {}).get("balance_sheet", {})
-    key = latest_period_key(bs)
-    return copy.deepcopy(bs.get(key, {})) if key else {}
-
-
-def last_bs_value(plan: dict[str, Any], key: str, default: float = 0.0) -> float:
-    value = last_bs(plan).get(key, default)
-    return float(value or 0.0)
-
-
-def last_nwc(plan: dict[str, Any]) -> float:
-    wc_hist = plan.get("historicals", {}).get("working_capital", {})
-    key = latest_period_key(wc_hist)
-    if key and is_number(wc_hist[key].get("nwc")):
-        return float(wc_hist[key]["nwc"])
-    bs = last_bs(plan)
-    return (
-        float(bs.get("ar", 0.0) or 0.0)
-        + float(bs.get("inventory", 0.0) or 0.0)
-        + float(bs.get("other_current_assets", 0.0) or 0.0)
-        - float(bs.get("ap", 0.0) or 0.0)
-        - float(bs.get("accrued_expenses", 0.0) or 0.0)
-        - float(bs.get("deferred_revenue", 0.0) or 0.0)
-    )
-
-
-def last_hist_is(plan: dict[str, Any]) -> dict[str, Any]:
-    hist = plan.get("historicals", {}).get("income_statement", {})
-    key = latest_period_key(hist)
-    return copy.deepcopy(hist.get(key, {})) if key else {}
-
-
-def compute_revenue(plan: dict[str, Any], periods: list[Period]) -> dict[str, Any]:
-    revenue_plan = plan.get("revenue", {})
-    model = revenue_plan.get("model", "total_growth")
-    total: list[float] = []
-    segment_rows: dict[str, list[float]] = {}
-
-    if model == "segments":
-        segments = revenue_plan.get("segments", {})
-        annualized: dict[str, float] = {}
-        for name, cfg in segments.items():
-            annualized[name] = float(cfg.get("base_revenue", 0.0) or 0.0)
-            segment_rows[name] = []
-        for period in periods:
-            period_total = 0.0
-            for name, cfg in segments.items():
-                growth = get_assumption(cfg.get("growth_rates"), period, 0.0)
-                growth = periodicize_annual_rate(growth, period.time_factor)
-                annualized[name] *= 1.0 + growth
-                value = annualized[name] * period.time_factor
-                segment_rows[name].append(value)
-                period_total += value
-            total.append(period_total)
-        return {
-            "revenue": total,
-            "segments": segment_rows,
-            "source_id": revenue_plan.get("source_id"),
-            "evidence_label": revenue_plan.get("evidence_label", "model_calculated"),
-        }
-
-    if model == "volume_price":
-        units = float(revenue_plan.get("base_units", 0.0) or 0.0)
-        price = float(revenue_plan.get("base_price", 0.0) or 0.0)
-        segment_rows["volume_price_revenue"] = []
-        for period in periods:
-            ug = periodicize_annual_rate(
-                get_assumption(revenue_plan.get("unit_growth_rates"), period, 0.0),
-                period.time_factor,
-            )
-            pg = periodicize_annual_rate(
-                get_assumption(revenue_plan.get("price_growth_rates"), period, 0.0),
-                period.time_factor,
-            )
-            units *= 1.0 + ug
-            price *= 1.0 + pg
-            value = units * price * period.time_factor
-            total.append(value)
-            segment_rows["volume_price_revenue"].append(value)
-        return {
-            "revenue": total,
-            "segments": segment_rows,
-            "source_id": revenue_plan.get("source_id"),
-            "evidence_label": revenue_plan.get("evidence_label", "model_calculated"),
-        }
-
-    base_revenue = float(
-        coalesce(
-            revenue_plan.get("base_revenue"),
-            last_hist_is(plan).get("revenue"),
-            default=0.0,
-        )
-        or 0.0
-    )
-    annualized_revenue = base_revenue
-    segment_rows["total_revenue"] = []
-    for period in periods:
-        growth = periodicize_annual_rate(
-            get_assumption(revenue_plan.get("growth_rates"), period, 0.0),
-            period.time_factor,
-        )
-        annualized_revenue *= 1.0 + growth
-        value = annualized_revenue * period.time_factor
-        total.append(value)
-        segment_rows["total_revenue"].append(value)
-    return {
-        "revenue": total,
-        "segments": segment_rows,
-        "source_id": revenue_plan.get("source_id"),
-        "evidence_label": revenue_plan.get("evidence_label", "model_calculated"),
-    }
-
-
-def compute_income_statement(
-    plan: dict[str, Any],
-    periods: list[Period],
-    revenue_result: dict[str, Any],
-    ppe_result: dict[str, list[float]] | None = None,
-    interest: list[float] | None = None,
-) -> dict[str, list[float]]:
-    costs = plan.get("costs", {})
-    cogs_plan = costs.get("cogs", {})
-    opex_plan = costs.get("opex", {})
-    revenue = revenue_result["revenue"]
-    da = (
-        ppe_result.get("depreciation", [0.0] * len(periods)) if ppe_result else [0.0] * len(periods)
-    )
-    interest_values = interest if interest is not None else [0.0] * len(periods)
-
-    out: dict[str, list[float]] = {
-        k: []
-        for k in [
-            "revenue",
-            "cogs",
-            "gross_profit",
-            "opex",
-            "ebitda",
-            "da",
-            "ebit",
-            "interest",
-            "ebt",
-            "book_taxes",
-            "cash_taxes",
-            "net_income",
-            "deferred_tax",
-            "nol_used",
-            "ending_nol",
-        ]
-    }
-    nol = float(plan.get("tax", {}).get("nol_balance", 0.0) or 0.0)
-    book_tax_rate = float(plan.get("tax", {}).get("book_tax_rate", 0.0) or 0.0)
-    cash_tax_rate = float(plan.get("tax", {}).get("cash_tax_rate", book_tax_rate) or 0.0)
-
-    for i, period in enumerate(periods):
-        rev = revenue[i]
-        if cogs_plan.get("method", "gross_margin") == "pct_revenue":
-            cogs = rev * get_assumption(cogs_plan.get("pct_revenue"), period, 0.0)
-        else:
-            gm = get_assumption(cogs_plan.get("gross_margin"), period, 0.0)
-            cogs = rev * (1.0 - gm)
-        if opex_plan.get("method", "pct_revenue") == "amount":
-            opex = get_assumption(opex_plan.get("amount"), period, 0.0)
-        else:
-            opex = rev * get_assumption(opex_plan.get("pct_revenue"), period, 0.0)
-        gp = rev - cogs
-        ebitda = gp - opex
-        ebit = ebitda - da[i]
-        ebt = ebit - interest_values[i]
-        if ebt > 0:
-            nol_used = min(nol, ebt)
-            taxable_income = max(0.0, ebt - nol_used)
-            book_tax = ebt * book_tax_rate
-            cash_tax = taxable_income * cash_tax_rate
-            nol = max(0.0, nol - nol_used)
-        else:
-            nol_used = 0.0
-            book_tax = 0.0
-            cash_tax = 0.0
-            nol += abs(ebt)
-        net_income = ebt - book_tax
-        deferred_tax = book_tax - cash_tax
-
-        values = {
-            "revenue": rev,
-            "cogs": cogs,
-            "gross_profit": gp,
-            "opex": opex,
-            "ebitda": ebitda,
-            "da": da[i],
-            "ebit": ebit,
-            "interest": interest_values[i],
-            "ebt": ebt,
-            "book_taxes": book_tax,
-            "cash_taxes": cash_tax,
-            "net_income": net_income,
-            "deferred_tax": deferred_tax,
-            "nol_used": nol_used,
-            "ending_nol": nol,
-        }
-        for key, value in values.items():
-            out[key].append(value)
-    return out
-
-
-def compute_ppe_and_da(
-    plan: dict[str, Any], periods: list[Period], revenue: list[float]
-) -> dict[str, list[float]]:
-    ppe_plan = plan.get("ppe", {})
-    begin_ppe = float(coalesce(last_bs_value(plan, "ppe_net", 0.0), default=0.0) or 0.0)
-    out = {
-        "beginning_ppe": [],
-        "capex": [],
-        "depreciation": [],
-        "disposals": [],
-        "ending_ppe": [],
-    }
-    for i, period in enumerate(periods):
-        capex_method = ppe_plan.get("capex_method", "pct_revenue")
-        if capex_method == "amount":
-            capex = get_assumption(ppe_plan.get("capex_amount"), period, 0.0)
-        else:
-            capex = revenue[i] * get_assumption(ppe_plan.get("capex_pct_revenue"), period, 0.0)
-
-        dep_method = ppe_plan.get("depreciation_method", "pct_beginning_ppe")
-        if dep_method == "pct_revenue":
-            depreciation = revenue[i] * get_assumption(
-                ppe_plan.get("depreciation_pct_revenue"), period, 0.0
-            )
-        elif dep_method == "amount":
-            depreciation = get_assumption(ppe_plan.get("depreciation_amount"), period, 0.0)
-        else:
-            annual_rate = get_assumption(
-                ppe_plan.get("depreciation_pct_beginning_ppe"), period, 0.0
-            )
-            depreciation = (begin_ppe + 0.5 * capex) * annual_rate * period.time_factor
-        disposals = get_assumption(ppe_plan.get("disposals"), period, 0.0)
-        ending_ppe = max(0.0, begin_ppe + capex - depreciation - disposals)
-
-        out["beginning_ppe"].append(begin_ppe)
-        out["capex"].append(capex)
-        out["depreciation"].append(depreciation)
-        out["disposals"].append(disposals)
-        out["ending_ppe"].append(ending_ppe)
-        begin_ppe = ending_ppe
-    return out
-
-
-def compute_working_capital(
-    plan: dict[str, Any], periods: list[Period], revenue: list[float], cogs: list[float]
-) -> dict[str, list[float]]:
-    wc_plan = plan.get("working_capital", {})
-    prev_nwc = last_nwc(plan)
-    out = {
-        "ar_days": [],
-        "inventory_days": [],
-        "ap_days": [],
-        "ar": [],
-        "inventory": [],
-        "other_current_assets": [],
-        "ap": [],
-        "accrued_expenses": [],
-        "deferred_revenue": [],
-        "nwc": [],
-        "change_nwc": [],
-    }
-    for i, period in enumerate(periods):
-        annual_revenue = revenue[i] / period.time_factor if period.time_factor else revenue[i]
-        annual_cogs = cogs[i] / period.time_factor if period.time_factor else cogs[i]
-        ar_days = get_assumption(wc_plan.get("ar_days"), period, 0.0)
-        inv_days = get_assumption(wc_plan.get("inventory_days"), period, 0.0)
-        ap_days = get_assumption(wc_plan.get("ap_days"), period, 0.0)
-        ar = annual_revenue * ar_days / 365.0
-        inventory = annual_cogs * inv_days / 365.0
-        oca = annual_revenue * get_assumption(
-            wc_plan.get("other_current_assets_pct_revenue"), period, 0.0
-        )
-        ap = annual_cogs * ap_days / 365.0
-        accrued = annual_revenue * get_assumption(
-            wc_plan.get("accrued_expenses_pct_revenue"), period, 0.0
-        )
-        deferred = annual_revenue * get_assumption(
-            wc_plan.get("deferred_revenue_pct_revenue"), period, 0.0
-        )
-        nwc = ar + inventory + oca - ap - accrued - deferred
-        change_nwc = nwc - prev_nwc
-
-        values = {
-            "ar_days": ar_days,
-            "inventory_days": inv_days,
-            "ap_days": ap_days,
-            "ar": ar,
-            "inventory": inventory,
-            "other_current_assets": oca,
-            "ap": ap,
-            "accrued_expenses": accrued,
-            "deferred_revenue": deferred,
-            "nwc": nwc,
-            "change_nwc": change_nwc,
-        }
-        for key, value in values.items():
-            out[key].append(value)
-        prev_nwc = nwc
-    return out
-
-
-def compute_debt_and_interest(
-    plan: dict[str, Any], periods: list[Period], integrated: dict[str, list[float]]
-) -> dict[str, list[float]]:
-    """Return debt schedule and cash sweep using already computed operating cash flow inputs."""
-    debt_plan = plan.get("debt", {})
-    sweep_plan = debt_plan.get("cash_sweep", {})
-    beginning_debt = float(debt_plan.get("beginning_debt", last_bs_value(plan, "debt", 0.0)) or 0.0)
-    beginning_cash = last_bs_value(plan, "cash", 0.0)
-    beginning_revolver = float(debt_plan.get("beginning_revolver_drawn", 0.0) or 0.0)
-    revolver_commitment = float(debt_plan.get("revolver_commitment", 0.0) or 0.0)
-    min_cash = float(sweep_plan.get("min_cash", 0.0) or 0.0)
-    sweep_pct = float(sweep_plan.get("sweep_pct", 0.0) if sweep_plan.get("enabled", True) else 0.0)
-
-    out = {
-        "beginning_cash": [],
-        "beginning_debt": [],
-        "beginning_revolver_drawn": [],
-        "scheduled_draws": [],
-        "required_draws": [],
-        "total_draws": [],
-        "mandatory_repayment": [],
-        "optional_repayment": [],
-        "total_repayments": [],
-        "interest_rate": [],
-        "interest": [],
-        "ending_debt": [],
-        "ending_revolver_drawn": [],
-        "revolver_availability": [],
-        "cash_before_sweep": [],
-        "cash_change": [],
-        "ending_cash": [],
-        "minimum_cash": [],
-    }
-
-    beg_debt = beginning_debt
-    beg_cash = beginning_cash
-    beg_revolver = beginning_revolver
-    for i, period in enumerate(periods):
-        rate = get_assumption(debt_plan.get("interest_rate"), period, 0.0)
-        scheduled_draw = get_assumption(debt_plan.get("optional_draws"), period, 0.0)
-        available_before_draw = max(0.0, revolver_commitment - beg_revolver)
-        scheduled_draw = (
-            min(max(0.0, scheduled_draw), available_before_draw)
-            if revolver_commitment > 0
-            else max(0.0, scheduled_draw)
-        )
-        mandatory_repay = min(
-            max(
-                0.0,
-                get_assumption(debt_plan.get("mandatory_amortization"), period, 0.0),
-            ),
-            beg_debt + scheduled_draw,
-        )
-        interest_base = max(0.0, beg_debt + 0.5 * scheduled_draw - 0.5 * mandatory_repay)
-        interest = interest_base * rate * period.time_factor
-
-        cfo = integrated["cash_flow_from_operations"][i]
-        capex = integrated["capex"][i]
-        dividends = integrated["dividends"][i]
-        buybacks = integrated["buybacks"][i]
-        issuance = integrated["issuance"][i]
-
-        cash_before_sweep = (
-            beg_cash
-            + cfo
-            - capex
-            - dividends
-            - buybacks
-            + issuance
-            + scheduled_draw
-            - mandatory_repay
-        )
-        required_draw = 0.0
-        revolver_after_scheduled = beg_revolver + scheduled_draw
-        debt_after_scheduled = beg_debt + scheduled_draw - mandatory_repay
-        if cash_before_sweep < min_cash:
-            need = min_cash - cash_before_sweep
-            availability = max(0.0, revolver_commitment - revolver_after_scheduled)
-            required_draw = min(need, availability) if revolver_commitment > 0 else need
-            cash_before_sweep += required_draw
-            revolver_after_scheduled += required_draw
-            debt_after_scheduled += required_draw
-
-        optional_repay = 0.0
-        if sweep_pct > 0.0 and cash_before_sweep > min_cash and debt_after_scheduled > 0.0:
-            optional_repay = min((cash_before_sweep - min_cash) * sweep_pct, debt_after_scheduled)
-        ending_cash = cash_before_sweep - optional_repay
-        ending_debt = max(0.0, debt_after_scheduled - optional_repay)
-
-        revolver_repay = min(optional_repay, revolver_after_scheduled)
-        ending_revolver = max(0.0, revolver_after_scheduled - revolver_repay)
-        availability = (
-            max(0.0, revolver_commitment - ending_revolver) if revolver_commitment > 0 else 0.0
-        )
-        total_draws = scheduled_draw + required_draw
-        total_repayments = mandatory_repay + optional_repay
-        cash_change = ending_cash - beg_cash
-
-        values = {
-            "beginning_cash": beg_cash,
-            "beginning_debt": beg_debt,
-            "beginning_revolver_drawn": beg_revolver,
-            "scheduled_draws": scheduled_draw,
-            "required_draws": required_draw,
-            "total_draws": total_draws,
-            "mandatory_repayment": mandatory_repay,
-            "optional_repayment": optional_repay,
-            "total_repayments": total_repayments,
-            "interest_rate": rate,
-            "interest": interest,
-            "ending_debt": ending_debt,
-            "ending_revolver_drawn": ending_revolver,
-            "revolver_availability": availability,
-            "cash_before_sweep": cash_before_sweep,
-            "cash_change": cash_change,
-            "ending_cash": ending_cash,
-            "minimum_cash": min_cash,
-        }
-        for key, value in values.items():
-            out[key].append(value)
-        beg_cash = ending_cash
-        beg_debt = ending_debt
-        beg_revolver = ending_revolver
-    return out
-
-
-def compute_cash_flow_statement(
-    income_statement: dict[str, list[float]],
-    wc: dict[str, list[float]],
-    ppe: dict[str, list[float]],
-    debt: dict[str, list[float]],
-    equity_flows: dict[str, list[float]],
-) -> dict[str, list[float]]:
-    out = {
-        "net_income": [],
-        "da": [],
-        "deferred_tax": [],
-        "change_nwc": [],
-        "cash_flow_from_operations": [],
-        "capex": [],
-        "cash_flow_from_investing": [],
-        "debt_draws": [],
-        "debt_repayments": [],
-        "dividends": [],
-        "buybacks": [],
-        "issuance": [],
-        "cash_flow_from_financing": [],
-        "cash_change": [],
-        "ending_cash": [],
-    }
-    n = len(income_statement["net_income"])
-    for i in range(n):
-        cfo = (
-            income_statement["net_income"][i]
-            + income_statement["da"][i]
-            + income_statement["deferred_tax"][i]
-            - wc["change_nwc"][i]
-        )
-        cfi = -ppe["capex"][i]
-        cff = (
-            debt["total_draws"][i]
-            - debt["total_repayments"][i]
-            - equity_flows["dividends"][i]
-            - equity_flows["buybacks"][i]
-            + equity_flows["issuance"][i]
-        )
-        values = {
-            "net_income": income_statement["net_income"][i],
-            "da": income_statement["da"][i],
-            "deferred_tax": income_statement["deferred_tax"][i],
-            "change_nwc": wc["change_nwc"][i],
-            "cash_flow_from_operations": cfo,
-            "capex": ppe["capex"][i],
-            "cash_flow_from_investing": cfi,
-            "debt_draws": debt["total_draws"][i],
-            "debt_repayments": debt["total_repayments"][i],
-            "dividends": equity_flows["dividends"][i],
-            "buybacks": equity_flows["buybacks"][i],
-            "issuance": equity_flows["issuance"][i],
-            "cash_flow_from_financing": cff,
-            "cash_change": debt["cash_change"][i],
-            "ending_cash": debt["ending_cash"][i],
-        }
-        for key, value in values.items():
-            out[key].append(value)
-    return out
-
-
-def compute_balance_sheet(
-    plan: dict[str, Any],
-    periods: list[Period],
-    income_statement: dict[str, list[float]],
-    wc: dict[str, list[float]],
-    ppe: dict[str, list[float]],
-    debt: dict[str, list[float]],
-    equity_flows: dict[str, list[float]],
-) -> dict[str, list[float]]:
-    out = {
-        "cash": [],
-        "ar": [],
-        "inventory": [],
-        "other_current_assets": [],
-        "ppe_net": [],
-        "other_assets": [],
-        "total_assets": [],
-        "ap": [],
-        "accrued_expenses": [],
-        "deferred_revenue": [],
-        "debt": [],
-        "other_liabilities": [],
-        "common_equity": [],
-        "retained_earnings": [],
-        "total_liabilities_equity": [],
-        "balance_check": [],
-    }
-    other_assets = float(
-        plan.get("other_balance_sheet", {}).get(
-            "other_assets", last_bs_value(plan, "other_assets", 0.0)
-        )
-        or 0.0
-    )
-    other_liabilities = float(
-        plan.get("other_balance_sheet", {}).get(
-            "other_liabilities", last_bs_value(plan, "other_liabilities", 0.0)
-        )
-        or 0.0
-    )
-    common_equity = float(
-        plan.get("equity", {}).get("common_equity", last_bs_value(plan, "common_equity", 0.0))
-        or 0.0
-    )
-    retained_earnings = last_bs_value(plan, "retained_earnings", 0.0)
-    cumulative_deferred_tax_liability = 0.0
-
-    for i in range(len(periods)):
-        common_equity += equity_flows["issuance"][i] - equity_flows["buybacks"][i]
-        retained_earnings += income_statement["net_income"][i] - equity_flows["dividends"][i]
-        cumulative_deferred_tax_liability += income_statement.get(
-            "deferred_tax", [0.0] * len(periods)
-        )[i]
-        current_other_liabilities = other_liabilities + cumulative_deferred_tax_liability
-        total_assets = (
-            debt["ending_cash"][i]
-            + wc["ar"][i]
-            + wc["inventory"][i]
-            + wc["other_current_assets"][i]
-            + ppe["ending_ppe"][i]
-            + other_assets
-        )
-        total_liab_eq = (
-            wc["ap"][i]
-            + wc["accrued_expenses"][i]
-            + wc["deferred_revenue"][i]
-            + debt["ending_debt"][i]
-            + current_other_liabilities
-            + common_equity
-            + retained_earnings
-        )
-        values = {
-            "cash": debt["ending_cash"][i],
-            "ar": wc["ar"][i],
-            "inventory": wc["inventory"][i],
-            "other_current_assets": wc["other_current_assets"][i],
-            "ppe_net": ppe["ending_ppe"][i],
-            "other_assets": other_assets,
-            "total_assets": total_assets,
-            "ap": wc["ap"][i],
-            "accrued_expenses": wc["accrued_expenses"][i],
-            "deferred_revenue": wc["deferred_revenue"][i],
-            "debt": debt["ending_debt"][i],
-            "other_liabilities": current_other_liabilities,
-            "common_equity": common_equity,
-            "retained_earnings": retained_earnings,
-            "total_liabilities_equity": total_liab_eq,
-            "balance_check": total_assets - total_liab_eq,
-        }
-        for key, value in values.items():
-            out[key].append(value)
-    return out
-
-
-def compute_covenants_or_liquidity(
-    plan: dict[str, Any],
-    periods: list[Period],
-    income_statement: dict[str, list[float]],
-    debt: dict[str, list[float]],
-) -> dict[str, list[float]]:
-    cov = plan.get("debt", {}).get("covenants", {})
-    out = {
-        "net_debt": [],
-        "liquidity": [],
-        "net_leverage": [],
-        "interest_coverage": [],
-        "min_liquidity": [],
-        "max_net_leverage": [],
-        "min_interest_coverage": [],
-        "liquidity_headroom": [],
-        "net_leverage_headroom": [],
-        "interest_coverage_headroom": [],
-        "covenant_breach_flag": [],
-    }
-    for i in range(len(periods)):
-        cash = debt["ending_cash"][i]
-        ending_debt = debt["ending_debt"][i]
-        liquidity = cash + debt["revolver_availability"][i]
-        ebitda = income_statement["ebitda"][i]
-        interest = income_statement["interest"][i]
-        net_debt = ending_debt - cash
-        net_lev = net_debt / ebitda if ebitda > 0 else float("inf")
-        icr = ebitda / interest if interest > 0 else float("inf")
-        min_liq = float(cov.get("min_liquidity", 0.0) or 0.0)
-        max_lev = float(cov.get("max_net_leverage", 1e9) or 1e9)
-        min_icr = float(cov.get("min_interest_coverage", 0.0) or 0.0)
-        liquidity_headroom = liquidity - min_liq
-        net_lev_headroom = max_lev - net_lev if math.isfinite(net_lev) else -1e9
-        icr_headroom = icr - min_icr if math.isfinite(icr) else 1e9
-        breach = (
-            1.0
-            if liquidity_headroom < -MODEL_TOLERANCE
-            or net_lev_headroom < -0.01
-            or icr_headroom < -0.01
-            else 0.0
-        )
-        values = {
-            "net_debt": net_debt,
-            "liquidity": liquidity,
-            "net_leverage": net_lev,
-            "interest_coverage": icr,
-            "min_liquidity": min_liq,
-            "max_net_leverage": max_lev,
-            "min_interest_coverage": min_icr,
-            "liquidity_headroom": liquidity_headroom,
-            "net_leverage_headroom": net_lev_headroom,
-            "interest_coverage_headroom": icr_headroom,
-            "covenant_breach_flag": breach,
-        }
-        for key, value in values.items():
-            out[key].append(value)
-    return out
-
-
-def equity_flows(plan: dict[str, Any], periods: list[Period]) -> dict[str, list[float]]:
-    eq = plan.get("equity", {})
-    out = {"dividends": [], "buybacks": [], "issuance": []}
-    for period in periods:
-        out["dividends"].append(get_assumption(eq.get("dividends"), period, 0.0))
-        out["buybacks"].append(get_assumption(eq.get("buybacks"), period, 0.0))
-        out["issuance"].append(get_assumption(eq.get("issuance"), period, 0.0))
-    return out
-
-
-def build_operating_scaffold(
-    plan: dict[str, Any], periods: list[Period]
-) -> tuple[
-    dict[str, Any],
-    dict[str, list[float]],
-    dict[str, list[float]],
-    dict[str, list[float]],
-]:
-    revenue_result = compute_revenue(plan, periods)
-    # First pass IS without D&A/interest gives COGS for WC and revenue for PP&E.
-    temp_is = compute_income_statement(
-        plan,
-        periods,
-        revenue_result,
-        ppe_result={"depreciation": [0.0] * len(periods)},
-        interest=[0.0] * len(periods),
-    )
-    ppe = compute_ppe_and_da(plan, periods, revenue_result["revenue"])
-    wc = compute_working_capital(plan, periods, revenue_result["revenue"], temp_is["cogs"])
-    return revenue_result, temp_is, ppe, wc
-
-
-def run_integrated_model(plan: dict[str, Any], scenario_name: str = "base") -> dict[str, Any]:
-    timeline = plan.get("timeline", {})
-    periods = build_timeline(
-        int(timeline["start_year"]),
-        int(timeline["horizon_periods"]),
-        timeline.get("periodicity", "annual"),
-    )
-    revenue_result, temp_is, ppe, wc = build_operating_scaffold(plan, periods)
-    eq = equity_flows(plan, periods)
-
-    # Resolve the mild interest/cash-sweep circularity by fixed-point iteration.
-    interest = [0.0] * len(periods)
-    integrated_for_debt = {
-        "cash_flow_from_operations": [0.0] * len(periods),
-        "capex": ppe["capex"],
-        "dividends": eq["dividends"],
-        "buybacks": eq["buybacks"],
-        "issuance": eq["issuance"],
-    }
-    debt = None
-    final_is = None
-    for _ in range(20):
-        final_is = compute_income_statement(plan, periods, revenue_result, ppe, interest=interest)
-        final_cfo = [
-            final_is["net_income"][i]
-            + final_is["da"][i]
-            + final_is["deferred_tax"][i]
-            - wc["change_nwc"][i]
-            for i in range(len(periods))
-        ]
-        integrated_for_debt["cash_flow_from_operations"] = final_cfo
-        debt = compute_debt_and_interest(plan, periods, integrated_for_debt)
-        new_interest = debt["interest"]
-        if max(abs(new_interest[i] - interest[i]) for i in range(len(periods))) < 1e-8:
-            interest = new_interest
-            break
-        interest = new_interest
-    final_is = compute_income_statement(plan, periods, revenue_result, ppe, interest=interest)
-    final_cfo = [
-        final_is["net_income"][i]
-        + final_is["da"][i]
-        + final_is["deferred_tax"][i]
-        - wc["change_nwc"][i]
-        for i in range(len(periods))
-    ]
-    integrated_for_debt["cash_flow_from_operations"] = final_cfo
-    debt = compute_debt_and_interest(plan, periods, integrated_for_debt)
-    final_is = compute_income_statement(
-        plan, periods, revenue_result, ppe, interest=debt["interest"]
-    )
-    cf = compute_cash_flow_statement(final_is, wc, ppe, debt, eq)
-    bs = compute_balance_sheet(plan, periods, final_is, wc, ppe, debt, eq)
-    cov = compute_covenants_or_liquidity(plan, periods, final_is, debt)
-
-    result = {
-        "scenario": scenario_name,
-        "periods": periods,
-        "revenue_detail": revenue_result,
-        "income_statement": final_is,
-        "working_capital": wc,
-        "ppe": ppe,
-        "debt": debt,
-        "cash_flow_statement": cf,
-        "balance_sheet": bs,
-        "covenants_liquidity": cov,
-        "equity_flows": eq,
-    }
-    result["checks"] = compute_checks(plan, result)
-    return result
-
-
-def apply_scenario(plan: dict[str, Any], scenario_name: str) -> dict[str, Any]:
-    scenario = plan.get("scenarios", {}).get(scenario_name, {})
-    return deep_merge(plan, scenario.get("overrides", {}))
-
-
-def run_scenarios(plan: dict[str, Any]) -> dict[str, dict[str, Any]]:
-    outputs: dict[str, dict[str, Any]] = {}
-    for scenario in ALLOWED_SCENARIOS:
-        scenario_plan = apply_scenario(plan, scenario)
-        outputs[scenario] = run_integrated_model(scenario_plan, scenario)
-    return outputs
-
-
-def shock_growth_rates(plan: dict[str, Any], delta: float) -> dict[str, Any]:
-    out = copy.deepcopy(plan)
-    rev = out.get("revenue", {})
-    if rev.get("model") == "segments":
-        for cfg in rev.get("segments", {}).values():
-            rates = cfg.get("growth_rates", {})
-            for key in list(rates.keys()):
-                rates[key] = float(rates[key]) + delta
-    else:
-        rates = rev.setdefault("growth_rates", {})
-        for key in list(rates.keys()):
-            rates[key] = float(rates[key]) + delta
-    return out
-
-
-def shock_map(plan: dict[str, Any], path: list[str], delta: float) -> dict[str, Any]:
-    out = copy.deepcopy(plan)
-    node = out
-    for key in path[:-1]:
-        node = node.setdefault(key, {})
-    leaf = node.get(path[-1], {})
-    if isinstance(leaf, dict):
-        for key in list(leaf.keys()):
-            if is_number(leaf[key]):
-                leaf[key] = float(leaf[key]) + delta
-    elif is_number(leaf):
-        node[path[-1]] = float(leaf) + delta
-    return out
-
-
-def run_sensitivities(plan: dict[str, Any]) -> list[dict[str, Any]]:
-    sens = plan.get("sensitivities", {})
-    rows: list[dict[str, Any]] = []
-
-    cases: list[tuple[str, float, dict[str, Any]]] = []
-    for delta in sens.get("revenue_growth_shocks", []):
-        cases.append(("revenue_growth", float(delta), shock_growth_rates(plan, float(delta))))
-    for delta in sens.get("gross_margin_shocks", []):
-        cases.append(
-            (
-                "gross_margin",
-                float(delta),
-                shock_map(plan, ["costs", "cogs", "gross_margin"], float(delta)),
-            )
-        )
-    for delta in sens.get("dso_day_shocks", []):
-        cases.append(
-            (
-                "dso_days",
-                float(delta),
-                shock_map(plan, ["working_capital", "ar_days"], float(delta)),
-            )
-        )
-    for delta in sens.get("capex_pct_revenue_shocks", []):
-        cases.append(
-            (
-                "capex_pct_revenue",
-                float(delta),
-                shock_map(plan, ["ppe", "capex_pct_revenue"], float(delta)),
-            )
-        )
-    for delta in sens.get("interest_rate_shocks", []):
-        cases.append(
-            (
-                "interest_rate",
-                float(delta),
-                shock_map(plan, ["debt", "interest_rate"], float(delta)),
-            )
-        )
-
-    for driver, delta, case_plan in cases:
-        result = run_integrated_model(case_plan, f"sensitivity_{driver}_{delta:+.4f}")
-        summary = summarize_result(result)
-        rows.append(
-            {
-                "case": f"{driver}_{delta:+.4f}",
-                "driver": driver,
-                "shock": delta,
-                "final_revenue": summary["final_revenue"],
-                "final_ebitda": summary["final_ebitda"],
-                "final_fcf": summary["final_fcf"],
-                "ending_cash": summary["ending_cash"],
-                "liquidity_trough": summary["liquidity_trough"],
-                "peak_net_leverage": summary["peak_net_leverage"],
-            }
-        )
-    return rows
-
-
-def compute_checks(plan: dict[str, Any], result: dict[str, Any]) -> dict[str, Any]:
-    periods = result["periods"]
-    bs = result["balance_sheet"]
-    cf = result["cash_flow_statement"]
-    debt = result["debt"]
-    ppe = result["ppe"]
-    wc = result["working_capital"]
-    is_ = result["income_statement"]
-    eq = result["equity_flows"]
-
-    checks: dict[str, Any] = {
-        "period_checks": [],
-        "max_balance_sheet_abs_error": 0.0,
-        "max_cash_tie_abs_error": 0.0,
-        "max_retained_earnings_abs_error": 0.0,
-        "max_debt_rollforward_abs_error": 0.0,
-        "max_ppe_rollforward_abs_error": 0.0,
-        "max_nwc_rollforward_abs_error": 0.0,
-    }
-    prior_re = last_bs_value(plan, "retained_earnings", 0.0)
-    prior_nwc = last_nwc(plan)
-    for i, period in enumerate(periods):
-        balance_error = bs["balance_check"][i]
-        cash_tie = cf["ending_cash"][i] - bs["cash"][i]
-        expected_re = prior_re + is_["net_income"][i] - eq["dividends"][i]
-        re_error = expected_re - bs["retained_earnings"][i]
-        debt_error = (
-            debt["beginning_debt"][i]
-            + debt["total_draws"][i]
-            - debt["total_repayments"][i]
-            - debt["ending_debt"][i]
-        )
-        ppe_error = (
-            ppe["beginning_ppe"][i]
-            + ppe["capex"][i]
-            - ppe["depreciation"][i]
-            - ppe["disposals"][i]
-            - ppe["ending_ppe"][i]
-        )
-        nwc_error = prior_nwc + wc["change_nwc"][i] - wc["nwc"][i]
-        checks["period_checks"].append(
-            {
-                "period": period.label,
-                "balance_sheet_error": balance_error,
-                "cash_tie_error": cash_tie,
-                "retained_earnings_error": re_error,
-                "debt_rollforward_error": debt_error,
-                "ppe_rollforward_error": ppe_error,
-                "nwc_rollforward_error": nwc_error,
-            }
-        )
-        checks["max_balance_sheet_abs_error"] = max(
-            checks["max_balance_sheet_abs_error"], abs(balance_error)
-        )
-        checks["max_cash_tie_abs_error"] = max(checks["max_cash_tie_abs_error"], abs(cash_tie))
-        checks["max_retained_earnings_abs_error"] = max(
-            checks["max_retained_earnings_abs_error"], abs(re_error)
-        )
-        checks["max_debt_rollforward_abs_error"] = max(
-            checks["max_debt_rollforward_abs_error"], abs(debt_error)
-        )
-        checks["max_ppe_rollforward_abs_error"] = max(
-            checks["max_ppe_rollforward_abs_error"], abs(ppe_error)
-        )
-        checks["max_nwc_rollforward_abs_error"] = max(
-            checks["max_nwc_rollforward_abs_error"], abs(nwc_error)
-        )
-        prior_re = bs["retained_earnings"][i]
-        prior_nwc = wc["nwc"][i]
-    return checks
-
-
-def summarize_result(result: dict[str, Any]) -> dict[str, float]:
-    is_ = result["income_statement"]
-    cf = result["cash_flow_statement"]
-    cov = result["covenants_liquidity"]
-    debt = result["debt"]
-    periods = result["periods"]
-    fcf = [cf["cash_flow_from_operations"][i] - cf["capex"][i] for i in range(len(periods))]
-    final_idx = len(periods) - 1
-    peak_net_leverage = max([x for x in cov["net_leverage"] if math.isfinite(x)] or [0.0])
-    return {
-        "final_revenue": is_["revenue"][final_idx],
-        "final_ebitda": is_["ebitda"][final_idx],
-        "final_ebitda_margin": is_["ebitda"][final_idx] / is_["revenue"][final_idx]
-        if is_["revenue"][final_idx]
-        else 0.0,
-        "final_fcf": fcf[final_idx],
-        "ending_cash": debt["ending_cash"][final_idx],
-        "ending_debt": debt["ending_debt"][final_idx],
-        "liquidity_trough": min(cov["liquidity"]),
-        "peak_net_leverage": peak_net_leverage,
-        "min_interest_coverage": min(
-            [x for x in cov["interest_coverage"] if math.isfinite(x)] or [999.0]
-        ),
-        "final_period": periods[final_idx].label,
-    }
-
-
-def _parse_iso_date(value: Any) -> date | None:
-    if not isinstance(value, str):
-        return None
-    try:
-        return datetime.strptime(value, "%Y-%m-%d").date()
-    except ValueError:
-        return None
-
-
-def source_freshness_warnings(plan: dict[str, Any]) -> list[dict[str, str]]:
-    warnings: list[dict[str, str]] = []
-    meta_as_of = _parse_iso_date(plan.get("meta", {}).get("as_of_date"))
-    dated_sources: list[date] = []
-    for src in plan.get("source_basis", []):
-        if not isinstance(src, dict):
-            continue
-        src_date = _parse_iso_date(src.get("as_of_date"))
-        if not src_date:
-            continue
-        dated_sources.append(src_date)
-        if not meta_as_of:
-            continue
-        age_days = (meta_as_of - src_date).days
-        covers = set(src.get("covers", [])) if isinstance(src.get("covers"), list) else set()
-        if age_days < 0:
-            warnings.append(
-                {
-                    "code": "SOURCE_DATE_AFTER_MODEL_AS_OF",
-                    "message": f"{src.get('id', 'source')} is dated after meta.as_of_date; confirm the model as-of date.",
-                }
-            )
-        elif (
-            covers & {"forecast", "revenue", "costs", "working_capital", "ppe", "debt"}
-            and age_days > 120
-        ):
-            warnings.append(
-                {
-                    "code": "STALE_FORECAST_SOURCE",
-                    "message": f"{src.get('id', 'source')} is {age_days} days older than meta.as_of_date for forecast-driver support.",
-                }
-            )
-        elif covers & {"historicals"} and age_days > 270:
-            warnings.append(
-                {
-                    "code": "STALE_HISTORICAL_SOURCE",
-                    "message": f"{src.get('id', 'source')} historical support is {age_days} days older than meta.as_of_date; refresh for decision-grade use.",
-                }
-            )
-    if dated_sources and (max(dated_sources) - min(dated_sources)).days > 365:
-        warnings.append(
-            {
-                "code": "SOURCE_DATE_SPREAD",
-                "message": "Source dates span more than one year; verify historicals, forecasts, guidance, and market context are contemporaneous.",
-            }
-        )
-    return warnings
-
-
-def evaluate_hard_failures_and_warnings(
-    plan: dict[str, Any], scenario_outputs: dict[str, dict[str, Any]]
-) -> tuple[list[dict[str, str]], list[dict[str, str]], dict[str, Any]]:
-    hard_failures: list[dict[str, str]] = []
-    warnings: list[dict[str, str]] = []
-    checks_summary: dict[str, Any] = {}
-
-    for scenario, result in scenario_outputs.items():
-        checks = result["checks"]
-        checks_summary[scenario] = {k: v for k, v in checks.items() if k != "period_checks"}
-        if checks["max_balance_sheet_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "BALANCE_SHEET_DOES_NOT_BALANCE",
-                    "message": f"{scenario}: balance sheet error exceeds tolerance.",
-                }
-            )
-        if checks["max_cash_tie_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "CASH_DOES_NOT_TIE",
-                    "message": f"{scenario}: ending cash does not tie to cash flow statement.",
-                }
-            )
-        if checks["max_retained_earnings_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "RETAINED_EARNINGS_ROLLFORWARD_FAIL",
-                    "message": f"{scenario}: retained earnings roll-forward fails.",
-                }
-            )
-        if checks["max_debt_rollforward_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "DEBT_ROLLFORWARD_FAIL",
-                    "message": f"{scenario}: debt roll-forward fails.",
-                }
-            )
-        if checks["max_ppe_rollforward_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "PPE_ROLLFORWARD_FAIL",
-                    "message": f"{scenario}: PP&E roll-forward fails.",
-                }
-            )
-        if checks["max_nwc_rollforward_abs_error"] > MODEL_TOLERANCE:
-            hard_failures.append(
-                {
-                    "code": "NWC_ROLLFORWARD_FAIL",
-                    "message": f"{scenario}: working capital roll-forward fails.",
-                }
-            )
-
-        cov = result["covenants_liquidity"]
-        if max(cov["covenant_breach_flag"] or [0.0]) > 0:
-            warnings.append(
-                {
-                    "code": "LIQUIDITY_OR_COVENANT_ISSUE",
-                    "message": f"{scenario}: liquidity or covenant headroom issue appears in forecast.",
-                }
-            )
-        if min(result["debt"]["ending_cash"] or [0.0]) < -MODEL_TOLERANCE:
-            warnings.append(
-                {
-                    "code": "NEGATIVE_CASH",
-                    "message": f"{scenario}: ending cash falls below zero.",
-                }
-            )
-
-    base = scenario_outputs.get("base")
-    downside = scenario_outputs.get("downside")
-    upside = scenario_outputs.get("upside")
-    if base and downside and upside:
-        b = summarize_result(base)
-        d = summarize_result(downside)
-        u = summarize_result(upside)
-        if (
-            abs(b["final_revenue"] - d["final_revenue"]) < MODEL_TOLERANCE
-            and abs(b["final_revenue"] - u["final_revenue"]) < MODEL_TOLERANCE
-        ):
-            hard_failures.append(
-                {
-                    "code": "SCENARIO_SWITCH_NO_OUTPUT_CHANGE",
-                    "message": "scenario switch changes labels but not material model outputs.",
-                }
-            )
-
-    source_basis = plan.get("source_basis", [])
-    if not source_basis:
-        hard_failures.append(
-            {
-                "code": "SOURCE_BASIS_MISSING",
-                "message": "source_basis is missing for material historicals or forecast drivers.",
-            }
-        )
-    else:
-        covers = {cover for src in source_basis for cover in src.get("covers", [])}
-        if "historicals" not in covers:
-            hard_failures.append(
-                {
-                    "code": "HISTORICAL_SOURCE_MISSING",
-                    "message": "source_basis does not cover historical financials.",
-                }
-            )
-        if not ({"revenue", "forecast", "costs"} & covers):
-            hard_failures.append(
-                {
-                    "code": "FORECAST_SOURCE_MISSING",
-                    "message": "source_basis does not cover material forecast drivers.",
-                }
-            )
-        if any(src.get("evidence_label") == "placeholder" for src in source_basis):
-            warnings.append(
-                {
-                    "code": "PLACEHOLDER_ASSUMPTIONS_ACTIVE",
-                    "message": "placeholder source basis remains active; model should be treated as screen-grade only.",
-                }
-            )
-        warnings.extend(source_freshness_warnings(plan))
-
-    # Senior judgment warnings.
-    hist_is = last_hist_is(plan)
-    hist_revenue = float(hist_is.get("revenue", 0.0) or 0.0)
-    if base and hist_revenue > 0:
-        first_revenue = base["income_statement"]["revenue"][0]
-        final_revenue = base["income_statement"]["revenue"][-1]
-        periods_count = max(1, len(base["periods"]))
-        annualized_cagr = (
-            (final_revenue / hist_revenue) ** (1.0 / periods_count) - 1.0
-            if final_revenue > 0
-            else -1.0
-        )
-        if annualized_cagr > 0.20:
-            warnings.append(
-                {
-                    "code": "AGGRESSIVE_REVENUE_RAMP",
-                    "message": "base case revenue CAGR exceeds 20%; verify capacity, market share, sales productivity, and demand evidence.",
-                }
-            )
-        if first_revenue > hist_revenue * 1.25:
-            warnings.append(
-                {
-                    "code": "STEP_UP_REVENUE",
-                    "message": "first forecast period revenue steps up more than 25% from latest historical period.",
-                }
-            )
-
-    if base:
-        gm_first = (
-            base["income_statement"]["gross_profit"][0] / base["income_statement"]["revenue"][0]
-        )
-        gm_last = (
-            base["income_statement"]["gross_profit"][-1] / base["income_statement"]["revenue"][-1]
-        )
-        if gm_last - gm_first > 0.05:
-            warnings.append(
-                {
-                    "code": "UNSUPPORTED_MARGIN_EXPANSION",
-                    "message": "gross margin expands by more than 500 bps; verify mix, pricing, utilization, input costs, and sourcing evidence.",
-                }
-            )
-        capex = base["ppe"]["capex"][-1]
-        da = base["ppe"]["depreciation"][-1]
-        rev_growth = (
-            (base["income_statement"]["revenue"][-1] / base["income_statement"]["revenue"][0] - 1.0)
-            if base["income_statement"]["revenue"][0]
-            else 0.0
-        )
-        if rev_growth > 0.10 and capex < da * 0.75:
-            warnings.append(
-                {
-                    "code": "CAPEX_TOO_LOW_FOR_GROWTH",
-                    "message": "capex is below 75% of D&A despite forecast revenue growth; verify maintenance and growth capex.",
-                }
-            )
-        hist_nwc_ratio = last_nwc(plan) / hist_revenue if hist_revenue else 0.0
-        forecast_nwc_ratio = (
-            base["working_capital"]["nwc"][-1] / base["income_statement"]["revenue"][-1]
-            if base["income_statement"]["revenue"][-1]
-            else 0.0
-        )
-        if hist_nwc_ratio - forecast_nwc_ratio > 0.05:
-            warnings.append(
-                {
-                    "code": "WORKING_CAPITAL_RELEASE_INCONSISTENT_WITH_HISTORY",
-                    "message": "forecast working capital intensity improves by more than 500 bps vs. history; verify DSO/DIO/DPO assumptions.",
-                }
-            )
-
-    return hard_failures, warnings, checks_summary
-
-
-def model_status(
-    hard_failures: list[dict[str, str]],
-    warnings: list[dict[str, str]],
-    plan: dict[str, Any],
-) -> str:
-    if hard_failures:
-        return "not-decision-ready"
-    if any(w.get("code") == "PLACEHOLDER_ASSUMPTIONS_ACTIVE" for w in warnings):
-        return "screen-grade"
-    if warnings:
-        return "senior-review-ready"
-    labels = {src.get("evidence_label") for src in plan.get("source_basis", [])}
-    if labels and labels <= {
-        "source_reported",
-        "connector_sourced",
-        "public_filing",
-        "web_verified",
-        "management_guidance",
-        "company_provided",
-    }:
-        return "decision-grade"
-    return "senior-review-ready"
-
-
-def fmt(value: Any) -> str:
-    if value is None:
-        return ""
-    if isinstance(value, float):
-        if math.isinf(value):
-            return "n/m"
-        return f"{value:,.1f}"
-    return str(value)
-
-
-def to_model_rows(plan: dict[str, Any], result: dict[str, Any]) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    scenario = result["scenario"]
-    periods = result["periods"]
-    unit = plan.get("meta", {}).get("units", "")
-
-    def add(
-        statement: str,
-        section: str,
-        line_item: str,
-        series: list[float],
-        formula_basis: str,
-        evidence_label: str = "model_calculated",
-        source_id: str = "",
-        notes: str = "",
-    ) -> None:
-        for period, value in zip(periods, series):
-            rows.append(
-                {
-                    "scenario": scenario,
-                    "statement": statement,
-                    "section": section,
-                    "line_item": line_item,
-                    "period": period.label,
-                    "value": value,
-                    "unit": unit,
-                    "evidence_label": evidence_label,
-                    "source_id": source_id,
-                    "formula_basis": formula_basis,
-                    "notes": notes,
-                }
-            )
-
-    is_ = result["income_statement"]
-    bs = result["balance_sheet"]
-    cf = result["cash_flow_statement"]
-    debt = result["debt"]
-    wc = result["working_capital"]
-    ppe = result["ppe"]
-    cov = result["covenants_liquidity"]
-
-    add(
-        "IS",
-        "Revenue",
-        "Revenue",
-        is_["revenue"],
-        "driver-based revenue forecast",
-        result["revenue_detail"].get("evidence_label", "model_calculated"),
-        result["revenue_detail"].get("source_id", ""),
-    )
-    add("IS", "COGS", "COGS", is_["cogs"], "revenue x (1 - gross margin)")
-    add("IS", "Gross Profit", "Gross Profit", is_["gross_profit"], "revenue - COGS")
-    add(
-        "IS",
-        "Opex",
-        "Operating Expense",
-        is_["opex"],
-        "revenue x opex percent or amount",
-    )
-    add("IS", "Profitability", "EBITDA", is_["ebitda"], "gross profit - opex")
-    add("IS", "Profitability", "D&A", is_["da"], "from PP&E schedule")
-    add("IS", "Profitability", "EBIT", is_["ebit"], "EBITDA - D&A")
-    add(
-        "IS",
-        "Financing",
-        "Interest Expense",
-        is_["interest"],
-        "average debt x interest rate",
-    )
-    add("IS", "Taxes", "EBT", is_["ebt"], "EBIT - interest")
-    add("IS", "Taxes", "Book Taxes", is_["book_taxes"], "positive EBT x book tax rate")
-    add(
-        "IS",
-        "Taxes",
-        "Cash Taxes",
-        is_["cash_taxes"],
-        "taxable income after NOL x cash tax rate",
-    )
-    add("IS", "Net Income", "Net Income", is_["net_income"], "EBT - book taxes")
-
-    for line in [
-        "cash",
-        "ar",
-        "inventory",
-        "other_current_assets",
-        "ppe_net",
-        "other_assets",
-        "total_assets",
-        "ap",
-        "accrued_expenses",
-        "deferred_revenue",
-        "debt",
-        "other_liabilities",
-        "common_equity",
-        "retained_earnings",
-        "total_liabilities_equity",
-        "balance_check",
-    ]:
-        add(
-            "BS",
-            "Balance Sheet",
-            line.replace("_", " ").title(),
-            bs[line],
-            "linked balance sheet schedule",
-        )
-
-    for line in [
-        "net_income",
-        "da",
-        "deferred_tax",
-        "change_nwc",
-        "cash_flow_from_operations",
-        "capex",
-        "cash_flow_from_investing",
-        "debt_draws",
-        "debt_repayments",
-        "dividends",
-        "buybacks",
-        "issuance",
-        "cash_flow_from_financing",
-        "cash_change",
-        "ending_cash",
-    ]:
-        add(
-            "CF",
-            "Cash Flow",
-            line.replace("_", " ").title(),
-            cf[line],
-            "cash flow statement roll-forward",
-        )
-
-    for line in [
-        "beginning_debt",
-        "scheduled_draws",
-        "required_draws",
-        "total_draws",
-        "mandatory_repayment",
-        "optional_repayment",
-        "total_repayments",
-        "interest_rate",
-        "interest",
-        "ending_debt",
-        "ending_revolver_drawn",
-        "revolver_availability",
-        "minimum_cash",
-    ]:
-        add(
-            "DEBT",
-            "Debt",
-            line.replace("_", " ").title(),
-            debt[line],
-            "debt roll-forward and cash sweep",
-        )
-
-    for line in [
-        "ar_days",
-        "inventory_days",
-        "ap_days",
-        "ar",
-        "inventory",
-        "other_current_assets",
-        "ap",
-        "accrued_expenses",
-        "deferred_revenue",
-        "nwc",
-        "change_nwc",
-    ]:
-        add(
-            "WORKING_CAPITAL",
-            "Working Capital",
-            line.replace("_", " ").title(),
-            wc[line],
-            "days/percent-driven working capital",
-        )
-
-    for line in ["beginning_ppe", "capex", "depreciation", "disposals", "ending_ppe"]:
-        add(
-            "PPE",
-            "PP&E",
-            line.replace("_", " ").title(),
-            ppe[line],
-            "PP&E roll-forward",
-        )
-
-    for line in [
-        "net_debt",
-        "liquidity",
-        "net_leverage",
-        "interest_coverage",
-        "liquidity_headroom",
-        "net_leverage_headroom",
-        "interest_coverage_headroom",
-        "covenant_breach_flag",
-    ]:
-        add(
-            "COVENANTS_LIQUIDITY",
-            "Liquidity",
-            line.replace("_", " ").title(),
-            cov[line],
-            "liquidity and covenant metrics",
-        )
-
-    for pc in result["checks"]["period_checks"]:
-        for key, value in pc.items():
-            if key == "period":
-                continue
-            rows.append(
-                {
-                    "scenario": scenario,
-                    "statement": "CHECKS",
-                    "section": "QA",
-                    "line_item": key.replace("_", " ").title(),
-                    "period": pc["period"],
-                    "value": value,
-                    "unit": unit,
-                    "evidence_label": "model_calculated",
-                    "source_id": "",
-                    "formula_basis": "machine-computed tie-out check",
-                    "notes": "hard failure if above tolerance",
-                }
-            )
-    return rows
-
-
-def assumption_rows(plan: dict[str, Any]) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-
-    def walk(prefix: str, node: Any, source_id: str = "", evidence_label: str = "") -> None:
-        if isinstance(node, dict):
-            sid = node.get("source_id", source_id)
-            ev = node.get("evidence_label", evidence_label)
-            for key, value in node.items():
-                if key in {"source_id", "evidence_label", "description", "notes"}:
-                    continue
-                walk(f"{prefix}.{key}" if prefix else key, value, sid, ev)
-        elif isinstance(node, list):
-            rows.append(
-                {
-                    "assumption_path": prefix,
-                    "period_or_key": "list",
-                    "value": json.dumps(node),
-                    "evidence_label": evidence_label,
-                    "source_id": source_id,
-                    "notes": "list assumption",
-                }
-            )
-        else:
-            bits = prefix.split(".")
-            period_or_key = bits[-1] if bits else ""
-            category = ".".join(bits[:-1]) if len(bits) > 1 else prefix
-            rows.append(
-                {
-                    "assumption_path": category,
-                    "period_or_key": period_or_key,
-                    "value": node,
-                    "evidence_label": evidence_label,
-                    "source_id": source_id,
-                    "notes": "",
-                }
-            )
-
-    for top in [
-        "revenue",
-        "costs",
-        "working_capital",
-        "ppe",
-        "debt",
-        "tax",
-        "equity",
-        "scenarios",
-        "sensitivities",
-    ]:
-        if top in plan:
-            walk(top, plan[top])
-    return rows
-
-
-def summary_rows(
-    plan: dict[str, Any], scenario_outputs: dict[str, dict[str, Any]]
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    for scenario, result in scenario_outputs.items():
-        s = summarize_result(result)
-        for key, value in s.items():
-            rows.append(
-                {
-                    "scenario": scenario,
-                    "metric": key,
-                    "value": value,
-                    "unit": plan.get("meta", {}).get("units", ""),
-                    "notes": "scenario summary",
-                }
-            )
-    return rows
-
-
-def check_rows(scenario_outputs: dict[str, dict[str, Any]]) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    for scenario, result in scenario_outputs.items():
-        for key, value in result["checks"].items():
-            if key == "period_checks":
-                continue
-            rows.append(
-                {
-                    "scenario": scenario,
-                    "check": key,
-                    "value": value,
-                    "pass": abs(float(value)) <= MODEL_TOLERANCE,
-                    "tolerance": MODEL_TOLERANCE,
-                }
-            )
-    return rows
-
-
-def source_rows(plan: dict[str, Any]) -> list[dict[str, Any]]:
-    return [dict(src) for src in plan.get("source_basis", [])]
-
-
-def p0_handoff(
-    plan: dict[str, Any],
-    scenario_outputs: dict[str, dict[str, Any]],
-    hard_failures: list[dict[str, str]],
-    warnings: list[dict[str, str]],
-    status: str,
-) -> dict[str, Any]:
-    scenarios = {name: summarize_result(result) for name, result in scenario_outputs.items()}
-    base = scenarios.get("base", {})
-    all_liquidity = [summary.get("liquidity_trough", 0.0) for summary in scenarios.values()]
-    return {
-        "operating_forecast_summary": {
-            "company_name": plan.get("meta", {}).get("company_name"),
-            "industry": plan.get("meta", {}).get("industry"),
-            "currency": plan.get("meta", {}).get("currency"),
-            "units": plan.get("meta", {}).get("units"),
-            "final_period": base.get("final_period"),
-        },
-        "scenario_outputs": scenarios,
-        "base_downside_upside_revenue_ebitda_fcf_cash": {
-            name: {
-                "final_revenue": s.get("final_revenue"),
-                "final_ebitda": s.get("final_ebitda"),
-                "final_fcf": s.get("final_fcf"),
-                "ending_cash": s.get("ending_cash"),
-            }
-            for name, s in scenarios.items()
-        },
-        "liquidity_trough": min(all_liquidity) if all_liquidity else None,
-        "key_operating_drivers": [
-            "revenue growth",
-            "gross margin",
-            "opex leverage",
-            "DSO/DIO/DPO",
-            "capex intensity",
-            "interest rate",
-            "cash sweep",
-        ],
-        "checks_passed_failed": {
-            "hard_failure_count": len(hard_failures),
-            "warning_count": len(warnings),
-        },
-        "model_status": status,
-        "paths": {
-            "workbook": "output/model.xlsx",
-            "plan": "output/plan.json",
-            "run_log": "output/run_log.json",
-            "support_note": "output/support_note.md",
-        },
-    }
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description=(
-            "Library module for three-statement-model-builder. "
-            "Use scripts/run_pipeline.py for deterministic CLI execution."
-        )
-    )
-    parser.parse_args()
-    print(
-        "This is a library module. Use scripts/run_pipeline.py to build a three-statement model export."
-    )
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/validate_plan.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/validate_plan.py
deleted file mode 100755
index 8e64ba7..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/validate_plan.py
+++ /dev/null
@@ -1,513 +0,0 @@
-#!/usr/bin/env python3
-"""Validate plan.json for Three Statement Model Builder.
-
-Usage:
-  python3 scripts/validate_plan.py path/to/plan.json
-
-Exit codes:
-  0 = valid
-  1 = invalid with actionable errors
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from datetime import date, datetime
-from typing import Any
-
-ALLOWED_PERIODICITIES = {"annual", "quarterly"}
-ALLOWED_EVIDENCE_LABELS = {
-    "source_reported",
-    "company_provided",
-    "connector_sourced",
-    "public_filing",
-    "web_verified",
-    "management_guidance",
-    "analyst_estimate",
-    "benchmark",
-    "assumption",
-    "placeholder",
-}
-ALLOWED_MODEL_EVIDENCE_LABELS = ALLOWED_EVIDENCE_LABELS | {"model_calculated"}
-ALLOWED_ACCOUNTING_BASIS = {"us_gaap", "ifrs", "management", "cash_basis", "unspecified"}
-
-
-def is_num(value: Any) -> bool:
-    return isinstance(value, (int, float)) and not isinstance(value, bool) and value == value
-
-
-def err(errors: list[str], message: str) -> None:
-    errors.append(message)
-
-
-def parse_iso_date(value: Any) -> date | None:
-    if not isinstance(value, str):
-        return None
-    try:
-        return datetime.strptime(value, "%Y-%m-%d").date()
-    except ValueError:
-        return None
-
-
-def require_obj(parent: dict[str, Any], key: str, errors: list[str], ctx: str) -> dict[str, Any]:
-    value = parent.get(key)
-    if not isinstance(value, dict):
-        err(errors, f"Missing or invalid object: {ctx}.{key}")
-        return {}
-    return value
-
-
-def require_list(parent: dict[str, Any], key: str, errors: list[str], ctx: str) -> list[Any]:
-    value = parent.get(key)
-    if not isinstance(value, list):
-        err(errors, f"Missing or invalid list: {ctx}.{key}")
-        return []
-    return value
-
-
-def validate_number_map(
-    value: Any,
-    errors: list[str],
-    ctx: str,
-    low: float = -1e12,
-    high: float = 1e12,
-    allow_empty: bool = False,
-) -> None:
-    if value is None:
-        if not allow_empty:
-            err(errors, f"{ctx} is required")
-        return
-    if is_num(value):
-        if not low <= float(value) <= high:
-            err(errors, f"{ctx} must be between {low} and {high}")
-        return
-    if not isinstance(value, dict):
-        err(errors, f"{ctx} must be a number or period map")
-        return
-    if not value and not allow_empty:
-        err(errors, f"{ctx} cannot be empty")
-        return
-    for key, item in value.items():
-        if not is_num(item) or not low <= float(item) <= high:
-            err(errors, f"{ctx}.{key} must be numeric between {low} and {high}")
-
-
-def validate_sources(
-    plan: dict[str, Any], errors: list[str], meta_as_of_date: date | None = None
-) -> None:
-    sources = require_list(plan, "source_basis", errors, "plan")
-    ids = set()
-    covered = set()
-    for i, src in enumerate(sources):
-        if not isinstance(src, dict):
-            err(errors, f"source_basis[{i}] must be an object")
-            continue
-        sid = src.get("id")
-        if not sid:
-            err(errors, f"source_basis[{i}].id is required")
-        elif sid in ids:
-            err(errors, f"duplicate source_basis id: {sid}")
-        ids.add(sid)
-        if not src.get("label"):
-            err(errors, f"source_basis[{i}].label is required")
-        if not src.get("source_type"):
-            err(errors, f"source_basis[{i}].source_type is required")
-        source_date_raw = src.get("as_of_date")
-        source_as_of_date = parse_iso_date(source_date_raw)
-        if not source_date_raw:
-            err(errors, f"source_basis[{i}].as_of_date is required")
-        elif source_as_of_date is None:
-            err(errors, f"source_basis[{i}].as_of_date must be YYYY-MM-DD")
-        elif meta_as_of_date and source_as_of_date > meta_as_of_date:
-            err(errors, f"source_basis[{i}].as_of_date cannot be after meta.as_of_date")
-        ev = src.get("evidence_label")
-        if ev not in ALLOWED_EVIDENCE_LABELS:
-            err(
-                errors,
-                f"source_basis[{i}].evidence_label must be one of {sorted(ALLOWED_EVIDENCE_LABELS)}",
-            )
-        covers = src.get("covers")
-        if not isinstance(covers, list) or not covers:
-            err(errors, f"source_basis[{i}].covers must be a non-empty list")
-        else:
-            covered.update(covers)
-    if sources:
-        if "historicals" not in covered:
-            err(errors, "source_basis must include at least one entry covering 'historicals'")
-        if not ({"forecast", "revenue", "costs", "working_capital", "ppe", "debt"} & covered):
-            err(
-                errors,
-                "source_basis must include at least one entry covering material forecast drivers",
-            )
-
-
-def validate_historicals(plan: dict[str, Any], errors: list[str]) -> None:
-    hist = require_obj(plan, "historicals", errors, "plan")
-    is_hist = require_obj(hist, "income_statement", errors, "historicals")
-    bs_hist = require_obj(hist, "balance_sheet", errors, "historicals")
-    require_obj(hist, "cash_flow", errors, "historicals")
-    require_obj(hist, "debt_schedule", errors, "historicals")
-    require_obj(hist, "ppe", errors, "historicals")
-    require_obj(hist, "working_capital", errors, "historicals")
-
-    if not is_hist:
-        err(errors, "historicals.income_statement must include at least one historical period")
-    for period, row in is_hist.items():
-        if not isinstance(row, dict):
-            err(errors, f"historicals.income_statement.{period} must be an object")
-            continue
-        for key in ["revenue", "cogs", "opex", "da", "interest", "taxes", "net_income"]:
-            if not is_num(row.get(key)):
-                err(errors, f"historicals.income_statement.{period}.{key} must be numeric")
-        if is_num(row.get("revenue")) and row["revenue"] <= 0:
-            err(errors, f"historicals.income_statement.{period}.revenue must be positive")
-
-    if not bs_hist:
-        err(errors, "historicals.balance_sheet must include at least one historical period")
-    for period, row in bs_hist.items():
-        if not isinstance(row, dict):
-            err(errors, f"historicals.balance_sheet.{period} must be an object")
-            continue
-        for key in [
-            "cash",
-            "ar",
-            "inventory",
-            "other_current_assets",
-            "ppe_net",
-            "other_assets",
-            "ap",
-            "accrued_expenses",
-            "deferred_revenue",
-            "debt",
-            "other_liabilities",
-            "common_equity",
-            "retained_earnings",
-        ]:
-            if not is_num(row.get(key)):
-                err(errors, f"historicals.balance_sheet.{period}.{key} must be numeric")
-        if all(
-            is_num(row.get(k))
-            for k in [
-                "cash",
-                "ar",
-                "inventory",
-                "other_current_assets",
-                "ppe_net",
-                "other_assets",
-                "ap",
-                "accrued_expenses",
-                "deferred_revenue",
-                "debt",
-                "other_liabilities",
-                "common_equity",
-                "retained_earnings",
-            ]
-        ):
-            assets = (
-                row["cash"]
-                + row["ar"]
-                + row["inventory"]
-                + row["other_current_assets"]
-                + row["ppe_net"]
-                + row["other_assets"]
-            )
-            liab_eq = (
-                row["ap"]
-                + row["accrued_expenses"]
-                + row["deferred_revenue"]
-                + row["debt"]
-                + row["other_liabilities"]
-                + row["common_equity"]
-                + row["retained_earnings"]
-            )
-            if abs(assets - liab_eq) > 0.1:
-                err(
-                    errors,
-                    f"historicals.balance_sheet.{period} does not balance; assets minus liabilities/equity = {assets - liab_eq:.2f}",
-                )
-
-
-def validate_revenue(plan: dict[str, Any], errors: list[str]) -> None:
-    rev = require_obj(plan, "revenue", errors, "plan")
-    model = rev.get("model")
-    if model not in {"segments", "total_growth", "volume_price"}:
-        err(errors, "revenue.model must be one of: segments, total_growth, volume_price")
-    if rev.get("evidence_label") and rev.get("evidence_label") not in ALLOWED_MODEL_EVIDENCE_LABELS:
-        err(errors, "revenue.evidence_label is invalid")
-    if model == "segments":
-        segments = rev.get("segments")
-        if not isinstance(segments, dict) or not segments:
-            err(errors, "revenue.segments must be a non-empty object when model='segments'")
-        else:
-            for name, cfg in segments.items():
-                if not isinstance(cfg, dict):
-                    err(errors, f"revenue.segments.{name} must be an object")
-                    continue
-                if not is_num(cfg.get("base_revenue")) or cfg.get("base_revenue", 0) <= 0:
-                    err(errors, f"revenue.segments.{name}.base_revenue must be positive")
-                validate_number_map(
-                    cfg.get("growth_rates"),
-                    errors,
-                    f"revenue.segments.{name}.growth_rates",
-                    -0.9,
-                    2.0,
-                )
-                if (
-                    cfg.get("evidence_label")
-                    and cfg.get("evidence_label") not in ALLOWED_MODEL_EVIDENCE_LABELS
-                ):
-                    err(errors, f"revenue.segments.{name}.evidence_label is invalid")
-    if model == "total_growth":
-        if not is_num(rev.get("base_revenue")) or rev.get("base_revenue", 0) <= 0:
-            err(errors, "revenue.base_revenue must be positive for total_growth")
-        validate_number_map(rev.get("growth_rates"), errors, "revenue.growth_rates", -0.9, 2.0)
-    if model == "volume_price":
-        for key in ["base_units", "base_price"]:
-            if not is_num(rev.get(key)) or rev.get(key, 0) <= 0:
-                err(errors, f"revenue.{key} must be positive for volume_price")
-        validate_number_map(
-            rev.get("unit_growth_rates"), errors, "revenue.unit_growth_rates", -0.9, 2.0
-        )
-        validate_number_map(
-            rev.get("price_growth_rates"), errors, "revenue.price_growth_rates", -0.9, 2.0
-        )
-
-
-def validate_costs(plan: dict[str, Any], errors: list[str]) -> None:
-    costs = require_obj(plan, "costs", errors, "plan")
-    cogs = require_obj(costs, "cogs", errors, "costs")
-    method = cogs.get("method", "gross_margin")
-    if method not in {"gross_margin", "pct_revenue"}:
-        err(errors, "costs.cogs.method must be gross_margin or pct_revenue")
-    if method == "gross_margin":
-        validate_number_map(cogs.get("gross_margin"), errors, "costs.cogs.gross_margin", -0.5, 0.95)
-    else:
-        validate_number_map(cogs.get("pct_revenue"), errors, "costs.cogs.pct_revenue", 0.0, 1.5)
-    opex = require_obj(costs, "opex", errors, "costs")
-    omethod = opex.get("method", "pct_revenue")
-    if omethod not in {"pct_revenue", "amount"}:
-        err(errors, "costs.opex.method must be pct_revenue or amount")
-    if omethod == "pct_revenue":
-        validate_number_map(opex.get("pct_revenue"), errors, "costs.opex.pct_revenue", 0.0, 1.5)
-    else:
-        validate_number_map(opex.get("amount"), errors, "costs.opex.amount", 0.0, 1e9)
-
-
-def validate_working_capital(plan: dict[str, Any], errors: list[str]) -> None:
-    wc = require_obj(plan, "working_capital", errors, "plan")
-    if wc.get("method", "days") != "days":
-        err(errors, "working_capital.method currently must be 'days'")
-    validate_number_map(wc.get("ar_days"), errors, "working_capital.ar_days", 0.0, 365.0)
-    validate_number_map(
-        wc.get("inventory_days"), errors, "working_capital.inventory_days", 0.0, 365.0
-    )
-    validate_number_map(wc.get("ap_days"), errors, "working_capital.ap_days", 0.0, 365.0)
-    validate_number_map(
-        wc.get("other_current_assets_pct_revenue"),
-        errors,
-        "working_capital.other_current_assets_pct_revenue",
-        0.0,
-        1.0,
-    )
-    validate_number_map(
-        wc.get("accrued_expenses_pct_revenue"),
-        errors,
-        "working_capital.accrued_expenses_pct_revenue",
-        0.0,
-        1.0,
-    )
-    validate_number_map(
-        wc.get("deferred_revenue_pct_revenue"),
-        errors,
-        "working_capital.deferred_revenue_pct_revenue",
-        0.0,
-        1.0,
-    )
-
-
-def validate_ppe_debt_tax_equity(plan: dict[str, Any], errors: list[str]) -> None:
-    ppe = require_obj(plan, "ppe", errors, "plan")
-    if ppe.get("capex_method", "pct_revenue") not in {"pct_revenue", "amount"}:
-        err(errors, "ppe.capex_method must be pct_revenue or amount")
-    if ppe.get("capex_method", "pct_revenue") == "pct_revenue":
-        validate_number_map(ppe.get("capex_pct_revenue"), errors, "ppe.capex_pct_revenue", 0.0, 1.0)
-    else:
-        validate_number_map(ppe.get("capex_amount"), errors, "ppe.capex_amount", 0.0, 1e9)
-    if ppe.get("depreciation_method", "pct_beginning_ppe") not in {
-        "pct_beginning_ppe",
-        "pct_revenue",
-        "amount",
-    }:
-        err(errors, "ppe.depreciation_method must be pct_beginning_ppe, pct_revenue, or amount")
-    if ppe.get("depreciation_method", "pct_beginning_ppe") == "pct_beginning_ppe":
-        validate_number_map(
-            ppe.get("depreciation_pct_beginning_ppe"),
-            errors,
-            "ppe.depreciation_pct_beginning_ppe",
-            0.0,
-            1.0,
-        )
-
-    debt = require_obj(plan, "debt", errors, "plan")
-    if not is_num(debt.get("beginning_debt")) or debt.get("beginning_debt", 0) < 0:
-        err(errors, "debt.beginning_debt must be non-negative")
-    if (
-        not is_num(debt.get("beginning_revolver_drawn", 0.0))
-        or debt.get("beginning_revolver_drawn", 0.0) < 0
-    ):
-        err(errors, "debt.beginning_revolver_drawn must be non-negative")
-    if not is_num(debt.get("revolver_commitment", 0.0)) or debt.get("revolver_commitment", 0.0) < 0:
-        err(errors, "debt.revolver_commitment must be non-negative")
-    validate_number_map(
-        debt.get("mandatory_amortization"),
-        errors,
-        "debt.mandatory_amortization",
-        0.0,
-        1e9,
-        allow_empty=True,
-    )
-    validate_number_map(
-        debt.get("optional_draws", {}), errors, "debt.optional_draws", 0.0, 1e9, allow_empty=True
-    )
-    validate_number_map(debt.get("interest_rate"), errors, "debt.interest_rate", 0.0, 1.0)
-    sweep = require_obj(debt, "cash_sweep", errors, "debt")
-    if not is_num(sweep.get("min_cash")) or sweep.get("min_cash", 0) < 0:
-        err(errors, "debt.cash_sweep.min_cash must be non-negative")
-    if not is_num(sweep.get("sweep_pct")) or not 0 <= sweep.get("sweep_pct", 0) <= 1:
-        err(errors, "debt.cash_sweep.sweep_pct must be between 0 and 1")
-    cov = debt.get("covenants", {})
-    if cov and not isinstance(cov, dict):
-        err(errors, "debt.covenants must be an object")
-
-    tax = require_obj(plan, "tax", errors, "plan")
-    if not is_num(tax.get("book_tax_rate")) or not 0 <= tax.get("book_tax_rate", 0) <= 0.6:
-        err(errors, "tax.book_tax_rate must be between 0 and 0.6")
-    if not is_num(tax.get("cash_tax_rate")) or not 0 <= tax.get("cash_tax_rate", 0) <= 0.6:
-        err(errors, "tax.cash_tax_rate must be between 0 and 0.6")
-    if not is_num(tax.get("nol_balance", 0.0)) or tax.get("nol_balance", 0.0) < 0:
-        err(errors, "tax.nol_balance must be non-negative")
-
-    eq = require_obj(plan, "equity", errors, "plan")
-    if not is_num(eq.get("common_equity")):
-        err(errors, "equity.common_equity must be numeric")
-    validate_number_map(
-        eq.get("dividends", {}), errors, "equity.dividends", 0.0, 1e9, allow_empty=True
-    )
-    validate_number_map(
-        eq.get("buybacks", {}), errors, "equity.buybacks", 0.0, 1e9, allow_empty=True
-    )
-    validate_number_map(
-        eq.get("issuance", {}), errors, "equity.issuance", 0.0, 1e9, allow_empty=True
-    )
-
-
-def validate_scenarios(plan: dict[str, Any], errors: list[str]) -> None:
-    scenarios = require_obj(plan, "scenarios", errors, "plan")
-    for name in ["base", "downside", "upside"]:
-        if name not in scenarios or not isinstance(scenarios.get(name), dict):
-            err(errors, f"scenarios.{name} is required")
-            continue
-        scenario = scenarios[name]
-        if "overrides" not in scenario or not isinstance(scenario.get("overrides"), dict):
-            err(errors, f"scenarios.{name}.overrides must be an object")
-        ev = scenario.get("evidence_label")
-        if ev and ev not in ALLOWED_EVIDENCE_LABELS:
-            err(errors, f"scenarios.{name}.evidence_label is invalid")
-
-
-def validate_sensitivities(plan: dict[str, Any], errors: list[str]) -> None:
-    sens = require_obj(plan, "sensitivities", errors, "plan")
-    allowed = {
-        "revenue_growth_shocks",
-        "gross_margin_shocks",
-        "dso_day_shocks",
-        "capex_pct_revenue_shocks",
-        "interest_rate_shocks",
-    }
-    for key, value in sens.items():
-        if key not in allowed:
-            err(errors, f"sensitivities.{key} is not supported")
-        if not isinstance(value, list):
-            err(errors, f"sensitivities.{key} must be a list")
-            continue
-        for item in value:
-            if not is_num(item):
-                err(errors, f"sensitivities.{key} entries must be numeric")
-
-
-def validate(path: str) -> list[str]:
-    errors: list[str] = []
-    try:
-        plan = json.loads(open(path, "r", encoding="utf-8").read())
-    except Exception as exc:
-        return [f"Could not read/parse JSON: {exc}"]
-
-    meta = require_obj(plan, "meta", errors, "plan")
-    meta_as_of_date: date | None = None
-    if meta:
-        if not meta.get("company_name"):
-            err(errors, "meta.company_name is required")
-        if not meta.get("industry"):
-            err(errors, "meta.industry is required")
-        if not meta.get("currency"):
-            err(errors, "meta.currency is required")
-        if not meta.get("units"):
-            err(errors, "meta.units is required")
-        if not meta.get("as_of_date"):
-            err(errors, "meta.as_of_date is required")
-        else:
-            meta_as_of_date = parse_iso_date(meta.get("as_of_date"))
-            if meta_as_of_date is None:
-                err(errors, "meta.as_of_date must be YYYY-MM-DD")
-        if meta.get("accounting_basis") not in ALLOWED_ACCOUNTING_BASIS:
-            err(errors, f"meta.accounting_basis must be one of {sorted(ALLOWED_ACCOUNTING_BASIS)}")
-
-    timeline = require_obj(plan, "timeline", errors, "plan")
-    if timeline:
-        if (
-            not isinstance(timeline.get("start_year"), int)
-            or not 1900 <= timeline.get("start_year", 0) <= 2200
-        ):
-            err(errors, "timeline.start_year must be an integer year")
-        if (
-            not isinstance(timeline.get("horizon_periods"), int)
-            or not 1 <= timeline.get("horizon_periods", 0) <= 80
-        ):
-            err(errors, "timeline.horizon_periods must be an integer between 1 and 80")
-        if timeline.get("periodicity") not in ALLOWED_PERIODICITIES:
-            err(errors, "timeline.periodicity must be annual or quarterly")
-        if timeline.get("periodicity") == "annual" and timeline.get("horizon_periods", 0) > 20:
-            err(errors, "annual horizon_periods should not exceed 20")
-        if timeline.get("periodicity") == "quarterly" and timeline.get("horizon_periods", 0) > 80:
-            err(errors, "quarterly horizon_periods should not exceed 80")
-
-    validate_sources(plan, errors, meta_as_of_date)
-    validate_historicals(plan, errors)
-    validate_revenue(plan, errors)
-    validate_costs(plan, errors)
-    validate_working_capital(plan, errors)
-    validate_ppe_debt_tax_equity(plan, errors)
-    validate_scenarios(plan, errors)
-    validate_sensitivities(plan, errors)
-    return errors
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Validate three-statement-model-builder plan.json")
-    parser.add_argument("plan_path", help="Path to plan.json")
-    args = parser.parse_args()
-
-    errors = validate(args.plan_path)
-    if errors:
-        print("Plan validation FAILED:", file=sys.stderr)
-        for item in errors:
-            print(f"- {item}", file=sys.stderr)
-        return 1
-    print("Plan validation OK")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/financial-markets/skills/three-statement-model-builder/scripts/xlsx_writer.py b/plugins/financial-markets/skills/three-statement-model-builder/scripts/xlsx_writer.py
deleted file mode 100644
index ffc109e..0000000
--- a/plugins/financial-markets/skills/three-statement-model-builder/scripts/xlsx_writer.py
+++ /dev/null
@@ -1,204 +0,0 @@
-"""Minimal XLSX writer for three-statement deterministic exports."""
-
-from __future__ import annotations
-
-import re
-import zipfile
-from pathlib import Path
-from typing import Any
-from xml.sax.saxutils import escape
-
-
-def _is_number(value: Any) -> bool:
-    return isinstance(value, (int, float)) and not isinstance(value, bool) and value == value
-
-
-def _xlsx_col_ref(col_1idx: int) -> str:
-    value = ""
-    col = col_1idx
-    while col:
-        col, rem = divmod(col - 1, 26)
-        value = chr(65 + rem) + value
-    return value
-
-
-def _xlsx_cell_ref(row_1idx: int, col_1idx: int) -> str:
-    return f"{_xlsx_col_ref(col_1idx)}{row_1idx}"
-
-
-def _xlsx_shared_string(text: str, shared_map: dict[str, int], shared_list: list[str]) -> int:
-    if text not in shared_map:
-        shared_map[text] = len(shared_list)
-        shared_list.append(text)
-    return shared_map[text]
-
-
-def sanitize_sheet_name(name: str) -> str:
-    cleaned = re.sub(r"[][\\/*?:]", "_", str(name))[:31]
-    return cleaned or "Sheet"
-
-
-def _cell_xml(
-    row_1idx: int,
-    col_1idx: int,
-    value: Any,
-    shared_map: dict[str, int],
-    shared_list: list[str],
-) -> str:
-    if value is None or value == "":
-        return ""
-    ref = _xlsx_cell_ref(row_1idx, col_1idx)
-    if isinstance(value, bool):
-        return f'<c r="{ref}" t="b"><v>{1 if value else 0}</v></c>'
-    if _is_number(value):
-        return f'<c r="{ref}"><v>{float(value)}</v></c>'
-    idx = _xlsx_shared_string(str(value), shared_map, shared_list)
-    return f'<c r="{ref}" t="s"><v>{idx}</v></c>'
-
-
-def _sheet_xml(
-    rows: list[dict[str, Any]], shared_map: dict[str, int], shared_list: list[str]
-) -> str:
-    if rows:
-        headers = list(rows[0].keys())
-    else:
-        headers = ["message"]
-        rows = [{"message": "no rows"}]
-    sheet_rows: list[str] = []
-    header_cells = "".join(
-        _cell_xml(1, c, value, shared_map, shared_list) for c, value in enumerate(headers, start=1)
-    )
-    sheet_rows.append(f'<row r="1">{header_cells}</row>')
-    for row_num, row in enumerate(rows, start=2):
-        cells = "".join(
-            _cell_xml(row_num, c, row.get(header, ""), shared_map, shared_list)
-            for c, header in enumerate(headers, start=1)
-        )
-        sheet_rows.append(f'<row r="{row_num}">{cells}</row>')
-    dimension = f"A1:{_xlsx_cell_ref(len(rows) + 1, len(headers))}"
-    return (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        '<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" '
-        'xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">'
-        f'<dimension ref="{dimension}"/>'
-        '<sheetViews><sheetView workbookViewId="0"><pane ySplit="1" topLeftCell="A2" activePane="bottomLeft" state="frozen"/></sheetView></sheetViews>'
-        "<sheetData>" + "".join(sheet_rows) + "</sheetData>"
-        "</worksheet>"
-    )
-
-
-def _shared_strings_xml(shared_list: list[str]) -> str:
-    def item_xml(text: str) -> str:
-        needs_preserve = text[:1].isspace() or text[-1:].isspace()
-        attr = ' xml:space="preserve"' if needs_preserve else ""
-        return f"<si><t{attr}>{escape(text)}</t></si>"
-
-    return (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        '<sst xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" '
-        f'count="{len(shared_list)}" uniqueCount="{len(shared_list)}">'
-        + "".join(item_xml(item) for item in shared_list)
-        + "</sst>"
-    )
-
-
-def _styles_xml() -> str:
-    return """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<styleSheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main">
-  <fonts count="1"><font><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/></font></fonts>
-  <fills count="2"><fill><patternFill patternType="none"/></fill><fill><patternFill patternType="gray125"/></fill></fills>
-  <borders count="1"><border><left/><right/><top/><bottom/><diagonal/></border></borders>
-  <cellStyleXfs count="1"><xf numFmtId="0" fontId="0" fillId="0" borderId="0"/></cellStyleXfs>
-  <cellXfs count="1"><xf numFmtId="0" fontId="0" fillId="0" borderId="0" xfId="0"/></cellXfs>
-  <cellStyles count="1"><cellStyle name="Normal" xfId="0" builtinId="0"/></cellStyles>
-</styleSheet>
-"""
-
-
-def _workbook_parts(sheet_xmls: list[tuple[str, str]]) -> tuple[str, str, str, str]:
-    workbook_sheets = []
-    rels = []
-    content_overrides = []
-    for idx, (name, _) in enumerate(sheet_xmls, start=1):
-        workbook_sheets.append(f'<sheet name="{escape(name)}" sheetId="{idx}" r:id="rId{idx}"/>')
-        rels.append(
-            f'<Relationship Id="rId{idx}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" Target="worksheets/sheet{idx}.xml"/>'
-        )
-        content_overrides.append(
-            f'<Override PartName="/xl/worksheets/sheet{idx}.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"/>'
-        )
-    style_rid = len(sheet_xmls) + 1
-    sst_rid = len(sheet_xmls) + 2
-    rels.append(
-        f'<Relationship Id="rId{style_rid}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles" Target="styles.xml"/>'
-    )
-    rels.append(
-        f'<Relationship Id="rId{sst_rid}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/sharedStrings" Target="sharedStrings.xml"/>'
-    )
-
-    workbook_xml = (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        '<workbook xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" '
-        'xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"><sheets>'
-        + "".join(workbook_sheets)
-        + "</sheets></workbook>"
-    )
-    workbook_rels_xml = (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        '<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">'
-        + "".join(rels)
-        + "</Relationships>"
-    )
-    root_rels_xml = """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
-  <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" Target="xl/workbook.xml"/>
-</Relationships>
-"""
-    content_types_xml = (
-        '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>'
-        '<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">'
-        '<Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>'
-        '<Default Extension="xml" ContentType="application/xml"/>'
-        '<Override PartName="/xl/workbook.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.main+xml"/>'
-        + "".join(content_overrides)
-        + '<Override PartName="/xl/styles.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.styles+xml"/>'
-        + '<Override PartName="/xl/sharedStrings.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sharedStrings+xml"/>'
-        + "</Types>"
-    )
-    return content_types_xml, root_rels_xml, workbook_xml, workbook_rels_xml
-
-
-def write_xlsx(
-    path: Path, sheets: dict[str, list[dict[str, Any]]], sheet_name: str = "Model"
-) -> None:
-    """Write a minimal multi-sheet .xlsx workbook using only stdlib zip/xml."""
-    path.parent.mkdir(parents=True, exist_ok=True)
-    if not sheets:
-        raise ValueError("No sheets to write")
-    if isinstance(sheets, list):
-        sheets = {sheet_name: sheets}  # type: ignore[assignment]
-
-    shared_map: dict[str, int] = {}
-    shared_list: list[str] = []
-    sheet_xmls: list[tuple[str, str]] = []
-    used_names: set[str] = set()
-    for raw_name, rows in sheets.items():
-        name = sanitize_sheet_name(raw_name)
-        base_name = name
-        suffix = 1
-        while name in used_names:
-            suffix += 1
-            name = sanitize_sheet_name(f"{base_name[:28]}{suffix}")
-        used_names.add(name)
-        sheet_xmls.append((name, _sheet_xml(rows, shared_map, shared_list)))
-
-    content_types_xml, root_rels_xml, workbook_xml, workbook_rels_xml = _workbook_parts(sheet_xmls)
-    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as archive:
-        archive.writestr("[Content_Types].xml", content_types_xml)
-        archive.writestr("_rels/.rels", root_rels_xml)
-        archive.writestr("xl/workbook.xml", workbook_xml)
-        archive.writestr("xl/_rels/workbook.xml.rels", workbook_rels_xml)
-        for idx, (_, sheet_xml) in enumerate(sheet_xmls, start=1):
-            archive.writestr(f"xl/worksheets/sheet{idx}.xml", sheet_xml)
-        archive.writestr("xl/sharedStrings.xml", _shared_strings_xml(shared_list))
-        archive.writestr("xl/styles.xml", _styles_xml())
diff --git a/plugins/financial-markets/skills/user-context/SKILL.md b/plugins/financial-markets/skills/user-context/SKILL.md
deleted file mode 100644
index fc235ff..0000000
--- a/plugins/financial-markets/skills/user-context/SKILL.md
+++ /dev/null
@@ -1,71 +0,0 @@
----
-name: user-context
-description: Start onboarding, initialize, inspect, save, update, forget, export, or explicitly reset the Public Equity Investing plugin's local user context, source setup, or optional automation setup. Use when the user explicitly asks to get started, orient, or manage Public Equity Investing saved preferences, source pointers, context storage, or recurring automation.
----
-
-# Public Equity Investing User Context
-
-This skill owns the Public Equity Investing plugin's local user-context storage foundation and explicit-only onboarding contract. It is intentionally narrow: it can initialize, inspect, save, update, forget, export, or explicitly reset local context, interpret the next onboarding action, guide the four-step intro/defaults -> connectors/plugins -> automation -> hero-workflow flow, and configure user-approved automations. Its read-only preflight does not inspect connectors, route sources, or create automations.
-
-## State Files
-
-Store Public Equity Investing state under:
-
-```text
-$CODEX_HOME/state/plugins/role-specific-plugins/financial-markets/
-```
-
-The storage foundation owns only:
-
-```text
-user-context.md
-onboarding-state.json
-```
-
-Do not create `category-state.json`.
-
-## Initialize
-
-When the user explicitly asks to initialize Public Equity Investing user context, run `python3 skills/user-context/scripts/init_user_context_state.py` with the shell working directory set to this plugin's root. Set the working directory before the first attempt; do not probe alternate relative paths.
-
-The initializer creates missing state files from bundled templates and preserves existing files unless the user explicitly requests overwrite behavior.
-
-## Inspect
-
-When the user explicitly asks to inspect saved Public Equity Investing context, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to this plugin's root. Set the working directory before the first attempt; do not probe alternate relative paths.
-
-Use the returned read-only JSON envelope to summarize initialization status, onboarding status, the deterministic `next_action`, the lightweight `onboarding_progress`, and empty memory categories. Treat `user-context.md` as user-editable durable context and `onboarding-state.json` as operational scaffolding.
-
-## Save Update Or Forget
-
-When the user explicitly asks to remember, save, update, forget, export, or apply durable Public Equity Investing preferences or source pointers, follow `skills/user-context/references/plugin-memory.md` from the plugin root. Store reusable user-approved context in `user-context.md`; keep live company updates and operational connector state out. Initialize missing state only when a save needs persistence.
-
-## Onboarding
-
-When the user explicitly asks to get started, orient, set up, resume, defer, quiet, or complete Public Equity Investing onboarding, follow `skills/user-context/references/onboarding.md` from the plugin root. Use the inspection helper's `next_action` as the current step and render its `copy_ref` template substantially verbatim. Keep the visible flow to four steps: intro and lightweight defaults, connectors and plugins, one optional automation, then a three-option hero-workflow chooser. Capture broader durable preferences only when the user explicitly asks or supplies them naturally.
-
-## Source Category Vocabulary
-
-`skills/user-context/plugin-author-config/source-category-config.json` reserves static Public Equity Investing source category ids, labels, and preference hints. The read-only preflight exposes those categories as `source_category_plan` and echoes any setup-owned routes already saved under `onboarding-state.json` `connector_confirmation`. Use `skills/user-context/references/source-category-runtime.md` from the plugin root for the source-resolution boundary. Preflight does not prove source readiness, select routes, inspect connectors, or create `category-state.json`.
-
-## Optional Automations
-
-`skills/user-context/plugin-author-config/automation-config.md` defines the small author-owned automation menu. When the user explicitly asks for a recurring Public Equity Investing workflow or accepts the optional onboarding automation step, follow `skills/user-context/references/automation.md` from the plugin root. Do not create, update, pause, resume, or remove automations during ordinary preflight.
-
-## Reset
-
-When the user explicitly asks to reset Public Equity Investing user context, run `python3 skills/user-context/scripts/reset_user_context_state.py` with the shell working directory set to this plugin's root. Set the working directory before the first attempt; do not probe alternate relative paths.
-
-The reset helper moves active state files into a timestamped sibling backup directory before clearing them from the active state directory.
-
-## Current Boundary
-
-- The Public Equity Investing router may run `scripts/user_context_preflight.py` as a soft read-only workflow preflight.
-- Do not initialize state merely because another Public Equity Investing skill runs.
-- Do not mutate state when running the read-only inspection helper.
-- Do not inspect apps, connectors, plugins, `.app.json`, or source readiness during preflight.
-- During user-approved explicit Source Setup, follow `skills/user-context/references/source-category-runtime.md` from the plugin root.
-- During user-approved explicit automation setup or maintenance, follow `skills/user-context/references/automation.md` from the plugin root.
-- Do not invoke onboarding unless the user explicitly asks to get started, orient, set up, or manage saved context.
-- Do not create or modify automations unless the user explicitly asks or accepts the optional automation setup step.
-- Do not add, read, or migrate `category-state.json`. The reset helper may back up and clear an older copy during an explicit reset.
diff --git a/plugins/financial-markets/skills/user-context/agents/openai.yaml b/plugins/financial-markets/skills/user-context/agents/openai.yaml
deleted file mode 100644
index bfc873a..0000000
--- a/plugins/financial-markets/skills/user-context/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Public Equity Investing User Context"
-  short_description: "Manage local Public Equity Investing context storage"
-  default_prompt: "Use $user-context to initialize, inspect, or explicitly reset local Public Equity Investing context storage."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/financial-markets/skills/user-context/plugin-author-config/automation-config.md b/plugins/financial-markets/skills/user-context/plugin-author-config/automation-config.md
deleted file mode 100644
index b2e1316..0000000
--- a/plugins/financial-markets/skills/user-context/plugin-author-config/automation-config.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Public Equity Investing Default Automations
-
-## Weekday Watchlist Brief
-
-- ID: `weekday-watchlist-brief`
-- Name: `Weekday Public Equity Watchlist Brief`
-- Frequency: Weekdays at 8:00 AM local time.
-- Launcher: `Set up a weekday Public Equity Investing watchlist brief in this conversation.`
-
-### Canonical Automation Prompt
-
-Pass this prompt to the automation tool substantially verbatim:
-
-```text
-Run a read-only weekday Public Equity Investing source check. Invoke the Public Equity Investing router and apply its user-context preflight before substantive work. Review only saved watchlist pointers that are already present. Do not perform broad research.
-
-Report only: Upcoming Catalysts, Stale Sources, and Missing Inputs. Include the ticker or watchlist pointer, the dated item or gap, the source pointer, and the as-of date when known.
-
-If no saved watchlist pointer is available, ask for one ticker or watchlist pointer. Do not invent a portfolio or watchlist. If context exists but there is nothing to surface, return: No upcoming catalysts, stale sources, or missing inputs identified today. Then state the source coverage and freshness limitations.
-
-Do not draft investment analysis or modify anything. Do not trade, send messages, edit source systems, or save new durable preferences without an explicit user request.
-```
diff --git a/plugins/financial-markets/skills/user-context/plugin-author-config/source-category-config.json b/plugins/financial-markets/skills/user-context/plugin-author-config/source-category-config.json
deleted file mode 100644
index a5c9ff1..0000000
--- a/plugins/financial-markets/skills/user-context/plugin-author-config/source-category-config.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
-  "schema_version": "public_equity_investing_source_category_config.v1",
-  "description": "Author-owned Public Equity Investing source category catalog. This file reserves static category ids, labels, and preference hints for the explicit source setup flow. It does not inspect connectors, claim readiness, select routes, or create category-state.json. User-specific source preferences and source-of-truth pointers live in $CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md.",
-  "categories": {
-    "company_filings_ir": {
-      "label": "company filings, IR materials, and reported financials",
-      "preferred_apps": [
-        "Quartr",
-        "Daloopa",
-        "Google Drive"
-      ]
-    },
-    "earnings_transcripts_presentations": {
-      "label": "earnings transcripts, presentations, and events",
-      "preferred_apps": [
-        "Quartr",
-        "FactSet",
-        "LSEG",
-        "S&P"
-      ]
-    },
-    "internal_research": {
-      "label": "internal research notes, expert context, and team discussions",
-      "preferred_apps": [
-        "Google Drive",
-        "Microsoft SharePoint",
-        "Slack",
-        "Microsoft Teams",
-        "Gmail",
-        "Outlook Email",
-        "Third Bridge"
-      ]
-    },
-    "portfolio_models_trackers": {
-      "label": "portfolio, watchlists, models, and thesis trackers",
-      "preferred_plugins": [
-        "Spreadsheets",
-        "Microsoft Excel"
-      ],
-      "preferred_apps": [
-        "Google Drive",
-        "Microsoft SharePoint",
-        "FactSet",
-        "Morningstar",
-        "Alpaca"
-      ]
-    },
-    "market_data_estimates": {
-      "label": "market data, consensus, estimates, and ownership",
-      "preferred_apps": [
-        "FactSet",
-        "LSEG",
-        "S&P",
-        "Morningstar",
-        "Daloopa",
-        "Alpaca"
-      ]
-    }
-  }
-}
diff --git a/plugins/financial-markets/skills/user-context/plugin-author-config/user-context-config.md b/plugins/financial-markets/skills/user-context/plugin-author-config/user-context-config.md
deleted file mode 100644
index 9f938ad..0000000
--- a/plugins/financial-markets/skills/user-context/plugin-author-config/user-context-config.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Public Equity Investing User Context Config
-
-Plugin authors edit this file when adding, removing, renaming, or clarifying the starter categories copied into a user's `user-context.md`. The initializer adds `## Saved Links And Context` with `status: not provided` beneath each category.
-
-## Default Categories
-
-# Investor Profile And Output Style
-
-- Description: Audience mode, mandate style, preferred memo or report depth, and PM or research-team conventions that should shape future Public Equity Investing work.
-
-# Portfolio Watchlist And Repository Pointers
-
-- Description: Watchlist locations, research repositories, approved trackers, and recurring review sources for public-equity workflows.
-
-# Research Source Priorities
-
-- Description: Preferred first-look providers, filing and transcript conventions, trusted internal research locations, and source-freshness rules.
-
-# Model Valuation And Estimate Conventions
-
-- Description: Model templates, valuation methods, estimate-source preferences, KPI definitions, and sector-specific conventions.
-
-# Thesis Catalyst And Risk Tracking Conventions
-
-- Description: Thesis-tracker locations, catalyst calendars, monitoring cadence, risk thresholds, and review triggers.
-
-# Compliance Circulation And Review Rules
-
-- Description: Distribution boundaries, citation expectations, compliance review, and client-facing or publication gates.
diff --git a/plugins/financial-markets/skills/user-context/references/automation.md b/plugins/financial-markets/skills/user-context/references/automation.md
deleted file mode 100644
index ae6f2cb..0000000
--- a/plugins/financial-markets/skills/user-context/references/automation.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Public Equity Investing Automation Setup
-
-Use this reference only when the user explicitly asks to create, inspect, update, pause, resume, or remove a Public Equity Investing automation, or accepts the optional automation step during saved-context onboarding.
-
-## Default Catalog
-
-Read `skills/user-context/plugin-author-config/automation-config.md` from the plugin root for the configured automation id, name, frequency, launcher, and canonical automation prompt. The catalog is an author-owned menu, not evidence that an automation exists.
-
-## Setup Rules
-
-1. Do not create an automation until the user explicitly accepts the configured launcher or directly asks for a recurring Public Equity Investing workflow.
-2. If `automation_update` is not already available, use `tool_search` to find it.
-3. Before creating an automation, inspect `$CODEX_HOME/automations/*/automation.toml` for an existing matching automation by id, name, or prompt intent. Prefer updating a matching automation over creating a duplicate.
-4. Use a `heartbeat` attached to the current thread with `destination="thread"` for the default watchlist brief. Keep the first version conversational and reviewable. Do not create a detached `cron` job unless the user explicitly requests one and provides an appropriate workspace.
-5. Pass cadence through the automation tool's schedule field. Keep schedule details out of the automation prompt.
-6. After creation or update, use `automation_update` readback when available and record concise operational metadata under `onboarding-state.json` `automations.configured.<automation-id>`.
-
-## Prompt Requirements
-
-Use the configured canonical automation prompt substantially verbatim. Do not replace it with a generic reminder. Preserve these requirements:
-
-- invokes the Public Equity Investing router and applies user-context preflight before substantive work;
-- reviews only saved watchlist pointers already present;
-- produces only `Upcoming Catalysts`, `Stale Sources`, and `Missing Inputs`;
-- includes the ticker or watchlist pointer, dated item or gap, source pointer, and as-of date when known;
-- returns an honest missing-context or no-material-changes result instead of inventing a portfolio or watchlist;
-- does not perform broad research or draft investment analysis;
-- does not trade, send messages, edit source systems, or save new durable preferences without an explicit user request;
-- remains concise and scan-friendly.
-
-## State Metadata
-
-Store only operational metadata under `onboarding-state.json`:
-
-```json
-{
-  "automations": {
-    "status": "completed",
-    "configured": {
-      "weekday-watchlist-brief": {
-        "automation_id": "<automation id>",
-        "name": "Weekday Public Equity Watchlist Brief",
-        "kind": "heartbeat",
-        "status": "ACTIVE"
-      }
-    }
-  }
-}
-```
-
-Do not copy automation metadata into `user-context.md`. Do not treat saved metadata as proof that the live automation still exists; inspect live automation state when a user asks about status or changes.
-
-## Cleanup
-
-Ask for explicit confirmation before deleting, replacing, or disabling a Public Equity Investing automation. After an approved cleanup, update the matching operational metadata instead of silently removing the history.
diff --git a/plugins/financial-markets/skills/user-context/references/onboarding-state-template.json b/plugins/financial-markets/skills/user-context/references/onboarding-state-template.json
deleted file mode 100644
index 7a41136..0000000
--- a/plugins/financial-markets/skills/user-context/references/onboarding-state-template.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "status": null,
-  "orientation": {},
-  "memory_preferences": {},
-  "source_setup": {},
-  "connector_confirmation": {},
-  "automations": {},
-  "hero_prompt_choice": {
-    "status": null,
-    "options": [
-      "earnings-deep-dive",
-      "long-short-pitch",
-      "idea-generation"
-    ],
-    "selected_skill": null,
-    "selected_anchor": null
-  }
-}
diff --git a/plugins/financial-markets/skills/user-context/references/onboarding.md b/plugins/financial-markets/skills/user-context/references/onboarding.md
deleted file mode 100644
index d7c6c7c..0000000
--- a/plugins/financial-markets/skills/user-context/references/onboarding.md
+++ /dev/null
@@ -1,146 +0,0 @@
-# Public Equity Investing User Context Onboarding
-
-Use this reference only when the user explicitly asks to get started, orient, set up, resume, defer, quiet, or complete Public Equity Investing onboarding. Ordinary Public Equity Investing workflows do not invoke onboarding yet.
-
-## Four-Step Spine
-
-Guide onboarding through exactly four visible steps:
-
-1. Intro and defaults
-2. Connectors and plugins
-3. Automation
-4. Hero workflows
-
-Keep one clear call to action at the end of each onboarding response. Treat a plain `yes`, `okay`, `continue`, or similar reply as approval for the current visible next step unless the user names a different step. Treat `skip`, `not now`, or similar as approval to skip or defer the current optional step and continue.
-
-Durable memory remains available throughout the plugin, but do not turn first-run onboarding into a broad preference questionnaire. Capture the two high-value defaults below when the user wants to provide them, then collect other durable preferences only when the user explicitly asks or supplies them naturally during later work.
-
-## Step 1: Intro And Defaults
-
-Render the canonical response template substantially verbatim. Do not inspect plugins, apps, connectors, or `.app.json` during this first response.
-
-### Orientation Response Template
-
-```text
-The Public Equity Investing plugin helps with listed-company research, earnings analysis, valuation, long/short pitches, thesis tracking, catalysts, and portfolio-risk workflows.
-
-Before we connect sources, are there any defaults you want me to use?
-
-- Reader-facing output: polished HTML research report, Word document (.docx), concise chat answer, or choose based on the task
-- Audience: PM or investment team, analyst working view, formal research note, or choose based on the task
-
-These preferences guide flexible reader-facing outputs only. Workflows with an obvious format, such as models, trackers, workbook updates, deck requests, document requests, or edits to an existing artifact, keep that format.
-
-Reply with any defaults you want saved, or say skip to continue to source setup.
-```
-
-Store only user-provided durable defaults in `user-context.md` by following `skills/user-context/references/plugin-memory.md` from the plugin root. Do not infer preferences. Update onboarding state before continuing:
-
-- When the user supplies one or both defaults, set `orientation.status` and `memory_preferences.status` to `completed`.
-- When the user skips defaults, set `orientation.status` to `completed` and `memory_preferences.status` to `skipped`.
-- When the user defers onboarding, set top-level `status` to `deferred`.
-- When the user quiets future setup guidance, set top-level `status` to `quiet`.
-
-After defaults are saved, skipped, or deferred for now, rerun preflight and render its next `copy_ref` so source setup follows immediately.
-
-### Additional Durable Memory
-
-When the user explicitly asks what else can be remembered, keep the answer concise:
-
-- Output style and recurring research conventions
-- Portfolio, watchlist, repository, and tracker pointers
-- Trusted filings, transcripts, research locations, and source priorities
-- Modeling, valuation, estimate-source, and KPI conventions
-- Thesis, catalyst, risk, and monitoring rules
-- Compliance, circulation, citation, and approval boundaries
-
-Keep live company updates and one-off research requests in the active workflow.
-
-## Step 2: Connectors And Plugins
-
-Render the canonical response template substantially verbatim. Do not inspect plugins, apps, connectors, or `.app.json` until the user approves this step or explicitly asks to configure Public Equity Investing sources.
-
-### Source Setup Response Template
-
-```text
-Next, I can check which Public Equity Investing plugins and source tools are exposed and save setup routes for future workflows. This does not read source contents. Should I check sources now?
-```
-
-When the user approves source setup, follow `skills/user-context/references/source-category-runtime.md` from the plugin root. Keep the result concise: name useful exposed routes, identify categories that still need a choice or access, and end with only the smallest necessary source question or a clear continue-with-current-sources action. Store operational route selections under `onboarding-state.json` `connector_confirmation`; do not store them in `user-context.md`.
-
-Update `source_setup.status` in `onboarding-state.json`:
-
-- Use `completed` after each category is classified and any required user choice is resolved, including when the user continues with known gaps.
-- Use `deferred` when the user wants to configure sources later.
-- Use `skipped` when the user wants to continue without source setup.
-
-Do not perform proof reads during this setup step. Actual source reads happen only when a focused workflow needs the source. After source setup is completed, skipped, or deferred, move directly to Step 3.
-
-## Step 3: Automation
-
-Render the canonical response template substantially verbatim. This step is optional and requires explicit acceptance before any automation is created.
-
-### Automation Setup Response Template
-
-```text
-Next, I can optionally set up a weekday Public Equity Investing source check in this conversation. It will report upcoming catalysts, stale sources, and missing inputs from saved watchlist pointers. It runs weekdays at 8:00 AM local time.
-
-Should I set that up?
-```
-
-When the user accepts, follow `skills/user-context/references/automation.md` from the plugin root. Store only concise operational metadata under `onboarding-state.json` `automations`; do not copy automation metadata into `user-context.md`.
-
-Update `automations.status` in `onboarding-state.json`:
-
-- Use `completed` after the accepted automation is created or an existing matching automation is refreshed.
-- Use `deferred` when the user wants to decide later.
-- Use `skipped` when the user does not want an automation.
-
-After automation setup is completed, skipped, or deferred, move directly to Step 4.
-
-## Step 4: Hero Workflows
-
-Render the canonical response template substantially verbatim. The numbered chooser is the final call to action; do not add a redundant completion question.
-
-### Hero Workflow Response Template
-
-```text
-Setup is done for now. Pick the first Public Equity Investing workflow you want to try:
-
-1. **Analyze Latest Earnings:** Produce a post-print investor deep dive for a named company.
-2. **Pressure-Test A Stock Idea:** Build a long/short pitch with a variant view, catalysts, risks, and falsifiers for a named ticker.
-3. **Screen A Market Theme:** Rank likely listed-equity beneficiaries, expectations risks, and false positives for a named theme.
-```
-
-Update `hero_prompt_choice` in `onboarding-state.json`:
-
-- When the user picks a workflow, set `status` to `selected`, save `selected_skill`, save a supplied `selected_anchor` when present, and set top-level `status` to `completed`.
-- When the user skips the chooser, set `status` to `skipped` and top-level `status` to `completed`.
-- When the user defers the chooser, set `status` to `deferred` and top-level `status` to `completed`.
-
-Use these mappings:
-
-- `1` or Analyze Latest Earnings -> `earnings-deep-dive`
-- `2` or Pressure-Test A Stock Idea -> `long-short-pitch`
-- `3` or Screen A Market Theme -> `idea-generation`
-
-When the user supplies enough intent or an anchor, proceed into the selected workflow. Otherwise ask only for the smallest missing anchor.
-
-## State Repair
-
-Render the canonical response template substantially verbatim. Do not overwrite or reset state unless the user explicitly requests it.
-
-### State Repair Response Template
-
-```text
-I could not interpret the Public Equity Investing onboarding state. It needs repair before setup progress can be read reliably. Would you like me to repair it?
-```
-
-## Current Boundary
-
-- Do not invoke onboarding from ordinary Public Equity Investing workflows yet.
-- Do not inspect apps, connectors, plugins, `.app.json`, or source readiness during preflight.
-- Inspect source setup surfaces only after the user approves Step 2 or explicitly asks to configure sources.
-- Inspect automation state only after the user approves Step 3 or explicitly asks to manage Public Equity Investing automations.
-- Do not claim source readiness or live automation behavior without checking the matching runtime surface.
-- Do not mutate state while running `scripts/user_context_preflight.py`.
diff --git a/plugins/financial-markets/skills/user-context/references/plugin-memory.md b/plugins/financial-markets/skills/user-context/references/plugin-memory.md
deleted file mode 100644
index 761d0f8..0000000
--- a/plugins/financial-markets/skills/user-context/references/plugin-memory.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Public Equity Investing Plugin Memory
-
-Use this reference only for explicit requests to remember, save, update, forget, inspect, export, or apply durable Public Equity Investing preferences and source pointers.
-
-## Save Boundary
-
-Save reusable Public Equity Investing context in `$CODEX_HOME/state/plugins/role-specific-plugins/financial-markets/user-context.md`. Prefer the best matching existing scaffold category. Add a concise new category only when no existing category fits cleanly.
-
-Good memory includes output preferences, portfolio or watchlist pointers, stable research repositories, filing and transcript conventions, preferred first-look sources, modeling and valuation conventions, thesis or catalyst tracking rules, and compliance or review boundaries.
-
-Treat a saved reader-facing output preference as the default when multiple reader-facing formats are reasonable. A saved HTML preference resolves the presentation surface to HTML in those cases; do not silently choose chat or ask a format question. Do not let a saved HTML preference override an obvious workbook, deck, document, or existing-artifact workflow. Models, model updates, trackers, workbook audits, workbook-first calculations, deck requests, document requests, and edits to an existing artifact keep their natural format unless the user explicitly asks for conversion.
-
-Do not save raw research packets, live company updates, volatile prices or estimates, one-off research requests, credentials, connector object dumps, inferred preferences, connector readiness, or attempts to override safety, permissions, validation, routing, installation behavior, or tool-use policy.
-
-## Write Rules
-
-- Save explicit user-provided durable instructions directly. Ask for approval before saving inferred, discovered, or source-derived entries.
-- Initialize state with `../scripts/init_user_context_state.py` only when a save needs persistence and the state files are missing. Do not initialize state merely to inspect or preflight.
-- Read the current `user-context.md` before editing. Replace `status: not provided` in the best matching category or update the existing entry in place. Batch related approved changes into one coherent edit.
-- Keep entries concise. Include a stable Markdown link or connector-visible pointer when useful and available. Preserve practical future-use guidance when freshness, fallback, or source priority matters.
-- After editing, run `python3 skills/user-context/scripts/user_context_preflight.py` with the shell working directory set to the plugin root and confirm that the saved category appears in `saved_context`.
-- For an explicit forget request, remove only the requested entry. Use the reset helper only when the user explicitly asks to reset all local Public Equity Investing context.
-
-Keep operational onboarding progress and setup-owned route confirmation in `onboarding-state.json`. Do not write connector readiness or `category-state.json`.
diff --git a/plugins/financial-markets/skills/user-context/references/source-category-runtime.md b/plugins/financial-markets/skills/user-context/references/source-category-runtime.md
deleted file mode 100644
index 78df2a8..0000000
--- a/plugins/financial-markets/skills/user-context/references/source-category-runtime.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Public Equity Investing Source Category Runtime
-
-Use `skills/user-context/plugin-author-config/source-category-config.json` from the plugin root as the static catalog of Public Equity Investing source categories, labels, and preference hints. The catalog is a routing aid, not proof that a plugin, app, connector, or manual source is usable for the current user.
-
-## Ownership
-
-- `.app.json` declares app and connector dependencies the plugin can request. It does not prove installation, authorization, preference, or readability.
-- `skills/user-context/plugin-author-config/source-category-config.json` owns static category ids, labels, and preferred plugins or apps.
-- `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` owns durable user-approved source preferences, source-of-truth pointers, and "do not use" rules.
-- `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json` owns operational setup confirmations under `connector_confirmation`.
-- Do not create, read, or migrate `category-state.json`.
-
-## Read-Only Preflight
-
-Preflight is a reader. Its `source_category_plan` returns static routing hints, saved setup confirmations, and unresolved setup gaps. It must not inspect apps, connectors, plugins, or `.app.json`; choose a plugin, app, connector, or manual route; claim readiness; perform proof reads; or write state.
-
-## Explicit Setup
-
-Source route selection belongs in the user-approved Source Setup onboarding step, an explicit source-setup turn, or later workflow-time repair. Do not inspect setup surfaces before the user opts in.
-
-During one setup pass:
-
-1. Start from unresolved categories in preflight `source_category_plan`.
-2. Read the session `Available plugins` and `Available skills` blocks. Call `functions.list_available_plugins_to_install` once and reuse the result for every category in the pass. Load `.app.json` only to map preferred app names to declared connector ids.
-3. Prefer a related installed plugin with visible plugin-owned skill or tool surface, then an installable related plugin, then an exposed app or connector, then manual or exported context.
-4. Ask before installing or authorizing anything. Ask the user to choose only when multiple plausible routes tie, an installable plugin needs approval, no suitable source is exposed, or IT/admin help may be required.
-5. Write the selected operational route under `onboarding-state.json` `connector_confirmation`. Do not write source setup state to `user-context.md`.
-
-A candidate is related when its `name` or `display_name` matches a preferred plugin or app, its `id` clearly names the preferred plugin or app, its `app_connector_ids` intersects the `.app.json` ids for a category preferred app, or its `description` clearly names the same provider or category and no better preferred match exists.
-
-If setup finds an installed app or connector and also finds a related plugin candidate, prefer the plugin because plugin-owned skills and tools can add workflow support. Keep the app or connector route as fallback if the user defers plugin installation or install visibility is pending.
-
-Use `status: active` only when the selected plugin, app, or connector surface is exposed clearly enough to attempt later. This is setup-only evidence, not a readiness claim. Use `needs_confirmation`, `missing`, `deferred`, `skipped`, or `unavailable` when the route is not active. Include `source_kind` and compact plugin, app, connector, or manual route details when known. Do not guess connector ids or infer active status from `.app.json`.
-
-Do not mark a newly installed plugin route as active until its plugin-owned skill or tool surface is visible. If installation succeeds but skills or tools are not visible until the next turn or session refresh, write `needs_confirmation` or `deferred`, not `active`, and ask the user to continue after refresh.
-
-Do not perform connector reads merely to prove setup. Install confirmed candidates one at a time. If the user approves an installable plugin or connector candidate returned by `functions.list_available_plugins_to_install`, call `functions.request_plugin_install` with `action_type: "install"`, `tool_type: <exact returned candidate tool_type>`, `tool_id: <exact returned candidate id>`, and `suggest_reason: <concise one-line reason>`. Do not guess `tool_id` values or call `request_plugin_install` without an exact returned candidate. Do not call `request_plugin_install` in parallel with other tools.
-
-## Workflow Use
-
-Attempt actual connector reads only when the active workflow needs the source. Before telling the user that a source is ready, use the smallest safe native read-only probe for that source. A successful read is evidence for that run only, not durable connector-readiness state. When a source is unavailable, continue from pasted, uploaded, or exported context when the workflow can still produce a useful limited answer.
diff --git a/plugins/financial-markets/skills/user-context/scripts/init_user_context_state.py b/plugins/financial-markets/skills/user-context/scripts/init_user_context_state.py
deleted file mode 100644
index 5005484..0000000
--- a/plugins/financial-markets/skills/user-context/scripts/init_user_context_state.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/usr/bin/env python3
-"""Initialize local Public Equity Investing user-context foundation files."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import PLUGIN_LABEL, STATE_DIR_HELP, resolve_state_dir
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-USER_CONTEXT_TEMPLATE = SKILL_ROOT / "plugin-author-config/user-context-config.md"
-ONBOARDING_STATE_TEMPLATE = SKILL_ROOT / "references/onboarding-state-template.json"
-
-USER_CONTEXT_NOTE = """<!--
-Public Equity Investing user-context scaffold. This file is user-editable.
-Unresolved `status: not provided` entries are setup prompts, not saved facts.
--->
-
-"""
-
-DEFAULT_CATEGORIES_MARKER = "## Default Categories"
-SAVED_CONTEXT_HEADING = "## Saved Links And Context"
-SAVED_CONTEXT_PLACEHOLDER = "status: not provided"
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=f"Create missing {PLUGIN_LABEL} local state files from bundled templates."
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument("--state-dir", type=Path, default=None, help=STATE_DIR_HELP)
-    parser.add_argument(
-        "--overwrite",
-        action="store_true",
-        help="Overwrite existing user-context.md and onboarding-state.json.",
-    )
-    return parser.parse_args()
-
-
-def validate_json(path: Path) -> None:
-    with path.open("r", encoding="utf-8") as handle:
-        json.load(handle)
-
-
-def build_user_context_scaffold() -> str:
-    template_text = USER_CONTEXT_TEMPLATE.read_text(encoding="utf-8")
-    if DEFAULT_CATEGORIES_MARKER not in template_text:
-        raise ValueError("user-context-config.md must include a Default Categories section")
-    category_text = template_text.split(DEFAULT_CATEGORIES_MARKER, 1)[1].strip()
-    sections = [
-        section.strip() for section in re.split(r"(?m)(?=^# .+$)", category_text) if section.strip()
-    ]
-    if not sections:
-        raise ValueError("user-context-config.md must define at least one category")
-
-    rendered_sections = []
-    for section in sections:
-        if not section.startswith("# "):
-            raise ValueError("user-context-config.md category entries must start with H1 headings")
-        if SAVED_CONTEXT_HEADING in section:
-            raise ValueError("the initializer owns saved-context placeholders")
-        rendered_sections.append(
-            f"{section.rstrip()}\n\n{SAVED_CONTEXT_HEADING}\n\n{SAVED_CONTEXT_PLACEHOLDER}"
-        )
-    return "\n\n".join(rendered_sections) + "\n"
-
-
-def write_text_if_needed(path: Path, text: str, overwrite: bool) -> str:
-    existed = path.exists()
-    if existed and not overwrite:
-        return "preserved"
-    path.write_text(text, encoding="utf-8")
-    return "overwritten" if existed else "created"
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-
-    missing_templates = [
-        str(path.relative_to(SKILL_ROOT))
-        for path in (USER_CONTEXT_TEMPLATE, ONBOARDING_STATE_TEMPLATE)
-        if not path.exists()
-    ]
-    if missing_templates:
-        print(f"Missing {PLUGIN_LABEL} bundled state source(s):", file=sys.stderr)
-        for missing in missing_templates:
-            print(f"- {missing}", file=sys.stderr)
-        return 1
-
-    try:
-        user_context_scaffold = build_user_context_scaffold()
-        validate_json(ONBOARDING_STATE_TEMPLATE)
-    except (json.JSONDecodeError, ValueError) as exc:
-        print(f"Invalid {PLUGIN_LABEL} bundled state source: {exc}", file=sys.stderr)
-        return 1
-
-    state_dir.mkdir(parents=True, exist_ok=True)
-    user_context_path = state_dir / "user-context.md"
-    onboarding_state_path = state_dir / "onboarding-state.json"
-
-    user_context_result = write_text_if_needed(
-        user_context_path,
-        USER_CONTEXT_NOTE + user_context_scaffold,
-        args.overwrite,
-    )
-    onboarding_state_result = write_text_if_needed(
-        onboarding_state_path,
-        ONBOARDING_STATE_TEMPLATE.read_text(encoding="utf-8"),
-        args.overwrite,
-    )
-
-    try:
-        validate_json(onboarding_state_path)
-    except json.JSONDecodeError as exc:
-        print(f"Invalid written {PLUGIN_LABEL} state JSON: {exc}", file=sys.stderr)
-        return 1
-
-    print(f"{PLUGIN_LABEL} state directory: {state_dir}")
-    print(f"user-context.md: {user_context_result}")
-    print(f"onboarding-state.json: {onboarding_state_result}")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/user-context/scripts/reset_user_context_state.py b/plugins/financial-markets/skills/user-context/scripts/reset_user_context_state.py
deleted file mode 100644
index 368bc6a..0000000
--- a/plugins/financial-markets/skills/user-context/scripts/reset_user_context_state.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""Back up and clear local Public Equity Investing user-context foundation files."""
-
-from __future__ import annotations
-
-import argparse
-import shutil
-import sys
-from datetime import datetime
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import PLUGIN_LABEL, STATE_DIR_HELP, STATE_FILENAMES, resolve_state_dir
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=f"Move active {PLUGIN_LABEL} local state files into a sibling backup directory."
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument("--state-dir", type=Path, default=None, help=STATE_DIR_HELP)
-    parser.add_argument(
-        "--backup-dir",
-        type=Path,
-        default=None,
-        help="Backup directory to create. Defaults to a timestamped sibling directory.",
-    )
-    parser.add_argument(
-        "--dry-run",
-        action="store_true",
-        help="Print what would move without changing files.",
-    )
-    return parser.parse_args()
-
-
-def resolve_paths(args: argparse.Namespace) -> tuple[Path, Path]:
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-    if args.backup_dir:
-        backup_dir = args.backup_dir.expanduser()
-    else:
-        timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-        backup_dir = state_dir.parent / f"{state_dir.name}-backup-{timestamp}"
-    return state_dir, backup_dir
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir, backup_dir = resolve_paths(args)
-    existing_state_files = [
-        state_dir / filename for filename in STATE_FILENAMES if (state_dir / filename).exists()
-    ]
-
-    if not existing_state_files:
-        print(f"No active {PLUGIN_LABEL} state files found in: {state_dir}")
-        return 0
-    if backup_dir.exists():
-        print(f"Backup directory already exists: {backup_dir}", file=sys.stderr)
-        return 1
-
-    if args.dry_run:
-        print(f"{PLUGIN_LABEL} state directory: {state_dir}")
-        print(f"Backup directory: {backup_dir}")
-        for path in existing_state_files:
-            print(f"would move: {path.name}")
-        print("Dry run only; no files were moved.")
-        return 0
-
-    backup_dir.mkdir(parents=True)
-    for path in existing_state_files:
-        shutil.move(str(path), str(backup_dir / path.name))
-
-    try:
-        state_dir.rmdir()
-        directory_status = "removed because it was empty"
-    except OSError:
-        directory_status = "kept because it still contains other files"
-
-    print(f"{PLUGIN_LABEL} state directory: {state_dir}")
-    print(f"Backup directory: {backup_dir}")
-    for path in existing_state_files:
-        print(f"moved: {path.name}")
-    print(f"state directory: {directory_status}")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/user-context/scripts/state_paths.py b/plugins/financial-markets/skills/user-context/scripts/state_paths.py
deleted file mode 100644
index b943421..0000000
--- a/plugins/financial-markets/skills/user-context/scripts/state_paths.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/env python3
-"""Shared Public Equity Investing state path resolution."""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-MARKETPLACE_ID = "role-specific-plugins"
-PLUGIN_ID = "financial-markets"
-PLUGIN_LABEL = "Public Equity Investing"
-STATE_FILENAMES = (
-    "user-context.md",
-    # Legacy readiness state is never created or read, but reset should clear it.
-    "category-state.json",
-    "onboarding-state.json",
-)
-
-STATE_DIR_HELP = (
-    f"{PLUGIN_LABEL} state directory. Defaults to "
-    f"<codex-home>/state/plugins/{MARKETPLACE_ID}/{PLUGIN_ID}."
-)
-
-
-def default_codex_home() -> Path:
-    env_home = os.environ.get("CODEX_HOME")
-    if env_home:
-        return Path(env_home).expanduser()
-    return Path.home() / ".codex"
-
-
-def configured_state_dir(codex_home: Path) -> Path:
-    return codex_home / "state/plugins" / MARKETPLACE_ID / PLUGIN_ID
-
-
-def resolve_state_dir(codex_home: Path | None, state_dir: Path | None) -> Path:
-    if state_dir:
-        return state_dir.expanduser()
-
-    resolved_home = codex_home.expanduser() if codex_home else default_codex_home()
-    return configured_state_dir(resolved_home)
diff --git a/plugins/financial-markets/skills/user-context/scripts/user_context_preflight.py b/plugins/financial-markets/skills/user-context/scripts/user_context_preflight.py
deleted file mode 100644
index b7a28ec..0000000
--- a/plugins/financial-markets/skills/user-context/scripts/user_context_preflight.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python3
-"""Inspect Public Equity Investing user-context state without mutating it."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-from pathlib import Path
-from typing import Any
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import PLUGIN_LABEL, STATE_DIR_HELP, resolve_state_dir
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-USER_CONTEXT_CONFIG = SKILL_ROOT / "plugin-author-config/user-context-config.md"
-SOURCE_CATEGORY_CONFIG = SKILL_ROOT / "plugin-author-config/source-category-config.json"
-DEFAULT_CATEGORIES_MARKER = "## Default Categories"
-SAVED_CONTEXT_HEADING = "## Saved Links And Context"
-SAVED_CONTEXT_PLACEHOLDER = "status: not provided"
-COMPLETE_ONBOARDING_STATUSES = {"complete", "completed", "quiet"}
-TERMINAL_ONBOARDING_STATUSES = COMPLETE_ONBOARDING_STATUSES | {"deferred"}
-RESOLVED_STEP_STATUSES = {"accepted", "complete", "completed", "selected", "skipped"}
-TERMINAL_STEP_STATUSES = {"declined", "deferred", "quiet"}
-HERO_PROMPT_OPTIONS = ["earnings-deep-dive", "long-short-pitch", "idea-generation"]
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=f"Inspect {PLUGIN_LABEL} local user-context state without changing it."
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument("--state-dir", type=Path, default=None, help=STATE_DIR_HELP)
-    return parser.parse_args()
-
-
-def configured_categories() -> list[str]:
-    config_text = USER_CONTEXT_CONFIG.read_text(encoding="utf-8")
-    if DEFAULT_CATEGORIES_MARKER not in config_text:
-        raise ValueError("user-context-config.md must include a Default Categories section")
-    category_text = config_text.split(DEFAULT_CATEGORIES_MARKER, 1)[1]
-    categories = re.findall(r"(?m)^# (.+)$", category_text)
-    if not categories:
-        raise ValueError("user-context-config.md must define at least one category")
-    return categories
-
-
-def configured_source_categories() -> dict[str, dict[str, Any]]:
-    payload = json.loads(SOURCE_CATEGORY_CONFIG.read_text(encoding="utf-8"))
-    categories = payload.get("categories")
-    if not isinstance(categories, dict) or not categories:
-        raise ValueError("source-category-config.json must define a non-empty categories object")
-    for category_id, metadata in categories.items():
-        if not isinstance(category_id, str) or not category_id:
-            raise ValueError("source-category-config.json contains an invalid category id")
-        if not isinstance(metadata, dict):
-            raise ValueError(
-                f"source-category-config.json category {category_id} must be an object"
-            )
-    return categories
-
-
-def read_user_context(path: Path, errors: list[str]) -> tuple[str, str | None]:
-    if not path.exists():
-        return "missing", None
-    try:
-        return "present", path.read_text(encoding="utf-8")
-    except OSError as exc:
-        errors.append(f"Could not read user-context.md: {exc}")
-        return "unreadable", None
-
-
-def read_onboarding_state(path: Path, errors: list[str]) -> tuple[str, dict[str, Any] | None]:
-    if not path.exists():
-        return "missing", None
-    try:
-        payload = json.loads(path.read_text(encoding="utf-8"))
-    except json.JSONDecodeError as exc:
-        errors.append(f"Could not parse onboarding-state.json: {exc.msg}")
-        return "malformed", None
-    except OSError as exc:
-        errors.append(f"Could not read onboarding-state.json: {exc}")
-        return "unreadable", None
-    if not isinstance(payload, dict):
-        errors.append("Could not parse onboarding-state.json: expected a JSON object")
-        return "malformed", None
-    return "present", payload
-
-
-def saved_context_by_category(user_context: str | None, categories: list[str]) -> dict[str, str]:
-    if user_context is None:
-        return {}
-    sections = {
-        match.group("name").strip(): match.group("body")
-        for match in re.finditer(r"(?ms)^# (?P<name>.+?)\n(?P<body>.*?)(?=^# |\Z)", user_context)
-    }
-    saved_context: dict[str, str] = {}
-    for category in categories:
-        section = sections.get(category)
-        if section is None:
-            continue
-        saved_context_match = re.search(
-            rf"(?ms)^{re.escape(SAVED_CONTEXT_HEADING)}\s*\n(?P<body>.*?)(?=^## |\Z)",
-            section,
-        )
-        if saved_context_match is None:
-            continue
-        saved_text = saved_context_match.group("body").strip()
-        if saved_text and saved_text != SAVED_CONTEXT_PLACEHOLDER:
-            saved_context[category] = saved_text
-    return saved_context
-
-
-def build_source_category_plan(
-    source_categories: dict[str, dict[str, Any]],
-    onboarding_state: dict[str, Any] | None,
-    errors: list[str],
-) -> dict[str, Any]:
-    raw_confirmation = (
-        onboarding_state.get("connector_confirmation") if onboarding_state is not None else None
-    )
-    if raw_confirmation is None:
-        raw_confirmation = {}
-    elif not isinstance(raw_confirmation, dict):
-        errors.append("Could not inspect connector_confirmation: expected a JSON object")
-        raw_confirmation = {}
-
-    categories: dict[str, dict[str, Any]] = {}
-    for category_id, metadata in source_categories.items():
-        raw_route = raw_confirmation.get(category_id)
-        configured_route = dict(raw_route) if isinstance(raw_route, dict) and raw_route else None
-        route_status = str((configured_route or {}).get("status") or "").casefold()
-        entry: dict[str, Any] = {
-            "label": metadata.get("label") or category_id,
-            "preferred_apps": list(metadata.get("preferred_apps") or []),
-            "confirmation_status": "saved_unverified" if configured_route else "unconfigured",
-            "setup_required": configured_route is None or route_status != "active",
-            "state_scope": "onboarding_only_not_durable_connector_readiness",
-            "readiness_status": "unverified",
-            "eager_read": False,
-        }
-        preferred_plugins = list(metadata.get("preferred_plugins") or [])
-        if preferred_plugins:
-            entry["preferred_plugins"] = preferred_plugins
-        relevant_skills = list(metadata.get("relevant_skills") or [])
-        if relevant_skills:
-            entry["relevant_skills"] = relevant_skills
-        if configured_route is not None:
-            entry["configured_route"] = configured_route
-        categories[category_id] = entry
-
-    return {
-        "resolution": "explicit_setup_then_attempt_actual_reads_only_when_a_workflow_needs_the_source",
-        "readiness_claimed": False,
-        "has_setup_gaps": any(entry["setup_required"] for entry in categories.values()),
-        "categories": categories,
-    }
-
-
-def onboarding_is_incomplete(
-    status: Any,
-    state_status: str,
-    onboarding_state: dict[str, Any] | None,
-) -> bool:
-    if state_status != "present" or not isinstance(status, str):
-        hero_prompt_status = block_status(onboarding_state, "hero_prompt_choice")
-        return hero_prompt_status not in RESOLVED_STEP_STATUSES | TERMINAL_STEP_STATUSES
-    return status.casefold() not in COMPLETE_ONBOARDING_STATUSES
-
-
-def block_status(onboarding_state: dict[str, Any] | None, block_name: str) -> str | None:
-    if onboarding_state is None:
-        return None
-    block = onboarding_state.get(block_name)
-    if not isinstance(block, dict):
-        return None
-    status = block.get("status")
-    return status.casefold() if isinstance(status, str) else None
-
-
-def read_automation_state(
-    onboarding_state: dict[str, Any] | None,
-    errors: list[str],
-) -> dict[str, Any]:
-    if onboarding_state is None:
-        return {}
-    automations = onboarding_state.get("automations")
-    if automations is None:
-        return {}
-    if not isinstance(automations, dict):
-        errors.append("Could not inspect automations: expected a JSON object")
-        return {}
-    return automations
-
-
-def onboarding_progress(onboarding_state: dict[str, Any] | None) -> dict[str, Any]:
-    steps = [
-        ("intro_defaults", "Intro and defaults", "orientation"),
-        ("connectors_plugins", "Connectors and plugins", "source_setup"),
-        ("automation", "Automation", "automations"),
-        ("hero_workflows", "Hero workflows", "hero_prompt_choice"),
-    ]
-    task_list = []
-    for step_id, label, block_name in steps:
-        status = block_status(onboarding_state, block_name)
-        if status in RESOLVED_STEP_STATUSES:
-            progress_status = "completed"
-        elif status in TERMINAL_STEP_STATUSES:
-            progress_status = status
-        else:
-            progress_status = "pending"
-        task_list.append({"id": step_id, "label": label, "status": progress_status})
-    return {"task_list": task_list, "hero_prompt_options": HERO_PROMPT_OPTIONS}
-
-
-def next_action(
-    onboarding_state_status: str,
-    onboarding_state: dict[str, Any] | None,
-    onboarding_status: Any,
-) -> dict[str, str] | None:
-    if onboarding_state_status in {"malformed", "unreadable"}:
-        return {
-            "id": "repair_onboarding_state",
-            "copy_ref": "skills/user-context/references/onboarding.md#state-repair-response-template",
-        }
-    if isinstance(onboarding_status, str):
-        if onboarding_status.casefold() in TERMINAL_ONBOARDING_STATUSES:
-            return None
-
-    orientation_status = block_status(onboarding_state, "orientation")
-    if orientation_status in TERMINAL_STEP_STATUSES:
-        return None
-    if orientation_status not in RESOLVED_STEP_STATUSES:
-        return {
-            "id": "offer_orientation",
-            "copy_ref": "skills/user-context/references/onboarding.md#orientation-response-template",
-        }
-
-    source_setup_status = block_status(onboarding_state, "source_setup")
-    if source_setup_status not in RESOLVED_STEP_STATUSES | TERMINAL_STEP_STATUSES:
-        return {
-            "id": "configure_sources",
-            "copy_ref": "skills/user-context/references/onboarding.md#source-setup-response-template",
-        }
-    automations_status = block_status(onboarding_state, "automations")
-    if automations_status not in RESOLVED_STEP_STATUSES | TERMINAL_STEP_STATUSES:
-        return {
-            "id": "configure_default_automation",
-            "copy_ref": "skills/user-context/references/onboarding.md#automation-setup-response-template",
-        }
-    hero_prompt_status = block_status(onboarding_state, "hero_prompt_choice")
-    if hero_prompt_status not in RESOLVED_STEP_STATUSES | TERMINAL_STEP_STATUSES:
-        return {
-            "id": "choose_hero_workflow",
-            "copy_ref": "skills/user-context/references/onboarding.md#hero-workflow-response-template",
-        }
-    return None
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-    errors: list[str] = []
-
-    try:
-        categories = configured_categories()
-        source_categories = configured_source_categories()
-    except (json.JSONDecodeError, OSError, ValueError) as exc:
-        print(f"Invalid {PLUGIN_LABEL} bundled user-context config: {exc}", file=sys.stderr)
-        return 1
-
-    user_context_status, user_context = read_user_context(state_dir / "user-context.md", errors)
-    onboarding_state_status, onboarding_state = read_onboarding_state(
-        state_dir / "onboarding-state.json",
-        errors,
-    )
-    onboarding_status = onboarding_state.get("status") if onboarding_state is not None else None
-    saved_context = saved_context_by_category(user_context, categories)
-
-    payload = {
-        "initialized": user_context_status == "present" and onboarding_state_status == "present",
-        "state_dir": str(state_dir),
-        "user_context_status": user_context_status,
-        "onboarding_state_status": onboarding_state_status,
-        "onboarding_status": onboarding_status,
-        "onboarding_incomplete": onboarding_is_incomplete(
-            onboarding_status,
-            onboarding_state_status,
-            onboarding_state,
-        ),
-        "next_action": next_action(
-            onboarding_state_status,
-            onboarding_state,
-            onboarding_status,
-        ),
-        "saved_context": saved_context,
-        "empty_categories": [category for category in categories if category not in saved_context],
-        "source_category_plan": build_source_category_plan(
-            source_categories,
-            onboarding_state,
-            errors,
-        ),
-        "automation_state": read_automation_state(onboarding_state, errors),
-        "onboarding_progress": onboarding_progress(onboarding_state),
-        "errors": errors,
-    }
-    print(json.dumps(payload, indent=2))
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/financial-markets/skills/user-context/tests/test_state_helpers.py b/plugins/financial-markets/skills/user-context/tests/test_state_helpers.py
deleted file mode 100644
index af1c184..0000000
--- a/plugins/financial-markets/skills/user-context/tests/test_state_helpers.py
+++ /dev/null
@@ -1,899 +0,0 @@
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-SCRIPT_DIR = SKILL_ROOT / "scripts"
-INIT_SCRIPT = SCRIPT_DIR / "init_user_context_state.py"
-RESET_SCRIPT = SCRIPT_DIR / "reset_user_context_state.py"
-PREFLIGHT_SCRIPT = SCRIPT_DIR / "user_context_preflight.py"
-MARKETPLACE_ID = "role-specific-plugins"
-PLUGIN_ID = "financial-markets"
-ROUTER_SKILL_ID = "public-equity-investing"
-ROUTER_SKILL = SKILL_ROOT.parent / ROUTER_SKILL_ID / "SKILL.md"
-MEETING_PREP_SKILL = SKILL_ROOT.parent / "meeting-prep" / "SKILL.md"
-COMPANY_TEARSHEET_SKILL = SKILL_ROOT.parent / "company-tearsheet" / "SKILL.md"
-COMPANY_TEARSHEET_SOURCE_REFERENCE = (
-    SKILL_ROOT.parent / "company-tearsheet" / "references/source-and-evidence.md"
-)
-ONBOARDING_REFERENCE = SKILL_ROOT / "references/onboarding.md"
-AUTOMATION_CONFIG = SKILL_ROOT / "plugin-author-config/automation-config.md"
-AUTOMATION_REFERENCE = SKILL_ROOT / "references/automation.md"
-PLUGIN_MEMORY_REFERENCE = SKILL_ROOT / "references/plugin-memory.md"
-SOURCE_CATEGORY_CONFIG = SKILL_ROOT / "plugin-author-config/source-category-config.json"
-SOURCE_CATEGORY_RUNTIME = SKILL_ROOT / "references/source-category-runtime.md"
-WORKFLOW_SOURCE_REFERENCE = SKILL_ROOT.parents[1] / "shared/workflow-source-resolution.md"
-SPECIALIST_SKILLS = {
-    "catalyst-calendar",
-    "company-tearsheet",
-    "comps-valuation",
-    "dcf-model-builder",
-    "deck-report-qc",
-    "earnings-deep-dive",
-    "earnings-preview",
-    "economic-impact-report",
-    "equity-model-update",
-    "event-driven-analyzer",
-    "financials-normalizer",
-    "idea-generation",
-    "initiating-coverage",
-    "long-short-pitch",
-    "meeting-prep",
-    "memo-builder",
-    "model-audit-tieout",
-    "portfolio-risk-management",
-    "scenario-sensitivity-generator",
-    "thesis-tracker",
-    "three-statement-model-builder",
-}
-SHARED_SOURCE_SKILLS = {
-    "catalyst-calendar": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "comps-valuation": {
-        "company_filings_ir",
-        "market_data_estimates",
-        "portfolio_models_trackers",
-    },
-    "earnings-deep-dive": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "earnings-preview": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "economic-impact-report": {
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "equity-model-update": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "event-driven-analyzer": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "financials-normalizer": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "idea-generation": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "initiating-coverage": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "long-short-pitch": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "memo-builder": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "portfolio-risk-management": {
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-    "thesis-tracker": {
-        "company_filings_ir",
-        "earnings_transcripts_presentations",
-        "internal_research",
-        "portfolio_models_trackers",
-        "market_data_estimates",
-    },
-}
-
-
-class PublicEquityInvestingStateHelpersTest(unittest.TestCase):
-    def setUp(self) -> None:
-        self.tmp = tempfile.TemporaryDirectory()
-        self.tmp_path = Path(self.tmp.name)
-        self.codex_home = self.tmp_path / "codex-home"
-        self.state_dir = self.codex_home / "state/plugins" / MARKETPLACE_ID / PLUGIN_ID
-
-    def tearDown(self) -> None:
-        self.tmp.cleanup()
-
-    def run_script(self, script: Path, *args: str) -> subprocess.CompletedProcess[str]:
-        return subprocess.run(
-            [sys.executable, str(script), *args],
-            text=True,
-            capture_output=True,
-        )
-
-    def test_init_uses_namespaced_state_dir_and_creates_only_expected_files(self) -> None:
-        proc = self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn(f"Public Equity Investing state directory: {self.state_dir}", proc.stdout)
-        self.assertEqual(
-            {path.name for path in self.state_dir.iterdir()},
-            {"user-context.md", "onboarding-state.json"},
-        )
-        self.assertFalse((self.state_dir / "category-state.json").exists())
-
-    def test_init_preserves_existing_files_without_overwrite(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text("keep me\n", encoding="utf-8")
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps({"status": "existing"}),
-            encoding="utf-8",
-        )
-
-        proc = self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn("user-context.md: preserved", proc.stdout)
-        self.assertIn("onboarding-state.json: preserved", proc.stdout)
-        self.assertEqual((self.state_dir / "user-context.md").read_text(), "keep me\n")
-
-    def test_init_overwrite_recreates_templates(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text("replace me\n", encoding="utf-8")
-        (self.state_dir / "onboarding-state.json").write_text("{}\n", encoding="utf-8")
-
-        proc = self.run_script(
-            INIT_SCRIPT,
-            "--codex-home",
-            str(self.codex_home),
-            "--overwrite",
-        )
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn("user-context.md: overwritten", proc.stdout)
-        self.assertIn(
-            "# Investor Profile And Output Style", (self.state_dir / "user-context.md").read_text()
-        )
-        self.assertEqual(
-            json.loads((self.state_dir / "onboarding-state.json").read_text()),
-            {
-                "status": None,
-                "orientation": {},
-                "memory_preferences": {},
-                "source_setup": {},
-                "connector_confirmation": {},
-                "automations": {},
-                "hero_prompt_choice": {
-                    "status": None,
-                    "options": ["earnings-deep-dive", "long-short-pitch", "idea-generation"],
-                    "selected_skill": None,
-                    "selected_anchor": None,
-                },
-            },
-        )
-
-    def test_reset_backs_up_known_files(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        for filename in ("user-context.md", "category-state.json", "onboarding-state.json"):
-            (self.state_dir / filename).write_text(filename, encoding="utf-8")
-        backup_dir = self.tmp_path / "backup"
-
-        proc = self.run_script(
-            RESET_SCRIPT,
-            "--state-dir",
-            str(self.state_dir),
-            "--backup-dir",
-            str(backup_dir),
-        )
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertFalse(self.state_dir.exists())
-        for filename in ("user-context.md", "category-state.json", "onboarding-state.json"):
-            self.assertEqual((backup_dir / filename).read_text(), filename)
-
-    def test_reset_dry_run_keeps_files(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text("memory", encoding="utf-8")
-
-        proc = self.run_script(RESET_SCRIPT, "--state-dir", str(self.state_dir), "--dry-run")
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertTrue((self.state_dir / "user-context.md").exists())
-        self.assertIn("Dry run only; no files were moved.", proc.stdout)
-
-    def test_preflight_reports_missing_state_without_creating_files(self) -> None:
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertFalse(payload["initialized"])
-        self.assertEqual(payload["user_context_status"], "missing")
-        self.assertEqual(payload["onboarding_state_status"], "missing")
-        self.assertTrue(payload["onboarding_incomplete"])
-        self.assertEqual(payload["next_action"]["id"], "offer_orientation")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#orientation-response-template",
-        )
-        self.assertFalse(self.state_dir.exists())
-
-    def test_preflight_reports_initialized_state_and_empty_categories(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertTrue(payload["initialized"])
-        self.assertIsNone(payload["onboarding_status"])
-        self.assertTrue(payload["onboarding_incomplete"])
-        self.assertEqual(payload["next_action"]["id"], "offer_orientation")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#orientation-response-template",
-        )
-        self.assertIn("Investor Profile And Output Style", payload["empty_categories"])
-        self.assertEqual(payload["errors"], [])
-
-    def test_preflight_excludes_populated_categories(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        user_context_path = self.state_dir / "user-context.md"
-        user_context_path.write_text(
-            user_context_path.read_text(encoding="utf-8").replace(
-                "## Saved Links And Context\n\nstatus: not provided",
-                "## Saved Links And Context\n\n- Watchlist tracker: https://example.com/watchlist",
-                1,
-            ),
-            encoding="utf-8",
-        )
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertNotIn("Investor Profile And Output Style", payload["empty_categories"])
-        self.assertIn("Portfolio Watchlist And Repository Pointers", payload["empty_categories"])
-        self.assertEqual(
-            payload["saved_context"]["Investor Profile And Output Style"],
-            "- Watchlist tracker: https://example.com/watchlist",
-        )
-
-    def test_preflight_reports_malformed_onboarding_state(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_path.write_text("{invalid\n", encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertFalse(payload["initialized"])
-        self.assertEqual(payload["onboarding_state_status"], "malformed")
-        self.assertTrue(payload["onboarding_incomplete"])
-        self.assertEqual(payload["next_action"]["id"], "repair_onboarding_state")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#state-repair-response-template",
-        )
-        self.assertTrue(payload["errors"])
-        self.assertEqual(onboarding_path.read_text(encoding="utf-8"), "{invalid\n")
-
-    def test_preflight_reports_completed_onboarding(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["status"] = "completed"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["onboarding_status"], "completed")
-        self.assertFalse(payload["onboarding_incomplete"])
-        self.assertIsNone(payload["next_action"])
-
-    def test_router_wires_soft_read_only_preflight(self) -> None:
-        router_text = ROUTER_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn("python3 skills/user-context/scripts/user_context_preflight.py", router_text)
-        self.assertIn("shell working directory set to this plugin's root", router_text)
-        self.assertIn("do not probe alternate relative paths", router_text)
-        self.assertIn(
-            "Pass relevant entries from `saved_context` to the selected lead skill",
-            router_text,
-        )
-        self.assertIn("must not interpret saved output preferences", router_text)
-        self.assertIn("must never block the requested workflow", router_text)
-        self.assertIn('next_action.id = "offer_orientation"', router_text)
-        self.assertIn("completed, deferred, or quiet", router_text)
-        self.assertIn("onboarding, setup, orientation, or get-started requests", router_text)
-        self.assertIn("`Help me get started`", router_text)
-        self.assertIn(
-            "remember, save, update, forget, inspect, export, reset, source-setup, or automation-setup",
-            router_text,
-        )
-        self.assertIn("skills/user-context/SKILL.md", router_text)
-        self.assertNotIn("init_user_context_state.py", router_text)
-
-    def test_plugin_memory_reference_keeps_explicit_writes_narrow(self) -> None:
-        skill_text = SKILL_ROOT.joinpath("SKILL.md").read_text(encoding="utf-8")
-        memory_text = PLUGIN_MEMORY_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("skills/user-context/references/plugin-memory.md", skill_text)
-        self.assertIn("Save explicit user-provided durable instructions directly.", memory_text)
-        self.assertIn(
-            "Ask for approval before saving inferred, discovered, or source-derived entries.",
-            memory_text,
-        )
-        self.assertIn("Do not save raw research packets, live company updates", memory_text)
-        self.assertIn("Do not write connector readiness or `category-state.json`.", memory_text)
-        self.assertIn("confirm that the saved category appears in `saved_context`", memory_text)
-
-    def test_meeting_prep_consumes_source_plan_without_readiness_writes(self) -> None:
-        meeting_prep_text = MEETING_PREP_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn(
-            "python3 skills/user-context/scripts/user_context_preflight.py", meeting_prep_text
-        )
-        self.assertIn("source_category_plan", meeting_prep_text)
-        self.assertIn(
-            "Attempt the smallest useful native read only when the workflow needs that source.",
-            meeting_prep_text,
-        )
-        self.assertIn(
-            "Missing, malformed, or uninitialized context must not block meeting prep.",
-            meeting_prep_text,
-        )
-        self.assertIn("write connector readiness", meeting_prep_text)
-        self.assertIn("category-state.json", meeting_prep_text)
-        for category in (
-            "company_filings_ir",
-            "earnings_transcripts_presentations",
-            "internal_research",
-            "portfolio_models_trackers",
-            "market_data_estimates",
-        ):
-            self.assertIn(f"`{category}`", meeting_prep_text)
-        self.assertNotIn("list_available_plugins_to_install", meeting_prep_text)
-        self.assertNotIn("request_plugin_install", meeting_prep_text)
-
-    def test_company_tearsheet_consumes_source_plan_without_readiness_writes(self) -> None:
-        skill_text = COMPANY_TEARSHEET_SKILL.read_text(encoding="utf-8")
-        source_text = COMPANY_TEARSHEET_SOURCE_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("python3 skills/user-context/scripts/user_context_preflight.py", skill_text)
-        self.assertIn("source_category_plan", skill_text)
-        self.assertIn(
-            "Attempt the smallest useful native read only when the workflow needs that source.",
-            skill_text,
-        )
-        self.assertIn(
-            "Missing, malformed, or uninitialized context must not block tearsheet work.",
-            skill_text,
-        )
-        self.assertIn("write connector readiness", skill_text)
-        self.assertIn("category-state.json", skill_text)
-        for category in (
-            "company_filings_ir",
-            "earnings_transcripts_presentations",
-            "internal_research",
-            "portfolio_models_trackers",
-            "market_data_estimates",
-        ):
-            self.assertIn(f"`{category}`", skill_text)
-        self.assertIn("callable provider apps/connectors or user-provided exports", source_text)
-        self.assertIn(
-            "Do not imply direct access when the runtime route is not callable.", source_text
-        )
-        self.assertNotIn("list_available_plugins_to_install", skill_text)
-        self.assertNotIn("request_plugin_install", skill_text)
-
-    def test_all_visible_specialists_consume_user_context_preflight(self) -> None:
-        actual_specialists = {
-            path.parent.name
-            for path in SKILL_ROOT.parent.glob("*/SKILL.md")
-            if path.parent.name not in {ROUTER_SKILL_ID, "user-context"}
-        }
-        self.assertEqual(actual_specialists, SPECIALIST_SKILLS)
-
-        for skill in sorted(SPECIALIST_SKILLS):
-            text = (SKILL_ROOT.parent / skill / "SKILL.md").read_text(encoding="utf-8")
-            with self.subTest(skill=skill):
-                self.assertIn("python3 skills/user-context/scripts/user_context_preflight.py", text)
-                self.assertIn("shell working directory set to this plugin's root", text)
-                self.assertIn("saved_context", text)
-                self.assertIn("source_category_plan", text)
-                self.assertIn("next_action", text)
-                self.assertIn("must not block", text)
-
-    def test_source_selecting_specialists_use_shared_lazy_resolution_reference(self) -> None:
-        reference_text = WORKFLOW_SOURCE_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("Attempt the smallest useful native read", reference_text)
-        self.assertIn("callable only when the runtime exposes a scoped route", reference_text)
-        self.assertIn("category-state.json", reference_text)
-
-        for skill, categories in sorted(SHARED_SOURCE_SKILLS.items()):
-            text = (SKILL_ROOT.parent / skill / "SKILL.md").read_text(encoding="utf-8")
-            with self.subTest(skill=skill):
-                self.assertIn("../../shared/workflow-source-resolution.md", text)
-                self.assertIn("Use `source_category_plan` lazily", text)
-                for category in categories:
-                    self.assertIn(f"`{category}`", text)
-
-    def test_preflight_offers_source_setup_after_orientation(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["next_action"]["id"], "configure_sources")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#source-setup-response-template",
-        )
-
-    def test_preflight_ignores_legacy_pending_memory_preferences(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["next_action"]["id"], "configure_sources")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#source-setup-response-template",
-        )
-
-    def test_preflight_offers_source_setup_when_memory_preferences_are_skipped(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_state["memory_preferences"]["status"] = "skipped"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertEqual(json.loads(proc.stdout)["next_action"]["id"], "configure_sources")
-
-    def test_preflight_offers_automation_setup_after_source_setup(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_state["memory_preferences"]["status"] = "completed"
-        onboarding_state["source_setup"]["status"] = "completed"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["next_action"]["id"], "configure_default_automation")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#automation-setup-response-template",
-        )
-
-    def test_preflight_offers_hero_workflows_after_automation_resolution(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_state["memory_preferences"]["status"] = "completed"
-        onboarding_state["source_setup"]["status"] = "completed"
-        onboarding_state["automations"]["status"] = "skipped"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["next_action"]["id"], "choose_hero_workflow")
-        self.assertEqual(
-            payload["next_action"]["copy_ref"],
-            "skills/user-context/references/onboarding.md#hero-workflow-response-template",
-        )
-
-    def test_preflight_stops_after_hero_workflow_selection(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_state["source_setup"]["status"] = "completed"
-        onboarding_state["automations"]["status"] = "skipped"
-        onboarding_state["hero_prompt_choice"]["status"] = "selected"
-        onboarding_state["hero_prompt_choice"]["selected_skill"] = "earnings-deep-dive"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertIsNone(payload["next_action"])
-        self.assertFalse(payload["onboarding_incomplete"])
-
-    def test_preflight_exposes_four_step_progress_and_hero_options(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        progress = json.loads(proc.stdout)["onboarding_progress"]
-        self.assertEqual(
-            [step["label"] for step in progress["task_list"]],
-            ["Intro and defaults", "Connectors and plugins", "Automation", "Hero workflows"],
-        )
-        self.assertEqual(
-            progress["hero_prompt_options"],
-            ["earnings-deep-dive", "long-short-pitch", "idea-generation"],
-        )
-
-    def test_preflight_echoes_automation_metadata_without_claiming_live_state(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        metadata = {
-            "status": "completed",
-            "configured": {
-                "weekday-watchlist-brief": {
-                    "automation_id": "automation-123",
-                    "name": "Weekday Public Equity Watchlist Brief",
-                    "kind": "heartbeat",
-                    "status": "active",
-                }
-            },
-        }
-        onboarding_state["automations"] = metadata
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertEqual(payload["automation_state"], metadata)
-        self.assertEqual(payload["errors"], [])
-
-    def test_automation_reference_keeps_setup_explicit_and_runtime_checked(self) -> None:
-        skill_text = SKILL_ROOT.joinpath("SKILL.md").read_text(encoding="utf-8")
-        onboarding_text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        config_text = AUTOMATION_CONFIG.read_text(encoding="utf-8")
-        automation_text = AUTOMATION_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("skills/user-context/references/automation.md", skill_text)
-        self.assertIn("### Automation Setup Response Template", onboarding_text)
-        self.assertIn("weekday-watchlist-brief", config_text)
-        self.assertIn(
-            "Do not create an automation until the user explicitly accepts", automation_text
-        )
-        self.assertIn("automation_update", automation_text)
-        self.assertIn("tool_search", automation_text)
-        self.assertIn("$CODEX_HOME/automations/*/automation.toml", automation_text)
-        self.assertIn("### Canonical Automation Prompt", config_text)
-        self.assertIn("Run a read-only weekday Public Equity Investing source check.", config_text)
-        self.assertIn(
-            "Report only: Upcoming Catalysts, Stale Sources, and Missing Inputs.", config_text
-        )
-        self.assertIn("Do not invent a portfolio or watchlist.", config_text)
-        self.assertIn(
-            "does not perform broad research or draft investment analysis;", automation_text
-        )
-        self.assertIn(
-            "Use the configured canonical automation prompt substantially verbatim.",
-            automation_text,
-        )
-        self.assertIn("Do not copy automation metadata into `user-context.md`.", automation_text)
-
-    def test_preflight_offers_automation_when_source_setup_is_skipped(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["orientation"]["status"] = "completed"
-        onboarding_state["memory_preferences"]["status"] = "completed"
-        onboarding_state["source_setup"]["status"] = "skipped"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertEqual(
-            json.loads(proc.stdout)["next_action"]["id"],
-            "configure_default_automation",
-        )
-
-    def test_onboarding_reference_uses_four_step_defaults_and_hero_copy(self) -> None:
-        onboarding_text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        plugin_memory_text = PLUGIN_MEMORY_REFERENCE.read_text(encoding="utf-8")
-        intake_text = (SKILL_ROOT.parents[1] / "shared/deliverable-intake-policy.md").read_text(
-            encoding="utf-8"
-        )
-
-        for step in (
-            "## Step 1: Intro And Defaults",
-            "## Step 2: Connectors And Plugins",
-            "## Step 3: Automation",
-            "## Step 4: Hero Workflows",
-        ):
-            self.assertIn(step, onboarding_text)
-        self.assertIn("Reader-facing output: polished HTML research report", onboarding_text)
-        self.assertIn("Word document (.docx)", onboarding_text)
-        self.assertIn("Audience: PM or investment team", onboarding_text)
-        self.assertIn("models, trackers, workbook updates, deck requests", onboarding_text)
-        self.assertIn("1. **Analyze Latest Earnings:**", onboarding_text)
-        self.assertIn("2. **Pressure-Test A Stock Idea:**", onboarding_text)
-        self.assertIn("3. **Screen A Market Theme:**", onboarding_text)
-        self.assertIn(
-            "Keep live company updates and one-off research requests in the active workflow.",
-            onboarding_text,
-        )
-        self.assertNotIn("## Memory Preferences", onboarding_text)
-        self.assertNotIn("## Complete Or Defer", onboarding_text)
-        self.assertIn("saved reader-facing output preference as the default", plugin_memory_text)
-        self.assertIn("saved HTML preference resolves the presentation surface to HTML", intake_text)
-        self.assertIn("saved HTML preference override an obvious workbook", intake_text)
-
-    def test_source_category_config_defines_static_catalog_only(self) -> None:
-        config = json.loads(SOURCE_CATEGORY_CONFIG.read_text(encoding="utf-8"))
-
-        self.assertEqual(
-            config["schema_version"], "public_equity_investing_source_category_config.v1"
-        )
-        self.assertEqual(
-            set(config["categories"]),
-            {
-                "company_filings_ir",
-                "earnings_transcripts_presentations",
-                "internal_research",
-                "portfolio_models_trackers",
-                "market_data_estimates",
-            },
-        )
-        self.assertIn("does not inspect connectors", config["description"])
-        self.assertIn("does not", config["description"])
-        for category in config["categories"].values():
-            self.assertTrue(category["label"])
-            self.assertTrue(category.get("preferred_apps") or category.get("preferred_plugins"))
-            self.assertLessEqual(
-                set(category),
-                {"label", "preferred_apps", "preferred_plugins", "relevant_skills"},
-            )
-
-    def test_source_setup_reference_keeps_discovery_out_of_preflight(self) -> None:
-        onboarding_text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        runtime_text = SOURCE_CATEGORY_RUNTIME.read_text(encoding="utf-8")
-        preflight_text = PREFLIGHT_SCRIPT.read_text(encoding="utf-8")
-
-        self.assertIn("### Source Setup Response Template", onboarding_text)
-        self.assertIn("This does not read source contents.", onboarding_text)
-        self.assertIn("functions.list_available_plugins_to_install", runtime_text)
-        self.assertIn("Do not perform connector reads merely to prove setup.", runtime_text)
-        self.assertIn("Do not create, read, or migrate `category-state.json`.", runtime_text)
-        self.assertIn("`app_connector_ids` intersects the `.app.json` ids", runtime_text)
-        self.assertIn("Keep the app or connector route as fallback", runtime_text)
-        self.assertIn(
-            "skills or tools are not visible until the next turn or session refresh", runtime_text
-        )
-        self.assertIn("Install confirmed candidates one at a time.", runtime_text)
-        self.assertIn('`action_type: "install"`', runtime_text)
-        self.assertIn("`tool_type: <exact returned candidate tool_type>`", runtime_text)
-        self.assertIn("`tool_id: <exact returned candidate id>`", runtime_text)
-        self.assertIn("`suggest_reason: <concise one-line reason>`", runtime_text)
-        self.assertIn("Do not guess `tool_id` values", runtime_text)
-        self.assertNotIn("list_available_plugins_to_install", preflight_text)
-        self.assertNotIn("request_plugin_install", preflight_text)
-
-    def test_preflight_returns_unconfigured_source_category_plan_without_readiness_claims(
-        self,
-    ) -> None:
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        plan = json.loads(proc.stdout)["source_category_plan"]
-        self.assertFalse(plan["readiness_claimed"])
-        self.assertTrue(plan["has_setup_gaps"])
-        self.assertEqual(
-            set(plan["categories"]),
-            {
-                "company_filings_ir",
-                "earnings_transcripts_presentations",
-                "internal_research",
-                "portfolio_models_trackers",
-                "market_data_estimates",
-            },
-        )
-        for category in plan["categories"].values():
-            self.assertEqual(category["confirmation_status"], "unconfigured")
-            self.assertEqual(category["readiness_status"], "unverified")
-            self.assertTrue(category["setup_required"])
-            self.assertFalse(category["eager_read"])
-            self.assertNotIn("configured_route", category)
-        self.assertFalse((self.state_dir / "category-state.json").exists())
-
-    def test_preflight_echoes_saved_source_route_without_claiming_readiness(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        saved_route = {
-            "status": "active",
-            "source_kind": "app",
-            "app": {"name": "Google Drive"},
-        }
-        onboarding_state["connector_confirmation"]["company_filings_ir"] = saved_route
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        plan = json.loads(proc.stdout)["source_category_plan"]
-        category = plan["categories"]["company_filings_ir"]
-        self.assertEqual(category["confirmation_status"], "saved_unverified")
-        self.assertEqual(category["readiness_status"], "unverified")
-        self.assertFalse(category["setup_required"])
-        self.assertEqual(category["configured_route"], saved_route)
-        self.assertTrue(plan["has_setup_gaps"])
-        self.assertFalse((self.state_dir / "category-state.json").exists())
-
-    def test_preflight_keeps_missing_saved_source_route_as_setup_gap(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["connector_confirmation"]["company_filings_ir"] = {"status": "missing"}
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        category = json.loads(proc.stdout)["source_category_plan"]["categories"][
-            "company_filings_ir"
-        ]
-        self.assertEqual(category["confirmation_status"], "saved_unverified")
-        self.assertTrue(category["setup_required"])
-
-    def test_preflight_suppresses_next_action_when_deferred(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["status"] = "deferred"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertTrue(payload["onboarding_incomplete"])
-        self.assertIsNone(payload["next_action"])
-
-    def test_preflight_suppresses_next_action_when_quiet(self) -> None:
-        self.assertEqual(
-            self.run_script(INIT_SCRIPT, "--codex-home", str(self.codex_home)).returncode,
-            0,
-        )
-        onboarding_path = self.state_dir / "onboarding-state.json"
-        onboarding_state = json.loads(onboarding_path.read_text(encoding="utf-8"))
-        onboarding_state["status"] = "quiet"
-        onboarding_path.write_text(json.dumps(onboarding_state), encoding="utf-8")
-
-        proc = self.run_script(PREFLIGHT_SCRIPT, "--codex-home", str(self.codex_home))
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        payload = json.loads(proc.stdout)
-        self.assertFalse(payload["onboarding_incomplete"])
-        self.assertIsNone(payload["next_action"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/goldens/dashboard-production-complete.png b/plugins/financial-markets/tests/goldens/dashboard-production-complete.png
deleted file mode 100644
index 6529710..0000000
Binary files a/plugins/financial-markets/tests/goldens/dashboard-production-complete.png and /dev/null differ
diff --git a/plugins/financial-markets/tests/test_artifact_packaging_and_qc.py b/plugins/financial-markets/tests/test_artifact_packaging_and_qc.py
deleted file mode 100644
index 174fcda..0000000
--- a/plugins/financial-markets/tests/test_artifact_packaging_and_qc.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""Artifact packaging and QC script regressions for Public Equity Investing."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from shared.artifact_packager import artifact_item, write_artifact_manifest  # noqa: E402
-
-DECK_QC = ROOT / "skills" / "deck-report-qc" / "scripts" / "inspect_deck_report.py"
-
-
-class ArtifactPackagingAndQCTests(unittest.TestCase):
-    def test_support_formats_cannot_be_primary_without_explicit_request(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            out = Path(tmp_dir)
-            primary = out / "scan.json"
-            primary.write_text("{}", encoding="utf-8")
-            with self.assertRaises(ValueError):
-                write_artifact_manifest(out, "unit-test", "html_report", primary)
-
-    def test_deck_qc_script_heroes_html_and_hides_support_artifacts(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            source = tmp / "memo.txt"
-            source.write_text(
-                "Revenue was $100m in FY2026. EBITDA was $20m. Source: company filing as of 2026-05-01. "
-                "Revenue was $105m in FY2026 on another page without clear reconciliation.",
-                encoding="utf-8",
-            )
-            outdir = tmp / "qc"
-            result = subprocess.run(
-                [sys.executable, str(DECK_QC), str(source), "--outdir", str(outdir)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            report = outdir / "public_equity_investing_deck_qc_report.html"
-            manifest = json.loads((outdir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertTrue(report.exists())
-            self.assertEqual(
-                report.resolve(), Path(manifest["primary_human_deliverable"]).resolve()
-            )
-            self.assertEqual("html_report", manifest["artifact_mode"])
-            self.assertFalse(manifest["support_artifacts_user_visible_default"])
-            self.assertTrue((outdir / "support" / "qc_issue_log.csv").exists())
-            self.assertTrue((outdir / "support" / "repeated_number_tieout.csv").exists())
-            for item in manifest["support_artifacts"]:
-                self.assertFalse(item["user_visible_default"])
-                self.assertTrue(item["support_reason"])
-                self.assertNotEqual("human_deliverable", item["role"])
-
-    def test_deck_qc_finalize_promotes_polished_report_and_reconciles_status(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            source = tmp / "memo.txt"
-            source.write_text(
-                "Revenue was $100m in FY2026. Revenue was $105m in FY2026.",
-                encoding="utf-8",
-            )
-            output_dir = tmp / "completed_review"
-            scan_dir = output_dir / "qc_support"
-            scan = subprocess.run(
-                [sys.executable, str(DECK_QC), str(source), "--outdir", str(scan_dir)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, scan.returncode, scan.stderr + scan.stdout)
-            self.assertTrue((scan_dir / "logs" / "deck_report_qc_dashboard_contract.json").exists())
-
-            final_report = output_dir / "public_equity_investing_deck_qc_report.html"
-            final_report.parent.mkdir(parents=True, exist_ok=True)
-            final_report.write_text(
-                "<html><body>Final reviewed QC report</body></html>", encoding="utf-8"
-            )
-            review_record = output_dir / "qc_review_record.json"
-            review_record.write_text(
-                json.dumps(
-                    {
-                        "status": "partial",
-                        "reason": "Internal QC completed; primary source confirmation remains open.",
-                        "completed_reviews": [
-                            "PDF pages rendered and material pages inspected",
-                            "Supporting workbook valuation tabs inspected",
-                        ],
-                        "missing_inputs": ["Dated external market-data source"],
-                    }
-                ),
-                encoding="utf-8",
-            )
-            finalize = subprocess.run(
-                [
-                    sys.executable,
-                    str(DECK_QC),
-                    "--finalize",
-                    "--outdir",
-                    str(output_dir),
-                    "--scan-dir",
-                    str(scan_dir),
-                    "--primary-report",
-                    str(final_report),
-                    "--review-record",
-                    str(review_record),
-                ],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, finalize.returncode, finalize.stderr + finalize.stdout)
-
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertEqual(
-                final_report.resolve(), Path(manifest["primary_human_deliverable"]).resolve()
-            )
-            status = manifest["blocked_or_partial_status"]
-            self.assertEqual(["Dated external market-data source"], status["missing_inputs"])
-            self.assertIn(
-                "Supporting workbook valuation tabs inspected", status["completed_reviews"]
-            )
-            self.assertFalse((scan_dir / "public_equity_investing_deck_qc_report.html").exists())
-            self.assertFalse((scan_dir / "manifest.json").exists())
-            self.assertFalse(
-                (scan_dir / "logs" / "deck_report_qc_dashboard_contract.json").exists()
-            )
-            support_paths = {Path(item["path"]).name for item in manifest["support_artifacts"]}
-            self.assertIn("scan.json", support_paths)
-            self.assertIn("qc_review_record.json", support_paths)
-
-    def test_workbook_manifest_support_artifact_shape(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            out = Path(tmp_dir)
-            workbook = out / "model.xlsx"
-            workbook.write_bytes(b"placeholder")
-            run_log = out / "run_log.json"
-            run_log.write_text("{}", encoding="utf-8")
-            manifest = write_artifact_manifest(
-                out,
-                "unit-test",
-                "workbook",
-                workbook,
-                support_artifacts=[
-                    artifact_item(
-                        run_log, "support_artifact", "json", "Run log", False, True, "Audit support"
-                    )
-                ],
-            )
-            self.assertEqual(str(workbook), manifest["primary_human_deliverable"])
-            self.assertEqual("primary_human_deliverable", manifest["first_read"]["role"])
-            self.assertEqual(
-                "only_briefly_unless_requested",
-                manifest["final_response_guidance"]["mention_support_artifacts"],
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_comps_model_artifact_metadata.py b/plugins/financial-markets/tests/test_comps_model_artifact_metadata.py
deleted file mode 100644
index 1f18e0f..0000000
--- a/plugins/financial-markets/tests/test_comps_model_artifact_metadata.py
+++ /dev/null
@@ -1,163 +0,0 @@
-"""Comps model artifact metadata and hero-deliverable contracts."""
-
-from __future__ import annotations
-
-import importlib.util
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-import zipfile
-from pathlib import Path
-from xml.etree import ElementTree as ET
-
-ROOT = Path(__file__).resolve().parents[1]
-SKILL = ROOT / "skills" / "comps-valuation"
-CREATE_SCRIPT = SKILL / "scripts" / "create_comps_template.py"
-FALLBACK_SCRIPT = SKILL / "scripts" / "materialize_screening_comps.py"
-AUDIT_SCRIPT = SKILL / "scripts" / "audit_comps_workbook.py"
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-
-
-def run_script(args: list[str]) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(
-        [sys.executable, *args], cwd=str(ROOT), text=True, capture_output=True, check=False
-    )
-
-
-def workbook_sheet_names(path: Path) -> list[str]:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        sheets = workbook.find(f"{{{NS_MAIN}}}sheets")
-        if sheets is None:
-            return []
-        return [sheet.attrib["name"] for sheet in sheets]
-
-
-class CompsModelArtifactMetadataTests(unittest.TestCase):
-    @unittest.skipUnless(
-        importlib.util.find_spec("xlsxwriter"), "xlsxwriter required for template workbook build"
-    )
-    def test_create_template_marks_xlsx_as_primary_human_deliverable(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            output = tmp / "comps_template.xlsx"
-
-            result = run_script(
-                [
-                    str(CREATE_SCRIPT),
-                    "--output",
-                    str(output),
-                    "--target",
-                    "ACME Software",
-                    "--ticker",
-                    "ACME",
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((tmp / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((tmp / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertEqual(str(output.resolve()), run_log["primary_human_deliverable"])
-            self.assertEqual("workbook", run_log["artifact_mode"])
-            self.assertFalse(run_log["support_artifacts_user_visible_default"])
-            workbook_row = next(
-                row for row in run_log["output_manifest"] if row["key"] == "workbook"
-            )
-            self.assertEqual("primary_human_deliverable", workbook_row["artifact_role"])
-            self.assertFalse(workbook_row["hidden_unless_requested"])
-            self.assertEqual(str(output.resolve()), manifest["primary_human_deliverable"])
-            self.assertTrue(
-                all(
-                    row["hidden_unless_requested"]
-                    for row in run_log["output_manifest"]
-                    if row["key"] in {"run_log", "manifest"}
-                )
-            )
-
-    def test_screening_fallback_creates_primary_xlsx_and_hides_support_files(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "fallback"
-
-            result = run_script(
-                [
-                    str(FALLBACK_SCRIPT),
-                    "--output-dir",
-                    str(output_dir),
-                    "--target",
-                    "ACME Software",
-                    "--ticker",
-                    "ACME",
-                    "--sector",
-                    "Software",
-                    "--peer-list",
-                    "MSFT:Microsoft,CRM:Salesforce",
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            workbook = output_dir / "screen_grade_comps_framework.xlsx"
-            run_log = json.loads((output_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertTrue(workbook.exists())
-            self.assertEqual(
-                ["Cover", "Peer Framework", "Missing Data Requests", "Source Requirements"],
-                workbook_sheet_names(workbook),
-            )
-            self.assertEqual(str(workbook.resolve()), run_log["primary_human_deliverable"])
-            self.assertEqual("workbook", run_log["artifact_mode"])
-            self.assertEqual("screen-grade", run_log["model_status"])
-            self.assertFalse(run_log["support_artifacts_user_visible_default"])
-            self.assertEqual(str(workbook.resolve()), manifest["primary_human_deliverable"])
-            workbook_row = next(
-                row for row in run_log["output_manifest"] if row["key"] == "workbook"
-            )
-            self.assertEqual("primary_human_deliverable", workbook_row["artifact_role"])
-            self.assertFalse(workbook_row["hidden_unless_requested"])
-            for row in run_log["output_manifest"]:
-                if row["key"] != "workbook":
-                    self.assertTrue(row["hidden_unless_requested"], row)
-                    self.assertNotEqual("primary_human_deliverable", row["artifact_role"], row)
-
-    @unittest.skipUnless(
-        importlib.util.find_spec("openpyxl"), "openpyxl required for comps workbook audit"
-    )
-    def test_audit_outputs_are_support_only_not_primary_deliverables(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "fallback"
-            audit_dir = Path(tmp_dir) / "audit"
-            result = run_script([str(FALLBACK_SCRIPT), "--output-dir", str(output_dir)])
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-
-            result = run_script(
-                [
-                    str(AUDIT_SCRIPT),
-                    str(output_dir / "screen_grade_comps_framework.xlsx"),
-                    "--json-out",
-                    str(audit_dir / "audit.json"),
-                    "--markdown-out",
-                    str(audit_dir / "audit.md"),
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((audit_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((audit_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertIsNone(run_log["primary_human_deliverable"])
-            self.assertEqual("support_only", run_log["artifact_mode"])
-            self.assertFalse(run_log["support_artifacts_user_visible_default"])
-            self.assertIsNone(manifest["primary_human_deliverable"])
-            self.assertEqual("support_only", manifest["artifact_mode"])
-            self.assertTrue(run_log["output_manifest"])
-            for row in run_log["output_manifest"]:
-                self.assertTrue(row["hidden_unless_requested"], row)
-                self.assertNotEqual("primary_human_deliverable", row["artifact_role"], row)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_comps_model_populated_expectations.py b/plugins/financial-markets/tests/test_comps_model_populated_expectations.py
deleted file mode 100644
index 425a78d..0000000
--- a/plugins/financial-markets/tests/test_comps_model_populated_expectations.py
+++ /dev/null
@@ -1,146 +0,0 @@
-"""Populated comps workbook expectations."""
-
-from __future__ import annotations
-
-import csv
-import importlib.util
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = ROOT / "skills" / "comps-valuation" / "scripts" / "create_comps_template.py"
-
-
-@unittest.skipUnless(
-    importlib.util.find_spec("xlsxwriter") and importlib.util.find_spec("openpyxl"),
-    "xlsxwriter and openpyxl required for populated comps workbook smoke test",
-)
-class CompsModelPopulatedExpectationsTests(unittest.TestCase):
-    def test_supplied_csv_populates_comps_workbook_but_caps_missing_data_status(self) -> None:
-        from openpyxl import load_workbook
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            input_csv = tmp / "peers.csv"
-            output = tmp / "comps.xlsx"
-            with input_csv.open("w", newline="", encoding="utf-8") as handle:
-                writer = csv.DictWriter(
-                    handle,
-                    fieldnames=[
-                        "ticker",
-                        "company",
-                        "peer_tier",
-                        "price",
-                        "basic_shares",
-                        "debt",
-                        "cash",
-                        "market_data_date",
-                        "revenue_ltm",
-                        "revenue_cy1",
-                        "ebitda_ltm",
-                        "ebitda_cy1",
-                        "source_name",
-                        "confidence",
-                        "inclusion_rationale",
-                    ],
-                )
-                writer.writeheader()
-                writer.writerow(
-                    {
-                        "ticker": "ACME",
-                        "company": "ACME Software",
-                        "peer_tier": "Target",
-                        "price": "100",
-                        "basic_shares": "50",
-                        "debt": "200",
-                        "cash": "100",
-                        "market_data_date": "2026-05-18",
-                        "revenue_ltm": "900",
-                        "revenue_cy1": "1000",
-                        "ebitda_ltm": "180",
-                        "ebitda_cy1": "220",
-                        "source_name": "User export",
-                        "confidence": "medium",
-                        "inclusion_rationale": "Subject company",
-                    }
-                )
-                writer.writerow(
-                    {
-                        "ticker": "PEER",
-                        "company": "Peer Co",
-                        "peer_tier": "Core",
-                        "price": "80",
-                        "basic_shares": "40",
-                        "debt": "50",
-                        "cash": "20",
-                        "market_data_date": "2026-05-18",
-                        "revenue_ltm": "700",
-                        "revenue_cy1": "820",
-                        "ebitda_ltm": "160",
-                        "ebitda_cy1": "190",
-                        "source_name": "User export",
-                        "confidence": "medium",
-                        "inclusion_rationale": "Close public peer",
-                    }
-                )
-                writer.writerow(
-                    {
-                        "ticker": "MISS",
-                        "company": "Missing Data Co",
-                        "peer_tier": "Core",
-                        "price": "",
-                        "basic_shares": "30",
-                        "debt": "40",
-                        "cash": "10",
-                        "market_data_date": "",
-                        "revenue_ltm": "500",
-                        "revenue_cy1": "",
-                        "ebitda_ltm": "90",
-                        "ebitda_cy1": "",
-                        "source_name": "",
-                        "confidence": "low",
-                        "inclusion_rationale": "Needs source work",
-                    }
-                )
-
-            result = subprocess.run(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    "--output",
-                    str(output),
-                    "--target",
-                    "ACME Software",
-                    "--ticker",
-                    "ACME",
-                    "--input-csv",
-                    str(input_csv),
-                ],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((tmp / "run_log.json").read_text(encoding="utf-8"))
-            workbook = load_workbook(output, data_only=False)
-
-            self.assertEqual("populated_supplied_data", run_log["workbook_mode"])
-            self.assertEqual("screen-grade", run_log["model_status"])
-            self.assertEqual(3, run_log["populated_peer_count"])
-            self.assertTrue(run_log["missing_required_fields"])
-            self.assertEqual("ACME", workbook["Universe"]["A2"].value)
-            self.assertEqual("Peer Co", workbook["Universe"]["B3"].value)
-            self.assertEqual(100, workbook["Market_Data"]["D2"].value)
-            self.assertEqual(1000, workbook["Financials"]["E2"].value)
-            self.assertTrue(str(workbook["Multiples"]["F2"].value).startswith("="))
-            self.assertEqual("User export", workbook["Sources"]["D2"].value)
-            self.assertNotEqual("decision-grade", run_log["model_status"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_comps_multiple_bridge_contract.py b/plugins/financial-markets/tests/test_comps_multiple_bridge_contract.py
deleted file mode 100644
index a36e536..0000000
--- a/plugins/financial-markets/tests/test_comps_multiple_bridge_contract.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Comps workbook PM bridge contract checks."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = ROOT / "skills" / "comps-valuation" / "scripts" / "create_comps_template.py"
-
-
-class CompsMultipleBridgeContractTests(unittest.TestCase):
-    def test_template_includes_pm_bridge_and_action_sections(self) -> None:
-        text = SCRIPT.read_text(encoding="utf-8")
-        for phrase in [
-            "Multiple_Bridge",
-            "PM_Action_Box",
-            "Peer Median To Selected Multiple Bridge",
-            "Current price",
-            "Implied value / share",
-            "Upside/downside to spot",
-            "What is priced in",
-            "Variant estimate path",
-            "PM action implication",
-            "Missing evidence",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_peer_universe_tracks_public_equity_investability_filters(self) -> None:
-        text = SCRIPT.read_text(encoding="utf-8")
-        for phrase in [
-            "Liquidity / Float",
-            "Index Membership",
-            "ETF Ownership / Flow",
-            "Short Interest / Borrow",
-            "ADR / Share-Class Issue",
-            "Consensus Coverage",
-            "Estimate Revision Relevance",
-            "Sector KPI Regime",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_comps_references_require_selected_multiple_bridge(self) -> None:
-        text = (
-            ROOT / "skills" / "comps-valuation" / "references" / "workbook" / "comps-framework.md"
-        ).read_text(encoding="utf-8")
-        self.assertIn("selected-multiple bridge", text)
-        self.assertIn("growth, margin, ROIC/quality", text)
-        self.assertIn("debt comps", text)
-        self.assertIn("Credit Markets", text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_core_pm_skill_sharpening.py b/plugins/financial-markets/tests/test_core_pm_skill_sharpening.py
deleted file mode 100644
index 57a42b6..0000000
--- a/plugins/financial-markets/tests/test_core_pm_skill_sharpening.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""Core public-equity PM skill sharpening contracts."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class CorePMSkillSharpeningTests(unittest.TestCase):
-    def test_company_tearsheet_requires_security_ownership_positioning_defaults(self) -> None:
-        text = (
-            read("skills/company-tearsheet/SKILL.md")
-            + "\n"
-            + read("skills/company-tearsheet/references/profile-templates.md")
-            + "\n"
-            + read("skills/company-tearsheet/references/metric-library.md")
-            + "\n"
-            + read("skills/company-tearsheet/references/quality-checks.md")
-        ).lower()
-        for phrase in [
-            "market cap",
-            "float",
-            "adv/liquidity",
-            "index membership",
-            "etf/passive ownership",
-            "top holders",
-            "short interest",
-            "borrow/crowding",
-            "factor exposure",
-            "governance",
-            "capital allocation",
-            "sell-side coverage",
-            "consensus setup",
-            "next analytical route",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_memo_builder_has_scratch_build_scaffold_and_pm_judgment(self) -> None:
-        text = (
-            read("skills/memo-builder/SKILL.md")
-            + "\n"
-            + read("skills/memo-builder/references/memo-modes.md")
-            + "\n"
-            + read("skills/memo-builder/references/output-contracts.md")
-            + "\n"
-            + read("skills/memo-builder/references/source-policy.md")
-        ).lower()
-        for phrase in [
-            "screen-grade-scratch-memo",
-            "intake checklist",
-            "source packet requirements",
-            "first-pass house view",
-            "variant wedge",
-            "what is priced in",
-            "estimate path",
-            "valuation/skew",
-            "downside mechanism",
-            "disconfirmers",
-            "action rules",
-            "evidence needed to upgrade",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_thesis_tracker_operating_model_is_explicit(self) -> None:
-        text = (
-            read("skills/thesis-tracker/SKILL.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/workflow-core.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/output-templates.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/thesis-schema.md")
-        ).lower()
-        for phrase in [
-            "pm owner",
-            "analyst owner",
-            "evidence owner",
-            "kpi owner",
-            "model owner",
-            "decision authority",
-            "review cadence",
-            "post-catalyst update sla",
-            "escalation triggers",
-            "next review gate",
-            "action threshold",
-            "append-only decision log",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_thesis_tracker_workbook_update_separates_readiness_and_decision_authority(
-        self,
-    ) -> None:
-        text = (
-            read("skills/thesis-tracker/SKILL.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/workflow-core.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/output-templates.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/quality-guardrails.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/thesis-schema.md")
-            + "\n"
-            + read("skills/thesis-tracker/references/DASHBOARD_PACK.md")
-        ).lower()
-        for phrase in [
-            "polished xlsx thesis tracker workbook",
-            "company-thesis status",
-            "security-thesis readiness",
-            "not decision-grade",
-            "draft threshold for pm confirmation",
-            "approved monitoring rule",
-            "current public price",
-            "render every",
-            "score chart",
-            "explicitly requests an html dashboard/report",
-            "override rationale",
-            "multiple core pillars",
-            "full action-rule",
-            "dedicated tabs",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_meeting_prep_has_first_class_public_equity_modes(self) -> None:
-        text = (
-            read("skills/meeting-prep/SKILL.md")
-            + "\n"
-            + read("skills/meeting-prep/references/meeting-type-playbooks.md")
-            + "\n"
-            + read("skills/meeting-prep/references/output-templates.md")
-            + "\n"
-            + read("skills/meeting-prep/references/DASHBOARD_PACK.md")
-        ).lower()
-        for phrase in [
-            "management_ir",
-            "expert_call",
-            "pm_internal_review",
-            "sell_side_call",
-            "earnings_call",
-            "investor_day",
-            "portfolio_watchlist_review",
-            "model_review",
-            "what not to ask",
-            "evidence requests",
-            "likely pushbacks",
-            "follow-up actions",
-        ]:
-            self.assertIn(phrase, text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_credit_language_boundary_contract.py b/plugins/financial-markets/tests/test_credit_language_boundary_contract.py
deleted file mode 100644
index 6c5aeaf..0000000
--- a/plugins/financial-markets/tests/test_credit_language_boundary_contract.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""Credit-language ownership boundaries for Public Equity Investing."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-AMBIGUOUS_PHRASES = [
-    "credit-" + "market research",
-    "credit " + "case",
-    "credit " + "workflow",
-    "private/" + "public " + "credit",
-    "public " + "credit underwriting",
-    "Credit Markets / " + "debt",
-    "Credit Markets / " + "structured",
-    "Credit Markets " + "or structured investor",
-    "security-" + "specific underwriting",
-    "credit/" + "debt valuation rules",
-    "debt underwriting " + "or credit work",
-]
-CORE_BOUNDARY_FILES = [
-    "shared/credit-markets-handoff.md",
-    "shared/equity-research-support-standard.md",
-    "shared/equity-valuation-pm-standard.md",
-    "skills/thesis-tracker/SKILL.md",
-    "skills/model-audit-tieout/references/audit-playbook.md",
-    "skills/event-driven-analyzer/references/restructuring_special_situations_playbook.md",
-    "skills/public-equity-investing/internal-support/excel-data-cleaner/references/examples.md",
-]
-
-
-def read(relative: str) -> str:
-    return (ROOT / relative).read_text(encoding="utf-8")
-
-
-class CreditLanguageBoundaryContractTests(unittest.TestCase):
-    def test_core_boundary_files_route_credit_instruments_to_credit_markets(self) -> None:
-        for relative in CORE_BOUNDARY_FILES:
-            text = read(relative)
-            self.assertIn("Credit Markets", text, relative)
-        self.assertIn("equity-risk context", read("shared/credit-markets-handoff.md"))
-        self.assertIn("common-equity", read("shared/equity-valuation-pm-standard.md"))
-        self.assertIn("listed-equity decision", read("shared/credit-markets-handoff.md"))
-
-    def test_ambiguous_credit_ownership_phrases_are_removed(self) -> None:
-        failures: list[str] = []
-        for relative in CORE_BOUNDARY_FILES:
-            text = read(relative)
-            for phrase in AMBIGUOUS_PHRASES:
-                if phrase in text:
-                    failures.append(f"{relative}: {phrase}")
-        self.assertEqual([], failures)
-
-    def test_thesis_tracker_is_equity_signal_not_credit_workflow(self) -> None:
-        text = read("skills/thesis-tracker/SKILL.md")
-        self.assertIn("Equity-risk credit signal / Credit Markets handoff", text)
-        self.assertIn("route credit-security", text)
-        self.assertIn("common-equity downside", text)
-
-    def test_event_driven_restructuring_uses_listed_equity_expression(self) -> None:
-        text = read(
-            "skills/event-driven-analyzer/references/restructuring_special_situations_playbook.md"
-        )
-        self.assertIn("dated public-equity event path", text)
-        self.assertIn("listed-equity expression", text)
-        self.assertIn("route debt-security expression to Credit Markets", text)
-
-    def test_guardrail_scripts_still_detect_credit_instruments_and_route_out(self) -> None:
-        sizing = read("skills/portfolio-risk-management/scripts/position_sizing_core.py")
-        hedge = read("skills/portfolio-risk-management/scripts/score_hedge_candidates.py")
-        cleaner = read(
-            "skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/clean_tabular_data.py"
-        )
-        combined = "\n".join([sizing, hedge, cleaner])
-        for phrase in ["route_to_credit_markets", "CDS", "bond", "loan", "DV01"]:
-            self.assertIn(phrase, combined)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_credit_markets_handoffs_for_risk.py b/plugins/financial-markets/tests/test_credit_markets_handoffs_for_risk.py
deleted file mode 100644
index eddd354..0000000
--- a/plugins/financial-markets/tests/test_credit_markets_handoffs_for_risk.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Credit Markets handoff boundaries for Public Equity risk workflows."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-TARGETS = [
-    "shared/credit-markets-handoff.md",
-    "skills/portfolio-risk-management/SKILL.md",
-    "skills/portfolio-risk-management/references/sizing-framework.md",
-    "skills/portfolio-risk-management/references/strategy-nuance.md",
-    "skills/portfolio-risk-management/references/hedge-workflow.md",
-    "skills/portfolio-risk-management/references/instrument-playbooks.md",
-    "skills/portfolio-risk-management/references/hedge-scorecard-schema.md",
-]
-
-CREDIT_TERMS = [
-    "CDS",
-    "bonds",
-    "loans",
-    "spread DV01",
-    "CS01",
-    "capital-structure",
-    "distressed",
-    "recovery",
-    "covenant",
-]
-
-
-class CreditMarketsHandoffForRiskTests(unittest.TestCase):
-    def test_each_target_routes_credit_work_to_credit_markets(self) -> None:
-        for relative_path in TARGETS:
-            text = (ROOT / relative_path).read_text(encoding="utf-8")
-            self.assertIn("Credit Markets", text, relative_path)
-            self.assertRegex(text.lower(), r"route|handoff|use credit markets", relative_path)
-
-    def test_combined_boundary_names_credit_instrument_family(self) -> None:
-        combined = "\n".join((ROOT / path).read_text(encoding="utf-8") for path in TARGETS)
-        for term in CREDIT_TERMS:
-            self.assertIn(term, combined)
-
-    def test_cds_and_spreads_are_signal_context_only(self) -> None:
-        combined = "\n".join((ROOT / path).read_text(encoding="utf-8") for path in TARGETS).lower()
-        for phrase in [
-            "cds/spreads only as common-equity risk context",
-            "only as equity-risk signals",
-            "use here only as public-equity risk context",
-            "common-equity downside context",
-        ]:
-            self.assertIn(phrase, combined)
-
-    def test_credit_macro_template_was_removed_from_position_sizing_mode(self) -> None:
-        script = (
-            ROOT
-            / "skills"
-            / "portfolio-risk-management"
-            / "scripts"
-            / "create_position_sizing_templates.py"
-        ).read_text(encoding="utf-8")
-        output_templates = (
-            ROOT
-            / "skills"
-            / "portfolio-risk-management"
-            / "references"
-            / "position-sizing-output-templates.md"
-        ).read_text(encoding="utf-8")
-        self.assertNotIn("credit_macro_inputs.csv", script)
-        self.assertNotIn("credit_macro_inputs.csv", output_templates)
-        self.assertIn("macro_proxy_inputs.csv", script)
-        self.assertIn("macro_proxy_inputs.csv", output_templates)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_credit_markets_handoffs_for_valuation.py b/plugins/financial-markets/tests/test_credit_markets_handoffs_for_valuation.py
deleted file mode 100644
index 379618a..0000000
--- a/plugins/financial-markets/tests/test_credit_markets_handoffs_for_valuation.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Credit Markets handoff boundaries for Public Equity valuation skills."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-TARGETS = [
-    "dcf-model-builder/SKILL.md",
-    "three-statement-model-builder/SKILL.md",
-    "comps-valuation/SKILL.md",
-    "scenario-sensitivity-generator/SKILL.md",
-    "model-audit-tieout/SKILL.md",
-    "equity-model-update/SKILL.md",
-]
-OLD_TABLE = "credit_liquidity_stress"
-
-
-class CreditMarketsHandoffForValuationTests(unittest.TestCase):
-    def test_skill_contracts_route_credit_security_work_to_credit_markets(self) -> None:
-        for relative in TARGETS:
-            text = (ROOT / "skills" / relative).read_text(encoding="utf-8")
-            self.assertIn("Use Credit Markets", text, relative)
-            for phrase in [
-                "bond comps",
-                "loan comps",
-                "CDS",
-                "spread/yield relative value",
-                "covenant-package analysis",
-                "debt-security valuation",
-                "recovery waterfall",
-            ]:
-                self.assertIn(phrase, text, relative)
-
-    def test_scenario_materializer_uses_equity_liquidity_downside_not_credit_table(self) -> None:
-        script = (
-            ROOT
-            / "skills"
-            / "scenario-sensitivity-generator"
-            / "scripts"
-            / "materialize_public_equity_sensitivities.py"
-        ).read_text(encoding="utf-8")
-        self.assertIn("equity_liquidity_downside", script)
-        self.assertNotIn(OLD_TABLE, script)
-        taxonomy = (
-            ROOT
-            / "skills"
-            / "scenario-sensitivity-generator"
-            / "references"
-            / "public-equity-investing-sensitivity-taxonomy.md"
-        ).read_text(encoding="utf-8")
-        self.assertIn("equity_liquidity_downside", taxonomy)
-        self.assertIn("Route credit-security valuation", taxonomy)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_dashboard_kind_contract.py b/plugins/financial-markets/tests/test_dashboard_kind_contract.py
deleted file mode 100644
index 6881abd..0000000
--- a/plugins/financial-markets/tests/test_dashboard_kind_contract.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""Dashboard kind contract regressions for Public Equity Investing."""
-
-from __future__ import annotations
-
-import json
-import sys
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from shared.dashboard.qa import EXPECTED_KIND, validate_payload  # noqa: E402
-
-PUBLIC_EQUITY_KIND = "public_equity_investing_dashboard.v1"
-LEGACY_KIND = "public_" + "markets_dashboard.v1"
-LEGACY_MARKERS = [
-    LEGACY_KIND,
-    "public_" + "markets_dashboard",
-    "Public " + "Markets " + "dashboard",
-]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-def load_json(path: Path) -> dict[str, object]:
-    return json.loads(path.read_text(encoding="utf-8"))
-
-
-def minimal_payload(kind: str = PUBLIC_EQUITY_KIND) -> dict[str, object]:
-    return {
-        "kind": kind,
-        "title": "Issuer dashboard",
-        "issuer": {"ticker": "ACME"},
-        "tabs": [
-            {
-                "id": "overview",
-                "label": "Overview",
-                "modules": [
-                    {"type": "decision_box", "data": {"stance": "Watch"}},
-                ],
-            }
-        ],
-        "sources": [{"id": "S1", "title": "Company release"}],
-    }
-
-
-class DashboardKindContractTests(unittest.TestCase):
-    def test_validator_and_schema_use_public_equity_kind(self) -> None:
-        schema = load_json(ROOT / "shared/dashboard/schema/dashboard_payload.schema.json")
-        self.assertEqual(PUBLIC_EQUITY_KIND, EXPECTED_KIND)
-        self.assertEqual(PUBLIC_EQUITY_KIND, schema["properties"]["kind"]["const"])
-
-    def test_public_equity_kind_validates_for_minimal_payload(self) -> None:
-        report = validate_payload(minimal_payload(), profile="draft")
-        self.assertEqual("passed", report["status"], report)
-
-    def test_default_validation_is_production_safe(self) -> None:
-        report = validate_payload(minimal_payload())
-        failures = "\n".join(report["hard_failures"])
-
-        self.assertEqual("failed", report["status"])
-        self.assertIn("production dashboards must include layout", failures)
-        self.assertIn("production dashboards must include metadata", failures)
-
-    def test_old_dashboard_kind_is_rejected(self) -> None:
-        report = validate_payload(minimal_payload(LEGACY_KIND))
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "kind must be" in failure and PUBLIC_EQUITY_KIND in failure
-                for failure in report["hard_failures"]
-            ),
-            report,
-        )
-
-    def test_all_dashboard_examples_use_public_equity_kind(self) -> None:
-        payload_dir = (
-            ROOT
-            / "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads"
-        )
-        payloads = sorted(payload_dir.glob("*.json"))
-        self.assertTrue(payloads, "expected dashboard example payloads")
-        for payload_path in payloads:
-            payload = load_json(payload_path)
-            self.assertEqual(PUBLIC_EQUITY_KIND, payload.get("kind"), payload_path.name)
-
-    def test_no_legacy_dashboard_identity_in_public_equity_plugin(self) -> None:
-        failures: list[str] = []
-        for path in ROOT.rglob("*"):
-            if not path.is_file():
-                continue
-            if path.suffix in {".pyc", ".png", ".jpg", ".jpeg", ".gif", ".xlsx", ".xls", ".pdf"}:
-                continue
-            if "__pycache__" in path.parts:
-                continue
-            try:
-                text = path.read_text(encoding="utf-8")
-            except UnicodeDecodeError:
-                continue
-            for marker in LEGACY_MARKERS:
-                if marker in text:
-                    failures.append(str(path.relative_to(ROOT)))
-                    break
-        self.assertEqual([], failures)
-
-    def test_hero_deliverable_policy_remains_html_xlsx_first(self) -> None:
-        framework = read("shared/final-deliverable-framework.md")
-        packager = read("shared/artifact_packager.py")
-        dashboard_builder = read(
-            "skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md"
-        )
-        combined = "\n".join([framework, packager, dashboard_builder])
-
-        for phrase in [
-            "HTML dashboard or report",
-            "XLSX workbook",
-            "JSON, CSV, Markdown, logs, and manifests as secondary files only",
-            "The JSON payload is an internal handoff contract, not the final user-facing artifact",
-            "the rendered HTML is the user-facing artifact",
-        ]:
-            self.assertIn(phrase, combined)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_dashboard_map_coverage.py b/plugins/financial-markets/tests/test_dashboard_map_coverage.py
deleted file mode 100644
index 40192bd..0000000
--- a/plugins/financial-markets/tests/test_dashboard_map_coverage.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Dashboard-map coverage for workbook/model-output Public Equity Investing workflows."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-INTERNAL_SUPPORT_ROOT = ROOT / "skills" / "public-equity-investing" / "internal-support"
-
-MODEL_OUTPUT_MAPS = [
-    "comps-valuation/references/workbook/dashboard-map.md",
-    "dcf-model-builder/references/dashboard-map.md",
-    "deck-report-qc/references/dashboard-map.md",
-    "model-audit-tieout/references/dashboard-map.md",
-    "equity-model-update/references/dashboard-map.md",
-    "three-statement-model-builder/references/dashboard-map.md",
-]
-
-
-class DashboardMapCoverageTests(unittest.TestCase):
-    def test_model_output_dashboard_maps_exist_and_have_contract_sections(self) -> None:
-        for relative in MODEL_OUTPUT_MAPS:
-            path = ROOT / "skills" / relative
-            self.assertTrue(path.exists(), relative)
-            text = path.read_text(encoding="utf-8")
-            for phrase in [
-                "## Decision Question",
-                "## Recommended Payload",
-                "## Recommended Tabs And Modules",
-                "## Required Evidence",
-                "## Do Not",
-            ]:
-                self.assertIn(phrase, text, relative)
-            self.assertIn("public_equity_investing_dashboard.v1", text, relative)
-
-    def test_dashboard_builder_indexes_model_output_maps(self) -> None:
-        text = (INTERNAL_SUPPORT_ROOT / "dashboard-builder" / "INTERNAL.md").read_text(
-            encoding="utf-8"
-        )
-        self.assertIn("Model-output dashboard maps", text)
-        for relative in [
-            item for item in MODEL_OUTPUT_MAPS if not item.startswith("deck-report-qc")
-        ]:
-            self.assertIn(relative, text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_deck_report_qc_equity_research_contract.py b/plugins/financial-markets/tests/test_deck_report_qc_equity_research_contract.py
deleted file mode 100644
index e0e7a29..0000000
--- a/plugins/financial-markets/tests/test_deck_report_qc_equity_research_contract.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Equity-research contract for deck-report-qc."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class DeckReportQCEquityResearchContractTests(unittest.TestCase):
-    def test_skill_classifies_equity_artifacts_not_credit_packs(self) -> None:
-        text = read("skills/deck-report-qc/SKILL.md")
-        for phrase in [
-            "sell-side initiation",
-            "PM pitch deck",
-            "ETF/index diligence note",
-            "public-equity IC memo",
-            "price target",
-            "benchmark weight",
-        ]:
-            self.assertIn(phrase, text)
-        self.assertNotIn("public-credit pack", text)
-        self.assertNotIn("credit memo, earnings note", text)
-
-    def test_issue_taxonomy_focuses_on_equity_decision_impact(self) -> None:
-        text = read("skills/deck-report-qc/references/issue-taxonomy.md")
-        self.assertIn("price target or rating support conflicts with source", text)
-        self.assertNotIn("recovery or spread data overstated", text)
-
-    def test_standalone_html_qc_is_default_with_optional_dashboard(self) -> None:
-        skill = read("skills/deck-report-qc/SKILL.md")
-        output_templates = read("skills/deck-report-qc/references/output-templates.md")
-        tieout = read("skills/deck-report-qc/references/extraction-and-tieout.md")
-        dashboard_pack = read("skills/deck-report-qc/references/DASHBOARD_PACK.md")
-
-        for phrase in [
-            "../../shared/html-artifact-standard.md",
-            "polished standalone HTML senior-review QC report",
-            "only when the user explicitly asks for a standardized dashboard",
-            "confirmed internal mismatch",
-            "externally verified error",
-            "local headless-browser screenshots",
-        ]:
-            self.assertIn(phrase, skill)
-        self.assertIn("Review scope and evidence limitations", output_templates)
-        self.assertIn("Decision-critical tie-out", output_templates)
-        self.assertIn("must appear before a narrative `Top Issues`", output_templates)
-        self.assertIn(
-            "document.documentElement.scrollWidth <= document.documentElement.clientWidth",
-            output_templates,
-        )
-        self.assertIn("Completion record", tieout)
-        self.assertIn("document itself must not horizontally overflow", tieout)
-        self.assertIn("Use this pack only when", dashboard_pack)
-
-    def test_optional_dashboard_pack_surfaces_equity_qc_decision_layer(self) -> None:
-        text = read("skills/deck-report-qc/references/DASHBOARD_PACK.md")
-        for phrase in [
-            "public-equity research support layer",
-            "EPS/revenue/KPI support",
-            "price-target support",
-            "benchmark/ETF/index exposure",
-            "market-data freshness",
-        ]:
-            self.assertIn(phrase, text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_deliverable_framework.py b/plugins/financial-markets/tests/test_deliverable_framework.py
deleted file mode 100644
index ac248fd..0000000
--- a/plugins/financial-markets/tests/test_deliverable_framework.py
+++ /dev/null
@@ -1,1552 +0,0 @@
-"""Regression checks for the Public Equity Investing deliverable framework."""
-
-from __future__ import annotations
-
-import json
-import re
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-sys.path.insert(0, str(ROOT))
-
-from shared.dashboard.qa import validate_payload  # noqa: E402
-from shared.dashboard.renderer import render_dashboard  # noqa: E402
-
-DASHBOARD_PACK_SKILLS = [
-    "company-tearsheet",
-    "comps-valuation",
-    "deck-report-qc",
-    "earnings-deep-dive",
-    "earnings-preview",
-    "economic-impact-report",
-    "event-driven-analyzer",
-    "portfolio-risk-management",
-    "idea-generation",
-    "initiating-coverage",
-    "long-short-pitch",
-    "meeting-prep",
-    "memo-builder",
-    "equity-model-update",
-    "scenario-sensitivity-generator",
-    "thesis-tracker",
-]
-
-DASHBOARD_SAMPLE_PAYLOADS = [
-    "comps-valuation-sample.json",
-    "initiating-coverage-sample.json",
-    "idea-generation-sample.json",
-    "portfolio-sizing-mode-sample.json",
-    "scenario-sensitivity-sample.json",
-    "portfolio-hedge-mode-sample.json",
-    "deck-report-qc-sample.json",
-    "hero-citations-sample.json",
-    "production-complete-sample.json",
-]
-
-INTAKE_OWNER_SKILLS = [
-    "catalyst-calendar",
-    "company-tearsheet",
-    "comps-valuation",
-    "dcf-model-builder",
-    "earnings-deep-dive",
-    "earnings-preview",
-    "economic-impact-report",
-    "equity-model-update",
-    "event-driven-analyzer",
-    "portfolio-risk-management",
-    "idea-generation",
-    "initiating-coverage",
-    "long-short-pitch",
-    "meeting-prep",
-    "memo-builder",
-    "model-audit-tieout",
-    "scenario-sensitivity-generator",
-    "thesis-tracker",
-    "three-statement-model-builder",
-]
-
-INTAKE_SUPPORT_SKILLS = [
-    "dashboard-builder",
-    "deck-report-qc",
-    "excel-data-cleaner",
-    "financial-source-of-truth",
-    "financials-normalizer",
-    "style-guide-adapter",
-]
-
-ARTIFACT_OWNING_SKILLS = {
-    "catalyst-calendar",
-    "company-tearsheet",
-    "comps-valuation",
-    "dcf-model-builder",
-    "deck-report-qc",
-    "earnings-deep-dive",
-    "earnings-preview",
-    "economic-impact-report",
-    "equity-model-update",
-    "event-driven-analyzer",
-    "financials-normalizer",
-    "idea-generation",
-    "initiating-coverage",
-    "long-short-pitch",
-    "meeting-prep",
-    "memo-builder",
-    "model-audit-tieout",
-    "portfolio-risk-management",
-    "scenario-sensitivity-generator",
-    "thesis-tracker",
-    "three-statement-model-builder",
-}
-
-INTERNAL_SUPPORT_PLAYBOOKS = {
-    "daloopa-provider-guide",
-    "dashboard-builder",
-    "excel-data-cleaner",
-    "financial-source-of-truth",
-    "quartr-provider-guide",
-    "sector-context-overlay",
-    "style-guide-adapter",
-}
-INTERNAL_SUPPORT_PATH = "skills/public-equity-investing/internal-support"
-
-
-def read(relative_path: str | Path) -> str:
-    path = Path(relative_path)
-    if not path.is_absolute():
-        path = ROOT / path
-    return path.read_text(encoding="utf-8")
-
-
-def load_json(relative_path: str) -> dict[str, object]:
-    return json.loads(read(relative_path))
-
-
-def minimal_dashboard_payload() -> dict[str, object]:
-    return {
-        "kind": "public_equity_investing_dashboard.v1",
-        "title": "Issuer dashboard",
-        "issuer": {"ticker": "ACME"},
-        "tabs": [
-            {
-                "id": "overview",
-                "label": "Overview",
-                "modules": [
-                    {"type": "decision_box", "data": {"stance": "Watch"}},
-                ],
-            }
-        ],
-        "sources": [{"id": "S1", "title": "Company release"}],
-    }
-
-
-def _payload_has_module(payload: dict[str, object], module_type: str) -> bool:
-    tabs = payload.get("tabs")
-    if not isinstance(tabs, list):
-        return False
-    for tab in tabs:
-        if not isinstance(tab, dict):
-            continue
-        modules = tab.get("modules")
-        if not isinstance(modules, list):
-            continue
-        for module in modules:
-            if isinstance(module, dict) and module.get("type") == module_type:
-                return True
-    return False
-
-
-class DeliverableFrameworkTests(unittest.TestCase):
-    def test_shared_framework_defines_artifact_lanes(self) -> None:
-        text = read("shared/final-deliverable-framework.md")
-
-        for phrase in [
-            "HTML dashboard or report",
-            "XLSX workbook",
-            "Concise chat answer",
-            "Support and audit files",
-            "HTML recommendation triggers",
-            "Full-depth default",
-            "Citation standard",
-            "Workbook cover standard",
-        ]:
-            self.assertIn(phrase, text)
-
-        self.assertIn(
-            "Markdown, raw JSON, CSV, manifests, run logs, and plan files are not the default user-facing deliverable",
-            text,
-        )
-
-    def test_html_artifacts_verify_headline_market_data_anchor(self) -> None:
-        standard = read("shared/html-artifact-standard.md")
-
-        for phrase in [
-            "market price, return, multiple, or market-derived value",
-            "hero card, headline KPI, or decision conclusion",
-            "correct issuer, security, or ticker",
-            "applicable as-of date",
-            "omit the figure from the headline view or label it unavailable",
-        ]:
-            self.assertIn(phrase, standard)
-
-    def test_adaptive_deliverable_intake_policy_defines_tool_and_fallback_rules(self) -> None:
-        policy = read("shared/deliverable-intake-policy.md")
-
-        for phrase in [
-            "request_user_input",
-            "no more than three questions",
-            "free-form `Other` response automatically",
-            "never include an",
-            "normal chat and wait",
-            "non-interactive run",
-            "preserve",
-            "do not ask again",
-            "Excel workbook (.xlsx) (Recommended)",
-            "Full working analysis (Recommended)",
-            "Depth is not readiness",
-            "concise plain-text",
-            "do not silently select depth or audience",
-            "append this",
-            "switch to Plan mode with `Shift + Tab`",
-            "do not require a mode change",
-            "substantive 60/90-day catalyst calendar",
-            "polished standalone HTML artifact",
-            "Presentation-Surface Precedence",
-            "Apply a saved reader-facing output preference as the default",
-            "Otherwise, resolve any new standalone reader-facing output to polished standalone HTML",
-            "Use chat-only output only when the user explicitly requests",
-            "A direct analytical question, a detail-page hero prompt",
-            "Do not choose chat-only output because a concise answer seems sufficient or more useful",
-            "Once the presentation surface resolves to HTML, treat that as a committed deliverable decision",
-            "do not later reconsider whether the analysis belongs in chat",
-        ]:
-            self.assertIn(phrase, policy)
-        self.assertNotIn("as a bias only", policy)
-
-        framework = read("shared/final-deliverable-framework.md")
-        self.assertIn("Do not infer chat-only intent from direct question wording", framework)
-        self.assertIn("Do not reopen the presentation decision after source gathering or analysis", framework)
-        self.assertNotIn("or one narrow event/section", framework)
-
-    def test_artifact_owning_skills_declare_natural_artifact_and_shared_precedence(self) -> None:
-        visible_skills = {path.parent.name for path in (ROOT / "skills").glob("*/SKILL.md")}
-        self.assertEqual(
-            ARTIFACT_OWNING_SKILLS,
-            visible_skills - {"public-equity-investing", "user-context"},
-        )
-
-        for skill in ARTIFACT_OWNING_SKILLS:
-            with self.subTest(skill=skill):
-                text = read(f"skills/{skill}/SKILL.md")
-                self.assertIn("Apply the presentation-surface precedence", text)
-                self.assertIn("../../shared/deliverable-intake-policy.md", text)
-                self.assertIn("This workflow's natural artifact is", text)
-                self.assertIn(
-                    "Do not choose chat-only output unless the user explicitly requests a lightweight response.",
-                    text,
-                )
-
-    def test_intake_policy_is_wired_to_framework(self) -> None:
-        framework = read("shared/final-deliverable-framework.md")
-
-        self.assertIn("shared/deliverable-intake-policy.md", framework)
-
-    def test_default_prompts_showcase_onboarding_earnings_and_theme_screening(self) -> None:
-        default_prompts = load_json(".codex-plugin/plugin.json")["interface"]["defaultPrompt"]
-
-        self.assertTrue(all(len(prompt) <= 128 for prompt in default_prompts))
-        self.assertEqual(
-            default_prompts,
-            [
-                "Help me get started",
-                "Analyze Apple's latest earnings: what changed, what is priced in, and what should an investor watch next",
-                "Screen listed-equity beneficiaries of AI data-center power demand and rank the best ideas, risks, and false positives",
-            ],
-        )
-
-    def test_artifact_owners_use_preflight_and_support_skills_inherit_preferences(self) -> None:
-        for skill in INTAKE_OWNER_SKILLS:
-            text = read(f"skills/{skill}/SKILL.md")
-            self.assertIn("../../shared/deliverable-intake-policy.md", text, skill)
-            self.assertIn("request_user_input", text, skill)
-            self.assertIn("Before source gathering or analysis", text, skill)
-
-        for skill in INTAKE_SUPPORT_SKILLS:
-            relative_path = (
-                f"{INTERNAL_SUPPORT_PATH}/{skill}/INTERNAL.md"
-                if skill in INTERNAL_SUPPORT_PLAYBOOKS
-                else f"skills/{skill}/SKILL.md"
-            )
-            text = read(relative_path)
-            self.assertIn("../../shared/deliverable-intake-policy.md", text, skill)
-            self.assertIn("do not re-prompt", text, skill)
-            self.assertIn("before source gathering", text, skill)
-
-        overlay = read(f"{INTERNAL_SUPPORT_PATH}/sector-context-overlay/INTERNAL.md")
-        self.assertIn("does not own a hero deliverable", overlay)
-        self.assertIn("do not call `request_user_input` independently", overlay)
-
-    def test_model_explicit_html_existing_workbook_and_reuse_scenarios_are_covered(self) -> None:
-        policy = read("shared/deliverable-intake-policy.md")
-        model = read("skills/dcf-model-builder/SKILL.md")
-
-        self.assertIn("model or valuation-package request", policy)
-        self.assertIn("Excel workbook (.xlsx)", policy)
-        self.assertIn("explicit HTML research report request skips the format question", policy)
-        self.assertIn("Duolingo's stock has plummeted since last May", policy)
-        self.assertIn("resolves Word format only", policy)
-        self.assertIn("unresolved depth and audience", policy)
-        self.assertIn("review of an existing workbook preserves `.xlsx`", policy)
-        self.assertIn("without repeated prompts", policy)
-        self.assertIn("../../shared/deliverable-intake-policy.md", model)
-
-    def test_new_research_and_model_work_start_with_intake(self) -> None:
-        framework = read("shared/final-deliverable-framework.md")
-        policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("Before source gathering", policy)
-        self.assertIn("begins source gathering or analysis", framework)
-
-    def test_implicit_invocation_is_limited_to_the_guarded_router(self) -> None:
-        policy = read("shared/invocation-policy.md")
-        framework = read("shared/final-deliverable-framework.md")
-        router = read("skills/public-equity-investing/SKILL.md")
-
-        for phrase in [
-            "Explicit invocation",
-            "Perfect-fit mandate",
-            "do not activate",
-            "A public company name by itself is not sufficient",
-        ]:
-            self.assertIn(phrase, policy)
-        self.assertIn("shared/invocation-policy.md", framework)
-        self.assertIn("shared/invocation-policy.md", router)
-
-        skill_names = {path.parent.name for path in (ROOT / "skills").glob("*/SKILL.md")}
-        yaml_paths = sorted((ROOT / "skills").glob("*/agents/openai.yaml"))
-        self.assertEqual(skill_names, {path.parents[1].name for path in yaml_paths})
-        for path in yaml_paths:
-            skill = path.parents[1].name
-            expected = skill == "public-equity-investing"
-            with self.subTest(skill=skill):
-                self.assertIn(
-                    f"allow_implicit_invocation: {str(expected).lower()}",
-                    read(path),
-                )
-
-    def test_router_resolves_bundled_paths_from_the_plugin_root(self) -> None:
-        router_path = ROOT / "skills" / "public-equity-investing" / "SKILL.md"
-        router = router_path.read_text(encoding="utf-8")
-
-        self.assertIn("Derive the plugin root once", router)
-        self.assertIn("Set the shell working directory to that plugin root", router)
-        self.assertIn("Do not apply `../..` to an already resolved plugin root", router)
-        for relative_path in [
-            "shared/invocation-policy.md",
-            "shared/final-deliverable-framework.md",
-            "shared/deliverable-intake-policy.md",
-            "skills/user-context/SKILL.md",
-            "skills/public-equity-investing/internal-support/policy.md",
-        ]:
-            with self.subTest(relative_path=relative_path):
-                self.assertIn(relative_path, router)
-                self.assertTrue((ROOT / relative_path).resolve().exists())
-
-    def test_router_selects_lead_skill_without_resolving_presentation(self) -> None:
-        router = read("skills/public-equity-investing/SKILL.md")
-        invocation_policy = read("shared/invocation-policy.md")
-
-        for phrase in [
-            "Pass relevant entries from `saved_context` to the selected lead skill as handoff context",
-            "must not interpret saved output preferences",
-            "The router owns admission and lead-skill selection only",
-            "load `skills/<lead-skill>/SKILL.md` from the plugin root before source gathering",
-            "must not continue substantive work as a substitute for the selected owner",
-            "without choosing or announcing format, depth, artifact architecture",
-            "The selected owner applies `shared/final-deliverable-framework.md`",
-            "reads `shared/deliverable-intake-policy.md`",
-        ]:
-            self.assertIn(phrase, router)
-        self.assertIn("The selected lead workflow, not the router", invocation_policy)
-        self.assertNotIn("must not be downgraded to chat-only output", router)
-
-    def test_internal_support_playbooks_are_packaged_but_not_visible_skills(self) -> None:
-        policy = read(f"{INTERNAL_SUPPORT_PATH}/policy.md")
-        router = read("skills/public-equity-investing/SKILL.md")
-        visible = {path.parent.name for path in (ROOT / "skills").glob("*/SKILL.md")}
-
-        self.assertEqual(23, len(visible))
-        self.assertIn("internal-support/policy.md", router)
-        for skill in INTERNAL_SUPPORT_PLAYBOOKS:
-            self.assertNotIn(skill, visible)
-            self.assertIn(f"internal-support/{skill}/INTERNAL.md", policy)
-            self.assertTrue((ROOT / INTERNAL_SUPPORT_PATH / skill / "INTERNAL.md").exists())
-            self.assertFalse((ROOT / "skills" / skill / "SKILL.md").exists())
-
-    def test_visible_workflows_that_name_internal_capabilities_resolve_policy(self) -> None:
-        internal_labels = INTERNAL_SUPPORT_PLAYBOOKS
-        for path in (ROOT / "skills").glob("*/SKILL.md"):
-            text = read(path)
-            if not any(label in text for label in internal_labels):
-                continue
-            self.assertTrue(
-                "internal-support/policy.md" in text
-                or "public-equity-investing" in text
-                or "support-layer-routing-contract.md" in text
-                or "final-deliverable-framework.md" in text,
-                path.parent.name,
-            )
-
-    def test_provider_guides_load_only_for_callable_selected_routes(self) -> None:
-        text = read("shared/workflow-source-resolution.md")
-        self.assertIn("Do not load provider guides merely because `.app.json` declares", text)
-        for provider in ["daloopa", "quartr"]:
-            with self.subTest(provider=provider):
-                self.assertIn(
-                    f"skills/public-equity-investing/internal-support/{provider}-provider-guide/INTERNAL.md",
-                    text,
-                )
-                self.assertNotIn(
-                    f"../skills/public-equity-investing/internal-support/{provider}-provider-guide/INTERNAL.md",
-                    text,
-                )
-                guide_root = ROOT / INTERNAL_SUPPORT_PATH / f"{provider}-provider-guide"
-                internal = read(guide_root / "INTERNAL.md")
-                self.assertIn("references/connector-playbook.md", internal)
-                self.assertIn("references/workbook-mode.md", internal)
-                self.assertTrue((guide_root / "references" / "connector-playbook.md").exists())
-                self.assertTrue((guide_root / "references" / "workbook-mode.md").exists())
-
-    def test_no_legacy_markdown_or_chat_default_wording(self) -> None:
-        docs = [
-            ROOT / "shared/final-deliverable-framework.md",
-            *ROOT.glob("skills/*/SKILL.md"),
-            *ROOT.glob(f"{INTERNAL_SUPPORT_PATH}/*/INTERNAL.md"),
-            *ROOT.glob("skills/*/references/*.md"),
-        ]
-        forbidden_patterns = [
-            r"chat-first",
-            r"markdown-first",
-            r"full markdown",
-            r"full\s+[a-z -]*markdown",
-            r"default chat",
-            r"default deliverable:.*markdown",
-            r"generated user-facing markdown",
-            r"return a full markdown",
-            r"markdown report",
-            r"markdown-led",
-            r"markdown as final",
-            r"markdown file as final",
-            r"rendered in chat",
-            r"paste .*verbatim",
-            r"user-facing markdown",
-            r"final markdown",
-            r"csv/markdown bundle",
-            r"structured json handoff",
-            r"use this only when .*asks.*dashboard",
-        ]
-
-        failures: list[str] = []
-        for relative_path in docs:
-            text = read(str(relative_path)).lower()
-            for pattern in forbidden_patterns:
-                if re.search(pattern, text):
-                    failures.append(f"{relative_path}: {pattern}")
-
-        self.assertEqual([], failures)
-
-    def test_dashboard_defaults_cover_substantial_public_equity_investing_workflows(self) -> None:
-        expectations = {
-            "skills/catalyst-calendar/SKILL.md": [
-                "recommended presentation path",
-                "in interactive runs",
-                "in non-interactive runs",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/earnings-preview/SKILL.md": [
-                "polished standalone html pre-earnings report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/earnings-deep-dive/SKILL.md": [
-                "polished standalone html post-earnings report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/memo-builder/SKILL.md": [
-                "polished standalone html investment committee memo",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/comps-valuation/SKILL.md": [
-                "polished standalone html comps report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/event-driven-analyzer/SKILL.md": [
-                "polished standalone html event report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/idea-generation/SKILL.md": [
-                "polished standalone html idea-triage report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/initiating-coverage/SKILL.md": [
-                "polished standalone html initiation report",
-                "explicitly asks for a standardized dashboard",
-            ],
-            "skills/portfolio-risk-management/SKILL.md": [
-                "polished standalone html risk decision report",
-                "explicitly asks for a standardized dashboard",
-            ],
-        }
-
-        for relative_path, phrases in expectations.items():
-            text = read(relative_path).lower()
-            for phrase in phrases:
-                self.assertIn(phrase.lower(), text, relative_path)
-
-    def test_memo_builder_uses_standalone_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/memo-builder/SKILL.md")
-        output_contracts = read("skills/memo-builder/references/output-contracts.md")
-        quality_workflow = read("skills/memo-builder/references/quality-workflow.md")
-        dashboard_pack = read("skills/memo-builder/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML investment committee memo", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("Do not repeat the same recommendation", skill_doc)
-        self.assertIn("do not fragment tickers, prices, EPS ranges", skill_doc)
-        self.assertIn("local headless-browser screenshots", skill_doc)
-        self.assertIn("annualized return / IRR versus a stated hurdle", skill_doc)
-        self.assertIn(
-            "sufficient to decline initiation today; insufficient to support initiation", skill_doc
-        )
-        self.assertIn("Standalone HTML Investment Committee Memo", output_contracts)
-        self.assertIn("Do not repeat the same recommendation", output_contracts)
-        self.assertIn("undiscounted terminal value appreciation", output_contracts)
-        self.assertIn("insufficient to support initiation", output_contracts)
-        self.assertIn("undiscounted terminal price appreciation", quality_workflow)
-        self.assertIn("return hurdle / IRR framing", quality_workflow)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("citation rendering remains readable", dashboard_pack)
-        self.assertIn("polished standalone HTML investment committee memo", intake_policy)
-
-    def test_initiating_coverage_uses_standalone_html_with_financed_growth_gate(self) -> None:
-        skill_doc = read("skills/initiating-coverage/SKILL.md")
-        architecture = read("skills/initiating-coverage/references/report-architecture.md")
-        valuation = read("skills/initiating-coverage/references/valuation-and-modeling.md")
-        source_evidence = read("skills/initiating-coverage/references/source-and-evidence.md")
-        output_templates = read("skills/initiating-coverage/references/output-templates.md")
-        quality_checklist = read("skills/initiating-coverage/references/quality-checklist.md")
-        dashboard_pack = read("skills/initiating-coverage/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML initiation report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("pro forma fully diluted capitalization", skill_doc)
-        self.assertIn("equity-value-to-revenue", skill_doc)
-        self.assertIn("Preliminary initiation underwrite", skill_doc)
-        self.assertIn("local headless-browser screenshots", skill_doc)
-        self.assertIn("do not fragment tickers, fiscal periods, dates", skill_doc)
-        self.assertIn("Complete market data and valuation inputs", skill_doc)
-        self.assertIn("Keep evidence confidence distinct from underwriting readiness", skill_doc)
-        self.assertIn("Standalone HTML Long-Only Initiation Report", architecture)
-        self.assertIn("Capital Return And Funding Gate", architecture)
-        self.assertIn("Capital-Intensive And Financed Growth Gate", valuation)
-        self.assertIn("after-financing return gate", valuation)
-        self.assertIn("Market Data And Valuation Inputs Completion Step", source_evidence)
-        self.assertIn("Underwriting status", source_evidence)
-        self.assertIn("Standalone HTML Long-Only Initiation Report", output_templates)
-        self.assertIn("Pro Forma Net Debt + Recorded Leases", output_templates)
-        self.assertIn("capital-intensive or externally financed growth", quality_checklist)
-        self.assertIn("Evidence confidence is separate from underwriting status", quality_checklist)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("citation rendering remains readable", dashboard_pack)
-        self.assertIn("polished standalone HTML initiation report", intake_policy)
-
-    def test_portfolio_risk_management_uses_conditional_standalone_html_risk_plan(self) -> None:
-        skill_doc = read("skills/portfolio-risk-management/SKILL.md")
-        sizing_framework = read("skills/portfolio-risk-management/references/sizing-framework.md")
-        source_protocol = read(
-            "skills/portfolio-risk-management/references/source-and-context-protocol.md"
-        )
-        sizing_templates = read(
-            "skills/portfolio-risk-management/references/position-sizing-output-templates.md"
-        )
-        hedge_workflow = read("skills/portfolio-risk-management/references/hedge-workflow.md")
-        hedge_templates = read(
-            "skills/portfolio-risk-management/references/hedge-output-templates.md"
-        )
-        quality_control = read("skills/portfolio-risk-management/references/quality-control.md")
-        dashboard_pack = read("skills/portfolio-risk-management/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML risk decision report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("scenario loss budget", skill_doc)
-        self.assertIn("absolute loss cap", skill_doc)
-        self.assertIn("Conditional risk screen", skill_doc)
-        self.assertIn("local headless-browser screenshots", skill_doc)
-        self.assertIn("do not fragment tickers, dates, percentages, basis-point amounts", skill_doc)
-        self.assertIn("Absolute loss cap", sizing_framework)
-        self.assertIn("Standalone HTML Integrated Risk Decision Report", sizing_templates)
-        self.assertIn("Hard-Cap Compliant Package", sizing_templates)
-        self.assertIn("one listed call contract per 100 shares short", hedge_workflow)
-        self.assertIn("A call spread is not compliant with an absolute cap", hedge_templates)
-        self.assertIn("scenario loss budget", source_protocol)
-        self.assertIn("Not implementation-ready", quality_control)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("citation rendering remains readable", dashboard_pack)
-        self.assertIn("polished standalone HTML risk decision report", intake_policy)
-
-    def test_dashboard_citation_contract_remains_compact_and_strict(self) -> None:
-        skill = read(
-            "skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md"
-        )
-        framework = read("shared/final-deliverable-framework.md")
-        renderer = read("shared/dashboard/renderer.py")
-        qa = read("shared/dashboard/qa.py")
-        browser_js = read("shared/dashboard/assets/dashboard.js")
-
-        for phrase in [
-            "Every material number",
-            "subtle numeric citation links",
-            "section-level source note",
-            "click-through to the source ledger",
-            "hover/focus previews",
-            "no numeric material without inline citation support",
-        ]:
-            self.assertIn(phrase, skill + framework)
-
-        for phrase in [
-            "_link_numeric_text",
-            "citation-link",
-            "section-citation-note",
-            "_find_numeric_citation_gaps",
-            "_find_unresolved_citations",
-        ]:
-            self.assertIn(phrase, renderer + qa)
-
-        self.assertIn("citation-popover", browser_js)
-
-    def test_dashboard_builder_indexes_typed_producer_packs(self) -> None:
-        text = read("skills/public-equity-investing/internal-support/dashboard-builder/INTERNAL.md")
-
-        self.assertIn("Producer Pack Index", text)
-        self.assertIn("Producer skills own the investment analysis", text)
-        self.assertIn("`dashboard-builder` owns the shared shell", text)
-        for skill in DASHBOARD_PACK_SKILLS:
-            self.assertIn(f"{skill}/references/DASHBOARD_PACK.md", text)
-        self.assertNotIn("catalyst-calendar/references/DASHBOARD_PACK.md", text)
-
-    def test_catalyst_calendar_uses_flexible_html_artifact_standard(self) -> None:
-        skill_doc = read("skills/catalyst-calendar/SKILL.md")
-        standard = read("shared/html-artifact-standard.md")
-
-        self.assertFalse((ROOT / "skills/catalyst-calendar/references/DASHBOARD_PACK.md").exists())
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertNotIn('Do not wait for the user to say "HTML"', skill_doc)
-        self.assertIn(
-            "default resolves the presentation surface to a polished HTML catalyst calendar",
-            skill_doc,
-        )
-        self.assertIn(
-            "default to the HTML catalyst calendar and `Full working analysis`", skill_doc
-        )
-        self.assertIn("90-Day Catalyst Calendar", skill_doc)
-        self.assertIn("Make the catalyst schedule the primary visual object", skill_doc)
-        self.assertIn("month-by-month or time-horizon visual summary", skill_doc)
-        self.assertIn("lower-priority confirmed issuer events", skill_doc)
-        self.assertIn("Do not show format-selection", skill_doc)
-        self.assertIn("Let the requested job determine the hierarchy and layout", standard)
-        self.assertIn("Prefer legibility, restrained color", standard)
-        self.assertIn("Format assumption", standard)
-        self.assertIn("standardized dashboard", standard)
-
-    def test_earnings_deep_dive_uses_flexible_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/earnings-deep-dive/SKILL.md")
-        standard = read("shared/html-artifact-standard.md")
-        output_modes = read("skills/earnings-deep-dive/references/OUTPUT_MODES.md")
-        dashboard_pack = read("skills/earnings-deep-dive/references/DASHBOARD_PACK.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML post-earnings report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("do not render an empty Q&A table", skill_doc)
-        self.assertIn("4-6 high-signal metric tiles", skill_doc)
-        self.assertIn("Prefer 4-5 tiles", skill_doc)
-        self.assertIn("Give each first-read element a distinct job", skill_doc)
-        self.assertIn(
-            "Company release reviewed; filing and transcript confirmation pending", skill_doc
-        )
-        self.assertIn(
-            "Keep citations traceable without making the page read like a control log", standard
-        )
-        self.assertIn("polished standalone HTML report", output_modes)
-        self.assertIn(
-            "only when the user explicitly requests the standardized dashboard", output_modes
-        )
-        self.assertIn("Use this only when", dashboard_pack)
-        self.assertIn("Do not render empty scenario cards", dashboard_pack)
-
-    def test_earnings_preview_uses_flexible_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/earnings-preview/SKILL.md")
-        output_spec = read("skills/earnings-preview/references/OUTPUT_SPEC.md")
-        dashboard_pack = read("skills/earnings-preview/references/DASHBOARD_PACK.md")
-        reference_router = read("skills/earnings-preview/references/REFERENCE_ROUTER.md")
-        qa_rules = read("skills/earnings-preview/references/QA_RULES.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML pre-earnings report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("Lead with the expectation bar and the stock-reaction debate", skill_doc)
-        self.assertIn("Do not force a fixed dashboard module inventory", skill_doc)
-        self.assertIn("do not fragment dates, times, ticker symbols", skill_doc)
-        self.assertIn("workflow-resolved standalone HTML pre-earnings report", skill_doc)
-        self.assertIn("expiry-tenor volatility context", skill_doc)
-        self.assertIn("polished standalone HTML report", output_spec)
-        self.assertIn("expiry-tenor volatility context", output_spec)
-        self.assertIn("Use this pack only for", dashboard_pack)
-        self.assertIn("expiry-tenor volatility context", dashboard_pack)
-        self.assertIn("chart axis or displayed scale", qa_rules)
-        self.assertIn("expiry-tenor volatility context", qa_rules)
-        self.assertIn("Standalone HTML full preview report", reference_router)
-
-    def test_event_driven_analyzer_uses_flexible_html_with_evidence_staging(self) -> None:
-        skill_doc = read("skills/event-driven-analyzer/SKILL.md")
-        output_templates = read("skills/event-driven-analyzer/references/output_templates.md")
-        dashboard_pack = read("skills/event-driven-analyzer/references/DASHBOARD_PACK.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML event report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("underwriting framework or entry screen", skill_doc)
-        self.assertIn("do not make the title more actionable than the evidence", skill_doc)
-        self.assertIn("For a spin-off or split-off", skill_doc)
-        self.assertIn("Use flexible standalone HTML", output_templates)
-        self.assertIn("Use this pack only for", dashboard_pack)
-        self.assertIn("Do not use an executable", dashboard_pack)
-
-    def test_idea_generation_uses_flexible_html_with_candidate_funnel(self) -> None:
-        skill_doc = read("skills/idea-generation/SKILL.md")
-        output_standards = read("skills/idea-generation/references/output-standards.md")
-        dashboard_pack = read("skills/idea-generation/references/DASHBOARD_PACK.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML idea-triage report", skill_doc)
-        self.assertIn("For a new standalone reader-facing idea screen", skill_doc)
-        self.assertNotIn("Treat a thematic screen that asks to rank", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("candidate funnel", skill_doc)
-        self.assertIn("expectations-heavy", skill_doc)
-        self.assertIn("needs exposure attribution", skill_doc)
-        self.assertIn("research-priority status, not an investment recommendation", skill_doc)
-        self.assertIn("Keep methodology and evidence-posture commentary compact", skill_doc)
-        self.assertIn("do not fragment years, ticker symbols, ranges", skill_doc)
-        self.assertIn("flexible standalone HTML idea-triage report", output_standards)
-        self.assertIn("Use this pack only for", dashboard_pack)
-        self.assertIn("verified positioning", dashboard_pack)
-
-    def test_company_tearsheet_uses_compact_flexible_html_when_selected(self) -> None:
-        skill_doc = read("skills/company-tearsheet/SKILL.md")
-        profile_templates = read("skills/company-tearsheet/references/profile-templates.md")
-        quality_checks = read("skills/company-tearsheet/references/quality-checks.md")
-        dashboard_pack = read("skills/company-tearsheet/references/DASHBOARD_PACK.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("When the user explicitly requests HTML for a tearsheet", skill_doc)
-        self.assertIn("polished standalone HTML tearsheet", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("compact issuer baseline", skill_doc)
-        self.assertIn("Trailing Valuation Snapshot", skill_doc)
-        self.assertIn("Do not include `Debate` in the heading", skill_doc)
-        self.assertIn(
-            "Do not thread it through earnings drivers, valuation, and multiple summary panels",
-            skill_doc,
-        )
-        self.assertIn("do not fragment tickers, years, dates", skill_doc)
-        self.assertIn("Standalone HTML tearsheet", profile_templates)
-        self.assertIn("Do not include `Debate` in the heading", profile_templates)
-        self.assertIn("compact evidence-gaps block", profile_templates)
-        self.assertIn("internal evidence labels", quality_checks)
-        self.assertIn(
-            "not threaded through earnings drivers, valuation, and multiple summary panels",
-            quality_checks,
-        )
-        self.assertIn("Use this pack only for", dashboard_pack)
-        self.assertIn("citation rendering remains readable", dashboard_pack)
-
-    def test_comps_valuation_uses_flexible_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/comps-valuation/SKILL.md")
-        output_templates = read("skills/comps-valuation/references/output-templates.md")
-        valuation_readthrough = read("skills/comps-valuation/references/valuation-readthrough.md")
-        source_rules = read("skills/comps-valuation/references/source-and-staleness-rules.md")
-        dashboard_pack = read("skills/comps-valuation/references/DASHBOARD_PACK.md")
-        p0_integrations = read("skills/comps-valuation/references/p0-integrations.md")
-        workbook_mode = read("skills/comps-valuation/references/workbook-mode.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML comps report", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("premium or discount is supported", skill_doc)
-        self.assertIn("Do not issue add, trim, hedge, sizing, or exit instructions", skill_doc)
-        self.assertIn("do not fragment tickers, prices, multiples", skill_doc)
-        self.assertIn("Standalone HTML comps report", output_templates)
-        self.assertIn("ordinary comps report into an action-rules dashboard", output_templates)
-        self.assertIn("illustrative trailing-multiple range", valuation_readthrough)
-        self.assertIn("third-party forward estimates", source_rules)
-        self.assertIn("not `consensus`", source_rules)
-        self.assertIn("third-party forward estimates", output_templates)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("citation rendering remains readable", dashboard_pack)
-        self.assertIn("standalone HTML comps report", p0_integrations)
-        self.assertIn("polished standalone HTML comps report", workbook_mode)
-
-    def test_deck_report_qc_uses_flexible_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/deck-report-qc/SKILL.md")
-        output_templates = read("skills/deck-report-qc/references/output-templates.md")
-        extraction_tieout = read("skills/deck-report-qc/references/extraction-and-tieout.md")
-        dashboard_pack = read("skills/deck-report-qc/references/DASHBOARD_PACK.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML senior-review QC report", skill_doc)
-        self.assertIn("only when the user explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("Review scope and evidence limitations", output_templates)
-        self.assertIn("Decision-critical tie-out", output_templates)
-        self.assertIn("must appear before a narrative `Top Issues`", output_templates)
-        self.assertIn(
-            "document.documentElement.scrollWidth <= document.documentElement.clientWidth",
-            skill_doc,
-        )
-        self.assertIn("confirmed internal mismatch", extraction_tieout)
-        self.assertIn("local headless browser", extraction_tieout)
-        self.assertIn("Use this pack only when", dashboard_pack)
-
-    def test_meeting_prep_uses_live_call_sheet_html_with_optional_dashboard(self) -> None:
-        skill_doc = read("skills/meeting-prep/SKILL.md")
-        output_templates = read("skills/meeting-prep/references/output-templates.md")
-        playbooks = read("skills/meeting-prep/references/meeting-type-playbooks.md")
-        dashboard_pack = read("skills/meeting-prep/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML live-meeting brief", skill_doc)
-        self.assertIn("explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("three or four must-ask questions", skill_doc)
-        self.assertIn("If Time Permits", skill_doc)
-        self.assertIn("Model / thesis implication", skill_doc)
-        self.assertIn("Place a compact `Conversation Flow` block immediately after", skill_doc)
-        self.assertIn("one short sentence", skill_doc)
-        self.assertIn("Standalone HTML management / IR call sheet", output_templates)
-        self.assertIn(
-            "The `Conversation Flow` block must appear before the detailed must-ask question cards",
-            output_templates,
-        )
-        self.assertIn("Avoid eight equally weighted question cards", output_templates)
-        self.assertIn("Live-use output for a public-company management or IR meeting", playbooks)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("polished standalone HTML live-meeting brief", intake_policy)
-
-    def test_long_short_pitch_uses_actionability_first_flexible_html(self) -> None:
-        skill_doc = read("skills/long-short-pitch/SKILL.md")
-        output_contract = read("skills/long-short-pitch/references/output-contract.md")
-        playbooks = read("skills/long-short-pitch/references/strategy-playbooks.md")
-        dashboard_pack = read("skills/long-short-pitch/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML trade-pitch report", skill_doc)
-        self.assertIn("only when the user explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("Implementation Gate", skill_doc)
-        self.assertIn("Illustrative Scenario Skew", skill_doc)
-        self.assertIn("AWS Infrastructure Commitment", skill_doc)
-        self.assertIn("Monitoring Triggers", skill_doc)
-        self.assertIn("Conditional Action Rules", skill_doc)
-        self.assertIn("do not fragment tickers, fiscal years, dates", skill_doc)
-        self.assertIn("Standalone HTML Trade-Pitch Report", output_contract)
-        self.assertIn("does not clear the trade for implementation", output_contract)
-        self.assertIn("not incremental revenue guidance", output_contract)
-        self.assertIn("Reserve `Monitoring Dashboard`", output_contract)
-        self.assertIn("Reported", output_contract)
-        self.assertIn("surface a compact `Implementation Gate`", playbooks)
-        self.assertIn("no-position or watchlist HTML pitch", playbooks)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("empty scenario fields", dashboard_pack)
-        self.assertIn("polished standalone HTML trade-pitch report", intake_policy)
-
-    def test_economic_impact_report_uses_transmission_first_flexible_html(self) -> None:
-        skill_doc = read("skills/economic-impact-report/SKILL.md")
-        report_template = read("skills/economic-impact-report/references/report-template.md")
-        quality_bar = read("skills/economic-impact-report/references/quality-bar.md")
-        dashboard_pack = read("skills/economic-impact-report/references/DASHBOARD_PACK.md")
-        intake_policy = read("shared/deliverable-intake-policy.md")
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill_doc)
-        self.assertIn("polished standalone HTML economic-impact report", skill_doc)
-        self.assertIn("only when the user explicitly asks for a standardized dashboard", skill_doc)
-        self.assertIn("Event Status And Market Baseline", skill_doc)
-        self.assertIn("Event-To-Equity Transmission Map", skill_doc)
-        self.assertIn("representative exposure candidates", skill_doc)
-        self.assertIn("Do not repeat citation chips", skill_doc)
-        self.assertIn(
-            "same transmission channel, first affected line item, and directional read-through",
-            skill_doc,
-        )
-        self.assertIn("broad-index performance as context only", skill_doc)
-        self.assertIn("exact research cut-off time and time zone", skill_doc)
-        self.assertIn("Event Status And Market Baseline", report_template)
-        self.assertIn("What Is Priced In Vs. What Requires Proof", report_template)
-        self.assertIn("Monitoring Triggers And Research Queue", report_template)
-        self.assertIn("Broad-index context", report_template)
-        self.assertIn("Combine exposures in one row only", report_template)
-        self.assertIn("repeated citation chips", quality_bar)
-        self.assertIn("thematic adjacency is not enough", quality_bar)
-        self.assertIn("Use this pack only when", dashboard_pack)
-        self.assertIn("ordinary standalone HTML economic-impact report", dashboard_pack)
-        self.assertIn("polished standalone HTML economic-impact report", intake_policy)
-
-    def test_substantial_workflows_have_typed_dashboard_packs(self) -> None:
-        for skill in DASHBOARD_PACK_SKILLS:
-            relative_path = f"skills/{skill}/references/DASHBOARD_PACK.md"
-            pack = read(relative_path)
-            skill_doc = read(f"skills/{skill}/SKILL.md")
-
-            self.assertIn("## Producer Role", pack, relative_path)
-            self.assertIn("## Recommended Payload", pack, relative_path)
-            self.assertIn("## Tabs And Modules", pack, relative_path)
-            self.assertIn("## Required Evidence", pack, relative_path)
-            self.assertIn("## Do Not", pack, relative_path)
-            self.assertIn("## QA Checks", pack, relative_path)
-            self.assertIn("`dashboard-builder` owns", pack, relative_path)
-            self.assertIn("metadata.payload_stage", pack, relative_path)
-            self.assertIn(
-                "public_equity_investing_dashboard.v1", skill_doc, f"skills/{skill}/SKILL.md"
-            )
-            self.assertIn("references/DASHBOARD_PACK.md", skill_doc, f"skills/{skill}/SKILL.md")
-
-            lowered = pack.lower()
-            forbidden = [
-                "markdown report",
-                "user-facing markdown",
-                "final markdown",
-                "raw json the lead",
-                "json as the lead",
-            ]
-            for phrase in forbidden:
-                self.assertNotIn(phrase, lowered, relative_path)
-
-    def test_dashboard_sample_payloads_validate_in_strict_mode(self) -> None:
-        payload_dir = "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads"
-        for filename in DASHBOARD_SAMPLE_PAYLOADS:
-            relative_path = f"{payload_dir}/{filename}"
-            payload = load_json(relative_path)
-            report = validate_payload(payload)
-
-            self.assertEqual("strict", payload["metadata"]["citation_policy"], relative_path)
-            self.assertEqual("failed" if report["hard_failures"] else "passed", report["status"])
-            self.assertEqual([], report["hard_failures"], f"{relative_path}: {report}")
-            self.assertTrue(payload.get("sources"), relative_path)
-            self.assertTrue(_payload_has_module(payload, "missing_evidence"), relative_path)
-
-    def test_strict_financial_chart_requires_render_ready_rows(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/financial-trend-incomplete-sample.json"
-        )
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "financial_trend_chart has fewer than 2 chart-ready rows" in failure
-                and "add missing_evidence" in failure
-                for failure in report["hard_failures"]
-            ),
-            report,
-        )
-
-    def test_non_strict_financial_chart_warns_on_incomplete_rows(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/financial-trend-incomplete-sample.json"
-        )
-        payload["metadata"]["citation_policy"] = "warn"
-        payload["metadata"]["payload_stage"] = "draft"
-        payload["metadata"]["readiness_posture"] = "working_draft"
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"])
-        self.assertEqual([], report["hard_failures"])
-
-    def test_production_profile_requires_complete_dashboard_contract(self) -> None:
-        payload = minimal_dashboard_payload()
-
-        report = validate_payload(payload, profile="production")
-        failures = "\n".join(report["hard_failures"])
-
-        self.assertEqual("failed", report["status"])
-        for expected in [
-            "payload.mode must be a non-empty string",
-            "production dashboards must include layout",
-            "production dashboards must include metadata",
-            "production dashboards must include a non-empty hero",
-            "production dashboards must include non-empty snapshot tiles",
-        ]:
-            self.assertIn(expected, failures)
-
-    def test_production_profile_complete_fixture_passes(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-complete-sample.json"
-        )
-
-        report = validate_payload(payload, profile="production")
-
-        self.assertEqual("passed", report["status"], report)
-        self.assertEqual([], report["hard_failures"], report)
-
-    def test_production_profile_fails_missing_source_ledger(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-missing-source-fails-sample.json"
-        )
-
-        report = validate_payload(payload, profile="production")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "strict dashboards must include a source ledger" in failure
-                for failure in report["hard_failures"]
-            ),
-            report,
-        )
-
-    def test_production_profile_fails_unresolved_citation(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-unresolved-citation-fails-sample.json"
-        )
-
-        report = validate_payload(payload, profile="production")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any("unknown source id 'S99'" in failure for failure in report["hard_failures"]),
-            report,
-        )
-
-    def test_warn_mode_support_payload_allows_source_and_numeric_gaps_as_warnings(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-support-source-gap-sample.json"
-        )
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"], report)
-        self.assertEqual([], report["hard_failures"], report)
-        self.assertTrue(
-            any(
-                "final dashboards should include a source ledger" in warning
-                for warning in report["warnings"]
-            )
-        )
-        self.assertTrue(
-            any("numeric material without citations" in warning for warning in report["warnings"])
-        )
-
-    def test_warn_mode_support_payload_allows_incomplete_chart_as_warning(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-incomplete-chart-support-sample.json"
-        )
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"], report)
-        self.assertEqual([], report["hard_failures"], report)
-        self.assertTrue(
-            any(
-                "financial_trend_chart has fewer than 2 chart-ready rows" in warning
-                for warning in report["warnings"]
-            ),
-            report,
-        )
-
-    def test_client_ready_posture_forces_production_validation(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {
-            "payload_stage": "draft",
-            "citation_policy": "warn",
-            "readiness_posture": "client_ready",
-            "readiness_label": "Client ready",
-        }
-
-        report = validate_payload(payload, profile="draft")
-        failures = "\n".join(report["hard_failures"])
-
-        self.assertEqual("failed", report["status"])
-        self.assertIn(
-            "production dashboards must set metadata.citation_policy to 'strict'", failures
-        )
-        self.assertIn("production dashboards must include a non-empty hero", failures)
-
-    def test_dashboard_render_script_defaults_to_production_profile(self) -> None:
-        payload = (
-            ROOT
-            / "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/draft-support-source-gap-sample.json"
-        )
-        script = (
-            ROOT
-            / "skills/public-equity-investing/internal-support/dashboard-builder/scripts/render_dashboard.py"
-        )
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output = Path(tmp_dir) / "dashboard.html"
-
-            default_result = subprocess.run(
-                [sys.executable, str(script), str(payload), str(output)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertNotEqual(0, default_result.returncode)
-            self.assertIn("production dashboards must include layout", default_result.stderr)
-
-            draft_result = subprocess.run(
-                [sys.executable, str(script), str(payload), str(output), "--profile", "draft"],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, draft_result.returncode, draft_result.stderr + draft_result.stdout)
-            self.assertTrue(output.exists())
-
-    def test_render_dashboard_default_uses_production_validation(self) -> None:
-        payload = minimal_dashboard_payload()
-
-        with self.assertRaisesRegex(ValueError, "production dashboards must include layout"):
-            render_dashboard(payload)
-
-        html = render_dashboard(payload, validation_profile="draft")
-        self.assertIn("Issuer dashboard", html)
-
-    def test_dashboard_renders_reader_actions_bar_with_print_control(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/production-complete-sample.json"
-        )
-
-        html = render_dashboard(payload)
-
-        self.assertIn("dashboard-utility-bar", html)
-        self.assertIn("Reader actions", html)
-        self.assertIn("Copy Full Report", html)
-        self.assertIn("data-copy-full-report", html)
-        self.assertIn("data-print-dashboard", html)
-        self.assertIn("Print / Save PDF", html)
-        self.assertIn("data-report-copy-root", html)
-        self.assertNotIn("print-action", html)
-        self.assertNotIn("Open workbook", html)
-        self.assertNotIn("Copy executive summary", html)
-        self.assertNotIn('<p class="eyebrow"></p>', html)
-
-    def test_dashboard_css_uses_flat_light_surface(self) -> None:
-        css = read("shared/dashboard/assets/dashboard.css")
-
-        self.assertIn("--page: #ffffff;", css)
-        self.assertIn("--surface-muted: #f8fafc;", css)
-        self.assertIn("--data-accent:", css)
-        self.assertIn("--status-positive:", css)
-        self.assertIn("--narrative-width: 72ch;", css)
-        self.assertIn("font-variant-numeric: tabular-nums lining-nums;", css)
-        self.assertIn(".dense-table th", css)
-        self.assertIn("min-height: 18px;", css)
-        for elevated_effect in (
-            "box-shadow",
-            "linear-gradient",
-            "radial-gradient",
-            "backdrop-filter",
-        ):
-            self.assertNotIn(elevated_effect, css)
-
-    def test_single_page_deduplicates_heading_and_aligns_numeric_columns(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["layout"] = "single_page"
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "table",
-                "title": "Overview",
-                "data": {
-                    "columns": [
-                        "metric",
-                        "revenue",
-                        {"key": "delta", "label": "Change", "type": "percent"},
-                    ],
-                    "rows": [{"metric": "Revenue", "revenue": "$120M", "delta": "12%"}],
-                },
-            }
-        ]
-
-        html = render_dashboard(payload, validate=False)
-
-        self.assertEqual(1, html.count("<h2>Overview</h2>"))
-        self.assertIn('<th class="is-numeric">Revenue</th>', html)
-        self.assertIn('<th class="is-numeric">Change</th>', html)
-        self.assertIn('<td class="is-numeric" data-label="Revenue">', html)
-
-    def test_dashboard_rejects_empty_scenario_cases(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["tabs"][0]["modules"] = [
-            {"type": "scenario_map", "data": {"cases": [{"label": "Bull", "citations": ["S1"]}]}}
-        ]
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any("omit empty scenario cards" in failure for failure in report["hard_failures"]),
-            report,
-        )
-
-    def test_dashboard_renders_scenario_summary_fields(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "scenario_map",
-                "data": {
-                    "cases": [
-                        {
-                            "label": "Bear",
-                            "status": "bear",
-                            "summary": "Underlying growth remains pressured.",
-                            "citations": ["S1"],
-                        }
-                    ]
-                },
-            }
-        ]
-
-        html = render_dashboard(payload, validate=False)
-
-        self.assertIn('<article class="scenario-card bear">', html)
-        self.assertIn("Bear", html)
-        self.assertIn("Underlying growth remains pressured.", html)
-
-    def test_market_events_resolve_visible_source_from_citation_ledger(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["sources"] = [
-            {"id": "S1", "title": "Company release", "url": "https://example.com/release"}
-        ]
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "market_events",
-                "data": {
-                    "events": [
-                        {
-                            "date": "May 27, 2026",
-                            "event": "Fiscal Q1 results",
-                            "impact": "Updated growth debate",
-                            "investor_read": "Monitor forward guidance",
-                            "citations": ["S1"],
-                        }
-                    ]
-                },
-            }
-        ]
-
-        html = render_dashboard(payload, validate=False)
-
-        self.assertIn("Company release", html)
-        self.assertIn("https://example.com/release", html)
-        self.assertNotIn('data-label="Source">Not sourced', html)
-
-    def test_strict_complete_financial_chart_passes(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "financial_trend_chart",
-                "data": {
-                    "periods": [
-                        {
-                            "period": "Q1 FY2026",
-                            "revenue": "100",
-                            "gross_profit": "60",
-                            "net_income": "20",
-                            "net_margin": "20%",
-                            "citations": ["S1"],
-                        },
-                        {
-                            "period": "Q2 FY2026",
-                            "revenue": "110",
-                            "gross_profit": "66",
-                            "net_income": "22",
-                            "net_margin": "20%",
-                            "citations": ["S1"],
-                        },
-                    ]
-                },
-            }
-        ]
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"])
-        self.assertEqual([], report["hard_failures"])
-
-    def test_strict_incomplete_chart_intent_passes_when_chart_is_omitted(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload["tabs"][0]["modules"] = [
-            {"type": "decision_box", "data": {"stance": "Watch", "citations": ["S1"]}},
-            {
-                "type": "missing_evidence",
-                "data": {
-                    "items": [
-                        "Financial trend chart omitted because revenue, gross profit, net income, and margin history are incomplete."
-                    ]
-                },
-            },
-        ]
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"])
-        self.assertEqual([], report["hard_failures"])
-
-    def test_chart_readiness_covers_eps_and_price_event_charts(self) -> None:
-        cases = [
-            (
-                "eps_actual_vs_estimate_chart",
-                {
-                    "periods": [
-                        {
-                            "period": "Q1",
-                            "estimated_eps": "1.00",
-                            "actual_eps": "1.10",
-                            "citations": ["S1"],
-                        },
-                        {
-                            "period": "Q2",
-                            "estimated_eps": "1.20",
-                            "actual_eps": "",
-                            "citations": ["S1"],
-                        },
-                    ]
-                },
-                "fewer than 2 chart-ready rows",
-            ),
-            (
-                "equity_price_event_chart",
-                {
-                    "prices": [
-                        {"date": "2026-01-01", "price": "10.00", "citations": ["S1"]},
-                        {"date": "2026-01-02", "price": "", "citations": ["S1"]},
-                    ],
-                    "events": [{"date": "2026-01-01", "event": "Launch", "citations": ["S1"]}],
-                },
-                "needs at least 10 daily chart-ready price rows; got 1",
-            ),
-        ]
-
-        for module_type, data, expected in cases:
-            payload = minimal_dashboard_payload()
-            payload["metadata"] = {"citation_policy": "strict"}
-            payload["tabs"][0]["modules"] = [{"type": module_type, "data": data}]
-
-            report = validate_payload(payload, profile="draft")
-
-            self.assertEqual("failed", report["status"], module_type)
-            self.assertTrue(
-                any(expected in failure for failure in report["hard_failures"]),
-                f"{module_type}: {report}",
-            )
-
-    def test_price_event_chart_rejects_three_point_daily_reaction_tape(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "equity_price_event_chart",
-                "data": {
-                    "price_granularity": "daily",
-                    "prices": [
-                        {"date": "2026-04-30", "price": 384.94, "citations": ["S1"]},
-                        {"date": "2026-05-13", "price": 402.62, "citations": ["S1"]},
-                        {"date": "2026-05-19", "price": 399.00, "citations": ["S1"]},
-                    ],
-                    "events": [
-                        {
-                            "date": "2026-04-30",
-                            "event": "Post-print close",
-                            "citations": ["S1"],
-                        }
-                    ],
-                },
-            }
-        ]
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "needs at least 10 daily chart-ready price rows; got 3" in failure
-                for failure in report["hard_failures"]
-            ),
-            report,
-        )
-
-    def test_price_event_chart_accepts_substantive_daily_reaction_tape(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "equity_price_event_chart",
-                "data": {
-                    "price_granularity": "daily",
-                    "prices": [
-                        {"date": f"2026-01-{day:02d}", "price": 100 + day, "citations": ["S1"]}
-                        for day in range(1, 11)
-                    ],
-                    "events": [
-                        {"date": "2026-01-05", "event": "Earnings print", "citations": ["S1"]}
-                    ],
-                },
-            }
-        ]
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"])
-        self.assertEqual([], report["hard_failures"])
-
-    def test_renderer_omits_thin_price_event_chart_when_validation_is_bypassed(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["tabs"][0]["modules"] = [
-            {
-                "type": "equity_price_event_chart",
-                "title": "Thin Price Tape",
-                "data": {
-                    "prices": [{"date": "2026-04-30", "price": 100, "citations": ["S1"]}],
-                    "events": [{"date": "2026-04-30", "event": "Earnings", "citations": ["S1"]}],
-                },
-            }
-        ]
-
-        html = render_dashboard(payload, validate=False)
-
-        self.assertNotIn("Thin Price Tape", html)
-        self.assertNotIn("price-event-chart", html)
-
-    def test_hero_dek_and_callout_render_citation_links(self) -> None:
-        payload = load_json(
-            "skills/public-equity-investing/internal-support/dashboard-builder/assets/example_payloads/hero-citations-sample.json"
-        )
-
-        html = render_dashboard(payload)
-
-        dek_index = html.index("The hero dek should append a source chip")
-        dek_chip_index = html.find("citation-chip", dek_index)
-        self.assertGreater(dek_chip_index, dek_index)
-        self.assertLess(dek_chip_index, dek_index + 600)
-
-        callout_index = html.index("Revenue grew")
-        numeric_link_index = html.find('class="citation-link"', callout_index)
-        linked_number_index = html.find("24.5%", callout_index)
-        self.assertGreater(numeric_link_index, callout_index)
-        self.assertLess(numeric_link_index, callout_index + 600)
-        self.assertGreater(linked_number_index, numeric_link_index)
-        self.assertLess(linked_number_index, numeric_link_index + 200)
-
-    def test_strict_dashboard_requires_source_ledger(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload["sources"] = []
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "strict dashboards must include a source ledger" in failure
-                for failure in report["hard_failures"]
-            )
-        )
-        self.assertFalse(any("source ledger" in warning for warning in report["warnings"]))
-
-    def test_strict_dashboard_missing_sources_requires_source_ledger(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["metadata"] = {"citation_policy": "strict"}
-        payload.pop("sources")
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "strict dashboards must include a source ledger" in failure
-                for failure in report["hard_failures"]
-            )
-        )
-
-    def test_top_level_strict_dashboard_requires_source_ledger(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["citation_policy"] = "strict"
-        payload["sources"] = []
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("failed", report["status"])
-        self.assertTrue(
-            any(
-                "strict dashboards must include a source ledger" in failure
-                for failure in report["hard_failures"]
-            )
-        )
-
-    def test_non_strict_dashboard_warns_on_missing_source_ledger(self) -> None:
-        payload = minimal_dashboard_payload()
-        payload["sources"] = []
-
-        report = validate_payload(payload, profile="draft")
-
-        self.assertEqual("passed", report["status"])
-        self.assertTrue(
-            any(
-                "final dashboards should include a source ledger" in warning
-                for warning in report["warnings"]
-            )
-        )
-        self.assertEqual([], report["hard_failures"])
-
-    def test_workbook_covers_are_insight_dashboards_and_audited(self) -> None:
-        standard = read("shared/workbook-artifact-standard.md")
-        helpers = read("shared/workbook_artifacts.py")
-        audit = read("skills/model-audit-tieout/scripts/audit_workbook.py")
-        skill = read("skills/model-audit-tieout/SKILL.md")
-
-        for phrase in [
-            "recommendation, net read, or decision question",
-            "model/workbook status and decision-readiness label",
-            "major sensitivities, catalyst/risk flags, or thesis breaks",
-            "A table-of-contents cover is not enough",
-        ]:
-            self.assertIn(phrase, standard)
-
-        for phrase in [
-            "Decision question",
-            "Executive read-through",
-            "KPI tiles",
-            "Chart-ready data",
-        ]:
-            self.assertIn(phrase, helpers)
-
-        self.assertIn('r"update_cover(?:_\\d+)?"', audit)
-        self.assertIn("is_update_cover", audit)
-        self.assertIn("chart-ready or visual data", audit)
-        self.assertIn("first-visible `Cover` tabs", skill)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_economic_impact_public_equity_contract.py b/plugins/financial-markets/tests/test_economic_impact_public_equity_contract.py
deleted file mode 100644
index d5d2c90..0000000
--- a/plugins/financial-markets/tests/test_economic_impact_public_equity_contract.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""Public-equity boundary tests for economic-impact-report."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class EconomicImpactPublicEquityContractTests(unittest.TestCase):
-    def test_skill_is_narrowed_to_public_equity_decision_impact(self) -> None:
-        text = read("skills/economic-impact-report/SKILL.md")
-
-        for phrase in [
-            "issuer, sector, earnings, valuation, positioning, and portfolio",
-            "not a generic cross-asset macro note",
-            "No Portfolio / Watchlist Fallback",
-            "general public-equity exposure map",
-            "Public Equity Framing",
-            "Issuer And Sector Impact",
-            "Earnings, Valuation, And Positioning Read-Through",
-            "public_equity_investing_dashboard.v1",
-            "../../shared/html-artifact-standard.md",
-            "polished standalone HTML economic-impact report",
-            "only when the user explicitly asks for a standardized dashboard",
-            "Event Status And Market Baseline",
-            "Event-To-Equity Transmission Map",
-            "representative exposure candidates",
-            "Do not repeat citation chips",
-            "share the same transmission channel, first affected line item, and directional read-through",
-            "broad-index performance as context only",
-            "exact research cut-off time and time zone",
-        ]:
-            self.assertIn(phrase, text)
-
-        for phrase in [
-            "standalone macro, rates, FX, futures, options, commodities, or credit strategy",
-            "route out",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_report_template_forces_public_equity_bottom_line(self) -> None:
-        text = read("skills/economic-impact-report/references/report-template.md")
-
-        for phrase in [
-            "Public Equity Bottom Line",
-            "General Exposure Map If No Portfolio Was Provided",
-            "Industries / countries / currencies / companies / commodities",
-            "What Is New Vs. Expected",
-            "Event Status And Market Baseline",
-            "Event-To-Equity Transmission Map",
-            "Transmission Map",
-            "Ranked Equity Impact Map",
-            "Issuer And Sector Impact",
-            "Earnings, Valuation, And Positioning Read-Through",
-            "Public Equity Framing",
-            "What Is Priced In Vs. What Requires Proof",
-            "Second-, Third-, And Fourth-Order Equity Effects",
-            "Monitoring Triggers And Research Queue",
-            "What Would Change The View",
-            "Research cut-off",
-            "Broad-index context",
-            "Combine exposures in one row only when they share the same transmission channel",
-        ]:
-            self.assertIn(phrase, text)
-
-        for stale in [
-            "Private Company Impact",
-            "Country And Currency Impact",
-            "Rates Impact",
-            "Options Impact",
-            "Futures Impact",
-            "Best cross-asset expression",
-        ]:
-            self.assertNotIn(stale, text)
-
-    def test_domain_checklists_keep_cross_asset_inputs_inside_equity_frame(self) -> None:
-        text = read("skills/economic-impact-report/references/domain-checklists.md")
-
-        for phrase in [
-            "Public Equity Issuers",
-            "Sector And Peer Group",
-            "Portfolio And Positioning",
-            "If no portfolio, watchlist, thesis, benchmark, or positions were provided",
-            "Build a general exposure map across industries, countries/currencies, public companies, relevant private companies, commodities",
-            "Macro, FX, Rates, Commodities, Options, And Futures As Inputs",
-            "Credit Signals As Equity Read-Through",
-            "common-equity downside signal",
-            "Credit Markets",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_dashboard_pack_rejects_standalone_cross_asset_output(self) -> None:
-        text = read("skills/economic-impact-report/references/DASHBOARD_PACK.md")
-
-        for phrase in [
-            "public-equity mechanism",
-            "Line item affected",
-            "general exposure map for industries, countries/currencies, public companies, relevant private companies, commodities",
-            "Priced-in status",
-            "PM action",
-            "Source posture",
-            "Missing evidence",
-            "Do not turn the dashboard into a standalone rates, FX, commodity, futures, options, or credit-security dashboard",
-        ]:
-            self.assertIn(phrase, text)
-
-        self.assertIn(
-            "Use this pack only when the user explicitly selects a standardized dashboard", text
-        )
-        self.assertIn("ordinary standalone HTML economic-impact report", text)
-
-    def test_quality_bar_fails_generic_macro_outputs(self) -> None:
-        text = read("skills/economic-impact-report/references/quality-bar.md")
-
-        for phrase in [
-            "It lacks issuer, sector, earnings, valuation, positioning, or portfolio implications.",
-            "It recommends a standalone rates, FX, options, futures, commodity, or credit-security expression",
-            "It fails to state what is already priced in",
-            "It does not identify what would change the PM action",
-            "If no portfolio/watchlist was provided",
-            "Event Status And Market Baseline",
-            "fixed dashboard module inventory",
-            "repeated citation chips",
-            "thematic adjacency is not enough",
-            "broad-index performance as context rather than primary evidence",
-            "exact research cut-off time and time zone",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_checker_accepts_public_equity_framing_section_name(self) -> None:
-        checker = read("skills/economic-impact-report/scripts/check_economic_impact_report.py")
-        for phrase in [
-            '"public equity framing"',
-            '"event status and market baseline"',
-            '"ranked equity impact map"',
-            '"what is priced in vs what requires proof"',
-            '"monitoring triggers and research queue"',
-            '"event to equity transmission map"',
-        ]:
-            self.assertIn(phrase, checker)
-        self.assertNotIn('"market framing"', checker)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_equity_model_update_rename.py b/plugins/financial-markets/tests/test_equity_model_update_rename.py
deleted file mode 100644
index 4325c76..0000000
--- a/plugins/financial-markets/tests/test_equity_model_update_rename.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Rename and routing guardrails for equity-model-update."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-OLD_SKILL = "public-model-update"
-NEW_SKILL = "equity-model-update"
-
-
-class EquityModelUpdateRenameTests(unittest.TestCase):
-    def test_skill_folder_and_front_matter_are_renamed(self) -> None:
-        old_path = ROOT / "skills" / OLD_SKILL
-        new_path = ROOT / "skills" / NEW_SKILL
-        self.assertFalse(old_path.exists())
-        self.assertTrue(new_path.exists())
-        text = (new_path / "SKILL.md").read_text(encoding="utf-8")
-        self.assertIn("name: equity-model-update", text)
-        self.assertIn("# Equity Model Update", text)
-        self.assertIn("target-price implications", text)
-
-    def test_no_stale_old_model_update_slug_remains(self) -> None:
-        stale = []
-        for path in ROOT.rglob("*"):
-            if path == Path(__file__):
-                continue
-            if not path.is_file() or path.suffix not in {
-                ".md",
-                ".py",
-                ".json",
-                ".yaml",
-                ".yml",
-                ".csv",
-                ".txt",
-            }:
-                continue
-            text = path.read_text(encoding="utf-8")
-            if (
-                OLD_SKILL in text
-                or ("Public " + "Model Update") in text
-                or ("public " + "model update") in text
-            ):
-                stale.append(str(path.relative_to(ROOT)))
-        self.assertEqual([], stale)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_equity_model_update_workbook.py b/plugins/financial-markets/tests/test_equity_model_update_workbook.py
deleted file mode 100644
index ce8850e..0000000
--- a/plugins/financial-markets/tests/test_equity_model_update_workbook.py
+++ /dev/null
@@ -1,851 +0,0 @@
-"""Workbook-first regressions for equity-model-update."""
-
-from __future__ import annotations
-
-import csv
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-import zipfile
-from pathlib import Path
-from xml.etree import ElementTree as ET
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = ROOT / "skills" / "equity-model-update" / "scripts" / "materialize_workbook_update.py"
-CSV_SCRIPT = ROOT / "skills" / "equity-model-update" / "scripts" / "materialize_model_update.py"
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-NS_PKG_REL = "http://schemas.openxmlformats.org/package/2006/relationships"
-
-sys.path.insert(0, str(ROOT))
-
-from shared.dashboard.qa import load_payload, validate_payload  # noqa: E402
-from shared.dashboard.renderer import render_dashboard  # noqa: E402
-
-
-def qn(ns: str, name: str) -> str:
-    return f"{{{ns}}}{name}"
-
-
-def write_rows(path: Path, rows: list[dict[str, str]]) -> None:
-    fields = sorted({field for row in rows for field in row})
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fields)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def read_csv_rows(path: Path) -> list[dict[str, str]]:
-    with path.open(encoding="utf-8") as handle:
-        return list(csv.DictReader(handle))
-
-
-def write_minimal_workbook(
-    path: Path, *, formula_cell: bool = False, local_defined_name: bool = False
-) -> None:
-    b2 = '<c r="B2"><f>A1+A2</f><v>3</v></c>' if formula_cell else '<c r="B2"><v>10</v></c>'
-    sheet_scoped_name = (
-        '<definedName name="_xlnm.Print_Area" localSheetId="0">\'Inputs\'!$A$1:$B$2</definedName>'
-        if local_defined_name
-        else ""
-    )
-    sheet_xml = f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<worksheet xmlns="{NS_MAIN}" xmlns:r="{NS_REL}">
-  <sheetData>
-    <row r="1"><c r="A1"><v>1</v></c></row>
-    <row r="2"><c r="A2"><v>2</v></c>{b2}</row>
-  </sheetData>
-</worksheet>
-"""
-    workbook_xml = f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<workbook xmlns="{NS_MAIN}" xmlns:r="{NS_REL}">
-  <sheets>
-    <sheet name="Inputs" sheetId="1" r:id="rId1"/>
-  </sheets>
-  <definedNames><definedName name="Input_Cell">'Inputs'!$B$2</definedName>{sheet_scoped_name}</definedNames>
-</workbook>
-"""
-    workbook_rels = f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Relationships xmlns="{NS_PKG_REL}">
-  <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet" Target="worksheets/sheet1.xml"/>
-</Relationships>
-"""
-    root_rels = f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Relationships xmlns="{NS_PKG_REL}">
-  <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" Target="xl/workbook.xml"/>
-</Relationships>
-"""
-    content_types = """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
-<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">
-  <Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>
-  <Default Extension="xml" ContentType="application/xml"/>
-  <Override PartName="/xl/workbook.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.main+xml"/>
-  <Override PartName="/xl/worksheets/sheet1.xml" ContentType="application/vnd.openxmlformats-officedocument.spreadsheetml.worksheet+xml"/>
-</Types>
-"""
-    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
-        zf.writestr("[Content_Types].xml", content_types)
-        zf.writestr("_rels/.rels", root_rels)
-        zf.writestr("xl/workbook.xml", workbook_xml)
-        zf.writestr("xl/_rels/workbook.xml.rels", workbook_rels)
-        zf.writestr("xl/worksheets/sheet1.xml", sheet_xml)
-
-
-def add_manual_calc_metadata(path: Path) -> None:
-    with zipfile.ZipFile(path) as zf:
-        entries = {info.filename: zf.read(info.filename) for info in zf.infolist()}
-
-    workbook_xml = entries["xl/workbook.xml"].decode("utf-8")
-    workbook_xml = workbook_xml.replace(
-        f'<workbook xmlns="{NS_MAIN}" xmlns:r="{NS_REL}">',
-        f'<workbook xmlns="{NS_MAIN}" xmlns:r="{NS_REL}"><calcPr calcMode="manual"/>',
-    )
-    entries["xl/workbook.xml"] = workbook_xml.encode("utf-8")
-    entries["xl/calcChain.xml"] = (
-        f'<?xml version="1.0" encoding="UTF-8" standalone="yes"?><calcChain xmlns="{NS_MAIN}"><c r="B2" i="1"/></calcChain>'
-    ).encode("utf-8")
-
-    with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
-        for filename, data in entries.items():
-            zf.writestr(filename, data)
-
-
-def workbook_sheet_paths(path: Path) -> dict[str, str]:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        rels = ET.fromstring(zf.read("xl/_rels/workbook.xml.rels"))
-        relmap = {
-            rel.attrib["Id"]: rel.attrib["Target"]
-            for rel in rels.findall(qn(NS_PKG_REL, "Relationship"))
-        }
-        paths: dict[str, str] = {}
-        sheets = workbook.find(qn(NS_MAIN, "sheets"))
-        assert sheets is not None
-        for sheet in sheets.findall(qn(NS_MAIN, "sheet")):
-            target = relmap[sheet.attrib[qn(NS_REL, "id")]]
-            paths[sheet.attrib["name"]] = (
-                f"xl/{target.lstrip('/')}" if not target.startswith("xl/") else target
-            )
-        return paths
-
-
-def workbook_sheet_names(path: Path) -> list[str]:
-    return list(workbook_sheet_paths(path))
-
-
-def defined_name_local_sheet_id(path: Path, name: str) -> str | None:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-    defined_names = workbook.find(qn(NS_MAIN, "definedNames"))
-    assert defined_names is not None
-    for defined_name in defined_names.findall(qn(NS_MAIN, "definedName")):
-        if defined_name.attrib.get("name") == name:
-            return defined_name.attrib.get("localSheetId")
-    return None
-
-
-def read_cell(path: Path, sheet_name: str, cell_ref: str) -> str:
-    with zipfile.ZipFile(path) as zf:
-        sheet_path = workbook_sheet_paths(path)[sheet_name]
-        root = ET.fromstring(zf.read(sheet_path))
-        for cell in root.iter(qn(NS_MAIN, "c")):
-            if cell.attrib.get("r") != cell_ref:
-                continue
-            formula = cell.find(qn(NS_MAIN, "f"))
-            if formula is not None:
-                return f"={formula.text or ''}"
-            value = cell.find(qn(NS_MAIN, "v"))
-            return "" if value is None or value.text is None else value.text
-    return ""
-
-
-def run_command(args: list[str], cwd: Path) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(args, cwd=str(cwd), text=True, capture_output=True, check=False)
-
-
-class EquityModelUpdateWorkbookTests(unittest.TestCase):
-    def test_skill_requires_final_presentation_workbook_metadata_promotion(self) -> None:
-        skill = (ROOT / "skills" / "equity-model-update" / "SKILL.md").read_text(encoding="utf-8")
-        executable_contract = (
-            ROOT / "skills" / "equity-model-update" / "references" / "executable-contract.md"
-        ).read_text(encoding="utf-8")
-        for phrase in [
-            "final presentation workbook",
-            "run_log.json",
-            "manifest.json",
-            "model_update_citations.json",
-            "final sheet names",
-        ]:
-            self.assertIn(phrase, skill)
-        self.assertIn("it replaces the intermediate workbook", executable_contract)
-
-    def test_safe_workbook_update_copies_and_updates_mapped_input_cell(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "company": "Northstar Instruments",
-                        "ticker": "NSIS",
-                        "fiscal_period": "FY2026",
-                        "model_section": "Inputs",
-                        "model_line": "Revenue growth",
-                        "source_metric": "FY2026 revenue growth",
-                        "current_model_value": "10",
-                        "proposed_model_value": "12.5",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "source_name": "Company release",
-                        "as_of_date": "2026-05-12",
-                        "confidence": "high",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            updated_workbook = out_dir / "updated_model.xlsx"
-            self.assertTrue(updated_workbook.exists())
-            self.assertEqual("10", read_cell(workbook, "Inputs", "B2"))
-            self.assertEqual("12.5", read_cell(updated_workbook, "Inputs", "B2"))
-            self.assertEqual("Inputs", workbook_sheet_names(updated_workbook)[0])
-            for sheet in [
-                "Update_Cover",
-                "Source_Map",
-                "Rebuild_Requirements",
-                "Change_Log",
-                "Tie_Out",
-                "Stale_Data",
-            ]:
-                self.assertIn(sheet, workbook_sheet_names(updated_workbook))
-
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((out_dir / "manifest.json").read_text(encoding="utf-8"))
-            citations = json.loads(
-                (out_dir / "model_update_citations.json").read_text(encoding="utf-8")
-            )
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-
-            self.assertEqual("xlsx_update_copy", run_log["workbook_mode"])
-            self.assertEqual(str(updated_workbook), run_log["primary_human_deliverable"])
-            self.assertEqual(str(updated_workbook), manifest["primary_human_deliverable"])
-            self.assertEqual(1, run_log["workbook_update_summary"]["applied_count"])
-            self.assertEqual("yes", source_rows[0]["applied_to_workbook"])
-            self.assertEqual("applied_to_copy", source_rows[0]["review_status"])
-            self.assertEqual("Inputs", citations["model_update_citations"][0]["sheet"])
-            self.assertEqual("B2", citations["model_update_citations"][0]["cell"])
-            self.assertTrue(citations["model_update_citations"][0]["applied"])
-            self.assertEqual(citations["model_update_citations"], citations["model_citations"])
-
-            citation_id = citations["model_citations"][0]["id"]
-            payload_path = out_dir / "dashboard_payload.json"
-            payload_path.write_text(
-                json.dumps(
-                    {
-                        "kind": "public_equity_investing_dashboard.v1",
-                        "mode": "equity_model_update",
-                        "layout": "single_page",
-                        "title": "NSIS model update",
-                        "issuer": {"ticker": "NSIS"},
-                        "metadata": {
-                            "payload_stage": "production",
-                            "freeze_time": "2026-05-18 09:00 ET",
-                            "source_posture": "Synthetic workbook update fixture.",
-                            "readiness_label": "PM-ready fixture",
-                            "readiness_posture": "pm_ready",
-                            "citation_policy": "strict",
-                            "decision_context": "Whether the copied workbook update is ready for review.",
-                        },
-                        "hero": {
-                            "headline": "NSIS copied-workbook update",
-                            "dek": {
-                                "value": "Inputs!B2 updated from source map",
-                                "citations": [citation_id],
-                            },
-                            "callout": {
-                                "value": "Safe update path used",
-                                "citations": [citation_id],
-                            },
-                        },
-                        "model_citations_path": "model_update_citations.json",
-                        "snapshot": [
-                            {
-                                "label": "Updated cell",
-                                "value": {"value": "Inputs!B2", "citations": [citation_id]},
-                                "detail": "Safe copied-workbook update",
-                            }
-                        ],
-                        "tabs": [
-                            {
-                                "id": "overview",
-                                "label": "Overview",
-                                "modules": [
-                                    {
-                                        "type": "decision_box",
-                                        "data": {
-                                            "stance": "Ready for review",
-                                            "summary": {
-                                                "value": "Inputs!B2 updated in copied workbook",
-                                                "citations": [citation_id],
-                                            },
-                                        },
-                                    }
-                                ],
-                            }
-                        ],
-                    }
-                ),
-                encoding="utf-8",
-            )
-            loaded_payload = load_payload(payload_path)
-            report = validate_payload(loaded_payload)
-            html = render_dashboard(loaded_payload)
-            self.assertEqual("passed", report["status"], report)
-            self.assertIn("Inputs!B2; source: Company release", html)
-
-    def test_formula_cell_routes_to_control_pack_without_overwrite(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook, formula_cell=True)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Calculated revenue growth",
-                        "source_metric": "FY2026 revenue growth",
-                        "current_model_value": "3",
-                        "proposed_model_value": "12.5",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "source_name": "Company release",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            control_pack = out_dir / "model_update_control_pack.xlsx"
-            self.assertTrue(control_pack.exists())
-            self.assertEqual("=A1+A2", read_cell(control_pack, "Inputs", "B2"))
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-
-            self.assertEqual("xlsx_control_pack", run_log["workbook_mode"])
-            self.assertEqual(str(control_pack), run_log["primary_human_deliverable"])
-            self.assertEqual(0, run_log["workbook_update_summary"]["applied_count"])
-            self.assertEqual(1, run_log["workbook_update_summary"]["blocked_count"])
-            self.assertEqual("Update_Cover", workbook_sheet_names(control_pack)[0])
-            self.assertEqual("ready_for_review", run_log["artifact_readiness"])
-            self.assertEqual("not_updated_requires_mapping_or_rebuild", run_log["model_readiness"])
-            self.assertFalse(run_log["recalculation_warning"]["recalc_required"])
-            self.assertIn(
-                "No recalculation is required from this run",
-                run_log["recalculation_warning"]["required_next_step"],
-            )
-            self.assertEqual("no", source_rows[0]["applied_to_workbook"])
-            self.assertEqual(
-                "formula_cell_not_overwritten_without_approval", source_rows[0]["blocked_reason"]
-            )
-
-    def test_reference_only_fact_does_not_become_an_update_candidate(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook, formula_cell=True)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Annual free cash flow forecast anchor",
-                        "source_metric": "Q1 FY2027 reported free cash flow",
-                        "source_value": "48.554",
-                        "current_model_value": "33.750",
-                        "mapping_treatment": "reference_only",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                        "notes": "Quarterly actual is not an annual forecast replacement.",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-            change_rows = read_csv_rows(out_dir / "change_log.csv")
-            tieout_rows = read_csv_rows(out_dir / "tieout_checklist.csv")
-            citations = json.loads(
-                (out_dir / "model_update_citations.json").read_text(encoding="utf-8")
-            )
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-
-            self.assertEqual("reference_only", source_rows[0]["update_action"])
-            self.assertEqual("reference_only", source_rows[0]["review_status"])
-            self.assertEqual("", source_rows[0]["proposed_model_value"])
-            self.assertEqual("", source_rows[0]["delta"])
-            self.assertEqual([], change_rows)
-            self.assertEqual("reference_only", tieout_rows[1]["status"])
-            self.assertIn("retained only as a cited reference", tieout_rows[1]["check_description"])
-            self.assertEqual("model_reference_cell", citations["model_citations"][0]["type"])
-            self.assertEqual(
-                "Annual free cash flow forecast anchor workbook reference",
-                citations["model_citations"][0]["title"],
-            )
-            self.assertEqual(1, run_log["workbook_update_summary"]["reference_only_count"])
-            self.assertEqual(0, run_log["workbook_update_summary"]["blocked_count"])
-
-    def test_non_update_treatments_normalize_spaces_and_hyphens_before_workbook_edit(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Revenue forecast",
-                        "source_metric": "Reported revenue",
-                        "source_value": "99",
-                        "current_model_value": "10",
-                        "mapping_treatment": "reference-only",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    },
-                    {
-                        "model_line": "Margin forecast",
-                        "source_metric": "Reported margin",
-                        "source_value": "88",
-                        "current_model_value": "1",
-                        "mapping_treatment": "missing model architecture",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "A1",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    },
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            control_pack = out_dir / "model_update_control_pack.xlsx"
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-            self.assertEqual("10", read_cell(control_pack, "Inputs", "B2"))
-            self.assertEqual("1", read_cell(control_pack, "Inputs", "A1"))
-            self.assertEqual("reference_only", source_rows[0]["mapping_treatment"])
-            self.assertEqual("missing_model_architecture", source_rows[1]["mapping_treatment"])
-            self.assertEqual("reference_only", source_rows[0]["review_status"])
-            self.assertEqual("blocked_missing_model_architecture", source_rows[1]["review_status"])
-
-    def test_promoted_cover_preserves_sheet_scoped_defined_names(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook, formula_cell=True, local_defined_name=True)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Calculated revenue growth",
-                        "source_metric": "FY2026 revenue growth",
-                        "proposed_model_value": "12.5",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            control_pack = out_dir / "model_update_control_pack.xlsx"
-            self.assertEqual("0", defined_name_local_sheet_id(workbook, "_xlnm.Print_Area"))
-            self.assertEqual(["Update_Cover", "Inputs"], workbook_sheet_names(control_pack)[:2])
-            self.assertEqual("1", defined_name_local_sheet_id(control_pack, "_xlnm.Print_Area"))
-
-    def test_rebuild_required_target_uses_rebuild_citation_label(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook, formula_cell=True)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Annual free cash flow forecast anchor",
-                        "source_metric": "Q1 FY2027 reported free cash flow",
-                        "source_value": "48.554",
-                        "mapping_treatment": "rebuild_required",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            citations = json.loads(
-                (out_dir / "model_update_citations.json").read_text(encoding="utf-8")
-            )
-            citation = citations["model_citations"][0]
-            self.assertEqual("model_rebuild_requirement", citation["type"])
-            self.assertEqual(
-                "Annual free cash flow forecast anchor rebuild requirement", citation["title"]
-            )
-            self.assertEqual("rebuild_required", citation["mapping_treatment"])
-
-    def test_missing_model_architecture_is_blocked_without_fake_delta(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Revenue forecast",
-                        "source_metric": "Q2 FY2027 revenue guidance",
-                        "source_value": "91.0",
-                        "mapping_treatment": "missing_model_architecture",
-                        "source_id": "src-guide",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-            change_rows = read_csv_rows(out_dir / "change_log.csv")
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-
-            self.assertEqual("rebuild_required", source_rows[0]["update_action"])
-            self.assertEqual("blocked_missing_model_architecture", source_rows[0]["review_status"])
-            self.assertEqual("missing_model_architecture", source_rows[0]["blocked_reason"])
-            self.assertEqual("", source_rows[0]["proposed_model_value"])
-            self.assertEqual([], change_rows)
-            self.assertEqual(1, run_log["workbook_update_summary"]["missing_architecture_count"])
-            self.assertIn(
-                "Rebuild_Requirements",
-                workbook_sheet_names(out_dir / "model_update_control_pack.xlsx"),
-            )
-
-    def test_missing_sheet_routes_to_workbook_control_pack(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Revenue growth",
-                        "source_metric": "FY2026 revenue growth",
-                        "proposed_model_value": "12.5",
-                        "workbook_sheet": "Missing Sheet",
-                        "workbook_cell": "B2",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            self.assertTrue((out_dir / "model_update_control_pack.xlsx").exists())
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-            change_rows = read_csv_rows(out_dir / "change_log.csv")
-            self.assertEqual("workbook_sheet_not_found", source_rows[0]["blocked_reason"])
-            self.assertEqual("blocked", change_rows[0]["status"])
-
-    def test_non_gaap_eps_does_not_trigger_gaap_recurring_driver_flag(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Diluted EPS",
-                        "source_metric": "Q1 non-GAAP diluted EPS",
-                        "source_value": "1.87",
-                        "proposed_model_value": "1.87",
-                        "earnings_basis": "non-GAAP",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    },
-                    {
-                        "model_line": "Diluted EPS",
-                        "source_metric": "Q1 GAAP diluted EPS",
-                        "source_value": "2.39",
-                        "proposed_model_value": "2.39",
-                        "earnings_basis": "GAAP",
-                        "source_id": "src-release",
-                        "as_of_date": "2026-05-12",
-                    },
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(CSV_SCRIPT),
-                    str(input_csv),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            source_rows = read_csv_rows(out_dir / "source_to_model.csv")
-            self.assertNotIn(
-                "gaap_eps_requires_recurring_driver_check", source_rows[0]["issue_flags"]
-            )
-            self.assertIn("gaap_eps_requires_recurring_driver_check", source_rows[1]["issue_flags"])
-
-    def test_csv_materializer_treats_workbook_request_as_handoff_not_hard_failure(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Revenue growth",
-                        "source_metric": "FY2026 revenue growth",
-                        "proposed_model_value": "12.5",
-                        "source_id": "src-release",
-                        "workbook_write_requested": "true",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(CSV_SCRIPT),
-                    str(input_csv),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            self.assertEqual("update_map_export", run_log["workbook_mode"])
-            self.assertEqual([], run_log["hard_failures"])
-            self.assertTrue(
-                any("materialize_workbook_update.py" in warning for warning in run_log["warnings"])
-            )
-
-    def test_workbook_update_surfaces_formula_cache_and_recalc_warning(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            workbook = tmp_path / "model.xlsx"
-            input_csv = tmp_path / "updates.csv"
-            out_dir = tmp_path / "out"
-            write_minimal_workbook(workbook, formula_cell=True)
-            add_manual_calc_metadata(workbook)
-            write_rows(
-                input_csv,
-                [
-                    {
-                        "model_line": "Revenue input",
-                        "source_metric": "FY2026 revenue",
-                        "current_model_value": "1",
-                        "proposed_model_value": "5",
-                        "workbook_sheet": "Inputs",
-                        "workbook_cell": "A1",
-                        "source_id": "src-release",
-                        "source_name": "Company release",
-                        "as_of_date": "2026-05-12",
-                    }
-                ],
-            )
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCRIPT),
-                    str(input_csv),
-                    "--workbook",
-                    str(workbook),
-                    "--out",
-                    str(out_dir),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((out_dir / "run_log.json").read_text(encoding="utf-8"))
-            preflight = run_log["workbook_preflight"]
-            recalc = run_log["recalculation_warning"]
-
-            self.assertEqual(1, preflight["formula_cell_count"])
-            self.assertEqual(1, preflight["cached_formula_value_count"])
-            self.assertTrue(preflight["calc_chain_present"])
-            self.assertEqual("manual", preflight["calc_mode"])
-            self.assertTrue(preflight["recalc_required"])
-            self.assertTrue(recalc["recalc_required"])
-            self.assertFalse(recalc["runtime_recalculated_formulas"])
-            self.assertTrue(
-                any(
-                    "does not recalculate downstream formula caches" in warning
-                    for warning in run_log["warnings"]
-                )
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_equity_research_support_layer_contracts.py b/plugins/financial-markets/tests/test_equity_research_support_layer_contracts.py
deleted file mode 100644
index eadb7b5..0000000
--- a/plugins/financial-markets/tests/test_equity_research_support_layer_contracts.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""Support-layer contract tests for Public Equity Investing."""
-
-from __future__ import annotations
-
-import csv
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-INTERNAL_SUPPORT_PATH = "skills/public-equity-investing/internal-support"
-SUPPORT_SKILLS = [
-    "financial-source-of-truth",
-    "financials-normalizer",
-    "excel-data-cleaner",
-    "deck-report-qc",
-    "style-guide-adapter",
-]
-INTERNAL_SUPPORT_SKILLS = {
-    "financial-source-of-truth",
-    "excel-data-cleaner",
-    "style-guide-adapter",
-}
-OWNING_SKILLS_WITH_SUPPORT_BLOCKS = [
-    "company-tearsheet",
-    "memo-builder",
-    "long-short-pitch",
-    "dcf-model-builder",
-    "three-statement-model-builder",
-    "comps-valuation",
-    "equity-model-update",
-    "thesis-tracker",
-    "meeting-prep",
-]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class EquityResearchSupportLayerContractTests(unittest.TestCase):
-    def test_shared_support_standard_exists(self) -> None:
-        text = read("shared/equity-research-support-standard.md")
-        for phrase in [
-            "Support-Layer Role",
-            "Not-Owner Rule",
-            "PM Judgment Layer",
-            "Connector Honesty Rule",
-            "Credit Markets Boundary",
-            "missing_required_source",
-            "Embedded Services Contract",
-        ]:
-            self.assertIn(phrase, text)
-        routing = read("shared/support-layer-routing-contract.md")
-        for phrase in [
-            "Embedded Service Rule",
-            "owning_workflow",
-            "decision_impact",
-            "readiness_effect",
-            "artifact_role",
-            "hidden_unless_requested",
-            "Hero Artifact Policy",
-        ]:
-            self.assertIn(phrase, routing)
-
-    def test_all_support_skills_reference_shared_standard(self) -> None:
-        for skill in SUPPORT_SKILLS:
-            relative_path = (
-                f"{INTERNAL_SUPPORT_PATH}/{skill}/INTERNAL.md"
-                if skill in INTERNAL_SUPPORT_SKILLS
-                else f"skills/{skill}/SKILL.md"
-            )
-            text = read(relative_path)
-            self.assertIn("shared/equity-research-support-standard.md", text, skill)
-            self.assertIn("shared/support-layer-routing-contract.md", text, skill)
-            self.assertIn("Embedded Support Routing", text, skill)
-            for phrase in [
-                "owning_workflow",
-                "decision_impact",
-                "readiness_effect",
-                "artifact_role",
-                "hidden_unless_requested",
-                "secondary/support artifacts",
-            ]:
-                self.assertIn(phrase, text, skill)
-
-    def test_support_standard_lists_public_equity_personas_and_not_owner_rule(self) -> None:
-        text = read("shared/equity-research-support-standard.md")
-        for phrase in [
-            "long-only PMs",
-            "long/short hedge funds",
-            "sell-side equity research",
-            "ETF/index diligence",
-            "do not own the investment conclusion",
-            "do not own memos, pitches, valuation, earnings calls, trade construction, credit research, or investment recommendations",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_owning_skills_have_support_invocation_blocks(self) -> None:
-        for skill in OWNING_SKILLS_WITH_SUPPORT_BLOCKS:
-            text = read(f"skills/{skill}/SKILL.md")
-            self.assertIn("When To Invoke Support", text, skill)
-            self.assertIn("shared/support-layer-routing-contract.md", text, skill)
-            self.assertIn("support", text.lower(), skill)
-
-    def test_internal_support_has_no_selectable_skill_metadata(self) -> None:
-        for skill in INTERNAL_SUPPORT_SKILLS:
-            self.assertFalse((ROOT / "skills" / skill).exists())
-            self.assertTrue((ROOT / INTERNAL_SUPPORT_PATH / skill / "INTERNAL.md").exists())
-
-    def test_support_scripts_mark_artifacts_secondary_by_default(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            input_csv = tmp / "financials.csv"
-            with input_csv.open("w", newline="", encoding="utf-8") as handle:
-                writer = csv.DictWriter(
-                    handle,
-                    fieldnames=[
-                        "entity",
-                        "source_id",
-                        "statement",
-                        "line_item_original",
-                        "period_label",
-                        "source_value",
-                        "source_name",
-                    ],
-                )
-                writer.writeheader()
-                writer.writerow(
-                    {
-                        "entity": "ACME",
-                        "source_id": "SRC-001",
-                        "statement": "income_statement",
-                        "line_item_original": "Revenue",
-                        "period_label": "FY2026",
-                        "source_value": "100",
-                        "source_name": "Company release",
-                    }
-                )
-            out_dir = tmp / "norm"
-            script = (
-                ROOT
-                / "skills"
-                / "financials-normalizer"
-                / "scripts"
-                / "normalize_extracted_financials.py"
-            )
-            result = subprocess.run(
-                [sys.executable, str(script), str(input_csv), "--output-dir", str(out_dir)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            manifest = json.loads((out_dir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertIsNone(manifest["primary_human_deliverable"])
-            self.assertFalse(manifest["support_artifacts_user_visible_default"])
-            self.assertTrue(
-                all(
-                    row["artifact_role"] != "primary_human_deliverable"
-                    for row in manifest["outputs"]
-                )
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_excel_data_cleaner_public_equity_domains.py b/plugins/financial-markets/tests/test_excel_data_cleaner_public_equity_domains.py
deleted file mode 100644
index a614981..0000000
--- a/plugins/financial-markets/tests/test_excel_data_cleaner_public_equity_domains.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""Public-equity domains for excel-data-cleaner."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class ExcelDataCleanerPublicEquityDomainsTests(unittest.TestCase):
-    def test_skill_focuses_on_public_equity_domains(self) -> None:
-        text = read(
-            "skills/public-equity-investing/internal-support/excel-data-cleaner/INTERNAL.md"
-        )
-        for phrase in [
-            "market/security data",
-            "portfolio/risk",
-            "ETF/index",
-            "consensus/provider exports",
-            "Credit tables may be profiled only as `credit_markets_handoff`",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_domain_playbook_routes_credit_and_adds_etf_index(self) -> None:
-        text = read(
-            "skills/public-equity-investing/internal-support/excel-data-cleaner/references/domain-playbook.md"
-        )
-        self.assertIn("Credit Markets handoff / equity-risk signal", text)
-        self.assertIn("route_to_credit_markets", text)
-        self.assertIn("## ETF / Index Data", text)
-        self.assertNotIn("## Public " + "Credit / Distressed", text)
-
-    def test_scripts_use_credit_handoff_domain(self) -> None:
-        profile = read(
-            "skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/profile_tabular_data.py"
-        )
-        cleaner = read(
-            "skills/public-equity-investing/internal-support/excel-data-cleaner/scripts/clean_tabular_data.py"
-        )
-        combined = profile + "\n" + cleaner
-        self.assertIn("credit_markets_handoff", combined)
-        self.assertIn("route_to_credit_markets", combined)
-        self.assertNotIn("public_credit_distressed", combined)
-        self.assertNotIn('"public_credit"', combined)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_financials_normalizer_public_equity_contract.py b/plugins/financial-markets/tests/test_financials_normalizer_public_equity_contract.py
deleted file mode 100644
index 7119c1d..0000000
--- a/plugins/financial-markets/tests/test_financials_normalizer_public_equity_contract.py
+++ /dev/null
@@ -1,225 +0,0 @@
-"""Public-equity contract for financials-normalizer."""
-
-from __future__ import annotations
-
-import csv
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class FinancialsNormalizerPublicEquityContractTests(unittest.TestCase):
-    def test_skill_reframes_to_public_equity_inputs(self) -> None:
-        text = read("skills/financials-normalizer/SKILL.md")
-        for phrase in [
-            "public-company source financials",
-            "consensus/guidance inputs",
-            "share-count support",
-            "net-debt and capital-allocation support",
-            "ETF/index constituent support",
-        ]:
-            self.assertIn(phrase, text)
-        self.assertNotIn("Classify job. Public " + "markets, Credit Markets", text)
-
-    def test_integration_guide_routes_credit_security_work_out(self) -> None:
-        text = read("skills/financials-normalizer/references/integration-guide.md")
-        self.assertIn("Credit Markets handoff / equity-risk debt and liquidity context", text)
-        self.assertIn("Route covenant packages, recovery waterfalls", text)
-        self.assertNotIn("## Credit Markets and distressed", text)
-
-    def test_schema_uses_equity_risk_debt_context_not_debt_schedule(self) -> None:
-        text = read("skills/financials-normalizer/references/normalization-schema.md")
-        self.assertIn("equity_risk_debt_liquidity_context", text)
-        self.assertIn("Credit-security schedules", text)
-        self.assertNotIn(
-            "`statement`: `income_statement`, `balance_sheet`, `cash_flow`, `kpi_schedule`, `segment`, `debt_schedule`",
-            text,
-        )
-
-    def test_standalone_model_ready_package_requires_comparability_and_validation(self) -> None:
-        skill = read("skills/financials-normalizer/SKILL.md")
-        schema = read("skills/financials-normalizer/references/normalization-schema.md")
-        qa = read("skills/financials-normalizer/references/qa-rules.md")
-        for phrase in [
-            "model-loading package",
-            "disclosure/comparability bridge",
-            "Validation_Checks",
-            "Do not infer fiscal period-end dates",
-            "reserve `consensus_estimate`",
-            "`comparable_rounded`",
-        ]:
-            self.assertIn(phrase, skill)
-        for phrase in [
-            "Standalone model-ready package",
-            "Disclosure_Comparability_Bridge",
-            "Validation_Checks",
-            "company guidance as consensus",
-            "`comparable_rounded`",
-        ]:
-            self.assertIn(phrase, schema)
-        self.assertIn("Do not report a number of passed checks", qa)
-        self.assertIn("changed cash-and-securities presentation", qa)
-
-    def test_helper_enforces_fiscal_dates_guidance_and_source_typing(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            input_csv = tmp / "financials.csv"
-            fieldnames = [
-                "entity",
-                "source_id",
-                "statement",
-                "line_item_original",
-                "line_item_standard",
-                "line_item_id",
-                "period_label",
-                "source_value",
-                "source_location",
-                "evidence_label",
-                "source_name",
-                "source_type",
-            ]
-            rows = [
-                {
-                    "entity": "NVDA",
-                    "source_id": "SRC-001",
-                    "statement": "income_statement",
-                    "line_item_original": "Revenue",
-                    "line_item_standard": "Revenue",
-                    "line_item_id": "revenue",
-                    "period_label": "Q1 FY2027",
-                    "source_value": "100",
-                    "source_location": "p. 1",
-                    "evidence_label": "fact_source_reported",
-                },
-                {
-                    "entity": "NVDA",
-                    "source_id": "SRC-001",
-                    "statement": "consensus_estimate",
-                    "line_item_original": "Revenue outlook",
-                    "line_item_standard": "Revenue guidance midpoint",
-                    "line_item_id": "guidance_revenue_midpoint",
-                    "period_label": "Q2 FY2027 outlook",
-                    "source_value": "110",
-                    "source_location": "p. 2",
-                    "evidence_label": "issuer_management_claim",
-                },
-                {
-                    "entity": "NVDA",
-                    "source_id": "SRC-001",
-                    "statement": "consensus_estimate",
-                    "line_item_original": "Revenue consensus",
-                    "line_item_standard": "Revenue consensus",
-                    "line_item_id": "consensus_revenue",
-                    "period_label": "Q2 FY2027 estimate",
-                    "source_value": "109",
-                    "source_location": "consensus p. 1",
-                },
-                {
-                    "entity": "NVDA",
-                    "source_id": "SRC-001",
-                    "statement": "debt_schedule",
-                    "line_item_original": "Debt due after five years",
-                    "line_item_standard": "Debt due after five years",
-                    "line_item_id": "debt_due_after_five_years",
-                    "period_label": "Q1 FY2027",
-                    "source_value": "50",
-                    "source_location": "p. 3",
-                    "evidence_label": "fact_source_reported",
-                },
-                {
-                    "entity": "NVDA",
-                    "source_id": "SRC-002",
-                    "statement": "kpi_schedule",
-                    "line_item_original": "Provenance marker",
-                    "line_item_standard": "Provenance marker",
-                    "line_item_id": "provenance_marker",
-                    "period_label": "Q1 FY2027",
-                    "source_value": "1",
-                    "source_location": "source_note.md",
-                    "evidence_label": "fact_source_reported",
-                    "source_type": "user_prompt",
-                },
-            ]
-            with input_csv.open("w", newline="", encoding="utf-8") as handle:
-                writer = csv.DictWriter(handle, fieldnames=fieldnames)
-                writer.writeheader()
-                writer.writerows(rows)
-            out_dir = tmp / "out"
-            script = (
-                ROOT
-                / "skills"
-                / "financials-normalizer"
-                / "scripts"
-                / "normalize_extracted_financials.py"
-            )
-            result = subprocess.run(
-                [sys.executable, str(script), str(input_csv), "--output-dir", str(out_dir)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            with (out_dir / "Normalized_Financials_Long.csv").open(
-                newline="", encoding="utf-8"
-            ) as handle:
-                normalized = list(csv.DictReader(handle))
-            self.assertEqual("", normalized[0]["period_end"])
-            self.assertEqual("forecast", normalized[1]["period_type"])
-            self.assertEqual("kpi_schedule", normalized[1]["statement"])
-            self.assertIn("separately from external consensus", normalized[1]["normalization_note"])
-            self.assertEqual("consensus_estimate", normalized[2]["statement"])
-            self.assertEqual("estimate_consensus", normalized[2]["evidence_label"])
-            self.assertIn("verify external source", normalized[2]["normalization_note"])
-            self.assertEqual("equity_risk_debt_liquidity_context", normalized[3]["statement"])
-            self.assertIn("legacy debt_schedule migrated", normalized[3]["normalization_note"])
-            with (out_dir / "Source_Index.csv").open(newline="", encoding="utf-8") as handle:
-                sources = {row["source_id"]: row for row in csv.DictReader(handle)}
-            self.assertEqual("uploaded_file", sources["SRC-002"]["source_type"])
-            issues = (out_dir / "Normalization_Issues.csv").read_text(encoding="utf-8")
-            self.assertIn("missing_period_end", issues)
-
-            validator = (
-                ROOT
-                / "skills"
-                / "financials-normalizer"
-                / "scripts"
-                / "validate_normalized_financials.py"
-            )
-            valid = subprocess.run(
-                [sys.executable, str(validator), str(out_dir / "Normalized_Financials_Long.csv")],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, valid.returncode, valid.stderr + valid.stdout)
-
-            normalized[1]["statement"] = "consensus_estimate"
-            with (out_dir / "Normalized_Financials_Long.csv").open(
-                "w", newline="", encoding="utf-8"
-            ) as handle:
-                writer = csv.DictWriter(handle, fieldnames=normalized[0].keys())
-                writer.writeheader()
-                writer.writerows(normalized)
-            invalid = subprocess.run(
-                [sys.executable, str(validator), str(out_dir / "Normalized_Financials_Long.csv")],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(1, invalid.returncode)
-            self.assertIn("consensus_estimate is reserved", invalid.stdout)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_index_etf_event_contracts.py b/plugins/financial-markets/tests/test_index_etf_event_contracts.py
deleted file mode 100644
index d9c7a38..0000000
--- a/plugins/financial-markets/tests/test_index_etf_event_contracts.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Index, ETF, and passive-flow public-equity contract tests."""
-
-from __future__ import annotations
-
-import importlib.util
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-RESOLVER = (
-    ROOT
-    / "skills"
-    / "public-equity-investing"
-    / "internal-support"
-    / "sector-context-overlay"
-    / "scripts"
-    / "resolve_sector_lens.py"
-)
-
-
-class IndexETFEventContractTests(unittest.TestCase):
-    def test_sector_resolver_detects_etf_index_mandate(self) -> None:
-        spec = importlib.util.spec_from_file_location("resolve_sector_lens", RESOLVER)
-        module = importlib.util.module_from_spec(spec)
-        assert spec.loader is not None
-        spec.loader.exec_module(module)
-        result = module.resolve(
-            "Assess MSFT as a large ETF/index constituent ahead of an S&P rebalance and passive flow event"
-        )
-        self.assertIn("etf_index", result["mandate_lenses"])
-        self.assertIn("passive flow relevance", result["mandate_output_requirements"]["etf_index"])
-
-    def test_core_docs_cover_etf_index_diligence(self) -> None:
-        docs = [
-            ROOT / "skills" / "company-tearsheet" / "SKILL.md",
-            ROOT / "skills" / "initiating-coverage" / "SKILL.md",
-            ROOT / "skills" / "memo-builder" / "SKILL.md",
-            ROOT / "skills" / "event-driven-analyzer" / "SKILL.md",
-            ROOT / "skills" / "catalyst-calendar" / "SKILL.md",
-        ]
-        for path in docs:
-            text = path.read_text(encoding="utf-8").lower()
-            self.assertTrue("etf" in text or "index" in text, str(path))
-            self.assertIn("public equity pm judgment layer", text, str(path))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_markdown_support_policy.py b/plugins/financial-markets/tests/test_markdown_support_policy.py
deleted file mode 100644
index 7393f12..0000000
--- a/plugins/financial-markets/tests/test_markdown_support_policy.py
+++ /dev/null
@@ -1,172 +0,0 @@
-"""Markdown sidecar policy regressions for Public Equity Investing scripts."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-RISK_SKILL = ROOT / "skills" / "portfolio-risk-management"
-IDEA_SKILL = ROOT / "skills" / "idea-generation"
-HEDGE_SKILL = ROOT / "skills" / "portfolio-risk-management"
-LONG_SHORT_SKILL = ROOT / "skills" / "long-short-pitch"
-SCENARIO_SKILL = ROOT / "skills" / "scenario-sensitivity-generator"
-
-
-def run_command(args: list[str], *, cwd: Path) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(args, cwd=str(cwd), text=True, capture_output=True, check=False)
-
-
-class MarkdownSupportPolicyTests(unittest.TestCase):
-    def test_risk_position_sizing_writes_support_note_not_report_by_default(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "risk"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(RISK_SKILL / "scripts" / "position_sizing_calculator.py"),
-                    "--input",
-                    str(RISK_SKILL / "references" / "example-risk-position-input.json"),
-                    "--out",
-                    str(output_dir),
-                ],
-                cwd=ROOT,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            self.assertTrue((output_dir / "support_note.md").exists())
-            self.assertFalse((output_dir / "report.md").exists())
-
-            run_log = json.loads((output_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertEqual("csv_support_note_export", run_log["workbook_mode"])
-            self.assertIsNone(manifest["primary_human_deliverable"])
-            self.assertTrue(
-                any(
-                    row["key"] == "support_note"
-                    and row["artifact_role"] == "narrative_support"
-                    and row["hidden_unless_requested"]
-                    for row in manifest["outputs"]
-                )
-            )
-            for row in manifest["outputs"]:
-                if row["path"].endswith((".md", ".json", ".csv")):
-                    self.assertNotEqual("primary_human_deliverable", row["artifact_role"])
-
-    def test_scorecard_helpers_write_support_notes_not_markdown_reports(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-
-            idea_csv = tmp_path / "ideas.csv"
-            idea_csv.write_text(
-                "ticker,company,idea_type,variant_perception_score,catalyst,source_as_of\n"
-                "ABC,ABC Co,long,4.0,Earnings revisions,2026-05-01\n",
-                encoding="utf-8",
-            )
-            idea_out = tmp_path / "ideas_out"
-            idea_result = run_command(
-                [
-                    sys.executable,
-                    str(IDEA_SKILL / "scripts" / "score_ideas.py"),
-                    str(idea_csv),
-                    "--output-dir",
-                    str(idea_out),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-            self.assertEqual(0, idea_result.returncode, idea_result.stderr + idea_result.stdout)
-            self.assertTrue((idea_out / "idea_scorecard_support_note.md").exists())
-            self.assertFalse((idea_out / "idea_scorecard.md").exists())
-
-            hedge_csv = tmp_path / "hedges.csv"
-            hedge_csv.write_text(
-                "hedge,hedge_type,risk_hedged,exposure_fit_score,as_of,source,live_pricing_status,borrow_status,option_chain_status,risk_model_status\n"
-                "SPY put,option,market beta,4.0,2026-05-01,user supplied,available,not_applicable,available,validated\n",
-                encoding="utf-8",
-            )
-            hedge_out = tmp_path / "hedge_out"
-            hedge_result = run_command(
-                [
-                    sys.executable,
-                    str(HEDGE_SKILL / "scripts" / "score_hedge_candidates.py"),
-                    str(hedge_csv),
-                    "--output-dir",
-                    str(hedge_out),
-                ],
-                cwd=ROOT,
-            )
-            self.assertEqual(0, hedge_result.returncode, hedge_result.stderr + hedge_result.stdout)
-            self.assertTrue((hedge_out / "hedge_scorecard_support_note.md").exists())
-            self.assertFalse((hedge_out / "hedge_scorecard.md").exists())
-
-    def test_long_short_scenario_helper_defaults_to_support_note_name(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            scenario_csv = tmp_path / "scenarios.csv"
-            scenario_csv.write_text(
-                "scenario,probability,current_value,target_value,key_drivers,timing,source_as_of\n"
-                "Base,60%,100,120,Multiple recovery,12 months,2026-05-01\n"
-                "Downside,40%,100,80,Estimate cut,6 months,2026-05-01\n",
-                encoding="utf-8",
-            )
-            markdown_out = tmp_path / "trade_scenarios_support_note.md"
-            json_out = tmp_path / "trade_scenarios.json"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(LONG_SHORT_SKILL / "scripts" / "materialize_trade_scenarios.py"),
-                    str(scenario_csv),
-                    "--markdown-out",
-                    str(markdown_out),
-                    "--json-out",
-                    str(json_out),
-                    "--run-date",
-                    "2026-05-18",
-                ],
-                cwd=ROOT,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            self.assertTrue(markdown_out.exists())
-            self.assertTrue(json_out.exists())
-            self.assertFalse((tmp_path / "trade_scenarios.md").exists())
-
-    def test_scenario_sensitivity_defaults_to_json_support_artifact(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            output = tmp_path / "scenario_tables.json"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(SCENARIO_SKILL / "scripts" / "materialize_public_equity_sensitivities.py"),
-                    "--tables",
-                    "price_target_scenario",
-                    "--output",
-                    str(output),
-                ],
-                cwd=ROOT,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            payload = json.loads(output.read_text(encoding="utf-8"))
-            self.assertIn("tables", payload)
-
-            run_log = json.loads((tmp_path / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((tmp_path / "manifest.json").read_text(encoding="utf-8"))
-            self.assertEqual("json_export", run_log["workbook_mode"])
-            self.assertIsNone(run_log["primary_human_deliverable"])
-            self.assertTrue(
-                all(
-                    row["artifact_role"] != "primary_human_deliverable"
-                    for row in manifest["outputs"]
-                )
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_model_audit_tieout.py b/plugins/financial-markets/tests/test_model_audit_tieout.py
deleted file mode 100644
index 96c5bf1..0000000
--- a/plugins/financial-markets/tests/test_model_audit_tieout.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""Model-audit artifact and issue-log regressions."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = ROOT / "skills" / "model-audit-tieout" / "scripts" / "audit_workbook.py"
-
-
-class ModelAuditTieoutTests(unittest.TestCase):
-    def test_substantive_audit_uses_standalone_html_and_audit_verdict_language(self) -> None:
-        skill = (ROOT / "skills" / "model-audit-tieout" / "SKILL.md").read_text(encoding="utf-8")
-        dashboard_map = (
-            ROOT / "skills" / "model-audit-tieout" / "references" / "dashboard-map.md"
-        ).read_text(encoding="utf-8")
-        templates = (
-            ROOT / "skills" / "model-audit-tieout" / "references" / "output-templates.md"
-        ).read_text(encoding="utf-8")
-        taxonomy = (
-            ROOT / "skills" / "model-audit-tieout" / "references" / "issue-taxonomy.md"
-        ).read_text(encoding="utf-8")
-        policy = (ROOT / "shared" / "deliverable-intake-policy.md").read_text(encoding="utf-8")
-
-        for phrase in [
-            "../../shared/html-artifact-standard.md",
-            "polished standalone HTML model-audit report",
-            "only when the user explicitly requests a standardized dashboard",
-            "Do not use for portfolio action until remediated and re-audited",
-            "Illustrative audit sensitivity",
-            "linked valuation/scenario decision output",
-            "local headless-browser screenshots",
-            "mention supporting audit files without linking them unless the user asks",
-            "external price anchor is unavailable",
-        ]:
-            self.assertIn(phrase, skill)
-
-        for finding_type in [
-            "Formula/control defect",
-            "Source contradiction",
-            "Unsupported assumption",
-            "Missing forecast refresh",
-            "Missing decision output",
-            "Not comparable without bridge",
-        ]:
-            self.assertIn(finding_type, skill)
-            self.assertIn(finding_type, taxonomy)
-
-        self.assertIn(
-            "finding count alone does not require a standardized dashboard", dashboard_map
-        )
-        self.assertIn("Illustrative audit sensitivity", templates)
-        self.assertIn("polished standalone HTML model-audit report", policy)
-
-    def test_audit_helper_outputs_html_issue_workbook_and_hidden_support(self) -> None:
-        try:
-            from openpyxl import Workbook
-        except Exception as exc:  # pragma: no cover
-            self.skipTest(f"openpyxl unavailable: {exc}")
-
-        with tempfile.TemporaryDirectory() as tmp:
-            tmp_path = Path(tmp)
-            workbook = tmp_path / "audit_me.xlsx"
-            wb = Workbook()
-            ws = wb.active
-            ws.title = "Inputs"
-            ws["A1"] = "Source date"
-            ws["B2"] = "=TODAY()+1.23"
-            hidden = wb.create_sheet("HiddenData")
-            hidden.sheet_state = "hidden"
-            hidden["A1"] = 42
-            wb.save(workbook)
-
-            out_dir = tmp_path / "audit_output"
-            result = subprocess.run(
-                [sys.executable, str(SCRIPT), str(workbook), "--out-dir", str(out_dir)],
-                cwd=str(SCRIPT.parent),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-
-            self.assertTrue((out_dir / "model_audit_report.html").exists())
-            self.assertTrue((out_dir / "model_audit_issues.xlsx").exists())
-            self.assertTrue((out_dir / "support" / "model_audit_findings.csv").exists())
-            self.assertTrue((out_dir / "support" / "formula_exception_log.csv").exists())
-            self.assertTrue((out_dir / "support" / "source_tieout_ledger.csv").exists())
-            findings = json.loads(
-                (out_dir / "logs" / "model_audit_findings.json").read_text(encoding="utf-8")
-            )
-            findings_text = json.dumps(findings)
-            for phrase in [
-                "first visible sheet is not a Cover tab",
-                "sheet is hidden",
-                "volatile function",
-                "numeric literal",
-            ]:
-                self.assertIn(phrase, findings_text)
-            manifest = json.loads((out_dir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertTrue(
-                str(manifest["primary_human_deliverable"]).endswith("model_audit_report.html")
-            )
-            self.assertTrue(
-                any(
-                    item["path"].endswith("model_audit_issues.xlsx")
-                    for item in manifest["companion_deliverables"]
-                )
-            )
-            self.assertFalse(manifest["support_artifacts_user_visible_default"])
-            self.assertTrue(manifest["support_artifacts"])
-            self.assertTrue(
-                all(not item["user_visible_default"] for item in manifest["support_artifacts"])
-            )
-
-    def test_update_cover_and_suffixed_cover_are_accepted_as_first_visible_cover_sheet(
-        self,
-    ) -> None:
-        try:
-            from openpyxl import Workbook
-        except Exception as exc:  # pragma: no cover
-            self.skipTest(f"openpyxl unavailable: {exc}")
-
-        for title in ("Update_Cover", "Update_Cover_2"):
-            with self.subTest(title=title), tempfile.TemporaryDirectory() as tmp:
-                tmp_path = Path(tmp)
-                workbook = tmp_path / "control_pack.xlsx"
-                wb = Workbook()
-                ws = wb.active
-                ws.title = title
-                ws["A1"] = "Bottom Line"
-                ws["A2"] = "Estimate change and valuation change are blocked pending rebuild."
-                ws["A3"] = "Revenue period and latest value chart source."
-                ws["A4"] = "Source posture, stale items, and workbook map."
-                wb.save(workbook)
-
-                out_dir = tmp_path / "audit_output"
-                result = subprocess.run(
-                    [sys.executable, str(SCRIPT), str(workbook), "--out-dir", str(out_dir)],
-                    cwd=str(SCRIPT.parent),
-                    text=True,
-                    capture_output=True,
-                    check=False,
-                )
-
-                self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-                findings = json.loads(
-                    (out_dir / "logs" / "model_audit_findings.json").read_text(encoding="utf-8")
-                )
-                findings_text = json.dumps(findings)
-                self.assertNotIn("first visible sheet is not a Cover tab", findings_text)
-                self.assertNotIn("cover/dashboard may be missing", findings_text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_model_valuation_pm_judgment.py b/plugins/financial-markets/tests/test_model_valuation_pm_judgment.py
deleted file mode 100644
index d544d4e..0000000
--- a/plugins/financial-markets/tests/test_model_valuation_pm_judgment.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""PM judgment contracts for Public Equity model and valuation skills."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-VALUATION_SKILLS = [
-    "dcf-model-builder",
-    "three-statement-model-builder",
-    "comps-valuation",
-    "scenario-sensitivity-generator",
-    "model-audit-tieout",
-    "equity-model-update",
-]
-
-
-class ModelValuationPMJudgmentTests(unittest.TestCase):
-    def test_shared_equity_valuation_standard_exists(self) -> None:
-        text = (ROOT / "shared" / "equity-valuation-pm-standard.md").read_text(encoding="utf-8")
-        for phrase in [
-            "What does the current stock price imply?",
-            "variant estimate path",
-            "What breaks first in downside?",
-            "What changes target, rating, sizing, hedge, trim, exit, or watchlist status?",
-            "What evidence is missing?",
-            "expected return versus hurdle",
-            "downside/upside ratio",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_model_and_valuation_skills_load_pm_standard(self) -> None:
-        for skill in VALUATION_SKILLS:
-            text = (ROOT / "skills" / skill / "SKILL.md").read_text(encoding="utf-8")
-            lower = text.lower()
-            self.assertIn("shared/equity-valuation-pm-standard.md", text, skill)
-            self.assertIn("shared/pm-judgment-heuristics.md", text, skill)
-            for phrase in [
-                "current stock price implies",
-                "variant estimate path",
-                "what breaks first in downside",
-                "target, rating, sizing, hedge, trim, exit, or watchlist",
-                "evidence is missing",
-            ]:
-                self.assertIn(phrase, lower, skill)
-
-    def test_model_dashboard_maps_include_equity_pm_modules(self) -> None:
-        checks = {
-            "dcf-model-builder/references/dashboard-map.md": [
-                "current price versus implied value/share",
-                "reverse DCF",
-                "scenario skew",
-            ],
-            "three-statement-model-builder/references/dashboard-map.md": [
-                "estimate path",
-                "EPS/FCF",
-                "what breaks first in downside",
-            ],
-            "comps-valuation/references/workbook/dashboard-map.md": [
-                "selected-multiple bridge",
-                "liquidity/float",
-                "ETF/index exposure",
-            ],
-            "model-audit-tieout/references/dashboard-map.md": [
-                "PM decision readiness",
-                "source posture",
-                "missing evidence",
-            ],
-        }
-        for relative, phrases in checks.items():
-            text = (ROOT / "skills" / relative).read_text(encoding="utf-8")
-            for phrase in phrases:
-                self.assertIn(phrase, text, relative)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_model_workbook_artifacts.py b/plugins/financial-markets/tests/test_model_workbook_artifacts.py
deleted file mode 100644
index d7b399a..0000000
--- a/plugins/financial-markets/tests/test_model_workbook_artifacts.py
+++ /dev/null
@@ -1,789 +0,0 @@
-"""Generated workbook regressions for Public Equity Investing model builders."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-import zipfile
-from pathlib import Path
-from xml.etree import ElementTree as ET
-
-ROOT = Path(__file__).resolve().parents[1]
-NS_MAIN = "http://schemas.openxmlformats.org/spreadsheetml/2006/main"
-NS_REL = "http://schemas.openxmlformats.org/officeDocument/2006/relationships"
-
-sys.path.insert(0, str(ROOT))
-
-from shared.dashboard.qa import load_payload, validate_payload  # noqa: E402
-from shared.dashboard.renderer import render_dashboard  # noqa: E402
-from shared.model_citations import validate_model_citations  # noqa: E402
-from shared.workbook_inspection import inspect_formula_workbook  # noqa: E402
-
-DCF_SKILL = ROOT / "skills" / "dcf-model-builder"
-THREE_STATEMENT_SKILL = ROOT / "skills" / "three-statement-model-builder"
-
-DCF_REQUIRED_FORMULA_SHEETS = [
-    "Cover",
-    "Executive Summary",
-    "Control Panel",
-    "Historical Financials",
-    "Revenue Build",
-    "Margin Cost Build",
-    "Working Capital",
-    "Capex D&A",
-    "Tax Schedule",
-    "Unlevered FCF",
-    "WACC",
-    "Terminal Value",
-    "DCF Valuation",
-    "Sensitivities",
-    "Checks",
-    "Source Notes",
-]
-
-THREE_STATEMENT_REQUIRED_FORMULA_SHEETS = [
-    "Cover",
-    "Executive Summary",
-    "Control Panel",
-    "Historical Financials",
-    "Revenue Build",
-    "Expense Build",
-    "Income Statement",
-    "Working Capital",
-    "PP&E D&A",
-    "Debt Interest",
-    "Tax",
-    "Balance Sheet",
-    "Cash Flow Statement",
-    "Scenarios",
-    "Checks",
-    "Source Notes",
-]
-
-
-def run_command(args: list[str], *, cwd: Path) -> subprocess.CompletedProcess[str]:
-    return subprocess.run(args, cwd=str(cwd), text=True, capture_output=True, check=False)
-
-
-def workbook_sheet_paths(path: Path) -> dict[str, str]:
-    with zipfile.ZipFile(path) as zf:
-        workbook = ET.fromstring(zf.read("xl/workbook.xml"))
-        rels = ET.fromstring(zf.read("xl/_rels/workbook.xml.rels"))
-        relmap = {rel.attrib["Id"]: rel.attrib["Target"] for rel in rels}
-        sheets = workbook.find(f"{{{NS_MAIN}}}sheets")
-        if sheets is None:
-            return {}
-        paths: dict[str, str] = {}
-        for sheet in sheets:
-            rid = sheet.attrib[f"{{{NS_REL}}}id"]
-            target = relmap[rid].lstrip("/")
-            paths[sheet.attrib["name"]] = target if target.startswith("xl/") else f"xl/{target}"
-        return paths
-
-
-def workbook_sheet_names(path: Path) -> list[str]:
-    return list(workbook_sheet_paths(path).keys())
-
-
-def workbook_formula_count(path: Path) -> int:
-    count = 0
-    with zipfile.ZipFile(path) as zf:
-        for sheet_path in workbook_sheet_paths(path).values():
-            root = ET.fromstring(zf.read(sheet_path))
-            count += len(root.findall(f".//{{{NS_MAIN}}}f"))
-    return count
-
-
-def workbook_external_links(path: Path) -> list[str]:
-    with zipfile.ZipFile(path) as zf:
-        return [name for name in zf.namelist() if name.startswith("xl/externalLinks/")]
-
-
-def workbook_cell(path: Path, sheet_name: str, ref: str) -> tuple[str | None, str | None]:
-    with zipfile.ZipFile(path) as zf:
-        root = ET.fromstring(zf.read(workbook_sheet_paths(path)[sheet_name]))
-        for cell in root.findall(f".//{{{NS_MAIN}}}c"):
-            if cell.attrib.get("r") != ref:
-                continue
-            formula = cell.find(f"{{{NS_MAIN}}}f")
-            value = cell.find(f"{{{NS_MAIN}}}v")
-            inline = cell.find(f"{{{NS_MAIN}}}is/{{{NS_MAIN}}}t")
-            return (
-                inline.text if inline is not None else value.text if value is not None else None,
-                formula.text if formula is not None else None,
-            )
-    return None, None
-
-
-def strip_formula_nodes(source: Path, target: Path) -> None:
-    with zipfile.ZipFile(source) as zin, zipfile.ZipFile(target, "w", zipfile.ZIP_DEFLATED) as zout:
-        for info in zin.infolist():
-            data = zin.read(info.filename)
-            if info.filename.startswith("xl/worksheets/") and info.filename.endswith(".xml"):
-                root = ET.fromstring(data)
-                for cell in root.findall(f".//{{{NS_MAIN}}}c"):
-                    for formula in list(cell.findall(f"{{{NS_MAIN}}}f")):
-                        cell.remove(formula)
-                data = ET.tostring(root, encoding="utf-8", xml_declaration=True)
-            zout.writestr(info, data)
-
-
-class ModelWorkbookArtifactTests(unittest.TestCase):
-    def test_model_builder_docs_default_to_formula_workbook_mode(self) -> None:
-        checks = [
-            (
-                DCF_SKILL,
-                "Default model-build artifact level is `banker_formula_workbook`",
-                "formula-first public-equity DCF workbooks",
-            ),
-            (
-                THREE_STATEMENT_SKILL,
-                "Default model-build artifact level is `banker_formula_workbook`",
-                "formula-first public-equity 3-statement operating model workbooks",
-            ),
-        ]
-        for skill_root, contract_phrase, agent_phrase in checks:
-            skill = (skill_root / "SKILL.md").read_text(encoding="utf-8")
-            output_spec = (skill_root / "references" / "output-spec.md").read_text(encoding="utf-8")
-            formula_contract = (
-                skill_root / "references" / "banker-formula-workbook-contract.md"
-            ).read_text(encoding="utf-8")
-            agent = (skill_root / "agents" / "openai.yaml").read_text(encoding="utf-8")
-
-            self.assertIn(contract_phrase, skill)
-            self.assertIn("Default path: ", skill)
-            self.assertIn("Use `python3 scripts/run_pipeline.py", skill)
-            self.assertIn("only for controlled computed values", skill)
-            self.assertIn(
-                "The default model-build path is the banker formula workbook path", output_spec
-            )
-            self.assertIn("`banker_formula_workbook`: default live formula workbook", output_spec)
-            self.assertIn(
-                "Formula mode is the default user-facing model-build path", formula_contract
-            )
-            self.assertIn(agent_phrase, agent)
-
-    def test_deterministic_exports_remain_values_workbooks(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "dcf"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "run_pipeline.py"),
-                    str(DCF_SKILL / "assets" / "plan_template.json"),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((output_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertEqual("deterministic_export", run_log["workbook_mode"])
-            self.assertEqual("Cover", workbook_sheet_names(output_dir / "model.xlsx")[0])
-            self.assertLess(workbook_formula_count(output_dir / "model.xlsx"), 5)
-            self.assertTrue((output_dir / "support_note.md").exists())
-            self.assertFalse((output_dir / "report.md").exists())
-            self.assertEqual(
-                (output_dir / "model.xlsx").resolve(),
-                Path(manifest["primary_human_deliverable"]).resolve(),
-            )
-            self.assertTrue(
-                any(
-                    row["key"] == "support_note"
-                    and row["artifact_role"] == "narrative_support"
-                    and row["hidden_unless_requested"]
-                    for row in manifest["outputs"]
-                )
-            )
-
-    def test_three_statement_deterministic_export_remains_values_workbook(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            work_dir = Path(tmp_dir)
-            result = run_command(
-                [
-                    sys.executable,
-                    str(THREE_STATEMENT_SKILL / "scripts" / "run_pipeline.py"),
-                    str(THREE_STATEMENT_SKILL / "assets" / "plan_template.json"),
-                ],
-                cwd=work_dir,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            output_dir = work_dir / "output"
-            run_log = json.loads((output_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-            self.assertEqual("deterministic_export", run_log["workbook_mode"])
-            self.assertEqual("Cover", workbook_sheet_names(output_dir / "model.xlsx")[0])
-            self.assertLess(workbook_formula_count(output_dir / "model.xlsx"), 5)
-            self.assertTrue((output_dir / "support_note.md").exists())
-            self.assertFalse((output_dir / "report.md").exists())
-            self.assertEqual(
-                (output_dir / "model.xlsx").resolve(),
-                Path(manifest["primary_human_deliverable"]).resolve(),
-            )
-            self.assertTrue(
-                any(
-                    row["key"] == "support_note"
-                    and row["artifact_role"] == "narrative_support"
-                    and row["hidden_unless_requested"]
-                    for row in manifest["outputs"]
-                )
-            )
-
-    def test_dcf_formula_workbook_has_required_artifacts_and_provenance(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "dcf_formula"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(DCF_SKILL / "assets" / "plan_template.json"),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            workbook = output_dir / "banker_formula_workbook.xlsx"
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-            model_citations = json.loads(
-                (output_dir / "model_citations.json").read_text(encoding="utf-8")
-            )
-
-            self.assertTrue(workbook.exists())
-            self.assertEqual("completed", run_log["status"])
-            self.assertEqual("banker_formula_workbook", run_log["workbook_mode"])
-            self.assertEqual("completed", manifest["status"])
-            self.assertEqual("banker_formula_workbook", manifest["artifact_mode"])
-            self.assertEqual("complete", manifest["blocked_or_partial_status"]["status"])
-            self.assertEqual(str(workbook), manifest["primary_human_deliverable"])
-            self.assertEqual("Cover", workbook_sheet_names(workbook)[0])
-            self.assertTrue(
-                set(DCF_REQUIRED_FORMULA_SHEETS).issubset(workbook_sheet_names(workbook))
-            )
-            inspection = inspect_formula_workbook(
-                workbook, required_sheets=DCF_REQUIRED_FORMULA_SHEETS, workbook_type="dcf"
-            )
-            self.assertGreaterEqual(workbook_formula_count(workbook), 800)
-            self.assertTrue(inspection["cover_first"])
-            self.assertTrue(inspection["required_formula_sheets_populated"])
-            self.assertTrue(
-                inspection["has_named_ranges"]
-                or run_log["workbook_inspection"]["anchor_map_present"]
-            )
-            self.assertEqual([], workbook_external_links(workbook))
-            self.assertEqual([], run_log["hard_failures"])
-            self.assertIn("model_citations", run_log["output_paths"])
-            self.assertEqual([], validate_model_citations(model_citations, strict=True))
-            self.assertGreaterEqual(len(model_citations["model_citations"]), 5)
-            self.assertIn("support_artifacts", manifest)
-            self.assertTrue(
-                any(
-                    item["path"].endswith("model_citations.json")
-                    and item["role"] == "support_artifact"
-                    for item in manifest["support_artifacts"]
-                )
-            )
-            self.assertTrue(
-                any(
-                    item["sheet"] == "DCF Valuation" and item["cell"] == "B18"
-                    for item in model_citations["model_citations"]
-                )
-            )
-
-    def test_dcf_formula_workbook_clears_template_history_and_maps_market_data(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            plan = json.loads(
-                (DCF_SKILL / "assets" / "plan_template.json").read_text(encoding="utf-8")
-            )
-            plan["meta"].update({"company": "Airbnb, Inc.", "ticker": "ABNB"})
-            plan["timeline"]["start_year"] = 2026
-            plan["historicals"].update(
-                {
-                    "latest_year": 2025,
-                    "revenue": 12241.0,
-                    "gross_profit_margin": 0.8296,
-                    "ebitda": 2635.0,
-                    "ebit": 2544.0,
-                    "cash_taxes": 232.0,
-                    "da": 91.0,
-                    "capex": 33.0,
-                    "change_nwc": 0.0,
-                    "net_working_capital": 0.0,
-                    "unlevered_fcf": 4613.0,
-                }
-            )
-            plan["market"] = {
-                "current_share_price": 134.5,
-                "source_id": "src_market_abnb",
-            }
-            plan["ev_to_equity_bridge"]["current_share_price"] = 134.5
-            plan["source_basis"].append(
-                {
-                    "id": "src_market_abnb",
-                    "topic": "market_data",
-                    "label": "web_research",
-                    "source_name": "Nasdaq ABNB quote endpoint",
-                    "source_type": "exchange_quote",
-                    "as_of_date": "2026-05-11",
-                    "confidence": "high",
-                    "notes": "Closing price used in DCF evaluation.",
-                }
-            )
-            plan_path = tmp / "abnb_plan.json"
-            plan_path.write_text(json.dumps(plan), encoding="utf-8")
-            output_dir = tmp / "abnb_formula"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(plan_path),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            workbook = output_dir / "banker_formula_workbook.xlsx"
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            self.assertNotEqual("not-decision-ready", run_log["model_status"])
-            self.assertNotIn("timeline_latest_historical_year_mismatch", run_log["hard_failures"])
-            self.assertEqual(("N/A", None), workbook_cell(workbook, "Revenue Build", "B5"))
-            self.assertEqual(("2025A", None), workbook_cell(workbook, "Revenue Build", "D5"))
-            self.assertEqual(("2026E", None), workbook_cell(workbook, "Revenue Build", "E5"))
-            self.assertEqual((None, None), workbook_cell(workbook, "Revenue Build", "B10"))
-            self.assertEqual(("12241.0", None), workbook_cell(workbook, "Revenue Build", "D10"))
-            self.assertEqual(("n.m.", None), workbook_cell(workbook, "Capex D&A", "B13"))
-            self.assertEqual(("n.m.", None), workbook_cell(workbook, "Capex D&A", "E13"))
-            self.assertEqual(
-                ("not provided", None), workbook_cell(workbook, "Historical Financials", "K21")
-            )
-            self.assertEqual(
-                ("Nasdaq ABNB quote endpoint", None), workbook_cell(workbook, "Source Notes", "B11")
-            )
-            self.assertEqual(("web_research", None), workbook_cell(workbook, "Source Notes", "C11"))
-            self.assertEqual(
-                ("Ending PP&E non-negative when roll-forward modeled", None),
-                workbook_cell(workbook, "Checks", "A21"),
-            )
-            self.assertIn("historical_ppe_or_reinvestment_support", run_log["missing_inputs"])
-            self.assertIn(
-                "earlier historical columns were intentionally blanked",
-                " ".join(run_log["warnings"]),
-            )
-
-    def test_dcf_formula_workbook_keeps_market_price_source_open_when_price_is_missing(
-        self,
-    ) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            plan = json.loads(
-                (DCF_SKILL / "assets" / "plan_template.json").read_text(encoding="utf-8")
-            )
-            plan["source_basis"].append(
-                {
-                    "id": "src_market_without_price",
-                    "topic": "market_data",
-                    "label": "web_research",
-                    "source_name": "Market-data endpoint",
-                    "source_type": "exchange_quote",
-                    "as_of_date": "2026-05-11",
-                    "confidence": "high",
-                    "notes": "Quote source was available.",
-                }
-            )
-            plan_path = tmp / "missing_price_plan.json"
-            plan_path.write_text(json.dumps(plan), encoding="utf-8")
-            output_dir = tmp / "missing_price_formula"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(plan_path),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            workbook = output_dir / "banker_formula_workbook.xlsx"
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            self.assertIn("current_share_price", run_log["missing_inputs"])
-            self.assertEqual(("Open", None), workbook_cell(workbook, "Source Notes", "H11"))
-            self.assertIn(
-                "Current share price was not supplied",
-                workbook_cell(workbook, "Source Notes", "F11")[0] or "",
-            )
-
-    def test_dcf_formula_workbook_rejects_negative_supported_ppe_rollforward(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            plan = json.loads(
-                (DCF_SKILL / "assets" / "plan_template.json").read_text(encoding="utf-8")
-            )
-            plan["historicals"]["ppe"] = 10.0
-            for scenario in plan["scenarios"].values():
-                scenario["capex_percent_revenue"] = 0.0
-                scenario["da_percent_revenue"] = 0.10
-            plan_path = tmp / "bad_reinvestment_plan.json"
-            plan_path.write_text(json.dumps(plan), encoding="utf-8")
-            output_dir = tmp / "bad_reinvestment_formula"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(plan_path),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(2, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            self.assertIn("negative_forecast_ppe_rollforward", run_log["hard_failures"])
-
-    def test_dcf_formula_workbook_rejects_negative_opening_ppe(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            plan = json.loads(
-                (DCF_SKILL / "assets" / "plan_template.json").read_text(encoding="utf-8")
-            )
-            plan["historicals"]["ppe"] = -10.0
-            for scenario in plan["scenarios"].values():
-                scenario["capex_percent_revenue"] = 0.20
-                scenario["da_percent_revenue"] = 0.0
-            plan_path = tmp / "negative_opening_ppe_plan.json"
-            plan_path.write_text(json.dumps(plan), encoding="utf-8")
-            output_dir = tmp / "negative_opening_ppe_formula"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(plan_path),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(2, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            self.assertIn("negative_historical_ppe", run_log["hard_failures"])
-
-    def test_three_statement_formula_workbook_has_required_artifacts_and_provenance(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            output_dir = Path(tmp_dir) / "three_statement_formula"
-            result = run_command(
-                [
-                    sys.executable,
-                    str(THREE_STATEMENT_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(THREE_STATEMENT_SKILL / "assets" / "plan_template.json"),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=THREE_STATEMENT_SKILL,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            workbook = output_dir / "banker_formula_workbook.xlsx"
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-            model_citations = json.loads(
-                (output_dir / "model_citations.json").read_text(encoding="utf-8")
-            )
-
-            self.assertTrue(workbook.exists())
-            self.assertEqual("completed", run_log["status"])
-            self.assertEqual("banker_formula_workbook", run_log["workbook_mode"])
-            self.assertEqual("completed", manifest["status"])
-            self.assertEqual("banker_formula_workbook", manifest["artifact_mode"])
-            self.assertEqual("complete", manifest["blocked_or_partial_status"]["status"])
-            self.assertEqual(str(workbook), manifest["primary_human_deliverable"])
-            self.assertEqual("Cover", workbook_sheet_names(workbook)[0])
-            self.assertTrue(
-                set(THREE_STATEMENT_REQUIRED_FORMULA_SHEETS).issubset(
-                    workbook_sheet_names(workbook)
-                )
-            )
-            inspection = inspect_formula_workbook(
-                workbook,
-                required_sheets=THREE_STATEMENT_REQUIRED_FORMULA_SHEETS,
-                workbook_type="three_statement",
-            )
-            self.assertGreaterEqual(workbook_formula_count(workbook), 1100)
-            self.assertTrue(inspection["cover_first"])
-            self.assertTrue(inspection["required_formula_sheets_populated"])
-            self.assertTrue(
-                inspection["has_named_ranges"]
-                or run_log["workbook_inspection"]["anchor_map_present"]
-            )
-            self.assertEqual([], workbook_external_links(workbook))
-            self.assertEqual([], run_log["hard_failures"])
-            self.assertIn("model_citations", run_log["output_paths"])
-            self.assertEqual([], validate_model_citations(model_citations, strict=True))
-            self.assertGreaterEqual(len(model_citations["model_citations"]), 5)
-            self.assertIn("support_artifacts", manifest)
-            self.assertTrue(
-                any(
-                    item["path"].endswith("model_citations.json")
-                    and item["role"] == "support_artifact"
-                    for item in manifest["support_artifacts"]
-                )
-            )
-            self.assertTrue(
-                any(
-                    item["sheet"] == "Income Statement" and item["cell"] == "I6"
-                    for item in model_citations["model_citations"]
-                )
-            )
-
-    def test_invalid_formula_plan_does_not_claim_formula_workbook(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            bad_plan = Path(tmp_dir) / "bad_plan.json"
-            bad_plan.write_text('{"meta": {}}', encoding="utf-8")
-            output_dir = Path(tmp_dir) / "bad_formula"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(bad_plan),
-                    "--output-dir",
-                    str(output_dir),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertNotEqual(0, result.returncode)
-            self.assertFalse((output_dir / "banker_formula_workbook.xlsx").exists())
-            self.assertFalse((output_dir / "banker_formula_workbook_run_log.json").exists())
-
-    def test_dcf_formula_workbook_hard_failures_return_nonzero_and_diagnostic_artifacts(
-        self,
-    ) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            bad_template = tmp / "dcf_template_without_formulas.xlsx"
-            strip_formula_nodes(
-                DCF_SKILL / "assets" / "templates" / "banker_formula_workbook_template.xlsx",
-                bad_template,
-            )
-            output_dir = tmp / "dcf_formula_bad"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(DCF_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(DCF_SKILL / "assets" / "plan_template.json"),
-                    "--output-dir",
-                    str(output_dir),
-                    "--template",
-                    str(bad_template),
-                ],
-                cwd=DCF_SKILL,
-            )
-
-            self.assertEqual(2, result.returncode, result.stderr + result.stdout)
-            self.assertTrue((output_dir / "banker_formula_workbook.xlsx").exists())
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertEqual("failed", run_log["status"])
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            workbook = output_dir / "banker_formula_workbook.xlsx"
-            self.assertEqual(("not-decision-ready", None), workbook_cell(workbook, "Cover", "B6"))
-            self.assertEqual(("not-decision-ready", None), workbook_cell(workbook, "Cover", "B35"))
-            self.assertEqual(
-                ("not-decision-ready", None), workbook_cell(workbook, "Control Panel", "B8")
-            )
-            self.assertIn(
-                "Not decision-ready",
-                workbook_cell(workbook, "Executive Summary", "B3")[0] or "",
-            )
-            self.assertIn(
-                "banker_formula_workbook_formula_count_below_threshold", run_log["hard_failures"]
-            )
-            self.assertIn(
-                "banker_formula_workbook_missing_required_formula_sheets", run_log["hard_failures"]
-            )
-            self.assertEqual("failed", manifest["status"])
-            self.assertEqual("not-decision-ready", manifest["blocked_or_partial_status"]["status"])
-            self.assertEqual(
-                "workbook_diagnostic_status", manifest["final_response_guidance"]["lead_with"]
-            )
-
-    def test_three_statement_formula_workbook_hard_failures_return_nonzero_and_diagnostic_artifacts(
-        self,
-    ) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            bad_template = tmp / "three_statement_template_without_formulas.xlsx"
-            strip_formula_nodes(
-                THREE_STATEMENT_SKILL
-                / "assets"
-                / "templates"
-                / "banker_formula_workbook_template.xlsx",
-                bad_template,
-            )
-            output_dir = tmp / "three_statement_formula_bad"
-
-            result = run_command(
-                [
-                    sys.executable,
-                    str(THREE_STATEMENT_SKILL / "scripts" / "build_banker_formula_workbook.py"),
-                    str(THREE_STATEMENT_SKILL / "assets" / "plan_template.json"),
-                    "--output-dir",
-                    str(output_dir),
-                    "--template",
-                    str(bad_template),
-                ],
-                cwd=THREE_STATEMENT_SKILL,
-            )
-
-            self.assertEqual(2, result.returncode, result.stderr + result.stdout)
-            self.assertTrue((output_dir / "banker_formula_workbook.xlsx").exists())
-            run_log = json.loads(
-                (output_dir / "banker_formula_workbook_run_log.json").read_text(encoding="utf-8")
-            )
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertEqual("failed", run_log["status"])
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            self.assertIn(
-                "banker_formula_workbook_formula_count_below_threshold", run_log["hard_failures"]
-            )
-            self.assertIn(
-                "banker_formula_workbook_missing_required_formula_sheets", run_log["hard_failures"]
-            )
-            self.assertEqual("failed", manifest["status"])
-            self.assertEqual("not-decision-ready", manifest["blocked_or_partial_status"]["status"])
-            self.assertEqual(
-                "workbook_diagnostic_status", manifest["final_response_guidance"]["lead_with"]
-            )
-
-    def test_dashboard_renders_model_citations_path_as_source_ledger(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            citations = {
-                "model_citations": [
-                    {
-                        "id": "model-output:dcf-value-per-share",
-                        "title": "DCF Value Per Share",
-                        "short_label": "Model: DCF Valuation!B18",
-                        "type": "model_cell",
-                        "workbook_path": "/tmp/banker_formula_workbook.xlsx",
-                        "sheet": "DCF Valuation",
-                        "cell": "B18",
-                        "range": "B18",
-                        "value": "$48.25",
-                        "formula": "=B16/B17",
-                        "line_item": "value_per_share",
-                        "source_id": "src_shares_001",
-                        "evidence_label": "reported",
-                    }
-                ]
-            }
-            (tmp_path / "model_citations.json").write_text(json.dumps(citations), encoding="utf-8")
-            payload = {
-                "kind": "public_equity_investing_dashboard.v1",
-                "mode": "dcf_model_dashboard",
-                "layout": "single_page",
-                "title": "DCF dashboard",
-                "issuer": {"ticker": "NSIS"},
-                "metadata": {
-                    "payload_stage": "production",
-                    "freeze_time": "2026-05-18 09:00 ET",
-                    "source_posture": "Synthetic formula-workbook fixture.",
-                    "readiness_label": "PM-ready fixture",
-                    "readiness_posture": "pm_ready",
-                    "citation_policy": "strict",
-                    "decision_context": "Whether workbook-backed valuation output is source-visible.",
-                },
-                "hero": {
-                    "headline": "DCF workbook dashboard",
-                    "dek": {
-                        "value": "DCF value is $48.25",
-                        "citations": ["model-output:dcf-value-per-share"],
-                    },
-                    "callout": {
-                        "value": "Workbook citation should render",
-                        "citations": ["model-output:dcf-value-per-share"],
-                    },
-                },
-                "model_citations_path": "model_citations.json",
-                "snapshot": [
-                    {
-                        "label": "DCF value/share",
-                        "value": {
-                            "value": "$48.25",
-                            "citations": ["model-output:dcf-value-per-share"],
-                        },
-                        "detail": "Base case model output",
-                    }
-                ],
-                "tabs": [
-                    {
-                        "id": "overview",
-                        "label": "Overview",
-                        "modules": [
-                            {
-                                "type": "decision_box",
-                                "data": {
-                                    "stance": "Watch",
-                                    "summary": {
-                                        "value": "DCF value is $48.25",
-                                        "citations": ["model-output:dcf-value-per-share"],
-                                    },
-                                },
-                            }
-                        ],
-                    }
-                ],
-            }
-            payload_path = tmp_path / "payload.json"
-            payload_path.write_text(json.dumps(payload), encoding="utf-8")
-
-            loaded = load_payload(payload_path)
-            report = validate_payload(loaded)
-            html = render_dashboard(loaded)
-
-            self.assertEqual("passed", report["status"], report)
-            self.assertIn("DCF Valuation!B18", html)
-            self.assertIn("formula: =B16/B17", html)
-            self.assertIn("source-model-output-dcf-value-per-share", html)
-            self.assertIn('class="citation-link"', html)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_pm_judgment_language.py b/plugins/financial-markets/tests/test_pm_judgment_language.py
deleted file mode 100644
index 168a24a..0000000
--- a/plugins/financial-markets/tests/test_pm_judgment_language.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""Static contract tests for the Public Equity Investing PM judgment layer."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-INTERNAL_SUPPORT_ROOT = ROOT / "skills" / "public-equity-investing" / "internal-support"
-
-CORE_SKILLS = [
-    "earnings-preview",
-    "earnings-deep-dive",
-    "initiating-coverage",
-    "company-tearsheet",
-    "idea-generation",
-    "long-short-pitch",
-    "memo-builder",
-    "thesis-tracker",
-    "catalyst-calendar",
-    "event-driven-analyzer",
-    "sector-context-overlay",
-]
-
-AUDIENCE_MODES = [
-    "long_only_pm",
-    "long_short_hf",
-    "sell_side_research",
-    "etf_index_diligence",
-    "public_equity_diligence",
-]
-
-PM_TERMS = [
-    "variant wedge",
-    "what is priced in",
-    "downside mechanism",
-    "falsifiers",
-    "action discipline",
-]
-
-
-class PMJudgmentLanguageTests(unittest.TestCase):
-    def test_shared_pm_judgment_standard_exists(self) -> None:
-        text = (ROOT / "shared" / "pm-judgment-heuristics.md").read_text(encoding="utf-8")
-        for mode in AUDIENCE_MODES:
-            self.assertIn(mode, text)
-        for term in PM_TERMS:
-            self.assertIn(term, text.lower())
-
-    def test_core_skills_reference_shared_pm_standard_and_modes(self) -> None:
-        for skill in CORE_SKILLS:
-            path = (
-                INTERNAL_SUPPORT_ROOT / skill / "INTERNAL.md"
-                if skill == "sector-context-overlay"
-                else ROOT / "skills" / skill / "SKILL.md"
-            )
-            text = path.read_text(encoding="utf-8")
-            self.assertIn("shared/pm-judgment-heuristics.md", text, skill)
-            self.assertIn("Public Equity PM Judgment Layer", text, skill)
-            for mode in AUDIENCE_MODES:
-                self.assertIn(mode, text, skill)
-
-    def test_plugin_scope_mentions_index_etf_and_sell_side(self) -> None:
-        combined = (
-            (ROOT / "shared" / "pm-judgment-heuristics.md").read_text(encoding="utf-8")
-            + "\n"
-            + (ROOT / ".codex-plugin" / "plugin.json").read_text(encoding="utf-8")
-        ).lower()
-        for phrase in ["etf/index", "constituent", "benchmark", "sell-side", "factor exposure"]:
-            self.assertIn(phrase, combined)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_pm_risk_workflow_contracts.py b/plugins/financial-markets/tests/test_pm_risk_workflow_contracts.py
deleted file mode 100644
index 0e03d39..0000000
--- a/plugins/financial-markets/tests/test_pm_risk_workflow_contracts.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""PM/risk workflow contracts for Public Equity Investing."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class PMRiskWorkflowContractTests(unittest.TestCase):
-    def test_shared_pm_risk_lens_exists(self) -> None:
-        text = read("shared/pm-judgment-heuristics.md").lower()
-        for phrase in [
-            "pm/risk workflow lens",
-            "alpha vs unwanted exposure",
-            "retained exposure",
-            "binding constraint",
-            "hedge-failure scenario",
-            "size-down/no-hedge alternative",
-            "add/trim/exit/cover/resize/roll/remove rules",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_portfolio_risk_skill_references_shared_standards(self) -> None:
-        skill = "portfolio-risk-management"
-        text = read(f"skills/{skill}/SKILL.md")
-        self.assertIn("shared/pm-judgment-heuristics.md", text, skill)
-        self.assertIn("shared/credit-markets-handoff.md", text, skill)
-        lowered = text.lower()
-        for phrase in [
-            "position_sizing",
-            "hedge_design",
-            "integrated_risk_plan",
-            "intended alpha",
-            "unwanted risk",
-            "retained exposure",
-            "binding constraint",
-            "size-down/no-hedge",
-        ]:
-            self.assertIn(phrase, lowered, skill)
-
-    def test_equity_risk_taxonomy_is_first_class(self) -> None:
-        combined = read("skills/portfolio-risk-management/SKILL.md").lower()
-        for phrase in [
-            "equity longs",
-            "equity shorts",
-            "pair trades",
-            "etfs/index",
-            "listed options",
-            "factor hedges",
-            "macro proxies",
-        ]:
-            self.assertIn(phrase, combined)
-
-    def test_dashboard_packs_surface_pm_risk_decision_layer(self) -> None:
-        relative_path = "skills/portfolio-risk-management/references/DASHBOARD_PACK.md"
-        text = read(relative_path).lower()
-        for phrase in [
-            "pm decision box",
-            "intended alpha",
-            "unwanted risk",
-            "retained exposure",
-            "basis risk",
-            "gross/net/beta/factor",
-            "size-down/no-hedge",
-            "credit markets handoff",
-        ]:
-            self.assertIn(phrase, text, relative_path)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_portfolio_risk_management_hedge_credit_boundary.py b/plugins/financial-markets/tests/test_portfolio_risk_management_hedge_credit_boundary.py
deleted file mode 100644
index 46f3e64..0000000
--- a/plugins/financial-markets/tests/test_portfolio_risk_management_hedge_credit_boundary.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""Portfolio risk hedge-design helper guardrails for credit instruments."""
-
-from __future__ import annotations
-
-import csv
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = ROOT / "skills" / "portfolio-risk-management" / "scripts" / "score_hedge_candidates.py"
-
-
-class PortfolioRiskHedgeCreditBoundaryTests(unittest.TestCase):
-    def test_credit_hedge_types_route_to_credit_markets(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp = Path(tmp_dir)
-            input_csv = tmp / "hedges.csv"
-            input_csv.write_text(
-                "hedge,hedge_type,risk_hedged,exposure_fit_score,thesis_preservation_score,basis_risk,implementation_status,as_of,source,live_pricing_status,borrow_status,option_chain_status,risk_model_status\n"
-                "ACME CDS,CDS,refinancing stress,5,4,entity basis,screen only,2026-05-18,user supplied,available,not_applicable,not_applicable,validated\n"
-                "SPY put,option,market beta,4,4,index basis,screen only,2026-05-18,user supplied,available,not_applicable,available,validated\n",
-                encoding="utf-8",
-            )
-            output_dir = tmp / "out"
-
-            result = subprocess.run(
-                [sys.executable, str(SCRIPT), str(input_csv), "--output-dir", str(output_dir)],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            with (output_dir / "hedge_scorecard.csv").open(newline="", encoding="utf-8") as handle:
-                rows = list(csv.DictReader(handle))
-            by_hedge = {row["hedge"]: row for row in rows}
-            self.assertEqual("route_to_credit_markets", by_hedge["ACME CDS"]["readiness_status"])
-            self.assertEqual(
-                "route_to_credit_markets", by_hedge["ACME CDS"]["implementation_status"]
-            )
-            self.assertIn(
-                "Credit hedge construction belongs in Credit Markets",
-                by_hedge["ACME CDS"]["warnings"],
-            )
-            self.assertNotEqual("route_to_credit_markets", by_hedge["SPY put"]["readiness_status"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_credit_boundary.py b/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_credit_boundary.py
deleted file mode 100644
index 1a28581..0000000
--- a/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_credit_boundary.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""Portfolio risk sizing-helper guardrails for credit instruments."""
-
-from __future__ import annotations
-
-import sys
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT_DIR = ROOT / "skills" / "portfolio-risk-management" / "scripts"
-sys.path.insert(0, str(SCRIPT_DIR))
-
-from position_sizing_core import sizing_rows  # noqa: E402
-
-
-class PortfolioRiskSizingCreditBoundaryTests(unittest.TestCase):
-    def test_credit_like_instrument_type_routes_to_credit_markets(self) -> None:
-        data = {
-            "portfolio": {"nav": 100000000, "max_loss_bps_nav": 50},
-            "position": {
-                "security": "ACME 2029 Notes",
-                "instrument_type": "bond",
-                "direction": "long",
-                "entry_price": 95,
-                "downside_price": 80,
-            },
-        }
-
-        with self.assertRaisesRegex(ValueError, "Use Credit Markets"):
-            sizing_rows(data)
-
-    def test_equity_instrument_still_sizes(self) -> None:
-        data = {
-            "portfolio": {"nav": 100000000, "max_loss_bps_nav": 50},
-            "position": {
-                "security": "ACME common stock",
-                "instrument_type": "common equity",
-                "direction": "long",
-                "entry_price": 100,
-                "downside_price": 80,
-                "confidence": "high",
-            },
-        }
-
-        rows, summary = sizing_rows(data)
-
-        self.assertTrue(rows)
-        self.assertGreater(summary["recommended_size_pct_nav"], 0)
-        self.assertNotEqual("insufficient data", summary["raw_binding_constraint"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_logic.py b/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_logic.py
deleted file mode 100644
index 80b2fa0..0000000
--- a/plugins/financial-markets/tests/test_portfolio_risk_management_sizing_logic.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""Executable PM sizing-constraint tests for portfolio-risk-management."""
-
-from __future__ import annotations
-
-import sys
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT_DIR = ROOT / "skills" / "portfolio-risk-management" / "scripts"
-sys.path.insert(0, str(SCRIPT_DIR))
-
-from position_sizing_core import sizing_rows  # noqa: E402
-
-
-class PortfolioRiskSizingLogicTests(unittest.TestCase):
-    def test_short_borrow_squeeze_constraint_can_bind_below_loss_vol_liquidity(self) -> None:
-        rows, summary = sizing_rows(
-            {
-                "portfolio": {
-                    "nav": 100_000_000,
-                    "max_loss_bps_nav": 150,
-                    "target_position_vol_contribution_bps": 500,
-                    "max_single_name_pct_nav": 10,
-                    "borrow_squeeze_capacity_pct_nav": 1.25,
-                },
-                "position": {
-                    "security": "Crowded Short Co",
-                    "ticker": "CSC",
-                    "instrument_type": "common equity",
-                    "direction": "short",
-                    "entry_price": 50,
-                    "downside_price": 65,
-                    "stress_price": 80,
-                    "annualized_volatility_pct": 40,
-                    "confidence": "high",
-                    "short_interest_pct_float": 35,
-                    "days_to_cover": 8,
-                    "borrow_cost_pct": 12,
-                },
-                "liquidity": {"adv_shares": 2_000_000, "price": 50, "required_exit_days": 5},
-            }
-        )
-        lenses = {row["sizing_lens"]: row for row in rows}
-        self.assertIn("borrow_squeeze_capacity", lenses)
-        self.assertEqual("borrow_squeeze_capacity", summary["raw_binding_constraint"])
-        self.assertAlmostEqual(1.25, summary["recommended_size_pct_nav"])
-
-    def test_benchmark_factor_constraint_can_bind_below_simple_limits(self) -> None:
-        rows, summary = sizing_rows(
-            {
-                "portfolio": {
-                    "nav": 100_000_000,
-                    "max_loss_bps_nav": 200,
-                    "target_position_vol_contribution_bps": 500,
-                    "max_single_name_pct_nav": 10,
-                    "benchmark_active_weight_limit_pct": 2.0,
-                    "factor_limit_pct_nav": 1.0,
-                },
-                "position": {
-                    "security": "Benchmark Risk Co",
-                    "ticker": "BRC",
-                    "instrument_type": "common equity",
-                    "direction": "long",
-                    "entry_price": 40,
-                    "downside_price": 30,
-                    "stress_price": 24,
-                    "annualized_volatility_pct": 35,
-                    "confidence": "high",
-                    "current_active_weight_pct": 0.5,
-                    "current_factor_exposure_pct_nav": 0.4,
-                    "factor_exposure_per_1pct_position": 1.0,
-                },
-                "liquidity": {"adv_shares": 2_000_000, "price": 40, "required_exit_days": 5},
-            }
-        )
-        lenses = {row["sizing_lens"]: row for row in rows}
-        self.assertIn("benchmark_active_weight_capacity", lenses)
-        self.assertIn("factor_limit_capacity", lenses)
-        self.assertEqual("factor_limit_capacity", summary["raw_binding_constraint"])
-        self.assertAlmostEqual(0.6, summary["recommended_size_pct_nav"])
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_public_equity_monitoring_contracts.py b/plugins/financial-markets/tests/test_public_equity_monitoring_contracts.py
deleted file mode 100644
index 31113df..0000000
--- a/plugins/financial-markets/tests/test_public_equity_monitoring_contracts.py
+++ /dev/null
@@ -1,170 +0,0 @@
-"""Monitoring, catalyst, and event contract tests for public-equity PM workflows."""
-
-from __future__ import annotations
-
-import importlib.util
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-class PublicEquityMonitoringContractTests(unittest.TestCase):
-    def test_thesis_tracker_has_portfolio_monitoring_contract(self) -> None:
-        text = (
-            (ROOT / "skills" / "thesis-tracker" / "SKILL.md").read_text(encoding="utf-8")
-            + "\n"
-            + (ROOT / "skills" / "thesis-tracker" / "references" / "thesis-schema.md").read_text(
-                encoding="utf-8"
-            )
-        ).lower()
-        for phrase in [
-            "portfolio monitoring mode",
-            "benchmark_weight",
-            "active_weight",
-            "portfolio_role",
-            "index_etf_flow_exposure",
-            "position_action",
-            "company_thesis_status",
-            "security_thesis_readiness",
-            "threshold_origin",
-            "threshold_approval_status",
-            "status_override_rationale_if_applicable",
-            "append-only",
-            "re-underwrite",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_catalyst_calendar_has_index_etf_decision_pressure_contract(self) -> None:
-        text = (
-            (ROOT / "skills" / "catalyst-calendar" / "SKILL.md").read_text(encoding="utf-8")
-            + "\n"
-            + (
-                ROOT
-                / "skills"
-                / "catalyst-calendar"
-                / "references"
-                / "catalyst-taxonomy-and-fields.md"
-            ).read_text(encoding="utf-8")
-            + "\n"
-            + (
-                ROOT / "skills" / "catalyst-calendar" / "references" / "source-and-data-protocol.md"
-            ).read_text(encoding="utf-8")
-        ).lower()
-        for phrase in [
-            "decision_pressure",
-            "index_rebalance",
-            "passive_flow",
-            "flow_vs_adv",
-            "confirmed dates",
-            "inferred windows",
-            "do not export inferred windows as exact",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_event_driven_has_passive_flow_event_contract(self) -> None:
-        text = (
-            (ROOT / "skills" / "event-driven-analyzer" / "SKILL.md").read_text(encoding="utf-8")
-            + "\n"
-            + (
-                ROOT / "skills" / "event-driven-analyzer" / "references" / "source_hierarchy.md"
-            ).read_text(encoding="utf-8")
-            + "\n"
-            + (
-                ROOT / "skills" / "event-driven-analyzer" / "references" / "scenario_math.md"
-            ).read_text(encoding="utf-8")
-        ).lower()
-        for phrase in [
-            "index / etf / passive flow event",
-            "flow_event",
-            "flow-vs-adv",
-            "trade expression menu",
-            "base-rate awareness",
-            "liquidity",
-            "borrow",
-            "exit plan",
-        ]:
-            self.assertIn(phrase, text)
-
-    @unittest.skipUnless(
-        importlib.util.find_spec("openpyxl"), "openpyxl required for XLSX thesis tracker smoke test"
-    )
-    def test_thesis_tracker_xlsx_manifest_is_primary_human_deliverable(self) -> None:
-        script = ROOT / "skills" / "thesis-tracker" / "scripts" / "materialize_thesis_tracker.py"
-        payload = {
-            "dashboard": {
-                "company_issuer": "Example Co",
-                "ticker_security": "EXM",
-                "direction": "long",
-                "current_thesis_status": "intact",
-                "conviction": "medium",
-                "position_rating": "add on proof",
-                "base_bull_bear_value": "$80 / $110 / $55",
-                "next_catalyst": "Q2 earnings",
-                "action_recommendation": "hold",
-            },
-            "sources": [
-                {
-                    "source_id": "S1",
-                    "source_name": "Company release",
-                    "source_type": "company filing",
-                    "date_as_of": "2026-05-01",
-                    "reliability": "high",
-                    "used_for": "dashboard",
-                }
-            ],
-        }
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_path = Path(tmp_dir)
-            input_path = tmp_path / "tracker_input.json"
-            output_dir = tmp_path / "tracker_output"
-            workbook_path = output_dir / "thesis_tracker.xlsx"
-            input_path.write_text(json.dumps(payload), encoding="utf-8")
-
-            result = subprocess.run(
-                [
-                    sys.executable,
-                    str(script),
-                    str(input_path),
-                    "--output-dir",
-                    str(output_dir),
-                    "--xlsx-out",
-                    str(workbook_path),
-                ],
-                cwd=str(ROOT),
-                text=True,
-                capture_output=True,
-                check=False,
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((output_dir / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertEqual(str(workbook_path), run_log["primary_human_deliverable"])
-            self.assertEqual("workbook", run_log["artifact_mode"])
-            self.assertEqual("xlsx_tracker_workbook", run_log["workbook_mode"])
-            self.assertEqual(str(workbook_path), manifest["primary_human_deliverable"])
-            self.assertEqual([str(workbook_path)], manifest["human_deliverables"])
-            self.assertFalse(manifest["support_artifacts_user_visible_default"])
-
-            xlsx_rows = [row for row in run_log["output_manifest"] if row["key"] == "xlsx"]
-            self.assertEqual(1, len(xlsx_rows))
-            self.assertEqual("primary_human_deliverable", xlsx_rows[0]["artifact_role"])
-            self.assertFalse(xlsx_rows[0]["hidden_unless_requested"])
-            self.assertTrue(
-                all(
-                    row["artifact_role"] != "primary_human_deliverable"
-                    for row in run_log["output_manifest"]
-                    if row["key"] != "xlsx"
-                )
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_scenario_skew_contract.py b/plugins/financial-markets/tests/test_scenario_skew_contract.py
deleted file mode 100644
index e911491..0000000
--- a/plugins/financial-markets/tests/test_scenario_skew_contract.py
+++ /dev/null
@@ -1,309 +0,0 @@
-"""Scenario skew outputs for Public Equity valuation work."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SCRIPT = (
-    ROOT
-    / "skills"
-    / "scenario-sensitivity-generator"
-    / "scripts"
-    / "materialize_public_equity_sensitivities.py"
-)
-sys.path.insert(0, str(ROOT))
-OLD_TABLE = "credit_liquidity_stress"
-
-
-class ScenarioSkewContractTests(unittest.TestCase):
-    def run_script(self, args: list[str]) -> subprocess.CompletedProcess[str]:
-        return subprocess.run(
-            [sys.executable, str(SCRIPT), *args],
-            cwd=str(SCRIPT.parent),
-            text=True,
-            capture_output=True,
-            check=False,
-        )
-
-    def test_price_target_scenario_outputs_pm_skew_columns(self) -> None:
-        payload = {
-            "base": {
-                "share_price": 100,
-                "upside_price_target": 140,
-                "base_price_target": 118,
-                "downside_price_target": 82,
-                "upside_probability": 0.3,
-                "base_probability": 0.5,
-                "downside_probability": 0.2,
-                "required_return": 0.12,
-            }
-        }
-        with tempfile.TemporaryDirectory() as tmp:
-            path = Path(tmp) / "assumptions.json"
-            path.write_text(json.dumps(payload), encoding="utf-8")
-            result = self.run_script(
-                ["--input", str(path), "--tables", "price_target_scenario", "--format", "json"]
-            )
-        self.assertEqual(0, result.returncode, result.stderr)
-        data = json.loads(result.stdout)
-        table = data["tables"][0]
-        for column in [
-            "required_return",
-            "expected_return_vs_hurdle",
-            "downside_upside_ratio",
-            "break_even_probability",
-            "skew_label",
-            "action_rule",
-        ]:
-            self.assertIn(column, table["columns"])
-        self.assertTrue(any(row["scenario"] == "probability_weighted" for row in table["rows"]))
-
-    def test_equity_liquidity_downside_replaces_credit_liquidity_table(self) -> None:
-        ok = self.run_script(["--tables", "equity_liquidity_downside", "--format", "json"])
-        self.assertEqual(0, ok.returncode, ok.stderr)
-        self.assertIn("equity_liquidity_downside", ok.stdout)
-        old = self.run_script(["--tables", OLD_TABLE, "--format", "json"])
-        self.assertNotEqual(0, old.returncode)
-        self.assertIn("Unknown table", old.stderr)
-
-    def test_scenario_html_default_and_optional_dashboard_are_pm_decision_contracts(self) -> None:
-        pack = (
-            ROOT / "skills" / "scenario-sensitivity-generator" / "references" / "DASHBOARD_PACK.md"
-        ).read_text(encoding="utf-8")
-        skill = (ROOT / "skills" / "scenario-sensitivity-generator" / "SKILL.md").read_text(
-            encoding="utf-8"
-        )
-        intake = (ROOT / "shared" / "deliverable-intake-policy.md").read_text(encoding="utf-8")
-
-        for phrase in [
-            "expected return versus hurdle",
-            "downside/upside ratio",
-            "break-even probability",
-            "skew label",
-            "PM action",
-            "Source posture",
-            "Missing evidence",
-            "HTML dashboard/report is the human deliverable",
-            "metadata.payload_stage",
-            "Use this pack only when",
-            "polished standalone HTML scenario report",
-            "citation rendering readable",
-        ]:
-            self.assertIn(phrase, pack)
-
-        self.assertIn("../../shared/html-artifact-standard.md", skill)
-        self.assertIn("polished standalone HTML scenario report", skill)
-        self.assertIn("explicitly asks for a standardized dashboard", skill)
-        self.assertIn("sourced discrete-event success/delay/break overlay", skill)
-        self.assertIn("local headless-browser screenshots", skill)
-        self.assertIn("primary transaction documents", skill)
-        self.assertIn("freshest accessible market-data source", skill)
-        self.assertIn("agreement-verified ticking-fee", skill)
-        self.assertIn("exact assumed close or break date", skill)
-        self.assertIn("lead with market-implied probabilities", skill)
-        self.assertIn("probability-weighted value secondary", skill)
-        self.assertIn("freshest accessible market-data source", pack)
-        self.assertIn("sample probability-weighted value secondary", pack)
-        self.assertIn("polished standalone HTML scenario report", intake)
-        self.assertIn("public_equity_investing_dashboard.v1", skill)
-        self.assertIn("references/DASHBOARD_PACK.md", skill)
-        self.assertIn("JSON/Markdown/CSV/run-log support files behind the HTML dashboard", skill)
-
-    def test_materializer_manifest_keeps_tables_as_support_artifacts(self) -> None:
-        with tempfile.TemporaryDirectory() as tmp:
-            output = Path(tmp) / "scenario_tables.json"
-            result = self.run_script(
-                ["--tables", "price_target_scenario", "--format", "json", "--output", str(output)]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr)
-            run_log = json.loads((Path(tmp) / "run_log.json").read_text(encoding="utf-8"))
-            manifest = json.loads((Path(tmp) / "manifest.json").read_text(encoding="utf-8"))
-
-            self.assertIsNone(run_log["primary_human_deliverable"])
-            self.assertFalse(run_log["support_artifacts_user_visible_default"])
-            self.assertFalse(manifest["support_artifacts_user_visible_default"])
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            self.assertIn("base.share_price", run_log["missing_required_inputs"])
-            self.assertTrue(
-                all(
-                    row["artifact_role"] != "primary_human_deliverable"
-                    for row in run_log["output_manifest"]
-                )
-            )
-
-    def test_complete_but_unsourced_scenario_is_capped_at_screen_grade(self) -> None:
-        payload = {
-            "base": {
-                "share_price": 100,
-                "upside_price_target": 140,
-                "base_price_target": 118,
-                "downside_price_target": 82,
-                "upside_probability": 0.3,
-                "base_probability": 0.5,
-                "downside_probability": 0.2,
-                "required_return": 0.12,
-            }
-        }
-        with tempfile.TemporaryDirectory() as tmp:
-            tmp_path = Path(tmp)
-            input_path = tmp_path / "assumptions.json"
-            output = tmp_path / "scenario_tables.json"
-            input_path.write_text(json.dumps(payload), encoding="utf-8")
-
-            result = self.run_script(
-                [
-                    "--input",
-                    str(input_path),
-                    "--tables",
-                    "price_target_scenario",
-                    "--format",
-                    "json",
-                    "--output",
-                    str(output),
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((tmp_path / "run_log.json").read_text(encoding="utf-8"))
-            self.assertEqual("screen-grade", run_log["model_status"])
-            self.assertEqual("screen_grade", run_log["readiness_effect"])
-            self.assertTrue(
-                any("Source/as-of posture" in warning for warning in run_log["warnings"])
-            )
-
-    def test_invalid_probabilities_are_not_decision_ready(self) -> None:
-        payload = {
-            "base": {
-                "share_price": 100,
-                "upside_price_target": 140,
-                "base_price_target": 118,
-                "downside_price_target": 82,
-                "upside_probability": 0.5,
-                "base_probability": 0.5,
-                "downside_probability": 0.5,
-            },
-            "metadata": {"as_of": "2026-05-18", "source_name": "User supplied scenario packet"},
-        }
-        with tempfile.TemporaryDirectory() as tmp:
-            tmp_path = Path(tmp)
-            input_path = tmp_path / "assumptions.json"
-            output = tmp_path / "scenario_tables.json"
-            input_path.write_text(json.dumps(payload), encoding="utf-8")
-
-            result = self.run_script(
-                [
-                    "--input",
-                    str(input_path),
-                    "--tables",
-                    "price_target_scenario",
-                    "--format",
-                    "json",
-                    "--output",
-                    str(output),
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            run_log = json.loads((tmp_path / "run_log.json").read_text(encoding="utf-8"))
-            self.assertEqual("not-decision-ready", run_log["model_status"])
-            self.assertIn("scenario.probabilities", run_log["missing_required_inputs"])
-            self.assertNotEqual("ok", run_log["probability_validation"])
-
-    def test_incomplete_dashboard_payload_is_draft_not_senior_review_ready(self) -> None:
-        from shared.dashboard.qa import validate_payload
-
-        with tempfile.TemporaryDirectory() as tmp:
-            tmp_path = Path(tmp)
-            output_path = tmp_path / "scenario_dashboard.json"
-            run_log_path = tmp_path / "run_log.json"
-
-            result = self.run_script(
-                [
-                    "--tables",
-                    "price_target_scenario",
-                    "--format",
-                    "json",
-                    "--dashboard-output",
-                    str(output_path),
-                    "--run-log",
-                    str(run_log_path),
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            dashboard_payload = json.loads(output_path.read_text(encoding="utf-8"))
-            report = validate_payload(dashboard_payload, profile="draft")
-            self.assertEqual("passed", report["status"], report)
-            self.assertEqual("draft", dashboard_payload["metadata"]["payload_stage"])
-            self.assertEqual("warn", dashboard_payload["metadata"]["citation_policy"])
-            self.assertNotEqual(
-                "senior_review_ready", dashboard_payload["metadata"]["readiness_posture"]
-            )
-            self.assertIn(
-                "base.share_price", dashboard_payload["metadata"]["missing_required_inputs"]
-            )
-
-    def test_materializer_can_emit_production_dashboard_payload(self) -> None:
-        from shared.dashboard.qa import validate_payload
-
-        payload = {
-            "base": {
-                "share_price": 100,
-                "upside_price_target": 145,
-                "base_price_target": 118,
-                "downside_price_target": 78,
-                "upside_probability": 0.25,
-                "base_probability": 0.55,
-                "downside_probability": 0.20,
-                "required_return": 0.12,
-            },
-            "metadata": {
-                "ticker": "ACME",
-                "issuer": "ACME Software",
-                "as_of": "2026-05-18",
-                "source_name": "User supplied scenario packet",
-                "missing_evidence": ["Need current consensus export before circulation."],
-            },
-        }
-        with tempfile.TemporaryDirectory() as tmp:
-            tmp_path = Path(tmp)
-            input_path = tmp_path / "assumptions.json"
-            output_path = tmp_path / "scenario_dashboard.json"
-            input_path.write_text(json.dumps(payload), encoding="utf-8")
-
-            result = self.run_script(
-                [
-                    "--input",
-                    str(input_path),
-                    "--tables",
-                    "price_target_scenario",
-                    "--format",
-                    "json",
-                    "--dashboard-output",
-                    str(output_path),
-                ]
-            )
-
-            self.assertEqual(0, result.returncode, result.stderr + result.stdout)
-            dashboard_payload = json.loads(output_path.read_text(encoding="utf-8"))
-            report = validate_payload(dashboard_payload, profile="production")
-
-            self.assertEqual("public_equity_investing_dashboard.v1", dashboard_payload["kind"])
-            self.assertEqual("scenario_sensitivity", dashboard_payload["mode"])
-            self.assertEqual("strict", dashboard_payload["metadata"]["citation_policy"])
-            self.assertEqual(
-                "senior_review_ready", dashboard_payload["metadata"]["readiness_posture"]
-            )
-            self.assertEqual("passed", report["status"], report)
-            self.assertEqual([], report["hard_failures"], report)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_sector_context_overlay_contract.py b/plugins/financial-markets/tests/test_sector_context_overlay_contract.py
deleted file mode 100644
index 0f52497..0000000
--- a/plugins/financial-markets/tests/test_sector_context_overlay_contract.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Sector overlay mandate and PM judgment contract tests."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SECTOR_SKILL_ROOT = (
-    ROOT / "skills" / "public-equity-investing" / "internal-support" / "sector-context-overlay"
-)
-SECTOR_ROOT = SECTOR_SKILL_ROOT / "references"
-MANDATE_HEADINGS = [
-    "### Long-only",
-    "### Long/short",
-    "### Sell-side / initiation",
-    "### ETF / index",
-    "### Public equity diligence",
-    "### Model / valuation",
-    "### Earnings / catalyst",
-    "### Source discipline",
-]
-
-
-class SectorContextOverlayContractTests(unittest.TestCase):
-    def test_sector_shared_contract_files_exist(self) -> None:
-        for filename in ["pm-judgment-heuristics.md", "research-output-overlay-contract.md"]:
-            path = SECTOR_ROOT / filename
-            self.assertTrue(path.exists(), filename)
-            text = path.read_text(encoding="utf-8")
-            self.assertIn("PM", text)
-
-    def test_all_sector_output_overlays_have_mandate_sections(self) -> None:
-        paths = sorted(SECTOR_ROOT.glob("*/output-overlays.md"))
-        self.assertGreaterEqual(len(paths), 8)
-        for path in paths:
-            text = path.read_text(encoding="utf-8")
-            for heading in MANDATE_HEADINGS:
-                self.assertIn(heading, text, str(path))
-
-    def test_sector_skill_requires_overlay_handoff_fields(self) -> None:
-        text = (SECTOR_SKILL_ROOT / "INTERNAL.md").read_text(encoding="utf-8")
-        for phrase in [
-            "sector_overlay.selected_lens",
-            "issuer_archetype",
-            "mandate_lens",
-            "pm_debate",
-            "dashboard_modules_to_add",
-            "source_gaps",
-        ]:
-            self.assertIn(phrase, text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_sector_overlay_credit_language_classification.py b/plugins/financial-markets/tests/test_sector_overlay_credit_language_classification.py
deleted file mode 100644
index c04222c..0000000
--- a/plugins/financial-markets/tests/test_sector_overlay_credit_language_classification.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Sector overlay credit-language classification for Public Equity Investing."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-SECTOR_ROOT = (
-    ROOT / "skills/public-equity-investing/internal-support/sector-context-overlay/references"
-)
-HANDOFF_OUTPUTS = [
-    "banks/output-overlays.md",
-    "biotech-pharma/output-overlays.md",
-    "consumer-internet-marketplaces/output-overlays.md",
-    "exchanges-market-infrastructure/output-overlays.md",
-    "insurance/output-overlays.md",
-    "oil-gas-ep/output-overlays.md",
-    "reits/output-overlays.md",
-    "saas-subscription-software/output-overlays.md",
-]
-
-
-def read(relative: str) -> str:
-    return (SECTOR_ROOT / relative).read_text(encoding="utf-8")
-
-
-class SectorOverlayCreditLanguageClassificationTests(unittest.TestCase):
-    def test_output_overlays_mark_credit_work_as_handoff(self) -> None:
-        for relative in HANDOFF_OUTPUTS:
-            text = read(relative)
-            self.assertIn("Credit Markets handoff", text, relative)
-            self.assertIn("Credit Markets owns debt-security", text, relative)
-            self.assertIn(
-                "Do not turn the sector overlay into a debt-security recommendation", text, relative
-            )
-
-    def test_sector_modeling_routes_debt_security_underwriting_to_credit_markets(self) -> None:
-        checks = {
-            "banks/modeling-rules.md": "Preferred/AT1/T2, holdco debt, and opco debt require Credit Markets",
-            "reits/modeling-rules.md": "Route preferred/debt issuing entity",
-            "insurance/modeling-rules.md": "Route surplus notes, hybrids, preferreds, debt",
-            "oil-gas-ep/modeling-rules.md": "Route unsecured-debt PDP cushion",
-            "biotech-pharma/modeling-rules.md": "route claim-seniority, collateral-quality, and recovery underwriting to Credit Markets",
-            "saas-subscription-software/modeling-rules.md": "Route debt/convert instrument underwriting",
-            "consumer-internet-marketplaces/modeling-rules.md": "route underwriting to Credit Markets",
-        }
-        for relative, phrase in checks.items():
-            self.assertIn(phrase, read(relative), relative)
-
-    def test_legitimate_equity_sector_language_is_preserved(self) -> None:
-        banks = read("banks/kpi-cheat-sheet.md")
-        reits = read("reits/kpi-cheat-sheet.md")
-        biotech = read("biotech-pharma/modeling-rules.md")
-        event = (ROOT / "skills/event-driven-analyzer/references/merger_arb_playbook.md").read_text(
-            encoding="utf-8"
-        )
-        self.assertIn("loan yield", banks)
-        self.assertIn("leasing spreads", reits)
-        self.assertIn("manufacturing", biotech)
-        self.assertIn("spread", event)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_source_data_qa_credit_handoffs.py b/plugins/financial-markets/tests/test_source_data_qa_credit_handoffs.py
deleted file mode 100644
index 68eddd5..0000000
--- a/plugins/financial-markets/tests/test_source_data_qa_credit_handoffs.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Credit Markets routing for source/data/QA support skills."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-TARGET_PATHS = [
-    "shared/equity-research-support-standard.md",
-    "skills/public-equity-investing/internal-support/financial-source-of-truth/INTERNAL.md",
-    "skills/public-equity-investing/internal-support/financial-source-of-truth/references/evidence-hierarchy.md",
-    "skills/public-equity-investing/internal-support/financial-source-of-truth/references/fact-assumption-labeling.md",
-    "skills/financials-normalizer/SKILL.md",
-    "skills/financials-normalizer/references/integration-guide.md",
-    "skills/public-equity-investing/internal-support/excel-data-cleaner/INTERNAL.md",
-    "skills/public-equity-investing/internal-support/excel-data-cleaner/references/domain-playbook.md",
-    "skills/deck-report-qc/SKILL.md",
-    "skills/public-equity-investing/internal-support/style-guide-adapter/references/writing-style-adaptation.md",
-]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class SourceDataQACreditHandoffTests(unittest.TestCase):
-    def test_credit_terms_route_or_remain_equity_context(self) -> None:
-        combined = "\n".join(read(path) for path in TARGET_PATHS)
-        for phrase in [
-            "Credit Markets handoff",
-            "equity-risk context",
-            "common-equity",
-            "route to Credit Markets",
-            "CDS/spread",
-            "debt-security",
-        ]:
-            self.assertIn(phrase, combined)
-
-    def test_old_public_credit_ownership_phrases_are_absent(self) -> None:
-        combined = "\n".join(read(path) for path in TARGET_PATHS)
-        for phrase in [
-            "public-credit pack",
-            "IC/credit recommendation",
-            "Public " + "Credit / Distressed",
-            "public_credit_distressed",
-            "Classify job. Public " + "markets, Credit Markets",
-            "Credit Markets trading sheet",
-        ]:
-            self.assertNotIn(phrase, combined)
-
-    def test_public_credit_memo_mentions_are_handoff_only(self) -> None:
-        combined = "\n".join(read(path) for path in TARGET_PATHS).lower()
-        for line in combined.splitlines():
-            if "public-credit memo" in line or "credit memo" in line:
-                self.assertTrue(
-                    "route" in line or "credit markets" in line or "not style-adapt" in line,
-                    line,
-                )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_stale_public_markets_language.py b/plugins/financial-markets/tests/test_stale_public_markets_language.py
deleted file mode 100644
index e314dd0..0000000
--- a/plugins/financial-markets/tests/test_stale_public_markets_language.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""Stale legacy branding regressions for Public Equity Investing."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-LEGACY_MARKERS = [
-    "Public " + "Markets",
-    "public-" + "markets",
-    "public_" + "markets",
-]
-
-SKIP_SUFFIXES = {".pyc", ".png", ".jpg", ".jpeg", ".gif", ".xlsx", ".xls", ".pdf", ".ico"}
-
-
-def iter_text_files() -> list[Path]:
-    files: list[Path] = []
-    for path in ROOT.rglob("*"):
-        if not path.is_file() or path.suffix in SKIP_SUFFIXES or "__pycache__" in path.parts:
-            continue
-        files.append(path)
-    return files
-
-
-class StalePublicMarketsLanguageTests(unittest.TestCase):
-    def test_no_legacy_branding_in_plugin_tree(self) -> None:
-        failures: list[str] = []
-        for path in iter_text_files():
-            try:
-                text = path.read_text(encoding="utf-8")
-            except UnicodeDecodeError:
-                continue
-            for marker in LEGACY_MARKERS:
-                if marker in text:
-                    failures.append(f"{path.relative_to(ROOT)}: {marker}")
-                    break
-        self.assertEqual([], failures)
-
-    def test_plugin_manifest_uses_replacement_branding(self) -> None:
-        plugin_json = (ROOT / ".codex-plugin/plugin.json").read_text(encoding="utf-8")
-        self.assertIn("public-equity-investing", plugin_json)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_style_guide_adapter_equity_artifact_contract.py b/plugins/financial-markets/tests/test_style_guide_adapter_equity_artifact_contract.py
deleted file mode 100644
index 7046e05..0000000
--- a/plugins/financial-markets/tests/test_style_guide_adapter_equity_artifact_contract.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Equity artifact style contract for style-guide-adapter."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class StyleGuideAdapterEquityArtifactContractTests(unittest.TestCase):
-    def test_style_is_not_evidence(self) -> None:
-        text = read(
-            "skills/public-equity-investing/internal-support/style-guide-adapter/INTERNAL.md"
-        )
-        self.assertIn("Style is not evidence", text)
-        self.assertIn(
-            "not factual support for prices, consensus, estimates, financials, ratings, targets, or investment claims",
-            text,
-        )
-
-    def test_writing_style_targets_equity_artifacts(self) -> None:
-        text = read(
-            "skills/public-equity-investing/internal-support/style-guide-adapter/references/writing-style-adaptation.md"
-        )
-        for phrase in [
-            "buy-side PM memos",
-            "sell-side research notes",
-            "public-equity decks",
-            "ETF/index diligence notes",
-            "client equity deliverables",
-        ]:
-            self.assertIn(phrase, text)
-        self.assertIn("do not style-adapt a public-credit memo as if it were locally owned", text)
-
-    def test_output_template_preserves_source_posture(self) -> None:
-        text = read(
-            "skills/public-equity-investing/internal-support/style-guide-adapter/references/output-templates.md"
-        )
-        for phrase in [
-            "source posture preserved",
-            "numbers/citations preserved",
-            "caveats preserved",
-            "confidence labels preserved",
-            "substantive edits separated from style edits",
-        ]:
-            self.assertIn(phrase, text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/financial-markets/tests/test_unsupported_connector_promises.py b/plugins/financial-markets/tests/test_unsupported_connector_promises.py
deleted file mode 100644
index 0a98127..0000000
--- a/plugins/financial-markets/tests/test_unsupported_connector_promises.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""Connector/provider honesty regressions for Public Equity support skills."""
-
-from __future__ import annotations
-
-import unittest
-from pathlib import Path
-
-ROOT = Path(__file__).resolve().parents[1]
-
-
-def read(relative_path: str) -> str:
-    return (ROOT / relative_path).read_text(encoding="utf-8")
-
-
-class UnsupportedConnectorPromisesTests(unittest.TestCase):
-    def test_shared_standard_forbids_implied_vendor_access(self) -> None:
-        text = read("shared/equity-research-support-standard.md")
-        for phrase in [
-            "Never imply live Bloomberg",
-            "unless that connector/app/tool is actually callable",
-            "use user-provided exports",
-            "request the export",
-            "missing_required_source",
-        ]:
-            self.assertIn(phrase, text)
-
-    def test_source_protocol_requires_callable_or_exported_provider_data(self) -> None:
-        text = read("skills/financials-normalizer/references/source-protocol.md")
-        self.assertIn("Callable connected internal system", text)
-        self.assertIn("Trusted financial data provider export or callable provider connector", text)
-        self.assertIn("Do not imply live access", text)
-
-    def test_style_sources_do_not_promise_connected_apps(self) -> None:
-        text = (
-            read("skills/public-equity-investing/internal-support/style-guide-adapter/INTERNAL.md")
-            + "\n"
-            + read(
-                "skills/public-equity-investing/internal-support/style-guide-adapter/references/source-and-safety.md"
-            )
-        )
-        self.assertNotIn("available in connected apps", text)
-        self.assertIn("callable runtime apps/connectors", text)
-        self.assertIn("Do not imply live access", text)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/product-design/.app.json b/plugins/product-design/.app.json
deleted file mode 100644
index 39a767e..0000000
--- a/plugins/product-design/.app.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "apps": {
-    "sites": {
-      "id": "REPLACE_WITH_SITES_APP_OR_CONNECTOR_ID"
-    }
-  }
-}
diff --git a/plugins/product-design/.codex-plugin/plugin.json b/plugins/product-design/.codex-plugin/plugin.json
index 5562f81..f3339ae 100644
--- a/plugins/product-design/.codex-plugin/plugin.json
+++ b/plugins/product-design/.codex-plugin/plugin.json
@@ -1,9 +1,10 @@
 {
   "name": "product-design",
-  "version": "0.1.41",
+  "version": "0.1.50",
   "description": "The Product Design plugin is built for turning early ideas into prototypes teams can review. It starts design and build work by confirming the brief, then helps teams explore product directions, audit user flows, prototype from a live URL, and make static screenshots interactive.",
   "author": {
     "name": "OpenAI",
+    "email": "support@openai.com",
     "url": "https://openai.com/"
   },
   "homepage": "https://github.com/openai/role-specific-plugins",
@@ -11,6 +12,12 @@
   "license": "MIT",
   "keywords": [
     "prototype",
+    "product design",
+    "prototyping",
+    "user experience",
+    "wireframes",
+    "design systems",
+    "figma",
     "frontend",
     "ux-research",
     "ux-audit",
@@ -18,17 +25,15 @@
     "research",
     "context-gathering",
     "ideation",
-    "image-generation",
-    "prototype"
+    "image-generation"
   ],
   "skills": "./skills/",
-  "apps": "./.app.json",
   "interface": {
     "displayName": "Product Design",
     "shortDescription": "Explore and prototype ideas",
     "longDescription": "Turn early ideas into prototypes teams can review. Explore product directions, audit user flows, research user friction, prototype from a live URL, and make static screenshots interactive. Start with a written brief, URL, screenshot, or existing design, then compare visual directions before building.\n\nUse available browser, Figma, Canva, image generation, and hosting tools to gather references, create concepts, review the result, and carry the work forward with your team.",
     "developerName": "OpenAI",
-    "category": "Design",
+    "category": "Creativity",
     "capabilities": [
       "Interactive",
       "Read",
@@ -40,7 +45,7 @@
     "defaultPrompt": [
       "Help me get started",
       "Turn this product idea into three visual directions",
-      "Clone this URL into an editable prototype"
+      "Design a landing page for my business"
     ],
     "composerIcon": "./assets/composerIcon.svg",
     "logo": "./assets/logo.png",
diff --git a/plugins/product-design/README.md b/plugins/product-design/README.md
index aac28be..2c3fdcc 100644
--- a/plugins/product-design/README.md
+++ b/plugins/product-design/README.md
@@ -6,35 +6,33 @@ Turn early product ideas, live URLs, and static screenshots into prototypes team
 
 Use Product Design when you want to understand an existing experience, explore a new direction, or create a prototype that people can interact with and review. You can start from a written brief, screenshots, a design image, a live URL, an existing prototype, or product references you have saved for future work, such as product URLs, Figma files, screenshots, codebase paths, or design tokens.
 
-## Optional setup
+## Get started
 
-Ask Codex:
+Try asking:
 
 `@Product Design Help me get started`
 
-Product Design has a short optional setup flow that helps save useful product context such as product URLs, relevant Figma files, screenshots, codebase paths, Storybook links, tokens, and brand assets.
+The plugin will walk you through how to save useful product context such as product URLs, relevant Figma files, screenshots, codebase paths, Storybook links, tokens, and brand assets.
 
 Already have a task in mind? Skip setup and ask for the workflow you need directly.
 
 ## Example workflows
 
-Hero workflows are bolded.
-
 | Workflow | Try this | Skill | Result |
 | --- | --- | --- | --- |
-| **Prototype a new idea** | `Build a clickable prototype for this product idea` | `prototype` | Three visual directions to choose from, followed by a runnable prototype based on your selection |
-| **Recreate a live surface** | `Clone this URL into an editable prototype` | `url-to-code` | An editable local prototype grounded in the visible source |
+| Prototype a new idea | `Build a clickable prototype for this product idea` | `ideate` → `image-to-code` | Three visual directions to choose from, followed by a runnable prototype based on your selection |
+| Recreate a live surface | `Clone this URL into an editable prototype` | `url-to-code` | An editable local prototype grounded in the visible source |
 | Implement a selected design | `Turn this selected mockup into a responsive prototype` | `image-to-code` | A faithful, interactive implementation of the design you selected |
 | Explore visual directions | `Turn this product idea into three visual directions` | `ideate` | Distinct concepts you can compare before choosing what to build |
-| Redesign an existing screen | `Redesign this settings screen and show me three directions before building` | `prototype` | Three grounded redesign options, followed by a prototype only after you choose a direction |
-| **Audit a product flow** | `Audit this onboarding flow and identify the highest-impact UX and accessibility issues` | `audit` | Screenshot-backed findings tied to the steps in the experience |
+| Redesign an existing screen | `Redesign this settings screen and show me three directions before building` | `ideate` → `image-to-code` | Three grounded redesign options, followed by a prototype only after you choose a direction |
+| Audit a product flow | `Audit this onboarding flow and identify the highest-impact UX and accessibility issues` | `audit` | Screenshot-backed findings tied to the steps in the experience |
 | Research user friction | `Research the biggest UX problems users are reporting for this product` | `research` | A source-grounded view of user pain, severity, confidence, and product opportunities |
 | Critique a prototype | `Critique this prototype and identify the highest-impact design issues` | `audit` | An evidence-grounded UX, design, and accessibility review |
 | Share a prototype | `Share this prototype with the team` | `share` | A working shareable URL after you choose a hosting target |
 
 ### Tip: Refine a prototype with Annotations
 
-When editing a prototype in the in-app browser, you can use the Annotations tool to point at the exact elements you want to change. Add comments on the UI or adjust CSS values directly, then send the annotations back to Codex for fast, focused edits.
+When editing a prototype in the in-app browser, you can use the Annotations tool to point at the exact elements you want to change. Add comments on the UI or adjust CSS values directly, then send the annotations back for fast, focused edits.
 
 ## Integrations
 
diff --git a/plugins/product-design/agents/openai.yaml b/plugins/product-design/agents/openai.yaml
index 079ed87..ad35a65 100644
--- a/plugins/product-design/agents/openai.yaml
+++ b/plugins/product-design/agents/openai.yaml
@@ -1,4 +1,4 @@
 interface:
   display_name: "Product Design"
-  short_description: "Product Design audits, research, brief confirmation, ideation, prototypes, and visual QA"
-  default_prompt: "For designing, building, or extending UI work, start by confirming the design brief. Help with product-flow audits, design research, ideation, prototyping, or prototype QA."
+  short_description: "Product Design audits, research, minimum briefs, ideation, prototypes, and visual QA"
+  default_prompt: "For designing, building, or extending UI work, resolve the minimum design brief and continue. Help with product-flow audits, design research, ideation, prototyping, or prototype QA."
diff --git a/plugins/product-design/package.json b/plugins/product-design/package.json
index fc72f0b..c618523 100644
--- a/plugins/product-design/package.json
+++ b/plugins/product-design/package.json
@@ -1,9 +1,9 @@
 {
   "name": "product-design",
-  "version": "0.1.41",
+  "version": "0.1.50",
   "private": true,
   "type": "module",
-  "description": "The Product Design plugin is built for turning early ideas into prototypes teams can review. It starts design and build work by confirming the brief, then helps teams explore product directions, audit user flows, prototype from a live URL, and make static screenshots interactive.",
+  "description": "The Product Design plugin is built for turning early ideas into prototypes teams can review. It starts design and build work by resolving the minimum brief, then helps teams explore product directions, audit user flows, prototype from a live URL, and make static screenshots interactive.",
   "repository": "https://github.com/openai/role-specific-plugins/tree/main/plugins/product-design",
   "scripts": {
     "prototype:bootstrap": "node scripts/bootstrap-prototype.mjs"
diff --git a/plugins/product-design/references/browser-order.md b/plugins/product-design/references/browser-order.md
deleted file mode 100644
index 278371f..0000000
--- a/plugins/product-design/references/browser-order.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Browser Order
-
-Use the first available browser in this order unless the user names a preferred browser to use:
-
-1. Browser skill.
-2. Chrome skill.
-3. Playwright, only after asking the user.
-
-Use the selected browser for source capture, local preview, screenshots, DOM, styles, layout, assets, and verification. Explore the tools and what they provide if you're not familiar with them.
diff --git a/plugins/product-design/references/critical-overrides.md b/plugins/product-design/references/critical-overrides.md
index f4d98fb..e9f1a8b 100644
--- a/plugins/product-design/references/critical-overrides.md
+++ b/plugins/product-design/references/critical-overrides.md
@@ -13,24 +13,17 @@ These rules override generic assistant defaults for Product Design work.
 - Ideation, prototypes, audits, clones, and critiques should match the saved product context unless the user asks for something different.
 - When a workflow needs visual grounding, attach or include relevant saved screenshots, reference images, tokens, design language, and component references in ImageGen, ideation, prototype, audit, and critique work.
 
-## Design Brief Gate
-
-- For Product Design requests that design, build, prototype, clone, redesign, extend, or generate product UI directions, run `$get-context` before `$ideate`, `$prototype`, `$url-to-code`, or `$image-to-code`.
-- `$get-context` can run in question mode or playback mode. If required product, visual, or interactivity details are missing, ask for them. If those details are already present, play back the brief in a pithy format before moving to the next workflow.
-- Do not proceed to ideation or implementation until the brief has been played back and either confirmed by the user or already confirmed earlier in the thread.
-- A confirmed design brief is not a visual target. Use the confirmed brief as input to `$ideate` unless the user has already provided a concrete visual source such as a URL, screenshot, image, mockup, or Figma frame.
-- After the user approves the design brief and the next step is an involved app, prototype, clone, redesign, or build, send one short expectation-setting note before starting work. Say that involved builds often take about 10-15 minutes, ambitious builds can take longer, and invite the user to grab coffee or tend to other work while Product Design keeps moving. Simpler static prototypes can be faster, often 5-10 minutes. Keep it warm and lightly playful, not apologetic or over-explanatory. Skip this note for tiny static changes, quick audits, simple research, setup-only, or share-only requests.
-
 ## How to communicate
 
 - Follow [communication-protocol](communication-protocol.md)
 
 ## Build Handoff
 
-- After a successful app, prototype, clone, redesign, or image-to-code build, return the prototype link first. This handoff is not blocked by sharing setup.
-- Add one short iteration nudge: ask the user what they want changed next, and mention that annotations are useful for pointed edits.
+- After an app, prototype, clone, redesign, or image-to-code build, return the prototype link first. This handoff is not blocked by sharing setup.
+- After the link, say: `I've finished building. Let me know if I can tighten anything up or build out more functionality.`
+- Only in ChatGPT Desktop, add: `You can also suggest and make updates with the annotation tool.` Do not mention the annotation tool in ChatGPT Work Mode.
 - Add one short share nudge. Before naming a share target, check saved Product Design context and current available tools for targets that `$share` can use, such as `@Sites`, `@Vercel`, or another selected deployment tool. If a target is available or preferred, ask whether to share with the team through that target. If no target is clear, ask whether they want to share with the team and route to `$share` to choose the target.
-- Keep the wording plain and human. Example: `Tell me what you'd like to change and we'll iterate. Annotations are great for pointed edits too. Want to share this with your team via @Sites?`
+- Keep the wording plain and human.
 
 ## Re-read this file
 
@@ -45,7 +38,7 @@ These rules override generic assistant defaults for Product Design work.
 - Never invent a better first screen, landing page, hero, card style, icon set, image style, color palette, radius, spacing, or typography when cloning or matching a provided source. Match the source.
 - Check the work like a senior designer. Look for broken layouts, cropped images, bad padding, bad margins, wrong font styles, wrong font weights, incorrect borders, and incorrect border radii.
 - Screenshots are not QA by themselves. Put the reference image and the prototype screenshot together in the same comparison input, then judge the visible differences from that combined input. Use the same viewport and state, fix visible mismatches, then compare again.
-- Build the current screen's interactions. Sidebars, menus, navs, forms, inputs, buttons, tabs, dropdowns, drawers, modals, popovers, carousels, tooltips, filters, toggles, and first-screen controls must be functional and populated with realistic mock data. Do not build new pages or routes unless the user asks for them.
+- Bring the app or website's core experience to life. Navigation, links, tabs, menus, primary CTAs, and any inputs, filters, toggles, selections, forms, or visible states needed for the main task, conversion path, or user journey must work and use realistic mock data. Controls outside the core experience may be visual-only. Do not build new pages or routes unless the user asks for them.
 
 ## Browser user
 
diff --git a/plugins/product-design/skills/prototype/references/existing-codebase-edits.md b/plugins/product-design/references/existing-codebase-edits.md
similarity index 61%
rename from plugins/product-design/skills/prototype/references/existing-codebase-edits.md
rename to plugins/product-design/references/existing-codebase-edits.md
index 658eaee..88fdfc1 100644
--- a/plugins/product-design/skills/prototype/references/existing-codebase-edits.md
+++ b/plugins/product-design/references/existing-codebase-edits.md
@@ -1,18 +1,18 @@
 # Existing App or Website Edits
 
-Use this only when editing an existing local apps or websites directly.
+Use this only when editing an existing local app or website directly.
 
 1. Inspect the running app and source code before changing it.
 2. Follow local `AGENTS.md` and any equivalent local design notes when they exist.
-3. Prefer the existing codebase's design system (tokens, style variables, components, layout patterns, and interaction patterns).
-4. Use existing layouts and components as much as you can.
+3. Prefer the existing codebase's design system: tokens, style variables, components, layout patterns, and interaction patterns.
+4. Reuse existing layouts and components as much as you can.
 5. Use realistic mock data that a user would expect to see in practice.
 6. Record durable design decisions in `AGENTS.md` or local design notes when they should guide future edits.
 
 Do not:
 
-- Invent a new design system when the existing prototype has one
+- Invent a new design system when the existing app has one
 - Replace the app structure unless the user asks for a redesign
 - Add made-up features just to fill the UI
 - Use text, punctuation, emoji, CSS shapes, placeholder boxes, or handcrafted SVGs as asset or icon stand-ins
-- Add long explanations inside the prototype UI.
+- Add long explanations inside the UI
diff --git a/plugins/product-design/skills/audit/SKILL.md b/plugins/product-design/skills/audit/SKILL.md
index 2e795d2..16f32b8 100644
--- a/plugins/product-design/skills/audit/SKILL.md
+++ b/plugins/product-design/skills/audit/SKILL.md
@@ -1,16 +1,16 @@
 ---
 name: audit
-description: "Audit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, placing them in Figma or a local folder, then reporting UX, design, and accessibility findings from that evidence. Use when the user asks to audit, critique, review, inspect, assess, or evaluate a product experience."
+description: "Audit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product experience."
 ---
 
 # Audit
 
-Use this skill when the user wants to audit or critique a product flow, journey, funnel, onboarding path, checkout path, settings path, screen, or other product experience.
+Use this skill when the user wants to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product flow, journey, funnel, onboarding path, checkout path, settings path, screen, or other product experience.
 
 The output is not a loose opinion. The output is:
 
 - Screenshots of the flow
-- Those screenshots placed in the chosen destination
+- Those screenshots rendered inline in the report
 - A numbered step list
 - UX and design findings tied to steps or screenshots
 - Accessibility risks tied to steps or screenshots
@@ -35,22 +35,21 @@ Before auditing:
 
 1. Identify the product or surface.
 2. Identify the flow or task.
-3. Identify the destination.
-4. Choose the capture tool.
-5. Capture the flow.
-6. Save, inspect, place, and annotate each screenshot.
+3. Choose the capture tool.
+4. Capture the flow.
+5. Save and inspect each screenshot.
+6. Return the audit inline with the accepted screenshots.
 
-Destination rules:
+Output rules:
 
-- If the user names Figma, use Figma.
-- If the user names a local folder, use that folder.
-- If the destination is missing, ask one question: "Should I put this in Figma or a local folder?"
+- Default to a concise inline report with screenshots rendered in the chat.
+- Saving screenshots and notes in the workspace is an internal implementation detail. Do not ask the user to choose a local folder.
+- If the user explicitly asks for Figma, create the Figma audit board in addition to the inline report.
+- After the inline report, ask once: `Want me to plot this out in Figma with the screenshots and notes?`
 
 Capture rules:
 
-- Use the Codex in-app Browser first.
-- If Browser cannot access, control, or screenshot the target, use Chrome [Internal].
-- If Browser and Chrome cannot complete the capture, ask before using Playwright as the fallback.
+- Follow the Browser Choice rule in [$index](../index/SKILL.md#browser-choice).
 - If none of those can capture valid screenshots or control the flow, stop and report the blocker.
 
 Browser capture order:
@@ -71,13 +70,13 @@ Figma rules:
 - Figma is not done until the screenshots are visibly placed in the Figma output.
 - After placing screenshots in Figma, render or inspect the board and confirm every flow step has the correct screenshot visible in the correct card.
 - If an image is missing, misplaced, blank, or only uploaded as an unused asset, fix it before handoff.
-- If Figma tools cannot create files or place images, save the audit locally and explain the missing Figma capability.
+- If Figma tools cannot create files or place images, return the inline audit and explain the missing Figma capability.
 
 Evidence rules:
 
 - Use only evidence captured in the current audit run.
 - Do not use memory, prior chats, old traces, cached screenshots, or prior generated artifacts as audit evidence unless the user explicitly provides them.
-- Do not audit until the product, flow, destination, and capture tool are known.
+- Do not audit until the product, flow, and capture tool are known.
 - Do not claim full accessibility compliance from screenshots alone.
 
 ## Capture And Audit The Flow
@@ -109,15 +108,16 @@ For every step:
 9. In the notes, report strengths, UX issues, accessibility risks, and any limits that made the step difficult to audit.
 10. Save accepted screenshots with numbered names, such as `01-start.png`, `02-form-filled.png`, and `03-confirmation.png`.
 11. Inspect the saved screenshot file before upload or handoff.
-12. Add each accepted screenshot to the chosen destination immediately.
-13. Add the notes for that step to the chosen destination immediately.
+12. Keep each accepted screenshot and its notes together for the final inline report.
+13. If the user explicitly requested Figma, add each accepted screenshot and its notes to the board immediately.
 
-If the destination is a local folder:
+Default inline report:
 
-- Save screenshots in that folder.
-- Save the notes in a file that can be shared at the end.
+- Render accepted screenshots in flow order.
+- Keep the report pithy: overall verdict, numbered steps, highest-impact changes, and evidence limits.
+- Tie every finding to the screenshot or step that supports it.
 
-If the destination is Figma:
+If the user explicitly requested Figma:
 
 - Place screenshots in order, left to right on the same row, with 200px between each one. Go to a new row every 15 screenshots, and separate those rows by 600px.
 - Underneath the screenshot, add text with the Step number and its name, and notes.
@@ -128,8 +128,8 @@ Acceptance checks:
 
 - Every important step in the requested flow has a valid screenshot or a named blocker.
 - Screenshots are saved in order.
-- Screenshots are placed in the chosen destination as they are captured.
-- Notes are placed in the chosen destination as they are written.
+- Screenshots are rendered inline in the final report.
+- When Figma was explicitly requested, screenshots and notes are placed in the board as they are captured.
 - Every note points to the screenshot or step it describes.
 - Notes explain strengths, UX issues, accessibility risks, and evidence limits when those apply.
 - Accessibility risks say what can be seen from screenshots and what still needs testing.
@@ -140,9 +140,10 @@ Blockers:
 - The flow cannot be completed.
 - A required step cannot be screenshotted.
 - The source changes in a way that makes the flow unclear.
-- Screenshots cannot be saved or placed in the destination.
-- Notes cannot be written or placed in the destination.
+- Screenshots cannot be saved or rendered inline.
+- Notes cannot be written.
 - The requested claim would require evidence that screenshots cannot provide.
+- Do not claim an audit if the actual flow could not be accessed and captured. Help Center pages, web searches, and other indirect evidence are research, not an audit.
 
 ## Final Response
 
diff --git a/plugins/product-design/skills/audit/agents/openai.yaml b/plugins/product-design/skills/audit/agents/openai.yaml
index 5fa8d11..b40ac32 100644
--- a/plugins/product-design/skills/audit/agents/openai.yaml
+++ b/plugins/product-design/skills/audit/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Audit or critique product UX and design from captured screenshots"
   default_prompt: "Audit this product flow and report UX, design, and accessibility findings."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/product-design/skills/design-qa/SKILL.md b/plugins/product-design/skills/design-qa/SKILL.md
index a2a0a8a..d21728c 100644
--- a/plugins/product-design/skills/design-qa/SKILL.md
+++ b/plugins/product-design/skills/design-qa/SKILL.md
@@ -30,6 +30,17 @@ Do not write the QA review from memory, code, or file paths alone. Open or captu
 
 Do not pretend separate image views are side-by-side comparison. Put the source image and the implementation screenshot together in the same comparison input, then judge the visible differences from that combined input.
 
+Design QA is an iteration loop. The first comparison may pass only when it finds no actionable P0/P1/P2 differences and no visual fixes are made in response.
+
+When a comparison finds any P0/P1/P2 issue:
+
+- Record the finding and keep the current result blocked.
+- Apply the fix.
+- Capture the revised implementation at the same viewport and state.
+- Compare the revised capture against the source again.
+
+A later pass must identify the earlier findings, the fixes made, and the post-fix visual evidence. Build, dependency, lint, deployment, and preview troubleshooting do not count as design-QA iterations.
+
 1. Identify the comparison target.
    - Determine the source design: Figma node, image, design board, screenshot, spec, or mockup.
    - Determine the implementation: local URL, deployed URL, app screen, component, screenshot, or code-rendered view.
@@ -38,7 +49,7 @@ Do not pretend separate image views are side-by-side comparison. Put the source
 
 2. Capture evidence.
    - For Figma, use design context and screenshot tools when available.
-   - For web/app implementations, open the target in a browser and capture screenshots at the intended viewport.
+   - For web/app implementations, follow the Browser Choice rule in [index](../index/SKILL.md#browser-choice). In ChatGPT Work Mode, follow the active build skill's "Previewing prototypes in ChatGPT Work Mode" section before opening a local implementation. Then capture screenshots at the intended viewport.
    - Capture additional states when relevant: mobile/desktop, hover/focus/active, empty/loading/error, dark/light, and key responsive breakpoints.
    - Save paths or URLs for screenshots when available so findings can cite evidence.
    - Capturing screenshots is not enough. Put the source image and the implementation screenshot together in the same comparison input before judging.
@@ -87,6 +98,8 @@ Every QA report must explicitly evaluate these surfaces:
 - `P2`: Moderate visual drift, inconsistent state, responsive issue, or fixable polish gap.
 - `P3`: Minor refinement that improves fidelity but does not block acceptance.
 
+Treat viewport overflow that hides persistent app controls, and mismatches that materially change above-the-fold content, major-region proportions, text wrapping, or interface density, as P2 or higher.
+
 ## Output Format
 
 Use this structure unless the user asks otherwise:
@@ -122,9 +135,11 @@ When this skill is used before handoff, save the latest QA report as project-roo
 - full-view comparison evidence
 - focused region comparison evidence, or why it was not needed
 - findings
-- patches made since the previous QA pass
+- comparison history for every P0/P1/P2 iteration: earlier findings, fixes made, and post-fix visual evidence
 - final result
 
+For ChatGPT Work Mode builds, `design-qa.md` must include the browser-rendered implementation screenshot, viewport, primary interactions tested, console errors checked, and final result. If browser-rendered evidence is missing, `final result` is `blocked`.
+
 `final result` must be exactly `passed` or `blocked`.
 
 Use `passed` when there are no actionable P0/P1/P2 findings. P3 findings may remain as follow-up polish.
diff --git a/plugins/product-design/skills/get-context/SKILL.md b/plugins/product-design/skills/get-context/SKILL.md
index 178d63a..14d8d3d 100644
--- a/plugins/product-design/skills/get-context/SKILL.md
+++ b/plugins/product-design/skills/get-context/SKILL.md
@@ -1,24 +1,21 @@
 ---
 name: get-context
-description: "Mandatory design-brief gate for Product Design build and design workflows. Use before ideation, prototyping, image-to-code builds, redesigns, or product UI work to clarify missing product, visual, and interactivity context or play back the supplied brief before proceeding."
+description: "Mandatory design-brief gate for clarifying the product and outcome. Use before ideation, image-to-code builds, redesigns, or product UI work to clarify missing product information and play back the brief before proceeding."
 ---
 
 # Get Context
 
-Gather only the context needed for the next design action. This skill resolves or confirms the design brief; it does not implement UI or create durable design artifacts.
 
 Run this skill at the start of Product Design requests that ask to design, build, prototype, clone, redesign, extend, or generate product UI directions.
 
-Use question mode when any of the following are unclear:
+Use question mode to clarify the following:
 
-- what product, site, feature, workflow, component, or screen is being designed
-- what visual source should determine how it looks
-- what concrete preferences or avoidances should shape visual exploration when no source exists
-- what level of interactivity the user expects
+- what product, site, feature, workflow, component, or screen is being designed, redesigned, or extended
+- what the feature, change, app, or website should help the user do
 
-Use playback mode when the user already provided the needed details. In playback mode, do not re-ask answered questions; play back the brief in a pithy format and name the next workflow.
+Do not re-ask answered questions. When both are clear, play back the brief and defaults in one pithy note, name the next workflow, and continue in the same turn. Playback is not a request for approval. The user can course-correct style, scope, or interactivity at any point.
 
-Hard boundary: do not implement UI, scaffold a prototype, start a server, or create files while context is still missing.
+Hard boundary: do not implement UI, scaffold a prototype, start a server, or create files while the design target or intended user outcome is still missing.
 
 ## Critical Overrides
 
@@ -33,49 +30,20 @@ Use saved product URLs, Figma files, screenshots, reference images, codebase pat
 
 Do not inspect every saved reference. Inspect only what the current task needs.
 
-## Get Context Script
+## Handoff To The Next Workflow
 
-The following three questions should be answered by the user. Adapt the questions based on what the user has provided so far in the conversation. If some or all fields are already known, skip the questions and summarize the design brief in your own words.
+1. When the next workflow is already clear, read that skill before sending the brief playback. Do not only name a skill you have not read.
 
-The questions to answer are:
+2. Before executing `$ideate`, `$url-to-code`, or `$image-to-code`, play back the minimum brief and any defaults in one pithy user-visible note.
 
-> What do you want the thing to do?
+3. If the target and intended user outcome are clear, continue to the next workflow in the same turn. Do not wait for explicit confirmation. If the user provides feedback, incorporate it and course-correct.
 
-> What existing product, design system, Figma file, screenshot, URL, image, or other visual source should it match? If none, what look are you going for? Mention existing design systems already in user-context if they exist.
-
-> What level of interactivity should the thing have?
-
-One of:
-
-- Full interactivity: all controls and states are completely functional and implemented.
-- Static: controls and states are minimally interactive, preferring speed.
-
-After the questions, reply with a pithy design brief that summarizes what you're about to explore. Avoid walls of text at all costs. Be clear and concise.
-
-Example script to follow:
-
-```
-Before I build, the Product Design workflow needs a quick design brief.
-
-What should the login page do? Email/password only, magic link, SSO, sign-up link, forgot password?
-Do you have an existing design system, app, Figma, or screenshot to match?
-If not, what look are you going for?
-Interactivity level: full working form states, or a faster mostly-static mock?
-
-```
-
-## Final message
-
-1. Before proceeding to `$ideate`, `$prototype`, `$url-to-code`, or `$image-to-code`, confirm the design brief by explaining it back to the user in a pithy format as a `final` message.
-
-2. Proceed only after the user confirms the design brief, unless the current thread already contains confirmation of that exact brief. If the user provides feedback, continue to refine the design brief with them.
-
-3. After the user confirms the design brief, send one short expectation-setting note before starting an involved app, prototype, clone, redesign, or build. Example confirmation message with expectations setting:
+4. Before starting an involved app, prototype, clone, redesign, or build, send one short expectation-setting note and continue. Example:
 
 ```text
-Lovely, brief locked. This kind of build usually takes about 10-15 minutes, and ambitious ones can take longer. Good moment to grab coffee or tend to something else; I'll keep moving and bring the prototype back when it is ready.
+This kind of build usually takes about 10-15 minutes, and ambitious ones can take longer. Good moment to grab coffee or tend to something else; I’ll keep moving and bring the prototype back when it is ready.
 ```
 
 Do not send this note for tiny static changes, quick audits, simple research, setup-only, or share-only requests.
 
-Done means the user has confirmed the design brief.
+Done means the design target and intended user outcome are clear, defaults have been played back, and any already-determined next skill has been read.
diff --git a/plugins/product-design/skills/get-context/agents/openai.yaml b/plugins/product-design/skills/get-context/agents/openai.yaml
index 2302afe..9c5ebbb 100644
--- a/plugins/product-design/skills/get-context/agents/openai.yaml
+++ b/plugins/product-design/skills/get-context/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Get Context"
   short_description: "Confirm the Product Design brief before design or build work"
-  default_prompt: "Confirm the design brief for this Product Design task before the next workflow starts."
+  default_prompt: "Let's design an app for my idea"
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/product-design/skills/ideate/SKILL.md b/plugins/product-design/skills/ideate/SKILL.md
index cffcd6d..c5feaba 100644
--- a/plugins/product-design/skills/ideate/SKILL.md
+++ b/plugins/product-design/skills/ideate/SKILL.md
@@ -1,6 +1,6 @@
 ---
 name: ideate
-description: "Generate image-based visual alternatives, remixes, or concept directions after Product Design get-context has confirmed the design brief. Use when the user asks for design variants, visual exploration, remixes, or image-generated approaches from provided context."
+description: "Generate image-based alternatives, remixes, or new design directions from a Product Design brief. Use when the user asks for design variants, visual exploration, remixes, or image-generated approaches from provided context."
 ---
 
 # Ideate
@@ -24,7 +24,7 @@ Do not inspect every saved reference. Inspect only what the current task needs.
 
 ## Workflow
 
-Do not generate images until `$get-context` has played back and confirmed the design brief for this exact request. If this skill was invoked directly and the current thread does not already contain that confirmed brief, route to [$get-context](../get-context/SKILL.md) first.
+Do not generate images until `$get-context` has satisfied the minimum required design brief.
 
 Before generating images:
 
@@ -33,7 +33,7 @@ Before generating images:
 - Identify the target: component, screen, feature/workflow, or broad product idea.
 - Identify the intended user, product surface, and goal.
 - Preserve hard constraints from the user.
-- Run `get-context` if the brief has not already been played back and confirmed.
+- Run `get-context` if the minimum required design brief isn't satisfied.
 
 2. Resolve context.
 
@@ -73,29 +73,29 @@ Before generating images:
 - Name the gap clearly and ask whether to troubleshoot access or continue without that source.
 - Do not generate images while silently ignoring a named reference.
 
-7. Ask only if context is too thin.
+7. Attach images and mocks provided by the user to the Image Gen call along with your design brief.
 
-- Ask one targeted question only when available context is insufficient to generate useful directions.
-- Prefer asking about style direction, target audience, or the reference surface.
-
-8. Attach images and mocks provided by the user to the Image Gen call along with your design brief.
-
-9. Generate 3 independent options that have distinct information hierarchy, layout strategy, interaction model, or product framing.
+8. Generate 3 independent options that have distinct information hierarchy, layout strategy, interaction model, or product framing.
 
 Rules you must follow:
 
 - Use the Image Gen prompt below.
 - Use the built-in Image Gen tool.
 - Generate exactly three independent images unless the user overrides the count.
-- Generate options in parallel when possible.
+- Launch each Image Gen call independently. Do not batch Image Gen calls with `Promise.all`, collect them into an ordered array, or replay them in request order.
 - Each option must be its own Image Gen result. Do not put multiple ideas in one image.
+- Give each direction a distinct, descriptive name before generation, but do not call it `option 1`, `option 2`, or `option 3` and do not put planned numeric labels in Image Gen prompts. Parallel results can arrive in a different order from the requests.
+- Number options only after the Image Gen results are present in the thread. The only authoritative option order is the order those generated-image results are displayed in the current thread. Ignore the planned concept order, original request chain, prompt submission order, `Promise.all` result order, batch order, array indexes, retry order, and assumed completion order.
+- After all results return, bind each visible option number to the result in that displayed order. Do not name or describe the options in the final selection message.
 - Attach provided screenshots, files, app captures, Figma references, and visual source material as moodboard inspiration when available.
 - Attach existing product screenshots, similar flows, Storybook captures, design tokens, and component references as grounding material when available.
+- When mock data includes dates or time-sensitive information, resolve the exact current date and include it in every Image Gen prompt. Derive visible dates from that anchor; preserve dates required by the user or source design.
 - If a screenshot, image, or visual file is available, attach the actual image to the Image Gen call. Do not rely on text descriptions of it.
 - Only claim a visual reference was attached if the Image Gen call actually received that image or a readable local image path.
 - If you cannot attach the image, say that clearly and ask whether to continue with text-only direction.
 - Preserve hard constraints from the brief in every image.
 - After generating options, stop for the user's selection before any build work begins.
+- When the user later selects option `N`, resolve it against the Nth displayed generated-image result from the most recent ideation set, not the original planned concept order. If the exact displayed result cannot be resolved, do not build from a guess; ask the user to name the concept or reattach/select the image.
 - The selected option is the visual target for `$image-to-code`.
 
 ## Feedback Loop
@@ -108,19 +108,19 @@ If the user likes parts of more than one option, combine those choices into a ne
 
 ## Image Gen Prompt
 
-Adapt this prompt to the confirmed design brief, attach any available image references, and send it to Image Gen:
+Adapt this prompt to the current design brief, attach any available image references, and send it to Image Gen:
 
 ```text
 Create realistic, production-quality UI designs with clear hierarchy, strong typography, intentional imagery, and purposeful spacing.
 
-Keep the design simple. Avoid busy interfaces. Every section should have a clear purpose, and every element should earn its place.
-
-Prioritize clarity, whitespace, and usability over decorative complexity.
+Design a focused primary screen, not a feature inventory. The product may support many workflows, but this frame should show the hero use case, one clear primary action, and only one or two supporting actions or content areas. Do not add cards, panels, tabs, badges, metrics, filters, or navigation items merely to advertise every feature. Let the rest of the product exist off-screen. Prefer strong hierarchy and generous whitespace; if the screen feels crammed, remove UI.
 
 ### Target Dimensions
 
 Pick the dimensions that best match the user's request and any provided visual reference.
 
+Default to a desktop web-app frame unless the user or reference clearly calls for mobile, tablet, or another format.
+
  - Mobile app: `390 x 844`
  - Tablet app: `834 x 1194`
  - Desktop app, dashboard, admin, or SaaS: `1440 x 1024`
@@ -128,6 +128,8 @@ Pick the dimensions that best match the user's request and any provided visual r
  - Modal, panel, widget, or component: natural container size
  - Provided screenshot, Figma frame, mockup, or reference image: match its dimensions and aspect ratio when the user wants to continue from that visual
 
+Use a natural viewport ratio for the intended surface. Never stretch, squash, or warp the generated screen, imagery, typography, or UI elements to fill the canvas. If the composition does not fit naturally, recompose or simplify the layout instead.
+
 Avoid crowding. Make the design fit the chosen dimensions cleanly, with realistic spacing, readable type, and no clipped content.
 
 ### Layout
@@ -156,24 +158,34 @@ Don'ts:
  - Do not add browser or device chrome around the mockup.
  - Do not put multiple ideas into a single image generation.
  - Vary each idea as much as possible while adhering to the constraints given entirely.
+
+### Data Freshness
+
+When the design includes dates or time-sensitive mock data, use the supplied current date as the anchor. Weekly views must show the real containing week with correct weekday/date pairs. Feeds, charts, notifications, and recent activity must use plausible chronological dates relative to today. Mark today when useful. Preserve dates required by the brief or source design.
 ```
 
 ## Output
 
-After generation, provide
+Wait until all Image Gen calls have returned before sending the final message that asks the user to choose.
+
+Do not send the final selection message until every requested generated image is visible exactly once in the main chat.
+
+If fewer Image Gen outputs are visible than requested, retry the missing generation. Do not send the selection message.
 
-1. A short, memorable name for each concept that distills its choices down.
+Number the returned Image Gen outputs in the order they appear in the conversation context:
 
-2. A short closing question that asks whether the user wants to keep exploring or choose one direction to continue.
+- First Image Gen output = Option 1
+- Second Image Gen output = Option 2
+- Third Image Gen output = Option 3
 
-If the image tool already displayed the generated images in the thread, do not embed the same images again in the final message.
+Ignore the planned concept order, original request chain, request order, `Promise.all` result order, batch order, array indexes, retry order, and tool submission order.
 
-If the image tool did not display the generated images, include one image per option.
+Do not name or describe the options. For the default three images, send only:
 
-Do not show the same generated image twice.
+`Which option should I build: 1, 2, or 3? Or tell me what you'd like to refine or personalize first.`
 
-Example closing question:
+Adjust the numbers only if the user requested a different count.
 
-> Want to explore more directions, or should I build one of these? If one works, tell me 1, 2, or 3.
+If the user chooses a number, acknowledge the chosen option before routing to `$image-to-code`, for example: `Building option 2!` Do not ask for confirmation when the mapping is clear.
 
-Done means the requested number of independent images was generated and the user has been asked to select one.
+Done means the requested number of independent images have been generated and the user has been asked to select one.
diff --git a/plugins/product-design/skills/ideate/agents/openai.yaml b/plugins/product-design/skills/ideate/agents/openai.yaml
index 4b24b80..cc729ce 100644
--- a/plugins/product-design/skills/ideate/agents/openai.yaml
+++ b/plugins/product-design/skills/ideate/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Ideate"
-  short_description: "Generate visual directions after brief confirmation"
-  default_prompt: "Confirm the design brief, then create visual design directions for this product idea."
+  short_description: "Generate design mocks for a product with Image Gen"
+  default_prompt: "Generate a few design directions for my app idea"
 policy:
   allow_implicit_invocation: true
diff --git a/plugins/product-design/skills/image-to-code/SKILL.md b/plugins/product-design/skills/image-to-code/SKILL.md
index 5bdd826..7e72914 100644
--- a/plugins/product-design/skills/image-to-code/SKILL.md
+++ b/plugins/product-design/skills/image-to-code/SKILL.md
@@ -1,6 +1,6 @@
 ---
 name: image-to-code
-description: "Implement a selected image, screenshot, mockup, or Image Gen reference as a faithful responsive frontend after Product Design get-context has confirmed the design brief."
+description: "Implement a selected image, screenshot, mockup, or Image Gen reference as a faithful, responsive frontend."
 ---
 
 # Image to Code
@@ -20,19 +20,44 @@ Use saved product URLs, Figma files, screenshots, reference images, codebase pat
 
 Do not inspect every saved reference. Inspect only what the current task needs.
 
+### [IMPORTANT] Previewing prototypes in ChatGPT Work Mode
+
+Starting `sites-preview` is not verification. Verification requires opening `http://terminal.local:4173/` in the cloud browser, inspecting the rendered page, testing primary interactions, checking browser console errors, and passing design QA.
+
+Do not substitute HTTP health, build success, preview-service status, or deployment success for browser verification. If the cloud browser cannot be used, report verification as blocked.
+
+For local prototype verification and design QA in ChatGPT Work Mode:
+
+1. Install dependencies if needed. The project must have an npm `dev` script.
+2. `sites-preview` runs `npm run dev -- --host 0.0.0.0 --port 4173 --strictPort`. The `dev` script must accept those flags.
+3. For Vite or Vinext projects, use `"dev": "vite"`. Do not use `vinext dev`. Configure Vite with `server.host: "0.0.0.0"` and `server.allowedHosts: ["terminal.local"]`.
+4. From the site root, run `sites-preview start "$PWD"`.
+5. Open `http://terminal.local:4173/` in the cloud browser. Do not use `localhost`, `127.0.0.1`, `0.0.0.0`, HTTPS, or another port.
+6. Verify the rendered site and its primary interactions before reporting completion.
+7. Never mention `terminal.local` or output that URL to the user. Deploy a checkpoint with `@Sites` and give the user only the deployed checkpoint URL.
+
+### Mobile prototypes
+
+For a mobile app or phone prototype, build and verify at 390 × 844 unless the user names a device. Use the starter's `.mobile-prototype` wrapper. Do not use `min-height: 100vh` on the app shell. The app surface must end at the 390 × 844 frame. Every screen must fit 390px wide with no horizontal scroll, clipped text, clipped controls, or off-screen primary actions.
+
 ## Workflow
 
 CRITICAL: THIS IS NOT GUIDANCE. THIS IS A CHECKLIST TO COMPLETE.
 
-0. Do not start unless `$get-context` has played back and confirmed the design brief for this exact request. If this skill was directly at-mentioned without a clear visual target or the current thread does not already contain a confirmed brief, route to [$get-context](../get-context/SKILL.md) first.
-
 1. Do not start unless you have a selected image, screenshot, mockup, or Image Gen result to recreate. A written brief is not enough.
 
-2. Treat the provided image as the design to recreate.
+2. Resolve the exact selected visual target before building.
 
-3. If the provided design is a mobile viewport, build a mobile app. If it's unclear, default to desktop.
+    - If the user selected a numbered `$ideate` option, use the Nth displayed generated-image result from the most recent ideation set. Do not use the original concept planning order or Image Gen prompt submission order.
+    - Use the concept-name list from `$ideate` only when it was explicitly written in the same displayed-image order.
+    - A generated-image result ID, selected image attachment, screenshot, mockup, or Figma frame is stronger than a bare ordinal. Prefer that exact reference when available.
+    - If the selected result cannot be resolved unambiguously, stop before implementation and ask the user to name the concept or reattach/select the image. Never guess and build a nearby option.
 
-4. Review the reference design, catalog every image asset in the design, and use the Image Gen tool to create individual images for each one. Zoom in so you can catch every asset that needs to be generated.
+3. Treat the resolved image as the design to recreate.
+
+4. If the provided design is a mobile viewport, build a mobile app. If it's unclear, default to desktop.
+
+5. Review the reference design, catalog every image asset in the design, and use the Image Gen tool to create individual images for each one. Zoom in so you can catch every asset that needs to be generated.
 
     Examples include:
 
@@ -53,38 +78,43 @@ CRITICAL: THIS IS NOT GUIDANCE. THIS IS A CHECKLIST TO COMPLETE.
     - Generated assets must share the same art direction, palette, rendering style, and design language as the reference mockup.
     - The built-in Image Gen tool does not support transparent images; post-process generated assets when transparency is required.
 
-5. Define all sections of the page. For each section, meticulously measure the layout, spacing between elements, and the size and space of the elements themselves.
+### Parallel asset production
+
+After cataloging and measuring the reference assets, spawn up to three asset subagents while the main agent builds the app structure.
+
+Give each subagent one raster asset task at a time with its reference crop, exact dimensions, focal point, style, output path, and consuming component. Asset subagents generate, inspect, save, and report the asset path only. They must not edit source code, run the browser, or deploy.
 
-6. Find freely available fonts that match the target design.
+Prioritize critical above-the-fold assets first, then reuse agents for supporting assets. Do not delegate standard UI icons or supplied brand logos.
 
-7. Find a freely available icon library that matches the target design. Do not default to Lucide icons. Search for the best match.
+6. Define all sections of the page. For each section, meticulously measure the layout, spacing between elements, and the size and space of the elements themselves.
+
+7. Find freely available fonts that match the target design.
+
+8. Find a freely available icon library that matches the target design. Do not default to Lucide icons. Search for the best match.
 
     Rules:
 
     - CRITICAL RULE: Do not create custom inline SVGs, handcrafted SVGs, HTML element drawings, div/span shapes, CSS drawings, gradients, emoji, or text glyphs. Use the built-in Image Gen tool to generate assets and use the closest matching icon library for icons.
 
-8. Build the app starting with [local-prototype-preflight](../../references/local-prototype-preflight.md). Build all interactions, ensuring the app is complete, functional, and interactive: all controls and states activated and functional.
+9. Build the app starting with [local-prototype-preflight](../../references/local-prototype-preflight.md). Unless the user asks for a static mock, full production behavior, or a different scope, bring the app or website to life with:
 
-    Examples include:
+    - Working navigation, links, tabs, menus, and primary CTAs.
+    - Functional inputs, filters, toggles, selections, and forms shown in the main experience.
+    - Visible UI states: hover, focus, selected, open/closed, loading, empty, and success where relevant.
+    - The main task, conversion path, or user journey working from start to finish when the product has one.
 
-    - Header, sidebar, tooltip, and modal interactions
-    - Hover and focus states
-    - Responsive navigation
-    - Clickable cards and buttons
-    - Animated affordances if implied by the design
-    - Newsletter forms, tags, filters, or navigation elements shown in the mockup
-    - Bring the thing to life. Do not deliver a static site; the less you do, the more the designer has to add.
+    Controls outside the core experience may be visual-only. Do not build auth, persistence, backend/API calls, integrations, or exhaustive edge cases unless requested.
 
     Rules:
 
     - Place every image asset you generated into its position before proceeding. I repeat, replace all placeholders, including CSS/SVG placeholders, before proceeding.
-    - Do not leave visible controls as static chrome. Do not create new pages or routes unless the user asks for them.
+    - Do not leave controls in the core experience as static chrome. Do not create new pages or routes unless the user asks for them.
 
-9. Run the local app.
+10. Run the local app.
 
-10. Capture the local app using [browser-order](../../references/browser-order.md).
+11. Capture the local app using the Browser Choice rule in [$index](../index/SKILL.md#browser-choice).
 
-11. Run [design-qa](../design-qa/SKILL.md) as the blocking build gate.
+12. Run [design-qa](../design-qa/SKILL.md) as the blocking build gate.
 
     Steps:
 
@@ -96,10 +126,10 @@ CRITICAL: THIS IS NOT GUIDANCE. THIS IS A CHECKLIST TO COMPLETE.
     - If source capture, prototype capture, or visual comparison is blocked, stop. `design-qa.md` must say `final result: blocked`.
     - Do not hand off unless `design-qa.md` exists and says `final result: passed`.
 
-12. Handoff the app or website.
+13. Handoff the app or website.
 
     - Only hand off after [design-qa](../design-qa/SKILL.md) passes.
     - Keep the prototype running locally.
-    - Provide the clickable local URL.
-    - Briefly describe the work as a designer would.
+    - In Codex Desktop, provide the clickable local URL. In ChatGPT Work Mode, deploy a checkpoint with `@Sites` and provide only its deployed URL; never output `terminal.local`.
+    - After the prototype link, use the shared build handoff from `critical-overrides.md`. Do not add a different completion message.
     - Include the post-build iteration and share nudge from [critical-overrides](../../references/critical-overrides.md#build-handoff).
diff --git a/plugins/product-design/skills/image-to-code/agents/openai.yaml b/plugins/product-design/skills/image-to-code/agents/openai.yaml
index e08a13a..eb5b537 100644
--- a/plugins/product-design/skills/image-to-code/agents/openai.yaml
+++ b/plugins/product-design/skills/image-to-code/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Image To Code"
-  short_description: "Build a responsive frontend from a selected visual target after brief confirmation"
-  default_prompt: "Confirm the design brief, then build this selected design as an interactive frontend."
+  short_description: "Build a responsive frontend after minimum brief playback"
+  default_prompt: "Play back the minimum design brief, then build this selected design as an interactive frontend."
 policy:
   allow_implicit_invocation: true
diff --git a/plugins/product-design/skills/index/SKILL.md b/plugins/product-design/skills/index/SKILL.md
index b679035..32b04b0 100644
--- a/plugins/product-design/skills/index/SKILL.md
+++ b/plugins/product-design/skills/index/SKILL.md
@@ -1,11 +1,11 @@
 ---
 name: index
-description: "Use to discover specific skills for the Product Design plugin, when it is at-mentioned directly, or for any mentions of potentially relevant work, including: UX research; product, screen, or flow audits; visual ideation; app or interface design, redesign, cloning, prototyping, or implementation from ideas, URLs, images, Figma, or code; design QA; and prototype sharing or deployment."
+description: "Use when Product Design is explicitly invoked, or when the user's main goal is to explore a design, research UX, audit or critique a flow, faithfully clone a visual source, check a built design, or share a prototype. Do not use Product Design for ordinary implementation unless the user explicitly asks for it."
 ---
 
 # Skill Purpose
 
-Route Product Design requests to the right Product Design skill. Treat an `@Product Design` mention, direct Product Design invocation, or broad request like "design this app", "build a prototype", "audit this flow", "research this product", or "share this prototype" as intent to use this plugin.
+Route Product Design requests to the right Product Design skill. Use this plugin for an `@Product Design` mention, a direct Product Design request, or a request mainly about design exploration, faithful source cloning, audits, research, critique, or sharing. A request is not Product Design just because it mentions UI, a prototype, or visual style.
 
 # Plugin Purpose
 
@@ -29,22 +29,57 @@ Speak to the user in a warm, fun, and collaborative way, prioritizing pithy expl
 
 ## Router Only
 
-This index routes Product Design requests; it does not satisfy focused workflows itself.
+This index chooses the next Product Design skill. It does not do that skill's work.
 
-When a request matches `$user-context`, `$get-context`, `$research`, `$ideate`, `$prototype`, `$image-to-code`, `$url-to-code`, `$audit`, `$design-qa`, or `$share`, load the focused skill and follow it.
+If the user names a focused skill, read that exact skill first. Do not replace it with a related skill.
 
-For visual ideation requests, load `$get-context` first, then `$ideate`.
+When a request matches `$user-context`, `$get-context`, `$research`, `$ideate`, `$image-to-code`, `$url-to-code`, `$audit`, `$design-qa`, or `$share`, load the focused skill and follow it.
+
+For requests to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on an existing product experience, load `$audit` directly; do not load `$get-context` first. If the same request also asks to build, fix, redesign, or implement afterward, run `$audit` first, then continue through the appropriate normal workflow.
+
+For visual ideation, `$ideate` is the focused workflow. Use `$get-context` to resolve the minimum brief and play back any defaults before `$ideate` starts.
+
+For clone or recreation of a live URL, load `$url-to-code` directly.
+
+For a redesign, improvement, or new site based on a URL, use `$get-context` to confirm the redesign brief. `Like <URL>` means redesign, not clone. Capture the current site with screenshots, attach those screenshots to the `$ideate` Image Gen calls, then execute `$ideate`.
+
+## Standard Chat Mode
+
+If Product Design is invoked in standard ChatGPT chat without Work Mode tools, do not start the workflow. Tell the user once:
+
+```text
+Chat isn't supported by the Product Design plugin. Please switch to the Work tab and paste this prompt in for the full experience.
+```
+
+Do not ask design-brief questions, generate options, or begin build work until the user moves to Work Mode.
+
+## Browser Choice
+
+In ChatGPT Work Mode, always use the cloud browser available to that chat. If it is not initially visible, load the Browser skill and follow its setup instructions before concluding it is unavailable.
+
+In Codex Desktop, use `@Browser` and explicitly select the in-app surface with `agent.browsers.get("iab")`.
+
+In Codex Desktop, use Chrome only when the user asks for it, the task needs an existing Chrome tab/login/profile/extension, or the in-app Browser is unavailable or blocked.
+
+If ChatGPT Work Mode does not expose both the cloud browser and `@Sites` after preflight, tell the user once:
+
+```text
+Cloud browser and Sites are not available in this chat. I can still build a single-page HTML prototype, but I cannot visually verify it or publish a live checkpoint, so fidelity and interaction polish may be lower. Continue with that fallback?
+```
+
+Only proceed after the user agrees. Do not claim the fallback is verified, open, hosted, or ready to share. This fallback applies to image-to-code and new prototypes. It does not apply to URL-to-code when browser capture is required.
 
 ## No Visual Target, No Build
 
 For new app, prototype, redesign, or UI build requests without a URL, screenshot, Figma frame, mockup, source image, or existing code target:
 
-- Run `$get-context`.
-- After the brief is approved, route to `$ideate`.
+- `$ideate` is the focused workflow.
+- Use `$get-context` to resolve the minimum brief.
+- Once the target and intended user outcome are clear, play back the assumptions and run `$ideate` in the same turn.
 - Show exactly three visual options and wait for the user to choose one.
 - Do not scaffold, edit files, or start a server before a visual option is selected.
 
-`Full working version`, `no refs`, `go for it`, `make an assumption`, or a confirmed brief do not waive this.
+`Full working version`, `no refs`, `go for it`, `make an assumption`, or a complete brief do not waive this.
 
 ## User Context
 
@@ -63,13 +98,7 @@ Adjust the context-gathering request to match the user's request. First-time set
 
 For setup-only requests, do not inspect the workspace, install dependencies, scaffold a prototype, generate images, run audits, or start implementation.
 
-When answering "what can you do?", "how do I get started?", or similar broad Product Design questions, end by asking whether the user wants to set up saved context.
-
-Use this close:
-
-```text
-Want to onboard Product Design with your context? Send product URLs, Figma files, screenshots, codebase paths, Storybook links, tokens, brand assets, or preferred share targets, and I'll save them for future work.
-```
+When answering "what can you do?", "how do I get started?", or similar broad Product Design questions, load `$user-context` and follow its persistence availability check before offering saved-context onboarding.
 
 Before routing to Product Design workflows, load [$user-context](../user-context/SKILL.md) and run its preflight script when local shell access is available.
 
@@ -91,7 +120,7 @@ Preflight, save, or answer from Product Design setup context. Route here before
 
 ### $get-context
 
-Route here first for design, build, prototype, redesign, extend, or UI exploration work. If details are missing, ask only for the missing product, visual, or interactivity context; if details are already present, play back the brief before proceeding. Confirm the design brief back to the user before Product Design ideation or implementation.
+Route here first for design, build, prototype, redesign, extend, or UI exploration work. Require only a clear design target and intended user outcome. Ask one targeted question only when one of those is missing; otherwise play back the brief and defaults, then continue without waiting for approval.
 
 ### $research
 
@@ -99,23 +128,19 @@ Run fast, source-grounded UX research on current user problems for a named digit
 
 ### $audit
 
-Capture and review a product flow, journey, screen, or multi-step product experience from screenshots. Route here for user-facing audit, critique, review, inspect, assess, or UI evaluation requests. It reports UX, design, and accessibility findings tied to captured evidence; do not use `design-qa` for user-facing audits.
+Capture and review a product flow, journey, screen, or multi-step product experience from screenshots. Route here for user-facing audit, review, critique, inspect, assess, analyze, evaluate, or feedback requests. It reports UX, design, and accessibility findings tied to captured evidence; do not use `design-qa` for user-facing audits.
 
 ### $ideate
 
-Generate image-based visual alternatives, remixes, or concept directions for a component, screen, feature, workflow, or product idea. Route here after `get-context` has confirmed the design brief and the user needs visual exploration, design variants, alternatives to an existing design, or idea discovery before choosing a visual target. Prefer this over prose-only ideation unless the user asks for prose.
-
-### $prototype
-
-Route coded prototype, redesign, clone, and UI build requests to the right Product Design workflow. Route here after `get-context` has confirmed the design brief and the user asks to build from a URL, image, mockup, Figma source, existing codebase, or product idea.
+Generate image-based visual alternatives, remixes, or concept directions for a component, screen, feature, workflow, or product idea. Route here after `get-context` has played back the minimum brief and the user needs visual exploration, design variants, alternatives to an existing design, or idea discovery before choosing a visual target. Prefer this over prose-only ideation unless the user asks for prose.
 
 ### $url-to-code
 
-Clone a live URL as a runnable frontend-only local app using Browser or Chrome source evidence. Route here after `get-context` has confirmed the brief and the user provides a production URL for a faithful local prototype or clone. It should not modify production code; use `prototype` first when source selection is still unclear.
+Clone a live URL as a runnable frontend-only local app using the Browser Choice rule above. Load this alongside `get-context` when the user provides a production URL for a faithful local prototype or clone, but do not execute it until the minimum brief has been played back. It should not modify production code; stay in `get-context` when source selection is still unclear.
 
 ### $image-to-code
 
-Implement a selected visual target as a faithful, responsive, interactive frontend. Route here after `get-context` has confirmed the brief and the user has chosen an ImageGen mock, screenshot, Figma frame, mockup, reference image, or other visual source. Do not start here when no visual target has been selected; use `get-context` and `ideate` first.
+Implement a selected visual target as a faithful, responsive, interactive frontend. Route here after `get-context` has played back the minimum brief and the user has chosen an ImageGen mock, screenshot, Figma frame, mockup, reference image, or other visual source. Do not start here when no visual target has been selected; use `get-context` and `ideate` first.
 
 ### $share
 
diff --git a/plugins/product-design/skills/index/agents/openai.yaml b/plugins/product-design/skills/index/agents/openai.yaml
index 6e953ce..f636167 100644
--- a/plugins/product-design/skills/index/agents/openai.yaml
+++ b/plugins/product-design/skills/index/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Product Design"
-  short_description: "Primary entrypoint for Product Design setup, brief confirmation, research, audits, ideation, prototypes, URL clones, image-to-code builds, QA, and sharing."
+  short_description: "Primary entrypoint for explicit Product Design work, design exploration, research, audits, critique, source clones, QA, and sharing."
   default_prompt: "What can Product Design do?"
 policy:
   allow_implicit_invocation: true
diff --git a/plugins/product-design/skills/prototype/SKILL.md b/plugins/product-design/skills/prototype/SKILL.md
deleted file mode 100644
index 560d4bd..0000000
--- a/plugins/product-design/skills/prototype/SKILL.md
+++ /dev/null
@@ -1,128 +0,0 @@
----
-name: prototype
-description: "Route coded prototype requests after Product Design get-context has confirmed the design brief. Use for building prototypes from URLs, images, mockups, Figma, existing code, or ideas that need visual exploration before build."
----
-
-# Design Prototypes
-
-You're tasked with creating a frontend-only, interactive website or app, that the user can click through and use.
-
-Select the best workflow from the following options to solve the user's request
-
-## Golden Rule of Prototyping
-
-- Do not build until you have a visual target and the chosen workflow reaches `$image-to-code` or `$url-to-code`.
-- A visual target is an app or website design to build from: a user-provided image, a source URL, or an ImageGen design selected by the user.
-- A `$get-context` brief is not a visual target.
-- After `$get-context`, route to `$ideate` unless the user already provided a concrete visual source.
-- If `$ideate` creates options, stop and wait for the user to choose one.
-- The chosen option becomes the required visual target for `$image-to-code`.
-
-## Critical Overrides
-
-- Refer to the Plugin router [$index](../index/SKILL.md) before proceeding.
-- Follow [$critical-overrides](../../references/critical-overrides.md).
-- Refer to [references/existing-codebase-edits.md](references/existing-codebase-edits.md) for best practices on making edits to existing codebases.
-
-## User Context
-
-Before starting, load [$user-context](../user-context/SKILL.md) and run its preflight script when local shell access is available.
-
-Use saved product URLs, Figma files, screenshots, reference images, codebase paths, Storybook, tokens, design systems, brand assets, component refs, browser preferences, and share targets as grounding material when relevant.
-
-Do not inspect every saved reference. Inspect only what the current task needs.
-
-## Setup Is Not Prototype Work
-
-If the user only asks to set up Product Design, do not continue in this skill. Use [$user-context](../user-context/SKILL.md).
-
-## Workflows
-
-The following workflows support cloning, redesigning, extending, and building products from scratch. Choose the workflow that best matches the user's request, or use the skills as helpers if the request does not match one workflow cleanly.
-
-Always start with `$get-context` to confirm the design brief before proceeding to the next workflow step. If required details are missing, `$get-context` asks for them. If the user already supplied the product, visual, and interactivity details, `$get-context` plays back the brief in your own words. Do not proceed to `$ideate`, `$url-to-code`, or `$image-to-code` until the brief has been played back and either confirmed by the user or already confirmed earlier in the thread.
-
-### Clone product
-
-"Recreate {URL}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the target URL, purpose, source constraints, and interactivity level.
-- `$url-to-code` - recreate the app or website shown at the URL.
-
-"Recreate {image}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including what the image represents, source constraints, and interactivity level.
-- `$image-to-code` - recreate the app or website design shown in the image.
-
-### New product
-
-"Build me {idea}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the product idea, user goal, visual direction, source constraints, and interactivity level.
-- `$ideate` - generate exactly three app or website design options; show them and wait for selection.
-- `$image-to-code` - only after the user selects an option.
-
-### Redesign product
-
-"Redesign this {image}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the redesign goal, visual direction, source constraints, and interactivity level.
-- `$ideate` - attach the provided app or website design to ImageGen; generate exactly three redesign options; show them and wait for selection.
-- `$image-to-code` - only after the user selects an option.
-
-"Redesign this {URL}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the target URL, redesign goal, source constraints, and interactivity level.
-- `$ideate` - attach provided app or website screenshot at the URL to ImageGen; generate exactly three redesign options; show them and wait for selection.
-- `$image-to-code` - only after the user selects an option; use the URL as reference for existing content, structure, and interactions.
-
-"Redesign my app"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the app surface, redesign goal, source constraints, and interactivity level.
-- Find the app or website in the codebase.
-- If there is more than one possible target, ask the user which one to redesign and whether they want to edit it directly or create a fresh prototype.
-- `$ideate` - use the existing app or website as the source; generate exactly three redesign options; show them and wait for selection.
-- `$image-to-code` - only after the user selects an option; refer to [references/existing-codebase-edits.md](references/existing-codebase-edits.md)
-
-### Extend product
-
-"Add {feature} to this {URL}"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the target URL, feature behavior, source constraints, and interactivity level.
-- `$url-to-code` - recreate the app or website and add the requested feature.
-
-"Add {feature} to my app"
-
-Required next steps:
-
-- `$get-context` - confirm the design brief, including the app surface, feature behavior, source constraints, and interactivity level.
-- Find the app or website in the codebase.
-- If there is more than one possible target, ask the user which one to extend.
-- Implement the requested feature in the existing app or website; refer to [references/existing-codebase-edits.md](references/existing-codebase-edits.md)
-
-When the user references a product name without a source URL or image, use the browser skill of choice to get actual screenshots of the product. Only fallback to web search images if the product isn't accessible.
-
-## Hard Rules
-
-- Written design direction is not a visual target. Use it as input to `$ideate` and ImageGen.
-- Do not build from a brief alone.
-- For redesigns, use `$ideate` to generate exactly three design options, show them, and wait for the user to choose one.
-- Do not build a redesign until the user has chosen a design option.
-- When a new product has no visual target after `$get-context`, use `$ideate` to generate exactly three design options, show them, and wait for the user to choose one.
-- If the user gives feedback, or says they like more than one design, use `$ideate` to create a new ImageGen mock with the feedback or combined direction before build.
-- Pass the user's written direction and attached design references into `$ideate` and ImageGen.
-- If the current folder looks like an existing prototype and it is unclear whether to edit it or create a new one, ask the user.
diff --git a/plugins/product-design/skills/prototype/agents/openai.yaml b/plugins/product-design/skills/prototype/agents/openai.yaml
deleted file mode 100644
index cada12a..0000000
--- a/plugins/product-design/skills/prototype/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Prototype"
-  short_description: "Route prototype, redesign, clone, and UI build requests after brief confirmation"
-  default_prompt: "Confirm the design brief, then build a Product Design prototype."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/product-design/skills/url-to-code/SKILL.md b/plugins/product-design/skills/url-to-code/SKILL.md
index bdbc762..bd02de5 100644
--- a/plugins/product-design/skills/url-to-code/SKILL.md
+++ b/plugins/product-design/skills/url-to-code/SKILL.md
@@ -1,10 +1,16 @@
 ---
 name: url-to-code
-description: "Clone a live URL as a runnable frontend-only local app using Browser/Chrome source evidence after Product Design get-context has confirmed the design brief."
+description: "Clone a live URL as a runnable frontend-only local app."
 ---
 
 # URL To Code
 
+If the user explicitly invokes this skill, continue.
+
+Only continue when the user asks to clone or recreate the current site.
+
+If the user says `like`, `better`, `redesign`, or `improve`, return to [$index](../index/SKILL.md).
+
 Clone `<target-url>` as a real interactive, frontend-only local app or website. The clone should look and interact like the source.
 
 ## Critical Overrides
@@ -22,11 +28,9 @@ Do not inspect every saved reference. Inspect only what the current task needs.
 
 ## Workflow
 
-0. Do not start unless `$get-context` has played back and confirmed the design brief for this exact request. If this skill was invoked directly and the current thread does not already contain that confirmed brief, route to [$get-context](../get-context/SKILL.md) first.
-
 1. CRITICAL STEP: Warn the user that they must follow the target website's terms before proceeding. This workflow is only for apps and websites the user owns, or has permission to recreate.
 
-2. Open the source URL using [browser-order](../../references/browser-order.md).
+2. Open the source URL using the Browser Choice rule in [$index](../index/SKILL.md#browser-choice).
 
 3. Check that the page is correct.
 
@@ -90,6 +94,22 @@ Do not inspect every saved reference. Inspect only what the current task needs.
 
 10. Run the local app.
 
+### Previewing prototypes in ChatGPT Work Mode
+
+Starting `sites-preview` is not verification. Verification requires opening `http://terminal.local:4173/` in the cloud browser, inspecting the rendered page, testing primary interactions, checking browser console errors, and passing design QA.
+
+Do not substitute HTTP health, build success, preview-service status, or deployment success for browser verification. If the cloud browser cannot be used, report verification as blocked.
+
+For local prototype verification in ChatGPT Work Mode:
+
+1. Install dependencies if needed. The project must have an npm `dev` script.
+2. `sites-preview` runs `npm run dev -- --host 0.0.0.0 --port 4173 --strictPort`. The `dev` script must accept those flags.
+3. For Vite or Vinext projects, use `"dev": "vite"`. Do not use `vinext dev`. Configure Vite with `server.host: "0.0.0.0"` and `server.allowedHosts: ["terminal.local"]`.
+4. From the site root, run `sites-preview start "$PWD"`.
+5. Open `http://terminal.local:4173/` in the cloud browser. Do not use `localhost`, `127.0.0.1`, `0.0.0.0`, HTTPS, or another port.
+6. Verify the rendered site and its primary interactions before reporting completion.
+7. Never mention `terminal.local` or output that URL to the user. Deploy a checkpoint with `@Sites` and give the user only the deployed checkpoint URL.
+
 11. Compare the local app against the original.
 
 - Check desktop.
@@ -109,8 +129,8 @@ Do not inspect every saved reference. Inspect only what the current task needs.
 
 - Only hand off after [design-qa](../design-qa/SKILL.md) passes.
 - Keep the prototype running locally.
-- Provide the clickable local URL.
-- Briefly describe the work as a designer would.
+- In Codex Desktop, provide the clickable local URL. In ChatGPT Work Mode, deploy a checkpoint with `@Sites` and provide only its deployed URL; never output `terminal.local`.
+- After the prototype link, use the shared build handoff from `critical-overrides.md`. Do not add a different completion message.
 - Include the post-build iteration and share nudge from [critical-overrides](../../references/critical-overrides.md#build-handoff).
 
 ## Hard Rules
@@ -121,4 +141,4 @@ Do not inspect every saved reference. Inspect only what the current task needs.
 - Do not implement a saved state without source screenshot plus the available DOM/style/layout evidence for that state.
 - Do not use hotlinked source assets in the final app.
 - Do not create temporary CSS icons, text glyphs, emoji marks, placeholder blocks, or handmade SVGs while "waiting" to resolve assets. Resolve assets first, then build.
-- If Browser and Chrome fail, ask before using Playwright. If no approved tool can capture valid source and prototype evidence, stop and report the design-qa blocker.
+- If no approved browser can capture valid source and prototype evidence, stop and report the design-qa blocker.
diff --git a/plugins/product-design/skills/url-to-code/agents/openai.yaml b/plugins/product-design/skills/url-to-code/agents/openai.yaml
index 166318d..e4bba16 100644
--- a/plugins/product-design/skills/url-to-code/agents/openai.yaml
+++ b/plugins/product-design/skills/url-to-code/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "URL To Code"
-  short_description: "Clone a live URL into a runnable frontend prototype after brief confirmation"
-  default_prompt: "Confirm the design brief, then clone this URL as a runnable frontend prototype."
+  short_description: "Clone live URLs of your projects into lightweight prototypes"
+  default_prompt: "Clone this URL as a runnable frontend prototype."
 policy:
-  allow_implicit_invocation: false
+  allow_implicit_invocation: true
diff --git a/plugins/product-design/skills/user-context/SKILL.md b/plugins/product-design/skills/user-context/SKILL.md
index 387e495..0faf643 100644
--- a/plugins/product-design/skills/user-context/SKILL.md
+++ b/plugins/product-design/skills/user-context/SKILL.md
@@ -22,6 +22,8 @@ Use this skill when the user asks to:
 
 - Refer to the Plugin router [$index](../index/SKILL.md) before proceeding.
 - Follow [$critical-overrides](../../references/critical-overrides.md).
+- Before offering onboarding or saving context for future conversations, confirm that local shell access is available, `$CODEX_HOME` can be resolved, and `$CODEX_HOME/state/plugins/product-design/` exists and is writable or can be created in a writable parent directory. If `user-context.md` already exists, confirm that it is writable too.
+- If any check cannot be completed or fails, persistent context is unavailable. Do not offer saved-context onboarding or claim that new context was saved for future conversations. If the user asks to save something, explain that it can be used in the current conversation but not saved for future conversations.
 
 ## Saved User Context
 
diff --git a/plugins/product-design/skills/user-context/references/onboarding.md b/plugins/product-design/skills/user-context/references/onboarding.md
index 51a0d42..e947625 100644
--- a/plugins/product-design/skills/user-context/references/onboarding.md
+++ b/plugins/product-design/skills/user-context/references/onboarding.md
@@ -2,6 +2,8 @@
 
 Use this reference when saved user context is missing, when the user asks what Product Design can remember, when the user asks to set up Product Design, or when the user provides product/design references to save.
 
+Before beginning setup, run the persistence availability check in [$user-context](../SKILL.md). If persistent context is unavailable, do not render the onboarding prompt below. Explain that references can be used in the current conversation but cannot be saved for future conversations.
+
 Setup is short. It is not a questionnaire and not a formal onboarding state machine.
 
 ## Step 1: Orientation
diff --git a/plugins/product-design/templates/prototype/AGENTS.md b/plugins/product-design/templates/prototype/AGENTS.md
index c0152ef..197fc5e 100644
--- a/plugins/product-design/templates/prototype/AGENTS.md
+++ b/plugins/product-design/templates/prototype/AGENTS.md
@@ -1,6 +1,6 @@
 # Prototype Instructions
 
-Run the local server yourself and open the preview in the in-app browser. Do not give the user server-start instructions when you can run it.
+Run the local server yourself and open the preview in the browser available to this environment. Do not give the user server-start instructions when you can run it.
 
 Before making substantial visual changes, use the Product Design plugin's `get-context` skill when the visual source is unclear or no longer matches the current goal. When the user gives durable prototype-specific design feedback, preferences, or decisions, record them in `AGENTS.md`.
 
diff --git a/plugins/product-design/templates/prototype/package.json b/plugins/product-design/templates/prototype/package.json
index 1b80f47..f77feb8 100644
--- a/plugins/product-design/templates/prototype/package.json
+++ b/plugins/product-design/templates/prototype/package.json
@@ -4,9 +4,9 @@
   "private": true,
   "type": "module",
   "scripts": {
-    "dev": "vite --host 127.0.0.1",
+    "dev": "vite",
     "build": "vite build",
-    "preview": "vite preview --host 127.0.0.1"
+    "preview": "vite preview"
   },
   "dependencies": {
     "@vitejs/plugin-react": "5.0.4",
diff --git a/plugins/product-design/templates/prototype/src/styles.css b/plugins/product-design/templates/prototype/src/styles.css
index 68d1602..f4d7a9c 100644
--- a/plugins/product-design/templates/prototype/src/styles.css
+++ b/plugins/product-design/templates/prototype/src/styles.css
@@ -10,4 +10,12 @@ body {
   min-width: 320px;
   min-height: 100vh;
   -webkit-font-smoothing: antialiased;
-}
\ No newline at end of file
+}
+
+.mobile-prototype {
+  width: min(100vw, 390px);
+  height: min(100dvh, 844px);
+  margin-inline: auto;
+  overflow: hidden;
+  position: relative;
+}
diff --git a/plugins/product-design/templates/prototype/vite.config.mjs b/plugins/product-design/templates/prototype/vite.config.mjs
index bbc4275..2cb4eb0 100644
--- a/plugins/product-design/templates/prototype/vite.config.mjs
+++ b/plugins/product-design/templates/prototype/vite.config.mjs
@@ -6,6 +6,8 @@ export default defineConfig({
     include: ["react", "react-dom/client"],
   },
   server: {
+    host: "0.0.0.0",
+    allowedHosts: ["terminal.local"],
     warmup: {
       clientFiles: ["./src/main.jsx"],
     },
diff --git a/plugins/sales/.app.json b/plugins/sales/.app.json
index 06f34fc..6d5eb0b 100644
--- a/plugins/sales/.app.json
+++ b/plugins/sales/.app.json
@@ -1,91 +1,124 @@
 {
   "apps": {
     "slack": {
-      "id": "REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_SLACK_APP_OR_CONNECTOR_ID",
+      "category": "Internal Messaging"
     },
     "microsoft_teams": {
-      "id": "connector_246af0940da3457da0e751171dc1ce60"
+      "id": "connector_246af0940da3457da0e751171dc1ce60",
+      "category": "Internal Messaging"
     },
     "zoom": {
-      "id": "REPLACE_WITH_ZOOM_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_ZOOM_APP_OR_CONNECTOR_ID",
+      "category": "Meeting Transcripts"
+    },
+    "zoom-zra": {
+      "id": "REPLACE_WITH_ZOOM_ZRA_APP_OR_CONNECTOR_ID",
+      "category": "Meeting Transcripts"
     },
     "granola": {
-      "id": "REPLACE_WITH_GRANOLA_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_GRANOLA_APP_OR_CONNECTOR_ID",
+      "category": "Meeting Transcripts"
     },
     "fireflies": {
-      "id": "REPLACE_WITH_FIREFLIES_APP_OR_CONNECTOR_ID"
+      "id": "connector_6912075cb358819187346bcafb601db8",
+      "category": "Meeting Transcripts"
     },
     "otter_ai": {
-      "id": "REPLACE_WITH_OTTER_AI_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_OTTER_AI_APP_OR_CONNECTOR_ID",
+      "category": "Meeting Transcripts"
     },
     "salesforce": {
-      "id": "REPLACE_WITH_SALESFORCE_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_SALESFORCE_APP_OR_CONNECTOR_ID",
+      "category": "CRM"
     },
     "hubspot": {
-      "id": "REPLACE_WITH_HUBSPOT_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_HUBSPOT_APP_OR_CONNECTOR_ID",
+      "category": "CRM"
     },
     "close": {
-      "id": "REPLACE_WITH_CLOSE_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_CLOSE_APP_OR_CONNECTOR_ID",
+      "category": "CRM"
     },
     "zoho": {
-      "id": "connector_fd0f007550a242459d6dd1f923668769"
+      "id": "connector_fd0f007550a242459d6dd1f923668769",
+      "category": "CRM"
     },
     "pipedrive": {
-      "id": "connector_36803b8cc4164d84aa3fbfc7ddb420e1"
+      "id": "connector_36803b8cc4164d84aa3fbfc7ddb420e1",
+      "category": "CRM"
     },
     "zoominfo": {
-      "id": "REPLACE_WITH_ZOOMINFO_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_ZOOMINFO_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "clay": {
-      "id": "REPLACE_WITH_CLAY_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_CLAY_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "hg_insights": {
-      "id": "REPLACE_WITH_HG_INSIGHTS_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_HG_INSIGHTS_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "rox": {
-      "id": "REPLACE_WITH_ROX_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_ROX_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "apollo": {
-      "id": "REPLACE_WITH_APOLLO_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_APOLLO_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "actively": {
-      "id": "REPLACE_WITH_ACTIVELY_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_ACTIVELY_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "meticulate": {
-      "id": "REPLACE_WITH_METICULATE_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_METICULATE_APP_OR_CONNECTOR_ID",
+      "category": "Sales Intelligence"
     },
     "gmail": {
-      "id": "connector_2128aebfecb84f64a069897515042a44"
+      "id": "connector_2128aebfecb84f64a069897515042a44",
+      "category": "Email"
     },
     "outlook_email": {
-      "id": "connector_4aaab2856305417b993eca9a216aaf6e"
+      "id": "connector_4aaab2856305417b993eca9a216aaf6e",
+      "category": "Email"
     },
     "outreach": {
-      "id": "REPLACE_WITH_OUTREACH_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_OUTREACH_APP_OR_CONNECTOR_ID",
+      "category": "Email"
     },
     "notion": {
-      "id": "REPLACE_WITH_NOTION_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_NOTION_APP_OR_CONNECTOR_ID",
+      "category": "Knowledge & Files"
     },
     "google_drive": {
-      "id": "connector_5f3c8c41a1e54ad7a76272c89e2554fa"
+      "id": "connector_5f3c8c41a1e54ad7a76272c89e2554fa",
+      "category": "Knowledge & Files"
     },
     "microsoft_sharepoint": {
-      "id": "connector_1e4f6a44acf14e3ca1d96672f8c945bc"
+      "id": "connector_1e4f6a44acf14e3ca1d96672f8c945bc",
+      "category": "Knowledge & Files"
     },
     "google_calendar": {
-      "id": "connector_947e0d954944416db111db556030eea6"
+      "id": "connector_947e0d954944416db111db556030eea6",
+      "category": "Calendar"
     },
     "outlook_calendar": {
-      "id": "connector_e6a7394682e24467ac68c60696f275a4"
+      "id": "connector_e6a7394682e24467ac68c60696f275a4",
+      "category": "Calendar"
     },
     "calendly": {
-      "id": "REPLACE_WITH_CALENDLY_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_CALENDLY_APP_OR_CONNECTOR_ID",
+      "category": "Scheduling"
     },
     "monday": {
-      "id": "REPLACE_WITH_MONDAY_APP_OR_CONNECTOR_ID"
+      "id": "connector_690aabb71bf481918b8d5b614ed3fd4c",
+      "category": "Work Management"
     },
     "docusign": {
-      "id": "REPLACE_WITH_DOCUSIGN_APP_OR_CONNECTOR_ID"
+      "id": "REPLACE_WITH_DOCUSIGN_APP_OR_CONNECTOR_ID",
+      "category": "Document Signing"
     }
   }
 }
diff --git a/plugins/sales/.codex-plugin/plugin.json b/plugins/sales/.codex-plugin/plugin.json
index 941a625..ae62557 100644
--- a/plugins/sales/.codex-plugin/plugin.json
+++ b/plugins/sales/.codex-plugin/plugin.json
@@ -1,7 +1,7 @@
 {
   "name": "sales",
-  "version": "0.2.16",
-  "description": "Practical sales workflows that help sellers prepare meetings, follow up after calls, plan deals, review pipeline, find internal answers, and reuse saved preferences.",
+  "version": "1.0.8",
+  "description": "Practical sales workflows for sellers. Prepare for meetings, follow up after calls, prioritize accounts, analyze signals, plan deals, build business cases, review forecasts, find customer evidence, coach reps, enrich data, and work from CRM context.",
   "author": {
     "name": "OpenAI"
   },
@@ -10,23 +10,37 @@
   "license": "MIT",
   "keywords": [
     "sales",
-    "competitive-brief",
+    "crm",
+    "hubspot",
+    "zoominfo",
+    "outreach",
+    "gmail",
+    "outlook",
+    "microsoft teams",
+    "sales intelligence",
     "account-intelligence",
     "meeting-preparation",
-    "product-feedback",
+    "call-follow-up",
+    "competitive-brief",
+    "deal-strategy",
+    "business-case",
     "forecasting",
+    "customer-quotes",
+    "sales-coaching",
     "pipeline",
-    "coaching",
-    "follow-up"
+    "account-prioritization",
+    "company-enrichment",
+    "salesforce",
+    "guided-flow"
   ],
   "skills": "./skills/",
   "apps": "./.app.json",
   "interface": {
     "displayName": "Sales",
-    "shortDescription": "Prepare sales work faster",
-    "longDescription": "Bring customer context into the work that moves deals forward. Find high-priority accounts and signals, prepare for customer meetings, complete follow-ups, propose customer-record updates, build close plans, and review deals at risk.\n\nStart with CRM details, call notes, docs, messages, calendar events, uploaded files, or pasted context. Connect tools like Salesforce, HubSpot, Slack, Outreach, Clay, Rox, and Actively to bring scattered account context together and turn the available evidence into practical next steps.",
+    "shortDescription": "Practical workflows for sellers",
+    "longDescription": "Bring customer context into the work that moves deals forward. Prepare for customer meetings, follow up after calls, understand account signals, prioritize accounts, plan active deals, build business cases and competitive briefs, review forecasts, find customer quotes and internal experts, coach reps from calls, and enrich company and contact data.\n\nStart with CRM details, calendar events, call notes, docs, messages, uploaded files, account lists, or pasted context. Connect tools like Salesforce, HubSpot, Slack, Outreach, Clay, Rox, and Actively to bring scattered context together and turn the available evidence into practical next steps.",
     "developerName": "OpenAI",
-    "category": "Business",
+    "category": "Business & Operations",
     "capabilities": [
       "Interactive",
       "Read",
@@ -36,9 +50,9 @@
     "privacyPolicyURL": "https://openai.com/policies/row-privacy-policy/",
     "termsOfServiceURL": "https://openai.com/policies/row-terms-of-use/",
     "defaultPrompt": [
-      "Let's get started",
-      "Prep me for my next customer meeting",
-      "Help me prioritize my accounts"
+      "What can you do?",
+      "Prepare me for my next important customer meeting. Give me the context, likely blockers, and questions I should ask.",
+      "Which accounts should I focus on this week? Rank them, explain why, and recommend the next move for each."
     ],
     "brandColor": "#FB6A22",
     "composerIcon": "./assets/codex-sales-plugin-composer-icon.svg",
diff --git a/plugins/sales/README.md b/plugins/sales/README.md
index 197117f..1a98e69 100644
--- a/plugins/sales/README.md
+++ b/plugins/sales/README.md
@@ -4,40 +4,39 @@ Bring customer context into the work that moves deals forward.
 
 ## When to use this plugin
 
-Use Sales when you need to prioritize accounts and signals, prepare for customer meetings, complete follow-ups, propose customer-record updates, build close plans, or review deals at risk.
+Use Sales when you need to prepare for customer meetings, follow up after calls, understand account movement, plan deals, build business cases, review forecasts, find customer quotes, coach reps from calls, build competitive guidance, find internal answers, prioritize accounts, enrich company or contact data, or use CRM and Sales Intelligence context.
 
-## Onboarding
+## Get started
 
-Ask Codex:
+Try asking:
 
 `@Sales Help me get started`
 
-Sales has a guided onboarding flow that helps connect the sources your team uses, save useful preferences, set up optional recurring support, and try a useful first workflow.
-
-Onboarding is an interactive, step-by-step conversation. Codex will guide you through setup, ask for approval before making changes, and help you try a first workflow.
+The plugin will help connect the sources the current workflow needs and recommend a useful first workflow.
 
 You can also jump directly into any workflow below.
 
-## Example workflows
+Sales strongly prefers using the runtime's `ask_user_input` tool for compact workflow choosers, clarification questions, fallback context intake, and review loops. When a selected workflow has a material missing source, Sales first looks for suitable installable providers and uses `request_plugin_install` to offer them before falling back to the plugin page or pasted context. When structured tools are unavailable, Sales falls back to the same choices in text.
 
-Hero workflows are bolded.
+## Example workflows
 
 | Workflow | Try this | Skill | Result |
 | --- | --- | --- | --- |
-| **Prepare for a customer meeting** | `Prep me for my next customer meeting` | `prepare-for-meeting` | A concise meeting brief with agenda, context, likely blockers, and next actions |
-| **Follow up after a call** | `Follow up from my latest customer call` | `follow-up-after-call` | A grounded recap, next-step list, external follow-up draft, and CRM-ready notes when useful |
-| Plan a deal strategy | `Build a strategy for moving the Acme deal forward` | `plan-deal-strategy` | A deal map, buying-committee view, procurement risks, and prioritized next actions |
-| **Prioritize accounts** | `Rank the accounts I should work this week and explain why` | `prioritize-accounts` | A focused account action view with evidence, reachable contacts, and planning-only next steps |
-| Review a forecast | `Review my forecast and tell me which deals could slip` | `review-forecast` | A manager-ready forecast readout with risks, change detection, and keep-or-downgrade recommendations |
-| Find internal answers | `Find the right people and docs for this customer's security question` | `find-key-internal-sources` | A ranked routing map of experts, documents, channels, and escalation paths |
-| Analyze account signals | `Tell me what changed for Acme and what I should do next` | `analyze-account-signals` | An evidence-backed account intelligence brief with a practical action plan |
-| Build a competitive brief | `Compare the competitors showing up in the Acme deal` | `build-competitive-brief` | A concise comparison matrix, objection package, and evidence-backed competitive brief |
-| Build a business case | `Turn these notes into a customer-led ROI story for Acme` | `build-business-case` | A customer-specific value narrative, value model, and follow-up question set |
-| Enrich account or contact data | `Enrich this account list and flag the best buying teams` | `enrich-company-and-contact-data` | A portable enrichment table for segmentation, prioritization, and outreach planning |
-| Find customer quotes | `Pull customer quotes about setup friction from recent calls` | `find-customer-quotes` | Transcript-grounded quotes with attribution and speaker-confidence notes |
-| Coach a rep | `Compare my recent calls with strong peer examples and suggest improvements` | `get-rep-call-feedback` | Evidence-backed coaching feedback tied to repeatable best practices |
-| Review rep call trends | `Show how my calls have changed over the last quarter` | `review-rep-call-trends` | A coaching readout covering improvements, regressions, stable patterns, and next actions |
-| Get a sales check-in | `Give me a sales check-in and suggest the highest-value next prompt` | `suggest-sales-next-step` | A short decision card with one concrete workflow to run next |
+| Prepare for a customer meeting | `Prep me for my next customer meeting` | `prepare-for-meeting` | A concise meeting brief with agenda, context, likely blockers, and next actions |
+| Follow up after a call | `Turn my latest customer call into a follow-up package` | `follow-up-after-call` | A grounded recap, customer and seller actions, email copy, CRM-ready text, and internal recap draft |
+| Analyze account signals | `What changed with Acme in the last two weeks?` | `analyze-account-signals` | An evidence-backed account brief or bounded watchlist summary with recommended actions |
+| Plan an active deal | `Build a strategy for the Acme renewal` | `plan-deal-strategy` | A deal map, buying committee, procurement risks, and prioritized next actions |
+| Review a forecast | `Review my forecast for risk and next actions` | `review-forecast` | A manager-ready rollup with risk posture, recommendation changes, and evidence gaps |
+| Build competitive guidance | `Build a competitive brief for Acme against Competitor Alpha` | `build-competitive-brief` | A source-backed HTML brief with comparison matrix, objection guidance, and caveats |
+| Build a business case | `Build a business case for Acme's support automation initiative` | `build-business-case` | A customer-led value story with workflows, assumptions, scenarios, proof, and open questions |
+| Find customer quotes | `Find customer quotes about setup friction from recent calls` | `find-customer-quotes` | Verbatim transcript-backed quotes with speaker confidence, provenance, and safe usage notes |
+| Coach a rep from calls | `Compare Jamie's discovery calls with strong peer examples` | `get-rep-call-feedback` | Evidence-backed peer exemplars, specific coaching moments, and a practical steal sheet |
+| Review rep call trends | `Review Jamie's call trends over the last three months` | `review-rep-call-trends` | Improvement, regression, stable patterns, calls to revisit, and next coaching actions |
+| Find internal sources | `Find who owns this security objection and what I should read` | `find-key-internal-sources` | Ranked experts, docs, channels, and a draft-ready first ask |
+| Prioritize accounts | `Rank the accounts I should work this week and explain why` | `prioritize-accounts` | A focused account action view with evidence, reachable contacts, and planning-only next steps |
+| Enrich account or contact data | `Enrich this account list and flag likely buying teams` | `enrich-company-and-contact-data` | A portable enrichment table for segmentation, ICP-fit review, and outreach planning |
+| Use Salesforce context | `Use Salesforce as the CRM source for this account` | `salesforce` | Connector guidance for Salesforce-backed account, opportunity, and contact context |
+| Use HubSpot context | `Use HubSpot as the CRM source for this account` | `hubspot` | Connector guidance for HubSpot-backed records, properties, drafts, and approved changes |
 
 ## Integrations
 
@@ -52,4 +51,4 @@ Sales can use configured tools when they are available and authorized:
 | Enrichment and signals | ZoomInfo, Clay, HG Insights, Rox, Actively, Apollo, Meticulate | Company and contact enrichment, market signals, ICP discovery, and account prioritization |
 | Other context | Monday.com, DocuSign | Optional task-tracker and agreement context when a workflow needs it |
 
-Connected tools provide the smoothest path, but they are not required for every task. You can start with uploaded files, pasted notes, transcripts, exports, spreadsheets, and public research when appropriate.
+Connected tools provide the smoothest path, but they are not required for every task. You can start with uploaded files, pasted notes, transcripts, exports, spreadsheets, and public research when appropriate. Source setup is lazy: Sales asks to connect only the tools needed for the task at hand.
diff --git a/plugins/sales/skills/analyze-account-signals/SKILL.md b/plugins/sales/skills/analyze-account-signals/SKILL.md
index 8e797a0..33d88a5 100644
--- a/plugins/sales/skills/analyze-account-signals/SKILL.md
+++ b/plugins/sales/skills/analyze-account-signals/SKILL.md
@@ -1,392 +1,218 @@
 ---
 name: analyze-account-signals
-description: Analyze fresh signals for a named account, owner portfolio, or watchlist and turn them into evidence-backed account intelligence using active Sales source categories and user-provided context.
+description: Use when the user wants to know what changed with one account, monitor an owner portfolio or watchlist, or rank accounts needing attention from recent evidence. Produce an evidence-backed account brief or bounded watchlist summary with recommended actions.
 ---
 
 # Analyze Account Signals
 
-Use this skill for account intelligence workflows that need either:
 
-- `adhoc` single-account signal analysis
-- `monitor` owner-portfolio or watchlist signal analysis
+## Context-Gathering Intake
 
-This skill turns fresh account evidence into a concise view of what changed, why it matters, and what to do next. The initial signal analysis is stateless and read-only: it can run directly from a user prompt or by an external automation, but the skill itself does not own standing monitoring state or recurring digests. After a reviewed signal action exists, it may offer supported posting, sharing, saving, or CRM writeback only after explicit user approval of that reviewed action.
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-## Skill Configuration
+Turn fresh account evidence into a concise view of what changed, why it matters, and what to do next. This skill owns a read-only account brief or bounded watchlist summary; it does not create tasks, post digests, store monitoring state, or write CRM updates.
 
-### User Context
+## Common Skill Instructions
 
-Mandatory pre-answer gate:
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the signal brief or monitor summary.
+## Key Dependency Categories
 
-### Audience And Language
+These categories are particularly important for this workflow; use other sources only when they materially improve the signal story.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+- [Blocking] ~~CRM for authoritative account identity, owner, opportunity, stage, amount, activity, customer-health, and account-status truth. It blocks the default account-signal readout unless sufficiently complete account truth is already grounded in context.
+- ~~Meeting Transcripts for recent customer language, decisions, commitments, objections, follow-ups, and stakeholder movement
+- ~~Email for customer-facing progression, unanswered asks, tone shifts, attachments, and promised next steps
+- ~~Internal Messaging for internal coordination, blockers, stakeholder alignment, and escalation signals
+- ~~Knowledge & Files for account plans, notes, briefs, implementation docs, and prior account context
+- ~~Calendar for recent or upcoming customer sessions, commitments, and scope reduction for large portfolios
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+Use ~~CRM as the default account anchor. Use sufficient user-provided/exported account truth only as a fallback when CRM cannot be used. Communications, files, and calendar evidence explain recency and movement; they do not override CRM-owned account or opportunity fields.
 
-### Source Resolution
+## Reference Loading
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+`SKILL.md` owns the normal mode split, bounded retrieval, and output shape. Load references only when their extra detail changes the decision:
 
-### Required Inputs
+- Use [references/request-schema.yaml](references/request-schema.yaml) when structured input, legacy aliases, selector precedence, or machine-readable output shape matters.
+- Use [references/signal-taxonomy.md](references/signal-taxonomy.md) when signal classification, confidence, recency, ranking, delta status, or display labels are ambiguous.
 
-This skill needs enough user-provided or explicitly inferable context to identify one account, account owner, owner portfolio, watchlist, territory, or provided signal set. Source access can suggest concrete candidates, enrich, or verify the information, but do not draft the full signal output until the account or monitor scope is selected, clearly inferable, or sufficiently supplied by the user.
+## Workflow Guidance
 
-The skill can still produce a limited signal brief or watchlist summary from pasted notes, uploaded/exported context, or user-linked context alone when that context is sufficient. Connectors add significant value and reduce the burden on the user, but do not stop solely because connectors are unavailable.
+### 1. Choose the mode and scope
 
-**Inputs:**
+- If the mode isn't clear, ask the user via `ask_user_input()`. For a bare invocation such as “Analyze account signals for me,” do not assume an Adhoc Account Brief or Monitor Summary; offer:
+  - `Adhoc Account Brief for one account`
+  - `Monitor Summary for accounts I own in CRM`
+  - `Monitor Summary for a watchlist, owner, or territory`
+- Use `Adhoc Account Brief` for one named account or “what changed with [account]?” requests. If the user selected Adhoc but did not supply an account, domain, or CRM id, search, rank, and offer up to five concrete account candidates via `ask_user_input()`. Do not retrieve deeper evidence or draft the brief until the user selects one.
+- Use `Monitor Summary` for owner, territory, portfolio, watchlist, daily-monitor, or “which accounts need attention?” requests. If the user selected Monitor but did not supply a watchlist, owner, territory, or portfolio, offer:
+  - `Accounts I own in CRM`
+  - `A named owner or territory`
+  - `A watchlist or account list I provide`
+  Do not broaden to a representative seller or retrieve deeper evidence until the user selects a scope.
+- Require exactly one account for Adhoc. Require a watchlist, owner, territory, portfolio, or sufficiently detailed account list for Monitor.
+- Default to the last 14 days unless the user provides another window. Preserve supplied focus areas such as expansion, churn, rollout blockers, or stakeholder movement.
+- Accept `sfdc_account_id` as a legacy alias for `crm_account_id`. When structured input conflicts with prompt wording, use the structured input as the source of truth and state the resolved scope.
+- After the mode and scope are known, if the anchor is ambiguous, make a bounded candidate pass before asking: start with ~~CRM when available, make at most three source reads, and offer up to five concrete candidates.
+- If the user says `okay` in response to concrete candidates presented in the current conversation, pick a suitable recent, important-looking, or high-signal account from those candidates instead of skipping. Ask one concise clarification when no suitable account, owner portfolio, or watchlist is visible.
 
-- [required for `Adhoc Account Brief`] One target account by company name, CRM account id, or sufficiently detailed user-provided account context
-- [required for `Monitor Summary`] Watchlist accounts, owner id/email, territory, portfolio, or sufficiently detailed user-provided account list
-- Time window, defaulting to `14d`
-- Focus areas when provided, such as expansion, churn, rollout blockers, or stakeholder movement
-- Output style, defaulting to `brief` for adhoc and `inbox_summary` for monitor
+### 2. Bound and resolve the account universe
 
-Supported modes:
+- For Adhoc, resolve one account by stable CRM id, exact name/domain, or sufficiently detailed user-provided account context.
+- For Monitor, use an explicit watchlist as the full scope. Otherwise use the requested owner/territory/portfolio in ~~CRM with a bounded first pass.
+- For owner-scoped Monitor, prefer primary-owner scope; paginate only while the provider indicates more results and until 50 accounts are collected.
+- If the primary-owner lookup returns zero accounts and the user explicitly requested account-team, territory, role, collaborator, or go-to-market-linked coverage, retry once with the selected ~~CRM app's supported filters and the same cap.
+- If the broader retry times out or fails, continue with primary-owner results when present; otherwise ask for a watchlist or narrower owner scope.
+- Keep Monitor to at most 50 accounts. If the resolved universe is larger, use upcoming external customer meetings in ~~Calendar over the next 14 days to reduce to at most 50; if that cannot reduce it, ask for a narrower scope instead of picking an arbitrary slice.
+- Prefer exact name/domain matches and do not trust the top resolver result blindly when several plausible accounts remain.
 
-- `Adhoc Account Brief`: use for prompts like `give me an account view on Stripe`, `account intelligence for Datadog`, or `what changed with Snowflake`.
-- `Monitor Summary`: use for prompts like `monitor my accounts`, `daily account watchlist`, or `which accounts need attention today`.
+### 3. Collect bounded recent evidence
 
-If the user does not explicitly provide mode, infer `Monitor Summary` for owner-, watchlist-, portfolio-, territory-, or daily-monitor phrasing. Otherwise default to `Adhoc Account Brief`.
+- For each selected account, collect primary ~~CRM truth and user-provided context first. Use ~~Knowledge & Files for account plans or notes that explain the active workstream.
+- For Adhoc, or when primary evidence is thin, deepen selectively in this order when relevant: ~~Email, ~~Knowledge & Files, ~~Meeting Transcripts, ~~Internal Messaging, then ~~Calendar.
+- For Monitor, finish the bounded primary pass across the account set before deepening individual accounts. Deepen only accounts with a possible material delta, ambiguity, or high-value risk/opportunity.
+- Parallelize only independent account lookups, with a practical cap of 10 at a time. Treat a batch timeout or schema error as a call-shape failure, not proof that the source is unavailable; retry failed accounts once by stable account id when available, then mark them unavailable and continue with surviving accounts.
+- Stop when the recent story is supported. If a recency source was checked and found no match, say `checked/no match` when that absence materially affects confidence.
 
-Use [references/request-schema.yaml](references/request-schema.yaml) when structured input validation, YAML normalization, or machine-readable field shape matters. Structured input wins over conflicting prompt wording. Preserve `sfdc_account_id` as a legacy alias for `crm_account_id`.
+### 4. Normalize, score, and interpret signals
 
-If required inputs remain missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. First attempt a bounded candidate pass through the source category that owns the missing anchor. Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. For account selection, start with `crm` when available, then use user-provided context, `calendar`, `meeting_notes`, `document_store`, `external_messaging`, `internal_messaging`, or saved book-of-business context as supporting evidence only when that fits the budget. Before asking the user to paste an account list, name an account, or manually define a watchlist, either offer concrete candidates from those source categories or say which attempted categories were unavailable or empty. Ask one friendly clarification only after that pass. Ask only for unresolved inputs; for each question, include up to 5 concrete lettered options from source-backed or user-provided candidates when available. If no concrete candidates are available, briefly say why and ask free-form.
+- Normalize evidence into the fixed taxonomy in `references/signal-taxonomy.md`; do not invent new signal labels unless the user explicitly extends the workflow.
+- For every signal preserve type, summary, source, recency, confidence, evidence, citation, and suggested action.
+- Deduplicate several sources describing the same event. Corroboration raises confidence; weak, stale, contradictory, or unsupported evidence lowers confidence and often becomes an Evidence gap.
+- Distinguish actionable account risk from hygiene-only context such as ownership gaps, missing routing fields, or ambiguous associations; surface hygiene only when it affects monitoring confidence or the suggested action.
+- For Adhoc, order signals by importance to the account story: risk/opportunity first, then dependencies, then supporting context.
+- For Monitor, rank fresh, high-confidence, action-relevant change. Prioritize Churn or retention risk and Expansion opportunity; down-rank stale or speculative signals and compress accounts with no material delta.
+- Give each ranked account an attention score of High, Medium, or Low and a directional posture of Expansion ready, Expansion blocked, Retention risk, or Execution risk.
 
-**Input Request Format Example:**
+### 5. Separate evidence from interpretation
 
-```md
-Which account, owner portfolio, or watchlist should I analyze?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-If the user says `okay` from guided exploration without naming an anchor, pick a suitable account from Sales context instead of skipping. Prefer a recent, important-looking, or high-signal account from CRM, calendar, recent calls, saved book-of-business context, or the current thread. If no suitable account, owner portfolio, or watchlist is visible, ask one concise clarification.
-
-Stop the workflow only when no account, owner portfolio, watchlist, territory, or sufficiently detailed user-provided signal set can be identified, or when a monitor scope is too broad to bound safely and cannot be narrowed by user input or source-backed candidate selection.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `crm`: default company-name-to-account resolver when available; authoritative account, opportunity, ownership, recent activity, customer-health, support, and account-status fields.
-- `document_store`: account plans, account notes, briefs, prior account context, implementation docs, and artifacts that explain what changed or what workstream is active.
-- `meeting_notes`: recent customer-call evidence, customer language, commitments, objections, decisions, follow-ups, and stakeholder movement.
-- `external_messaging`: customer-facing thread progression, unanswered asks, rollout chatter, tone shifts, attachments, and promised next steps.
-- `internal_messaging`: internal coordination, follow-up, stakeholder alignment, blocker context, and escalation signals.
-- `calendar`: recent or upcoming customer sessions, near-term commitments, external meetings, and monitor scope reduction for overly broad portfolios.
-- user-provided context: pasted notes, uploads, exports, account lists, CRM snippets, call notes, docs, and linked context that can serve as working evidence.
+- Put clickable source links close to the claims they support. For CRM, use connector-provided record URLs; construct a URL from an id only when trusted connector metadata exposes the instance base URL. Use plain source labels when no stable link exists and do not expose naked record ids as the primary action path.
+- Keep raw evidence and strategic interpretation visibly separate. Every recommended action must trace to a signal or evidence item.
+- If CRM and manual account truth are both unavailable, state that the recency anchor is unavailable and do not imply the result is current.
 
-These are semantic source categories, not fixed connector names. Resolve each attempted source to the specific available app, connector, or helper skill through Sales preflight `context.sources` before using connector-specific logic.
+### Next Step Options
 
-Source obligations by intent:
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Deepen one account or close the smallest material evidence gap.
+- Draft a concise internal account-team update grounded in the signals.
+- Hand the selected account to deal strategy or meeting prep when the evidence points to an active motion or upcoming conversation.
+- Check whether a matching daily or weekly automation already reruns this `analyze-account-signals` skill for the same monitor scope; if none exists, offer to create one that reruns the skill and flags which accounts need attention, why it matters, and the next best sales action.
+- Draft CRM-ready updates for review when missing or stale fields are affecting confidence.
 
-- For `latest`, `what changed`, account-monitoring, or watchlist prompts, use `crm` or sufficient user-provided/exported account truth as the account anchor, then check at least one recency source when available: `meeting_notes`, `calendar`, `external_messaging`, or recent account documents.
-- For `Adhoc Account Brief`, `crm` is required when CRM is available and an account anchor is supplied, inferred, or confirmed. Use CRM as the account resolver and source of account/opportunity truth. If `crm` is unavailable but manual account context is sufficient, continue with a limitation; if both are missing, stop.
-- For `Monitor Summary`, keep the account universe bounded by the provided watchlist, owner, territory, portfolio, or resolved `crm` scope before enrichment. Do not broaden to arbitrary accounts unless the user asks or the first pass is empty and the user confirms a broader scope.
-- If a recency source is checked and has no relevant result, say `checked/no match` in source notes or evidence gaps when that absence materially affects confidence. Do not imply fresh customer evidence exists when only CRM current-state fields were available.
+Next steps to avoid:
+- Creating tasks, posting updates, or writing CRM changes automatically.
 
-Authority and gaps:
+### Automation Offer Guard
 
-- Treat recent `crm` account activity, user-provided context, and supporting communication sources as the recency anchor on every run.
-- Treat `crm` as the system of record for account and opportunity fields when available.
-- Treat `meeting_notes`, `calendar`, `external_messaging`, `internal_messaging`, and `document_store` as recency or explanation sources, not replacements for CRM-owned account, opportunity, owner, stage, amount, close-date, or commercial-posture truth.
-- Do not let document, messaging, public, or third-party enrichment context override CRM-owned account truth.
-- Treat `document_store` and communication sources as supporting context for what changed, what is active, and what needs attention soon.
-- Treat user-provided notes, uploads, exports, and linked context as valid working evidence; label limitations when they cannot be verified against connector sources.
-- If `crm` and manual account context are both unavailable for a target account, say the recency anchor is unavailable and do not pretend the result is current.
-- If optional sources fail, continue with the strongest available evidence and explicitly note material evidence gaps.
-- Do not use public web research, external news search, external enrichment, or task trackers in v1 unless the user explicitly extends the workflow and provides the source, export, or matching connector.
+For `Monitor Summary` outputs, a recurring seller account watch is the preferred next step when the user has provided a reusable scope such as a watchlist, owner, territory, or portfolio and the summary would benefit from daily or weekly refresh. Frame the value in sales language: catching pipeline movement, expansion signals, retention risk, stalled next steps, stakeholder changes, upcoming customer meetings, and account-team blockers before they get missed.
 
-### Context Gathering Principles
+The automation must be a scheduled rerun of this skill, not a separate custom digest. When creating or describing the automation, make the prompt call this skill directly and preserve the same normalized request shape the skill accepts where possible:
 
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the analyze-account-signals experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Analyze Account Signals
-This skill helps turn fresh customer, account, product, market, meeting, email, CRM, and internal-message signals into a concise view of what changed and what to do next.
+```text
+Use the Sales `analyze-account-signals` skill in `Monitor Summary` mode.
+Rerun it for the same seller account scope: [watchlist, owner, territory, portfolio, or CRM filter].
+Return the standard Monitor Summary output.
 
+mode: "monitor"
+owner_id: "[owner id when used]"
+owner_email: "[owner email when used]"
+watchlist_accounts: [same watchlist when used]
+time_window: "[same or agreed recency window, default 14d]"
+focus_areas: [same focus areas such as expansion, churn, rollout blockers, stakeholder movement]
+output_style: "inbox_summary"
 ```
 
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Analyze Account Signals next-step behavior and action text. Onboarding, draft creation, CRM writeback, posting or sharing, tracker creation, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the account-signal continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `analyze-account-signals` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the signal brief** | The first substantive account-signal brief has just been produced. | `Anything you'd change in the signal brief before we act on it?` |
-| **2. Review the revised signal brief** | The user asked for any change to signal selection, priority, interpretation, confidence, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to draft a seller follow-up, account-team update, escalation note, CRM note, or tracker?` |
-| **3. Prepare the signal action** | The user accepts the brief, says no changes are needed, says a revision looks good, or asks to operationalize the signal. | Offer to produce the most useful next artifact or draft action, prioritizing seller follow-up, account-team update, escalation note, CRM-ready note, or tracker over broad suggestions. |
-| **4. Review the artifact or draft** | A follow-up, account-team update, escalation note, CRM-ready note, or tracker exists, or the user asks to change it. | `Anything you'd change before I save, post, share, or write this where it belongs?` |
-| **5. Post, share, save, or write approved signal action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as posting the account-team update, sharing the escalation note, saving the tracker, or writing the approved CRM note. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the signal brief, treat a lightweight acknowledgement as acceptance and move to `Prepare the signal action`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to post, share, save, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-## Procedure
-
-### 1) Normalize the request
-
-Normalize the prompt, pasted YAML, or attached YAML using the input model.
-
-Rules:
-
-- Use explicit fields from structured input when present.
-- If both `company_name` and `crm_account_id` are present, trust the account id.
-- Accept `sfdc_account_id` as a legacy alias for `crm_account_id`.
-- If both `watchlist_accounts` and `owner_id`/`owner_email` are present in monitor mode, use the watchlist and skip owner lookup.
-- Accept `watchlist_accounts` as a list of names, ids, or mixed entries.
-- Preserve user-provided `focus_areas`.
-
-### 2) Select the account scope
-
-For `Adhoc Account Brief`, operate on exactly one account.
-
-For `Monitor Summary`:
-
-- Use `watchlist_accounts` as the full scope when present and non-empty.
-- Otherwise select the available `crm` connector, apply any relevant helper skills returned for that source category, discover the provider's owner/account fields or filters, and query accounts owned by the requested seller or owner with a bounded result set.
-- Keep the first pass primary-owner scoped; paginate only while the provider indicates more results and until `50` accounts are collected.
-- If the primary-owner lookup returns zero accounts and the user explicitly asked for account-team, territory, role, collaborator, or go-to-market-linked coverage, retry once with the selected `crm` connector's supported filters and the same cap.
-- If the broader retry times out or fails, continue with primary-owner results when present; otherwise stop and ask for a watchlist or narrower owner scope.
-- If the resulting scope has more than `50` accounts, use `calendar` to find upcoming external customer meetings in the next `14` days and reduce to at most `50` meeting-linked accounts.
-- If the scope is larger than `50` and `calendar` is unavailable or yields no upcoming meeting-linked accounts, stop and ask for a watchlist or narrower scope instead of picking an arbitrary slice.
-- Resolve each final account individually and collect bounded recent evidence.
-- If parallel execution is available, run up to `10` independent single-account evidence lookups in parallel. Otherwise process sequentially with the same per-account shape.
+For territory or portfolio scopes that are not expressible as `owner_id`, `owner_email`, or `watchlist_accounts`, keep the stable scope in the plain-language prompt text and let this skill resolve the account universe using its normal Monitor Summary guidance.
 
-### 3) Resolve account identity
+The recurring output should follow this skill's `Monitor Summary` format: ranked accounts requiring attention, material deltas, run metadata, and no-major-delta handling. Keep it read-only; it may recommend next sales actions, but must not create tasks, post digests, store monitoring state outside the automation, or write CRM updates unless the user separately asks and approves.
 
-Resolution order for each account:
+Before offering a recurring monitor, check whether the user already has a matching local automation installed. Inspect local automation records under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset, and match by name, prompt, skill name, mode, account set, owner, territory, portfolio, or other stable scope details. Treat active and paused matches as already installed.
 
-1. Provided `crm_account_id`
-2. `crm` account resolver when available
-3. User-provided domain, account notes, or `document_store` account-plan context as a tie-breaker when `crm` resolution is ambiguous, low-confidence, or inconsistent with the requested company
+- If a matching automation exists, do not suggest creating another one. Continue with the next most relevant non-automation follow-up.
+- If no matching automation exists, end with one clear offer to check/create a daily or weekly rerun of `analyze-account-signals` for the same scope. Describe the recurring output as a short seller-ready digest of accounts needing attention, why each matters commercially, and the recommended next action. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, do not mention tool details; offer to help set up a recurring check when automations are available.
 
-Rules:
+## Modes
 
-- Do not trust the top ranked resolver result blindly when there are multiple plausible matches.
-- Prefer exact company-name or domain-aligned matches.
-- Note ambiguity handling in the final output when it affects confidence.
+- `Adhoc Account Brief` — one account, default brief output.
+- `Monitor Summary` — bounded watchlist/owner/portfolio view, default inbox-style output.
 
-### 4) Collect primary evidence
+## Output Format
 
-Always collect, when available:
-
-- `crm` account, opportunity, ownership, recent activity, and open customer-health or support signals
-- user-provided account context
-- `document_store` account plans, account notes, briefs, or prior context
-
-For monitor mode:
-
-- Apply the same bounded evidence pass once per selected account.
-- Prefer exact account id lookups and parallelize only across independent single-account calls.
-- Treat a timeout or schema error on a multi-account request shape as a call-shape failure, not as proof that `crm` or `document_store` is broadly unavailable.
-- Retry only failed accounts once, preferring exact `account_id`.
-- If a specific account still fails after the retry, mark it as `primary_evidence=unavailable` and continue with surviving accounts.
-- Do not deepen every account with heavier sources before the per-account primary evidence pass completes.
-
-Deepen with `document_store`, `meeting_notes`, `external_messaging`, or `internal_messaging` when:
-
-- the run is adhoc;
-- primary evidence is too thin to support interpretation;
-- identity resolution required notes or communications context.
-
-### 5) Collect supporting evidence
-
-Use supporting sources only to strengthen, corroborate, or refine the account story. Do not let weaker sources override stronger account truth.
-
-Supporting source order:
-
-1. `external_messaging`
-2. `document_store`
-3. `meeting_notes`
-4. `internal_messaging`
-5. `calendar`
-
-For portfolios larger than `50`, `calendar` moves earlier and acts as the required scope-reduction source before enrichment begins.
-
-Use supporting sources for corroboration, richer context, stakeholder movement, blocker details, and near-term commitments. Do not use supporting sources for overriding clear CRM account or opportunity truth, inventing account metrics, or replacing account resolution logic.
-
-### 6) Normalize signals
-
-Convert collected evidence into the fixed signal taxonomy. Use only these default signal types unless the skill is explicitly extended later:
-
-- `momentum`
-- `expansion_opportunity`
-- `churn/risk`
-- `blocker/dependency`
-- `stakeholder_movement`
-- `upcoming_commitment_or_deadline`
-- `evidence_gap`
-
-For each signal, preserve:
-
-- `signal_type`
-- `summary`
-- `source`
-- `recency`
-- `confidence`
-- `evidence`
-- `citation`
-- `recommended_next_step`
-
-Use [references/signal-taxonomy.md](references/signal-taxonomy.md) when signal classification, confidence, recency, monitor ranking, delta classification, or user-facing label mapping is ambiguous.
-
-Rules:
-
-- Multiple pieces of corroborating evidence may support one signal.
-- Do not create duplicate signals when several sources describe the same event.
-- If evidence is weak, stale, or contradictory, lower confidence and add an evidence-gap note.
-- Distinguish actionable account risks from hygiene-style context such as ownership gaps, missing routing fields, or ambiguous associations; surface the latter only when they affect monitoring confidence or the recommended next step.
-- Preserve source identifiers internally, but prefer user-actionable links in the final output over raw ids.
-
-### 7) Score and rank
-
-For adhoc mode:
-
-- Do not rank the account against others.
-- Sort signals by importance to the account story: risk/opportunity first, then dependencies, then supporting context.
+### Adhoc Account Brief
 
-For monitor mode:
+```md
+# Account Snapshot
 
-- Rank accounts by fresh, high-confidence, action-relevant change.
-- Prioritize accounts with strong `churn/risk` or `expansion_opportunity` signals.
-- Rank corroborated multi-source signals above single-source weak signals.
-- Down-rank stale or speculative signals.
-- Omit or compress accounts with no material delta.
-- When monitor scope was filtered by upcoming meetings, rank within that filtered set only.
-- Assign an attention score (`high`, `medium`, or `low`) per ranked account.
-- Assign a directional posture per ranked account: `expansion_ready`, `expansion_blocked`, `retention_risk`, or `execution_risk`.
-- Classify each material signal change as `new`, `updated`, `worsened`, `resolved`, or `unchanged`.
+- **Account:** [Linked account when available]
+- **Time Window:** [Window]
+- **Current Posture:** [1-2 grounded lines]
 
-### 8) Cite and separate evidence from inference
+## Key Recent Signals
 
-Keep raw evidence separate from interpretation. Every recommendation must be traceable to explicit evidence and, when the source exposes a usable URL, a clickable citation.
+- **[Plain-English signal label]** — [What changed] · [Fresh/Recent/Stale] · [High/Medium/Low confidence] · [Source link/label]
 
-Citation rules:
+## Strategic Interpretation
 
-- When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL.
-- In `Account Snapshot` and `Ranked Accounts Requiring Attention`, render the company name as a Markdown link to the resolved CRM account when a link can be built.
-- For CRM evidence, prefer connector-provided record URLs. If only a CRM record id is available, construct a record URL only when trusted connector metadata exposes the CRM instance base URL; otherwise use the record name and object label without inventing a URL.
-- For Google Docs, Drive files, Slack messages, calendar events, meeting notes, or files, use connector-provided document URLs, file URLs, permalinks, event links, or meeting links when available.
-- Put citations close to the claim they support.
-- If a source has no usable URL, use a plain-language provenance label such as `CRM activity`, `Drive account plan`, or `Slack search result`; do not expose a naked id unless the user asks for ids or ambiguity requires it.
+- [What the signals likely mean for land, expand, retention, or execution risk; label inference]
 
-## Output Shapes
+## Recommended Actions
 
-Use concise Markdown headings and bullets. Use plain-English labels instead of raw enum tokens in user-facing output. Internal enum tokens may be preserved for machine parsing, but they should not be the primary displayed label.
+1. [Action] — [Evidence or signal link] — [Expected outcome]
 
-### Adhoc Account Brief
+## Open Questions / Missing Evidence
 
-Return these sections in order:
+- [Unknown or gap] — [smallest follow-up evidence that would reduce uncertainty]
 
-1. `Account Snapshot`
-2. `Key Recent Signals`
-3. `Strategic Interpretation`
-4. `Recommended Actions`
-5. `Open Questions / Missing Evidence`
+---
 
-`Account Snapshot` includes the linked account name when available, time window used, and one or two lines on current account posture.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-`Key Recent Signals` lists normalized signals ordered by importance. Each signal should state what changed, sources, citation links when available, recency, and confidence.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-`Strategic Interpretation` explains what the signals likely mean for land, expand, retention, or execution risk, making high-confidence conclusions distinct from tentative conclusions.
+### Monitor Summary
 
-`Recommended Actions` is a short, prioritized action list. Each action must cite the signal or evidence it is based on.
+```md
+# Ranked Accounts Requiring Attention
 
-`Open Questions / Missing Evidence` lists unresolved unknowns, missing sources, and the follow-up evidence that would reduce uncertainty.
+| Account | What Changed | Why It Matters | Attention | Posture | Suggested Action | Confidence / Evidence |
+| --- | --- | --- | --- | --- | --- | --- |
+| [Linked account] | [Delta] | [Impact] | [High/Medium/Low] | [Posture] | [Action] | [Link/label] |
 
-### Monitor Summary
+## Material Deltas
 
-Return these sections in order:
+| Date | Account | Signal | Status | Delta Summary | Impact | Next Step | Confidence |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| [Date] | [Account] | [Label] | [New/Updated/Worsened/Resolved] | [Delta] | [Impact] | [Action] | [Confidence] |
 
-1. `Ranked Accounts Requiring Attention`
-2. `Material Deltas`
-3. `Run Metadata`
-4. `No Major Delta`, optional
+## Run Metadata
 
-For each ranked account, include:
+- **Time Window:** [Window]
+- **Data Freshness Cutoff:** [Cutoff or limitation]
+- **Scope:** [Universe, checked count, ranked count]
+- **Delta Since Last Run:** [Only when prior-run context exists]
 
-- linked account name when a CRM record link is available
-- what changed
-- why it matters
-- recommended next step
-- confidence / evidence note
-- attention score (`high`, `medium`, or `low`)
-- directional posture (`expansion_ready`, `expansion_blocked`, `retention_risk`, or `execution_risk`)
+## No Major Delta
 
-For `Material Deltas`, include only material changes from the selected time window. Each row or bullet must include date, signal, status, delta summary, impact, next step, and confidence.
+[Include only when useful.]
 
-For `Run Metadata`, include time window used, data freshness cutoff, account scope size, accounts checked, accounts ranked, and a concise `delta_since_last_run` note when prior-run context is provided by the caller.
+---
 
-Use these user-facing labels in monitor output:
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-- `momentum` -> `Momentum`
-- `expansion_opportunity` -> `Expansion opportunity`
-- `churn/risk` -> `Churn or retention risk`
-- `blocker/dependency` -> `Blocker or dependency`
-- `stakeholder_movement` -> `Stakeholder movement`
-- `upcoming_commitment_or_deadline` -> `Milestone or deadline`
-- `evidence_gap` -> `Evidence gap`
-- `new` -> `New this period`
-- `updated` -> `Updated this period`
-- `worsened` -> `Worsened this period`
-- `resolved` -> `Resolved this period`
-- `unchanged` -> `No change this period`
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-## Fallback Behavior
+## Rules
 
-- If account resolution fails, stop that account and say what identifier is missing or ambiguous.
-- If `crm` and manual account context are both unavailable for a target account, say the recency anchor is unavailable and do not pretend the result is current.
-- If optional sources fail, continue with the strongest available evidence and explicitly note any material evidence gaps.
-- If a monitor scope is larger than `50` and `calendar` cannot reduce it to meeting-linked accounts, stop and ask for a watchlist or narrower scope.
-- If a monitor run returns no accounts with a material delta, return a concise summary saying no accounts currently require prioritization based on the available evidence.
+- Do not fabricate accounts, owners, opportunities, metrics, signals, dates, links, or recommended actions.
+- Do not use public web/news research, external enrichment, or task trackers unless the user explicitly extends the workflow and supplies a relevant source.
+- Do not create tasks, posts, digests, CRM updates, or other writebacks in this workflow.
+- Use plain-English display labels rather than raw taxonomy tokens in user-facing output.
+- Rewrite unsupported claims as questions, risks, or evidence gaps rather than assertions.
+- If a monitor run has no material delta, say so concisely instead of padding the ranking.
 
-## Rules
+## Failure Handling
 
-- Keep evidence and interpretation visibly separate.
-- Every recommendation must be traceable to evidence.
-- Degrade cleanly when optional connectors are unavailable.
-- Do not use public web research or news enrichment in v1 unless the user explicitly asks for it.
-- Do not create task issues, `internal_messaging` posts, `crm` updates, or other writebacks in v1.
-- Do not present raw CRM ids as the main user-facing way to act on an account.
-- Do not imply certainty that the sources do not support.
-- Rewrite unsupported claims as questions, risks, or evidence gaps instead of assertions.
-- Do not expose internal tool names in user-facing prose unless the user is explicitly asking about the workflow itself.
+If account resolution fails, state what identifier is missing or ambiguous and ask for the smallest clarification. If optional sources fail, continue with the strongest grounded evidence and name material gaps. If Monitor scope cannot be safely reduced to 50 or fewer accounts, ask for a watchlist or narrower scope.
diff --git a/plugins/sales/skills/analyze-account-signals/references/request-schema.yaml b/plugins/sales/skills/analyze-account-signals/references/request-schema.yaml
index 9e6572a..5068d88 100644
--- a/plugins/sales/skills/analyze-account-signals/references/request-schema.yaml
+++ b/plugins/sales/skills/analyze-account-signals/references/request-schema.yaml
@@ -22,9 +22,9 @@ owner_email: ""            # fallback owner identifier for monitor mode
 watchlist_accounts: []
 # Example:
 # watchlist_accounts:
-#   - "Datadog"
+#   - "ExampleCorp"
 #   - "001US00000ABC123"
-#   - company_name: "Stripe"
+#   - company_name: "ExampleCo"
 #     crm_account_id: "001US00000XYZ456"
 
 # Recency window for source queries and signal freshness.
diff --git a/plugins/sales/skills/analyze-account-signals/references/signal-taxonomy.md b/plugins/sales/skills/analyze-account-signals/references/signal-taxonomy.md
index ecbed61..797922b 100644
--- a/plugins/sales/skills/analyze-account-signals/references/signal-taxonomy.md
+++ b/plugins/sales/skills/analyze-account-signals/references/signal-taxonomy.md
@@ -89,6 +89,7 @@ Each signal must store:
 - `recency`
 - `confidence`
 - `evidence`
+- `citation`
 - `recommended_next_step`
 
 ## Confidence guidance
@@ -99,7 +100,7 @@ Use a simple three-level confidence scale:
 - `low`: weak, stale, or contradictory evidence; use mainly to frame uncertainty
 
 Rules:
-- recent `crm` activity or corroborated communication evidence can support `high` confidence on recent account-state changes
+- recent ~~CRM activity or corroborated communication evidence can support `high` confidence on recent account-state changes
 - multi-source corroboration raises confidence
 - unsupported interpretation must not be labeled `high`
 - conflicting evidence should lower confidence and usually produce an `evidence_gap` signal
diff --git a/plugins/sales/skills/answers-ask-user-input/SKILL.md b/plugins/sales/skills/answers-ask-user-input/SKILL.md
new file mode 100644
index 0000000..9727d2e
--- /dev/null
+++ b/plugins/sales/skills/answers-ask-user-input/SKILL.md
@@ -0,0 +1,86 @@
+---
+name: answers-ask-user-input
+description: "Use when a small amount of missing context would materially improve the answer and tappable options plus a free-text answer can gather it efficiently."
+---
+
+# Ask User Input
+
+## `ask_user_input`
+
+purpose: present tappable options plus a free-text option to gather user preferences before providing advice. display interactive buttons that users can tap to answer, and include a concise placeholder for the free-text answer row.
+
+when to use this tool:
+- use for elicitation -- when you need to understand the user's preferences, constraints, or goals to give useful advice
+- examples:
+  - "help me plan a workout routine": ask about goals (strength/cardio/weight loss), time available, equipment access
+  - "help me find a book to read": ask about genres, mood, recent favorites
+  - "I'm thinking about getting a pet": ask about lifestyle, living situation, time commitment
+  - "help me pick a gift for my friend": ask about occasion, budget, friend's interests
+
+question quality requirements:
+- each question should uncover a concrete preference, constraint, or tradeoff that would actually change your recommendation. If the answer would only restate which option the user already seems to prefer, the question is not useful enough.
+- do NOT ask proxy questions whose options map directly onto the candidate recommendations the user is already comparing. Ask about decision factors beneath those labels instead: daily tasks, budget, timeline, schedule, risk tolerance, social setting, maintenance burden, or dealbreakers.
+- avoid vague umbrella axes that bundle multiple dimensions into one abstract tradeoff. Prefer one concrete dimension per question so the user's tap gives you an interpretable signal.
+- examples:
+  - bad: user asks "Should I be an EMT or a firefighter?" and you ask "How do you feel about physical intensity vs medical focus?" with options that correspond to firefighter vs EMT.
+  - better: ask "Which day-to-day work sounds more appealing?" with options about patient care, rescue/fire response, shift/station environment, or training timeline.
+
+free-text placeholder requirements:
+- every question MUST include free_text_placeholder
+- make the placeholder short, concrete, and specific to the question, such as "Add another constraint" or "Describe your ideal pace"
+- do not use generic placeholders like "Type your answer" unless the question truly has no more specific hint
+
+when NOT to use this tool:
+- user asks "A or B": they want your analysis and recommendation, not the options repeated back as buttons
+- user is venting or processing emotions (e.g. "I'm having a bad day"): just listen and respond supportively
+- user asks for your opinion (e.g. "what do you think of eggs?"): give your perspective directly
+- factual questions (e.g. "what's the capital of France?"): just answer
+- user needs prose feedback (e.g. "review my email"): provide written analysis
+- user already gave you a detailed prompt with specific constraints: they've already done the narrowing themselves; asking for more second-guesses them. Proceed with their constraints and state any assumption you make inline
+
+key instructions:
+- always include a brief conversational message before presenting options -- don't show options silently
+- you can ask up to 3 questions. Three is a ceiling, not a target.
+- use 2-4 short, distinct options per question. You can use up to 10, but prefer 2-4.
+- after calling this tool, your turn is done. Don't keep writing.
+- prefer asking questions that can be multi-select. Most questions aren't mutually exclusive.
+
+Invocation:
+// Insert directly:
+genui{"ask_user_input": {"questions": {...}}}
+// This widget is not eligible for UUID Mode.
+
+Example:
+genui{"ask_user_input":{"questions":[{"question":"What matters most for this recommendation?","options":["Lower cost","Less effort","Best quality"],"type":"multi_select","free_text_placeholder":"Add another priority"}]}}
+
+Args schema:
+```text
+// AskUserInputWidgetData
+{
+// Questions
+//
+// Ordered list of one-question pages to render.
+// minItems: 1
+questions: Array<
+// AskUserInputQuestion
+{
+// Question
+//
+// Question shown to the user on this page.
+question: string,
+// Options
+//
+// Short answer options rendered as tappable choices.
+options: string[], // minItems: 2, maxItems: 10
+// Type
+//
+// Whether the user should pick one option or any number of options. Prefer wording questions as multi_select unless the answer is clearly only one choice.
+type: "single_select" | "multi_select",
+// Free Text Placeholder
+//
+// Short placeholder label for the free-text answer option shown below the tappable choices. Make it specific to this question.
+free_text_placeholder: string,
+}
+>,
+}
+```
diff --git a/plugins/sales/skills/apollo/SKILL.md b/plugins/sales/skills/apollo/SKILL.md
new file mode 100644
index 0000000..30d495e
--- /dev/null
+++ b/plugins/sales/skills/apollo/SKILL.md
@@ -0,0 +1,212 @@
+---
+name: apollo
+description: Use only when a focused Sales workflow has selected a present and connected Apollo connector, or the user explicitly asks for Apollo prospecting, enrichment, Company Details, records, sequences, or outbound planning. Apply Apollo v2-specific behavior only after verifying app version 2.0.0 or later.
+---
+
+# Apollo User Guide
+
+Use Apollo for focused Sales Intelligence work after the parent Sales workflow selects it. Keep the parent workflow authoritative for the seller's task; this guide owns Apollo-specific version, credit, identifier, mutation, and outbound safety rules.
+
+## Common Skill Instructions
+
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
+
+## Apollo Version Gate
+
+This guide's v2-specific behavior applies only when Apollo app version >= 2.0.0 is verified from visible app metadata, platform context, or explicit v2-only surfaces.
+
+- V2-only evidence includes exposed company-search or Company Details preview wrappers, bulk account or contact create-update, sequence create-update, schedule lookup, campaign or sequence approval, or standalone Apollo usage-stats credit tooling.
+- Generic People Search, Contact Search, Organization Search, enrichment, email account lookup, single-record create-update, sequence search, add/remove sequence contacts, job postings, or analytics do not prove v2.
+- If version is unknown, unavailable, or below 2.0.0, do not apply v2-only assumptions or call v2-only mutations, enrollment, activation, or v2-only credit-consuming flows. Generic enrichment can still proceed under the normal Apollo-credit approval rules; otherwise explain the limitation.
+
+## Apollo Operation Ranking
+
+1. Explicit user request, named company or person, domain, email, Apollo ID, sequence, or stated action
+2. The narrowest read-only lane needed by the active Sales workflow
+3. Strong identifier resolution and a compact candidate preview
+4. Search and shortlist before credit-consuming enrichment or Company Details
+5. A reviewed mutation or launch preview
+6. An explicitly approved exact action followed by verification
+
+Never turn a draft, search, clarification, or general approval into a mutation, enrollment, activation, or send.
+
+## Key Dependency Categories
+
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
+
+- ~~Sales Intelligence, specifically a present and connected Apollo connector, for Apollo prospecting, enrichment, Company Details, records, and sequence planning
+- ~~CRM for customer status, opportunity facts, ownership, and forecast truth
+- The parent Sales workflow for Email, Calendar, Meeting Transcripts, Internal Messaging, or Knowledge & Files context
+- User-provided names, domains, emails, IDs, ICP constraints, sequence intent, and approval when connector context is incomplete
+
+If Apollo is missing, unavailable, ambiguous, version-limited, or unsupported by the exposed surface, state the limitation.
+
+## Connector Boundary
+
+Choose the narrowest Apollo lane that fits the request. Use the live Apollo tool schema; do not invent replacement tools when a named action is unavailable.
+
+- Identity and Apollo credit awareness: user profile and credit usage reads.
+- Discovery: People Search, Contact Search, Organization Search, existing sequence search, and email account lookup.
+- Company Details and enrichment: organization enrichment, bulk organization enrichment, people enrichment, bulk people enrichment, and organization job postings when exposed.
+- Account/contact records: account create-update, contact create-update, and bulk account/contact create-update when exposed.
+- Sequences and launch: sequence search, sequence create-update when exposed, schedule lookup when exposed, sender account lookup, sequence approval or activation when exposed, add contacts to sequence, and remove or stop contacts from sequences.
+- Analytics: read-only Apollo sales analytics reports when relevant.
+
+Do not present Apollo as CRM ground truth. Apollo can support prospecting, enrichment, contactability, account/contact record preparation, and outbound sequence work; CRM-owned customer status, opportunity facts, ownership, and forecast truth should still come from ~~CRM when available.
+
+## Credit-Aware Behavior
+
+Say "Apollo credit," not "API credit."
+
+Before broad, open-ended, or credit-consuming Apollo work, make the scope and likely Apollo credit exposure visible and get explicit approval. Credit-consuming actions commonly include Company Search, Organization Search, Company Details or organization enrichment, people enrichment, bulk enrichment, job postings, and any other Apollo tool whose live description says it consumes credits.
+
+Use free or lower-cost narrowing steps first when practical:
+
+1. Search or resolve the working set.
+2. Show a compact preview of the candidates, filters, and count.
+3. Ask for approval before credit-consuming enrichment or Company Details.
+4. Enrich only the selected or materially useful rows.
+
+Do not add unnecessary friction for a small, clearly requested lookup when the live tool policy already permits it, but still mention Apollo credit use when the action consumes meaningful credits.
+
+## Search And Resolution
+
+### People And Contacts
+
+- Use People Search for net-new prospects in Apollo's database. It should not be treated as returning raw email addresses or phone numbers unless the live tool response explicitly does so and the user has asked for contactability.
+- Use Contact Search for contacts already added to the team's Apollo account.
+- Combine title, seniority, company/domain, location, technology, company size, and revenue filters when they improve precision.
+- If the user's persona phrase is ambiguous, state one concise assumption instead of stopping.
+- For broad role families, prefer one well-scoped search with combined title variants when the schema supports it; split only when the first pass is sparse or off-target.
+- Do not infer missing emails, phone numbers, LinkedIn profiles, or confidence scores.
+
+### Companies
+
+- Prefer domains, Apollo organization IDs, or exact company names plus location/industry when resolving companies.
+- For company-only discovery, preview the filters and possible Apollo credit exposure before running a credit-consuming company search.
+- Keep company-only results company-only: do not add people columns, contactability labels, or outreach actions unless the user separately asks for contacts.
+- If name-only rows are ambiguous, leave them unresolved or ask for domains rather than guessing.
+
+### Identifiers
+
+Use Apollo-returned IDs internally when a downstream Apollo action requires them. Do not fabricate account IDs, contact IDs, organization IDs, person IDs, sequence IDs, sender account IDs, schedule IDs, preview keys, or confirmation flags.
+
+Do not expose internal IDs in ordinary seller-facing output unless the user is explicitly debugging an Apollo integration, validating connector behavior, or preparing an admin handoff that needs exact identifiers.
+
+## Enrichment And Company Details
+
+Use Company Details language for Apollo organization enrichment when it produces firmographic context such as employees, industry, location, description, funding, revenue, corporate phone, or related company facts.
+
+For People to Company Details:
+
+1. Convert the seller's ICP into focused people filters.
+2. Run the appropriate people or contact search.
+3. Show the people table first.
+4. Offer Company Details as the next step.
+5. Preview the selected company domains or organizations and Apollo credit exposure.
+6. Ask for explicit approval.
+7. After approval, run Company Details and show the combined table.
+
+For people enrichment:
+
+- Prefer the strongest available identifiers: Apollo person ID, business email, LinkedIn URL, domain plus name, or company plus name.
+- Bulk people enrichment should stay within the live tool's batch size and should be limited to the rows the user needs.
+- Keep `reveal_personal_emails` false by default. Use any personal-email or phone reveal option only when the user explicitly asks, the live tool policy supports it, and the action is lawful and appropriate for legitimate B2B work.
+
+## Mutations
+
+Account/contact create-update tools are mutation-risk tools. Use them only after a reviewed preview and explicit confirmation.
+
+Before account/contact creates:
+
+- Normalize the proposed records.
+- Show key fields and the intended action.
+- Warn that Apollo may create duplicates when dedupe is not supported or not enabled.
+- Use dedupe options when the live tool supports them unless the user explicitly asks not to.
+
+Before account/contact updates:
+
+- Require verified existing Apollo account/contact IDs from prior tool results or clear user-provided IDs.
+- Show a field-level before/after diff.
+- Send only intended changed fields.
+- Never use create/update tools for search, preview, analysis, or draft-only requests.
+
+If bulk account/contact tools are exposed, apply the same preview, duplicate-risk, ID, and confirmation gates to the full batch. Do not split a risky batch into multiple mutations to bypass review.
+
+## Sequences And Launch
+
+Separate drafting, creation/update, enrollment, activation, and sending. Never bundle launch-risk actions together.
+
+Ordinary seller-safe sequence work may include:
+
+- Searching existing sequences by name or audience.
+- Reviewing or drafting sequence content in chat.
+- Looking up sender accounts for planning.
+- Inspecting schedules when a read-only schedule lookup is exposed.
+
+Mutation or launch-risk sequence work requires explicit approval after a preview or diff:
+
+- Sequence creation or update, when exposed, requires a reviewed payload. Default new sequences to inactive when the live schema supports an active flag.
+- Sequence update requires a verified existing sequence ID, current state, a clear diff, and explicit confirmation.
+- Sequence approval, activation, or campaign approval is critical launch risk and requires a separate explicit confirmation. Never activate by default.
+- Adding contacts to a sequence can send real emails. First search and disambiguate the sequence, retrieve valid sender email accounts, show the sender, sequence name, number of contacts, and active/paused enrollment status, then wait for explicit confirmation before enrolling.
+- Removing or stopping contacts from sequences requires verified contact IDs, verified sequence IDs, the mode, and confirmation. For stop actions, capture the stop reason when the live schema requires or benefits from it.
+
+Do not enroll contacts, activate sequences, approve campaigns, or start sending unless the user has approved that exact reviewed action and the live Apollo surface supports it.
+
+## Safety Rules
+
+- No raw personal email or phone reveal by default.
+- No Apollo credit-consuming Company Search, Organization Search, Company Details, people enrichment, organization enrichment, bulk enrichment, or job-posting lookup without making Apollo credit exposure visible and getting approval when the action is broad, batch, Company Details, or otherwise material.
+- No account/contact mutation without preview and explicit approval.
+- No sequence creation, update, activation, approval, enrollment, removal, stopping, or sending without the relevant reviewed workflow gate.
+- No generic "high Buying Intent" claims unless Apollo returns a supported intent or signal field that matches the user's requested concept.
+- No production-readiness claims from local, mock, prototype, or simulation evidence.
+- Preserve safe partial work when a request is blocked: show the search, preview, draft, diff, or planning step that can be done safely.
+
+## Output Rules
+
+Use seller language:
+
+- Say "Company Details," not "account context."
+- Say "Apollo credit," not "API credit."
+- Say "sequence draft," "inactive sequence," "reviewed diff," "enrollment confirmation," or "activation confirmation" precisely.
+
+Prefer compact tables:
+
+- People Search: First, Last, Title, Company, Domain, Location, Email Availability, Phone Availability.
+- People plus Company Details: First, Last, Title, Company, Domain, Employees, Industry, Location, Description, Email Availability, Phone Availability.
+- Company Search or company-only: Company, Domain, Employees, Industry, Location, Description.
+- Existing sequences: Name, Active, Archived, Steps, Max Emails Per Day.
+- Sender accounts: Sender Account, Active, Verified, Warmup.
+- Batch preview: Record, Key Fields, Risk, Action.
+- Batch update diff: ID Source, Record, Field, Current, Proposed.
+
+Prefer clickable connector-returned links when available; never construct guessed links from opaque IDs.
+Keep sourced facts separate from `Inference:`. If a requested row or field is not returned, say so instead of filling it in.
+
+Avoid raw tool names in seller-facing prose unless the user is explicitly discussing connector validation, evals, implementation, or admin troubleshooting.
+
+#### Output Format
+
+    # [Apollo Search / Company Details / Sequence Plan]
+
+    ## Results
+
+    | Company / Person / Sequence | Key Evidence | Useful Detail | Status |
+    | --- | --- | --- | --- |
+    | [Result] | [Returned signal] | [Returned field] | [Qualified, Near, Draft, or Needs approval] |
+
+    ## Before Any Action
+
+    - [Credit exposure, ambiguity, reviewed diff, sender/enrollment gate, or exact approval needed]
+
+    ## Gaps / Limitations
+
+    - [Missing field, version gate, connector surface, or source limitation]
+
+    ---
+
+    {Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+    {Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
diff --git a/plugins/sales/skills/apollo/agents/openai.yaml b/plugins/sales/skills/apollo/agents/openai.yaml
new file mode 100644
index 0000000..5a2089f
--- /dev/null
+++ b/plugins/sales/skills/apollo/agents/openai.yaml
@@ -0,0 +1,6 @@
+interface:
+  display_name: "Apollo User Guide"
+  short_description: "Apollo prospecting, enrichment, and outbound gates"
+  default_prompt: "Use $apollo to guide this Sales Apollo prospecting workflow."
+policy:
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/build-business-case/SKILL.md b/plugins/sales/skills/build-business-case/SKILL.md
index 7ec1d31..6bd5e8c 100644
--- a/plugins/sales/skills/build-business-case/SKILL.md
+++ b/plugins/sales/skills/build-business-case/SKILL.md
@@ -1,405 +1,168 @@
 ---
 name: build-business-case
-description: Build customer-led business cases, ROI narratives, value models, executive summaries, and customer-ready value stories from uneven customer context, metrics, transcripts, notes, and public evidence.
+description: Review commercial proposals and build customer-led business cases, ROI or value models, pricing or investment rationales, executive summaries, and customer-ready value stories tied to a customer, workflow, initiative, or decision.
 ---
 
 # Build Business Case
 
-Use this skill for customer-led business cases, ROI narratives, value models, executive summaries, customer-ready value stories, and follow-up question sets tied to a specific customer, workflow, or decision context.
+Turn uneven account evidence into a customer-led, decision-useful value case. This skill owns the business-case logic and drafts; it is not generic product positioning, a presentation-design workflow, or a writeback workflow.
 
-Do not use this skill for generic product positioning with no customer angle.
-
-Core invariant: start with the customer, not the product. Follow this chain on every run:
+Start with the customer, not the product. Follow this chain on every run:
 
 `customer context -> account-native anchor -> workflow -> use case -> value drivers -> metrics -> quantified impact -> narrative -> public research when useful`
 
-If evidence is sparse, produce a clearly labeled structural hypothesis or follow-up question set instead of fake specificity or fake math.
-
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate:
-
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the business-case output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the business case.
-
-### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
-
-### Source Resolution
-
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
-
-### Required Inputs
-
-This skill needs enough user-provided, connector-visible, public, or explicitly inferable context to identify the customer angle and the business workflow or decision context.
-
-The skill can still produce a limited structural case from pasted notes, uploaded/exported account context, discovery notes, customer metrics, public-company context, or user-linked materials when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
-
-**Inputs:**
-
-Require:
-
-- [required] `customer_or_context`: customer, account, company, segment, or clearly described customer scenario
-- [required] `workflow_or_decision_context`: workflow, initiative, use case, decision, pain, metric goal, or provided notes that anchor the value case
-
-Accept when provided:
-
-- buyer role, industry, solution scope, customer metrics, current tools, known constraints, source materials, strategic priorities, proof points, analogous wins, output mode, and public-research preference
-
-**Context Rules:**
-
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup before relying on docs, notes, messages, public research, or third-party enrichment alone. If CRM returns one high-confidence account match, use it as the primary customer candidate; if multiple plausible account matches remain, ask the user to choose from concrete candidates.
-- Before asking the user to paste customer context, metrics, or discovery notes, first try the workflow source categories that can identify the missing customer or value-motion anchor: `crm`, `meeting_notes`, and `document_store`, then public research only when an account-native source is unavailable or too thin. Manual input is the fallback after those categories are unavailable, empty, or insufficient for a stable candidate.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
-
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
-
-If customer, workflow, and decision context are all too weak to form a stable first-pass hypothesis, ask only the minimum clarifying question needed.
+If the evidence cannot support credible math, produce a clearly labeled structural case and the smallest validation plan needed to make it finance-ready.
 
-**Input Request Format Example:**
+## Common Skill Instructions
 
-```md
-Which customer and workflow, initiative, or decision context should I build the business case around?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when the active thread, recent meeting, CRM opportunity context, account docs, discovery notes, pasted metrics, or public-company prompt clearly names the customer and value motion.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, browser UI, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `crm`: account identity, opportunity context, stage, amount, close timing, account owner, activity history, deal notes, and opportunity-linked value or decision context.
-- `meeting_notes`: discovery notes, transcripts, stakeholder language, workflow pain, buyer priorities, objections, quantified claims, decision process, and validation gaps.
-- `document_store`: account plans, discovery docs, prior business cases, customer notes, proof points, value frameworks, internal account strategy, and source-of-truth context.
-- `data_enrichment`: company profile, fit signals, stakeholder background, tech stack, funding, and org context when those details sharpen the value story.
-- user-provided context: pasted notes, customer metrics, public or internal excerpts, uploaded/exported materials, account context, current tools, known constraints, and explicit assumptions.
-- public research: company-controlled public materials, investor relations, annual reports, 10-Ks, quarterly filings, earnings commentary, company website pages, and recent material news.
-
-Source obligations by intent:
-
-- Start with the highest-signal account-native anchor: user-provided metrics/notes, discovery notes, source-pack docs, `crm` opportunity context, or account documents. Do not begin with broad public research when an account-native anchor exists.
-- Use `meeting_notes` when value claims depend on discovery evidence, stakeholder language, customer pain, quantified claims, objections, or decision process. If checked and no relevant discovery evidence is found, label the validation gap instead of inventing customer-confirmed value.
-- For customer/account business cases, `crm` is required when CRM is available and a customer/account anchor is supplied, inferred, or confirmed. Use CRM for account identity, opportunity posture, commercial anchors, close timing, decision context, owner, activity history, and deal notes. Use `document_store` when account strategy, source packs, or prior business-case materials materially affect the case. If either source is unavailable, continue from sufficient account-native or user-provided evidence and name the gap.
-- Use `data_enrichment` only as optional background for fit, company, contact, and market context. It must not substitute for customer-confirmed pain, metrics, decision process, CRM truth, or meeting-note evidence.
-- Use public research only as optional enrichment for strategic priorities, external operating pressure, and public-company context. It must not substitute for customer workflow metrics or account-native truth.
-
-Authority and gaps:
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-- Prioritize evidence in this order when sources conflict or completeness varies:
-  1. customer-provided metrics;
-  2. product telemetry or usage data;
-  3. discovery notes or `meeting_notes`;
-  4. `crm`, `document_store`, or internal account notes;
-  5. public filings, earnings materials, website, investor materials, and recent material company news;
-  6. analogous wins or directional benchmarks.
-- Public research is useful for strategic priorities, executive wording, operating pressure, and "why now" context. It must not replace account-native truth.
-- Do not let public research, docs, notes, messages, or third-party enrichment override CRM-owned customer/account truth.
-- Use `meeting_notes` and account-native docs for customer-confirmed pain, metrics, decision process, and ROI validation; public or analogous evidence can frame hypotheses but cannot make them customer-confirmed.
-- Do not use public research to invent baseline workflow metrics, internal tooling certainty, economic buyer certainty, workflow owners, or customer-confirmed ROI.
-- If public sources are unavailable, thin, or low-signal, continue with internal or user-provided evidence and label the gap.
+## Key Dependency Categories
 
-Commercial anchor preservation:
+Use only lanes that materially improve the case.
 
-- Preserve commercial anchors from account-native sources, including pilot amount, expansion path, target contract value, budget range, close timing, forecast status, and paid-pilot terms.
-- Label commercial anchors as `Known` when they appear in customer-provided context, CRM, opportunity snapshots, source-pack documents, account-native docs, or meeting notes.
-- Do not drop commercial anchors merely because CRM search is noisy, empty, or missing adjacent opportunity fields. Instead, say which source supplied the commercial fact and separately name the CRM gap.
-- Example: `Known from document_store source pack: SGD 420K pilot target and SGD 1.8M expansion path. Missing from CRM connector search: opportunity stage, owner, and close date.`
-
-### Context Gathering Principles
-
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-For ordinary business-case requests, optimize for the highest-value first artifact, not exhaustive enrichment. Find the highest-signal account-native anchor, draft the decision-useful case, and offer targeted deepening only when the next decision needs it.
-
-Default route:
-
-- Prefer the highest-signal canonical anchor over broad shallow search. If one source contains the customer, workflow, decision audience, commercial stakes, metrics, urgency, and caveats, fetch it first.
-- Use adjacent sources only to validate material gaps such as approval path, budget ownership, workflow risk, decision timing, or customer-facing follow-up.
-- Treat optional enrichment as conditional. Public research, broad meeting enumeration, generic account search, and inbox search should run only when they can change the strategic framing, confidence level, customer-facing artifact, or next step.
-- Stop the first pass once enough authoritative data exists to produce the core business case and name evidence gaps. Do not continue low-yield searches merely because more connectors are available.
-- If source retrieval is slow or repeatedly noisy, draft from the best available account-native anchor and label the missing source, rather than spending the ordinary path on broad reconstruction.
-
-
-### First-Run Banner
-
-If Sales preflight says the build-business-case experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Build Business Case
-This skill helps turn customer context, workflow pain, metrics, discovery notes, transcripts, CRM evidence, docs, and public context into a customer-led business case. It produces a clear view of the workflow, value drivers, assumptions, quantified or structural impact, proof points, and open validation questions for a seller or account team to use.
+- [Blocking] ~~CRM for authoritative account, opportunity, owner, stage, amount, close timing, contacts, and commercial context. It blocks customer/account-specific cases unless authoritative account and commercial context is already grounded; generic scenario cases can proceed without it.
+- ~~Meeting Transcripts for discovery evidence, stakeholder language, workflow pain, objections, quantified claims, decisions, and validation gaps
+- ~~Knowledge & Files for account plans, source packs, discovery notes, prior value cases, proof points, and account-native metrics
+- ~~Sales Intelligence only when company, fit, stakeholder, or market context closes a specific evidence gap
 
-Definitions:
-- Value hypothesis: a directional claim about potential customer impact that still depends on assumptions or missing inputs.
-- Finance-ready case: a version of the business case with enough sourced inputs to support quantified ROI or scenario math.
-- Analogous win: evidence from a similar customer or situation that can support a hypothesis without pretending it is proof for this customer.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Build a business case for a customer.`
-- `@Sales build a business case for Acme's support automation initiative.`
-- `@Sales turn these discovery notes into a value case for reducing manual sales operations work.`
-- `@Sales create a structural business case for Globex even though we are missing exact metrics.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Build Business Case next-step behavior and action text. Onboarding, artifact creation, spreadsheet creation, draft creation, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the business-case continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `build-business-case` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the business case** | The first substantive business case has just been produced. | `Anything you'd change in the business case before we make it customer-ready?` |
-| **2. Review the revised business case** | The user asked for any change to value framing, numbers, confidence labels, audience, customer language, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to turn it into a customer-ready executive summary, champion talk track, value table, or spreadsheet model?` |
-| **3. Prepare the value artifact** | The user accepts the case, says no changes are needed, says a revision looks good, or asks to operationalize it. | Offer to produce the most useful next artifact or draft action, prioritizing executive summary, champion talk track, value-model table, spreadsheet model, customer-ready note, or internal account-team note over broad suggestions. |
-| **4. Review the artifact or draft** | An executive summary, talk track, value table, spreadsheet, customer-ready note, or internal note exists, or the user asks to change it. | `Anything you'd change before I save, share, or draft this where it belongs?` |
-| **5. Save, share, or draft approved business-case action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the spreadsheet, sharing the executive summary, creating the customer-facing draft, or saving the internal note. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the business case, treat a lightweight acknowledgement as acceptance and move to `Prepare the value artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to save, share, or draft a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+Use public research only when it sharpens strategic priorities, executive wording, operating pressure, or “why now.” Prefer company-controlled, investor, regulatory, and other primary sources. Never let public or enrichment context substitute for customer-confirmed pain, workflow metrics, decision process, or CRM-owned truth.
 
 ## Reference Loading
 
-`SKILL.md` owns the normal execution path, evidence order, guardrails, and default output shape. Load references selectively:
+`SKILL.md` owns the normal customer-led flow, evidence order, guardrails, and default package. Load references only when their extra detail matters:
 
-- Use [references/input-and-output.md](references/input-and-output.md) when inputs are especially uneven, the user names an output mode, or you need the full required-content contract.
-- Use [references/workflow.md](references/workflow.md) when a complex request needs the expanded step-by-step workflow or you are checking behavior against the full workflow detail.
+- Use [references/input-and-output.md](references/input-and-output.md) when inputs are uneven, the user names an output mode, or the full required-content contract matters.
+- Use [references/workflow.md](references/workflow.md) when a complex case needs the expanded customer-to-value sequence and examples.
 - Use [references/value-model-and-evidence.md](references/value-model-and-evidence.md) when quantification, confidence labeling, evidence conflict, or value-bucket logic needs more detail.
-- Use [references/output-and-presentation.md](references/output-and-presentation.md) only for final response/output pattern details, formatted artifact presentation guidance, or behavior tests.
-
-## Procedure
-
-### 1. Resolve customer and decision context
-
-Determine:
-
-- who the customer is;
-- what industry or segment they are in;
-- what they are trying to achieve;
-- what pressure makes the work urgent;
-- which buyer, team, or stakeholder matters;
-- what constraints are visible.
-
-Anchor the narrative in the customer objective and business pressure. Do not jump to product recommendations before this step is complete.
+- Use [references/output-and-presentation.md](references/output-and-presentation.md) only for a formatted artifact, exact presentation/output requirements, or behavior tests.
 
-### 2. Find the account-native business-case anchor
+## Workflow Guidance
 
-When the request names a customer, account, pilot, or workflow but does not include pasted source material, look for the highest-signal business-case anchor before broad enrichment.
+### 1. Resolve the customer and decision anchor
 
-Search the preferred `document_store` first when available, then CRM, meeting notes, and email only as needed. Use narrow exact-match queries before broad account searches:
+- Require a customer/account/scenario and a workflow, initiative, pain, metric goal, or decision context. Proceed from pasted notes, links, exports, or a clearly anchored thread when sufficient.
+- If an inferred required anchor is ambiguous, make a bounded pass using at most three source reads, offer up to five concrete candidates, and ask the user to choose before broad enrichment.
+- Treat company-like names as possible account anchors. When ~~CRM is available, resolve the account and matching opportunity before relying on indirect context.
+- If the customer and workflow are both too weak for a stable hypothesis, ask only the smallest clarifying question.
 
-- `{customer} business case`
-- `{customer} value case`
-- `{customer} CFO value case`
-- `{customer} ROI`
-- `{customer} pilot target`
-- `{customer} expansion path`
-- `{customer} {workflow} pilot`
-- known shorthand or acronym variants when visible or user-provided
+### 2. Find the account-native anchor first
 
-High-signal anchors include pages, docs, records, or source-pack items whose title or snippet contains terms such as `business case`, `value case`, `CFO value case`, `ROI`, `pilot target`, `expansion path`, `paid pilot`, `value model`, `executive case`, or `source pack`.
+- Start with user-provided metrics, notes, links, or exports. Otherwise look for the highest-signal account-native artifact before broad search: exact-match ~~Knowledge & Files queries such as `[customer] business case`, `value case`, `ROI`, `pilot target`, or `expansion path`; then the matching ~~CRM opportunity; then relevant ~~Meeting Transcripts.
+- Fetch the strongest anchor first. High-signal anchors contain the named customer, workflow, decision audience, commercial stakes, metrics, urgency, or caveats.
+- Use adjacent sources only to validate material gaps such as workflow pain, approval path, budget ownership, timing, risks, or customer-facing follow-up.
+- Preserve commercial anchors from account-native evidence, including pilot amount, expansion path, target value, budget range, close timing, and paid-pilot terms. If CRM is thin, keep the sourced fact and separately name the CRM gap.
+  - Example: `Known from ~~Knowledge & Files source pack: SGD 420K pilot target and SGD 1.8M expansion path. Missing from ~~CRM: opportunity stage, owner, and close date.`
+- Stop the first pass once the core case is supported; do not continue low-yield searches merely because more sources exist.
+- For a named public company, run a focused public-primary pass unless the user disables it or the account-native anchor already answers the material why-now question. Use it to sharpen strategic priorities and executive wording, never to delay the first useful case or replace account-native proof.
 
-If a high-signal anchor is found, fetch it first and use it as the primary account-native source. Then fetch only the few adjacent records needed to validate workflow, risks, approvals, budget ownership, and customer follow-up. Do not enumerate broad meeting lists, public research, generic CRM records, or inbox results before checking whether the anchor already contains the required business-case facts.
+### 3. Build the customer value logic
 
-Use the backed-up skill or baseline run as a behavioral reference for this retrieval pattern: the strongest OPB baseline selected `OPB: Build CFO value case` first because it contained the named customer, executive audience, pilot amount, expansion path, workflow metrics, urgency, and explicit caveats. Preserve that anchor-first behavior while applying this skill's stricter evidence labeling and source-provenance rules.
+- Name the customer objective, business pressure, relevant buyer or team, constraints, and actual workflow before naming seller capabilities.
+- Describe the workflow in business language: actors, steps, handoffs, bottlenecks, tools, KPIs, and what good looks like.
+- Prioritize one to three use cases. For each, explain the workflow problem, why it matters now, who cares, supporting evidence, and remaining validation.
+- Map each use case to the fewest applicable value buckets: `Enhanced Productivity`, `Cost Reduction`, `Risk Reduction`, `Revenue Acceleration`, and `Time to Market`.
+- Make the causal chain explicit: workflow change -> operational effect -> business outcome -> metric.
 
-### 3. Run public research when useful
+### 4. Quantify only what the evidence supports
 
-For public companies, or when public context can materially sharpen strategic priorities, executive wording, operating pressure, or "why now" context, run public research when it improves the business case enough to justify the source read. For private, low-public-signal, or account-native requests, treat public research as conditional and do not let it delay the first useful business case.
+- Label material claims and inputs as `Known`, `Inferred`, `Assumed`, or `Missing`.
+- Use this evidence order when sources conflict: customer-provided metrics; product telemetry or usage data; discovery or transcript evidence; ~~CRM, ~~Knowledge & Files, or internal account notes; public primary materials; analogous wins or directional benchmarks.
+- For a finance-ready case, show formula, inputs, source/label, low/base/high scenarios, caveats, and confidence. Useful formula patterns include:
+  - Productivity: `users x tasks per period x time saved per task x labor cost x adoption rate`
+  - Cost: `current cost baseline - future cost baseline`
+  - Risk: `incidents avoided x average cost per incident x confidence factor`
+  - Revenue: `impacted revenue pool x improvement rate x confidence factor`
+  - Time to Market: `cycle-time reduction x value of earlier release or deployment`
+- If required inputs are missing, keep the math structural: show the formula, mark missing inputs, state what can be said directionally, and list the smallest questions that would make it finance-ready.
 
-Gather and synthesize, when available:
+### 5. Draft the decision-useful package
 
-- investor relations materials;
-- 10-K, annual report, or quarterly filings;
-- earnings call commentary;
-- shareholder letters or quarterly results decks;
-- company website, product pages, and corporate pages;
-- recent material news that changes priorities or urgency.
+- Translate operational impact into the customer’s business outcomes, then explain why the seller solution fits this workflow and buyer need.
+- Keep public strategic context separate from account-native proof. Use analogous wins as support for a hypothesis, never as proof for this customer.
+- Cite material claims with useful clickable links when available; use a plain source label only when no stable link exists.
 
-Use concrete dates for recent developments when they materially affect urgency. Do not let public evidence override stronger customer, system, or conversation evidence. Do not bloat the output with generic company background.
+### Next Step Options
 
-### 4. Identify the business workflow before the use case
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Improve the case using the user's guidance or close the smallest material validation gap.
+- Produce a customer-ready executive summary or value narrative for review.
+- Produce a champion talk track or internal account-team note for review.
+- Build a value-model table or spreadsheet-ready model structure.
+- Turn the case into a concise meeting pre-read or decision document after the content is reviewed.
 
-Before identifying use cases, understand the actual workflow, business process, or functional motion the customer is trying to improve.
+Next steps to avoid:
+- Saving, sharing, posting, creating a document, or updating a system before the user reviews the content and explicitly requests that action.
 
-For each workflow, inspect:
+## Modes
 
-- actors and teams involved;
-- major steps and handoffs;
-- bottlenecks, manual work, delays, or quality breakdowns;
-- tools or systems involved;
-- KPIs and what "good" looks like.
+- `Default Package` — the full business case below.
+- `Structural Case` — use when the customer and workflow are clear but credible quantified inputs are missing.
+- `Finance-Ready Case` — use when sourced inputs support formulas and low/base/high scenarios.
+- `Focused Output` — use when the user asks only for an executive summary, ROI table, customer-ready narrative, differentiators, or validation questions; include a compact evidence posture and gaps.
 
-Name the workflow in business language, not product language.
+## Output Format
 
-### 5. Prioritize use cases
+Use this structure for the default package.
 
-Prioritize one to three use cases tied to the customer's goals and workflow constraints. Do not list attractive but weakly supported use cases.
-
-For each use case, explain:
-
-- the workflow problem it addresses;
-- why it matters now;
-- which buyer or team cares;
-- what evidence supports it;
-- what still needs validation.
-
-### 6. Map value drivers
-
-Use these value buckets exactly. Inline the logic rather than inventing new categories:
-
-- `Enhanced Productivity`
-- `Cost Reduction`
-- `Risk Reduction`
-- `Revenue Acceleration`
-- `Time to Market`
-
-Map each use case to the fewest value buckets needed. Make the causal chain explicit. Do not treat "value" as a vague synonym for "better."
-
-### 7. Identify required metrics
-
-For each use case, identify the minimum metrics required for credible quantification, separating known inputs from missing inputs.
-
-Common metrics include:
-
-- number of users or workflows;
-- task volume and frequency;
-- time per task and time saved per task;
-- labor cost and adoption rate;
-- cycle time, defect rate, error rate, or incident cost;
-- conversion rate, revenue influenced, launch speed, tool spend, or contractor spend.
-
-Do not imply quantifiability when required inputs are missing.
-
-### 8. Quantify only when inputs support it
-
-Build directional or quantified value hypotheses only when the evidence supports them.
-
-When credible, show:
-
-- formula logic;
-- known inputs;
-- assumptions;
-- low, base, and high scenarios;
-- caveats.
-
-If inputs are too weak for quantified scenarios, keep the case structural and state exactly what data would be required to move from structural case to finance-ready case.
-
-### 9. Draft the customer-led narrative
-
-Translate operational impact into business outcomes, then write the narrative in customer-led, seller-ready language.
+```md
+# Business Case: [Customer / Initiative]
 
-The narrative must connect:
+## Executive Summary
+[Customer objective, why now, priority workflow/use cases, likely value story, seller fit, and confidence.]
 
-- customer priority;
-- customer challenge;
-- relevant workflow;
-- relevant use case;
-- expected business impact;
-- why the seller solution fits;
-- what assumptions matter;
-- what still needs validation.
+## Strategic Initiatives
+- [Known/Inferred/Assumed/Missing: initiative + source]
 
-The narrative should sound consultative, outcome-oriented, grounded, and tailored. It should not sound like a product datasheet.
+## Key Challenges
+- [Customer or workflow challenge + evidence label/source]
 
-### 10. Render the default package
+## Priority Workflows
+- **[Workflow]:** [Actors, bottleneck, KPI, and why it matters]
 
-Unless the user asks for a different shape, return a decision-useful package with these sections:
+## Priority Use Cases
+1. **[Use case]** — [Problem, buyer/team, why now, evidence, validation gap]
 
-1. `Executive Summary`
-2. `Strategic Initiatives`
-3. `Key Challenges`
-4. `Priority Workflows`
-5. `Priority Use Cases`
-6. `Value Hypothesis by Use Case`
-7. `Metrics and Assumptions`
-8. `ROI or Value View`
-9. `Solution Differentiators`
-10. `Proof Points or Analogous Wins`
-11. `Caveats and Open Questions`
+## Value Hypothesis by Use Case
+| Use Case | Value Bucket | Causal Chain | Evidence Posture | Confidence |
+| --- | --- | --- | --- | --- |
+| [Use case] | [Exact bucket] | [Change -> effect -> outcome -> metric] | [Known/Inferred/Assumed/Missing] | [High/Medium/Low] |
 
-Output expectations:
+## Metrics and Assumptions
+| Metric / Input | Value | Label | Source | Why It Matters |
+| --- | --- | --- | --- | --- |
+| [Input] | [Value or Missing] | [Known/Inferred/Assumed/Missing] | [Link/label] | [Formula role] |
 
-- `Executive Summary` must include what the customer is trying to achieve, why it matters now, the most relevant workflows and use cases, the likely value story, and why the seller solution is relevant.
-- `Metrics and Assumptions` must include required inputs, known data, assumptions, and confidence level.
-- `ROI or Value View` should include low, base, and high cases only where possible.
-- `Solution Differentiators` must tie differentiators to the workflow, buyer need, and expected business effect.
-- `Proof Points or Analogous Wins` should keep public-company statements separate from account-native proof.
-- `Caveats and Open Questions` must name what still needs validation and what public research could not determine.
+## ROI or Value View
+| Use Case | Formula | Low | Base | High | Caveats |
+| --- | --- | --- | --- | --- | --- |
+| [Use case] | [Formula] | [Value/Structural] | [Value/Structural] | [Value/Structural] | [Gap/confidence] |
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+## Solution Differentiators
+- [Differentiator tied to workflow, buyer need, and expected business effect]
 
-## Evidence And Assumption Rules
+## Proof Points or Analogous Wins
+- **Account-native proof:** [Evidence or Missing]
+- **Analogous / public support:** [Evidence, clearly not customer proof]
 
-- Label important claims as `Known`, `Inferred`, `Assumed`, or `Missing` whenever evidence is mixed.
-- Label public-derived insights clearly when they materially shape strategic context or "why now" framing.
-- Keep public strategic context separate from account-native proof.
-- If public evidence conflicts with stronger internal evidence, prefer internal evidence and call out the tension when it materially changes the case.
-- Use analogous wins and benchmarks to shape hypotheses, not to pretend customer proof exists.
-- If confidence is low, say why. Do not soften uncertainty into polished prose.
+## Caveats and Open Questions
+- [Most important validation gap and smallest next collection step]
 
-Never:
+---
 
-- invent customer numbers without labeling them as assumptions;
-- blur customer facts and directional assumptions;
-- convert public strategic language into fake ROI;
-- use earnings-call or public-company rhetoric as proof of company-specific business impact;
-- use public statements to infer exact internal workflow owners unless supported;
-- write generic hype language disconnected from customer value;
-- hide missing data.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-Always:
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-- anchor the case in customer business reality;
-- make workflow explicit before use cases or seller solution fit;
-- prioritize one to three value drivers when several could apply;
-- distinguish known facts, inferences, assumptions, and missing evidence;
-- say when the case is structural rather than finance-ready;
-- show sources for key facts when available;
-- tie use cases back to strategic priorities;
-- explain caveats openly;
-- keep the output auditable and decision-useful.
+## Rules
 
-## Presentation Artifacts
+- Do not invent customer numbers, buyers, workflow owners, tools, approvals, commercial terms, ROI, or source links.
+- Do not turn public strategic language, benchmarks, or analogous wins into customer-confirmed impact.
+- Do not hide missing data behind polished prose; say whether the case is structural or finance-ready and why.
+- Prioritize one to three use cases and the fewest value buckets that explain the decision.
+- Keep this workflow read-only. Do not post, send, share, create a document, or update CRM unless the user explicitly asks in a later step.
 
-If no presentation artifact is requested, ignore presentation styling and return strong plain Markdown.
+## Failure Handling
 
-If the user asks for a formatted artifact, load [references/output-and-presentation.md](references/output-and-presentation.md) and keep presentation rules subordinate to the workflow, evidence hierarchy, assumptions handling, value-model logic, and output content requirements.
+If no credible customer/workflow anchor exists, state the blocker and ask for the smallest missing input. If optional lanes are unavailable, continue with the strongest grounded evidence and make the resulting confidence limits visible.
diff --git a/plugins/sales/skills/build-business-case/references/input-and-output.md b/plugins/sales/skills/build-business-case/references/input-and-output.md
index 481d6d9..ffc0858 100644
--- a/plugins/sales/skills/build-business-case/references/input-and-output.md
+++ b/plugins/sales/skills/build-business-case/references/input-and-output.md
@@ -11,8 +11,8 @@ Typical inputs may include:
 - account notes
 - discovery call transcript
 - customer metrics
-- `crm` summary
-- `meeting_notes` transcript
+- ~~CRM summary
+- ~~Meeting Transcripts transcript
 - product usage data
 - customer website
 - 10-K, annual report, or investor presentation
@@ -37,9 +37,9 @@ Useful selectors and hints may include:
 
 Default behavior:
 
-- default `public_research` to `true`
+- default `public_research` to `true` for named public companies when it can sharpen why-now context
 - default `output_mode` to `default_package`
-- if `company_name` is present, run a public research pass by default
+- if a named public company is present, run a focused public research pass by default unless the account-native anchor already answers the material why-now question
 - if the user explicitly disables public research, skip it
 - if no named company is present, do not force public research
 - if public sources are unavailable, thin, or low-signal, continue with internal evidence and label the gap
@@ -64,10 +64,10 @@ Input handling rules:
 
 ## Internal account-scoped evidence order
 
-- Attempt `crm` account resolution, account history, opportunity context, and activity review first when the request is tied to a named account.
-- Use `document_store` for account plans, discovery notes, prior business cases, and narrative account context.
-- Use `meeting_notes` next for transcript-backed workflow, stakeholder, and blocker evidence.
-- Use user-provided enrichment exports or public sources only as a fallback when `crm` and `meeting_notes` evidence still leave a critical evidence gap, especially for quantification inputs.
+- Attempt ~~CRM account resolution, account history, opportunity context, and activity review first when the request is tied to a named account.
+- Use ~~Knowledge & Files for account plans, discovery notes, prior business cases, and narrative account context.
+- Use ~~Meeting Transcripts next for transcript-backed workflow, stakeholder, and blocker evidence.
+- Use user-provided enrichment exports or public sources only as a fallback when ~~CRM and ~~Meeting Transcripts evidence still leave a critical evidence gap, especially for quantification inputs.
 - Keep `Known`, `Inferred`, `Assumed`, and `Missing` labels visible whenever evidence is mixed.
 
 ## Standard outputs
diff --git a/plugins/sales/skills/build-business-case/references/output-and-presentation.md b/plugins/sales/skills/build-business-case/references/output-and-presentation.md
index ae6947d..16a5ae8 100644
--- a/plugins/sales/skills/build-business-case/references/output-and-presentation.md
+++ b/plugins/sales/skills/build-business-case/references/output-and-presentation.md
@@ -35,14 +35,9 @@ Typography for presentation artifacts:
 
 Color and palette for presentation artifacts:
 
-- use near-black or dark navy text
-- use a very light neutral background
-- use corporate darks around `#000944` and `#00264F`
-- use restrained corporate blues around `#3D8DFF`, `#6DCBF4`, and `#D0EDFA`
-- use status accents only when useful:
-  - positive `#85DF7B`
-  - caution `#F7CB59`
-  - negative `#F67576`
+- use the user's supplied brand conventions when available;
+- otherwise use near-black or dark navy text, a very light neutral background, and restrained neutral or blue accents;
+- use status accents only when they clarify meaning, not as decoration.
 
 Color rules:
 
@@ -182,7 +177,7 @@ Default output rules:
 - keep the package decision-useful
 - keep prioritization explicit
 - label `Known`, `Inferred`, `Assumed`, and `Missing` when evidence is mixed
-- use public research by default for named companies
+- use focused public research by default for named public companies when it sharpens why-now context
 - do not claim precision you cannot support
 
 ## Example prompts to include when building and testing the skill
@@ -201,7 +196,7 @@ Use these prompts when evaluating whether the skill is behaving in a customer-le
 
 Acceptance checks:
 
-- public research is included by default for named public companies
+- focused public research is included by default for named public companies when it materially sharpens the case
 - the output improves strategic-priority framing and "why now" logic
 - the output does not invent internal baselines or buyer certainty from public materials
 - public evidence is clearly separated from account-native proof
diff --git a/plugins/sales/skills/build-business-case/references/value-model-and-evidence.md b/plugins/sales/skills/build-business-case/references/value-model-and-evidence.md
index 219dcc6..9b24283 100644
--- a/plugins/sales/skills/build-business-case/references/value-model-and-evidence.md
+++ b/plugins/sales/skills/build-business-case/references/value-model-and-evidence.md
@@ -135,8 +135,8 @@ When evidence conflicts or is incomplete, prioritize sources roughly in this ord
 
 1. customer-provided metrics
 2. product telemetry or usage data
-3. discovery notes or `meeting_notes`
-4. `crm`, `document_store`, or internal account notes
+3. discovery notes or ~~Meeting Transcripts
+4. ~~CRM, ~~Knowledge & Files, or internal account notes
 5. public filings, earnings materials, website, investor materials, and recent material company news
 6. analogous wins or directional benchmarks
 
diff --git a/plugins/sales/skills/build-business-case/references/workflow.md b/plugins/sales/skills/build-business-case/references/workflow.md
index 80568b6..42f6879 100644
--- a/plugins/sales/skills/build-business-case/references/workflow.md
+++ b/plugins/sales/skills/build-business-case/references/workflow.md
@@ -36,9 +36,22 @@ Operating rules:
 - Do identify the pressure that makes the workflow important now.
 - Do not jump to product recommendations before this step is complete.
 
-### Step 2: Run the public research pass
+### Step 2: Find the account-native business-case anchor
 
-If a named company is present and public research is not explicitly disabled, run a public research pass before final workflow prioritization.
+Before broad research, look for the strongest account-native artifact:
+
+- user-provided metrics, notes, links, or exports;
+- exact-match ~~Knowledge & Files queries such as `[customer] business case`, `value case`, `ROI`, `pilot target`, or `expansion path`;
+- the matching ~~CRM opportunity;
+- relevant ~~Meeting Transcripts.
+
+Fetch the strongest anchor first. High-signal anchors contain the named customer, workflow, decision audience, commercial stakes, metrics, urgency, or caveats. Preserve commercial facts such as pilot amount, expansion path, target value, budget range, close timing, and paid-pilot terms; if ~~CRM is thin, keep the sourced fact and separately name the CRM gap.
+
+Use adjacent sources only to validate material gaps such as workflow pain, approval path, budget ownership, timing, risks, or customer-facing follow-up. Stop the first pass once the core case is supported.
+
+### Step 3: Run the public research pass
+
+If a named public company is present and public research is not explicitly disabled, run a focused public research pass unless the account-native anchor already answers the material why-now question. Do not let this delay the first useful case.
 
 Gather and synthesize, when available:
 
@@ -65,7 +78,7 @@ Operating rules:
 - Do not use earnings rhetoric as proof of company-specific impact.
 - Do not bloat the output with generic company background.
 
-### Step 3: Understand the customer workflow
+### Step 4: Understand the customer workflow
 
 Before identifying use cases, understand the actual workflow, business process, or functional motion the customer is trying to improve.
 
@@ -107,7 +120,7 @@ Operating rules:
 - Do describe the workflow in business language, not product language.
 - Do not stay at the level of broad corporate transformation themes.
 
-### Step 4: Identify the highest-priority use cases
+### Step 5: Identify the highest-priority use cases
 
 Do not list every possible use case.
 
@@ -132,7 +145,7 @@ Operating rules:
 - Do tie each use case to a specific workflow problem.
 - Do not include attractive but weakly supported use cases.
 
-### Step 5: Define the value drivers
+### Step 6: Define the value drivers
 
 For each prioritized use case, define what kind of business value it creates.
 
@@ -154,7 +167,7 @@ Operating rules:
 - Do map each use case to the fewest value buckets needed to explain the business case clearly.
 - Do not treat "value" as a vague synonym for "better."
 
-### Step 6: Identify the metrics required
+### Step 7: Identify the metrics required
 
 For each use case, identify what must be measured.
 
@@ -190,7 +203,7 @@ Operating rules:
 - Do separate known metrics from missing metrics.
 - Do not imply quantifiability when the required inputs are missing.
 
-### Step 7: Quantify the value
+### Step 8: Quantify the value
 
 Build directional or quantified value hypotheses.
 
@@ -218,7 +231,7 @@ Operating rules:
 - Do keep the case structural if customer data is weak.
 - Do not fake precision.
 
-### Step 8: Translate the impact into business outcomes
+### Step 9: Translate the impact into business outcomes
 
 Operational improvements should be translated into business value.
 
@@ -242,7 +255,7 @@ Operating rules:
 - Do explain why the KPI matters economically.
 - Do not leave the case stuck at the level of workflow efficiency.
 
-### Step 9: Draft the business narrative
+### Step 10: Draft the business narrative
 
 After the analysis, write the narrative in a way that an executive can understand.
 
diff --git a/plugins/sales/skills/build-competitive-brief/SKILL.md b/plugins/sales/skills/build-competitive-brief/SKILL.md
index 78be0cf..58dbeb9 100644
--- a/plugins/sales/skills/build-competitive-brief/SKILL.md
+++ b/plugins/sales/skills/build-competitive-brief/SKILL.md
@@ -1,388 +1,223 @@
 ---
 name: build-competitive-brief
-description: Build a multi-competitor build-competitive-brief report, comparison matrix, and battlecard-style objection package using user-provided materials, optional connector-assisted research, and public evidence.
+description: "Use when the user wants a competitor or vendor comparison, market-landscape analysis, battlecard, objection package, positioning brief, or account-specific competitive view. Produce an evidence-backed comparison, guidance, and brief, using supplied materials, connected research, and public evidence when appropriate."
 ---
 
 # Build Competitive Brief
 
-## Overview
+Create a seller-ready competitive brief that separates verified facts from field signal and inference. Default to a self-contained HTML artifact plus a concise chat readout; use chat or Markdown only when the user explicitly asks for it. This skill owns research, synthesis, and the brief artifact, not external posting or CRM writes.
 
-Produce a durable competitive brief for strategy, product marketing, enablement, or sales teams. Default to a concise evidence-backed brief that can support downstream messaging, account prep, and objection handling.
+## Common Skill Instructions
 
-This workflow accepts manual materials, but it should use active source categories before asking for manual input. It must work with:
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-- a seller company, product scope, or workflow scope
-- competitor names
-- competitor discovery when the user does not name competitors
-- user-provided notes, decks, or transcripts
-- optional account context
-- public web research
+## Key Dependency Categories
 
-## Skill Configuration
+Use the categories that materially apply to the request; do not broaden a search merely because a source is available.
 
-### User Context
+- ~~CRM for account, opportunity, contact, ownership, known competitor, and active-deal truth
+- ~~Knowledge & Files for battlecards, competitive docs, account plans, product positioning, proof points, and prior briefs
+- ~~Internal Messaging for recent field signals, account-team observations, objection examples, and source-of-truth routing when internal context matters
+- ~~Meeting Transcripts for prior objections, competitor mentions, buyer language, decisions, and commitments
+- ~~Sales Intelligence only when it sharpens competitor discovery, company context, or a concrete evidence gap
 
-Mandatory pre-answer gate:
-
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the output.
-
-### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
-
-### Source Resolution
-
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
-
-### Required Inputs
-
-This skill needs enough user-provided, connector-visible, public, or explicitly inferable context to identify a competitor set, named account/deal with competitor context, or clear market category to compare.
-
-The skill can still produce a limited competitive brief from pasted notes, uploaded/exported materials, prior battlecards, public evidence, or user-linked context when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
-
-**Inputs:**
-
-Require one anchor:
-
-- [required] competitor names or a competitor set
-- [required] named account/deal with competitor context
-- [required] product, workflow, segment, or market category to compare
-
-Accept when provided:
-
-- seller company, account, product scope, buyer context, industry, region, customer segment, known objections, existing materials, output mode, and shareable artifact request
-
-**Context Rules:**
-
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- When an ambiguous company-like proper noun could be either a competitor or a customer/account, include a bounded CRM lookup when `crm` is available before drafting. If CRM returns a high-confidence customer/account match and the prompt did not clearly ask for that company as a competitor, treat it as account/deal context or ask the user to choose from concrete candidates.
-- Before asking the user to paste battlecards, competitor lists, or account context, first try the source categories that can resolve the missing anchor: `crm` for account/deal context, `document_store` for competitive docs and prior briefs, and `internal_messaging` for field signals when the user wants internal context. Use public research for public competitor facts after the anchor is resolved or when no internal/account source can supply candidates.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
-
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
-
-**Input Request Format Example:**
-
-```md
-Which competitors, account, or market category should I compare?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when the account, meeting context, prior brief, source pack, or user-provided notes clearly name the competitor set or category.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `crm`: account context, opportunity status, known competitor fields, active deal motion, and account-specific competitive pressure.
-- `document_store`: competitive docs, battlecards, account plans, enablement notes, product positioning, prior briefs, and proof points.
-- `internal_messaging`: field signals, account-team observations, competitive chatter, objection examples, and source-of-truth routing when the user wants internal context.
-- user-provided context: pasted notes, decks, transcripts, prior briefs, account context, objection lists, and uploaded/exported materials.
-- public research: official competitor pages, product docs, pricing pages, customer stories, public filings, launch posts, investor materials, and reputable public coverage.
-
-Authority and gaps:
-
-- Prioritize user-provided and internal competitive materials over generic public research for sales posture.
-- For named account/deal competitive briefs, `crm` is required when CRM is available and an account/deal anchor is supplied, inferred, or confirmed. Use CRM for account context, opportunity status, known competitor fields, active deal motion, and account-specific competitive pressure; do not let public research, docs, messages, or third-party enrichment override CRM-owned account/deal truth.
-- Use public research to validate current positioning, launches, pricing, partnerships, and public proof; do not use it to invent private account status or deal presence.
-- Treat internal docs and messaging as sales-context and field-signal sources; do not present them as current competitor facts when public product, pricing, release, or customer-proof evidence is needed and unavailable.
-- Treat internal field signals as directional unless they are corroborated by account evidence or source-of-truth docs.
-- If a source is unavailable, blocked, stale, or thin, continue with the strongest remaining evidence and label the limitation.
-
-### Context Gathering Principles
-
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the build-competitive-brief experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Build Competitive Brief
-This skill helps compare competitors, alternatives, or market options for a deal, segment, or sales motion. It can use user-provided competitor names, CRM/deal context, internal docs, customer notes, public research, Slack, and prior battlecards. It produces a grounded competitive brief with comparison points, likely objections, proof, caveats, and talk tracks.
-
-Definitions:
-- Battlecard: a compact seller guide for how to position against a competitor, including where they win, where they are weaker, and what to say.
-- Defensive prep: preparation for a competitor that may be relevant even when there is not enough evidence to say the competitor is active in the live deal.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Build a competitive brief for a competitor set.`
-- `@Sales build a competitive brief for Acme comparing OpenAI, Anthropic, and Gemini.`
-- `@Sales prep battlecard-style objections for the vendors showing up in my Globex deal.`
-- `@Sales compare the competitor set in this account plan and highlight traps for the sales team.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Build Competitive Brief next-step behavior and action text. Onboarding, artifact creation, draft creation, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the competitive-brief continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `build-competitive-brief` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the competitive brief** | The first substantive competitive brief has just been produced. | `Anything you'd change in the competitive framing before we turn it into something reusable?` |
-| **2. Review the revised competitive brief** | The user asked for any change to positioning, matrix details, objection handling, proof points, uncertainty notes, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks good, want me to draft the customer-safe response, internal battlecard note, objection talk track, or account-team update?` |
-| **3. Prepare the competitive action** | The user accepts the brief, says no changes are needed, says a revision looks good, or asks to operationalize it. | Offer to produce the most useful next artifact or draft action, prioritizing customer-safe response, battlecard note, objection talk track, account-team update, or enablement snippet over broad suggestions. |
-| **4. Review the artifact or draft** | A customer-safe response, battlecard note, talk track, account-team update, or enablement snippet exists, or the user asks to change it. | `Anything you'd change before I save, post, share, or draft this where it belongs?` |
-| **5. Save, post, share, or draft approved competitive action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the battlecard, posting the account-team update, sharing the enablement snippet, or creating the customer response draft. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the competitive brief, treat a lightweight acknowledgement as acceptance and move to `Prepare the competitive action`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to save, post, share, or draft a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+Use public research for current public competitor facts. Prefer official product, pricing, launch, customer-story, investor, and regulatory sources; use secondary coverage only when primary sources are insufficient.
 
 ## Reference Loading
 
-`SKILL.md` owns the normal modes, source order, workflow, output defaults, and artifact behavior. Load references selectively:
-
-- Use `references/request-schema.yaml` only when structured input validation, YAML normalization, or machine-readable field shape matters.
-- Use `references/source-priority.md` when source conflicts, public-proof downgrade behavior, or connector/source selection needs more detail than the workflow below.
-- Use `references/output-contract.md` when exact section ordering, compressed brief variants, or machine-readable compatibility matters.
+`SKILL.md` owns the normal research flow, HTML-by-default behavior, and evidence guardrails. Load references only when their extra detail matters:
 
-## Modes
-
-Choose the narrowest mode that satisfies the request.
-
-- `brief`: default mode; produce a canonical competitive brief with evidence and response guidance
-- `objections`: focus on objection handling, proof points, and do-not-say guidance
-- `positioning`: focus on comparison framing, where the competitor is dangerous, and how to respond
-- `account_overlay`: focus on account-specific implications when the user supplies a target account
-
-If the user does not specify a mode, use `brief`.
-
-## Inputs
-
-Normalize the request using the fields and defaults below.
-
-Accept when provided:
-- `seller_company_name`
-- `account_name`
-- `product_scope`
-- `workflow_scope`
-- `buyer_context`
-- `competitor_scope`
-- `max_matrix_competitors`
-- `competitor_names`
-- `industry`
-- `region`
-- `customer_segment`
-- `mode`
-- `known_objections`
-- `existing_materials`
-
-Normalization rules:
-- If `seller_company_name` is missing, infer it from the prompt, uploaded materials, or available business context; otherwise ask one targeted clarification.
-- Accept either one competitor or a list, and normalize to `competitor_names`.
-- If the user does not name competitors, discover likely competitors from seller context, buyer context, product scope, and public evidence.
-- Support zero to many discovered competitors, but render at most five competitors in one comparison matrix by default.
-- Preserve exact competitor, seller, and account names from the user.
-- Use neutral placeholders in examples, such as `MyCompany`, `Competitor Alpha`, and `ExampleCorp`, instead of assuming the seller is the current company.
-
-## Workflow
-
-Run these steps in order.
-
-### 1. Normalize the request
-
-- Map singular competitor input to `competitor_names`.
-- Set `brief` as the default mode.
-- Preserve the user’s exact seller names, competitor names, account names, and objection wording.
-
-### 2. Gather seller context
-
-- Identify `seller_company_name`, product or workflow scope, buyer context, and optional account context.
-- Use public seller-company sources first for positioning, product lines, target segments, customer proof, recent launches, and comparison language.
-- Treat this as the baseline for the seller side of the brief or matrix.
-- If the seller cannot be inferred, ask for it instead of assuming the current company.
-
-### 3. Gather the source corpus first
+- Use [references/request-schema.yaml](references/request-schema.yaml) for structured input, mode normalization, or matrix bounds.
+- Use [references/source-priority.md](references/source-priority.md) when source conflict, authority order, or public-proof downgrade behavior is ambiguous.
+- Use [references/output-contract.md](references/output-contract.md) for exact section order, compressed brief variants, or machine-readable compatibility.
 
-Prioritize:
-1. user-provided notes, decks, transcripts, and prior briefs
-2. `document_store` knowledge-base or competitive-doc sources when available
-3. `internal_messaging` field signals only when the user asks for internal context
-4. public web validation and public-proof sweep when relevant
-5. public research or user-provided market intelligence only when it materially sharpens the story
+## Terms
 
-Do not let generic web research replace stronger user or internal materials when those exist.
+- **Battlecard:** a compact seller guide for how to position against a competitor, including where they win, where they are weaker, and what to say.
+- **Defensive prep:** preparation for a competitor that may be relevant even when there is not enough evidence to say the competitor is active in the live deal.
 
-Minimum evidence coverage:
-- competitor-only runs should use multiple distinct evidence units when available
-- account-scoped runs should add account evidence when the user provided it or the connectors make it easy to fetch
-- if a source lane is unavailable, state that directly and continue with the strongest remaining evidence path
+## Workflow Guidance
 
-Required public-proof sweep when relevant:
-- if the brief relies on current competitor positioning, launches, pricing, partnerships, or customer proof, explicitly validate those claims
-- search official company pages first
-- then use primary-source public materials such as investor relations pages, earnings materials, official launch posts, product pages, or public customer stories
-- use secondary coverage only when those primary sources are insufficient
+### 1. Resolve the comparison anchor
 
-Source-extraction rules:
-- do not compress every source into the same vague reframe
-- for each high-priority source, extract at least one concrete nugget into:
-  - `what_they_are_claiming`
-  - `what_is_confirmed`
-  - `why_buyers_find_it_compelling`
-  - `how_to_counter_or_reframe`
-  - `what_not_to_overclaim`
+- Require one useful anchor: named competitor(s), a named account or deal with competitor context, or a clear product, workflow, segment, or market category to compare.
+- Preserve the user's exact seller, competitor, account, and objection wording. Infer the seller from the prompt or provided material only when credible; otherwise ask one targeted question.
+- When the user gives a clear product, workflow, segment, or market category but no competitor names, discover likely competitors from seller context, buyer context, product scope, maintained competitive material, and public evidence. Keep the discovered set focused and label uncertainty.
+- User confirmation is required for any required anchor inferred from sources rather than provided by the user.
+- If the anchor is ambiguous, do a bounded candidate pass before asking: at most three source reads, only enough to offer up to five concrete candidates with one-line rationale.
+- When a company-like name could be a competitor or customer, use a bounded ~~CRM check when available. If it resolves to a customer and the user did not clearly name it as a competitor, treat it as account context or ask the user to choose.
+- Do not gather broad enrichment before the user confirms an inferred required anchor.
 
-### 4. Build competitor dossiers before synthesis
+### 2. Gather evidence in authority order
 
-For each in-scope competitor, capture the most relevant available facts:
+Use this order unless the user specifies a narrower lane:
 
-- profile and target market
-- what they sell
-- positioning versus alternatives
-- pricing and pricing talk track when supported
-- recent launches, releases, or news
-- customer proof or review themes
-- where they win
-- where the seller can separate
-- objections, talk tracks, and landmines
+1. User-provided notes, decks, transcripts, prior briefs, and linked material
+2. ~~Knowledge & Files competitive docs, battlecards, account plans, and positioning
+3. ~~CRM for named account or deal truth
+4. ~~Meeting Transcripts and ~~Internal Messaging when they materially change the talk track
+5. Official public competitor and seller sources
+6. Investor, regulatory, or other primary public materials
+7. ~~Sales Intelligence and reputable secondary coverage for a specific remaining gap
 
-Example dossier labels:
+- Do not let generic web research replace stronger user or internal material.
+- Establish a seller baseline from seller-controlled sources when the seller is known: positioning, product lines, target segments, customer proof, recent launches, and comparison language.
+- Validate claims about current positioning, launches, pricing, partnerships, and public proof with public primary sources.
+- For competitor-only runs, seek multiple distinct evidence units when available. For account-scoped runs, add account evidence when the user supplied it or a connected source can fetch it narrowly.
+- Treat internal field signals as directional unless corroborated by account evidence, maintained docs, or public proof.
+- For each important source, extract a concrete evidence unit: what is claimed, what is confirmed, why buyers may care, how to counter or reframe, and what not to overclaim.
+- Cite material claims with clickable links when available. If a lane is unavailable, stale, thin, or conflicting, say so and continue with the strongest remaining evidence.
 
-- `Seller`: `MyCompany`
-- `Primary competitor`: `Competitor Alpha`
-- `Watchlist competitor`: `Competitor Beta`
-- `Account`: `ExampleCorp`
+### 3. Build dossiers before synthesis
 
-### 5. Run account-context lanes when account context matters
+For each in-scope competitor, capture the relevant available facts:
 
-- When `account_name` is present, use user-provided account notes first.
-- If `crm`, `document_store`, or `internal_messaging` can sharpen the account context materially, use those lanes selectively instead of broad retrieval.
-- If direct competitor evidence is not found in the account context, label the result as `defensive prep` instead of implying the competitor is confirmed in the live deal.
+- profile, target market, and what they sell
+- positioning and buyer appeal
+- pricing or pricing talk track only when supported
+- recent launches, releases, partnerships, or public proof
+- where they win and where the seller can separate
+- likely objections, discovery questions, talk tracks, and landmines
 
-### 6. Gather supporting evidence only when it sharpens the brief
-
-- Use `internal_messaging` only for field-signal evidence that changes the talk track or next move.
-- Use public research or user-provided market intelligence only to validate claims, gather public proof, or close a concrete evidence gap.
-- Keep field signals labeled as directional rather than durable proof.
-- Keep unsupported or conflicting facts in an uncertainty note instead of smoothing them into the narrative.
+Do not collapse competitors into one generic story. Keep distinct evidence and uncertainty for each.
 
-### 7. Build the comparison matrix when useful
+### 4. Add account context carefully
 
-Always include the seller company in comparison logic when it is known.
+- When an account is named, use user-provided account notes first and ~~CRM for account and opportunity truth.
+- Use ~~Knowledge & Files, ~~Meeting Transcripts, or ~~Internal Messaging selectively when they sharpen the live account implication.
+- Do not let public research or generic field chatter invent account status, competitor presence, or deal posture.
+- If there is no direct evidence that a competitor is active in the deal, label the account section Defensive prep.
 
-Use the shape that fits the request:
+### 5. Synthesize the seller response
 
-- `1-v-1`: one seller column, one competitor column, and one `Readout` or `Winner` column
-- `1-v-many`: `Area`, seller read, `Status`, and `Main pressure`
+- Default to brief; use objections, positioning, or account_overlay when the request calls for a narrower shape.
+- Compare at most five competitors in one matrix unless the user explicitly asks for more.
+- For 1-v-1, use Area | Seller | Competitor | Readout. For 1-v-many, use Area | Seller read | Status | Main pressure.
+- Use statuses such as Lead, Pressure from [Competitor], Mixed, and Not determined; do not force a winner when evidence does not support one.
+- Keep the tone factual and calm. Include what to say, what to clarify, proof to use, and what not to overclaim; avoid a feature bakeoff unless requested.
+- The package should answer what each competitor is doing, where they are dangerous, why they win, why they lose, how to compete, and what the seller or strategist should say next.
 
-Example `1-v-1` row:
+Example 1-v-1 row:
 
 | Area | MyCompany | Competitor Alpha | Readout |
 | --- | --- | --- | --- |
 | Deployment fit | Strong for governed enterprise rollout | Strong for fast team-level adoption | Mixed; clarify buyer governance needs |
 
-Example `1-v-many` row:
+Example 1-v-many row:
 
 | Area | MyCompany read | Status | Main pressure |
 | --- | --- | --- | --- |
 | Buyer simplicity | Strong if the buyer values one accountable platform | Pressure from Competitor Beta | Competitor Beta may look simpler for a narrow departmental use case |
 
-Status examples should stay generic:
+### 6. Create and verify the artifact
+
+- Default output: create a self-contained UTF-8 HTML brief with inline CSS and no external assets, then provide a concise chat summary. If the user explicitly asks for chat, plain text, or Markdown only, skip HTML and render the same substance in chat.
+- Use a user-provided destination when supplied. Otherwise write a sanitized-seller-compintel-date.html file in the current workspace; use a safe temporary directory when a workspace file would be inappropriate. Ask before overwriting an existing user-visible file.
+- Use a restrained, neutral, deck-inspired layout unless the user supplies brand conventions. Do not hardcode a company palette, logo, product name, or visual system.
+- Include top-level sections or tabs: Overview, Competitors, Guidance, and Sources.
+- Useful generic tooltip or card labels include Trigger, Discovery questions, Positioning, Verified signals, Where they win, Where the seller can separate, Why this works, Proof, Use when, and Landmine / do not say.
+- Sources must show clickable provenance, evidence limitations, and which dependency categories were used, unavailable, or not relevant.
+- Read the generated file back and verify the required sections, source provenance, and embedded styling are present. When a browser or Playwright-style renderer is available, render it once and fix blank pages, broken layout, unreadable contrast, or missing CSS before returning the link.
+- Return the clickable artifact link first, followed by a short readout and a useful next-step offer. Do not narrate HTML mechanics in ordinary user-facing copy.
+
+### Next Step Options
+
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Add an account-specific overlay that separates confirmed deal evidence from defensive prep.
+- Create an objections-only or positioning-only version for the immediate seller need.
+- Prepare a meeting brief using the competitive risks and discovery questions.
+- Draft a concise internal enablement or account-team update for review.
+- Check whether a matching weekly automation already reruns this `build-competitive-brief` skill for the same competitor, product, category, or account scope; if none exists, offer to create one that gives the seller a reusable competitive digest.
+- Close the smallest material competitive evidence gap.
+
+Next steps to avoid:
+- Sending, posting, or turning unsupported competitor claims into customer-facing assertions.
+
+### Automation Offer Guard
+
+For durable competitor, category, product, or account-specific competitive scopes, a weekly competitive digest is the preferred automation offer when the user would benefit from reusable field intelligence rather than a one-off battlecard. Frame the value in sales language: what changed in the competitive landscape, why buyers may care, where the seller can separate, what to say, what to clarify, and what not to overclaim.
+
+The automation must be a scheduled rerun of this skill, not a separate custom research digest. When creating or describing the automation, make the prompt call this skill directly and preserve the same competitive scope and mode:
+
+```text
+Use the Sales `build-competitive-brief` skill.
+Rerun it weekly for the same competitive scope: [competitor, product, workflow, category, segment, region, or account].
+Return the standard competitive brief output, including source posture and landmines.
+
+seller_company_name: "[seller company when known]"
+product_scope: "[same product scope]"
+workflow_scope: "[same workflow or buyer problem]"
+competitor_scope: "[same competitor category or market category]"
+competitor_names: [same named competitors when used]
+account_name: "[same account when account-specific]"
+mode: "[brief, objections, positioning, or account_overlay]"
+known_objections: [same recurring objections when used]
+```
+
+For broad category or product scopes where named competitors are discovered by the skill, keep the stable scope in the plain-language prompt text and let this skill resolve likely competitors using its normal comparison-anchor guidance. Do not offer this automation for a narrow one-time objection, meeting-specific prep, or sparse evidence gap unless the user clearly wants a recurring competitive watch.
 
-- `Lead`
-- `Pressure from Competitor Alpha`
-- `Mixed`
-- `Not determined`
+The recurring output should follow this skill's competitive brief contract: context, seller baseline, competitive landscape summary, competitor snapshots, comparison takeaways, response strategy, what not to overclaim, and sources. Keep it read-only; it may recommend talk tracks, discovery questions, proof, and internal updates for review, but must not post, send, share, or turn unsupported competitor claims into customer-facing assertions unless the user separately asks and approves.
 
-### 8. Apply tone and framing guardrails
+Before offering the weekly competitive digest, check whether the user already has a matching local automation installed. Inspect local automation records under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset, and match by name, prompt, skill name, cadence, competitor, product, category, segment, account, mode, known objections, or other stable scope details. Treat active and paused matches as already installed.
 
-- Keep the tone factual and calm.
-- Do not turn the artifact into a feature bakeoff unless the user explicitly asks for that style.
-- Do not trash competitors or overstate confidence.
-- Always include what to say, what to clarify, and what not to overclaim where relevant.
+- If a matching automation exists, do not suggest creating another one. Continue with the next most relevant non-automation follow-up.
+- If no matching automation exists, end with one clear offer to check/create a weekly rerun of `build-competitive-brief` for the same scope. Describe the recurring output as a seller-ready competitive digest with what changed, why it matters, what to say, proof to use, and landmines to avoid. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, do not mention tool details; offer to help set up a recurring competitive digest when automations are available.
 
-### 9. Build the structured package
+## Modes
 
-- Build the brief from evidence units, not generic opinion.
-- The package should answer:
-  - what the competitor is doing
-  - where they are dangerous
-  - why they win
-  - why they lose
-  - how to compete
-  - what the seller or strategist should say next
-- If the user asks for a reusable artifact, format the response so it can be copied into a document or wiki. Do not assume a renderer script is available.
+- brief — default; canonical competitive brief, matrix, response guidance, and sources
+- objections — buyer objections, proof points, discovery questions, talk tracks, and do-not-say guidance
+- positioning — comparison framing, danger zones, differentiators, and response strategy
+- account_overlay — account-specific implications, clearly separating confirmed deal evidence from defensive prep
+- chat_or_markdown_only — same evidence contract without an HTML artifact, only when explicitly requested
 
-### 10. Optionally produce a shareable HTML artifact
+## Output Format
 
-If the user explicitly asks for a shareable artifact, HTML brief, battlecard page, or executive page, write a self-contained HTML file directly and return its path plus a short chat summary.
+For the HTML artifact and Markdown-only fallback, preserve this content contract:
+
+```md
+# [Seller] Competitive Brief
 
-HTML artifact conventions:
+## Context
+[Scope, buyer/account context, mode, and evidence posture]
 
-- filename pattern: `{seller-company}-compintel-{YYYY-MM-DD}.html`
-- create the file in the current workspace unless the user provides a destination; sanitize the seller-company filename segment and ask before overwriting an existing file
-- top-level sections or tabs: `Overview`, `Competitors`, `Guidance`, `Sources`
-- include source provenance and connector-lane status in `Sources`
-- use a restrained, deck-inspired corporate layout
-- use the user's provided brand conventions when available; otherwise use neutral, public web-safe styling
-- do not hardcode the current company's brand, palette, product names, or visual system
-- after writing, read the file back to verify it contains the required sections and source provenance; when a browser or Playwright-style renderer is available, render the local HTML once to catch blank pages, broken layout, or missing embedded CSS before returning the path
+## Seller Baseline
+[Seller positioning and relevant proof]
 
-Useful generic tooltip labels:
+## Competitive Landscape Summary
+[What matters most and why]
 
-- `Trigger`
-- `Discovery questions`
-- `Positioning`
-- `Verified signals`
-- `Where they win`
-- `Where MyCompany can separate`
-- `Why this works`
-- `Proof`
-- `Use when`
-- `Landmine / do not say`
+## Competitor Snapshot
+### [Competitor]
+- **What they claim:** [Grounded claim]
+- **What is confirmed:** [Evidence-backed fact]
+- **Why buyers may care:** [Buyer appeal]
+- **Where they win:** [Supported strength]
+- **Where we can separate:** [Supported response]
+- **Landmine / do not say:** [Overclaim to avoid]
 
-### 11. Handle conflicts and sparse evidence
+## Comparison Matrix Takeaways
+[Matrix plus the few implications that change seller behavior]
 
-- If sources conflict, say so and explain which source categories are more trustworthy.
-- If evidence is sparse, narrow the brief, label weak spots, and end with the smallest useful set of follow-up questions.
+## Response Strategy
+- **Say:** [Talk track]
+- **Clarify:** [Discovery question]
+- **Prove:** [Evidence]
 
-## Output Rules
+## What Not To Overclaim
+- [Uncertainty, conflict, or unsupported claim]
+
+## Sources
+- [Clickable source + what it supported]
+- [Dependency category status and material gaps]
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-- Default to a concise brief with explicit source provenance. Use these sections unless the user asks for a narrower shape: `Context`, `Seller Baseline`, `Competitive Landscape Summary`, `Competitor Snapshot`, `Comparison Matrix Takeaways`, `Response Strategy`, `What Not To Overclaim`, and `Sources`.
-- Include uncertainty notes when claims depend on limited or fast-changing public evidence.
-- If `mode=account_overlay`, add account implications without implying direct competitive presence unless it is evidenced.
-- Preserve useful examples by replacing internal company/product names with `MyCompany`, seller, product, account, or competitor placeholders. Do not delete examples just because they originally named an internal company.
+Add Account Implications for account_overlay, and label unsupported competitor presence as Defensive prep.
 
 ## Example Prompts
 
@@ -392,7 +227,10 @@ Useful generic tooltip labels:
 - `Build an account-specific defensive prep brief for ExampleCorp where Competitor Alpha may be in the deal.`
 - `Create a shareable HTML competitive brief for MyCompany vs Competitor Alpha.`
 
-## Failure Handling
+## Rules
 
-- If no reliable evidence exists beyond the competitor name, return a minimal brief structure plus the exact evidence gaps that block a stronger output.
-- If a connector lane fails, continue with manual inputs and public evidence instead of stopping.
+- Never fabricate competitor capabilities, pricing, customer proof, account posture, or source links.
+- Keep facts, field signals, and inference visibly separate.
+- If evidence is sparse, narrow the brief and name the smallest evidence gaps that would improve it.
+- Do not post, send, share, or update systems from this workflow unless the user later explicitly requests a separate supported action.
+- Always cite material source-backed claims with hyperlinks when useful links are available.
diff --git a/plugins/sales/skills/build-competitive-brief/references/output-contract.md b/plugins/sales/skills/build-competitive-brief/references/output-contract.md
index 8fc6098..ba28d2c 100644
--- a/plugins/sales/skills/build-competitive-brief/references/output-contract.md
+++ b/plugins/sales/skills/build-competitive-brief/references/output-contract.md
@@ -26,6 +26,8 @@ If `mode=objections`, focus the output on objection handling and proof points.
 
 If `mode=account_overlay`, add one section called `Account Implications` and clearly label any directional inference.
 
+The default delivery is a self-contained HTML artifact plus a concise chat readout. Use the same section contract in chat or Markdown only when the user explicitly asks for that mode.
+
 Use placeholder examples when showing structure:
 
 - seller: `MyCompany`
diff --git a/plugins/sales/skills/build-competitive-brief/references/request-schema.yaml b/plugins/sales/skills/build-competitive-brief/references/request-schema.yaml
index 5c77d17..832fb2c 100644
--- a/plugins/sales/skills/build-competitive-brief/references/request-schema.yaml
+++ b/plugins/sales/skills/build-competitive-brief/references/request-schema.yaml
@@ -29,7 +29,7 @@ properties:
     type: string
   mode:
     type: string
-    enum: [brief, objections, positioning, account_overlay]
+    enum: [brief, objections, positioning, account_overlay, chat_or_markdown_only]
   known_objections:
     type: array
     items:
@@ -38,5 +38,3 @@ properties:
     type: array
     items:
       type: string
-  business_context_path:
-    type: string
diff --git a/plugins/sales/skills/build-competitive-brief/references/source-priority.md b/plugins/sales/skills/build-competitive-brief/references/source-priority.md
index fababb9..5fa424c 100644
--- a/plugins/sales/skills/build-competitive-brief/references/source-priority.md
+++ b/plugins/sales/skills/build-competitive-brief/references/source-priority.md
@@ -3,11 +3,11 @@
 Use this order unless the user specifies a tighter lane:
 
 1. User-provided materials
-2. `document_store` knowledge-base or competitive-doc sources
-3. Recent `internal_messaging` field evidence when the user asks for internal context
-4. Official competitor and partner sources
-5. Investor or regulatory materials when relevant
-6. public research or user-provided market intelligence when relevant
-7. Secondary coverage
+2. ~~Knowledge & Files knowledge-base, competitive-doc, battlecard, or account-plan sources
+3. ~~CRM for named-account or active-deal truth
+4. ~~Meeting Transcripts and ~~Internal Messaging when recent field evidence materially changes the talk track
+5. Official competitor, seller, and partner sources
+6. Investor, regulatory, or other primary public materials when relevant
+7. ~~Sales Intelligence or reputable secondary coverage for a specific remaining gap
 
-Keep source provenance explicit. Do not collapse different sources into one generic claim.
+Keep source provenance explicit. Do not collapse different sources into one generic claim. If sources conflict, explain which source is more authoritative for that claim and keep field signal directional until corroborated.
diff --git a/plugins/sales/skills/enrich-company-and-contact-data/SKILL.md b/plugins/sales/skills/enrich-company-and-contact-data/SKILL.md
index 01ce297..702eb67 100644
--- a/plugins/sales/skills/enrich-company-and-contact-data/SKILL.md
+++ b/plugins/sales/skills/enrich-company-and-contact-data/SKILL.md
@@ -1,307 +1,174 @@
 ---
 name: enrich-company-and-contact-data
-description: Build portable sales enrich-company-and-contact-data outputs for company and contact discovery, firmographic or technographic completion, ICP list building, segmentation, trigger analysis, market scans, and enrichment-backed comparison work using configured source categories and user-provided inputs.
+description: "Use for data-first company, contact, lead, and prospect discovery or enrichment, including firmographic or technographic completion, named-company profiling, entity resolution, ICP matching, prospect-list building, segmentation, trigger or sales-signal analysis, market and territory scans, and enrichment-backed comparisons. Exclude meeting preparation, outreach-led company research, and prioritization of an already established account book."
 ---
 
 # Enrich Company And Contact Data
 
-Use this skill for data-first go-to-market enrichment: company/contact lookup, firmographic or technographic completion, ICP discovery, record enrichment, market segmentation, signal scans, similar-company research, and enrichment-backed comparison tables.
 
-Use another focused Sales workflow when the user's real goal is primarily meeting prep, pipeline prioritization, deal strategy, competitive narrative, business case, or forecast review. Those skills may still use this skill as a bounded enrichment lane, but they stay authoritative for the seller task itself.
+## Context-Gathering Intake
 
-## Skill Configuration
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-### User Context
+Prepare a sales person with a trusted, decision-ready view of companies or contacts: what is known, what is a strong match, what is still uncertain, and what action the data supports. This skill owns evidence resolution, ICP-fit and coverage comparisons, and source-grounded signal analysis; it does not own rep-work priority, outreach execution, or CRM writes.
 
-Mandatory pre-answer gate:
+## Common Skill Instructions
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the output.
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### Audience And Language
+## Enrichment Ranking
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+Use this priority order when the request is broad or the working set is ambiguous.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+1. Explicit user-provided rows, domains, contacts, companies, ICP criteria, requested fields, or stated ranking rule
+2. The named CRM account set, territory, target list, or documented ICP that the user points to
+3. Entities that satisfy every hard filter, such as geography, industry, company size, technology, role, or seniority
+4. Entities with high-confidence identity resolution and enough comparable evidence to support the requested output
+5. Entities with a clear fit implication, reachable buying-team path, or source-grounded external signal
+6. Near matches and unresolved entities, clearly separated from qualified results
 
-### Source Resolution
+Do not silently broaden a supplied list into discovery, merge ambiguous entities, or rank by a criterion the user cannot inspect.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+If the user asks to score or tier a list, keep the judgment limited to ICP fit, enrichment completeness, identity confidence, or defined signal strength. If the user asks which accounts to work now, where to focus, or what rep action deserves priority, route to `prioritize-accounts`.
 
-### Required Inputs
+## Key Dependency Categories
 
-This skill needs enough user-provided, connector-visible, public, or explicitly inferable context to identify the entity set and enrichment task shape.
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
 
-The skill can still produce a limited enrichment table from pasted lists, CSV/exported rows, explicit domains, user-provided ICP notes, or public context when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because enrichment connectors are unavailable.
+- [Blocking] ~~Sales Intelligence for company/contact discovery, firmographics, technographics, intent, lookalikes, and provider-native signals. It blocks discovery, contact discovery, lookalikes, intent, and provider-native enrichment when equivalent requested data is not already grounded.
+- ~~CRM for account identity, customer status, ownership, lifecycle stage, opportunity context, and duplicate resolution
+- ~~Knowledge & Files for ICP definitions, territory rules, target lists, segmentation rules, and enrichment conventions
+- User-provided rows, CSVs, exports, domains, emails, and ICP notes when they already define the work
+- Public research only for narrow validation or gaps that configured sources cannot answer
 
-**Inputs:**
+Start with the category that owns the requested fields, then attempt only additional categories that materially improve coverage, identity confidence, or the decision. If a material category is unavailable, stale, conflicting, or provider-limited, state the limitation and its impact on coverage or confidence.
 
-Require:
+## Workflow Guidance
 
-- [required] entity scope: companies, contacts, account list, ICP, territory, segment, or pasted/exported rows
-- [required] task shape: discover, enrich, compare, segment/score, or scan signals
+These enrichment-specific steps modify the corresponding default workflow stages in the index skill. Continue the remaining default stages, including the first output and proposed next steps.
 
-Accept when provided:
+- 1. Clarify and Gather Context
+    - Resolve the smallest useful mode, entity scope, task shape, requested fields, ranking rule, and result limit.
+    - If the anchor is ambiguous, make at most two narrow source calls to surface concrete candidates, then ask the user to choose before deeper enrichment. When two or three concrete modes, entities, or candidates are available, use `ask_user_input()`; otherwise ask one narrow text question.
+- 2. First Draft
+    - Start from the user-defined working set or the canonical source for the request, then retrieve only evidence that materially improves the result.
+    - When the selected connected Sales Intelligence provider is ZoomInfo, load `zoominfo` before provider-specific search, enrichment, intent, similarity, or recovery. When it is Apollo, load `apollo`; apply Apollo v2-specific guidance only after verifying app version 2 or later.
+    - For broad discovery, search before heavy enrichment and enrich only the final shortlist unless the user explicitly asks for exhaustive treatment.
+    - Verify hard filters before calling a row qualified. Put close but unsupported candidates in Near Matches, Unclear, or Excluded.
+    - Render the smallest useful table or shortlist, with field-level clickable source links, confidence, and visible gaps.
 
-- requested fields, ranking or segmentation criteria, result limit, provider preference, geography, industry, company size, technology, title/seniority, funding/growth/intent signals, and output format
+## Overall Rules
 
-**Context Rules:**
+- Cite sourced claims with hyperlinks whenever links are available. If a source cannot provide a link, name the source and the limitation.
+- CRM owns internal account truth; enrichment providers own provider-native external fields; user-provided records define the working set unless the user asks for discovery.
+- Use Sales Intelligence for provider-native discovery, contact discovery, lookalikes, and signal scans; use CRM for existing customers, ownership, lifecycle stage, opportunity context, and named CRM lists; use Knowledge & Files for named ICP, territory, target-list, or segmentation rules that are not supplied.
+- Do not use indirect or mirrored sources, broad web search, or Computer Use as substitutes for the authoritative category.
+- Keep sourced facts separate from `Inference:`. Never invent emails, phones, titles, technologies, funding, hiring signals, intent, or missing fields.
+- Do not claim exhaustive coverage from a bounded query. Keep provider limits, weak matches, and missing lanes visible.
+- Do not update CRM, execute outreach, create records, or send messages in this workflow. If the user wants a CRM change, prepare the proposed field updates and require explicit approval before a separate write action.
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible existing customer/account anchors unless the request clearly asks for external discovery only. When `crm` is available, include a bounded CRM lookup to resolve account identity, customer status, duplicate candidates, or account-list scope before relying on enrichment providers, public research, or docs alone.
-- Before asking the user to paste a list, CSV, ICP, or field set, first try the source categories that can resolve the missing entity scope or task shape: `data_enrichment` for provider-native company/contact discovery, `crm` for existing account/customer scope, and `document_store` for ICP, territory, segmentation, or target-account lists. Manual/exported rows are the fallback after those categories are unavailable, empty, or not specific enough.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+## Output Contract
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+Every mode must include a compact `## Sources & Coverage` section before proposed next steps:
 
-**Input Request Format Example:**
+- **Used:** [Linked source or source label] — [fields, rules, or signals it supplied]
+- **Unavailable or limited:** [Material category or provider gap] — [impact on confidence or coverage]
+- **Coverage:** [Working set, query bound, or whether the result is exhaustive]
 
-```md
-Which company, contact list, account list, or ICP should I enrich?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when the user has supplied a list, export, ICP, or onboarding/check-in context clearly names the list to work.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
+### Next Step Options
 
-- `data_enrichment`: company/contact lookup, firmographics, technographics, intent signals, similar-company discovery, contact recommendations, and provider-native enrichment fields.
-- `crm`: account identity, customer status, ownership, lifecycle stage, open opportunity context, duplicate resolution, and internal account truth.
-- `document_store`: ICP definitions, target-account lists, territory docs, segmentation rules, enrichment conventions, and saved source-of-truth guidance.
-- user-provided context: pasted lists, CSV/exported rows, domains, emails, company/contact names, ICP notes, ranking criteria, and requested fields.
-- public research: narrow validation of facts not covered by configured enrichment providers when the user asks or the first pass would otherwise be misleading.
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Export the results to a spreadsheet with the visible evidence, confidence, and unresolved fields preserved.
+- Prepare reviewed updates to the source of truth, such as CRM records, for missing or corrected fields.
+- Verify near matches, ambiguous identities, or the smallest material coverage gap.
+- Refine the filters, ICP rule, requested fields, or result limit using the user's guidance.
+- Hand a qualified shortlist to account prioritization or company research when the user wants the next selling action.
 
-Authority and gaps:
+Next steps to avoid:
+- Executing outreach, silently broadening the working set, or updating CRM without explicit approval.
 
-- Do not treat enrichment providers as CRM systems of record.
-- For customer/account enrichment or account-list cleanup, use CRM when available for account identity, customer status, ownership, lifecycle stage, open opportunity context, duplicate resolution, and internal account truth. Do not let enrichment providers, public research, docs, or user-provided guesses overwrite CRM-owned account truth.
-- Treat public research as validation or gap-fill for external facts only; do not use it to overwrite provider-native enrichment or CRM-owned account status when those sources are available.
-- User-provided records define the working set unless the user asks for discovery.
-- Use public research only for narrow validation or when no configured provider/manual evidence can answer the requested field.
-- If configured enrichment access is missing, continue from manual/exported context when sufficient and label provider gaps.
+## Modes
 
-### Context Gathering Principles
+### 1. Enrich Provided Records
 
-Optimize for marginal value, not absolute speed.
+- Use when the user supplies companies, contacts, domains, emails, rows, a CSV/export, or a CRM-backed list and wants missing fields completed or cleaned.
+- Preserve the full supplied set, normalize obvious duplicates, and surface identity ambiguity instead of silently dropping or merging rows.
+- Use CRM for customer/account truth when available, then enrich requested external fields.
 
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the enrich-company-and-contact-data experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+#### Output Format
 
 ```md
-## Enrich Company And Contact Data
-This skill helps complete sparse company, account, and contact inputs with useful sales context. It can use a list, territory, ICP, CSV/export, CRM data, enrichment connectors, docs, or public research when appropriate. It produces a cleaned and ranked table with filled fields, confidence, sources, gaps, and recommended next actions.
-
-Definitions:
-- ICP: ideal customer profile, the criteria that define what a strong-fit company or contact looks like for the sales motion.
-- Firmographic context: company attributes such as industry, size, geography, revenue band, funding, or business model.
-- Technographic context: evidence about the tools, platforms, infrastructure, or technology choices a company uses.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Enrich a company or contact list.`
-- `@Sales enrich this account list with firmographics and likely buying teams.`
-- `@Sales clean up these contacts and flag which ones are worth pursuing first.`
-- `@Sales find missing company details for the top accounts in my territory.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Enrich Company And Contact Data next-step behavior and action text. Onboarding, spreadsheet creation, draft creation, CRM writeback, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the enrichment continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `enrich-company-and-contact-data` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the enrichment table** | The first substantive enrichment table has just been produced. | `Anything you'd change in the fields, scoring, rows, or confidence thresholds before we act on this table?` |
-| **2. Review the revised enrichment table** | The user asked for any change to columns, ranking criteria, confidence thresholds, rows, filters, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If the table looks right, want me to move it into a spreadsheet, segmentation artifact, contact-selection guide, or CRM update draft?` |
-| **3. Prepare the enrichment artifact** | The user accepts the table, says no changes are needed, says a revision looks good, or asks to operationalize the enrichment. | Offer to produce the most useful next artifact or draft action, prioritizing spreadsheet, segmentation artifact, contact-selection guide, outreach input, or CRM-ready update text over broad suggestions. |
-| **4. Review the artifact or draft** | A spreadsheet, segmentation artifact, contact-selection guide, outreach input, or CRM-ready update exists, or the user asks to change it. | `Anything you'd change before I save, share, or write the approved enrichment updates?` |
-| **5. Save, share, or write approved enrichment action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the spreadsheet, sharing the contact-selection guide, creating reviewed outreach drafts, or writing approved CRM update text. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the enrichment table, treat a lightweight acknowledgement as acceptance and move to `Prepare the enrichment artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to save, share, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+# Enrichment Results
 
-### Source Links
+| Input Record | Resolved Entity | [Requested Field] | [Requested Field] | Confidence / Notes | Missing Or Unresolved |
+| --- | --- | --- | --- | --- | --- |
+| [Original input] | [Matched company/contact + source link] | [Grounded value + source link] | [Grounded value + source link] | [High/Medium/Low + reason] | [Gap or ambiguity] |
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+## Key Readout
 
-## Operating Modes
+- [Most useful pattern or implication]
+- [Important source gap, duplicate, or weak match]
+- [Recommended next check or action]
 
-Choose the narrowest mode that matches the user's request.
-
-- `discover_companies`: build a company list from an ICP, market description, trigger, or comparison seed
-- `discover_contacts`: find relevant stakeholders or buyers for named companies or a defined segment
-- `enrich_records`: complete or improve a supplied company/contact set
-- `compare_accounts`: contrast known companies using requested attributes or research dimensions
-- `segment_or_score`: cluster, tier, or prioritize a bounded set using explicit criteria
-- `signal_scan`: surface companies or accounts matching defined external signals
-
-If the user does not specify a mode, infer it from the request. Do not broaden the task beyond what the prompt requires.
-
-## Inputs
-
-Normalize the request into the smallest useful working shape:
-
-- `mode`
-- `entity_scope`: companies, contacts, or mixed
-- `input_records`: names, domains, emails, companies, contacts, or source list when supplied
-- `search_criteria`: industry, region, company size, business model, technology, title, seniority, hiring, funding, growth, intent, or other explicit filters
-- `requested_fields`: fields the user wants returned or completed
-- `ranking_or_segmentation_rule`: the criterion for ordering or grouping results, when requested
-- `result_limit`
-- `provider_preference`, when the user names a provider
-
-Ask a concise clarification only when the request is impossible to execute responsibly, such as:
-
-- the desired entity type is unclear in a way that changes the workflow
-- the user asks for a ranked or filtered list without enough criteria to know what qualifies
-- the task requires a named provider or account scope that cannot be inferred from the prompt
-
-Otherwise, choose a conservative reasonable interpretation and proceed.
-
-## Workflow
-
-Run these steps in order.
-
-### 1. Lock the task shape
-
-- Identify the smallest mode that satisfies the request.
-- Separate hard constraints from softer preferences.
-- Preserve exact user wording for company names, titles, industries, and priority rules.
-- Decide whether the output should be a shortlist, a comparison table, an enriched record set, or a compact narrative with supporting rows.
-
-### 2. Resolve the working set
-
-Use the request type to choose the path:
-
-- For discovery work, start from the search criteria or seed companies.
-- For enrichment work, start from the supplied records and normalize obvious duplicates before lookup.
-- For comparison work, preserve the full named set and avoid silently dropping difficult matches.
-- For signal scans, define the exact signal or proxy before retrieving data.
-
-When identity ambiguity matters, surface it. Do not quietly merge two companies, two contacts, or two competing interpretations into one row.
-
-### 3. Choose the evidence lane
-
-- Prefer user-supplied rows when they already define the scope.
-- Use a configured `data_enrichment` app when the task needs external lookup, structured enrichment, topic research, lookalikes, or provider-native signals.
-- Use `crm` or `document_store` only as a supporting lane for disambiguation or account framing, not as a replacement for provider-native enrichment when the task is explicitly external-data-first.
-- Use public research sparingly and label it clearly when present.
+---
 
-If no configured provider is available, continue from user-provided inputs and public sources only when the task still remains useful. State the missing lane plainly when it materially limits quality.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-### 4. Retrieve only what materially improves the answer
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-- Narrow first, then deepen.
-- For broad candidate discovery, search before heavier enrichment.
-- Enrich only the final shortlist or the provided record set unless the user explicitly wants exhaustive batch treatment.
-- For company/contact discovery, return the fields the user asked for plus only the minimum context needed to interpret quality.
-- For comparisons, gather comparable fields for every entity before synthesizing winners or rankings.
-- For signal scans, keep the signal definition visible and avoid converting a weak proxy into a stronger claim.
+### 2. Discover Companies Or Contacts
 
-Be credit-aware when the selected provider makes cost material. Do not add ceremony to a small, clearly requested call, but avoid wasteful wide enrichments when a narrower pass would answer the question well.
+- Use when the user asks for new companies, contacts, likely buyers, decision makers, lookalikes, or ICP matches.
+- Start from explicit search criteria or seed companies. Return only qualified matches as clean hits and keep near matches separate.
+- For contacts, verify role, seniority, and company assignment before recommending a person.
 
-### 5. Verify before summarizing
+#### Output Format
 
-Before calling something a match, verify the hard constraints that matter:
+```md
+# Qualified Matches
 
-- geography
-- company size or revenue band
-- public versus private status when requested
-- seniority, role, or company assignment for contacts
-- industry, technology, or other qualifying fields when surfaced
-- any explicit ranking or segmentation criterion
+| Company / Contact | Why It Fits | Key Evidence | Confidence | Source |
+| --- | --- | --- | --- | --- |
+| [Entity] | [Hard criteria satisfied] | [Compact grounded evidence] | [High/Medium/Low] | [Clickable link] |
 
-If a candidate is close but not qualified, move it to `Near Matches`, `Unclear`, or `Excluded` instead of presenting it as a clean hit.
+## Near Matches
 
-### 6. Render the right output shape
+- [Entity] — [Why it is close but not qualified]
 
-Use the smallest structure that preserves decision value.
+## Gaps / Caveats
 
-For discovery:
+- [Coverage, provider, identity, or source limitation]
 
-- `Qualified Matches`
-- `Near Matches` when useful
-- `Why These Fit`
-- `Gaps / Caveats`
+---
 
-For enrichment:
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-- `Input Record`
-- `Resolved Entity`
-- requested enriched fields
-- `Confidence / Notes`
-- `Missing or Unresolved`
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-For comparison:
+### 3. Compare, Segment, Or Scan Signals
 
-- comparison table with one row per company or contact
-- explicit ranking or readout only when the underlying criterion is supported
-- `What Is Still Unclear`
+- Use when the user asks to compare a bounded set, score or tier a list, or find entities matching a defined external signal.
+- Keep the visible comparison, tiering rule, or signal/proxy definition in the output so the user can inspect the judgment.
+- Do not convert a weak proxy into a stronger claim.
 
-For segmentation or scoring:
+#### Output Format
 
-- segment or tier labels
-- the visible rule used to assign each label
-- concise rationale
-- `Evidence Gaps`
+```md
+# [Comparison / Segmentation / Signal Scan]
 
-For signal scans:
+| Entity | [Comparison Field Or Tier] | Evidence / Signal | Confidence | Source | Notes |
+| --- | --- | --- | --- | --- | --- |
+| [Entity] | [Grounded value or visible tier] | [Observed signal or rationale] | [High/Medium/Low] | [Clickable link] | [Gap or caveat] |
 
-- returned entities
-- the precise signal or proxy observed
-- confidence or caveat
-- the smallest next check that would improve trust
+## What Is Still Unclear
 
-## Quality Rules
+- [Missing field, unsupported ranking input, or next verification step]
 
-- Keep sourced facts separate from `Inference:`.
-- Do not invent missing fields, emails, phones, titles, technologies, funding, hiring signals, or account intent.
-- Do not claim exhaustiveness from a bounded provider query.
-- Do not overstate a provider-specific result as universal market truth.
-- Rank only by explicit criteria or returned provider signals that the user can understand.
-- If a request is blocked by unavailable source access, weak matching, or partial provider output, say so directly.
-- Prefer compact, inspectable tables over long prose when the task is data-first.
-- Make provider limitations visible when they affect confidence, coverage, cost, or rerun quality.
+---
 
-## When To Hand Off
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-- Route to `prioritize-accounts` when the user wants "which accounts should I work now?" rather than a data list.
-- Route to `prepare-for-meeting` when the user wants a meeting brief or attendee prep.
-- Route to `plan-deal-strategy` when the user wants a buying committee, risk register, or next-action plan for an active deal.
-- Route to `build-competitive-brief` when the user wants a positioning brief, objection map, or market narrative rather than structured enrichment.
-- Route to `build-business-case` when the user wants a business case, ROI structure, or quantified value story.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
diff --git a/plugins/sales/skills/enrich-company-and-contact-data/agents/openai.yaml b/plugins/sales/skills/enrich-company-and-contact-data/agents/openai.yaml
index 5269b34..5ebeb24 100644
--- a/plugins/sales/skills/enrich-company-and-contact-data/agents/openai.yaml
+++ b/plugins/sales/skills/enrich-company-and-contact-data/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Enrich Company And Contact Data"
   short_description: "Company, contact, and market enrichment for sales workflows"
-  default_prompt: "Enrich a company or contact list."
+  default_prompt: "Find and qualify a short list of companies that fit my criteria."
 policy:
   allow_implicit_invocation: false
diff --git a/plugins/sales/skills/find-customer-quotes/SKILL.md b/plugins/sales/skills/find-customer-quotes/SKILL.md
index 75af0d0..db079de 100644
--- a/plugins/sales/skills/find-customer-quotes/SKILL.md
+++ b/plugins/sales/skills/find-customer-quotes/SKILL.md
@@ -1,322 +1,173 @@
 ---
 name: find-customer-quotes
-description: Retrieve theme-specific customer or prospect quotes from transcripts, call notes, or exported recordings using transcript-first evidence and explicit speaker-confidence rules.
+description: "Use when the user wants verbatim customer or prospect quotes, voice-of-customer evidence, theme validation, objection evidence, product-friction examples, or support for a product area, use case, or sales narrative. Retrieve from transcripts, call notes, supplied transcript-like recordings or exports, and other grounded call material using explicit speaker-confidence and provenance rules."
 ---
 
 # Find Customer Quotes
 
-Use this skill to pull theme-specific exemplar customer or prospect quotes from transcripts, call notes, exported recordings with transcript text, or pasted transcript-like evidence.
 
-This is a transcript-first workflow. Prefer `meeting_notes` search and fetch when the connector is available; otherwise use user-provided transcript exports, pasted transcript text, or grounded call notes. Do not use public web search, CRM summaries, memory, or generic notes as substitutes for customer quote evidence.
+## Context-Gathering Intake
 
-## Skill Configuration
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-### User Context
+Extract high-confidence customer or prospect language from transcript-like evidence. This skill owns quote discovery, verification, selection, and readable provenance; it is not call analytics, paraphrase generation, legal review, or a posting workflow.
 
-Mandatory pre-answer gate:
+## Common Skill Instructions
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the quote output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits quote retrieval or confidence.
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### Audience And Language
+## Key Dependency Categories
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+Use the narrowest evidence lane that can support the requested quote set.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+- [Blocking] ~~Meeting Transcripts for transcript search, fetched transcript text, speaker labels, participants, dates, companies, and source links. It blocks the default live-source quote-extraction path; explicit transcript-like material already in context satisfies the need.
+- ~~CRM only for account identity, customer/prospect status, and company or segment filters that narrow transcript search
+- ~~Knowledge & Files for uploaded or exported transcripts and grounded call notes that preserve direct language and speaker context
 
-### Source Resolution
+Transcript-like evidence is required. ~~CRM, account summaries, public research, internal notes, and memory can narrow scope but are never quote evidence. If no live or user-provided transcript-like material exists, ask for a transcript export, pasted transcript text, recording export with text, or grounded call notes.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Reference Loading
 
-### Required Inputs
+`SKILL.md` owns the normal transcript-first path, thresholds, and readable output. Use [references/extraction-and-output.md](references/extraction-and-output.md) when transcript parsing is ambiguous, speaker-role confidence is hard to judge, quote ranking or deduplication needs the full rules, JSON is requested, or failure handling needs the exact response shape.
 
-This skill needs enough user-provided, connector-visible, or explicitly inferable context to identify the quote theme and at least one transcript-like evidence lane.
+## Terms
 
-The skill can still produce a limited quote set from pasted transcripts, uploaded/exported transcripts, call-note exports, or user-linked transcript text when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
+- **Quote candidate:** a verbatim snippet that may be useful, but still needs speaker, context, and usage checks before being treated as customer evidence.
+- **Customer confidence:** confidence that the speaker is actually a customer or prospect rather than an internal teammate, partner, or unknown speaker.
+- **Fallback evidence:** relevant non-customer or lower-confidence material that may explain a gap but should not be presented as a customer quote.
+- **Safe usage notes:** guidance on attribution, sensitivity, confidence, and where the quote can responsibly be reused.
 
-**Inputs:**
+## Workflow Guidance
 
-Require:
+### 1. Resolve theme and evidence scope
 
-- [required] `theme_or_narrative`: feedback theme, objection, product area, use case, narrative, customer pain, buying criterion, or other quote topic
-- [required] `transcript_evidence`: live `meeting_notes` access or user-provided transcript-like material that preserves enough speaker or context evidence to judge customer/prospect likelihood
+- If the theme or transcript-like evidence scope isn't clear, ask the user via `ask_user_input()`. For a bare invocation such as “Find customer quotes for me,” do not assume a theme or transcript set; offer:
+  - `Use the most common blocker from recent Meeting Transcript evidence`
+  - `Use a theme, objection, product area, or narrative I specify`
+  - `Use a specific transcript or call set I provide`
+- For a broad request such as “Find customer quotes about the most common blocker in my recent customer calls,” treat both the recommended theme and broad recent-call scope as a clarification case; offer:
+  - `Use the blocker theme you recommend from recent Meeting Transcript evidence`
+  - `Use a blocker theme I specify`
+  - `Use a specific transcript or call set I provide`
+  Do not search transcript evidence or draft a quote set until the user selects one.
+- Require a feedback theme, objection, product area, use case, narrative, or a specific transcript set after that clarification. Preserve the user’s wording.
+- Default to five quotes per theme. Process many themes in small batches.
+- Normalize time windows: use explicit dates when supplied; treat “last month” as trailing 30 days unless the user says “last calendar month”; use calendar meaning for “last quarter” and trailing meaning for “last N days/weeks.”
+- If a date phrase is too ambiguous to convert responsibly, ask one brief clarification unless the user clearly signals flexibility.
+- For an ambiguous account, segment, or transcript set, make a bounded pass and offer up to five concrete candidates via `ask_user_input()`. Do not search transcript evidence or draft a quote set until the user selects one. Use ~~CRM only to resolve filters; do not gather CRM content as quote evidence.
+- If a recent call-follow-up, pasted transcript, upload, or linked call clearly supplies the theme and evidence source, proceed without asking.
 
-Accept when provided:
+### 2. Search transcript evidence first
 
-- company, account segment, product, date range, source set, or call list filters
-- `quotes_per_theme`; default `5`
-- output format; default readable Markdown, JSON only when requested or needed downstream
+- Start with ~~Meeting Transcripts when available, using the theme plus one or two intent terms such as `pain point`, `blocker`, `request`, `limitation`, or `need`.
+- Apply company, segment, and date filters only when the user supplied or confirmed them. Start with 10-15 results and a relevance threshold around 0.7.
+- If the selected ~~Meeting Transcripts source exposes an account-segment filter with a known enum, use the connector-visible value that matches the user's wording.
+- Do not invent additional user intent. If the user asks only for a theme, keep the query theme-centric.
+- If sparse, simplify the query, lower relevance modestly to about 0.6, then expand toward 20-40 results only when coverage still needs it.
+- Fetch the top-ranked transcripts first and expand only until enough high-confidence, diverse evidence exists or the first pass is clearly thin.
+- For pasted, uploaded, or exported material, parse only transcript-like text that preserves enough speaker/context evidence to classify customer or prospect likelihood.
+- Keep a mapping of theme, search-result metadata, fetched transcript content, connector-specific refetch handle, and call or transcript URL when available. Do not assume a fixed transcript schema; inspect the fetched shape and adapt.
 
-**Context Rules:**
+### 3. Extract verbatim candidates
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- When company, account, segment, or source-set filters are ambiguous, include a bounded CRM lookup when `crm` is available to resolve account identity or segment candidates, but do not use CRM as quote evidence.
-- Before asking the user to paste transcript exports or call notes, first try the source category that can own quote evidence: `meeting_notes`. Use `crm` only to resolve ambiguous account, company, or segment filters, never as quote evidence. Manual transcript material is the fallback after `meeting_notes` is unavailable, empty, or lacks relevant transcript-like evidence.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+- Keep only direct, substantial language that expresses a pain point, blocker, constraint, concern, unmet need, request, purchase condition, or other theme-relevant signal.
+- Keep wording verbatim. Do not clean up grammar, join fragments, or convert paraphrases into quotes.
+- Exclude paraphrases that are not direct quotes unless the user explicitly asks for paraphrases. If requested, label them separately and never present them as verbatim quotes.
+- Exclude obvious internal seller statements, generic praise, garbled fragments, and tiny snippets without usable meaning.
+- Preserve transcript, call, date, company, speaker label, participant context, and source link when available.
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+### 4. Verify speaker and rank precisely
 
-If neither live transcript access nor user-provided call material is available, stop and ask for transcript exports, pasted transcript text, or call notes. Do not fabricate quotes from memory, CRM summaries, public web research, or account notes.
+- For each candidate, inspect nearby transcript context and assign `speaker_role_guess` as `customer`, `prospect`, `internal_seller`, or `unknown`; `customer_confidence` from 0.0-1.0; `theme_relevance` from 0.0-1.0; and a concise evidence note.
+- Keep only candidates with `customer_confidence >= 0.8` and `theme_relevance >= 0.75` by default.
+- If coverage is thin, prefer fewer quotes over lowering customer confidence. You may lower theme relevance slightly, to about 0.65, only when speaker evidence remains strong.
+- Rank by customer confidence, theme relevance, diversity across calls/customers, specificity/actionability, then readability while remaining verbatim.
+- If no candidate passes the customer threshold, report `0/[target]` customer/prospect quotes. Do not backfill the main quote set with unknown or internal speakers.
 
-**Input Request Format Example:**
+### 5. Deduplicate and select exemplars
 
-```md
-Which feedback theme, objection, product area, or transcript set should I use?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when a recent call-follow-up, product-feedback, planning thread, pasted transcript, or uploaded/exported call material clearly names the theme and evidence source.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
+- Normalize whitespace and punctuation for comparison only; never alter displayed quote text.
+- Drop exact and near-duplicate quotes.
+- Default to one quote per call. Use a second quote from the same call only when it adds materially different evidence or is needed to reach the target with strong quotes.
+- Prefer breadth across customers when identity is available.
+- Keep relevant non-customer transcript snippets only in a separate fallback section, labeled by role such as `internal evidence`, `vendor-eval evidence`, `partner-readiness evidence`, or `unknown non-customer evidence`.
 
-- `meeting_notes`: transcript search, fetched transcripts, call metadata, call/transcript URLs, speaker labels, participant context, dates, companies, and connector-specific refetch handles.
-- `crm`: account identity, customer/prospect status, and segment filters that narrow transcript search. Never use CRM as quote evidence.
-- user-provided context: pasted transcripts, uploaded/exported transcripts, recording exports with transcript text, grounded call notes, theme labels, company or segment filters, and quote-format preferences.
+### Next Step Options
 
-Source obligations by intent:
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Search an adjacent or narrower theme to improve evidence coverage.
+- Expand or tighten the transcript, account, segment, or time-window scope.
+- Package the selected quotes into a business case, competitive brief, meeting prep, or evidence appendix.
+- Draft a source-safe internal summary of what the quotes support and what they do not support.
+- Close the smallest speaker-identity or transcript-quality gap.
 
-- Quote extraction requires transcript-like evidence from `meeting_notes`, exported transcripts, pasted transcript text, recording exports, or grounded call notes that preserve speaker/context confidence.
-- `crm`, account summaries, docs, public web, and saved memory may suggest filters, account context, or segment labels, but they are not quote evidence and must not be used to invent or paraphrase customer language.
-- If `meeting_notes` is available, search and fetch the narrowest relevant transcript set before asking for manual transcript material. If checked and no relevant transcript is found, say so and ask for transcript exports or pasted text.
+Next steps to avoid:
+- Paraphrasing quotes, presenting ambiguous speakers as customers, or implying legal or compliance approval.
 
-Authority and gaps:
+## Modes
 
-- Evidence lane order:
-  1. `meeting_notes` search and fetch when available.
-  2. Uploaded/exported transcripts, pasted transcript text, recording exports with transcript text, or grounded call notes that clearly distinguish customer/prospect language.
-- Use grounded call notes only when they contain transcript-like direct quotes or enough context to preserve speaker-role confidence.
-- Do not treat CRM fields, account summaries, public web pages, internal notes, or memory as quote evidence.
-- Use CRM, account context, docs, and saved memory only to narrow transcript search or explain scope; if those lanes are unavailable, continue when the transcript-like evidence and user theme are sufficient.
-- Default bias is precision over recall. Return fewer than `quotes_per_theme` when customer-speaker confidence is weak.
-- When no high-confidence customer/prospect quotes pass threshold but transcript-like sources contain relevant internal, vendor-eval, partner-readiness, or implementation evidence, the skill may return that material only in a separate fallback section. Do not count it toward `quotes_per_theme` or present it as customer/prospect language.
+- `Theme Quote Set` — default; grouped readable Markdown for one or more themes.
+- `Specific Transcript` — extract from user-named or supplied calls only.
+- `JSON` — use only when the user explicitly asks for machine-readable output or a downstream workflow requires it.
 
-### Context Gathering Principles
+## Output Format
 
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the find-customer-quotes experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+Use readable Markdown by default.
 
 ```md
-## Find Customer Quotes
-This skill helps pull customer or prospect language for a feedback theme, objection, product area, use case, or sales narrative. It can use transcripts, call notes, pasted excerpts, meeting notes, docs, or exported recordings. It produces quote candidates with speaker/context, source links when available, theme fit, confidence, and safe usage notes.
-
-Definitions:
-- Quote candidate: a verbatim snippet that may be useful, but still needs speaker, context, and usage checks before being treated as customer evidence.
-- Customer confidence: the confidence that the speaker is actually a customer or prospect rather than an internal teammate, partner, or unknown speaker.
-- Fallback evidence: relevant non-customer or lower-confidence material that may explain a gap but should not be presented as a customer quote.
-- Safe usage notes: guidance on attribution, sensitivity, confidence, and where the quote can responsibly be reused.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Find customer quotes about a feedback theme.`
-- `@Sales find customer quotes about setup friction from recent enterprise calls.`
-- `@Sales pull prospect quotes about data residency from the last month of call notes.`
-- `@Sales find quotes that support the "faster account research" story from these transcripts.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Find Customer Quotes next-step behavior and action text. Onboarding, artifact creation, draft creation, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the quote-finding continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `find-customer-quotes` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the quote set** | The first substantive quote set has just been produced. | `Anything you'd change in the quote filters, confidence bar, or usage notes before we package these?` |
-| **2. Review the revised quote set** | The user asked for any change to themes, filters, quote selection, confidence, usage notes, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If these are the right quotes, want me to package them into a customer-evidence snippet, proof-point note, enablement blurb, or internal post?` |
-| **3. Prepare the quote package** | The user accepts the quote set, says no changes are needed, says a revision looks good, or asks to operationalize the quotes. | Offer to produce the most useful next artifact or draft action, prioritizing customer-evidence snippet, proof-point note, enablement blurb, internal post, or shareable quote package over broad suggestions. |
-| **4. Review the quote package or draft** | A snippet, proof-point note, enablement blurb, internal post, or quote package exists, or the user asks to change it. | `Anything you'd change before I save, post, or share this quote package?` |
-| **5. Save, post, or share approved quote package** | A reviewed quote package or draft exists and does not need edits. | Offer the specific supported action, such as saving the package, posting the approved internal note, or sharing the enablement blurb. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the quote set, treat a lightweight acknowledgement as acceptance and move to `Prepare the quote package`. If the previous continuation offered to prepare a package or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to save, post, or share a specific reviewed package, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+# Customer Quotes
 
-## Procedure
+## [Theme]
 
-### 1. Parse and normalize themes
+**Coverage:** [returned]/[target] high-confidence customer/prospect quotes · [spread/quality note]
 
-- Convert single or many themes into a list.
-- Preserve the user's theme and segment labels.
-- Process large theme sets in small batches.
-- Default `quotes_per_theme` to `5`.
-- Normalize time filters:
-  - if the user supplied `date_range`, use it;
-  - if the user supplied a natural-language recency window, convert it to an absolute `date_range`;
-  - treat `last month` as ambiguous; default to trailing `30` days unless the user explicitly says `last calendar month`;
-  - prefer calendar interpretations for phrases like `last quarter` or `this quarter`; prefer trailing windows when the user says `last N days/weeks`;
-  - if the phrase is too ambiguous to convert responsibly, ask one brief clarifying question unless the user clearly signals flexibility.
-- For each theme, derive a short search phrase: include the theme itself and one or two intent words like `pain point`, `blocker`, `request`, `limitation`, or `need`.
-- Do not overstuff search queries with instructions.
+- “[Verbatim quote]”
+  - **Speaker:** [Name or Unknown] · **Customer confidence:** [0.00] · **Theme relevance:** [0.00]
+  - **Context:** [Company/call/date when known]
+  - **Why it fits:** [Concise transcript-grounded evidence note]
+  - **Source:** [Clickable transcript/call link, or no useful link available]
 
-### 2. Search transcript evidence per theme
+## Gaps
 
-Use `meeting_notes` search with a concise topical query when available.
+- [Theme with weak coverage, speaker ambiguity, transcript quality issue, or missing evidence]
 
-Recommended starting parameters per theme:
+## Internal / Non-Customer Evidence
 
-- `query`: theme-focused phrase
-- `company`: only if the user explicitly wants a specific company
-- `account_segment`: only if explicitly requested
-- if the selected `meeting_notes` exposes an account-segment filter with a known enum, use the connector-visible value that matches the user's wording
-- `date_range`: only if the user supplied one
-- `limit`: `10` to `15`
-- `score_threshold`: `0.7`
+- “[Verbatim fallback snippet]”
+  - **Speaker / context:** [Known context] · **Role:** [non-customer classification] · **Customer confidence:** [0.00] · **Theme relevance:** [0.00]
+  - **Usage note:** Useful for [internal purpose]; do not present as a customer quote.
+  - **Source:** [Link/label]
 
-Fallbacks if sparse results:
-
-1. Retry with a simpler query: theme plus the single strongest keyword.
-2. Lower `score_threshold` modestly, for example to `0.6`.
-3. Increase `limit` toward `20` to `40` only when the first pass is thin and the requested quote count or coverage needs it.
-
-Do not invent additional user intent. If the user asks only for a theme, keep the query theme-centric.
-
-### 3. Fetch or parse transcripts
-
-- Fetch top-ranked search result ids first; expand only until there is enough high-confidence coverage or the first pass is clearly thin.
-- For manual/export inputs, parse only transcript-like material that preserves enough speaker or context evidence to classify customer/prospect likelihood.
-- Keep a mapping of theme, search result metadata, fetched transcript content, connector-specific id or internal refetch handle, and call/transcript URL from the fetch response when available.
-- Do not assume a fixed transcript schema. Inspect the fetched payload shape and adapt.
-
-### 4. Extract candidate quotes
-
-Run a theme-specific extraction pass over each transcript.
-
-Include candidates that:
-
-- directly express a pain point, blocker, constraint, concern, unmet need, request, or purchase condition;
-- are relevant to the theme;
-- are substantial enough to be useful, not generic praise;
-- appear to be spoken by an external participant or are ambiguous but promising.
-
-Exclude:
-
-- obvious internal seller-side statements;
-- paraphrases that are not direct quotes unless the user explicitly asks for paraphrases;
-- concatenated or garbled fragments that combine multiple thoughts;
-- extremely short fragments with no clear meaning.
-
-Keep quotes verbatim. Do not clean up wording beyond preserving readable punctuation around the quoted text.
-
-### 5. Verify speaker likelihood and rank
-
-Run a second pass over extracted candidates with local transcript context.
-
-For each candidate, provide:
-
-- `speaker_role_guess`: `customer`, `prospect`, `internal_seller`, or `unknown`
-- `customer_confidence`: `0.0` to `1.0`
-- `theme_relevance`: `0.0` to `1.0`
-- `evidence`: concise explanation grounded in transcript text or labels
-
-Default thresholds:
-
-- Keep only `customer_confidence >= 0.8`.
-- Keep only `theme_relevance >= 0.75`.
-- If fewer than `quotes_per_theme` remain, prefer returning fewer quotes over lowering `customer_confidence`. You may lower `theme_relevance` slightly, for example to `0.65`, if speaker evidence stays strong.
-- If no candidates pass the customer-confidence threshold, preserve that result as `0/{quotes_per_theme}`. Do not backfill the quote list with internal or unknown-speaker snippets.
-- If relevant non-customer snippets were found, classify them separately as `internal_evidence`, `vendor_eval_evidence`, `partner_readiness_evidence`, or `unknown_non_customer_evidence`. Keep only snippets with strong theme relevance and enough context to explain why they are useful but excluded from the customer/prospect quote set.
-
-Ranking priorities:
-
-1. `customer_confidence`
-2. `theme_relevance`
-3. diversity across calls/customers
-4. specificity and actionability of the pain point
-5. clarity/readability while staying verbatim
-
-Use [references/extraction-and-output.md](references/extraction-and-output.md) when transcript parsing is ambiguous, speaker-role confidence is hard to judge, quote ranking/deduplication needs the full rules, the user asks for JSON, or failure handling needs the exact response shape.
-
-### 6. Deduplicate and select exemplars
-
-- Normalize whitespace and punctuation for comparison only.
-- Drop exact duplicates.
-- Drop quotes that are near-identical with trivial wording changes.
-- Default to one quote per call.
-- Use two quotes from the same call only when needed to reach the requested count with strong quotes or when the second quote adds materially different evidence.
-- Prefer breadth across customers when customer/account identity is available.
-- If a theme has insufficient high-confidence quotes, return fewer quotes and include a short gap note.
-
-### 7. Render quote output
-
-Default to a readable grouped Markdown summary. Use JSON only when the user explicitly asks for JSON, the results will be consumed downstream by another tool/workflow, or a machine-readable artifact is needed for comparison/review.
-
-Readable summary per theme:
+---
 
-- theme name;
-- number of quotes returned over target;
-- one-line coverage quality note, such as `strong spread across 8 calls`;
-- quote list, each quote on its own bullet, with quote text, customer/company when available, speaker name when known, compact confidence, and transcript link when available.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-Minimum fields per quote:
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-- `theme`
-- `quote` verbatim
-- `speaker_name`, using `unknown` if unavailable
-- `speaker_role_guess`
-- `customer_confidence`
-- `theme_relevance`
-- `evidence`
-- `transcript_url` or a clear note when no useful link is available
+Omit empty optional sections. When both high-confidence customer quotes and fallback evidence exist, include fallback evidence only if the user asked for it or it materially explains a gap.
 
-Optional footer:
+Include compact Method Notes when search breadth, transcript formatting, repeated calls, or speaker-label quality lowered confidence. Keep each theme's coverage explicit even when the result is `0/[target]`.
+Do not print `speaker_role_guess` in readable summaries unless the user explicitly asks for role metadata or a quote needs an ambiguity caveat.
+Use clickable source links when available; otherwise say `no useful link available`.
 
-- `Gaps` for themes with weak coverage
-- `Internal / Non-Customer Evidence` when no customer/prospect quotes pass threshold but relevant transcript evidence exists from internal, vendor-eval, partner-readiness, or otherwise non-customer speakers
-- `Method Notes` when transcript formatting, sparse results, repeated calls, or speaker-label quality reduced confidence
+## Example Prompts
 
-Fallback evidence rules:
+- `Find customer quotes about a feedback theme.`
+- `Find customer quotes about setup friction from recent enterprise calls.`
+- `Pull prospect quotes about data residency from the last month of call notes.`
+- `Find quotes that support the "faster account research" story from these transcripts.`
 
-- Keep the main theme result explicit, for example `Theme: sales plugin setup friction (0/{quotes_per_theme} customer/prospect quotes)`.
-- Render fallback snippets under a heading such as `Internal / Non-Customer Evidence`, not under `Quote Candidates` or the customer quote list.
-- For each fallback snippet, include the verbatim text, speaker/context when known, `speaker_role_guess`, customer confidence, theme relevance, source link, and a short `usage note` such as `Useful for an internal product brief; do not present as a customer quote`.
-- Prefer a compact set of the strongest, most actionable fallback snippets over an exhaustive dump. Do not include garbled or bracket-reconstructed text unless the uncertainty is clearly noted.
-- When both high-confidence customer/prospect quotes and fallback evidence exist, omit fallback evidence unless the user explicitly asked for internal evidence too or the fallback materially explains a gap.
+## Rules
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+- Do not fabricate or infer quote text, speaker names, titles, roles, companies, dates, transcript links, or call metadata.
+- Do not treat every quote from a relevant call as relevant, or every relevant quote as customer-spoken.
+- Do not treat missing speaker labels as high confidence unless surrounding context strongly establishes an external participant.
+- Keep quotes verbatim and make provenance visible near each quote.
+- Prefer precision, diversity, and honest gaps over a padded list.
+- This workflow is not for exhaustive call analytics, exact speaker identity verification when transcripts lack evidence, or legal/compliance review.
+- Keep this workflow read-only. Do not post, send, package, or save quotes unless the user explicitly asks in a later step.
 
-## Guardrails
+## Failure Handling
 
-- Do not fabricate quotes, speaker names, titles, roles, companies, transcript links, or call metadata.
-- Do not treat every quote in a relevant call as theme-relevant.
-- Do not treat every relevant quote as customer-spoken.
-- Do not treat no-speaker-label transcripts as high confidence unless surrounding context clearly supports external speaker likelihood.
-- Keep quotes verbatim.
-- Prefer fewer, high-confidence exemplars over a padded list.
-- Prefer diverse call/customer coverage over taking multiple quotes from the same call.
-- If transcript formatting is poor, say so explicitly and surface uncertainty.
-- This skill is not for exhaustive call analytics, exact speaker identity verification when transcripts lack evidence, or legal/compliance review.
+If no transcript-like evidence is available, state that quote extraction cannot be grounded and ask for the smallest usable source. If transcript formatting is poor or speaker identity is ambiguous, return fewer quotes, name the limitation, and keep lower-confidence evidence out of the customer/prospect set.
diff --git a/plugins/sales/skills/find-customer-quotes/references/extraction-and-output.md b/plugins/sales/skills/find-customer-quotes/references/extraction-and-output.md
index 09a478e..45ebcae 100644
--- a/plugins/sales/skills/find-customer-quotes/references/extraction-and-output.md
+++ b/plugins/sales/skills/find-customer-quotes/references/extraction-and-output.md
@@ -23,14 +23,14 @@ Minimum fields per quote:
 - `customer_confidence` (`0.0`-`1.0`)
 - `theme_relevance` (`0.0`-`1.0`)
 - `evidence` (brief reason the speaker is likely a customer and why the quote matches the theme)
-- `transcript_url` (the call/transcript link from the `meeting_notes` fetch response)
+  - `transcript_url` (the call or transcript link from the ~~Meeting Transcripts fetch response)
 
 Strongly recommended extra fields when available:
 
 - `call_date`
 - `company`
 - `call_title`
-- `connector_file_id` (optional internal/debug field; useful for refetching with `meeting_notes`)
+- `connector_file_id` (optional internal/debug field; useful for refetching with ~~Meeting Transcripts)
 - `transcript_snippet` (short surrounding context, not a rewrite)
 
 If a theme has insufficient high-confidence quotes, return fewer quotes and include a short gap note.
@@ -46,7 +46,7 @@ Per theme, include:
 - short one-line note on coverage quality, for example, `strong spread across 8 calls`
 - quote list, each quote on its own bullet, with quote text, customer/company when available, speaker name when known, compact confidence, and a transcript link labeled `[Transcript]`
 
-For readable summaries, do not print `speaker_role_guess` unless the user explicitly asks for role metadata or a quote needs an ambiguity caveat.
+For readable summaries, do not print `speaker_role_guess` unless the user explicitly asks for role metadata or a quote needs an ambiguity caveat. Omit extra internal retrieval metadata unless the user asks.
 
 Optional footer:
 
@@ -204,7 +204,7 @@ Return structured gaps instead of failing the whole task.
 
 Examples:
 
-- no `meeting_notes` search results for theme
+- no ~~Meeting Transcripts search results for theme
 - search results exist but transcript fetch fails
 - transcript content lacks enough speaker evidence
 - enough relevant quotes found, but not enough high-confidence customer quotes
@@ -274,7 +274,7 @@ Gaps
 - sales plugin setup friction: Reviewed transcript-like evidence, but speaker context was internal/vendor-eval rather than customer/prospect.
 
 Internal / Non-Customer Evidence
-- "Salesforce needed reauthentication..." - Internal Seller (speaker_role_guess=internal_seller, customer_conf=0.05, relevance=0.90, [Transcript](https://example.com/transcript/222222222)). Usage note: Useful for an internal product brief; do not present as a customer quote.
+- "The CRM needed reauthentication..." - Internal Speaker (speaker_role_guess=internal_seller, customer_conf=0.05, relevance=0.90, [Transcript](https://example.com/transcript/222222222)). Usage note: Useful for an internal product brief; do not present as a customer quote.
 ```
 
 ## Migration note
diff --git a/plugins/sales/skills/find-key-internal-sources/SKILL.md b/plugins/sales/skills/find-key-internal-sources/SKILL.md
index da6f726..28b4e59 100644
--- a/plugins/sales/skills/find-key-internal-sources/SKILL.md
+++ b/plugins/sales/skills/find-key-internal-sources/SKILL.md
@@ -1,313 +1,159 @@
 ---
 name: find-key-internal-sources
-description: Find the best internal experts, documents, and chat channels for a customer question, product topic, objection, implementation issue, account task, or other internal topic using user-provided context, optional connector-assisted search, and evidence-backed ranking.
+description: "Use when a seller needs to find the best internal experts, owners, approvers, documents, channels, source-of-truth materials, or escalation routes for a customer question, product topic, competitive objection, implementation issue, account task, or other sales-support need. Route questions such as 'who knows about this?', 'what should I read?', 'where is the source of truth?', and 'which internal channel or document should I use?' here."
 ---
 
 # Find Key Internal Sources
 
-Use this skill to locate the people, docs, channels, source-of-truth pages, decision forums, and escalation paths that can answer a customer question or unblock a sales task.
+Find the smallest reliable internal route that helps a seller get an answer or unblock work: the right people, maintained docs, public channels, decision forums, and escalation paths. The default output is a quick, evidence-backed routing map plus a draft-ready first ask. This workflow is read-only and never posts, assigns, or updates systems.
 
-This skill produces a practical routing map from grounded internal evidence. It is useful for customer questions, product topics, objections, implementation issues, account tasks, launch/rollout questions, and source-of-truth gaps. The initial routing map is read-only: it may draft a question or handoff, but it does not post, message, assign, or update systems. After a reviewed ask or handoff exists, it may offer supported posting or sharing only after explicit user approval of that reviewed action.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-### User Context
+## Key Dependency Categories
 
-Mandatory pre-answer gate:
+Use only the categories needed for the selected route; do not fan out across every source by default.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the routing map also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the routing map.
+- ~~Knowledge & Files for maintained source-of-truth pages, owner metadata, wikis, field guides, playbooks, FAQs, runbooks, launch docs, and decision records
+- ~~Internal Messaging for public channels, channel topics, threads, active contributors, escalation routes, support channels, and decision forums
+- ~~CRM for customer or account identity, ownership, opportunity context, and CRM-visible blockers that clarify an account-specific question
 
-### Audience And Language
+Prefer canonical ownership and source-of-truth material over chat mentions. ~~CRM can resolve account truth, but it does not by itself prove who owns the internal answer.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Reference Loading
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+`SKILL.md` owns the normal route selection, bounded search, ranking, and output map. Load references only when their extra detail matters:
 
-### Source Resolution
+- Use [references/search-patterns.md](references/search-patterns.md) when query fan-out, compound facets, source-specific search, or stopping rules need more detail.
+- Use [references/company-context.md](references/company-context.md) only after the generic pass, when user-provided or connector-visible terminology is likely to change the route.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Terms
 
-For ownership or source-of-truth routing requests, prefer the source that most directly owns the requested fact before broad search. A company knowledge base, people directory, team directory, wiki owner field, go-link index, verified routing page, or maintained source-of-truth hub should be treated as the first-pass source when available, even if it resolves through the same `document_store` category as ordinary docs. Use `internal_messaging` as supporting evidence for recent practice, escalation texture, or active contributors; do not make message search the default authority for stable ownership when a canonical routing source can answer the question.
+- **Source-of-truth page:** the maintained doc, wiki, tracker, or page that should be treated as the most authoritative answer source.
+- **DRI:** directly responsible individual, the person accountable for a topic, decision, or follow-through.
+- **SME:** subject matter expert, someone with practical depth on the topic even if they are not the accountable owner.
+- **Decision forum:** the recurring meeting, channel, doc, or group where tradeoffs and approvals are handled.
 
-### Required Inputs
+## Workflow Guidance
 
-This skill needs enough user-provided or explicitly inferable context to identify the customer question, account blocker, product topic, initiative, implementation issue, or source-of-truth gap to route.
+### 1. Resolve the topic and route
 
-The skill can still produce a limited routing map from pasted notes, call follow-up output, meeting prep context, uploaded/exported context, or user-linked context alone when that context is sufficient. Connectors add significant value and reduce the burden on the user, but do not stop solely because connectors are unavailable.
+- Require a concrete topic_or_task: customer question, product topic, objection, implementation issue, account blocker, initiative, or source-of-truth gap.
+- Infer it from the active thread or provided Sales output when clear. If still ambiguous, make a bounded candidate pass of at most three source reads, then ask one friendly question with up to five concrete candidates.
+- Treat ambiguous company-like names as possible account anchors. Use a bounded ~~CRM lookup when available before relying on internal docs or messages for account-specific routing.
+- Default to quick. Use deep only when requested or when a high-stakes question clearly needs broader corroboration.
 
-**Inputs:**
+Choose the smallest answer route that satisfies the request:
 
-Require:
+- owner_route — who owns, approves, knows, or should be contacted
+- doc_route — what to read, which page is maintained, or what wording is approved
+- channel_route — where to ask, discuss, escalate, or get support
+- full_map — experts, docs, and channels together when the request actually needs all three
 
-- [required] `topic_or_task`: the core customer question, product topic, account blocker, initiative, implementation issue, or internal source-of-truth gap
+### 2. Search canonical sources first
 
-Optional:
+- For owner_route, start with maintained ownership, directory, routing, or source-of-truth pages; use messages to confirm current practice or fill gaps.
+- For doc_route, start with maintained docs, title/heading matches, linked hubs, owner fields, and recency; fetch only the strongest candidate before ranking.
+- For channel_route, start with public channel names, topics, purposes, and documented escalation routes; inspect recent public threads only when they change confidence.
+- Use ~~CRM first for account identity and deal context when the route is customer-specific, then use ~~Knowledge & Files or ~~Internal Messaging for internal ownership.
+- In quick, make one canonical source attempt, one narrow fallback when the first pass is empty, thin, or misleading, and at most one fetch or thread read per top candidate.
+- Broaden only when the first pass is weak, the topic spans distinct surfaces, or the user asked for deep. Stop when a supported route is good enough, results stabilize, or additional searching produces low-confidence duplicates.
 
-- `output_depth`: `quick` for top 3 each, or `deep` for top 5 to 8 each. Default to `quick`.
-- account, customer, team, product, source, or urgency context when provided
+### 3. Build useful search facets
 
-**Context Rules:**
+- Start with exact topic terms, then add aliases, abbreviations, legacy/current names, product/team names, and task-shape terms such as owner, approval, policy, runbook, playbook, FAQ, support, or escalation.
+- For a compound question, keep separate tracks for the product/account surface and the control/process surface. Do not let a strong policy hit replace product routing, or a launch page replace approval ownership.
+- Use [references/search-patterns.md](references/search-patterns.md) when query fan-out, source-specific search, or stopping rules need more detail.
+- Use company-specific terminology only when it comes from the user or connector-visible source truth. Do not invent internal names, channel patterns, URLs, or ownership conventions.
+- Apply organization-specific expansions only after generic candidate retrieval, keep base scoring primary, and cap the total context-based boost per candidate at `+0.25`.
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup to resolve account identity, owner, or opportunity context before relying on docs or internal messages alone for a customer/account-specific routing map.
-- Before asking the user to provide a customer question, topic, or source pointer, first try to infer concrete candidates from the active thread or prior Sales output, then the workflow source categories that can cheaply own the missing route: `crm` for customer/account context, `document_store` for canonical docs and owner metadata, and `internal_messaging` for channels, active contributors, and escalation texture.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+### 4. Pull, score, and rank candidates
 
-If `topic_or_task` remains missing or ambiguous, first attempt a bounded candidate pass through the active thread, recent Sales workflow context, saved team hubs, and only the source categories that can cheaply produce concrete choices, within the Fast Candidate Resolution budget. Ask one friendly clarification after that pass. Ask only for the unresolved topic; include concrete lettered options from source-backed or user-provided candidates when available.
+Normalize candidates by type, title or name, URL, source, evidence, ownership signal, and freshness. Deduplicate near-identical entries.
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+Rank by:
 
-**Input Request Format Example:**
+1. direct relevance to the requested answer route
+2. authority and maintenance signal
+3. freshness
+4. cross-source confirmation
+5. practical usefulness for the seller's next action
 
-```md
-What customer question, product topic, or internal source-of-truth gap should I route?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when call follow-up, meeting prep, account docs, Slack, saved team hubs, or the active thread clearly surfaced a specific blocker, question, or topic.
-
-### Internal Ownership
-
-Treat internal ownership as broader than "one person who knows the answer." Useful ownership can include DRIs, approvers, SMEs, maintainers, accountable teams, launch or rollout owners, recurring decision forums, canonical docs, escalation channels, support channels, and feedback channels. Rank ownership signals by how directly they help the seller get a reliable answer or next action.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
+- Prefer direct evidence links over profile-only or mention-only matches.
+- Prefer maintained source-of-truth pages, field guides, FAQs, and playbooks over one-off notes.
+- Prefer channels whose topic, purpose, linked guide, or recent threads show an ownership path over channels that merely mention the topic.
+- For people inferred mainly from ~~Internal Messaging, require a recent ownership or expertise signal, defaulting to the last 90 days; omit or down-rank stale candidates.
+- Exclude deactivated or inactive users. Default to public channels only; include private channels, group DMs, direct messages, or externally shared channels only when the user explicitly asks and access is appropriate.
+- Keep distinct routes when a topic spans product/GTM guidance and policy, security, implementation, pricing, or approval ownership.
 
-- `crm`: customer/account identity, owner, opportunity/account context, and CRM-visible account blockers that clarify the customer question or implementation issue.
-- `document_store`: canonical docs, wikis, pages, databases, source-of-truth hubs, decision records, runbooks, launch docs, owner metadata, recency signals, and linked artifacts.
-- `internal_messaging`: public channels, threads, messages, recurring contributors, active expert signals, escalation channels, support channels, feedback channels, decision forums, and source-of-truth routes.
-- user-provided context: pasted notes, prior Sales outputs, account context, customer question wording, uploaded/exported docs, and linked context that can seed or constrain the search.
+### 5. Render the routing map
 
-Authority and gaps:
+- Return only the depth the request needs. For a single route, keep the other required sections compact with Not searched in quick pass or No high-confidence candidate found.
+- Explain each candidate's answer-path type: DRI, approver, SME, maintainer, accountable team, decision forum, launch owner, escalation channel, support channel, or feedback channel.
+- Include a one-line rationale, direct link when available, evidence signal, and freshness or confidence when it affects trust.
+- If the user's wording conflates two surfaces, add a short framing note explaining the split and continue with both routes unless the distinction changes which sources are safe to use.
+- Recommended First Ask must be a concrete draft the seller can send to the best owner or public channel. Draft it in chat; do not post it.
+- Coverage Gaps must name unavailable or weak categories, the effect on confidence, and the smallest useful next step.
 
-- Prefer sources that directly help the seller get a reliable answer or next action.
-- For customer/account-specific routing, use CRM when available to resolve CRM-owned account truth before internal docs/messages; CRM does not replace source-of-truth docs/channels for who owns the answer.
-- Do not present a CRM owner, account field, or opportunity note as the internal expert or source-of-truth route unless `document_store`, `internal_messaging`, user context, or explicit user-provided evidence supports that route.
-- Search multiple source categories in parallel only when the requested answer shape needs them, or when parallel reads reduce elapsed time without widening scope. Do not use broad `internal_messaging` plus `document_store` fanout as the default for simple owner, doc, or channel routes.
-- If one source is unavailable, continue with the other when it can produce useful routing evidence and record the gap.
-- Default to public `internal_messaging` channels only. Include private channels only when the user explicitly asks.
-- Exclude private channels, group DMs, direct messages, and externally shared channels unless the user explicitly asks and access is appropriate.
-- Exclude deactivated or inactive `internal_messaging` users from expert recommendations.
-- Use company-specific terminology only from user-provided context, connector-visible source truth, or Sales plugin-scoped user context. Do not hardcode company-specific process names, channel patterns, product names, URLs, or boost rules into this skill.
+### Next Step Options
 
-### Context Gathering Principles
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Draft the first ask to the recommended owner, expert, or public channel.
+- Open and synthesize the strongest source-of-truth document for the seller's question.
+- Prepare a concise escalation note when the supported route requires escalation.
+- Create a reusable routing note with the verified people, docs, channels, and caveats.
+- Find the smallest missing source that would resolve an ownership or approval gap.
 
-Optimize for marginal value, not exhaustive coverage.
+Next steps to avoid:
+- Posting, assigning, escalating, or updating ownership automatically.
 
-- Classify the requested answer shape before searching: `owner route` for experts/POCs/approvers, `doc route` for source-of-truth or approved wording, `channel route` for where to ask, and `full map` only when the user asks for people, docs, and channels together.
-- Start with the source category that owns that answer shape and the narrowest query that could answer it. For owner routes, start with canonical ownership, directory, or routing sources when available; for doc routes, start with source-of-truth docs; for channel routes, start with channel/topic search or saved escalation routes.
-- In default `quick` mode, spend a small bounded first pass before answering: one canonical source attempt, one narrow fallback attempt when the first pass is empty/thin/misleading, and at most one fetch or thread read per top candidate unless the user asked for `deep`.
-- Treat roughly three to five minutes of active retrieval, two slow connector calls, or one timeout/rate-limit on a non-required enrichment source as the practical quick-mode budget. When that budget is reached, yield back to the user instead of continuing silent retrieval: return the best supported routing map or checkpoint, name limitations in `Coverage Gaps`, and make the final natural continuation one concrete choice to clarify or give a steer, continue deeper, or act on the current best route.
-- Broaden only when the first pass is empty, thin, misleading, or the user's requested output explicitly needs multiple source types. Do not fan out across docs and messages merely because both are available.
-- Answer once the core artifact is supported by a direct source. Use optional sources to improve confidence only when they materially change who to ask, what to read, where to ask, or what wording is safe to reuse.
+## Modes
 
-### First-Run Banner
+- quick — default; up to three candidates each for experts, docs, and channels, with bounded retrieval
+- deep — five to eight candidates each when evidence quality supports them
+- owner_route, doc_route, channel_route, full_map — answer-shape routes; combine with quick or deep
 
-If Sales preflight says the find-key-internal-sources experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+## Output Format
 
 ```md
-## Find Key Internal Sources
-This skill helps locate the people, docs, Slack channels, source-of-truth pages, decision forums, and escalation paths that can answer a customer question or unblock a sales task.
-
-Definitions:
-- Source-of-truth page: the maintained doc, wiki, tracker, or page that should be treated as the most authoritative answer source.
-- DRI: directly responsible individual, the person accountable for a topic, decision, or follow-through.
-- SME: subject matter expert, someone with practical depth on the topic even if they are not the accountable owner.
-- Decision forum: the recurring meeting, channel, doc, or group where tradeoffs and approvals are handled.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Find the key internal sources for a customer question.`
-- `@Sales find who owns the Enterprise SSO rollout path for Acme.`
-- `@Sales find the docs and channels for answering a customer's security review question.`
-- `@Sales route this implementation blocker to the right internal people and source of truth.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Find Key Internal Sources next-step behavior and action text. Onboarding, draft creation, posting or sharing, source-list creation, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the internal-routing continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `find-key-internal-sources` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the routing map** | The first substantive routing map has just been produced. If quick-mode retrieval stopped at the retrieval budget, make the next action the most practical recovery path: ask for a narrow steer, offer a deeper search, or suggest using the current best route when it is good enough. | `Does this routing map look good enough to use, or should I dig one level deeper?` |
-| **2. Review the revised routing map** | The user asked for any change to owners, docs, channels, confidence, scope, wording, or search depth. Make the change in chat first. | `Anything else you'd change? If that looks good, want me to draft the first Slack ask, internal handoff note, source-of-truth reading list, or customer-safe response?` |
-| **3. Prepare the routing action** | The user accepts the map, says no changes are needed, says a revision looks good, or asks to act on the route. | Offer to produce the most useful next artifact or draft action, prioritizing a Slack ask, internal handoff, escalation note, source-of-truth reading list, or customer-safe response over broad suggestions. |
-| **4. Review the ask, handoff, or response** | A Slack ask, handoff note, reading list, escalation note, or customer-safe response exists, or the user asks to change it. | `Anything you'd change before I save, post, or share this?` |
-| **5. Post, share, or save approved routing action** | A reviewed ask, handoff, response, or reading list exists and does not need edits. | Offer the specific supported action, such as posting the approved Slack ask, saving the reading list, or sharing the handoff note. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the routing map, treat a lightweight acknowledgement as acceptance and move to `Prepare the routing action`. If the previous continuation offered to prepare an ask, handoff, response, or list, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to post, share, or save a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-## Procedure
-
-### 1. Normalize the request
-
-- Extract `topic_or_task` from the user request, prior call follow-up output, meeting prep, account docs, internal messaging context, saved team hubs, or the active thread when safe.
-- Default `output_depth` to `quick`.
-- Keep user-provided customer/account context, urgency, and desired audience as prioritization context.
-- If no usable topic can be identified, ask the clarification in `Required Inputs` instead of producing a generic source map.
-
-### 2. Choose the answer route and build search facets
-
-Choose the smallest route that satisfies the user's request:
-
-- `owner route`: the user asks who owns the topic, who knows, who approves, who to contact, or which expert/POC/lawyer/specialist to ask. Start with canonical ownership or routing sources, then use docs or `internal_messaging` only to fill gaps.
-- `doc route`: the user asks what to read, what the source of truth is, or what approved wording exists. Start with verified or maintained source-of-truth docs and fetch only the most relevant top result before ranking.
-- `channel route`: the user asks where to ask, where discussion happens, or which escalation/support channel to use. Start with channel/topic/source-of-truth route search and use message search only when it changes confidence.
-- `full map`: the user asks for experts, docs, and channels, or the request clearly needs multiple answer paths. Use the full workflow and search the necessary source categories.
+# Internal Routing Map: [Topic]
 
-Do not produce every section at full depth when the user asked for only one route. Keep required output sections, but mark unsearched or low-value sections as `Not searched in quick pass` or `No high-confidence candidate found in quick pass` when appropriate.
+[Optional framing note when the topic spans distinct ownership surfaces.]
 
-Build query facets from:
+## Experts
+- **[Name / team]** — [DRI / approver / SME / other type]; [why this route]. [Evidence link] · [confidence/freshness]
 
-- primary terms from `topic_or_task`
-- aliases, abbreviations, product/team/system names, and legacy/current names when available
-- task-shape terms such as troubleshooting, incident, RFC, approval, compliance, runbook, playbook, owner, rollout, escalation, or support
-- user-provided account/customer context when it helps disambiguate the route
-- route-shape terms that identify the answer path, such as field guide, GTM, specialist, enablement, FAQ, source of truth, security, policy, exception, approval, implementation, support, or escalation
+## Docs
+- **[Doc]** — [source-of-truth / field guide / FAQ / other type]; [why it matters]. [Link] · [confidence/freshness]
 
-Use [references/search-patterns.md](references/search-patterns.md) for query construction details when fan-out quality, source-specific search behavior, or stopping rules are ambiguous.
+## Channels
+- **[#channel]** — [support / escalation / decision forum / other type]; [why ask here]. [Link] · [confidence/freshness]
 
-For compound topics that combine a product or account surface with a policy, security, retention, implementation, pricing, launch, or support question, split the search into at least two tracks:
+## Recommended First Ask
+> [Draft-ready question or handoff, addressed to the best owner or public channel.]
 
-- the underlying control/process track, such as the policy, approval, exception, enablement, or implementation process;
-- the product or GTM routing track, such as the product field guide, launch hub, specialist team, seller FAQ, customer-facing collateral, or support channel.
+## Coverage Gaps
+- [Missing or weak source, confidence impact, and smallest useful next step]
 
-Keep both tracks in the candidate pool until ranking. Do not let a strong process result crowd out product-specific seller routes, and do not let product launch/GTM hits replace policy or implementation ownership when the question needs both.
-
-### 3. Expand search only as needed
-
-- Start with exact or canonical topic terms per source.
-- Add synonym and contextual variants only when the first pass is empty, thin, ambiguous, or the compound topic needs both process and product routing tracks.
-- Search `internal_messaging` and `document_store` in parallel only when it reduces elapsed time without widening scope and the selected answer route needs both categories.
-- For `internal_messaging`, search public channel names/descriptions and public messages/threads by default. Prefer server-side visibility filters when available; otherwise post-filter by visibility metadata.
-- For `document_store`, search both exact topic wording and route-shape variants for central hubs, field guides, FAQs, playbooks, and source-of-truth pages. Prioritize title and heading matches before body-only matches. Prefer pages linked from central hubs/wikis and pages with active updates.
-- When a source result points to a central guide, channel topic, channel purpose, linked doc, thread, or source-of-truth hub, fetch or inspect that result before final ranking when the connector exposes a readable link or object id.
-- Stop expansion when the core answer is supported, top results stabilize across two or more query variants, additional variants add low-confidence duplicates, or the quick-mode retrieval budget is reached.
-
-### 4. Pull source candidates
-
-Pull candidates from:
-
-- `internal_messaging`: candidate channels, channel topics/purposes, thread/message evidence, recurring contributors with recent participation, decision forums, escalation paths, specialist-team routes, and support/feedback channels.
-- `document_store`: candidate docs, pages, databases, field guides, playbooks, source-of-truth hubs, owner signals, recency, linked docs, and listed support or specialist routes.
-
-For people recommendations derived primarily from `internal_messaging`, require at least one ownership or expertise signal from the last `90 days`; down-rank or omit stale chat-derived candidates. Canonical owner pages, maintained directories, verified routing docs, or current policy/source-of-truth docs can satisfy ownership without recent message evidence, but label stale or unverified owner metadata when freshness affects confidence.
-
-### 5. Apply organization-specific context safely
-
-Apply organization-specific behavior only from user-provided context, connector-visible source truth, or Sales plugin-scoped user context read through `sales:user-context`.
-
-Do not add org-specific channel patterns, product names, boost rules, account data, URLs, or private process names to bundled plugin files. Use [references/company-context.md](references/company-context.md) only for generic rules about how to apply Sales plugin-scoped terminology safely.
-
-Apply company context after generic candidate retrieval:
-
-1. Use it to add source-specific query expansions.
-2. Use it to boost or down-rank candidates.
-3. Keep generic scoring as the base and cap context-based boosts.
-
-### 6. Aggregate, score, and rank
-
-Normalize all candidates into a shared structure:
-
-- `type`
-- `title_or_name`
-- `url`
-- `source`
-- `evidence`
-- `owner_signals`
-- `timestamps`
-
-Deduplicate near-identical entries.
-
-Score using:
-
-- relevance
-- authority
-- freshness
-- cross-source confirmation
-- practical seller route quality, including whether the item gives a clear place to ask, a team to tag, an owner to contact, or approved customer-facing wording
-
-For ownership recommendations, include the ownership type when inferable, such as DRI, approver, SME, maintainer, accountable team, decision forum, launch owner, escalation channel, support channel, or feedback channel. Prefer fewer high-confidence items over long noisy lists.
-
-Ranking tie-breakers:
-
-- Prefer direct evidence links over profile-only or channel-only matches when recommending experts.
-- Prefer verified or recently maintained field guides, playbooks, FAQs, and source-of-truth hubs over one-off notes when the user needs seller routing or approved wording.
-- Prefer channels whose topic, purpose, central guide, or recent threads show an ownership path over channels that merely mention the topic.
-- Keep distinct answer paths when a topic spans multiple ownership surfaces, such as product/GTM guidance plus policy/implementation approval. Do not collapse them into a single generic owner.
-
-### 7. Render the routing map
-
-Return `Experts`, `Docs`, `Channels`, `Recommended First Ask`, and `Coverage Gaps` sections.
-
-For every item, include:
-
-- one-line rationale
-- direct source link when available
-- ownership or answer-path type
-- evidence or signal that supports the recommendation
-- freshness or confidence when it affects trust
-
-Explain what kind of ownership or answer path each recommendation appears to provide, not only why it matched the query. If a source has no useful URL or stable connector-visible link, use a plain text label and say `(no useful link available)` when that absence matters.
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL.
-
-When the evidence suggests the user's phrasing may conflate two surfaces, such as a product surface and an API/process/policy control, include a short framing note before the ranked sections. Keep it practical: name the split, explain why it affects the answer path, and continue with the routing map instead of stopping for clarification unless the distinction changes which sources are safe to use.
-
-## Output Shapes
-
-Use compact Markdown headings and bullets. Keep the result focused on the routing decision, not a full research memo.
-
-Required sections:
+---
 
-1. `Experts`
-2. `Docs`
-3. `Channels`
-4. `Recommended First Ask`
-5. `Coverage Gaps`
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-For `quick` output, return up to 3 items each for experts, docs, and channels, and use the quick-mode retrieval budget from `Context Gathering Principles`. For `deep` output, return 5 to 8 items each when the evidence quality supports that many.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-`Recommended First Ask` should give the seller one concrete next message or question to send to the best owner/channel. Keep it draft-ready, but do not post it.
+## Example Prompts
 
-`Coverage Gaps` should name unavailable or weak sources, how that affects confidence, and the smallest useful next step.
+- `Find the key internal sources for a customer question.`
+- `Find who owns the Enterprise SSO rollout path for ExampleCorp.`
+- `Find the docs and channels for answering a customer's security review question.`
+- `Route this implementation blocker to the right internal people and source of truth.`
 
 ## Rules
 
-- Keep the routing-map phase read-only. Do not post to `internal_messaging`, send messages, assign owners, or update docs unless the user has explicitly approved a reviewed ask, handoff, or update in a separate action step.
-- Do not fabricate owners, experts, channels, docs, source-of-truth paths, or links.
-- Rank ownership signals by practical usefulness for the seller's next action.
-- Default to public channels only; include private channels only when the user explicitly asks.
-- Exclude deactivated/inactive `internal_messaging` accounts from expert recommendations.
-- Keep facts and inference separate. Label uncertain ownership as `Likely` or `Possible`.
+- Do not fabricate owners, experts, channels, documents, source-of-truth paths, links, or ownership certainty.
+- Keep facts and inference separate; label uncertain ownership Likely or Possible.
+- Keep the routing-map phase read-only. Do not post, send, assign, or update anything.
 - Prefer fewer high-confidence routes over long noisy lists.
+- Always cite sources with hyperlinks when useful links are available; say (no useful link available) when the absence matters.
diff --git a/plugins/sales/skills/find-key-internal-sources/agents/openai.yaml b/plugins/sales/skills/find-key-internal-sources/agents/openai.yaml
index 7bb981c..bd5e714 100644
--- a/plugins/sales/skills/find-key-internal-sources/agents/openai.yaml
+++ b/plugins/sales/skills/find-key-internal-sources/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Find internal experts, docs, and channels"
   default_prompt: "Find the key internal sources for a customer question."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/find-key-internal-sources/references/company-context.md b/plugins/sales/skills/find-key-internal-sources/references/company-context.md
index 08e9de5..f2538e0 100644
--- a/plugins/sales/skills/find-key-internal-sources/references/company-context.md
+++ b/plugins/sales/skills/find-key-internal-sources/references/company-context.md
@@ -2,16 +2,16 @@
 
 Use this file only as generic guidance for applying organization-specific terminology after the generic search workflow is already working.
 
-This generalized version intentionally ships without company-specific product names, channel patterns, URLs, process names, source priorities, or boost rules. Do not add those facts to this bundled file. Use user-provided context, connector-visible source truth, or Sales plugin-scoped user context read through `user-context` when it exists.
+This generalized version intentionally ships without company-specific product names, channel patterns, URLs, process names, source priorities, or boost rules. Do not add those facts to this bundled file. Use user-provided context or connector-visible source truth.
 
 ## How To Apply
 
 1. Run the generic workflow first.
-2. Apply user-provided or Sales plugin-scoped terminology, aliases, and team names as query expansions.
+2. Apply user-provided or connector-visible terminology, aliases, and team names as query expansions.
 3. Apply any local boost rules only after base scoring.
 4. Cap total company-context boost per candidate at `+0.25`.
 
-## Good Inputs For Plugin-Scoped User Context
+## Good Grounded Inputs
 
 - product or team names
 - known abbreviations or legacy names
@@ -22,4 +22,4 @@ This generalized version intentionally ships without company-specific product na
 
 - Do not let local boosts overpower base relevance.
 - Do not use this file to hardcode one company's ownership model into the generic skill.
-- Keep future company-specific additions outside the plugin package, in Sales plugin-scoped user context or source systems.
+- Keep future company-specific additions outside the plugin package, in user-provided context or source systems.
diff --git a/plugins/sales/skills/find-key-internal-sources/references/search-patterns.md b/plugins/sales/skills/find-key-internal-sources/references/search-patterns.md
index 7d27ab0..353dbf0 100644
--- a/plugins/sales/skills/find-key-internal-sources/references/search-patterns.md
+++ b/plugins/sales/skills/find-key-internal-sources/references/search-patterns.md
@@ -1,7 +1,7 @@
 # Search Patterns
 
 ## Purpose
-Use these patterns to produce high-quality queries across `internal_messaging` and `document_store` when the focused workflow needs fan-out. These are optional expansion patterns, not a requirement to exhaust every variant before answering.
+Use these patterns to produce high-quality queries across ~~Internal Messaging and ~~Knowledge & Files when the focused workflow needs fan-out. These are optional expansion patterns, not a requirement to exhaust every variant before answering.
 
 ## Query Construction
 
@@ -41,11 +41,11 @@ Run only the variants needed for the selected answer route and current confidenc
 
 4. Route: `"<team_or_product>" "field guide"`, `"<team_or_product>" "GTM"`, `"<team_or_product>" "specialist"`, `"<control_or_process>" "source of truth"`, and `"<control_or_process>" "escalation"` when those facets exist.
 
-If the source supports semantic search, keep queries short and topical. If the source is lexical-heavy, include explicit aliases and abbreviations. After the bounded generic pass, apply org-specific expansions from user-provided context, connector-visible source truth, or Sales plugin-scoped user context read through `user-context` using the rules in `company-context.md` only when those expansions are likely to change the recommended route.
+If the source supports semantic search, keep queries short and topical. If the source is lexical-heavy, include explicit aliases and abbreviations. After the bounded generic pass, apply org-specific expansions from user-provided context or connector-visible source truth using the rules in `company-context.md` only when those expansions are likely to change the recommended route.
 
 ## Source-Specific Notes
 
-- `internal_messaging`:
+- ~~Internal Messaging:
   - Search both channel names/descriptions and messages.
   - Default to public channels only; include private channels only on explicit user request.
   - Prefer server-side visibility filters when available; otherwise post-filter by channel visibility metadata.
@@ -54,16 +54,16 @@ If the source supports semantic search, keep queries short and topical. If the s
   - Inspect channel topics/purposes when channel search results are candidate ownership routes.
   - Prefer direct thread/message links as evidence for expert recommendations when available.
   - Follow connector-visible links from strong threads to docs, field guides, FAQs, or source-of-truth pages before ranking.
-  - Capture recurring handles as expert candidates only when their `internal_messaging` account is active (not deactivated).
+  - Capture recurring handles as expert candidates only when their ~~Internal Messaging account is active (not deactivated).
   - Require recent participation signals (default: within the last `90 days`) before elevating a person as an expert.
 
-- `document_store`:
+- ~~Knowledge & Files:
   - Prioritize title + heading matches, then body-only matches.
   - Prefer docs with clear owners and recent edits.
   - Search for central routing artifacts, including field guides, launch hubs, GTM pages, FAQs, playbooks, source-of-truth hubs, and support/escalation sections.
   - Fetch top candidate docs when snippets indicate they contain support channels, specialist routes, owner lists, customer-facing wording, or linked references.
 
-- `document_store` wiki/page sources:
+- ~~Knowledge & Files wiki/page sources:
   - Prefer pages linked from central hubs/wikis and pages with active updates.
   - Use parent/child relationships to find canonical pages, not one-off notes.
 
diff --git a/plugins/sales/skills/follow-up-after-call/SKILL.md b/plugins/sales/skills/follow-up-after-call/SKILL.md
index 5a187c2..5acf683 100644
--- a/plugins/sales/skills/follow-up-after-call/SKILL.md
+++ b/plugins/sales/skills/follow-up-after-call/SKILL.md
@@ -1,398 +1,204 @@
 ---
 name: follow-up-after-call
-description: Turn a recent customer, partner, or important internal call transcript or grounded call notes into a seller-ready follow-up package with a recap, next steps, external comms when applicable, CRM next steps when applicable, and an internal follow-up draft.
+description: "Use after a completed customer, prospect, partner, or important internal sales call, discovery session, demo, or conversation, including when the user supplies or uploads a transcript, notes, recording summary, or other grounded evidence. Advise on, assess, qualify, recap, and produce seller-ready follow-up actions and drafts."
 ---
 
 # Follow Up After Call
 
-Use this skill for seller post-call follow-up after an external customer meeting, partner call, or important internal call with sales follow-through value.
 
-It produces a seller-ready follow-up package from grounded call evidence:
+## Context-Gathering Intake
 
-- call summary
-- customer and seller next steps
-- external follow-up copy when applicable
-- CRM-ready next-step text for customer/account calls
-- internal team recap draft when useful
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-Default behavior:
+Turn grounded call evidence into a seller-ready follow-up package that preserves what was actually said, makes ownership visible, and gives the seller copy they can use immediately. This skill owns the post-call synthesis and drafts; it does not send, post, create email drafts, or update CRM.
 
-- draft-only output unless the user explicitly asks for private draft creation
-- copy/paste CRM output only; never write to `crm`
-- draft-only `internal_messaging` team recap output under `Internal Follow-Up`
-- private `external_messaging` email draft creation only when requested or clearly appropriate, supported by the selected App, and never sent without explicit approval
-- explicit placeholders for unknowns
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### User Context
+## Key Dependency Categories
 
-Mandatory pre-answer gate:
+These categories are particularly important for this workflow; use other sources only when they materially improve the package.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the package also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the follow-up package.
+- [Blocking] ~~Meeting Transcripts for the primary transcript, grounded notes, participant language, decisions, commitments, objections, and source links. It blocks the default live-source follow-up path; explicit grounded call evidence already in context satisfies the need.
+- ~~Calendar for recent-call identification, attendees, timing, and upcoming related meetings
+- ~~CRM for account, opportunity, contact, owner, and CRM-ready next-step context
+- ~~Email for customer-facing thread context when the call evidence or promised follow-up lives there
+- ~~Internal Messaging for internal account context and a safe suggested destination for the team recap
+- ~~Knowledge & Files for exported transcripts, call notes, account notes, and follow-up assets
 
-### Audience And Language
+Grounded call evidence is required. CRM, calendar, files, and messages can enrich the package but cannot substitute for a transcript, grounded notes, or a customer-facing thread that records the call. If evidence is missing, stale, or conflicting, state the limitation rather than reconstructing the call.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Reference Loading
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+`SKILL.md` owns the normal evidence-first workflow, chat-first draft behavior, and output package. Load references only when their extra detail changes the decision:
 
-### Source Resolution
+- Use [references/request-schema.yaml](references/request-schema.yaml) when normalizing structured input, relative call dates, meeting-recording identifiers, or fallback evidence paths.
+- Use [references/opportunity-and-channel-selection.md](references/opportunity-and-channel-selection.md) when opportunity ranking or internal destination safety is ambiguous.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Workflow Guidance
 
-### Required Inputs
+### 1. Resolve the call and evidence
 
-This skill needs enough user-provided context to identify the required information. Source access can suggest concrete candidates, enrich, or verify the information, but do not draft the package until grounded call evidence is supplied or confirmed by the user.
+- If the user supplies a transcript, notes, link, or clearly identified call, start there.
+- If the call-resolution path isn't clear, ask the user via `ask_user_input()`. For a bare invocation such as “Follow up after my call,” do not assume a meeting; offer:
+  - `Use my most appropriate recent grounded call`
+  - `Use a call, account, attendee, or date I specify`
+  - `Use transcript or notes I provide`
+- For “my latest call,” “today’s call,” “earlier today,” “yesterday,” “last week,” “my most appropriate recent meeting,” or a named account without a source, treat the wording as a retrievable anchor: search the narrowest recent window in ~~Calendar and ~~Meeting Transcripts, then offer up to five concrete candidates via `ask_user_input()`. Do not retrieve deeper context or draft a package until the user selects one.
+- When the call or account anchor is ambiguous, make at most three source reads to produce concrete candidates; do not gather broad enrichment before the user chooses.
+- Treat ambiguous company-like names, partner names, and account shorthands as possible account anchors. When ~~CRM is available and account identity affects which call to retrieve, use a bounded ~~CRM lookup to disambiguate the account before relying on weaker account context; it never substitutes for grounded call evidence.
+- If exactly one plausible grounded candidate remains, ask the user to confirm it via `ask_user_input()` before drafting. If no grounded evidence can be found, ask for the transcript, notes, or specific call rather than producing a recap from surrounding account context.
+- Treat pasted notes, uploads, and user-linked material as valid working evidence; label them as user-provided when they cannot be verified.
 
-The skill can still produce a limited follow-up package from pasted transcripts, grounded notes, uploaded/exported context, or user-linked context alone when that context is sufficient. Connectors add significant value and reduce the burden on the user, but do not stop solely because connectors are unavailable.
+### 2. Gather only useful enrichment
 
-**Inputs:**
+- Prefer grounded evidence in this order: ~~Meeting Transcripts transcript; retrievable meeting-recording transcript; other exported transcript; pasted transcript or grounded notes; ~~Knowledge & Files meeting notes; then an ~~Email thread or message as a recovery lane.
+- Fetch a user-provided call or transcript handle directly. Otherwise search with the account plus date or timeframe and at most two meeting-topic terms, then fetch the best match.
+- Preserve supplemental source-of-truth, call-notes, or transcript links alongside the primary evidence rather than replacing it.
+- For an external account call, use ~~CRM when available to resolve the relevant account or opportunity and sharpen names, commercial context, and the one-sentence CRM next step.
+- Check ~~Calendar for a related upcoming meeting when timing changes the next step. Use ~~Email, ~~Internal Messaging, and ~~Knowledge & Files only when they add promised actions, blockers, owners, links, or destination context.
+- If the transcript explicitly names a meeting or file that could affect the follow-up, do one targeted lookup in ~~Calendar or ~~Knowledge & Files using the exact title and date when available. Include the link if found; otherwise say it was not found in that checked source/window rather than claiming no such meeting or file exists.
+- Stop once the package is grounded and the highest-value account/timing context is covered; name missing enrichment instead of continuing broad searches.
 
-Require:
+### 3. Classify the call and extract commitments
 
-- [required] Grounded call evidence, such as a transcript, recording transcript, pasted notes, call notes doc, or message thread
+- Decide whether the call is external customer/partner-facing or internal. External email copy and CRM text apply only to external calls.
+- Extract decisions, commitments, asks, objections, blockers, owners, and dates. Use `Unknown` or `TBD` when the evidence does not establish them.
+- If multiple CRM opportunities are plausible, choose the one matching the call topic, attendees, product, timing, and recent activity. If none is credible, keep the CRM sentence generic and append `(paste into the relevant opp)`.
+- When opportunity intent, candidate ranking, or the safest internal channel is not obvious, use [references/opportunity-and-channel-selection.md](references/opportunity-and-channel-selection.md).
 
-Recommended inputs:
+### 4. Draft in chat
 
-- `account_name`
-- `call_date`
+- Return the package in chat by default, even when an email or internal channel is available.
+- Do not create an email draft, send outreach, post an internal message, or write CRM in this workflow.
+- Keep verbatim email and internal-message copy in Markdown block quotes so the seller can review and copy it.
 
-Accepted optional inputs:
+### Next Step Options
 
-- `primary_contact_name`
-- `primary_contact_email`
-- `known_chat_channel`
-- `opportunity_name`
-- `opportunity_link`
-- `account_context_notes`
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Refine the external email, internal recap, CRM sentence, or action list using the user's guidance.
+- Create the reviewed email draft or post the reviewed internal recap when the user explicitly requests it.
+- Add the reviewed recap and action items to an existing meeting note or create a new follow-up note.
+- Prepare CRM-ready updates for review.
+- Check for a matching automation and offer to create one unless a matching automation already exists; if one exists, offer to review or adjust it.
 
-Defaults:
+Next steps to avoid:
+- Sending, posting, or updating CRM before the user reviews and explicitly requests the action.
 
-- `chat_mode=draft_only`
-- `crm_mode=copy_paste_only`
-- `unknown_marker=Unknown`
-- `missing_due_date_marker=TBD`
+### Automation Offer Guard
 
-Use [references/request-schema.yaml](references/request-schema.yaml) when structured input validation, YAML normalization, or machine-readable field shape matters.
+Always offer a follow-up automation as the end-of-output next step unless a matching automation already exists. If the output identifies a natural timing anchor such as a next meeting, follow-up date, customer decision date, or agreed check-in, use that timing in the offer; otherwise offer to help choose the trigger or cadence. Frame the automation as a scheduled rerun of this skill, not a custom digest: it should use the same call/account anchor where possible and return the standard follow-up package with recap, action items, external copy, CRM-ready text, and internal recap.
 
-**Context Rules:**
+Before ending with an automation offer, check whether a matching local automation already exists under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset. Match by prompt, skill name, account, meeting title, attendee, opportunity, trigger time, or other stable scope details. Treat active and paused matches as already installed.
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting the package.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup before relying on calendar, docs, messages, meeting notes, or public context alone. If CRM returns one high-confidence account match, use it as the primary account candidate; if multiple plausible account matches remain, ask the user to choose from concrete candidates.
-- Before asking the user to paste a transcript or notes, first try the workflow source categories that can identify or supply call evidence: `calendar` for recent call candidates, `meeting_notes` for transcripts or call notes, `document_store` for exported call notes, and `external_messaging` only when call details plausibly live in a customer-facing thread. Manual pasted evidence is the final fallback after those categories are unavailable, empty, or insufficient.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+- If a matching automation exists, offer to review or adjust it rather than creating another one.
+- If no matching automation exists, end with one clear offer to create the automation, describing the output as a seller-ready follow-up package produced after the next call or at the agreed follow-up time. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, avoid tool details and simply offer to help set up a recurring or scheduled follow-up check when automations are available.
 
-If required inputs remain missing or ambiguous, first attempt a bounded candidate pass through the source category that owns the missing anchor, within the Fast Candidate Resolution budget. Ask one friendly clarification only after that pass. Ask only for unresolved inputs; include up to 5 concrete lettered options from source-backed or user-provided candidates when available.
+## Modes
 
-If call evidence is missing, search recent `calendar` or `meeting_notes` candidates first; only ask the user after offering up to 5 defaults or confirming no plausible candidates were found.
+### 1. Full Follow-Up Package
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+Default when the user asks to follow up after a call. Return every section below in order.
 
-**Input Request Format Example:**
+### 2. Focused Draft
 
-```md
-Which call should I use?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Stop only when no recent call, meeting, transcript, note set, customer/account, timeframe, or sufficiently detailed user-provided call evidence can be identified.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `calendar`: recent meeting candidates, titles, attendees, times, and first-pass call selection when the user asks for a recent call without naming a specific transcript or notes source.
-- `meeting_notes`: primary transcript, call notes, meeting recording transcript when available, participant language, decisions, commitments, asks, risks, and source links.
-- `document_store`: exported transcripts, call notes, account notes, follow-up docs, and account context that sharpens seller action.
-- `crm`: account and opportunity context, stakeholder/account history, opportunity candidates, and CRM-ready next-step framing. Never write to CRM.
-- `external_messaging`: customer-facing thread context, private email draft creation when allowed, and recovery evidence when call details live in an email/chat thread.
-- `internal_messaging`: internal account-channel selection, internal context, and draft-only team recap destination guidance. Never post or send.
-- user-provided context: pasted transcripts, grounded notes, uploads, exports, links, and call context that can serve as working evidence.
-
-Source obligations by intent:
-
-- For post-call follow-up, grounded call evidence is required from `meeting_notes`, a meeting recording transcript, exported transcript, pasted transcript, grounded notes, or a customer-facing message thread. CRM, docs, or summaries can enrich the package but cannot substitute for call evidence.
-- Do not use `calendar`, `crm`, `document_store`, `internal_messaging`, or public context to produce call recaps, commitments, quotes, objections, or participant language unless those sources contain grounded call notes or customer-facing thread evidence.
-- When the user asks for the latest or recent call without naming one, check `calendar` and `meeting_notes` for concrete call candidates before asking. If both are unavailable and user-provided evidence is insufficient, ask for transcript, notes, or the specific call.
-- For customer follow-up packages, check `calendar` for upcoming related meetings when available. If a relevant future meeting is found, use it in next-step timing; if checked and no relevant meeting is found, say so briefly only when timing matters.
-- For customer/account follow-up packages, `crm` is required when CRM is available and a customer/account anchor is supplied, inferred, or confirmed. Use it for opportunity, account, contact, owner, recent activity, and CRM-ready next-step context when it can materially improve next steps, names, wording, or risk framing. If `crm` is unavailable or yields no useful context, continue from grounded call evidence and label the CRM gap.
-- Stop after the follow-up package has grounded call evidence plus the highest-value timing/account enrichment; offer to deepen optional lanes instead of continuing broad searches.
-
-Primary evidence order:
-
-1. `meeting_notes` transcript
-2. `meeting_notes` meeting recording transcript when available
-3. other exported meeting transcript when available
-4. pasted transcript or grounded notes
-5. `document_store` meeting notes
-6. `external_messaging` thread or message retrieval as a recovery path
-
-Authority and gaps:
-
-- Keep CRM/account context authoritative for CRM-owned customer truth in customer/account packages, but not as a substitute for call evidence itself.
-- If a preferred evidence lane is unavailable, continue with the next supported lane before dropping to fallback evidence.
-- If no grounded evidence lane is available, stop and explain the missing source.
-- If `crm` and `document_store` are unavailable, continue without account enrichment and label the gap.
-- If optional account, calendar, email, or internal-message enrichment is slow or unavailable, do not delay a package that already has grounded call evidence; continue and label the missing enrichment.
-- If private `external_messaging` or `internal_messaging` evidence requires access not yet authorized, ask for consent before retrieval.
-- If meeting recording cannot be retrieved directly, ask for exported transcript text or a linked doc rather than dropping the source from scope.
+Use when the user asks only for an email, internal recap, CRM note, or next-step extraction. Still ground it in call evidence; return the requested section plus a compact `Call Summary`, material gaps, and the status line.
 
-### Context Gathering Principles
+## Output Format
 
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the follow-up-after-call experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+Use bold section labels, not top-level headings, for the normal package.
 
 ```md
-## Follow Up After Call
-This skill turns a recent customer, partner, or important internal call into a follow-up package. It can use calendar events, call transcripts, meeting notes, CRM, docs, email, Slack, or pasted notes. It can draft customer-facing copy, internal recaps, and CRM-ready updates for review, but it does not send, post, or write into CRM without approval.
-```
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Follow Up After Call next-step behavior and action text. Onboarding, draft creation, CRM writeback, posting or sharing, document creation, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the call-follow-up continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `follow-up-after-call` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
+**Call Summary**
 
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the follow-up package** | The first substantive recap, next steps, and draft package has just been produced. | `Anything you'd change before this becomes the working follow-up?` |
-| **2. Review the revised follow-up package** | The user asked for any change to the package, including a shorter recap, edited next step, tone change, recipient change, source adjustment, or formatting tweak. Make the change in chat first. | `Anything else you'd change? If that looks good, want me to draft the customer email, internal recap, CRM next steps, or action tracker for review?` |
-| **3. Prepare the follow-through artifact** | The user accepts the package, says no changes are needed, says a revision looks good, or asks to operationalize the follow-up. | Offer to produce the most useful next artifact or draft action, prioritizing customer email, internal recap, CRM-ready next steps, action tracker, or follow-up doc over broad suggestions. |
-| **4. Review the artifact or draft** | An email draft, internal recap, CRM-ready update, action tracker, or follow-up doc exists, or the user asks to change it. | `Anything you'd change before I save, post, or write this where it belongs?` |
-| **5. Post, save, or write approved follow-through** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as creating or saving the external draft, posting the approved internal recap, saving the follow-up doc, or writing the approved CRM next steps. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
+- **Primary evidence:** [Clickable transcript/notes link or source label]
+- **TL;DR:** [2-4 grounded bullets]
+- **Context / Goal:** [Why the call happened]
+- **Key Points:** [Important customer/internal signals]
+- **Decisions / Commitments:** [What was agreed]
+- **Risks / Blockers:** [What could slow follow-through]
 
-#### Acceptance Handling
+**Next Steps**
 
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the follow-up package, treat a lightweight acknowledgement as acceptance and move to `Prepare the follow-through artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to post, save, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+**Customer**
+- [ ] [Action] — [Owner or Unknown] — [Due date or TBD] — [Notes]
 
-## Procedure
+**Seller**
+- [ ] [Action] — [Owner or Unknown] — [Due date or TBD] — [Notes]
 
-### 1. Normalize the request
+**External Comms**
 
-- Convert the user request into the normalized input shape.
-- Keep source provenance for every material fact.
-- Treat a request such as `follow up from my call with <company> earlier today`, `this morning`, `yesterday`, or another recent timeframe as a retrievable source request, not as missing evidence.
-- Normalize company/account names into `account_name`, normalize relative dates or timeframes into `call_date` when possible, and proceed to source retrieval before asking the user to paste notes.
-- If the user provides only vague recollections with no transcript, notes, account/company, date/timeframe, direct link, or retrievable source handle, stop and explain the missing evidence in one line.
+Subject options (recommended first):
+1. [Subject]
+2. [Subject]
+3. [Subject]
 
-### 2. Ingest the best grounded source
+Recommended subject: [Subject]
 
-Supported production paths:
+> Hi <FirstName>,
+>
+> [150-220 word grounded follow-up email]
+>
+> Best,
+> [Seller]
 
-- `meeting_notes` transcript via a successful connector read
-- `meeting_notes` meeting recording transcript when the source exists and a runtime retrieval path or exported transcript is available
-- other exported meeting transcript when the source exists and a runtime retrieval path or exported transcript is available
-- pasted transcript or pasted notes
-- `document_store` notes via a successful docs read
-- `external_messaging` thread or message retrieval as a secondary recovery path
+Draft link: Not created; copy drafted in chat only.
 
-Source-specific rules:
+**CRM Next Steps**
 
-- For `meeting_notes`, fetch a provided `file-` call id directly. Otherwise search with `account_name` plus call date/timeframe and 0 to 2 meeting topics, then fetch the best match.
-- For prompts like `my call with <company> earlier today`, search today's meeting notes and transcripts for that company/account before asking for pasted notes.
-- For meeting recording, accept a direct record link, exported transcript, linked doc, or minimum identifiers: record title, call date/timeframe, customer name, and 1 to 2 keywords.
-- For `external_messaging`, use it as a recovery lane when the call evidence lives in chat and transcript systems are not the best available source.
-- When a meeting record exposes useful links in metadata or the meeting description, preserve them as supplemental source links. If a link is explicitly labeled source of truth, notes, transcript, or call notes, include it alongside the primary transcript evidence and in the internal draft's `Source` section when relevant.
+[Exactly one sentence with call date, seller action, customer action, and outcome/success criterion.]
 
-If multiple meeting recording candidates remain plausible after retrieval, present candidate titles with one-line rationale and ask the user to choose before drafting.
+**Internal Follow-Up**
 
-### 3. Pull account context when it sharpens the seller action
+Suggested destination: [Safe internal channel/link, or “No verified internal channel found”]
 
-When `account_name` is known or can be resolved confidently from the evidence, use `crm` and `document_store` to sharpen account state.
+> **[Meeting title]**
+>
+> [One concise summary paragraph]
+>
+> **Attendees:** [Names or Unknown]
+>
+> **Key Notes**
+> - [Grounded note]
+>
+> **Decisions**
+> - [Decision or None confirmed]
+>
+> **Action Items**
+> - [Action] — [Owner or TBD] — [Due date or TBD]
+>
+> **Open Questions / Risks**
+> - [Question or risk]
+>
+> **Source**
+> - [Clickable call-notes/transcript link, or no useful link available]
 
-- Prefer `crm` first when the package would benefit from opportunity context, stakeholders, or account history.
-- Use `document_store` account plans or notes only when they materially change the recency lane.
-- If `crm`/`document_store` context is unavailable or resolution is ambiguous, continue with the source evidence and label the missing account-context lane explicitly.
+No email draft, Slack post, or CRM update was created.
 
-### 4. Build the seller follow-up package
-
-Always return the package in this order:
-
-1. `Call Summary`
-2. `Next Steps`
-3. `External Comms`
-4. `CRM Next Steps`
-5. `Internal Follow-Up`
-6. Draft/update status line
-
-Render standard bold section labels, including `**Next Steps**`; do not replace them with loose labels such as `Main blockers / follow-ups`. Keep the package evidence-grounded, use explicit placeholders for unknowns, and never invent attendees, dates, commitments, pricing, or decisions. Verbatim draft content, including the email body and internal follow-up draft, must be formatted as Markdown block quotes.
-
-### 5. Determine whether CRM next steps apply
-
-CRM next steps apply only to external customer or account relations.
-
-- For internal calls, write exactly: `Not applicable: this was an internal call.`
-- For external calls, determine the most relevant `crm` opportunity. If no clear opportunity is available, still draft the one-sentence update and label it `(paste into the relevant opp)`.
-- Do not claim `crm` was updated.
-
-Use [references/opportunity-and-channel-selection.md](references/opportunity-and-channel-selection.md) when opportunity ranking is ambiguous or internal-channel selection requires more than the safety defaults below.
-
-### 6. Build the Internal Follow-Up draft safely
+---
 
-Default to the `internal_messaging` category for internal follow-up, suggest the safest relevant channel when one can be identified, reject channels that appear externally shared, and return draft text only.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-The `Internal Follow-Up` draft should be a single structured team recap for the selected or suggested channel, not a generic internal note or a top-level-plus-thread split unless the user asks for a thread. Use the selected internal messaging app's native formatting, including user mentions for attendees or owners when available. If no useful call-notes link exists, say so rather than inventing one. If internal-only status is unclear, add a short warning to verify the channel before posting.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-### 7. Keep the package commercially useful
+## Output Rules
 
 - Bias toward decisions, risks, next steps, buying process, procurement, technical blockers, and stakeholder movement.
-- Keep `External Comms` concise and seller-usable when an external email is applicable.
-- For internal calls, mark external email and CRM next steps not applicable instead of drafting external email.
-- Keep the CRM sentence to exactly one sentence.
-- Keep the internal draft compact, internal-facing, and shaped as a team recap.
-
-## Output Shapes
-
-Use bold section labels, not H1/H2/H3 headings, for the normal package.
-
-### Call Summary
-
-Required subsections:
-
-- `Primary evidence`
-- `TL;DR`
-- `Context / Goal`
-- `Key Points`
-- `Decisions / Commitments`
-- `Risks / Blockers`
-
-Rules:
-
-- `Primary evidence` should name the source lane and source handle. Render the source handle as a clickable Markdown link when the connector exposes a useful URL; use a plain source label only when no useful link is available.
-- If the primary evidence source exposes supplemental source-of-truth notes, call notes, or transcript links, list them in `Primary evidence` after the primary source instead of replacing the primary source.
-- `TL;DR` should contain 2 to 4 bullets.
-- Do not invent details absent from the source.
-- When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL.
-
-### Next Steps
-
-Always include both groups:
-
-- `Customer`
-- `Seller`
-
-Line shape:
-
-```md
-- [ ] Action - Owner - Due date (or TBD) - Notes
-```
-
-If there are no concrete next steps for one side, include one line with `Unknown` or `TBD` markers instead of omitting the section.
-
-### External Comms
-
-Required elements for external calls:
-
-- `Subject options (recommended first):`
-- exactly 3 numbered subject lines
-- `Recommended subject:`
-- email body formatted as block quote text
-- `Draft link:` when an email draft was actually created or saved through `external_messaging`
-
-Rules:
-
-- If the selected call was internal, write `Not applicable: this was an internal call.` and do not draft external email content.
-- Keep the draft about 150 to 220 words.
-- Format the verbatim email body as a Markdown block quote. Do not use a fenced code block for ordinary email copy.
-- If names or emails are missing, use placeholders such as `<FirstName>` and `<Team>`.
-- Do not include sensitive internal language.
-- Draft creation through `external_messaging` is allowed when the user asked for a follow-up package, follow-up email, or post-call follow-through and the selected App supports private draft creation. This does not authorize sending.
-- If the user asks for chat-only output or no connector write, do not create a draft.
-- If a real draft was created or saved, render `Draft link:` as a clickable Markdown link to the draft URL, message URL, or web URL returned by the App. If multiple drafts were created, list one clickable link per draft.
-- If the workflow only produced email copy in chat, write `Draft link: Not created; copy drafted in chat only.`
-- If a real draft was created but the App exposes no useful URL, write `Draft link: <recipient or subject> (no useful draft link available)`.
-- Never fabricate a draft link from an opaque id or guessed mailbox URL.
-- Never send outreach without explicit user approval.
-
-### CRM Next Steps
-
-Rules:
-
-- CRM next steps apply only to external customer or account relations.
-- For internal calls, write exactly: `Not applicable: this was an internal call.`
-- For external calls, use exactly one sentence.
-- For external calls, include call date, next seller action, customer action, and outcome or success criteria.
-- If the opportunity is unresolved, append `(paste into the relevant opp)`.
-- Do not claim `crm` was updated.
-
-### Internal Follow-Up
-
-Rules:
-
-- Suggest the safest relevant Slack or other `internal_messaging` channel before the draft when one can be identified. If no channel was user-provided, search or resolve likely internal channels from the account name, call title, meeting topic, or saved account-room context when the selected app and permissions allow it.
-- Render the suggested channel as a clickable Markdown link when the `internal_messaging` App exposes a stable channel URL or permalink. Never fabricate a channel URL from an opaque id or guessed workspace path.
-- Before writing `no verified internal channel link found`, search `internal_messaging` once for the meeting/account/project name and use the best safe destination link if found.
-- Default to one consolidated internal message. Split into a top-level message plus thread reply only when the user asks for a threaded format or an identified channel norm clearly requires it.
-- Format the verbatim internal follow-up draft as a Markdown block quote when returning it inside the package. Do not use a fenced code block for ordinary chat copy. When creating an actual private draft in `internal_messaging`, write the message content directly rather than wrapping it as a quote.
-- The internal follow-up draft is a team meeting recap, not a generic internal note.
-- Include a brief summary, next steps, owners or TBDs, and structured sections when source evidence supports them.
-- Start with a bold meeting title and one concise summary paragraph that names the launch path, business impact, decision point, or main follow-through theme. Do not repeat the same TL;DR again later in the message.
-- Include attendees on one line. Use the selected internal messaging app's native user mention format for attendees when available; otherwise use readable names.
-- Use bold section labels in this order when source evidence supports them: `Key Notes`, `Decisions`, `Action Items`, `Open Questions / Risks`, and `Source`.
-- De-duplicate content across sections.
-- Include owners or `TBD` in action items. Use native user mentions for owners when available.
-- Include clickable Markdown source links when available, including a clickable Markdown call-notes link when the meeting record exposes one. Prefer the primary transcript link first, then supplemental source-of-truth notes or call-notes links exposed by the meeting record.
-- If no useful call-notes link exists, state `Call notes: no useful link available` or equivalent inside the draft rather than inventing a link.
-- Use the selected `internal_messaging` App's markdown/message format.
-- Use no `##` headings in draft text.
-- Prefer concise bullet lists; keep the draft compact enough for Slack or Teams scanning, but allow the structured sections to run longer than 12 lines when the call has real decisions or action items.
-- Always return draft text only, never a sent-message claim.
-- If channel safety is unclear, add a short warning line after the draft text.
-
-### Draft / Update Status Line
-
-After `Internal Follow-Up`, include one plain status line describing what was and was not created outside chat. If nothing was created or updated, write exactly: `No email draft, Slack post, or CRM update was created.`
-
-If the workflow created a private email draft or other explicit draft through a connector, name what was created and still state that nothing was sent or posted and CRM was not updated unless the user explicitly approved that action and it actually succeeded. Never claim `internal_messaging`, `external_messaging`, or `crm` was updated unless a tool result proves the exact action.
+- For an internal call, write exactly `Not applicable: this was an internal call.` under both `External Comms` and `CRM Next Steps`; do not draft external copy.
+- Keep the external email concise, customer-safe, and free of sensitive internal language. Never invent recipients, commitments, pricing, dates, or draft links.
+- Keep the CRM text to exactly one sentence. For an unresolved opportunity, append `(paste into the relevant opp)`.
+- Suggest an internal destination only when it appears internal and relevant. If channel safety is unclear, say to verify before posting; never fabricate a channel URL.
+- Before saying no verified internal destination exists, search ~~Internal Messaging once for the account, meeting title, or workstream when that source is available.
+- Use useful clickable source links when available, and name source gaps that materially lower confidence.
+- Default to one consolidated internal recap; split it into a top-level message plus thread only when the user asks or a verified channel norm requires it.
+- Use native mentions for attendees and owners when the selected app supports them; otherwise use readable names.
+- Keep the internal draft free of `##` headings. Its opening summary should name the launch path, business impact, decision point, or main follow-through theme without repeating the TL;DR.
+- Keep the internal recap compact, de-duplicated, and team-facing; include owners or `TBD` rather than implying ownership.
+- Always include the plain status line above after `Internal Follow-Up` unless the user explicitly asks for a different status format.
 
 ## Failure Handling
 
-When blocked:
-
-1. State the blocker in one line.
-2. Say whether the blocker is hard or degraded.
-3. Emit the best partial package you can without fabricating facts.
-
-## Rules
-
-- Keep the package seller-ready, concise, and explicit about missing information.
-- Never claim `internal_messaging`, `external_messaging`, or `crm` was updated unless a tool result proves the exact user-approved action succeeded.
-- Never send outreach without explicit approval.
-- When an actual email draft was created or saved, include its clickable draft link; when only copy was drafted in chat, say no email draft was created.
-- After the package, ask for iteration first, and include any onboarding resume or move-on path in the same final natural continuation so the response never has two competing CTAs.
-- Once the user accepts the result or asks to move on, offer to create drafts in email and/or the best suggested `internal_messaging` channel as applicable.
-- If an iteration introduced a reusable preference, apply `skills/user-context/references/plugin-memory.md`: narrow, low-risk accepted preferences can be summarized and saved automatically; broad, sensitive, ambiguous, or high-risk preferences require explicit save approval.
+If the call cannot be grounded, state the blocker in one line, say what evidence is needed, and return only safe partial material such as candidate calls or a blank fill-in structure. If optional enrichment is unavailable, still produce the grounded package and label the missing lane.
diff --git a/plugins/sales/skills/follow-up-after-call/agents/openai.yaml b/plugins/sales/skills/follow-up-after-call/agents/openai.yaml
index 12b8485..2edf90c 100644
--- a/plugins/sales/skills/follow-up-after-call/agents/openai.yaml
+++ b/plugins/sales/skills/follow-up-after-call/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Follow Up After Call"
   short_description: "Post-call recap, email, CRM note, and internal draft"
-  default_prompt: "Help me follow up from a recent call."
+  default_prompt: "Assess these notes from a recent sales conversation and suggest follow-up."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/follow-up-after-call/references/opportunity-and-channel-selection.md b/plugins/sales/skills/follow-up-after-call/references/opportunity-and-channel-selection.md
index 4e175bd..095c0d5 100644
--- a/plugins/sales/skills/follow-up-after-call/references/opportunity-and-channel-selection.md
+++ b/plugins/sales/skills/follow-up-after-call/references/opportunity-and-channel-selection.md
@@ -1,6 +1,6 @@
 # Opportunity And Channel Selection
 
-## `crm` opportunity selection
+## ~~CRM Opportunity Selection
 
 Goal: choose the most relevant opportunity for the one-sentence next-steps text.
 
@@ -21,7 +21,7 @@ Support the choice with 1 to 2 direct-evidence bullets.
 Priority:
 
 1. user-provided opportunity name or link
-2. `crm` opportunity context or `document_store` account notes if available
+2. ~~CRM opportunity context or ~~Knowledge & Files account notes if available
 3. user clarification when candidates remain unclear
 
 ### Step 3: rank candidates
@@ -44,9 +44,9 @@ If no credible candidate exists:
 - still draft the sentence
 - append `(paste into the relevant opp)`
 
-## `internal_messaging` channel selection
+## ~~Internal Messaging Channel Selection
 
-User preference: `internal_messaging` channel search first, with a linked channel recommendation when a safe channel can be resolved.
+Default: search ~~Internal Messaging first and provide a linked channel recommendation when a safe channel can be resolved.
 
 Priority:
 
@@ -58,18 +58,18 @@ Priority:
 Internal-only rules:
 
 - reject channels with metadata indicating external or shared status
-- reject channels whose name, topic, or purpose suggests external sharing in the selected `internal_messaging` App
+- reject channels whose name, topic, or purpose suggests external sharing in the selected ~~Internal Messaging App
 - if safety is unclear, return draft text only and include a warning to verify the channel before posting
 
 Link rules:
 
 - Prefer the selected App's stable channel URL, channel permalink, or connector-provided web URL.
 - If the App exposes a channel id and display name but no useful URL, render the channel name and say `(no useful channel link available)`.
-- Never invent Slack URLs from channel ids, names, or guessed workspace paths.
+- Never invent internal-messaging URLs from channel ids, names, or guessed workspace paths.
 - If no channel search/read path is available, suggest a best-effort internal channel description from the call evidence and mark the link as unverified.
 
 Default behavior:
 
 - always return a draft
 - never post or send
-- if a later workflow sends the recap, show it locally or in the user's test private channel before any target-channel post
+- if the user later asks for a supported posting action, review the exact draft and destination before any separate approval step
diff --git a/plugins/sales/skills/follow-up-after-call/references/request-schema.yaml b/plugins/sales/skills/follow-up-after-call/references/request-schema.yaml
index be60818..0954c86 100644
--- a/plugins/sales/skills/follow-up-after-call/references/request-schema.yaml
+++ b/plugins/sales/skills/follow-up-after-call/references/request-schema.yaml
@@ -32,10 +32,10 @@ defaults:
 input_notes:
   source_type:
     required: true
-    guidance: "Choose the best grounded evidence path. Prefer `meeting_notes` first, then meeting recording when those sources can be retrieved in the user's environment; use pasted text, `document_store`, or `external_messaging` when they are the best available fallback."
+    guidance: "Choose the best grounded evidence path. Prefer ~~Meeting Transcripts first, then meeting recording when those sources can be retrieved in the user's environment; use pasted text, ~~Knowledge & Files, or ~~Email when they are the best available fallback."
   evidence:
     required: true
-    guidance: "Use direct transcript text, a retrievable source reference such as a `meeting_notes` call id, meeting recording title/link, `external_messaging` link, or `document_store` URL, or a linked/exported transcript artifact."
+    guidance: "Use direct transcript text, a retrievable ~~Meeting Transcripts source reference, meeting recording title/link, ~~Email link, ~~Knowledge & Files URL, or a linked/exported transcript artifact."
   account_name:
     required: false
     guidance: "Use when already known or when it can be resolved confidently from the source."
@@ -53,7 +53,7 @@ input_notes:
     guidance: "Only use if it is clearly an internal account channel."
   opportunity_name:
     required: false
-    guidance: "If given, use as the primary candidate for the `crm` next-steps sentence."
+    guidance: "If given, use as the primary candidate for the ~~CRM next-steps sentence."
   opportunity_link:
     required: false
     guidance: "Use only to disambiguate the target opportunity."
@@ -65,6 +65,6 @@ normalization_rules:
   - "Normalize unknown names, dates, and owners to the configured markers."
   - "Treat a company/account plus a recent relative timeframe, such as 'my call with Acme earlier today,' as a retrievable evidence reference for meeting notes or transcripts, not as a request to ask for pasted notes first."
   - "Preserve direct evidence wording for decisions, asks, and commitments when possible."
-  - "Treat `crm` and `document_store` account context as enrichment lanes, not the primary call-evidence source."
+  - "Treat ~~CRM and ~~Knowledge & Files account context as enrichment lanes, not the primary call-evidence source."
   - "For meeting recording, require enough identifiers to distinguish the transcript: title, date or timeframe, customer name, and one to two keywords when a direct link is not provided."
   - "If direct meeting recording retrieval is unavailable in the current environment, retain the source_type and degrade to exported transcript text or linked doc evidence."
diff --git a/plugins/sales/skills/get-rep-call-feedback/SKILL.md b/plugins/sales/skills/get-rep-call-feedback/SKILL.md
index 98219d2..a1860cb 100644
--- a/plugins/sales/skills/get-rep-call-feedback/SKILL.md
+++ b/plugins/sales/skills/get-rep-call-feedback/SKILL.md
@@ -1,285 +1,167 @@
 ---
 name: get-rep-call-feedback
-description: Compare one rep’s call history against peer examples to extract repeatable best practices and produce evidence-backed coaching feedback.
+description: Use when the user wants evidence-backed coaching for one rep's calls, especially by comparing the rep with peer examples to identify repeatable best practices, specific upgrade moments, and practical next-call language.
 ---
 
-# get-rep-call-feedback
+# Get Rep Call Feedback
 
-## Goal
-Produce high-signal coaching feedback for a target rep by benchmarking their recent `meeting_notes` calls against peer reps' calls, extracting **repeatable best practices**, and translating them into **very specific moments** where the target rep can apply those patterns in future calls.
 
-This skill optimizes for:
-- **Great examples from peers** (what “good” sounds like in practice)
-- **Specific coaching moments** for the target rep (exact spots to apply those moves)
-- **Evidence-led** excerpts and observable patterns (no invented content)
+## Context-Gathering Intake
 
-## Skill Configuration
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-### User Context
+Turn grounded call evidence into practical coaching for one rep. This skill compares the target rep with relevant peer examples, extracts repeatable moves, and maps them to exact moments the target rep can improve. It owns the coaching readout only; it does not create scorecards, send feedback, post messages, or save coaching artifacts.
 
-Mandatory pre-answer gate:
+## Common Skill Instructions
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the output.
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### Audience And Language
+## Key Dependency Categories
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+These categories are particularly important for this workflow; use other sources only when they materially improve call selection or coaching context.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+- [Blocking] ~~Meeting Transcripts for target and peer call search, summaries, transcript moments, speaker context, dates, companies, and clickable call links. It blocks the default live-source feedback path; explicit transcript-like call evidence already in context satisfies the need.
+- ~~Knowledge & Files for user-provided or exported transcripts, call notes, manager examples, and prior coaching context
 
-### Source Resolution
+Transcript-like call evidence is required for behavioral claims. Manager notes, CRM outcomes, and generic impressions can shape focus, but cannot substitute for observed call evidence. If live transcripts are unavailable, continue from sufficiently detailed pasted or exported material and label the coverage limit.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Reference Loading
 
-### Required Inputs
+`SKILL.md` owns the normal benchmarked-coaching loop and output format. Load references only when their extra detail matters:
 
-This skill needs enough user-provided, connector-visible, or explicitly inferable context to identify a target rep and a call set, transcripts, notes, or coaching theme.
+- Use [references/call-transcripts-connector-playbook.md](references/call-transcripts-connector-playbook.md) when date-window conversion, attendee-filter limitations, threshold fallback, or sparse-result recovery needs more detail.
+- Use [references/request-schema.yaml](references/request-schema.yaml) for structured input normalization.
+- Use [references/source-priority.md](references/source-priority.md) when evidence quality is mixed.
+- Use [references/output-contract.md](references/output-contract.md) when checking required sections or downstream compatibility.
 
-The skill can still produce limited coaching feedback from pasted transcripts, uploaded/exported call notes, manager-provided examples, or user-linked transcript text when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
+## Terms
 
-**Inputs:**
+- **MAP:** mutual action plan, a shared customer-and-seller plan with owners, milestones, and dates.
 
-Require:
+## Workflow Guidance
 
-- [required] target rep or clearly supplied target call set
-- [required] call evidence: transcripts, call notes, meeting-note access, exported calls, or pasted transcript-like material
+### 1. Resolve the target and comparison set
 
-Accept when provided:
+- If neither a benchmarked target/comparison set nor a focused-coaching anchor is clear, ask the user via `ask_user_input()`. For a bare invocation such as “give me call feedback,” do not assume a target rep, peer set, or coaching focus; offer: `My calls compared with two relevant peers`, `A named rep compared with named peers`, and `Focused coaching on a supplied call set or theme`.
+- If the user names only “my recent calls” without a peer basis or focused-coaching anchor, offer the same explicit choices before gathering deeper evidence. Do not infer a representative rep, peer set, or feedback mode from a fresh request.
+- Require a target rep or clearly supplied target call set, plus transcript-like call evidence.
+- For benchmarked feedback, prefer user-named peer reps. If peers are omitted, use named top performers only when the user explicitly asks for that comparison; otherwise ask for at least two peers or offer source-derived candidates after a bounded search.
+- Accept an explicit coaching theme, call type, product focus, account, time window, audience, or requested depth when provided. Do not force optional scope.
+- Use product focus only when the user requests it, the dataset mixes motions or products enough to make comparison noisy, or one grounded area is needed to find better exemplars. Choose one area or a small user-specified set from the user's wording or call evidence; never run placeholder-only product searches.
+- If a required target, peer set, or supplied call-set anchor remains ambiguous after the user selects a lane, make at most three narrow source reads, offer up to five concrete candidates, and confirm any inferred anchor before gathering deeper evidence.
+- If the user supplies transcripts, a clear target/peer/window for benchmarked feedback, or a clear target or call set plus a focused-coaching anchor, proceed without a setup detour.
 
-- peer exemplar reps, time window, call type, product focus, coaching theme, manager notes, output audience, and requested depth
+### 2. Build a fair call sample
 
-**Context Rules:**
+- Start with ~~Meeting Transcripts. Search by the rep's exact email or display name and convert relative windows to absolute dates before retrieval.
+- Default to the trailing 30 days for the target and trailing 60 days for peers unless the user supplies a window; use the same user-specified window for both sides unless asked otherwise.
+- Aim for at least 15 target calls and at least 15 peer calls total. Use about 15 per peer only when strict peer-by-peer benchmarking is requested.
+- Keep the target and peer mix comparable by motion, segment, product focus, and topic when those dimensions are visible. Do not compare mostly discovery calls with mostly procurement calls without calling out the mismatch.
+- Search summaries first. Use a few bounded query variations around the rep plus the requested motion or theme; lower thresholds or broaden the window only when it is likely to improve the sample.
+- If the user provides a company or account, include it in the query text and any available structured filter; do not rely on structured filters alone.
+- If targets are not met after a bounded pass, produce a limited feedback memo, state the shortfall, and name the next search that would improve confidence instead of padding the analysis.
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Before asking the user to paste transcripts, call notes, rep names, or peer examples, first try `meeting_notes` for target-rep call candidates, peer-example candidates, call metadata, and transcript access. Manual transcript-like material is the fallback after `meeting_notes` is unavailable, empty, or too thin for evidence-backed coaching.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+### 3. Find peer exemplars and target moments
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+- Search for the moment, not just the meeting label: agenda control, discovery, value framing, objection handling, stakeholder mapping, pricing, security, procurement, next steps, or mutual action plan.
+- Prefer peer moves repeated across multiple calls, or several strong moments in one clearly relevant call.
+- Fetch only the calls needed to validate the pattern and show useful moments. For each fetched call, retain readable title/date/company context, motion, live link, connector-specific refetch handle for internal retrieval only, and three to eight observed moments.
+- Tag evidence internally as Peer exemplar, Target strength, or Target opportunity.
+- For each target opportunity, identify the exact moment, the peer move that fits it, and concrete language the rep could use next time.
 
-**Input Request Format Example:**
+### 4. Synthesize coaching without a scorecard
 
-```md
-Which rep and call set should I review?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when the user supplies transcripts or a clear rep/time window.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
+- Do not create a formal rubric, rating, ranking, or scorecard.
+- Turn repeated peer behavior into a short best-practice list: what the peer does, why it works, one to three supporting peer examples, and a stealable line when the transcript supports one.
+- Map those practices to specific target moments: “In this target call, when this happened, use this peer move; here is how it could sound.”
+- Normally surface five to ten stealable peer moments and five to ten specific target upgrades; use fewer when the evidence does not support that many.
+- Keep strengths visible alongside upgrades so the output is useful for coaching, not just critique.
 
-Source categories:
+### Next Step Options
 
-- `meeting_notes`: call search, transcript fetches, call metadata, call/transcript URLs, speaker labels, participant context, dates, companies, and refetch handles.
-- user-provided context: pasted transcripts, uploaded/exported call notes, manager notes, coaching themes, rep/call lists, and peer exemplar examples.
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Focus the coaching on one skill, motion, account, or call moment.
+- Add a practical 30-day practice plan grounded in the observed opportunities.
+- Draft a concise manager-to-rep or self-coaching note for review.
+- Compare an additional bounded call set when it would improve confidence or find better exemplars.
+- Turn the steal sheet into a reusable coaching document after review.
 
-Authority and gaps:
+Next steps to avoid:
+- Creating a scorecard, inferring performance or personality, or sending feedback automatically.
 
-- Use transcript/call-note evidence for behavioral claims. Do not infer coaching moments from memory or CRM summaries alone.
-- Treat CRM outcomes, manager notes, scorecards, and peer examples as coaching context only; do not use them as substitutes for transcript-backed or call-note-backed observed behavior.
-- Prefer live transcript/call URLs over raw connector ids in final output.
-- If transcript access is missing, continue only from transcript-like manual material that preserves enough context to support coaching claims.
-- If the call set is too small or uneven, produce a limited feedback memo and name the coverage gap instead of padding recommendations.
+## Modes
 
-### Context Gathering Principles
+- Benchmarked Feedback — default when target and peer evidence are available.
+- Focused Coaching — use when the user names one theme, motion, account, or small call set.
+- Limited Feedback — use when peer evidence, transcript depth, or comparable sample size is thin; clearly separate supported observations from coverage gaps.
 
-Optimize for marginal value, not absolute speed.
+## Output Format
 
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the get-rep-call-feedback experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+Return these sections in order.
 
 ```md
-## Get Rep Call Feedback
-This skill helps compare a rep's call behavior with evidence from their calls and, when available, peer examples. It can use transcripts, call notes, scorecards, manager notes, CRM context, or pasted examples. It produces strengths, coaching opportunities, concrete examples, and practical next practice steps without overclaiming beyond the evidence.
-
-Definitions:
-- MAP: mutual action plan, a shared customer-and-seller plan with owners, milestones, and dates.
-```
-
-Starter prompts:
-
-- Primary default prompt: `Give feedback on a rep's calls.`
-- `@Sales give feedback on Jamie's last three discovery calls.`
-- `@Sales compare this rep's demo calls with strong peer examples and suggest coaching points.`
-- `@Sales review these transcripts for talk-listen balance, qualification, and next-step clarity.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Get Rep Call Feedback next-step behavior and action text. Onboarding, coaching artifact creation, draft creation, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the call-feedback continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `get-rep-call-feedback` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the coaching feedback** | The first substantive call-feedback output has just been produced. | `Anything you'd change in the coaching feedback before we turn it into something the rep or manager can use?` |
-| **2. Review the revised feedback** | The user asked for any change to coaching points, examples, peer moves, tone, audience, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to draft the rep-facing note, practice plan, manager summary, or enablement snippet?` |
-| **3. Prepare the coaching artifact** | The user accepts the feedback, says no changes are needed, says a revision looks good, or asks to operationalize it. | Offer to produce the most useful next artifact or draft action, prioritizing rep-facing coaching note, practice plan, manager summary, enablement snippet, or rep-facing action list over broad suggestions. |
-| **4. Review the artifact or draft** | A coaching note, practice plan, manager summary, enablement snippet, or action list exists, or the user asks to change it. | `Anything you'd change before I save, post, or share this coaching artifact?` |
-| **5. Save, post, or share approved coaching action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the practice plan, sharing the manager summary, or posting the approved rep-facing note. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
+# Call Feedback: [Target Rep]
 
-#### Acceptance Handling
+## TL;DR
 
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the coaching feedback, treat a lightweight acknowledgement as acceptance and move to `Prepare the coaching artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to save, post, or share a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+- [3-6 evidence-backed coaching takeaways]
 
-### Source Links
+## Dataset Coverage
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+- **Target calls reviewed:** [N; goal >=15]
+- **Peer calls reviewed:** [N total; peers included; goal >=15]
+- **Coverage window:** [Absolute target window] / [Absolute peer window]
+- **Comparable mix:** [Motion, segment, product focus, or mismatch]
+- **Confidence / limits:** [Shortfall, missing transcripts, or none material]
 
-## Operating rules
-- **Be evidence-led**: Ground everything in call excerpts, transcript moments, or repeated patterns. If you can’t find enough calls, say so and propose a tighter search strategy.
-- **Never invent** call content, attendees, outcomes, metrics, or deal context.
-- **Use inline numbered markdown links to live transcript/call URLs in final user-facing output.** Keep raw connector ids only for retrieval/refetch logic.
-- `SKILL.md` owns the normal collection loop and output format. Use `references/call-transcripts-connector-playbook.md` only when date-window conversion, attendee-filter limitations, score-threshold fallback, or sparse-result recovery needs more detail. State the absolute coverage window in `Dataset coverage`.
+## Peer Exemplars: Stealable Moves
 
----
-
-## Optional product focus (use only when helpful)
-Many reps sell one product area; don’t overcomplicate this. Only apply product focus when:
-- the user explicitly asks (e.g., “focus on API calls”), or
-- the dataset mixes motions/products and comparisons would be noisy, or
-- you need to hone in on one area to find better peer exemplars.
-
-**If product focus is not requested and not needed:** skip it.
-
-**If product focus is needed:** pick **one** product area, or a small set the user specifies, and add keywords from the user's wording, account context, provided product taxonomy, or call evidence. Do not run placeholder-only product searches.
-
----
+### [Behavior]
 
-## Workflow
+- **What they do:** [Observable move]
+- **Why it works:** [Observable effect]
+- **Examples:** [Readable call context + short excerpt + inline call link]
+- **Stealable line:** “[Only transcript-supported language]”
 
-### 1) Parse the user request
-Extract:
-- **Target rep email** (required)
-- **Peer rep emails** (optional but strongly recommended)
-- Optional scope: date range, company/account, call type/motion, topics (pricing/security/procurement), and **optional product focus**
+## Target Rep: Specific Upgrade Opportunities
 
-If peers are omitted:
-- Proceed only if the user explicitly names “top performers” to use as peers.
-- Otherwise, ask for **at least 2 peers** (or propose a peer list if the user provides team/segment constraints).
+### [Upgrade]
 
----
-
-### 2) Collect calls (repeat until sufficient)
-
-Use `meeting_notes` search and fetch tools. Search from the most reliable rep/timeframe filters first, convert relative windows to absolute dates before retrieval, put rep names or emails directly in search queries when attendee filters are unreliable, and use only a few bounded query variations when results are sparse.
+- **Moment observed:** [Readable target call context + short excerpt + inline call link]
+- **Peer exemplar move:** [Readable peer context + short excerpt + inline call link]
+- **Apply it like this:** [Specific behavior and suggested language]
 
-#### Dataset targets (strongly encouraged)
-- **Target rep**: find **at least 15 calls**
-- **Peers**: find **at least 15 calls total** across peers (or **~15 per peer** if the user requests strict peer-by-peer benchmarking)
+## Next-Call Steal Sheet
 
-If you can’t reach these targets after a bounded first pass:
-- State the shortfall clearly (e.g., “Found 9 target calls in the last month”)
-- Expand date ranges or broaden keywords only when that is likely to change the coaching quality; otherwise proceed with a smaller sample, name the limitation, and offer to run a deeper collection pass.
+- [10-15 practical behaviors or example lines]
 
-Collect target and peer calls with the same bounded search discipline, aiming for a comparable call mix by motion, segment, and product focus only when product focus is enabled. If the user gave a company/account, include it in the query string and any available structured filter, but do not rely on structured filters alone.
+## Optional 30-Day Practice Plan
 
----
-
-### 3) Find strong peer exemplars
-Search for the moment you want, not just the meeting motion: objection handling, agenda control, next steps, stakeholder mapping, value framing, security review, pricing, procurement, or business case. Prefer best practices that repeat across multiple peer calls or multiple moments in one clearly strong call.
+[Include only when requested.]
 
 ---
 
-### 4) For each fetched call, capture “coachable moments”
-For every call you fetch, extract:
-- Call title, date, company/account if available, and live transcript/call URL
-- Raw call id or `file-*` id for internal retrieval only
-- Motion/type (best-effort from transcript cues)
-- **Optional product focus flag** (only if enabled/needed)
-- 3–8 key moments, e.g.:
-  - Opening / agenda
-  - Discovery depth and sequencing
-  - Value framing and proof
-  - Objection handling
-  - Stakeholder mapping / buying process
-  - Next steps / MAP
-
-Tag moments as:
-- **Peer exemplar** (keep as a “great call” example)
-- **Target opportunity** (a moment where applying a peer move would help)
-- **Target strength** (keep/scale)
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
----
-
-### 5) Synthesize best practices (no formal rubric)
-Do **not** create a formal rubric or scorecard.
-
-Instead, produce:
-- **A short list of best practices that show up repeatedly in peer calls**
-- For each best practice:
-  - What the rep does (behavior)
-  - Why it works (observable effect)
-  - 1-3 peer examples (readable title/date/company context + inline numbered transcript links + short excerpt)
-  - A “stealable” line or mini-script (if transcript supports it)
-
-Then map to the target rep:
-- Identify **specific moments** in target calls where the peer move would apply
-- Write: **“In target call X, when Y happened, do Z instead. Here’s how it would sound.”**
-
----
-
-## Output format (what to deliver)
-
-1) **TL;DR** (3–6 bullets)
-
-2) **Dataset coverage**
-   - # target calls reviewed (goal ≥15)
-   - # peer calls reviewed (goal ≥15 total; note peers included)
-   - Date ranges used + any scoping notes (e.g., product focus enabled)
-
-3) **Peer exemplars: the best calls + why they’re great**
-   - 5–10 “stealable moments,” each with call citations + excerpts
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-4) **Target rep: specific upgrade opportunities**
-   - 5–10 coaching points formatted as:
-     - **Moment observed** (readable title/date/company context + inline numbered transcript links + excerpt)
-     - **Peer exemplar move** (same citation style + excerpt)
-     - **Apply it like this** (very specific, with suggested language)
+## Example Prompts
 
-5) **Next-call “steal sheet”**
-   - 10–15 practical behaviors + example lines
+- `Give feedback on a rep's calls.`
+- `Give feedback on Jamie's last three discovery calls.`
+- `Compare this rep's demo calls with strong peer examples and suggest coaching points.`
+- `Review these transcripts for talk-listen balance, qualification, and next-step clarity.`
 
-6) Optional (only if requested): **30-day practice plan**
+## Rules
 
----
+- Ground every coaching claim in a call excerpt, transcript moment, or repeated observable pattern; never invent call content, outcomes, attendees, metrics, or deal context.
+- Cite examples with readable title/date/company context and compact inline numbered Markdown links. Prefer live transcript or call URLs; never expose raw connector ids.
+- Keep excerpts short, normally one to three lines and no more than 25 quoted words from one source.
+- Use peer examples only when the call mix is reasonably comparable; state mismatches and lower confidence when it is not.
+- If a useful call has no stable link, use readable context and say that no direct link was available.
+- Keep recommendations behavior-based and immediately usable. Do not infer intent, personality, or performance from thin evidence.
 
-## Output constraints
-- Quote short excerpts (1–3 lines) when available and useful.
-- Attribute examples with readable title/date/company context plus inline numbered markdown links to the live transcript/call URL.
-- Keep recommendations practical and behavior-based.
-- If dataset targets aren’t met, explicitly state limitations and provide next search moves.
-- Prefer the live transcript/call URL over raw connector ids.
-- If a call URL is missing unexpectedly, use readable title/date/company context only and note that a direct link was unavailable. Do not print the raw id.
+## Failure Handling
 
-## References
-- See `references/call-transcripts-connector-playbook.md` for detailed search iteration patterns, attendee-filter limitations, and date-range defaults when the compact rules above are not enough.
+If the target, peer set, or call evidence cannot be resolved, state the smallest missing input and offer concrete candidates when available. If evidence is sparse, return the limited memo with supported observations, explicit coverage gaps, and the next bounded search that would improve it.
diff --git a/plugins/sales/skills/get-rep-call-feedback/references/call-transcripts-connector-playbook.md b/plugins/sales/skills/get-rep-call-feedback/references/call-transcripts-connector-playbook.md
index 98a9fa8..c953764 100644
--- a/plugins/sales/skills/get-rep-call-feedback/references/call-transcripts-connector-playbook.md
+++ b/plugins/sales/skills/get-rep-call-feedback/references/call-transcripts-connector-playbook.md
@@ -1,7 +1,7 @@
-# `meeting_notes` connector playbook (rep benchmarking)
+# ~~Meeting Transcripts playbook (rep benchmarking)
 
 ## Tools
-Use the selected `meeting_notes` App's search and fetch functions:
+Use the selected ~~Meeting Transcripts App's search and fetch functions:
 - search with `query`, optional company/account filters, `date_range`, `limit`, and `score_threshold`
 - fetch by connector-specific id from search results
 
diff --git a/plugins/sales/skills/get-rep-call-feedback/references/output-contract.md b/plugins/sales/skills/get-rep-call-feedback/references/output-contract.md
index 4e24f01..4ee341c 100644
--- a/plugins/sales/skills/get-rep-call-feedback/references/output-contract.md
+++ b/plugins/sales/skills/get-rep-call-feedback/references/output-contract.md
@@ -2,10 +2,11 @@
 
 Required sections:
 
-1. Coverage
-2. Best Practices From Peers
-3. Coaching Moments For The Target Rep
-4. Calls To Revisit
-5. Top 3 Practice Actions
+1. TL;DR
+2. Dataset Coverage
+3. Peer Exemplars: Stealable Moves
+4. Target Rep: Specific Upgrade Opportunities
+5. Next-Call Steal Sheet
+6. Optional 30-Day Practice Plan, only when requested
 
 Every claim should point back to at least one call, transcript, or note.
diff --git a/plugins/sales/skills/get-rep-call-feedback/references/source-priority.md b/plugins/sales/skills/get-rep-call-feedback/references/source-priority.md
index 7784d16..b3234f6 100644
--- a/plugins/sales/skills/get-rep-call-feedback/references/source-priority.md
+++ b/plugins/sales/skills/get-rep-call-feedback/references/source-priority.md
@@ -2,7 +2,7 @@
 
 Prefer sources in this order:
 
-1. `meeting_notes`
+1. ~~Meeting Transcripts
 2. exported call summaries
 3. full transcripts
 4. structured call notes
diff --git a/plugins/sales/skills/hubspot/SKILL.md b/plugins/sales/skills/hubspot/SKILL.md
index 58e6a73..370bec4 100644
--- a/plugins/sales/skills/hubspot/SKILL.md
+++ b/plugins/sales/skills/hubspot/SKILL.md
@@ -1,47 +1,142 @@
 ---
 name: hubspot
-description: HubSpot CRM connector guide for Sales workflows that use HubSpot for CRM reads, drafts, notes, or proposed record changes.
+description: Use only when a focused Sales workflow has selected a connected HubSpot CRM, or the user explicitly asks for HubSpot guidance, reads, drafts, notes, or reviewed record changes. Do not use when another CRM is authoritative.
 ---
 
 # HubSpot User Guide
 
-Use this as the HubSpot-specific user guide whenever the user or workflow is using HubSpot as the CRM connector, including drafting HubSpot-backed updates, summaries, notes, or proposed record changes. Keep the surrounding workflow skill authoritative for the sales task itself; this guide adds HubSpot-specific access checks, object lookup patterns, pagination, URLs, and write safety.
+Prepare trustworthy HubSpot-backed context or a safely reviewed HubSpot action while keeping the surrounding Sales workflow authoritative for the business task.
 
-Do not use this guide when the workflow uses another CRM connector or no CRM connector.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-### User Context
+## HubSpot Operation Ranking
 
-Mandatory pre-answer gate: Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before answering, searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`. Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance. Do not read or reinterpret raw Sales state files unless preflight fails, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Use this priority order when the requested HubSpot job or record is ambiguous.
 
-Use returned user context as lookup hints and drafting guidance. Always verify HubSpot object, property, and enum metadata before querying or writing; saved user context does not replace `get_user_details`, `search_properties`, `get_properties`, or write-safety confirmation.
+1. Explicit user request, named record, HubSpot ID, company, deal, contact, ticket, or stated action
+2. Read-only CRM context needed by the active Sales workflow
+3. Exact record or a high-confidence candidate resolved from the user's words and business context
+4. The smallest property set, associations, activity context, or page of results that answers the request
+5. A reviewed draft of proposed HubSpot changes
+6. An explicitly approved, supported HubSpot write followed by verification
 
-### Audience And Language
+Do not silently pick among plausible records, broaden a read into a write, or treat clarification as write approval.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Key Dependency Categories
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
 
-### Source Links
+- ~~CRM, specifically the exposed HubSpot connector, for HubSpot-backed identity, records, properties, associations, links, and approved writes
+- The parent Sales workflow for Calendar, Meeting Transcripts, Email, Internal Messaging, Knowledge & Files, or Sales Intelligence context that HubSpot does not own
+- User-provided record IDs, names, exports, property values, and business context when connector access is unavailable or a record needs disambiguation
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+If HubSpot is missing, unavailable, stale, ambiguous, or limited by the exposed connector surface, say so and label the answer as not HubSpot-backed.
 
-## Rules
+## Workflow Guidance
 
-1. Call `get_user_details` first; check object read/write availability.
-2. Clarify scope: object type, owner/team, pipeline, timeframe, stage, and whether writes are requested.
-3. Use `search_properties` for fields, max 5 `keywords`; use `get_properties` for enum values.
-4. Use `search_crm_objects` for records, counts, filters, pagination, and associations; use `get_crm_objects` for known IDs. Do not use deprecated `search` or `fetch`.
-5. Include clickable HubSpot URLs with UTM params for returned records. State filters, totals, pagination, and whether analysis is sampled.
+These HubSpot-specific steps override the default workflow in the index skill.
 
-## Writes
+- 1. Resolve access and target
+    - Use get_user_details first to confirm the connected portal and exposed object read/write availability.
+    - Resolve whether the user wants a read, a draft, or an approved write; identify object type, owner or team, pipeline, timeframe, stage, and exact record when those affect the answer.
+    - For an ambiguous record, do the smallest bounded candidate lookup and ask the user to choose before using it for a summary or write.
+- 2. Discover properties and records
+    - Use search_properties with at most five focused keywords when fields are uncertain; use get_properties to confirm enum values and writable properties.
+    - Use search_crm_objects for search, counts, filters, pagination, and associations. Use get_crm_objects for known IDs. Do not use deprecated search or fetch.
+    - Retrieve only the properties and associations that affect the sales decision. State filters, totals, page or sample limits, and whether analysis is sampled.
+- 3. Draft, approve, and verify
+    - For proposed changes, show an exact field-level draft before calling manage_crm_objects.
+    - Write only after the user explicitly approves the exact reviewed change or batch. Batch at most 10 objects and confirm associations separately.
+    - A reviewed-batch approval applies only to that exact batch; never offer a blanket confirmation bypass.
+    - Inspect the write response and verify with a narrow read when the response does not prove the outcome.
 
-Before `manage_crm_objects`, show exact proposed changes and get approval:
+## HubSpot Rules
 
-| Object Type | ID | Property | Current Value | New Value |
-|---|---:|---|---|---|
+- Prefer exact IDs, domains, emails, and full names plus company over vague name-only matching.
+- Before querying or writing with a property or enum, confirm property labels, internal names, enum values, and writeability; do not guess them.
+- Keep HubSpot as CRM truth for HubSpot-owned account, contact, deal, ticket, owner, pipeline, and activity facts. Do not let enrichment or web context overwrite CRM truth.
+- Include clickable connector-returned or trusted HubSpot record URLs with UTM parameters when available.
+- Do not write inferred data, overwrite user-entered context without clear consent, or mutate an association without explicit approval.
+- Do not imply support for objects, properties, pagination behavior, bulk sizes, or writes that the live connector does not expose.
 
-For repeated writes, the user may approve a specific reviewed batch after seeing the exact proposed changes. Do not offer a blanket confirmation bypass for the chat.
+## Connector Boundary
 
-Batch at most 10 objects. Confirm associations explicitly. Do not write inferred data or overwrite user-entered context without clear consent.
+Use only the HubSpot surfaces that are actually exposed:
+
+- Identity and access: get_user_details
+- Property discovery: search_properties, get_properties
+- Record search and reads: search_crm_objects, get_crm_objects
+- Reviewed writes: manage_crm_objects
+
+For unsupported broad exports, delete, merge, dedupe, arbitrary automation, or unexposed object operations, state the missing surface and offer a narrower read, a draft, or another approved source.
+
+## Modes
+
+### 1. HubSpot Read
+
+- Use for company, deal, contact, ticket, owner, activity, property, or association context.
+- Resolve the target, retrieve only decision-relevant fields, and call out ambiguity or gaps.
+
+#### Output Format
+
+    # [HubSpot Record Or Context]
+
+    ## Summary
+
+    - [Most important CRM fact with trusted link]
+    - [Current deal, company, contact, activity, or association signal]
+    - [Risk, missing property, ambiguity, or source gap]
+
+    ---
+
+    {Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+    {Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+
+### 2. HubSpot Update Draft
+
+- Use when the user asks to prepare a note, property change, association, create, or update but has not approved the write.
+- Keep current and proposed values separate and identify the exact target.
+
+#### Output Format
+
+    # Proposed HubSpot Update
+
+    **Target:** [Record name, type, and trusted link or ID]
+
+    | Property / Association | Current Value | Proposed Value | Reason / Source |
+    | --- | --- | --- | --- |
+    | [Label] | [Current or Unknown] | [Proposed] | [Grounded reason + link] |
+
+    ## Before I Write
+
+    - [Missing input, association confirmation, validation risk, or approval needed]
+
+    ---
+
+    {Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+    {Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+
+### 3. Approved HubSpot Write
+
+- Use only after explicit approval for the exact reviewed object or batch.
+- Verify before reporting completion when the write response is inconclusive.
+
+#### Output Format
+
+    # HubSpot Updated
+
+    **Record:** [Name, type, and trusted link or ID]
+
+    - **Changed:** [Property or association]
+    - **Result:** [Verified outcome]
+    - **Not changed:** [Unsupported or skipped item]
+
+    ---
+
+    {Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+    {Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
diff --git a/plugins/sales/skills/index/SKILL.md b/plugins/sales/skills/index/SKILL.md
index 2c714db..7240cd1 100644
--- a/plugins/sales/skills/index/SKILL.md
+++ b/plugins/sales/skills/index/SKILL.md
@@ -1,161 +1,157 @@
 ---
 name: index
-description: "Use to discover specific skills for the Sales plugin, when it is at-mentioned directly, or for any mentions of potentially relevant work, including: meeting prep or call follow-up; account research, monitoring, or prioritization; internal source finding; competitive briefs; deal strategy; pipeline or forecast review; company or contact enrichment; customer quote retrieval; rep coaching; business cases; sales company research; and CRM or data enrichment workflows."
+description: "Use this Sales index first for explicit Sales mentions and clear seller workflows: prospecting, lead qualification, account research, monitoring or prioritization, meeting prep, call follow-up, outreach research, deal strategy, pipeline or forecast review, CRM-backed context or data enrichment, internal source finding or sales support, customer quotes or evidence, business cases, competitive briefs, rep coaching, sales company research, and company or contact enrichment. For implicit use require clear seller, prospect, account, opportunity, pipeline, forecast, CRM, or customer-facing sales intent."
 ---
 
-# Skill Purpose
+# Sales Index Skill
 
-Route broad Sales requests to the right focused workflow. Treat invocation of this index as strong intent to use this plugin; default to loading and following any relevant focused skill for related sales work.
 
-## Skill Configuration
+## Context-Gathering Intake
 
-### User Context
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-Mandatory pre-answer gate: Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before answering, searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`. Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance. Do not read or reinterpret raw Sales state files unless preflight fails, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+After this index is invoked, treat it as a router rather than the final workflow. If any focused skill plausibly owns the request, select and follow the best match; do not answer through the index alone. Handle broad orientation requests directly through the canonical orientation response, and handle other plugin-level questions directly only when no focused skill owns them.
 
-Route direct context requests to `user-context` as the primary workflow. This includes remember/save/recall/inspect/customize/setup requests and broad future-facing instructions or corrections that may be reusable after Sales work. Route requests to run Sales Company Research, learn how the user's team sells from available company context, discover useful Sales resources, or fill missing company-context setup to `sales-company-research`; that skill uses `user-context` for save/update policy. Let `user-context` own direct state reads/writes, confirmation wording, action-oriented continuation, and successful-run learning; this index should only identify and route those cases.
+MANDATORY: Read the frontmatter description for ALL skills in this plugin, and based on that, decide which to trigger and read more deeply.
 
-### Audience And Language
+## Plugin Purpose
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+Sales provides evidence-grounded workflows for customer-facing preparation, follow-up, account and prospect research, pipeline decisions, deal strategy, customer evidence, internal navigation, business cases, coaching, and CRM-backed context.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+## Broad Orientation
 
-### Source Links
+For broad orientation requests such as “what can you do?”, “help me get started”, “what should I try?”, or “how do I use Sales?”, do not choose a focused workflow. Load `references/orientation-response.md` and return its user-facing content as written. Treat that file as the canonical, updatable output surface for this branch. Offer the full skill catalog only when the user asks for it.
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+## Cross-Skill Best Practices
 
-### Broad Orientation And Help Requests
+These should be used as configuration and rules to be followed by default across all skills.
 
-For broad orientation and help requests:
+### Audience and Language
 
-- Handle open-ended Sales asks from this index before choosing a focused workflow.
-- Route requests such as `what can you do?`, `help`, `orient me`, `what should I try first?`, `how do I use Sales?`, setup-adjacent capability questions, and similar plugin-level requests here when the user needs orientation more than a specific artifact.
-- Answer from the skill map in this file using the default shape below.
-- Include concrete low-friction next prompts that start with `@Sales`, name the sales job, and use a realistic anchor.
-- Include a short setup context section from the `sales_preflight.context.sources` envelope when any source category is not active, needs a user choice, was skipped, or is otherwise unresolved.
-- Keep setup context seller-facing: name the practical source category, use model judgment to explain the likely user-experience impact from the category label, preferred apps, setup action, and suggested next prompts, then give the smallest next action or fallback.
-- Show at most three highest-impact gaps by default, and never more than five setup-context bullets total. Prioritize gaps in the order most relevant to the examples you are suggesting rather than following a hard-coded impact catalog.
-- If all categories are active, keep setup context to one sentence such as `Your core Sales sources look ready; I'll still try each source only when a workflow needs it.`
-- When the current context or available sources identify a real meeting, call, account, deal, forecast, account list, customer question, or feedback theme, use the relevant focused skill's inline first-run and next-step guidance to make the suggested prompt contextual.
-- For setup context wording, be direct and practical, for example: `You won't be able to properly use meeting follow-up until a transcript or meeting-notes provider is connected, but you can paste call notes or a transcript export for now.`
-- Use static examples only when no suitable context is available.
-- Do not expose raw status names, onboarding state fields, connector ids, or implementation terms.
-- Do not perform connector reads merely to answer a capability question; use the preflight setup summary and any current session app/tool availability already visible in context.
+- Users of this plugin are not expected to know code or internal implementation details
+- Use simple, high-level language that communicates the key information needed about the work, not about what's happening under the hood. Don't narrate mechanical processes during the rollout.
+- These users are experts in their domain, and want information about why you made certain logical decisions, want to provide input to improve outputs and apply their taste, and want to learn enough about what's happening so they can reason about and trust the outputs.
+- These users want to be treated as intelligent collaborators who are in the driver seat for key decisions, you should work with them to ensure you're on the right track and giving them what they need.
 
-Use this default answer shape for broad orientation and help requests:
+### Dependencies
 
-```md
-Sales can help with:
-- Meeting prep and daily customer-meeting briefs
-- Follow-up packages after calls, demos, or discovery notes
-- Internal source-finding for customer questions and objections
-- Deal strategy, account prioritization, forecast review, and account-signal briefs
-- Competitive briefs, customer quote pulls, rep coaching, enrichment, and business cases
+Skills refer to source categories with placeholders such as `~~CRM`, `~~Calendar`, or `~~Meeting Transcripts`.
 
-Setup context:
-- {Only include when useful: source category readiness or gap plus practical impact}
+The configured apps and their categories live in this plugin's `.app.json`. Treat this as the canonical mapping for which apps can satisfy each category. Other discoverable apps in the user's environment, such as custom internal MCP connectors, can also satisfy a category when they credibly expose equivalent information.
 
-Good first prompts:
-- `@Sales prepare me for my next customer meeting.`
-- `@Sales follow up from my latest customer call.`
-- `@Sales prioritize my accounts for pipeline focus this week.`
-```
-
-### Routing And Skill Selection
-
-If several focused skills apply, sequence them in the order that creates the most useful seller workflow. For example, meeting prep may precede deal strategy, and customer-call follow-up may feed a forecast or account-intelligence update. Keep this index as a router; do not perform focused workflow logic here.
-
-Before finalizing future Sales instruction edits, run `python3 plugins/sales/skills/user-context/scripts/validate_user_context_preflight.py` from the repository root. Treat a missing mandatory pre-answer gate in any `SKILL.md`, including helper skills, as an audit finding to fix before release.
-
-Prefer examples that route to focused skills without extra setup, such as:
-
-```text
-@Sales prepare me for my next customer meeting.
-@Sales follow up from my latest customer call.
-@Sales prioritize my accounts for pipeline focus this week.
-```
-
-For follow-up messages such as "yes", "walk me through it", "what happened?", or "show the steps" immediately after a completed guided workflow offers an agent-journey walkthrough, route to `user-context` and use `../user-context/references/onboarding.md#agent-journey-walkthrough`. Explain the observable steps, tool/app calls, retrievals, source gaps, and artifact assembly at a beginner-friendly level without revealing hidden reasoning.
-
-# Plugin Purpose
-
-Sales provides portable, evidence-grounded sales workflows for account research, competitive intelligence, meeting prep, deal strategy, pipeline and forecast review, customer follow-up, product-feedback evidence, internal navigation, business case, and rep coaching. It uses active workflow source categories such as CRM, calendar, meeting notes provider, external customer messaging, internal messaging, and document store tools when they can reduce manual input; pasted notes, uploaded files, exports, or public research remain supported fallback and enrichment paths.
-
-# Skills
-
-## analyze-account-signals
-
-Build a single-account intelligence brief or ranked owner/watchlist summary from recent account signals. Route here for account views, account monitoring, "what changed with this customer?", or portfolio-watchlist requests. It is read-only and must not create tasks, post digests, or store schedule state.
+#### Category Resolution
 
-## build-competitive-brief
+1. Identify the categories used by the selected workflow in its dependency categories section.
+2. Treat every listed category as useful but non-blocking by default. A category blocks the first output only when the focused skill explicitly marks it `[Blocking]`.
+3. A `[Blocking]` category is satisfied when either a suitable installed app is available or the user has already provided equivalent context. Do not request installation merely because the connector is absent when the needed information is already grounded in the conversation, pasted notes, links, or files.
+4. Check whether an installed app matches each category you plan to use. The initially surfaced app list is only a hint and is NEVER sufficient evidence that a provider is absent. Before making any negative availability claim, saying a connector is not installed, naming an installation gap, or offering an install, you MUST search the live/lazy tool registry for the provider name and credible category-equivalent providers. A live tool match is sufficient to treat that provider as installed and available for dependency resolution, even when it was omitted from the surfaced app list. If the provider is found but its tools are missing on the first discovery pass, recheck discovery once. Only after both checks fail may you describe it as unavailable. Do not infer readiness or absence from metadata, recommended-install lists, manifests, vendored skills, or the initially surfaced app list alone.
+5. Note that only one suitable app is needed to satisfy a category. Use additional apps when they materially improve coverage, freshness, confidence, or actionability.
 
-Build multi-competitor competitive research, comparison matrices, and battlecard-style objection packages from user materials, optional connector-assisted research, and public evidence. Route here for vendor comparisons, market-landscape questions, competitive positioning, and objection preparation.
+#### Missing Source Resolution
 
-## follow-up-after-call
+- Apply this sequence whenever a material category selected for the current workflow has no verified usable source, whether the category is blocking or non-blocking:
+  1. If equivalent user-provided context already exists, proceed without requesting installation.
+  2. Before declaring the category unavailable, look up suitable installable providers through the runtime's install/connect surfaces, such as recommended plugins or an exposed app/connector listing or search. Use `.app.json` to identify candidate providers for the category, but do not treat a manifest entry alone as proof that a provider is installable or ready.
+  3. For a `[Blocking]` category, briefly explain why the source is needed, what evidence it would add, and that the first output cannot proceed without it. If a suitable provider is available, offer it through the install/connect UI before asking for fallback context. Prefer a user-named provider; otherwise recommend the best available match. When multiple options are materially different and no preference is known, offer a bounded choice.
+  4. For a `[Blocking]` category with no suitable provider, or when the install/connect attempt is declined or fails, offer the user a choice: check the installed plugin's page in the Plugins tab for other provider options, or provide the smallest useful uploaded or pasted context needed to proceed. Pause the first output until the source or equivalent context is available; otherwise return a clearly blocked result.
+  5. For a non-blocking category, do not open the install/connect UI, ask for fallback context, or pause before the first useful output. Continue with a safe partial output, state the practical limitation, and only after presenting that result offer the suitable provider or fallback context as an optional improvement.
+- Prefer canonical plugins over connectors only when choosing among installation options. Do not request a second app solely because it is more canonical when an installed app already satisfies the category.
 
-Turn a customer call transcript or grounded call notes into a seller-ready follow-up package with recap, next steps, follow-up email draft, CRM-ready note text, and internal recap draft. Route here after calls, discovery notes, demos, or transcript uploads.
+#### Source authority
 
-## enrich-company-and-contact-data
+- Strongly prefer `~~CRM` for customer truth, account ownership, opportunity status, contacts, and pipeline context.
+- If CRM is unavailable, clearly state that customer information came from less authoritative sources.
+- Use web search only as fallback context or additional enrichment.
+- Do not use browser automation as a fallback for unavailable connectors.
+- When the Salesforce or Hubspot Connector is the CRM, use the appropriate vendored skills in this plugin.
+- When connected ZoomInfo is selected for Sales Intelligence work, load the vendored ZoomInfo skill before provider-specific search, enrichment, or recovery.
+- When connected Apollo is selected for Sales Intelligence work, load the vendored Apollo skill.
 
-Clean up sparse go-to-market inputs into sales-ready company lists, contact candidates, firmographic or technographic completion, segmentation, signal scans, and enrichment-backed comparison tables. Route here when the request is data-first rather than meeting, deal, forecast, narrative, or coaching-first. If the selected enrichment provider is ZoomInfo, also load `zoominfo` before provider-specific connector work.
+### User Input Modalities
 
-## plan-deal-strategy
+You have multiple available methods of getting input from the user:
+- User input elicitation: You can ask the user to answer questions with a generic form, usually via the `ask_user_input()` function. Prefer this whenever there are questions with strong suggested defaults, where it would be faster to accept via a click than typing a response. Bias strongly to preferring this tool over a text-based question.
+- Plugin install elicitation: You can ask the user to install plugins, connectors or apps with a special UI, usually via the `request_plugin_install()` function.
+- Text: You can always ask the user questions via markdown in a chat context.
 
-Build a post-discovery deal strategy pack with a deal map, buying committee map, procurement risk register, and prioritized next actions. Route here when the user needs to decide how to move an active deal forward from grounded deal evidence.
-
-## hubspot
-
-HubSpot connector guide for Sales workflows that use HubSpot as the selected `crm`. Route here only as a helper after another focused sales workflow has selected HubSpot, or when the user explicitly asks for Sales HubSpot CRM connector rules. Do not route here for non-HubSpot CRM use.
-
-## find-key-internal-sources
-
-Find the best internal experts, documents, and team-chat channels for a customer question, product topic, objection, implementation issue, account task, or other internal topic. Route here for "who knows about this?", "what should I read before answering this customer?", "where is the source of truth?", or "which internal channel/doc should I use?" sales-support requests.
-
-## sales-company-research
-
-Run explicit or scheduled Sales Company Research to discover durable internal resources, save high-confidence Sales plugin memory, and ask focused questions for missing links, access, or source-of-truth choices. Route here for company research, resource discovery, filling missing Sales context, recurring Sales Company Research automation runs, or broad questions about which company resources would materially improve future Sales workflows. Do not route ordinary customer questions or one-off owner/doc/channel lookup here; use `find-key-internal-sources` for those.
-
-## prepare-for-meeting
+Example
+```
+*thinking*
+{need CRM account truth and CRM is marked [Blocking]; Salesforce is available to install}
+{tell user: "Salesforce is needed to establish the account set and ownership, so I cannot produce the first ranking without it or equivalent account context."}
+{request_plugin_install(['Salesforce'])}
 
-Create concise customer meeting briefs, daily customer-meeting digests, and scheduled most-important meeting prep from invite context, prior interactions, account notes, likely objections, internal workstream context, and asset suggestions. Route here for upcoming customer meetings, daily customer-facing schedules, or recurring meeting prep automation runs that should prefer customer/account meetings when available and otherwise help with the day's most important qualifying meeting.
+{Email is useful but non-blocking; Gmail is available to install}
+{continue to the first useful output, state the email limitation, then offer Gmail as an optional next step}
 
-## suggest-sales-next-step
+{need clarification between three valid options}
+{ask_user_input(['Option 1', 'Option 2', 'Option 3'])}
 
-Run scheduled or manual Sales check-ins that summarize what the user has been doing with Sales, inspect timely calendar/source signals, and recommend one next Sales workflow to try, defaulting to an untried Sales workflow when no stronger context exists. Route here for recurring weekday sales check-in or daily check-in automation runs, manual "run my sales check-in" requests, plugin adoption reviews, or questions about how to get more value from Sales. Do not use this skill for ordinary sales artifacts; route those to the focused workflow that owns the artifact.
+{final answer to the user}
+```
 
-## prioritize-accounts
+### Workflow Steps
 
-Prioritize rep-ready pipeline by ranking accounts, suppressing in-flight motion, selecting reachable contacts, and drafting planning-only action packages. Route here for "which accounts should I work now?", territory planning, pipeline creation, or ICP/account-list prioritization.
+Your goal is to provide the user with the most value with the least amount of mental load and burden. You should default to making assumptions to produce user value more quickly, but if there are questions that materially change the output, and help de-risk the downstream value of longer workflows, you should ask. This is also an opportunity to help the user discover additional value and next steps they might not have been aware of. Avoid dead-ends and always provide a short offer for a helpful next step given their intent.
 
-## find-customer-quotes
+The offer for a next step should always append to any output formatting specified in a particular skill.
 
-Retrieve theme-specific customer or prospect quotes from transcripts, call notes, or exported recordings with speaker-confidence and provenance rules. Route here for voice-of-customer quote pulls, theme validation, or product-friction evidence requests. If live transcript connectors are unavailable, use the skill's manual/export lane when the user supplies transcript or call-note material.
+Here is the default flow you should follow for each skill:
 
-## review-forecast
+#### 1. Resolve Dependencies and Clarify
+- Review the dependency categories listed in the skill. Treat unlabeled categories as useful but non-blocking. Resolve missing `[Blocking]` categories before the first output; defer non-blocking install offers and fallback requests until after the first useful output.
+- Do a quick context gathering pass to better understand the problem and constraints
+- If needed, ask the user to resolve any high-impact, high-uncertainty questions. Use the ask_user_input tool with a batch of questions. This must happen within the first 20s of the rollout. After these questions you should be clear to execute on the First Output.
 
-Generate forecast reviews with risk analysis, recommendation posture, and change detection from CRM truth, account notes, and optional call or chat evidence. Route here for seller-book reviews, forecast risk checks, commit/pipeline hygiene, or deal-by-deal forecast recommendations.
+#### 2. Gather Context
+- After assessing user-provided context, start with an available category that owns the core source of truth, then attempt only additional available categories that can materially improve the selected artifact, confidence, or next action.
+- In an intermediate update message to the user, highlight which material dependencies are available that you'll try to use.
+- Aim for a balance of completeness and speed: broaden when the first pass is empty, thin, conflicting, or a decision depends on the missing evidence; stop once the artifact is grounded.
+- If a material dependency category isn't available, or a material search returned no useful context, communicate the practical limitation to the user.
 
-## user-context
+#### 3. First Output
+- After sufficient context has been gathered, provide the user with an output
+- Default to providing output in chat, but if the skill or user instruction prefers another output like html or a document, use that instead.
+- Remember to identify the likely underlying user goal behind the request and try to address that, as well as satisfying their object-level request.
+- Below are two common elements that should be used by default in all outputs.
 
-Preflight, manage, or answer from Sales plugin user context. Route here before Sales workflows to load saved context, and for direct remember, save, recall, inspect, setup, customization, context-maintenance, or approved Sales Company Research save requests. This skill owns Sales plugin-scoped user context and memory policy.
+##### Limitations and Improvements
 
-## salesforce
+The first output is "best effort" and tries to give the most useful response relative to the connectors and context available. In this output, you should give the user context on the strength of your answer and instruct how it can be improved through installing new connectors.
 
-Salesforce connector guide for Sales workflows that use Salesforce as the selected `crm`. Route here only as a helper after another focused sales workflow has selected Salesforce, or when the user explicitly asks for Sales Salesforce CRM connector rules. Do not route here for non-Salesforce CRM use.
+Details:
+- Start with 1–2 sentences describing the answer’s strengths, grounding, and known gaps.
+- For available connectors with no relevant evidence, mention what was checked and what was or wasn't found.
+- For unresolved non-blocking categories, state the practical limitation and name a suitable provider as an optional improvement when one is available. Offer its install/connect UI only after the first output, normally in Next Steps.
+- For unresolved blocking categories, report whether a provider was offered, no suitable provider was found, or the install/connect attempt failed or was declined. Mention the plugin-page, pasted/uploaded-context, or IT fallback only after no suitable installable provider was found or the install/connect attempt failed or was declined.
 
-## get-rep-call-feedback
+**Output Format**
+```
+## Confidence and Gaps
 
-Compare one rep's call history against peer examples to extract repeatable best practices and evidence-backed coaching feedback. Route here for peer-benchmark coaching requests that name a target rep and peer set or ask to derive peer exemplars.
+This brief provides a solid orientation from the invite and shared notes, but it does not yet capture prior-call decisions, unresolved commitments, or authoritative account and opportunity context.
 
-## review-rep-call-trends
+Potentially helpful context:
 
-Analyze one rep's recent calls over time to detect improvement, regression, and stable patterns with objective evidence and practical coaching actions. Route here for trend-oriented coaching, progress checks, or "how has this rep changed?" requests.
+1. **[Category]:** [Provider] is available to install and could add [specific missing evidence].
+2. **[Category]:** No suitable installable provider was found, so [plugin-page, pasted/uploaded-context, or IT fallback] is the next path for [specific missing evidence].
+```
 
-## build-business-case
+##### 4. Next Steps
+- *Always* offer one clear next step to help the user get more value and discover useful adjacent functionality.
+- When the focused skill provides `Next Step Options`, choose the single most relevant transition from that list. Do not present the whole menu, offer an action already completed, or suggest an action that conflicts with the workflow's ownership or safety rules.
+- When the focused skill does not provide options, use your judgment. Some common fallback options:
+  - Install new connectors if they could materially improve output quality
+  - Iterate on and improve the output
+  - Create a document, presentation, spreadsheet, or html report
+  - Draft response(s) in Slack or Email to help with next steps
+  - Take another action in a relevant tool
+  - Set up an automation to follow up or refresh the output in the future
 
-Build customer-led business cases, ROI narratives, value models, executive summaries, customer-ready value stories, and follow-up question sets from uneven customer context, metrics, transcripts, notes, and public evidence. Route here when the user needs credible customer-value reasoning rather than generic positioning.
+**Output Format**
 
-## zoominfo
+```
+{other message outputs}
 
-ZoomInfo provider guide for Sales enrichment, company/contact research, lookalikes, contact recommendations, intent signals, and result-quality recovery. Route here only as a helper after `enrich-company-and-contact-data` or another focused workflow has selected ZoomInfo, or when the user explicitly asks for Sales ZoomInfo connector rules.
+Anything you'd change, or would you like me to [single most relevant next step]?
+```
diff --git a/plugins/sales/skills/index/agents/openai.yaml b/plugins/sales/skills/index/agents/openai.yaml
index f2b27f0..bb4f976 100644
--- a/plugins/sales/skills/index/agents/openai.yaml
+++ b/plugins/sales/skills/index/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Index"
-  short_description: "Route broad sales workflow and setup requests"
+  short_description: "Sales workflows for meetings, accounts, and deals"
   default_prompt: "Help me choose the right Sales workflow for this sales task."
 policy:
   allow_implicit_invocation: true
diff --git a/plugins/sales/skills/index/references/orientation-response.md b/plugins/sales/skills/index/references/orientation-response.md
new file mode 100644
index 0000000..e30d98f
--- /dev/null
+++ b/plugins/sales/skills/index/references/orientation-response.md
@@ -0,0 +1,25 @@
+Sales turns customer, company, conversation, and pipeline context into practical next steps. It can help you prepare for meetings, follow up after calls, research accounts, prioritize opportunities, build business cases, review pipeline risk, and find the right customer evidence or internal answers.
+
+## Simpler Use Cases
+
+These workflows usually work well with a basic set of connectors—and they also work if you paste in notes, a transcript, an account brief, or other relevant context.
+
+| Use case | Sample prompt |
+| --- | --- |
+| Prepare for an important meeting | @Sales prepare me for my next customer meeting. Give me the context, likely blockers, desired outcomes, and questions I should ask. |
+| Follow up after a customer call | @Sales turn these call notes into a recap, clear next steps, and a customer-ready follow-up email. |
+| Research a company and draft outreach | @Sales research this company and suggest a relevant outreach angle, then draft a concise first message. |
+| Find an internal answer for a customer | @Sales a customer asked about data residency. Find the best internal sources and draft the answer I should send. |
+
+## More Advanced Use Cases
+
+These workflows become more powerful with specialized connectors such as CRM, customer activity, data enrichment, or call recordings. If a needed source is not enabled, ask your admin to connect it.
+
+| Use case | Sample prompt |
+| --- | --- |
+| Prioritize accounts | @Sales which accounts should I focus on this week? Rank them, explain why, and recommend the next move for each. |
+| Understand what changed in an account | @Sales review my most important active account. Tell me what changed, what matters, and what I should do next. |
+| Triage pipeline and forecast risk | @Sales review my open pipeline for this quarter. Rank deals likely to slip and recommend the next action for each. |
+| Pull customer evidence from calls | @Sales find five verbatim customer quotes about setup friction from recent calls and summarize the pattern they show. |
+
+Name a use case and I’ll tee up a personalized sample prompt to show it in action.
diff --git a/plugins/sales/skills/plan-deal-strategy/SKILL.md b/plugins/sales/skills/plan-deal-strategy/SKILL.md
index 5bb1649..6bb9e8a 100644
--- a/plugins/sales/skills/plan-deal-strategy/SKILL.md
+++ b/plugins/sales/skills/plan-deal-strategy/SKILL.md
@@ -1,396 +1,177 @@
 ---
 name: plan-deal-strategy
-description: Build a post-discovery deal strategy pack with a deal map, buying committee map, procurement risk register, and prioritized next actions from grounded deal evidence.
+description: Use when the user wants strategy for one active deal, renewal, negotiation, buying process, or initial sales motion for an offer or product. Build a grounded deal map or practical sales plan with objections, sequencing, posture, and prioritized next actions. Use prepare-for-meeting instead for multi-account same-day customer call queues.
 ---
 
 # Plan Deal Strategy
 
-Use this skill for post-discovery deal strategy, not first-call prep.
 
-This skill turns grounded deal evidence into a practical strategy pack for moving an active account forward. It is for situations where deal evidence likely exists in CRM, meeting notes, external or internal messaging threads, docs, calendar milestones, or user-provided notes, and the user needs a concrete plan to advance or unblock the deal.
+## Context-Gathering Intake
 
-Do not use this skill for:
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-- first discovery calls with no prior account context
-- generic prospecting
-- meeting prep that only needs an agenda and attendee context
+Turn active-deal evidence or clearly supplied offer context into a practical strategy for advancing a sale or shaping an initial sales motion. This skill owns the deal map, buying committee, negotiation posture, objections, sequencing, and action plan; it is not first-call prep, data-first prospecting, or a writeback workflow.
 
-For those requests, route to `prepare-for-meeting` when there is a customer meeting anchor, or ask for basic account/context notes and produce lightweight manual prep instead.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### User Context
+## Key Dependency Categories
 
-Mandatory pre-answer gate:
+These categories are particularly important for this workflow; use other sources only when they materially improve the strategy.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the strategy pack.
+- [Blocking] ~~CRM for authoritative account and opportunity identity, stage, amount, close date, owner, contacts, and recent activity. It blocks the default active-deal strategy path unless sufficiently grounded deal truth is already in context.
+- ~~Meeting Transcripts for stakeholder language, objections, commitments, decisions, procurement signals, and call continuity
+- ~~Email for customer-facing progression, approvals, negotiation, legal/procurement exchange, and unanswered asks
+- ~~Internal Messaging for internal blockers, owner alignment, escalation paths, and execution risk
+- ~~Knowledge & Files for account plans, mutual action plans, implementation docs, risk logs, and strategy artifacts
+- ~~Calendar for upcoming decision forums, executive meetings, procurement/legal milestones, and timing pressure
+- ~~Sales Intelligence only when it sharpens a stakeholder or company hypothesis after the deal is anchored
 
-### Audience And Language
+Keep ~~CRM authoritative for CRM-owned deal truth. Supporting sources sharpen risks, stakeholders, timing, and actions; they do not invent or override stage, amount, owner, close date, or active-deal presence.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Reference Loading
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+`SKILL.md` owns the normal deal-strategy flow and output package. Load references only when their extra detail matters:
 
-### Source Resolution
+- Use [references/request-schema.yaml](references/request-schema.yaml) for structured input, legacy aliases, mode enums, stage hints, time-window bounds, or seed-evidence normalization.
+- Use [references/risk-rubric.md](references/risk-rubric.md) when risk type, severity, likelihood, confidence, prioritization, or mitigation fields are ambiguous.
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Workflow Guidance
 
-### Required Inputs
+### 1. Qualify and choose a mode
 
-This skill needs enough user-provided or explicitly inferable context to identify an active account, opportunity, renewal, buying process, or deal. Source access can suggest concrete candidates, enrich, or verify the information, but do not draft the full strategy pack until a specific deal/account anchor is selected, clearly inferable, or sufficiently supplied by the user.
+- Use this only when there is an active account, opportunity, renewal, buying process, or sufficiently detailed deal context after discovery.
+- Seek enough evidence to identify the current deal motion and at least one blocker, stakeholder question, or next-action need. If the anchor is sufficient but evidence is thin, produce a low-confidence pack with explicit gaps rather than inventing detail.
+- If the request is first-call prep with little prior evidence, route to `prepare-for-meeting` when a meeting anchor exists; otherwise ask for basic account context and provide lightweight manual prep.
+- Default to `Full Strategy Pack`. If the user asks for only one artifact, use the matching partial mode and keep the investigation scoped to it.
 
-The skill can still produce a limited strategy pack from pasted notes, uploaded/exported context, or user-linked context alone when that context is sufficient. Connectors add significant value and reduce the burden on the user, but do not stop solely because connectors are unavailable.
+### 2. Resolve the deal anchor
 
-**Inputs:**
+- Start with the user-named account, opportunity, renewal, initiative, link, or notes.
+- Accept `sfdc_account_id` as a legacy alias for `crm_account_id`.
+- When available, use ~~CRM first to identify the account and the opportunity that best matches the named product, solution, renewal, stakeholders, timing, and recent activity.
+- If the user asks for deal strategy but did not supply an account, opportunity, renewal, initiative, link, or notes, use ~~CRM first to search, rank, and offer up to five concrete current-opportunity candidates via `ask_user_input()`. For a bare invocation such as “Plan the deal strategy for my most appropriate current opportunity,” do not assume a target; offer the connector-backed candidates and ask the user to choose before broad enrichment or drafting.
+- Build a small alias set from the selected opportunity name, product or solution terms, activity titles, and user wording. Use it to validate related meetings, threads, and docs and reject same-account artifacts for a different deal motion.
+- If multiple plausible deals remain, make at most three source reads to offer up to five concrete candidates via `ask_user_input()`, then ask the user to choose before broad enrichment. If exactly one inferred candidate remains, ask the user to confirm it via `ask_user_input()` before broad enrichment or drafting.
+- If CRM is unavailable but user-provided evidence is sufficient, continue with a clear CRM gap. Do not treat same-account evidence for another opportunity as proof for the selected motion.
 
-- [required] Active account, opportunity, renewal, buying process, or sufficiently detailed deal notes
-- [required] Enough evidence to identify the current deal motion, blocker, stakeholder question, or next-action need
-- CRM account or opportunity identity when available
-- Time window, defaulting to the last 90 days unless the user provides a narrower or broader window
-- Output mode, defaulting to `Full Strategy Pack`
+### 3. Gather evidence by lane
 
-Supported modes:
+- Use the selected account and deal-motion aliases to find relevant recent evidence in ~~Meeting Transcripts, ~~Email, ~~Internal Messaging, ~~Knowledge & Files, and ~~Calendar.
+- Prefer the narrowest relevant window; default to the last 90 days unless the user provides another window.
+- Do not require transcript, thread, or document links up front; use the selected account and deal-motion aliases to discover relevant artifacts.
+- Read enough to support the current motion, stakeholder posture, procurement risks, and next actions. If the first same-account artifact belongs to another deal motion, continue to the next targeted result when available; otherwise skip it, label the gap, or ask the user to choose.
+- Record each lane as available, unavailable, empty, stale, conflicting, or user-provided-only. Stop once the core strategy is supported; do not chase indirect substitutes for unavailable source truth.
 
-- `Full Strategy Pack`
-- `Deal Map Only`
-- `Buying Committee Only`
-- `Procurement Risk Only`
-- `Next Actions Only`
+### 4. Build the strategy
 
-Use [references/request-schema.yaml](references/request-schema.yaml) when structured input validation, YAML normalization, or machine-readable field shape matters. Preserve `sfdc_account_id` as a legacy alias for `crm_account_id`.
+- Build a concise deal map: initiative, target outcome, motion/timeline, active workstreams, recent account or service context that materially changes deal execution when evidenced, blockers, and dependencies.
+- Build the buying committee from evidenced people and roles. Use roles `economic_buyer`, `decision_maker`, `champion`, `influencer`, `blocker`, `procurement`, `legal`, `security`, or `unknown`; stance `supportive`, `neutral`, `skeptical`, `blocking`, or `unknown`; and influence `high`, `medium`, `low`, or `unknown`. Label inferred role, stance, or influence with `Inference:`.
+- Build procurement risks with severity, likelihood, confidence, owner or suggested owner, mitigation, timing, and source. Load `references/risk-rubric.md` when classification or prioritization is ambiguous.
+- Produce 5-7 high-signal actions by default. Each action needs an owner or suggested owner, due date or `TBD`, expected outcome, linked risk/stakeholder, and source.
 
-If required inputs remain missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. First attempt a bounded candidate pass through the source category that owns the missing anchor. Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. For account and opportunity selection, start with `crm` when available, then use user-provided context, `calendar`, `meeting_notes`, `document_store`, `external_messaging`, or `internal_messaging` as supporting evidence only when that fits the budget. Before asking the user to paste deal notes or name an account/opportunity manually, either offer concrete candidates from those source categories or say which attempted categories were unavailable or empty. Ask one friendly clarification only after that pass. Ask only for unresolved inputs; for each question, include up to 5 concrete lettered options from source-backed or user-provided candidates when available. If no concrete candidates are available, briefly say why and ask free-form.
+### 5. Separate evidence from inference
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+- Cite useful source links inline when available; use plain source labels only when no stable link exists.
+- Do not link meeting join URLs, generic room URLs, or opaque connector ids as evidence.
+- If evidence conflicts, lower confidence and add an evidence gap instead of forcing a conclusion.
+- Use exact dates and confirmed customer-side owners only when directly evidenced. Otherwise use relative timing, `TBD`, or `Suggested owner:`; avoid exact-day due dates when confidence is low or evidence is sparse.
+- Use public or user-provided market context only when it sharpens a company or stakeholder hypothesis after the deal is anchored. Treat it as supporting context, never a hard dependency or override for account/deal truth.
 
-**Input Request Format Example:**
+### Next Step Options
 
-```md
-Which account or active deal should I build the strategy around?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Stop the workflow only when no account, deal, renewal, buying process, or sufficiently detailed user-provided deal context can be identified, or when the user requests write actions without giving the needed destination and permission.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `crm`: authoritative account and opportunity identity, stage, close date, amount, known contacts, owner, account status, recent CRM activity, customer-health context, and other opportunity metadata.
-- `meeting_notes`: transcript-backed stakeholder language, objections, commitments, decisions, follow-ups, procurement/security/legal signals, and call continuity.
-- `external_messaging`: customer-facing thread progression, approvals, negotiation movement, procurement/legal exchange, unanswered asks, tone shifts, attachments, and promised next steps.
-- `internal_messaging`: internal blocker visibility, owner alignment, deal coordination, execution risk, escalation paths, and confidence shifts. Do not use it to override `crm` truth.
-- `document_store`: account strategy docs, mutual action plans, implementation docs, risk logs, team planning artifacts, relevant assets, and account notes.
-- `calendar`: upcoming decision forums, executive meetings, procurement/legal milestones, timing pressure, and near-term follow-up moments.
-- user-provided enrichment exports or public research: optional structured company, contact, usage, market, or public context that can sharpen committee hypotheses. Never treat this as a blocker for completion.
-
-These are semantic source categories, not fixed connector names. Resolve each attempted source to the specific available app, connector, or helper skill through Sales preflight `context.sources` before using connector-specific logic.
-
-Authority and gaps:
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Turn the prioritized actions into a mutual action plan or execution tracker.
+- Prepare the next customer, partner, or internal meeting around the highest-risk decision.
+- Draft stakeholder-specific email or Slack language for review.
+- Draft CRM-ready opportunity updates for review.
+- Deepen one blocker, stakeholder, or procurement risk where evidence is still thin.
 
-- Use each source category for the facts it owns.
-- For active account/deal strategy, `crm` is required when CRM is available and an account, opportunity, renewal, or buying-process anchor is supplied, inferred, or confirmed. Keep CRM truth authoritative for account/opportunity metadata, ownership, commercial posture, customer status, and recent CRM activity; do not let docs, messages, notes, public context, or third-party enrichment override CRM-owned deal truth.
-- Use `meeting_notes`, `external_messaging`, `internal_messaging`, `document_store`, and `calendar` to sharpen risks, stakeholders, timing, and next actions; do not use them to invent opportunity status, ownership, stage, amount, close date, renewal posture, or active deal presence.
-- When a specific opportunity, renewal, product, or solution is selected, supporting evidence must match that deal motion before it shapes the plan. Account-level evidence is useful background, but same-account transcripts, threads, docs, or meetings that point to a different active opportunity should be skipped or labeled as a gap.
-- Treat user-provided notes, uploads, exports, and linked context as valid working evidence; label limitations when they cannot be verified against connector sources.
-- If a source is unavailable, blocked, stale, ambiguous, or missing required fields, name the gap and either ask for access/input, proceed with a clear limitation, or omit the unsupported claim.
-- If evidence conflicts, lower confidence and add an evidence gap rather than forcing a false resolution.
-- If discovery yields sparse evidence, continue with a low-confidence output when the user-provided or discovered anchor is sufficient.
+Next steps to avoid:
+- Sending messages, updating CRM, or creating downstream artifacts without a separate explicit request.
 
-### Context Gathering Principles
+## Modes
 
-Optimize for marginal value, not absolute speed.
+- `Full Strategy Pack` — default; return every section below.
+- `Deal Map Only` — return Deal Snapshot, Deal Map, Evidence Gaps, and Inference Notes.
+- `Buying Committee Only` — return Deal Snapshot, Buying Committee Map, Evidence Gaps, and Inference Notes.
+- `Procurement Risk Only` — return Deal Snapshot, Procurement Risk Register, Evidence Gaps, and Inference Notes.
+- `Next Actions Only` — return Deal Snapshot, Prioritized Next Actions, Evidence Gaps, and Inference Notes.
 
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
+## Output Format
 
-### First-Run Banner
-
-If Sales preflight says the plan-deal-strategy experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+For a full strategy pack, return sections in this exact order.
 
 ```md
-## Plan Deal Strategy
-This skill helps turn grounded deal evidence into a practical strategy pack for moving an active account forward. It can use CRM, meeting notes, transcripts, account docs, email, Slack, competitive context, and user-provided deal notes.
-
-Definitions:
-- Buying committee map: a view of the people who influence, approve, use, block, or sign off on the purchase, including their role in the decision.
-- Economic buyer: the person with budget authority or final business accountability for the purchase.
-- Champion: a customer-side advocate who wants the solution to succeed and can help navigate the organization.
-- Procurement risk register: a list of process, legal, budget, security, commercial, or timing issues that could slow or stop the deal.
-- Mutual action plan: a shared customer-and-seller plan with owners, milestones, and dates for reaching a decision or launch.
-
-```
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-- Do not offer doc creation, CRM updates, plugin memory setup, or weekly discovery in that same response.
-
-### Next Step Guidance
-
-This section is the single owner for Plan Deal Strategy next-step behavior and action text. Onboarding, artifact creation, draft creation, CRM writeback, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the deal-strategy continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `plan-deal-strategy` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the strategy pack** | The first substantive strategy pack has just been produced. | `Anything you'd change in the strategy pack before we make it operational?` |
-| **2. Review the revised strategy pack** | The user asked for any change to the strategy pack, including a small rewrite, added risk, source adjustment, shorter summary, or formatting tweak. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to turn it into an owner/action tracker, MAP outline, internal recap, or CRM-ready update?` |
-| **3. Prepare the action artifact** | The user accepts the strategy, says no changes are needed, says a revision looks good, or asks to operationalize the plan. | Offer to create the most useful downstream artifact or draft action, prioritizing an owner/action tracker, MAP outline, internal recap, customer follow-up draft, or CRM-ready update over broad suggestions. |
-| **4. Review the artifact or draft** | The tracker, MAP outline, recap, follow-up draft, or CRM-ready update exists, or the user asks to change it. | `Anything you'd change before I save, share, post, or write this where it belongs?` |
-| **5. Post, share, save, or write approved action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the tracker, posting the internal recap, drafting the customer note in the external messaging tool, or writing the approved CRM update. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the strategy pack, treat a lightweight acknowledgement as acceptance and move to `Prepare the action artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to post, share, save, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-## Procedure
-
-Run these steps in order.
-
-### 1) Qualify the request
-
-- If the user asks for prep for an upcoming first call with little/no prior evidence, do not run this skill.
-- Return a one-line reason and route to `prepare-for-meeting` when there is a customer meeting anchor, or ask for basic account/context notes and produce lightweight manual prep instead.
-- If the user asks for a partial artifact, use the matching mode and keep the rest of the workflow scoped to that artifact.
-
-### 2) Resolve account and deal anchor
-
-- Resolve account identity from `account_name`, `crm_account_id`, user-provided notes, linked context, or a source-backed candidate.
-- Accept `sfdc_account_id` as a legacy alias for `crm_account_id` when older inputs use it.
-- Resolve the best opportunity anchor when possible from `opportunity_id`, `opportunity_name`, ranked CRM candidates, or clearly supplied user context.
-- When the user names both an account and opportunity, product, solution, renewal, or initiative, use the named deal motion to choose among same-account CRM opportunities before gathering supporting evidence.
-- Build a small alias set from the selected opportunity name, product or solution terms, CRM activity titles, and user wording. Use those aliases to validate related meetings, transcripts, threads, and docs.
-- Pull `crm` truth first when available for stage, amount, close date, known contacts, and recent account activity or customer-health context that materially changes deal strategy.
-- If CRM is unavailable but the user has provided sufficient deal context, continue and record the CRM gap in coverage.
-
-### 3) Pull evidence lanes
-
-Start from account identity and the selected opportunity or deal motion when available. For each enabled lane, proactively search or fetch relevant artifacts inside the requested time window. Treat user-provided links and notes as accelerators, not prerequisites.
-
-Expected lane behavior:
-
-- `crm` establishes deal truth and commercial context when available.
-- Recent account activity or customer-health context can sharpen blockers, urgency, and risk when directly relevant to the active deal.
-- `meeting_notes` contributes transcript-backed stakeholder language, objections, and commitments.
-- `external_messaging` contributes customer-facing thread progression, approvals, and procurement/legal motion.
-- `internal_messaging` contributes internal blockers, owner alignment, and execution risk.
-- `document_store` contributes account plans, MAP artifacts, and strategy docs.
-- `calendar` contributes near-term milestones and decision forums.
-- User-provided enrichment exports, public research, or market intelligence can add optional structured commercial context.
-
-Discovery requirements:
-
-- Do not require the user to provide transcript links, thread links, or document links upfront.
-- Use the account plus selected opportunity, product, solution, or initiative aliases to discover relevant artifacts in `meeting_notes`, `external_messaging`, `internal_messaging`, `document_store`, and `calendar` for the selected time window when sources are available.
-- If the first same-account artifact is about a different opportunity, do not use it as deal-plan evidence. Continue with the next targeted result when available; otherwise continue with a clear source gap or ask the user to choose among concrete candidates.
-- If a lane is unavailable or returns nothing relevant, continue and record the lane as unavailable, empty, or unverified in coverage.
-- Do not chase indirect, mirrored, or convoluted paths to reconstruct unavailable source truth.
-
-### 4) Build the deal map
+# Deal Strategy: [Account / Opportunity]
 
-Produce a concise map with:
+## Deal Snapshot
 
-- business initiative and target outcome
-- current stage and timeline posture
-- key active workstreams
-- recent account or service context that materially changes deal execution, when evidenced
-- top blockers and dependencies
+- **Account:** [Name + source]
+- **Opportunity:** [Name or Unknown]
+- **Stage:** [CRM-backed value or Unknown]
+- **Close Date:** [Evidenced date or Unknown]
+- **Time Window:** [Window]
+- **Run Mode:** [Mode]
+- **Coverage Summary:** [Available, unavailable, empty, stale, or user-provided-only lanes]
 
-Every material claim must be source-backed or explicitly labeled `Inference:`.
+## Deal Map
 
-### 5) Build the buying committee map
+- **Initiative:** [Business initiative]
+- **Target Outcome:** [Customer/business outcome]
+- **Current Motion:** [Stage, timeline posture, and deal movement]
+- **Active Workstreams:** [3-6 grounded bullets]
+- **Top Blockers or Dependencies:** [Up to 5 grounded bullets]
 
-For each material stakeholder, capture:
+## Buying Committee Map
 
-- person, title, and org area
-- role in deal (`economic_buyer`, `decision_maker`, `champion`, `influencer`, `blocker`, `procurement`, `legal`, `security`, `unknown`)
-- stance (`supportive`, `neutral`, `skeptical`, `blocking`, `unknown`)
-- influence (`high`, `medium`, `low`, `unknown`)
-- confidence (`high`, `medium`, `low`) with source evidence
+| Stakeholder | Title | Org | Role | Stance | Influence | Last Signal | Confidence | Source |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| [Name] | [Title] | [Org] | [Role] | [Stance] | [Influence] | [Signal] | [High/Medium/Low] | [Link/label] |
 
-If role or stance is inferred rather than directly stated, label it `Inference:`.
+## Procurement Risk Register
 
-### 6) Build procurement risk register
+| Risk ID | Risk Summary | Risk Type | Severity | Likelihood | Owner or Suggested Owner | Mitigation | Target Date | Confidence | Source |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| R1 | [Risk] | [Type] | [High/Medium/Low] | [High/Medium/Low] | [Owner] | [Step] | [Date/TBD] | [High/Medium/Low] | [Link/label] |
 
-Cover at minimum:
+## Prioritized Next Actions
 
-- risk summary
-- risk type
-- severity and likelihood
-- source evidence
-- owner
-- mitigation step
-- target date or `TBD`
+1. **Action:** [Concrete action]
+   - **Owner or Suggested Owner:** [Owner]
+   - **Due:** [Date, relative timing, or TBD]
+   - **Linked Risk IDs:** [IDs or None]
+   - **Linked Stakeholders:** [Names or None]
+   - **Expected Outcome:** [Outcome]
+   - **Source:** [Link/label]
 
-Use [references/risk-rubric.md](references/risk-rubric.md) when risk classification, severity/likelihood, confidence, prioritization, or mitigation fields are ambiguous.
+## Evidence Gaps
 
-Owner and timeline precision rules:
+- **Gap:** [Material missing/conflicting evidence]
+  - **Impact:** [How confidence or execution is affected]
+  - **Smallest Next Collection Step:** [Most efficient way to close it]
 
-- Use `Owner:` only when ownership is explicitly supported by evidence.
-- If customer-side ownership is inferred or unconfirmed, label it `Suggested owner:`.
-- Use exact calendar dates only when tied to explicit evidence such as a calendar event, stated deadline, or documented commitment.
-- If no explicit date exists, use relative timing (`this week`, `next week`, `this month`) or `TBD`.
+## Inference Notes
 
-### 7) Generate prioritized next actions
+- Inference: [Claim inferred rather than directly stated, with basis]
 
-Create actions that are concrete and owner-bound.
-
-Each action must include:
-
-- action statement
-- owner
-- due date or `TBD`
-- expected outcome
-- linked stakeholder(s) and/or linked risk(s)
-- source reference
-
-Owner and due-date precision rules:
-
-- For customer-side actions without explicit ownership confirmation, use `Suggested owner:` instead of asserting ownership.
-- Prefer relative due windows over exact dates unless there is direct date evidence.
-- Avoid exact-day precision when confidence is `low` or when evidence is sparse.
-
-Prefer 5 to 7 high-signal actions by default. Prefer fewer strong actions over broad advice.
-
-### 8) Optional public or user-provided market context
-
-Treat public research or user-provided market intelligence as optional context, not a hard dependency or assumed source category.
-
-When available and useful, use it to:
-
-- enrich company firmographics and context
-- validate or refine stakeholder hypotheses
-- add external context that sharpens committee mapping
-
-If unavailable, continue with internal evidence, user-provided context, or both, and keep confidence explicit.
-
-### 9) Cite and separate evidence from inference
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
-
-Do not link to meeting join URLs, generic room links, or opaque connector refetch IDs.
-
-## Output Shapes
-
-Use human-readable labels in Title Case for normal output. Use `snake_case` keys only when the user explicitly asks for JSON, YAML, or machine-readable output.
-
-Return sections in this exact order for a full strategy pack:
-
-1. `Deal Snapshot`
-2. `Deal Map`
-3. `Buying Committee Map`
-4. `Procurement Risk Register`
-5. `Prioritized Next Actions`
-6. `Evidence Gaps`
-7. `Inference Notes`
-
-For partial modes, return only the requested artifact plus `Evidence Gaps`, `Inference Notes`, and `Sources` when useful.
-
-### Deal Snapshot
-
-Include:
-
-- Account
-- Opportunity
-- Stage
-- Close Date
-- Time Window
-- Run Mode
-- Coverage Summary, including which source lanes were available, unavailable, empty, or user-provided-only
-
-### Deal Map
-
-Keep this compact and execution-oriented:
-
-- Initiative
-- Target Outcome
-- Current Motion
-- Active Workstreams, 3 to 6 bullets
-- Top Blockers or Dependencies, up to 5 bullets
-
-### Buying Committee Map
-
-Return a table with these columns:
-
-- Stakeholder
-- Title
-- Org
-- Role
-- Stance
-- Influence
-- Last Signal
-- Confidence
-- Source
-
-### Procurement Risk Register
-
-Return a table with these columns:
-
-- Risk ID
-- Risk Summary
-- Risk Type
-- Severity
-- Likelihood
-- Owner or Suggested Owner
-- Mitigation
-- Target Date
-- Confidence
-- Source
-
-### Prioritized Next Actions
-
-Return a numbered list. Each action must include:
-
-- Action
-- Owner or Suggested Owner
-- Due
-- Linked Risk IDs
-- Linked Stakeholders
-- Expected Outcome
-- Source
-
-### Evidence Gaps
-
-List only material gaps that lower confidence or block execution:
-
-- Gap
-- Impact
-- Smallest Next Collection Step
+---
 
-### Inference Notes
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-List only claims that were inferred rather than directly stated. Prefix each with `Inference:`.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
 ## Rules
 
 - Do not fabricate stakeholders, approvals, blockers, owners, commitments, amounts, stages, close dates, or exact dates.
-- Keep `crm` truth authoritative for opportunity metadata when available.
-- Use supporting lanes to sharpen risk and action decisions, not to override authoritative CRM facts.
-- Keep facts and inference separate.
-- If evidence is conflicting, lower confidence and add an evidence gap.
-- If discovery yields sparse evidence, continue with a low-confidence output and explicit evidence gaps when the account/deal anchor is sufficient.
-- Use human-readable labels in Title Case in normal output.
-- Keep this skill read-only unless the user explicitly asks for write actions in downstream systems.
+- Keep facts and inference separate; omit empty inference notes rather than padding them.
+- Prefer fewer strong actions over broad advice, and prioritize the risks most likely to block signature or slip timing.
+- Use human-readable Title Case labels in normal output; use machine-readable keys only when the user asks for JSON or YAML.
+- Keep this workflow read-only. Do not update CRM, send messages, post recaps, or create downstream artifacts unless the user explicitly asks in a later step.
+- If evidence is sparse but the deal anchor is sufficient, produce a low-confidence pack with explicit gaps rather than stopping.
+
+## Failure Handling
+
+If no credible active-deal anchor exists, state the blocker, offer concrete candidates when available, and ask for the smallest missing input. If optional lanes are unavailable, continue with the strongest grounded evidence and make the resulting confidence limits visible.
diff --git a/plugins/sales/skills/plan-deal-strategy/agents/openai.yaml b/plugins/sales/skills/plan-deal-strategy/agents/openai.yaml
index f7329c1..e2e4be9 100644
--- a/plugins/sales/skills/plan-deal-strategy/agents/openai.yaml
+++ b/plugins/sales/skills/plan-deal-strategy/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Plan Deal Strategy"
-  short_description: "Post-discovery deal strategy from grounded evidence"
+  short_description: "Grounded strategies for active deals and sales motions"
   default_prompt: "Build a deal strategy for an active account."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/plan-deal-strategy/references/request-schema.yaml b/plugins/sales/skills/plan-deal-strategy/references/request-schema.yaml
index fd5747d..50a74c8 100644
--- a/plugins/sales/skills/plan-deal-strategy/references/request-schema.yaml
+++ b/plugins/sales/skills/plan-deal-strategy/references/request-schema.yaml
@@ -7,13 +7,13 @@ properties:
     description: Customer account name.
   crm_account_id:
     type: string
-    description: "`crm` account ID if known."
+    description: "~~CRM account ID if known."
   sfdc_account_id:
     type: string
     description: "Legacy alias for `crm_account_id`; normalize to `crm_account_id` before use."
   opportunity_id:
     type: string
-    description: "`crm` opportunity ID if known."
+    description: "~~CRM opportunity ID if known."
   opportunity_name:
     type: string
   time_window_days:
diff --git a/plugins/sales/skills/prepare-for-meeting/SKILL.md b/plugins/sales/skills/prepare-for-meeting/SKILL.md
index d495901..7dbae5e 100644
--- a/plugins/sales/skills/prepare-for-meeting/SKILL.md
+++ b/plugins/sales/skills/prepare-for-meeting/SKILL.md
@@ -1,290 +1,179 @@
 ---
 name: prepare-for-meeting
-description: Create concise pre-meeting briefs and daily prep for customer meetings or the user's most important meeting of the day, using authoritative internal context plus supplementary public web enrichment.
+description: "Use when the user wants to prepare for one or more upcoming meetings with customers, prospects, partners, accounts, or important stakeholders; requests daily or date-based meeting preparation; asks to organize a same-day customer call queue; asks for the most important qualifying meeting of the day; or identifies meetings only by company, attendee, topic, or date. Produce an individual meeting brief or a chronological multi-meeting agenda as appropriate."
 ---
 
-# Prepare For Meeting
+# Prepare for Meeting
 
-Prepare a seller or account director for an upcoming customer meeting, or for the most important meeting of the day in scheduled mode. Optimize for the meeting in front of the user: agenda, decisions, high-priority topics, likely blockers, and next actions. Keep broader account context short and subordinate.
 
-A single-meeting brief must use the provided `Output Shapes` format exactly. Fold broader account, product, deal, support, partnership, or public-company context into `Background Context` only when it changes how the user should handle the meeting.
+## Context-Gathering Intake
 
-Use this skill primarily for customer-facing or account-related meetings. In scheduled mode, prefer customer/account meetings; if none qualify, choose only the most important non-routine work meeting where prep would materially help. Do not choose routine internal team meetings, 1:1s, interviews, performance conversations, or generic meetings unless the user explicitly asks or the evidence shows clear business stakes.
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-## Skill Configuration
+Prepare a sales person for a critical customer or internal meeting, with all relevant context needed to help them reach the stated or assumed goal of the meeting.
 
-### User Context
+## Common Skill Instructions
 
-Mandatory pre-answer gate:
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the brief.
+## Meeting Ranking
 
-### Audience And Language
+Use this priority order for selecting meetings if the specific meeting is ambiguous.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+1. Explicit user request, named meeting, named account, named topic, or stated preference
+2. Meetings the user owns, leads, presents in, or is expected to drive
+3. Customer, prospect, partner, renewal, opportunity, or strategic account meetings
+4. Large or cross-stakeholder internal meetings with decisions, dependencies, launches, escalations, or leadership visibility
+5. Meetings with buyer, executive, technical decision-maker, or senior stakeholder attendance
+6. Meetings with explicit decisions, blockers, risks, commitments, or time-sensitive next steps
+7. Meetings where prep is likely to materially improve the user’s next action
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+Routine internal syncs, 1:1s, recruiting meetings, and performance discussions are lower priority, but may still qualify when explicitly requested, user-owned, or clearly high-stakes.
 
-### Source Resolution
+## Key Dependency Categories
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
 
-### Required Inputs
+- [Blocking] ~~Calendar for meeting identity, invite context, agenda, and attendees. It blocks only when the meeting must be discovered or selected.
+- ~~Meeting Transcripts for prior decisions, objections, commitments, and continuity
+- ~~Email for prior meeting notes, recent questions, and customer-facing context
+- ~~Internal Messaging for internal strategy, blockers, owners, and dependencies
+- ~~Knowledge & Files for prior meeting notes, strategy docs, and assets
+- ~~CRM for account, opportunity, renewal, and contact truth
+- ~~Sales Intelligence only when it materially improves preparation
 
-This skill needs enough user-provided context to identify the required information. Source access can suggest concrete candidates, enrich, or verify the information, but do not draft the full brief until a specific meeting/account anchor is selected or clearly inferable, supplied, or confirmed by the user.
+Avoid unsupported claims. If context is missing, stale, or conflicting, state the limitation.
 
-The skill can still produce a limited brief from user-provided details alone, but connectors add significant value and reduce burden on the user to manually provide context. Do not stop solely because connectors are unavailable.
+## Workflow Guidance
 
-**Inputs:**
+These meeting-specific steps modify and override the default workflow in the index skill.
 
-- [required] Company, account, key attendee, meeting title, invite, or date/time
-- [required] Meeting attendees or attendee organizations
-- [required] Meeting goal, agenda, topic, or meeting type
-- Past context, transcripts, notes, emails, docs, or CRM context when available
+- 1. Resolve Dependencies and Clarify
+    - If the mode isn't clear, ask the user via `ask_user_input()`. For a bare invocation such as “run the sales meeting prep workflow,” do not assume upcoming-meeting prep; offer: `Prep for an upcoming meeting`, `Overview of today's meetings`, and `Overview of tomorrow's meetings`.
+    - If intent is "prep for an upcoming meeting" and the user did not supply a meeting, account, attendee, topic, or date, search, rank, and offer up to three candidates via `ask_user_input()`. Do not retrieve deeper context or draft a brief until the user selects one. All suggested events must be in the future relative to the user's current time.
+        - This search should just be for the next 3 business days, with 25 max results, and no broad free-text query unless the user supplied an account, attendee, topic, or keyword
+    - Resolve the account or workstream from the user request, invite, attendees, attendee domains, and nearby context. If CRM has multiple opportunities, use the one that matches the meeting topic, attendees, product, and recent activity; do not let an unrelated same-account opportunity drive the brief. If no credible anchor exists, state the gap instead of inventing one.
+    - After the first draft, offer the most relevant follow-up from the Next Step Options below.
 
-**Context Rules:**
+### Next Step Options
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup before relying on calendar, docs, internal messages, meeting notes, or public context alone. If CRM returns one high-confidence account match, use it as the primary account candidate; if multiple plausible account matches remain, ask the user to choose from concrete candidates.
-- Before asking the user to name a meeting, paste an invite, or provide attendee/agenda details, first try the workflow source category that owns the missing meeting anchor: `calendar` for meeting candidates, titles, attendees, timing, invite body, and agenda. For customer/account ambiguity, try `crm` before relying on `document_store`, `internal_messaging`, `meeting_notes`, or public context alone. Manual meeting details are the fallback after those categories are unavailable, empty, or insufficient.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+Use these as high-value transitions. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Install new connectors if they could materially improve output quality
+- Improve or refine the brief based on the user's guidance.
+- Add the prep to an existing meeting note or create a new meeting document as a pre-read. If needed and possible, offer to attach the document to the calendar invite. Be mindful of the broader audience when creating this prep doc; don't add your draft verbatim.
+- Draft a concise Slack or email update for attendees, owners, or internal stakeholders.
+- For `Daily Prep Digest` outputs, check whether a matching daily automation already reruns this `prepare-for-meeting` skill in that mode; if none exists, offer to create one that gives the seller a morning view of today's customer meetings, watchouts, and suggested closes.
+- Set a heartbeat automation to follow-up with summary and action items after the meeting.
 
-If required inputs remain missing or ambiguous, first attempt a bounded candidate pass through the source category that owns the missing input, within the Fast Candidate Resolution budget. Ask one friendly clarification only after that pass. Ask only for unresolved inputs; for each question, include up to 5 concrete lettered options from source-backed or user-provided candidates when available. Never present a generic instruction as a default. If no concrete candidates are available, briefly say why and ask free-form.
+Next steps to avoid:
+- Talk tracks or facilitation scripts
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+### Automation Offer Guard
 
-**Input Request Format Example:**
+For `Daily Prep Digest` outputs, a daily meeting-prep brief is the preferred automation offer when the user benefits from starting each day with a seller-ready view of customer, prospect, partner, renewal, opportunity, or high-stakes internal meetings. Frame the value in sales language: knowing which meetings matter today, what account or deal context changed, where the watchouts are, and how to close each meeting toward a concrete next step.
 
-```md
-{Natural clarification question, e.g. "Which meeting should I prepare?"}
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `calendar`: meeting existence, meeting title, time, attendees, invite body, stated agenda, and first-pass meeting focus.
-- `crm`: account status, opportunity/deal context, contacts, owner, recent activity, and account records that materially change meeting prep.
-- `meeting_notes`: prior calls, internal prep calls, objections, stakeholder language, commitments, decisions, follow-ups, and transcript-backed continuity.
-- `document_store`: account plans, implementation docs, mutual action plans, relevant assets, account notes, and team guidance.
-- `external_messaging`: recent customer questions, commitments, attachments, tone shifts, scheduling context, and promised next steps.
-- `internal_messaging`: account-team strategy, risks, blockers, internal coordination context, and discussion history that changes how the meeting should be handled.
-- `data_enrichment` / public web search: company/contact enrichment, attendee roles, firmographics, official company context, recent news, filings, announcements, funding, earnings, leadership changes, and first-time account ramp-up. Use public web only after a meeting/account anchor exists, keep searches targeted to the anchored company, attendees, and topic, and skip it when internal sources already provide enough context.
-
-These are semantic source categories, not fixed connector names. Resolve each attempted source to the specific available app, connector, or helper skill through Sales preflight `context.sources` before using connector-specific logic.
-
-Source obligations by intent:
-
-- For `next`, `upcoming`, `today`, `tomorrow`, scheduled, daily-digest, or invite-dependent meeting prep, `calendar` is required to resolve the meeting title, time, attendees, invite body, and stated agenda unless the user supplied equivalent meeting details. For vague prompts such as "prep me for a meeting" or "prep me for my next meeting," stop after calendar candidate discovery and return options; enrichment starts only after user selection. If `calendar` is unavailable but user-provided details are sufficient, continue with a clear limitation; if the meeting anchor remains ambiguous, ask with concrete calendar-backed candidates when available.
-- Do not infer a calendar-selected meeting from `crm`, `external_messaging`, `internal_messaging`, `document_store`, `meeting_notes`, public web, or enrichment. Those sources may suggest candidate context, but selected meeting evidence still needs `calendar`, user-provided details, or user confirmation.
-- For customer/account meetings, `meeting_notes` is conditional but expected when accessible and likely to contain prior customer or internal prep context. Search by account, attendee, meeting title, and opportunity/topic terms. If checked and no relevant prior context is found, say so briefly instead of implying transcript-backed continuity.
-- For customer/account meetings, `crm` is required when CRM is available and a customer/account anchor is supplied, inferred, or confirmed. Use CRM for account status, opportunity/deal context, owner, contacts, recent activity, and records that materially change the agenda, risks, recommended close, or broader account posture. When multiple opportunities or solutions exist, prefer the one that matches the meeting title/topic, user ask, attendees, product or solution aliases, and recency. If CRM is unavailable or yields no relevant account/deal context, name the gap.
-- Do not run every source by default. Stop once the meeting anchor is resolved and the brief has enough high-value evidence; name unchecked or unavailable optional sources as limitations or expansion opportunities when they could matter.
-
-Authority and gaps:
-
-- Use each source category for the facts it owns; do not substitute mirrored sources, browser UI, Computer Use, or web search for authoritative connector evidence.
-- Do not treat calendar, docs, Slack/internal messaging, email, meeting notes, public context, or third-party enrichment as substitutes for CRM-owned customer truth.
-- Treat public web as lower-authority enrichment after a meeting/account anchor exists; do not use it to invent private account status, commitments, objections, support posture, or deal facts.
-- If a source is unavailable, blocked, stale, ambiguous, or missing required fields, name the gap and either ask for access/input, proceed with a clear limitation, or omit the unsupported claim.
-- Do not wait on optional enrichment once the meeting is anchored and the available evidence is enough for a useful prep brief; continue with a labeled limitation.
-- If continuity is thin, say so and use public/company context only as first-time ramp-up, with `Inference:` for synthesis.
-- If evidence is weak or only broadly sourced, omit it or place it in background rather than turning it into tactical guidance.
-
-### Context Gathering Principles
-
-Optimize for marginal value, not absolute speed.
+The automation must be a scheduled rerun of this skill, not a separate custom calendar summary. When creating or describing the automation, make the prompt call this skill directly and preserve the same digest shape:
 
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the prepare-for-meeting experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Prepare For Meeting
-This skill creates a concise brief for an upcoming customer or high-value sales-adjacent meeting using calendar, account context, CRM, meeting notes, transcripts, docs, email, internal messaging, saved plugin memory, and user-provided guidance.
+```text
+Use the Sales `prepare-for-meeting` skill in `Daily Prep Digest` mode.
+Rerun it daily for today's qualifying seller meetings in the user's timezone.
+Return the standard Daily Prep Digest output with chronological meeting briefs and priority watchouts.
 
+mode: "Daily Prep Digest"
+date: "today"
+meeting_scope: "customer, prospect, partner, renewal, opportunity, or high-stakes internal meetings"
 ```
 
-If this is the first prepare-for-meeting demo from guided Sales onboarding, onboarding owns the framed CTA presentation. Return the `Review the brief` action text from `Next Step Guidance` for onboarding to place in its common frame, and do not add a second direct-run continuation.
-
-### Next Step Guidance
-
-This section is the single owner for Prepare For Meeting next-step behavior and action text. Onboarding, automation readbacks, document creation, calendar linking, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the meeting-prep continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-The next-step flow is mandatory and opt-out. While `prepare-for-meeting` is the active workflow, every turn must either advance the journey, offer the earliest valid next step, or explicitly match one of the allowed exit reasons below. This applies across the whole turn, including after tool calls, connector writes, document updates, Slack/email handoffs, readbacks, short answers, local rewrites, one-paragraph summaries, confirmations, FYI/source cards, partial results, source-gap notes, status updates, explanations of what changed, and answers to "what's next?".
-
-Before sending any final response, inspect the visible assistant-written content. If it does not end with exactly one concrete journey next action, add the earliest valid continuation from `Journey States` or name the specific exit reason. Generic advice, bare confirmations, source links, "done" messages, and "next best step" summaries do not satisfy this invariant when a concrete journey state remains available.
-
-Exit the next-step flow only when one of these reasons applies: the user explicitly asks for no follow-up, the user clearly declines the offered path, the user explicitly closes the workflow, the user starts a new unrelated intent, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not exits; make the unresolved question the final natural continuation. Each Sales skill may define its own allowed omission reasons, but if a skill is silent, the next-step flow is mandatory by default.
-
-After every accepted action or successful tool call in this journey, recompute the current artifact state and move to the earliest next valid continuation. Treat follow-up prompts such as "what's next?", "continue", "next", and "go on" as requests to resume the current journey, not as new intent, unless the user names a different goal. Do not replace the journey continuation with generic next-best advice while a concrete journey state remains available.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **Review the brief** | The first substantive prep output for a selected meeting has just been produced. | `Anything we should expand on, or anything you'd change?` |
-| **Review the revised brief** | The user asked for any change to the brief, including a small rewrite, added question, one-paragraph summary, source adjustment, or formatting tweak. Make the change in chat first. | If Daily Meeting Prep is eligible, use `Anything else you'd change? If that looks good, want me to set up Sales Daily Meeting Prep for weekday morning calendar checks?` Otherwise use `Anything else you'd change? If that looks good, want me to move it into a dedicated meeting doc?` |
-| **Offer Daily Meeting Prep** | The user has tried meeting prep, accepted the brief or a revision, and `sales_preflight` does not show `daily_meeting_prep` active, installed, ready, verified, declined, skipped, or deferred. This is especially important during guided Sales onboarding after the first Prepare For Meeting demo is accepted. | Offer to set up Sales Daily Meeting Prep so Sales checks the calendar each weekday, prepares one high-value qualifying meeting when useful, and returns `DONT_NOTIFY` when no meeting is worth prepping. |
-| **Create a meeting doc** | The user accepts the brief, says no changes are needed, says a revision looks good, or asks to operationalize the prep after Daily Meeting Prep is already active, installed, ready, verified, declined, skipped, deferred, unavailable, not applicable, or explicitly bypassed by the user. | Offer to create a lightweight meeting doc from the accepted non-empty brief, with space for decisions, owners, and follow-ups. |
-| **Iterate the meeting doc** | A meeting doc exists and the user asks to change it, or readback shows a content/formatting issue. | Offer the specific doc edit or cleanup needed. |
-| **Link or draft share note** | A meeting doc exists and does not need edits. | Offer to link it to the calendar event or draft a short Slack/email note for review with the doc, goal, open questions, and decisions needed. Do not offer to post or send the note directly unless the user explicitly asks for posting/sending or approves a reviewed draft. |
-| **Set post-meeting reminder** | The doc handoff step has been completed or declined, follow-up has not been handled, and notes/transcript availability is not already confirmed. This still applies after the meeting has ended when a future check can be scheduled for notes/transcript availability. | Offer an event-aware automatic follow-up that starts checking at the event-derived time, or a small post-end buffer when the meeting has already ended, and cancels itself after meeting notes/transcript are found and follow-up is handled. |
-| **Post-meeting follow-up** | The meeting has ended and notes, transcript, or recap evidence is already available, or the user explicitly asks to check available notes now and draft the follow-up. | Offer or run `follow-up-after-call` for the recap, customer/internal follow-up, action tracker, and CRM-ready next steps. |
+The recurring output should follow this skill's `Daily Prep Digest` format: today's meetings, each meeting's goal, key context, watchout, suggested close, and cross-meeting priority watchouts. Keep it read-only; it may recommend preparation and follow-up actions, but must not create meeting notes, attach documents, send messages, post updates, or write CRM unless the user separately asks and approves.
 
-Daily Meeting Prep is the primary accepted-brief continuation owned by this skill. Treat `daily_meeting_prep` as ineligible when `sales_preflight.context.automations.daily_meeting_prep.status` is `active`, `installed`, `ready`, `verified`, `declined`, `skipped`, `skipped_for_now`, `deferred`, `deferred_environment_api_limitations`, `unavailable`, or `not_applicable`, or when other readback clearly shows the user already has a matching Sales Daily Meeting Prep automation. Do not offer meeting doc creation as the accepted-brief default while Daily Meeting Prep remains eligible, unless the user explicitly asks for the doc instead.
+Before offering the daily prep brief, check whether the user already has a matching local automation installed. Inspect local automation records under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset, and match by name, prompt, skill name, mode, cadence, meeting scope, or other stable scope details. Treat active and paused matches as already installed.
 
-#### Post-Meeting Reminder
+- If a matching automation exists, do not suggest creating another one. Continue with the next most relevant non-automation follow-up.
+- If no matching automation exists, end with one clear offer to check/create a daily rerun of `prepare-for-meeting` for the Daily Prep Digest. Describe the recurring output as a morning seller brief for the meetings that need attention, why each matters commercially, and the recommended close or next step. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, do not mention tool details; offer to help set up a recurring meeting-prep brief when automations are available.
 
-When offering `Set post-meeting reminder`, include the automatic trigger behavior and the exact proposed schedule derived from the meeting event, for example: `Want me to automatically start checking 5 minutes before the meeting ends, at {date/time/timezone}, then pull the notes/transcript when they show up and draft the recap plus action tracker?` Use the selected calendar event's scheduled end time minus a default 5-minute lead buffer in the event timezone. The proposed first run must be in the future; if the lead-buffer time has already passed, use the next future event-aware time before the scheduled end when possible, otherwise use a small future post-end buffer. If the meeting end time is unknown, ask for it in the final continuation instead of guessing. If the meeting already ended and notes/transcript evidence is available, use `Post-meeting follow-up` instead of scheduling; if availability is unknown or not yet found, still offer the event-aware check/reminder instead of generic next-step advice.
+## Overall Rules
+- If asking the user a straightforward question where the set of potential answers are bounded and high-confidence, use the user_input form tool if available.
+- Always cite sources using hyperlinks so users can click through to source docs
 
-When the user accepts `Set post-meeting reminder`, create the narrowest event-aware reminder or automation the available tool supports. Prefer a one-time future wakeup when it can reliably run after notes are available; otherwise create a short-lived heartbeat whose first run is the exact event-aware start time above and whose prompt says to return `DONT_NOTIFY` until notes/transcript are found. The reminder instructions must name the meeting, meeting date/time, prep doc or calendar link when available, sources to check for notes/transcript, and intended follow-up action: pull notes/transcript, summarize decisions and action items, and run or offer `follow-up-after-call`. They must also say that once notes/transcript are found and the follow-up has been drafted or offered, the automation should cancel/delete itself so it does not keep running. Do not create a daily, weekly, generic 7 PM, arbitrary long-interval, or untethered recurring automation unless the user explicitly asks for that recurrence. If the tool cannot schedule a future event-aware first run, report the limitation and ask for an explicit fallback time instead of creating a generic schedule. After creating the reminder, read back the exact first-run date/time, cadence if any, and self-cancel condition as the final natural continuation.
+## Modes
+### 1. Single Meeting Prep
 
-#### Daily Meeting Prep Automation
+- Use when the user names one meeting, account, attendee, invite, or topic.
+- You can pull in relevant information from other related meeting and context, but ensure that you make the link to the target meeting clear.
 
-When the user accepts `Offer Daily Meeting Prep`, load `[$sales:user-context](../user-context/SKILL.md)` and its `references/automation.md` plus `plugin-author-config/automation-config.md`. Use the `daily_meeting_prep` entry from the later journey automation section, not the default onboarding automation list. Create or repair a dedicated `Sales Daily Meeting Prep` heartbeat automation only after user approval, with its configured weekday 9:00 AM local cadence and launcher instructions. Read back the automation and target thread before claiming it is set up. If the user declines, skips, or defers this offer, record that disposition under `automations.daily_meeting_prep` when the state file is available so future accepted briefs do not keep re-offering the same automation. If the environment cannot persist the automation, say that plainly and continue the meeting-prep journey; do not mark initial Sales onboarding incomplete because this later journey automation was skipped or deferred.
+#### Output Format
 
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. Also treat "what's next?", "continue", "next", and similar prompts as requests to advance the current journey unless the user names a different goal. If the previous continuation was `Review the brief`, treat a lightweight acknowledgement as accepting the brief as-is; if Daily Meeting Prep is eligible, move to `Offer Daily Meeting Prep`, otherwise move to `Create a meeting doc`. If the previous continuation was `Review the revised brief`, treat a lightweight acknowledgement such as "okay looks good" as accepting the revision; if the visible continuation already offered Sales Daily Meeting Prep, treat the acknowledgement as approval to create or repair that automation, otherwise move to `Offer Daily Meeting Prep` when eligible and `Create a meeting doc` only when the automation is ineligible or explicitly bypassed. If the previous continuation was `Offer Daily Meeting Prep`, treat a lightweight acknowledgement as approval to create or repair the automation. After resolving the accepted action or resume request, apply the `Final Continuation Invariant`.
-
-Keep the continuation separate from the brief format so it does not rename, reorder, or add sections inside the provided shape.
+```md
+# [Meeting Name]
 
-### Context Sufficiency
+**Date:** [Date / Time]
+**Attendees:** [Names + roles]
 
-Gather enough relevant context to make the brief grounded and correct:
+## Summary
 
-- Do not stop after the first available source when another accessible source could materially change the agenda, risk framing, account posture, recommended close, or next action.
-- Prefer high-value evidence gathering during research and tighter synthesis in the final brief.
-- Use as much direct, high-signal context as is helpful for confidence, but include only the highest-leverage facts, gaps, and inferences in the user-facing output.
-- When time, access, or source availability limits context gathering, name the limitation and its likely impact.
+- [Core meeting objective]
+- [Current account, opportunity, or workstream signal]
+- [Top implication, risk, or source gap]
 
-### Search Discipline And Yielding
+## Goal
 
-Use bounded, direct searches rather than exhaustive retrieval:
+[Specific outcome: decision, alignment, feedback, commitment, or next step.]
 
-- Start with the source categories most likely to change the meeting objective, agenda, risks, close, or next action.
-- For each attempted source, use targeted queries and stop after useful evidence, a clear no-result, or two failed attempts.
-- Do not chase indirect, mirrored, or convoluted paths to reconstruct unavailable source truth; use `Authority and gaps`.
-- If repeated errors, auth gaps, rate limits, ambiguity, or missing access block reliable prep, stop searching and tell the user the core issue, what evidence is available, and the shortest unblock option.
-- Prefer a limited brief with `Known Limitations` or a concise clarification over long-running retrieval.
+## Open Questions
 
+- [Most important unresolved question]
+- [Question tied to invite, CRM, notes, or message context]
+- [Decision or clarification needed]
 
-## Prep Modes
+## Proposed Agenda
 
-Prep modes decide what job the skill is doing before output formatting begins:
+1. [Decision-oriented topic]
+2. [Risk, blocker, or decision to resolve]
+3. [Confirm next steps and owners]
 
-1. `Single Meeting Prep`: use when the user asks for prep for one specific customer meeting, account, invite, attendee, or topic.
-2. `Daily Prep Digest`: use when the user asks for a day-based summary or morning prep digest. Start from `calendar`, keep meetings with at least one external attendee, and produce one chronological digest.
-3. `Scheduled Meeting Selection`: use only for system-triggered automations, heartbeats, recurring check-ins, or scheduled runs. Start from `calendar`, prepare exactly one meeting where prep would materially help, briefly say why it was selected, and return `DONT_NOTIFY` if no qualifying meeting exists. Choose among candidates using the strongest available impact signals:
+## Background Context
+[Max 4 bullets, non-overlapping with above]
 
-- customer or prospect importance;
-- executive or buyer attendance;
-- late-stage opportunity or renewal context;
-- explicit agenda urgency;
-- unresolved risk or decision topics;
-- strategic account status;
-- recent customer-facing activity;
-- high-stakes internal decision-making;
-- launch or product dependencies;
-- leadership visibility;
-- whether a prep brief would improve the user's next action.
+- [Compact account, deal, or workstream context]
+- [Relevant attendee or prior-meeting context with citation]
+- [Useful assets, constraints, dependencies, and source gaps]
 
-## Procedure
 
-Use this lifecycle as the normal operating model: resolve the meeting and gather high-value context, draft the prep, iterate in chat, create or draft the accepted artifact only after the user is happy with the content, then help drive the next operational step. Do not jump straight from the first draft to document/deck creation unless the user asked for an artifact up front or the accepted output already exists.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-1. Start from the meeting source.
-   Use `calendar` for day-based, scheduled, invite-dependent, or user-requested calendar selection, including `next`, `upcoming`, `today`, `tomorrow`, or latest scheduled meeting prompts. Read title, timing, attendee list, description, and invite body before adding narrative unless the user supplied equivalent meeting details.
-2. Qualify the meeting for the active prep mode.
-   For `Single Meeting Prep` and `Daily Prep Digest`, keep the customer/account anchor and stop if the meeting is not clearly customer-facing or account-related. For `Scheduled Meeting Selection`, customer/account meetings win; otherwise choose only a high-impact non-routine work meeting.
-   For non-customer meetings selected only by `Scheduled Meeting Selection`, do not add customer-specific objections, CRM facts, public account research, customer-facing assets, or `Broader Account Context` unless the meeting evidence actually names a customer or account.
-3. Define a one-sentence focus hypothesis before gathering supporting context.
-   Use the invite title/body, user-supplied target, attendee roles, recent scheduling or email context, internal discussion that references this meeting or a same-day call, and current account workstreams. Use the hypothesis to prioritize sources, not to exclude strong contrary evidence.
-4. Explain attendee relevance for single-meeting briefs.
-   Identify external attendees first; include sourced roles or history when reliable. Group internal attendees by role or relevance when individual titles are not sourced. Do not guess titles; say when only email identity is known.
-5. Pull current account or workstream state.
-   For customer/account meetings, use `crm` and `document_store` for account state and broader context. Match the active opportunity or deal to the meeting topic before using deal facts; if another same-account opportunity appears unrelated, exclude it or label the conflict instead of letting it drive the brief. Capture account-level facts for `Broader Account Context`; promote them into `Upcoming Meeting` only when they directly affect the objective, agenda, risks, questions, or close. For non-customer meetings selected by `Scheduled Meeting Selection`, use internal sources for workstream decisions, blockers, owners, and dependencies instead of forcing account context.
-6. Pull continuity and recent context.
-   Search `meeting_notes` by exact external attendee emails or names, account name and aliases, opportunity or solution names, invite title terms, and focused topic terms. If no direct prior external customer call is found, say so and use relevant internal meeting records only as internal continuity. Then pull `external_messaging` for unanswered questions, scheduling shifts, tone changes, and promised next steps. Then pull `internal_messaging` and `document_store` for internal strategy that changes the agenda, decision framing, likely objections, or next actions.
-7. Run a concise public web enrichment pass only when it materially improves preparation.
-   Useful targets include high-confidence external attendee title/company matches, official company or product pages, public customer pages, product or partnership announcements, filings, earnings, funding, leadership changes, and reputable news. If internal continuity is thin, label the brief as first-time or low-context and add public ramp-up context with `Inference:` where needed. If public evidence conflicts with internal sources, defer to internal source truth and mention the discrepancy in `Known Limitations`.
-8. Focus the agenda.
-   Use invite agenda when present; otherwise recommend a short agenda based on the focus hypothesis and strongest current signals. Broad account-plan items enter the focused agenda only when another strong signal ties them to this meeting, such as the invite, attendee roles, recent message context, prior continuity, a same-day customer call, or a recent note assigning owners.
-9. Add a light two-sided alignment frame when needed.
-   Use one when a topic involves disagreement, unclear ownership, incompatible expectations, or negotiation around pricing, capacity, data retention, support, roadmap commitments, co-sell, procurement, security, implementation ownership, or executive sensitivity. Use the compact shape `Problem`, `Customer side`, `Seller side`, and `Goal for this meeting`; name people only when sourced and label inferred motives or constraints.
-10. Recommend up to 3 existing customer-facing assets to bring.
-    Explain why each helps. Do not fabricate, generate, or rewrite assets in this skill.
-11. Cite tactical claims and separate facts from synthesis.
-    For normal single-meeting briefs, put citations inline on the `Background Context` bullets instead of collecting them in a separate sources section. Each factual `Background Context` bullet should end with the source title, record name, channel/thread, or meeting/date label as visible Markdown link text when a source exposes a useful URL. Use plain labels with `(no useful link available)` when needed. Do not link to meeting join URLs, generic room links, or opaque connector refetch IDs.
-12. Assemble the final answer against `Output Shapes` before sending.
-    Select the applicable provided shape after the prep mode and meeting type are known, then fill that shape directly. The selected output shape is the only user-facing structure for the brief body; after it, apply `Next Step Guidance` unless an explicit exception applies.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+### 2. Daily Prep Digest
 
-Use [references/meeting-selection-and-agenda-rules.md](references/meeting-selection-and-agenda-rules.md) when qualification, domain resolution, agenda selection, attendee enrichment, public enrichment, or asset selection is ambiguous.
+Use when the user asks for today’s, tomorrow’s, or another date-based meeting summary.
 
-## Output Shapes
+Start from calendar, apply the shared selection rules, keep qualifying meetings, and order them chronologically. Keep separate briefs for separate meetings.
 
-Use this output shape for normal single-meeting briefs, including customer/account meetings and high-value internal workstream meetings:
+### Output Format
 
 ```md
-# [Meeting Name]
-
-**Date:** [Date / Time]
-**Attendees:** [Names + roles]
-
-## Summary
+## Today's Meetings
 
-* [Core meeting objective]
-* [Current account, opportunity, or workstream signal]
-* [Top implication, risk, or source gap for this meeting]
+### [Time] — [Meeting Name]
 
-## Goal
-
-[The specific outcome to achieve: decision, feedback, alignment, commitment, or next step.]
+**Attendees:** [Names + roles]
 
-## Open Questions
+- **Goal:** [What to accomplish]
+- **Key context:** [Relevant account, deal, project, or workstream signal]
+- **Watchout:** [Risk, blocker, dependency, or source gap]
+- **Suggested close:** [Next step, owner, commitment, or decision]
 
-* [Most important unresolved question]
-* [Question this meeting should answer, tied to CRM, transcript, or invite gaps when relevant]
-* [Decision or clarification needed]
+## Priority Watchouts
 
-## Proposed Agenda
+- [Most important cross-meeting risk]
+- [Meeting needing special preparation]
+- [Missing context or follow-up needed before meetings]
 
-1. [Topic or question to discuss]
-2. [Topic or decision to resolve, tailored to meeting type and deal/workstream stage]
-3. [Confirm next steps / owners]
+---
 
-## Background Context
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-* [Compact account/deal or workstream snapshot with inline source citation]
-* [Relevant attendees, prior-call/internal-prep continuity, or customer/team context with inline source citation]
-* [Useful assets, constraints, and material source gaps with inline source citation when factual]
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
 ```
-
-Clarification and digest requests are not normal single-meeting briefs and still use their specialized shapes:
-
-- `Clarification Stop`: end with only the unresolved required input question.
-- `Daily Prep Digest`: use `**Today's External Meetings**`, one compact chronological brief per meeting, and `**Priority Watchouts**`.
-
-For normal single-meeting briefs, use only the provided single-meeting output shape for the brief body. Fill the placeholders with the best available sourced facts. Do not add, rename, reorder, or omit headings inside the brief body. After the brief body, end with the natural continuation selected by `Next Step Guidance`.
diff --git a/plugins/sales/skills/prepare-for-meeting/agents/openai.yaml b/plugins/sales/skills/prepare-for-meeting/agents/openai.yaml
index c2f8e80..9a02732 100644
--- a/plugins/sales/skills/prepare-for-meeting/agents/openai.yaml
+++ b/plugins/sales/skills/prepare-for-meeting/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Prepare For Meeting"
-  short_description: "Customer meeting briefs from calendar and notes"
+  short_description: "Customer meeting briefs from calendars and notes"
   default_prompt: "Prepare me for an upcoming customer meeting."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/prepare-for-meeting/references/meeting-selection-and-agenda-rules.md b/plugins/sales/skills/prepare-for-meeting/references/meeting-selection-and-agenda-rules.md
deleted file mode 100644
index 74650f1..0000000
--- a/plugins/sales/skills/prepare-for-meeting/references/meeting-selection-and-agenda-rules.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# Meeting Selection And Agenda Rules
-
-Use this reference only when the main skill instructions leave meeting qualification, agenda scope, attendee enrichment, or asset choice ambiguous.
-
-## Qualification Edge Cases
-
-- For single-meeting briefs and daily digests, require at least one external attendee or another explicit account/customer signal.
-- Reject routine internal meetings, recruiting meetings, 1:1s, performance conversations, and operational syncs unless the user explicitly asked for them or the invite evidence shows clear business stakes.
-- In scheduled most-important mode, select a non-customer meeting only when no customer/account meeting qualifies and the work meeting is non-routine, high-impact, and prep would materially improve the user's next action.
-
-## Account Or Workstream Resolution
-
-- Resolve the likely account from external attendee domains when the user did not name one.
-- If multiple external companies appear, anchor on the company most central to the request and call out the others.
-- If no credible account anchor can be resolved for a customer-mode brief, stop and report the gap instead of improvising a customer brief.
-- For a scheduled non-customer meeting, resolve the workstream from title, invite body, attendees, internal docs, and recent internal messages instead of inventing an account anchor.
-- When CRM has multiple opportunities for the account, prefer the one that matches the user ask, meeting title or invite topic, attendee roles, product or solution aliases, and recent activity.
-- Do not let an unrelated same-account opportunity drive the brief. Mention it only as excluded or conflicting context when that helps the seller.
-
-## Agenda Scope
-
-Use this priority order for `Focused Agenda`:
-
-1. Explicit user ask or named account/topic.
-2. Calendar title, invite body, attached agenda, and attendee list.
-3. Recent external email or `external_messaging` about the meeting.
-4. Prior meeting notes with the same customer and topic.
-5. `internal_messaging` that references the upcoming meeting, a same-day customer call, the same attendees, or a current blocker.
-6. CRM or document-store items with current dates, owners, or open next actions.
-7. Broad account-plan strategy, only as background or when directly tied to the signals above.
-
-Agenda phrasing rules:
-
-- Prefer phrasing that names the decision to make, not just the topic to discuss.
-- If the title points to a narrow implementation, pilot, support escalation, security review, procurement step, or launch review, make that topic the agenda spine.
-- Move broader commercial, executive, product, or partnership context into background unless it changes a decision needed in this meeting.
-- Use internal sales-review or prep-call transcripts as internal continuity when they match the account and topic; do not present them as customer history unless the source shows customer participation.
-
-## Multi-Meeting Handling
-
-- In a daily digest, order qualifying meetings chronologically.
-- Keep separate briefs for separate meetings, even when they involve the same account.
-- Reuse supported account framing across same-account meetings, but do not merge distinct meetings into one summary.
-
-## Enrichment And Assets
-
-- Add public attendee title and company only from high-confidence LinkedIn/profile matches; never infer titles from names alone, and omit weak or ambiguous matches.
-- Prefer official company pages, official product/customer pages, filings, press releases, investor materials, and reputable news for public account enrichment.
-- Use public context only when it sharpens the meeting background, questions, risks, or close.
-- Recommend up to 3 existing assets only when there is evidence they exist and would help this meeting.
diff --git a/plugins/sales/skills/prioritize-accounts/SKILL.md b/plugins/sales/skills/prioritize-accounts/SKILL.md
index 8279871..c7696b2 100644
--- a/plugins/sales/skills/prioritize-accounts/SKILL.md
+++ b/plugins/sales/skills/prioritize-accounts/SKILL.md
@@ -1,416 +1,168 @@
 ---
 name: prioritize-accounts
-description: Prioritize rep-ready pipeline by ranking accounts, suppressing in-flight motion, selecting the best reachable contact, and producing a connector-grounded account action view plus a concise planning-only action package from CRM, user-provided lists, saved context, and optional enrichment evidence.
+description: "Use when the user asks which existing accounts to work now, which leads are hottest in a grounded list or pipeline, how to rank a territory, book, pipeline, ICP list, or account list, where to focus this week, or what rep-ready next actions to take. Prioritize accounts using current evidence, suppress or account for in-flight motion, and identify reachable contacts when supported. Use enrichment instead for net-new company discovery or missing-data completion."
 ---
 
 # Prioritize Accounts
 
-Use this skill to help an individual sales rep decide which accounts to work now, why now, and what to do next.
+Prepare a sales person with a small, evidence-grounded account action view: which accounts deserve attention now, why they rose, who to engage, and what safe next step to take.
 
-This is a rep-facing, account-first workflow. It prioritizes accounts before contacts, branches clearly across `net_new`, `expansion`, or `mixed`, suppresses accounts already in motion, and returns a small set of executable Suggested Focus rows. Its primary output is a connector-grounded account action view that can be opened in Codex when connected source records or useful source links are available. It plans and drafts only: it does not execute outreach, create records, update CRM, or send messages unless the user explicitly asks for that in a separate step.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-### User Context
+## Account Ranking
 
-Mandatory pre-answer gate:
+Use this priority order when the account set, ranking basis, or motion is ambiguous.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the account-prioritization output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the prioritization.
+1. Explicit user request, named account list, owner scope, territory, ranking basis, or stated preference
+2. CRM-backed open pipeline or user-provided CRM-equivalent account truth
+3. Accounts with a source-grounded why-now signal, clear net_new or expansion branch, and reachable primary contact
+4. Accounts with meaningful deal value, timing, momentum, risk, or actionability
+5. Accounts without active motion, duplicate outreach risk, ownership ambiguity, or another hard suppressor
+6. Accounts that fit the rep's stated capacity window
 
-### Audience And Language
+Default broad requests to open pipeline deals, deal value / ACV adjusted by timing, momentum, risk, and actionability, with a mixed motion goal. Do not create the candidate universe from enrichment, public context, calendar, or messages alone.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Key Dependency Categories
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+It is **critical** to use **all** of these categories if you can, if any relevant plugins or connectors are available, to ensure high quality responses. If the user declines, or connectors aren't available, state that limitation in your final answer. You can also use other relevant connectors as needed.
 
-### Source Resolution
+- [Blocking] ~~CRM for account truth, ownership, customer status, opportunities, stage, amount, forecast posture, contacts, and recent activity. It blocks unless an authoritative account universe and account truth are already grounded in context.
+- ~~Calendar for active-motion suppression and timing
+- ~~Meeting Transcripts for objections, buying process, and stakeholder continuity
+- ~~Email for recent engagement and promised next steps
+- ~~Internal Messaging for duplicate motion, blockers, and owners
+- ~~Knowledge & Files for ICP guidance, target lists, territory rules, account plans, sales plays, and suppression conventions
+- ~~Sales Intelligence for fit, contactability, tech stack, funding, hiring, and source-grounded why-now context after CRM anchors the set
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+## Overall Rules
 
-### Required Inputs
+- Always cite sources using hyperlinks when useful links are available.
+- Sources must list every material dependency category, its status, and whether it affected the recommendation.
+- Keep the account as the primary unit of work and prefer fewer executable rows over a noisy list.
+- Put each account in exactly one of Suggested Focus, Monitor, or Suppress Or Block. When an account has both actionable work and a suppressor such as duplicate-outreach risk, keep it in Suggested Focus and describe the constrained action there; use Suppress Or Block only when no rep action should be taken now.
+- CRM owns the account universe and account truth. Other sources can enrich, suppress, or re-rank after the universe is anchored.
+- Calendar, meeting notes, messages, documents, and Sales Intelligence may change timing, suppression, contact choice, confidence, or why-now context after CRM anchors the account set; they must not create or overwrite the account universe.
+- Never fabricate contacts, urgency, opportunity posture, why-now signals, ownership, or account facts.
+- Do not execute outreach, create records, update CRM, or send messages in this workflow unless the user explicitly requests the separate approved action.
+- Use references/suppression-and-fallbacks.md when a row may be suppressed, blocked, monitored, or recovered through a fallback lane.
 
-This skill is CRM-anchored. It needs CRM data, a CRM export, or user-provided CRM-equivalent account truth to identify and populate the account universe before producing the account action view.
+### Next Step Options
 
-Do not produce a standard account action view from enrichment, calendar, documents, messages, or public context alone. Those sources can enrich and re-rank after CRM anchors the account set, but they must not replace CRM truth.
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Take the next logical action for one selected account, such as drafting a message, preparing an internal ask, or unblocking a dependency.
+- Draft CRM-ready updates for review to address missing, stale, or contradictory fields that affected the ranking.
+- Research one selected account more deeply when the next action depends on missing context.
+- Refine the ranking, suppression rules, or motion goal using the user's guidance.
+- Check whether a matching weekly automation already reruns this `prioritize-accounts` skill for the same account set; if none exists, offer to create one that gives the seller a weekly account worklist with why-now context and safe next actions.
 
-**Inputs:**
+Next steps to avoid:
+- Blanket outreach, bulk CRM writes, or taking action across the whole ranked list without explicit user direction.
 
-Require:
+### Automation Offer Guard
 
-- [required] `crm_account_truth`: live CRM data, CRM export, or user-provided CRM-equivalent account truth that includes candidate accounts, owner scope, pipeline view, customer status, or opportunity/account records
-- [defaulted] `account_set`: candidate accounts, owner scope, territory, ICP, segment, pipeline view, or other account universe to prioritize; default `open pipeline deals` when the user does not specify
-- [defaulted] `ranking_basis`: what "best" means for this run, such as deal value, likelihood to close soon, product or usage signal, expansion potential, named-account priority, or mixed rep actionability; default `deal value / ACV`
+For substantive ranking outputs, a weekly account-priority brief is the preferred automation offer when the user has a reusable account set, owner scope, territory, pipeline view, target list, or named watchlist. Frame the value in sales language: deciding where to spend the week, which accounts moved into focus, which should be monitored or suppressed, and what next action is safe to take without creating duplicate outreach or CRM churn.
 
-Accept when provided:
+The automation must be a scheduled rerun of this skill, not a separate custom digest. When creating or describing the automation, make the prompt call this skill directly and preserve the same account scope and ranking intent:
 
-- `motion_goal`: `net_new`, `expansion`, or `mixed`; default `mixed`
-- `batch_size`; default `5`
-- `capacity_window`; default to `batch_size`
-- `signal_preference`
-- `product_focus`
-- `exclude_accounts`
-- `uploaded_examples`
+```text
+Use the Sales `prioritize-accounts` skill.
+Rerun it weekly for the same seller account set: [owner scope, territory, pipeline view, target list, or watchlist].
+Return the standard Account Action View when source links are available; otherwise return the Chat Fallback output.
 
-**Context Rules:**
-
-- For broad requests such as "prioritize accounts", assume `account_set=open pipeline deals`, `ranking_basis=deal value / ACV`, `motion_goal=mixed`, and the user's saved CRM preference as source of truth.
-- Always attempt CRM first for account truth, owner scope, open opportunities, stage, amount, forecast posture, close date, account status, recent activity, and CRM next step fields.
-- If live CRM is unavailable, inaccessible, not installed, or not authorized, offer to connect/install the CRM source when that capability is available. If installation/connection is not available in the current context, ask the user for a CRM export, account list with CRM fields, owner scope, territory, or enough CRM-equivalent context to proceed.
-- If CRM is available but the assumed account set returns no candidates, stop before broadening. Briefly report the empty result and ask whether to broaden to recent account activity, expansion customers, net-new targets, a different owner scope, or a user-provided CRM export/list.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible account-set or account-list anchors. Use a bounded CRM lookup to resolve account identity, ownership, customer status, or candidate-list scope before asking.
-- Ask a clarification question only when CRM and prompt context cannot determine the account universe, when multiple CRM scopes would produce materially different results, or when the user's requested ranking basis conflicts with available CRM truth.
-- When asking, present up to 5 concrete CRM-backed candidates or next-step choices with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
-
-When a required CRM anchor is ambiguous, do the minimum CRM lookup needed to produce concrete choices, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
-
-Proceed with default assumptions when CRM resolves the account universe. State assumptions in `Scope` instead of blocking.
-
-### Default Intake Behavior
-
-Default broad prioritization requests to:
-
-- account set: open pipeline deals from CRM
-- ranking basis: deal value / ACV, adjusted by timing, momentum, risk, and actionability
-- source of truth: saved CRM preference when available, otherwise the available CRM connector/source
-- enrichment: pull any available and potentially relevant Calendar, meeting notes, internal messaging, external messaging, document-store, and enrichment context after CRM anchors the account universe
-
-Only ask the user for more context when CRM cannot be accessed, CRM returns no usable account truth, the owner/account scope cannot be resolved, or the user asks for a ranking basis that cannot be inferred from CRM and available enrichment.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `crm`: account truth, owner scope, territory or book, customer status, open opportunities, stage, amount, forecast posture, recent activity, contacts, account hierarchy, suppression signals, and CRM-backed account lists.
-- `calendar`: recent or upcoming external meetings that suppress, down-rank, or explain active motion.
-- `meeting_notes`: recent customer-call context, objections, decision process, urgency, stakeholder coverage, and continuity signals that materially change ranking or next action.
-- `document_store`: account plans, territory docs, named-account lists, ICP guidance, sales plays, saved team priorities, or source-of-truth ranking/suppression conventions.
-- `data_enrichment`: account fit, contact availability, tech stack, funding, hiring, and other account trigger context when CRM or user-provided CRM-equivalent truth is thin.
-- `external_messaging`: recent customer engagement, timing, objections, stakeholder continuity, or duplicate-motion signals.
-- `internal_messaging`: active seller/team motion, internal blockers, owner ambiguity, deal-desk or account-team signals, and suppression evidence when the user wants internal context.
-- user-provided context: pasted account lists, CRM exports, enrichment tables, ICP notes, named-account lists, prior Sales outputs, examples, and explicit ranking criteria.
-
-Source obligations by intent:
-
-- `crm` is required as the source of account truth for the standard account action view. If live CRM is unavailable, use a CRM export or user-provided CRM-equivalent account truth only after making clear that live CRM was unavailable. If neither live CRM nor CRM-equivalent truth exists, stop and offer to connect/install CRM when possible, or ask for a CRM export, account list with CRM fields, owner scope, territory, or account context.
-- Use `data_enrichment` only after the CRM account universe or CRM-equivalent export is anchored. It can improve fit, contactability, and why-now context, but it must not create the candidate universe by itself or overwrite CRM/user-provided CRM truth.
-- Use `calendar`, `meeting_notes`, `external_messaging`, `internal_messaging`, and `document_store` whenever they are available and plausibly relevant to suppression, active-motion detection, timing, risk, contact choice, deal context, or next action. If checked and no relevant evidence appears, note the no-match only when it affects confidence.
-- For "best/top N accounts" prompts, return the requested number of Suggested Focus rows whenever the confirmed candidate universe contains enough viable accounts. Use `Low` confidence or `partial` status for weaker but still actionable rows; return fewer than N only when fewer viable candidates remain after suppression/blocking, and say why.
-
-Authority and gaps:
-
-- Prefer `crm` truth and existing internal motion before enrichment or drafting.
-- Prefer known account context before generic web-style personalization.
-- Treat `calendar`, `meeting_notes`, `external_messaging`, and `internal_messaging` as timing, suppression, and why-now signals; do not use them to create the candidate account universe or overwrite CRM/user-provided account truth.
-- Do not let public research, enrichment exports, docs, messages, or inferred personalization override CRM-owned account truth.
-- Use public research, enrichment exports, or user-provided market intelligence only to fill account coverage gaps when CRM or user-provided CRM truth is thin.
-- Do not use lower-confidence lanes to overwrite stronger `crm` or user-provided truth.
-- If no account truth is available from `crm`, CRM export, or user-provided CRM-equivalent inputs, stop and explain the blocker briefly.
-
-### Context Gathering Principles
-
-Optimize for marginal value, not absolute speed.
-
-- Start with CRM and the narrowest owner/account scope.
-- After CRM anchors the candidate set, gather available Calendar, meeting-note, messaging, document-store, and enrichment evidence that materially improves prioritization, suppression, confidence, or next action.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the prioritize-accounts experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Prioritize Accounts
-This skill helps decide which accounts to work now and why. It can use account lists, territories, CRM, firmographic/enrichment context, calendar, recent engagement, pipeline data, saved ICP guidance, and user-provided priorities. It produces a source-grounded account action view plus a ranked account list with reasons, confidence, suppressions, key contacts or gaps, and recommended next actions.
-
-Definitions:
-- ICP: ideal customer profile, the criteria that define what a strong-fit account looks like for the sales motion.
-- Suppression: a reason to deprioritize or exclude an account for now, such as active motion, unclear ownership, or unsafe duplicate outreach.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Prioritize accounts for pipeline focus.`
-- `@Sales prioritize these target accounts for pipeline focus this week.`
-- `@Sales rank my territory accounts by where I should spend time next.`
-- `@Sales use this ICP and account list to pick the best ten outreach targets.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Prioritize Accounts next-step behavior and action text. Onboarding, spreadsheet creation, draft creation, CRM writeback, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the account-prioritization continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `prioritize-accounts` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the account ranking** | The first substantive ranking or Suggested Focus list has just been produced. | `Anything you'd change in the ranking, suppression rules, or Suggested Focus list before we act on it?` |
-| **2. Review the revised ranking** | The user asked for any change to criteria, suppression rules, rows, contact choices, scoring, source weighting, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to turn the accepted ranking into outreach drafts, CRM next actions, an owner tracker, or a spreadsheet?` |
-| **3. Prepare the activation artifact** | The user accepts the ranking, says no changes are needed, says a revision looks good, or asks to operationalize the list. | Offer to produce the most useful next artifact or draft action, prioritizing outreach drafts, CRM next actions, an owner/action tracker, contact-selection guidance, or a spreadsheet over broad suggestions. |
-| **4. Review the artifact or draft** | Outreach drafts, CRM-ready actions, a tracker, contact-selection guide, or spreadsheet exists, or the user asks to change it. | `Anything you'd change before I save, share, or write the approved account actions?` |
-| **5. Save, share, or write approved account action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the spreadsheet, sharing the owner tracker, creating reviewed outreach drafts, or writing approved CRM next actions. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the ranking, treat a lightweight acknowledgement as acceptance and move to `Prepare the activation artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to save, share, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-### Account Action View Output
-
-For every substantive ranking run with CRM, another valuable searchable connected source, or stable source-of-truth links in the row payload, make the account action view the primary output. Use the bundled template:
-
-`assets/account-priority-pane.template.html`
-
-The template is standalone UTF-8 HTML with inline CSS and browser JavaScript only. It has no external assets, no script imports, no npm dependencies, no Node dependency, and no helper scripts.
-
-Generate one local file in an OS-appropriate temporary directory:
-
-- macOS/Linux: `${TMPDIR:-/tmp}/prioritize-accounts/index.html`
-- Windows: `%TEMP%\prioritize-accounts\index.html`
-
-Return the actual clickable view link first in the final response. Do not hardcode macOS-only `/private/tmp` in Windows-facing output. URL-encode spaces if emitting a `file://` URL.
-
-In user-facing copy, call the output an account action view, account ranking, or priority view. Do not mention templates, JSON, placeholders, Markdown, local files, or HTML mechanics unless the user explicitly asks how the view is built.
-
-Do not generate the account action view when no CRM search, connected source search, or useful stable source links are available for the run. In that case, render the structured ranking directly in chat and say the account action view was skipped because there were no searchable source records or source-of-truth links for the view to open. A purely pasted, unlinked account list can still produce a ranking, but it should not create a local HTML view unless the pasted/exported data includes useful source links or the user explicitly asks for an offline view.
-
-View generation procedure:
-
-1. Build the account action package using the `Suggested Focus`, `Monitor`, `Suppress Or Block`, `Evidence Gaps`, and `Source & Run Details` contracts below. Serialize Suggested Focus rows under the existing `workNow` payload key for template compatibility.
-2. Serialize the same package as JSON using the payload shape below.
-3. Escape the JSON for safe embedding in a script tag by replacing every `<` with `\u003c`.
-4. Replace the exact template placeholder `__PRIORITIZE_ACCOUNTS_DATA_JSON__` with the escaped JSON.
-5. Write the resulting HTML as UTF-8 to the local output path.
-6. Sanity check that the generated file contains no unresolved placeholder.
-7. Final response: put the file link first, then a short summary and the required natural continuation from `Next Step Guidance`.
-
-Do not ask the user to run Node, npm, Python, Salesforce CLI, or any helper script. If local file links do not open in the client, start a tiny local HTTP server yourself only when necessary and return the localhost URL.
-
-Preferred payload shape:
-
-```json
-{
-  "title": "Prioritize Accounts",
-  "generatedAt": "2026-05-29T19:30:00Z",
-  "source": {
-    "label": "Salesforce/CRM",
-    "status": "Connected sources used"
-  },
-  "scope": {
-    "accountSet": "Open pipeline deals",
-    "rankingBasis": "Deal value / ACV",
-    "sourceOfTruth": "Salesforce/CRM plus recent meetings",
-    "sourceScope": "Salesforce/CRM plus recent meetings",
-    "actionOwner": "Rep-owned next actions",
-    "accountOwner": "Rep-owned next actions",
-    "ownershipActionModel": "Rep-owned next actions",
-    "motionGoal": "mixed",
-    "batchSize": 5,
-    "capacityWindow": 5,
-    "sourcesChecked": ["crm", "calendar", "meeting_notes"],
-    "evidenceLanes": ["crm", "calendar", "meeting_notes"],
-    "assumptions": "Assumption-based only when applicable"
-  },
-  "workNow": [
-    {
-      "rank": 1,
-      "account": "Acme Corporation",
-      "accountUrl": "https://example.my.salesforce.com/001000000000000",
-      "motion": "expansion",
-      "whyNow": "Source-grounded timing reason.",
-      "primaryContact": "Dana Price, VP Operations",
-      "secondaryContact": "Lee Chen, Director Data Platform",
-      "oppRecommendation": "work account before opp",
-      "nextStep": "Confirm security reviewer and send pilot success criteria.",
-      "sequenceAngle": "Pilot success criteria tied to expansion timeline.",
-      "confidence": "High",
-      "status": "ready_now"
-    }
-  ],
-  "monitor": [
-    {
-      "account": "Beta Inc.",
-      "accountUrl": "https://example.my.salesforce.com/001000000000001",
-      "motion": "net_new",
-      "reasonToMonitor": "Good fit, but no immediate trigger.",
-      "triggerToRevisit": "New executive hire or product signal.",
-      "status": "monitor"
-    }
-  ],
-  "suppressOrBlock": [
-    {
-      "account": "Gamma LLC",
-      "accountUrl": "https://example.my.salesforce.com/001000000000002",
-      "motion": "expansion",
-      "suppressorOrBlocker": "Recent external meeting already in motion.",
-      "evidence": "CRM activity and calendar meeting.",
-      "status": "suppressed"
-    }
-  ],
-  "evidenceGaps": [
-    "Missing direct contact path for some otherwise strong accounts."
-  ]
-}
+account_set: "[same resolved account set]"
+ranking_basis: "[same ranking basis or motion goal]"
+motion_goal: "[net_new, expansion, mixed, or user-specified]"
+suppression_rules: "[same known suppression rules when relevant]"
 ```
 
-The account action view intentionally mirrors the action-package components: `Suggested Focus`, `Monitor`, `Suppress Or Block`, `Evidence Gaps`, and `Source & Run Details`. Keep the view and any chat fallback semantically identical. If local file creation is impossible, or if no useful source search/linking is available, render the structured package directly and state why the account action view could not be created.
-
-## Procedure
-
-### 1. Normalize the rep request
-
-- Resolve whether the user wants `net_new`, `expansion`, or `mixed`.
-- Resolve whether the request is account-led, owner-led, territory-led, ICP-led, or pipeline-view-led.
-- Set an execution cap from `capacity_window` or `batch_size`.
-- Preserve any explicitly supplied candidate accounts as the top evaluation set.
-- Use `references/request-schema.yaml` only when structured input validation, YAML normalization, or machine-readable field shape matters.
-- If the user request is broad, proceed with the default CRM-backed assumptions from `Default Intake Behavior`.
-- If CRM is unavailable or cannot resolve the account universe, offer to connect/install CRM when possible or ask for CRM-equivalent account context.
-
-### 2. Build the candidate account set
-
-- Use `crm` as the required account truth source for the standard run.
-- Prefer user-supplied CRM scopes, owner scopes, or CRM-backed account lists before broad discovery.
-- If the default or assumed source returns no owned open pipeline, no matching account list, or no candidate set for the stated scope, stop before broadening the request. Briefly report the empty result and ask whether to broaden to recent account activity, expansion customers, net-new targets, or a user-provided account list or territory.
-- Do not silently pivot from "best accounts to go after" to "accounts where the user can help or coordinate" unless the user chooses that broader action model.
-- Fetch enough candidates before suppression to satisfy the requested cap when possible, but do not widen beyond the confirmed account set unless the first pass is empty, thin, or misleading.
-- If the user requested an explicit count, preserve it as the target Suggested Focus row count throughout selection. Do not demote an otherwise viable Nth account to `Monitor` solely because confidence is lower; use confidence and status labels instead.
-
-### 3. Branch and classify each account
-
-- Classify each account as `net_new`, `expansion`, or `unknown_branch`.
-- Prefer `crm`-backed evidence for customer status, current owner, open opportunities, recent or upcoming meetings, and current stage of any existing motion.
-- Treat missing owner, weak account identity, or no usable contact path as execution blockers, not minor enrichment gaps.
-- Do not blend `net_new` and `expansion` logic in the same row. Pick one branch and state it.
-- If branch evidence is weak, mark the row `partial` or `blocked` instead of guessing.
-
-### 4. Apply suppressors before drafting
-
-Always suppress when strongly evidenced:
-
-- active open opportunity;
-- recent or upcoming external meeting already in motion;
-- clearly active seller, SE, account-team, or internal team motion that would make new outreach duplicative;
-- unresolved account ownership or contact-path ambiguity that makes immediate rep action unsafe to recommend.
-
-Do not draft suppressed accounts unless the user explicitly asks to override suppression. Use `references/suppression-and-fallbacks.md` when suppression, blocking, monitoring, or fallback-lane behavior needs more detail than these rules.
-
-### 5. Score for rep actionability
+The recurring output should follow this skill's account-priority contract: Suggested Focus, Monitor, Suppress Or Block, Evidence Gaps, and Source & Run Details. Keep it read-only; it may recommend account actions, messages to draft, or CRM corrections for review, but must not execute outreach, create records, update CRM, or send messages unless the user separately asks and approves.
 
-Rank accounts using explicit evidence, not opaque scoring. Prioritize:
+Before offering the weekly account-priority brief, check whether the user already has a matching local automation installed. Inspect local automation records under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset, and match by name, prompt, skill name, cadence, account set, owner, territory, pipeline view, ranking basis, motion goal, or other stable scope details. Treat active and paused matches as already installed.
 
-- strongest `why_now`;
-- reachable stakeholder path;
-- clear branch fit;
-- absence of suppressors;
-- fit with the rep's capacity window.
+- If a matching automation exists, do not suggest creating another one. Continue with the next most relevant non-automation follow-up.
+- If no matching automation exists, end with one clear offer to check/create a weekly rerun of `prioritize-accounts` for the same scope. Describe the recurring output as a seller-ready weekly worklist of accounts to focus, accounts to monitor, accounts to suppress, why each matters, and the recommended next move. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, do not mention tool details; offer to help set up a recurring account-priority brief when automations are available.
 
-Favor fewer, higher-confidence executable rows over broad list generation.
+## Modes
 
-### 6. Select contacts and next action
+### 1. Account Action View
 
-- Resolve one primary contact per account row.
-- Prefer `crm` contact truth first.
-- If `crm` is thin, use user-provided contacts, `document_store` account context, public research, or user-provided market intelligence as fallback.
-- Use `meeting_notes`, `external_messaging`, and `internal_messaging` only as corroboration or continuity lanes, not as the sole source of contact fit.
-- Add a secondary contact only when it materially supports multithreading.
-- Never fabricate a contact. If the path is weak, keep the row out of Suggested Focus.
+- This is the required default mode whenever stable connected-source or source-of-truth links are available.
+- Do not choose Chat Fallback merely because the candidate set is small, the result can fit in chat, or some links need to be constructed from a trusted connector-returned org or instance base URL.
+- Use for substantive ranking runs when CRM, another searchable connected source, or stable source-of-truth links can open useful row context.
+- Build Work now, Watch, Paused, Evidence Gaps, and Source & Run Details. Map Suggested Focus to Work now, Monitor to Watch, and Suppress Or Block to Paused so the pane and any chat readout stay semantically identical.
+- Use the bundled template exactly:
 
-### 7. Gather why-now and message-angle evidence
+  `assets/account-priority-pane.template.html`
 
-- Use `crm` first for account and opportunity truth.
-- After CRM anchors the account set, pull in any available and potentially relevant `calendar`, `meeting_notes`, `external_messaging`, `internal_messaging`, `document_store`, and `data_enrichment` context that could improve prioritization, suppression, timing, confidence, or next action.
-- Prefer known account context over generic personalization tactics.
-- Use `calendar` to suppress or down-rank accounts with meetings already in flight.
-- Use `external_messaging`, `internal_messaging`, and `meeting_notes` only when they materially change timing, urgency, contact choice, or the sequence angle.
-- Keep sourced facts separate from inference.
+- The template is standalone UTF-8 HTML with inline CSS and browser JavaScript only. It has no external assets, no script imports, no npm dependencies, no Node dependency, and no helper scripts.
+- Do not inline, rewrite, restyle, summarize, or recompose the template. The only allowed HTML mutation is replacing its exact data placeholder with the escaped payload described below.
 
-### 8. Render the account action package
+#### View Generation Rules
 
-Build the account action package with these components:
+- Generate the view whenever stable connected-source or source-of-truth links are available. A pasted unlinked list stays in chat unless the user explicitly asks for an offline view.
+- Generate one local file in an OS-appropriate temporary directory:
+  - macOS/Linux: `${TMPDIR:-/tmp}/prioritize-accounts/index.html`
+  - Windows: `%TEMP%\prioritize-accounts\index.html`
+- Build the account action package using Work now, Watch, Paused, Evidence Gaps, and Source & Run Details.
+- Serialize the three account groups under `workNow`, `watch`, and `paused` respectively.
+- Give every account row the seller-facing fields required by the payload schema: `rank`, `account`, `motion`, `stage`, `value`, `whyItMatters`, `nextAction`, `owner`, `dueDate`, `confidence`, and `status`. Include `accountUrl`, `primaryContact`, and `evidence` when grounded and available.
+- Keep row copy compact enough to scan: `whyItMatters` should explain the timing, value, momentum, or risk in one or two short sentences, and `nextAction` should name one immediate safe action, watch trigger, or unblocking step.
+- Never invent display fields. Use `Value unavailable`, `Unassigned`, or `No date set` when the connected source does not provide value, owner, or due-date truth.
+- Serialize the same package as JSON that conforms exactly to `references/template-payload.schema.json`. Treat the schema's required root keys and account-row fields as the canonical payload contract.
+- Escape the JSON for safe embedding in a script tag by replacing every `<` with `\u003c`.
+- Replace the exact template placeholder `__PRIORITIZE_ACCOUNTS_DATA_JSON__` with the escaped JSON.
+- Write the resulting HTML as UTF-8 to the local output path.
+- Sanity check that the generated file contains no unresolved placeholder.
+- Return the actual clickable view link first in the final response, then a short summary and proposed next step. Do not hardcode macOS-only `/private/tmp` in Windows-facing output. URL-encode spaces if emitting a `file://` URL.
+- If local file links do not open in the client, start a tiny local HTTP server yourself only when necessary and return the localhost URL.
+- Do not mention templates, JSON, placeholders, local files, or HTML mechanics in ordinary user-facing copy.
 
-1. `Suggested Focus`
-2. `Monitor`
-3. `Suppress Or Block`
-4. `Evidence Gaps`
-5. `Source & Run Details`
+### 2. Chat Fallback
 
-Put `Suggested Focus` first in the account action view. Sellers should see the priority accounts, why-now evidence, contacts, recommendations, and suggested next steps before run metadata. Put source of truth, sources checked, account set, ranking basis, counts, assumptions, and last-updated details at the bottom in `Source & Run Details`.
+- Use when no useful source search/linking is available, local view creation is impossible, the user asks for plain text, or a parent flow requires chat-only output.
+- State why the account action view was skipped when the missing source/link condition caused the fallback.
 
-In `Source & Run Details`, name the resolved account set, ranking basis, source of truth, action owner, batch size or capacity window, and main sources checked. If the run used assumptions, label the result as assumption-based. If the source was broadened after a no-results guard, label it as partial rather than a definitive "best accounts" result.
+#### Output Format
 
-In the account action view, Suggested Focus rows must show explicit priority order. Set `rank` to `1`, `2`, `3`, and so on in the serialized view payload, where `1` is the highest-priority account to work first. The chat fallback can rely on table order, but the view should display the priority number as plain text.
+```md
+# Prioritize Accounts
 
-Use this exact Suggested Focus row schema for the account action view and for any chat fallback:
+## Suggested Focus
 
-```md
 | Account | Why Now | Primary Contact | Secondary Contact | Opp Recommendation | Suggested Next Step | Sequence Angle | Confidence | Status |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-```
+| [Account + source link] | [Grounded timing reason] | [Name + role] | [Name + role or None] | [Practical recommendation] | [Immediate rep action] | [Compact outreach hook] | [High/Medium/Low] | [ready_now/partial] |
 
-Rules:
+## Monitor
 
-- Classify motion internally as `net_new` or `expansion`, but do not render Motion as a column in the account action view or chat fallback.
-- Include `accountUrl` when a CRM, document, spreadsheet, or other source-of-truth record URL is available. In Salesforce, prefer the UI record URL for the Account. The account name renders as a clickable link when this field is populated.
-- Use human-readable dates in all user-visible row text, for example `Jun 1` or `Jun 1, 2026` instead of `2026-06-01`. Machine fields such as `generatedAt` may remain ISO because the view formats them for display.
-- `Why Now` must be source-grounded. If it is not well supported, the row should not be in Suggested Focus.
-- `Secondary Contact` may be `None` when multithreading is not supported by evidence.
-- `Opp Recommendation` should be concise and practical, for example `work account before opp`, `create opp after discovery`, or `expand existing motion later`.
-- `Suggested Next Step` must be an immediate rep action and should be populated from `nextStep` or `suggestedNextStep` in the serialized payload. This is an assistant recommendation, not a CRM field value.
-- `Sequence Angle` should summarize the outreach hook, not render a full sequence.
-- `Confidence` should be a plain-language rating such as `High`, `Medium`, or `Low`.
-- `Status` must be `ready_now` or `partial` in Suggested Focus; move `blocked` and `suppressed` rows to later sections.
-
-Use this `Monitor` row schema:
-
-```md
 | Account | Reason To Monitor | Trigger To Revisit | Status |
 | --- | --- | --- | --- |
-```
 
-Use this `Suppress Or Block` row schema:
+## Suppress Or Block
 
-```md
 | Account | Suppressor Or Blocker | Evidence | Status |
 | --- | --- | --- | --- |
-```
 
-Use `Evidence Gaps` for a flat list of the most important missing inputs, connector truth, or account gaps that limited the run.
+## Sources
 
-After building these rows, fill `assets/account-priority-pane.template.html` using `Account Action View Output` only when useful source search/linking is available. The account action view is the primary presentation in that case. In the chat response, return the view link first and then a concise readout of the ranking, source of truth, and evidence gaps. Render the full chat tables only when the user asks for plain text, when no useful source search/linking is available, when local file creation is impossible, or when a parent flow explicitly requires chat-only output.
+- [Sources that were included in the analysis]
+- [Sources that were not included, why, and potential next steps to resolve]
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+## Source & Run Details
 
-## Output Rules
+- **Account set:** [Resolved set]
+- **Ranking basis:** [Visible rule]
+- **Source of truth:** [CRM or CRM-equivalent source]
+- **Motion goal:** [net_new / expansion / mixed]
+- **Sources checked:** [Sources]
+- **Assumptions:** [Assumption-based only when applicable]
 
-- This is a prioritization and activation workflow, not a generic lead list.
-- For substantive ranking runs, make the account action view the primary output and put its link first.
-- Keep the account row as the primary unit of work.
-- Prefer account-level prioritization over contact sprawl.
-- Show evidence gaps that prevented action.
-- Do not claim urgency, contact fit, or motion status without evidence.
-- Do not fabricate contacts, urgency, opportunity posture, or account facts.
-- Prefer smaller, more executable Suggested Focus output over larger noisy output.
-- When the user requested a specific count, prefer exactly that many executable Suggested Focus rows over a smaller polished list unless suppression, blocking, or insufficient account truth leaves fewer viable candidates.
-- If the user explicitly asks for email drafts in a separate execution step and a real draft is created or saved through `external_messaging`, include a clickable Markdown link to each created draft using the URL returned by the app. If only copy is drafted in chat, say no email draft was created.
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
diff --git a/plugins/sales/skills/prioritize-accounts/agents/openai.yaml b/plugins/sales/skills/prioritize-accounts/agents/openai.yaml
index 398821f..09fd8b2 100644
--- a/plugins/sales/skills/prioritize-accounts/agents/openai.yaml
+++ b/plugins/sales/skills/prioritize-accounts/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Rank accounts and open a local action pane"
   default_prompt: "Prioritize accounts for pipeline focus."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/prioritize-accounts/assets/account-priority-pane.template.html b/plugins/sales/skills/prioritize-accounts/assets/account-priority-pane.template.html
index 1d0549e..3157ea8 100644
--- a/plugins/sales/skills/prioritize-accounts/assets/account-priority-pane.template.html
+++ b/plugins/sales/skills/prioritize-accounts/assets/account-priority-pane.template.html
@@ -3,51 +3,79 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
-    <title>Prioritize Accounts</title>
+    <meta name="color-scheme" content="light dark" />
+    <title>Priority Ledger — Sales Focus</title>
     <style>
       :root {
         color-scheme: light;
-        --bg: #f7f8fa;
-        --panel: #ffffff;
-        --panel-soft: #f9fafb;
-        --text: #182230;
-        --muted: #667085;
-        --line: #e4e7ec;
-        --line-strong: #d0d5dd;
-        --blue: #0b5cab;
-        --blue-soft: #e8f1ff;
-        --green: #16794c;
-        --green-soft: #e9f7ef;
-        --amber: #b65e00;
-        --amber-soft: #fff4df;
-        --red: #b42318;
-        --red-soft: #fff1f0;
-        --shadow: 0 1px 2px rgba(16, 24, 40, 0.04);
+        --bg: #f7faf8;
+        --bg-elevated: #ffffff;
+        --bg-hover: #f0f6f3;
+        --bg-control: rgba(255, 255, 255, 0.88);
+        --line: #dbe4df;
+        --line-strong: #bdcbc4;
+        --text: #16211c;
+        --muted: #68756f;
+        --subtle: #89948f;
+        --accent: #167a55;
+        --accent-strong: #0c6041;
+        --accent-soft: #dff3e9;
+        --accent-contrast: #ffffff;
+        --danger: #a73a35;
+        --backdrop: rgba(19, 29, 24, 0.42);
+        --shadow: 0 16px 50px rgba(27, 45, 35, 0.12);
+        --app-font-sans: var(
+          --font-sans,
+          ui-sans-serif,
+          -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          sans-serif
+        );
+      }
+
+      @media (prefers-color-scheme: dark) {
+        :root {
+          color-scheme: dark;
+          --bg: #090f12;
+          --bg-elevated: #0d1519;
+          --bg-hover: rgba(18, 28, 32, 0.72);
+          --bg-control: rgba(15, 22, 26, 0.72);
+          --line: #243036;
+          --line-strong: #445159;
+          --text: #f3f5f4;
+          --muted: #899399;
+          --subtle: #5e696f;
+          --accent: #68d6a7;
+          --accent-strong: #a3eccb;
+          --accent-soft: #183028;
+          --accent-contrast: #072218;
+          --danger: #ff8580;
+          --backdrop: rgba(2, 6, 8, 0.66);
+          --shadow: -28px 0 80px rgba(0, 0, 0, 0.35);
+        }
       }
 
       * {
         box-sizing: border-box;
       }
 
+      html,
+      body {
+        min-height: 100%;
+        margin: 0;
+        background: var(--bg);
+      }
+
       html {
         scrollbar-gutter: stable;
         -webkit-text-size-adjust: 100%;
         text-size-adjust: 100%;
       }
 
-      html,
-      body,
-      #app {
-        min-height: 100%;
-      }
-
       body {
-        margin: 0;
-        background: var(--bg);
         color: var(--text);
-        font-family:
-          ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI",
-          "Roboto", "Helvetica Neue", Arial, sans-serif;
+        font-family: var(--app-font-sans);
         font-size: 14px;
         line-height: 1.5;
         -webkit-font-smoothing: antialiased;
@@ -56,7 +84,7 @@
 
       button,
       input,
-      select {
+      a {
         color: inherit;
         font: inherit;
       }
@@ -65,1162 +93,1343 @@
         cursor: pointer;
       }
 
-      .shell {
-        display: grid;
-        grid-template-rows: auto 1fr;
+      button:focus-visible,
+      input:focus-visible,
+      a:focus-visible,
+      summary:focus-visible {
+        border-radius: 6px;
+        outline: 2px solid var(--accent);
+        outline-offset: 3px;
+      }
+
+      svg {
+        display: block;
+      }
+
+      .app-shell {
         min-height: 100vh;
+        background: var(--bg);
       }
 
       .topbar {
         position: sticky;
         top: 0;
-        z-index: 5;
-        display: flex;
+        z-index: 10;
+        display: grid;
+        grid-template-columns: 1fr minmax(320px, 450px) 1fr auto;
         align-items: center;
-        justify-content: space-between;
-        gap: 16px;
-        min-height: 58px;
-        border-bottom: 1px solid var(--line);
-        background: rgba(255, 255, 255, 0.96);
-        box-shadow: var(--shadow);
-        padding: 10px max(16px, calc(((100vw - 1320px) / 2) + 18px));
+        gap: 28px;
+        min-height: 76px;
+        border-bottom: 1px solid color-mix(in srgb, var(--line) 72%, transparent);
+        background: color-mix(in srgb, var(--bg) 92%, transparent);
+        padding: 0 27px;
+        -webkit-backdrop-filter: blur(16px);
+        backdrop-filter: blur(16px);
       }
 
-      .brand {
+      .brand-lockup {
         display: flex;
         align-items: center;
+        gap: 18px;
         min-width: 0;
-        gap: 10px;
+        font-size: 15px;
+      }
+
+      .brand-lockup strong {
+        overflow: hidden;
+        letter-spacing: -0.01em;
+        font-weight: 650;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
       .brand-mark {
         display: grid;
-        place-items: center;
-        width: 32px;
-        height: 32px;
+        width: 34px;
+        height: 34px;
         flex: 0 0 auto;
-        border-radius: 7px;
-        background: var(--blue);
-        color: #ffffff;
-        font-weight: 900;
+        place-items: center;
+        border-radius: 50%;
+        background: var(--accent);
+        color: var(--accent-contrast);
+        transform: rotate(-45deg);
       }
 
-      .brand-text {
-        min-width: 0;
+      .divider {
+        width: 1px;
+        height: 20px;
+        flex: 0 0 auto;
+        background: var(--line);
       }
 
-      .brand-text strong {
-        display: block;
+      .current-view {
         overflow: hidden;
+        color: var(--muted);
         text-overflow: ellipsis;
         white-space: nowrap;
       }
 
-      .brand-text strong {
-        color: #092b5a;
-        font-size: 15px;
-      }
-
-      .controls {
+      .search-box {
         display: flex;
         align-items: center;
-        justify-content: flex-end;
-        gap: 8px;
-        min-width: 0;
+        gap: 11px;
+        height: 44px;
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        background: var(--bg-control);
+        color: var(--muted);
+        padding: 0 14px;
+        transition:
+          border-color 150ms ease,
+          background 150ms ease;
       }
 
-      .control {
-        min-height: 34px;
-        border: 1px solid var(--line);
-        border-radius: 6px;
-        background: #ffffff;
+      .search-box:focus-within {
+        border-color: color-mix(in srgb, var(--accent) 62%, var(--line));
+        background: var(--bg-elevated);
+      }
+
+      .search-box input {
+        width: 100%;
+        min-width: 0;
+        border: 0;
+        outline: 0;
+        background: transparent;
         color: var(--text);
-        padding: 7px 9px;
+        font-size: 14px;
       }
 
-      .search {
-        width: min(280px, 32vw);
+      .search-box input::placeholder {
+        color: var(--subtle);
       }
 
-      .main {
+      .search-clear {
         display: grid;
-        gap: 22px;
-        width: min(1120px, 100%);
-        margin: 0 auto;
-        padding: 22px 18px 34px;
+        width: 26px;
+        height: 26px;
+        flex: 0 0 auto;
+        place-items: center;
+        border: 0;
+        border-radius: 50%;
+        background: transparent;
+        color: var(--muted);
+        padding: 0;
       }
 
-      .scenario-panel {
-        display: grid;
-        gap: 6px;
+      .search-clear:hover {
+        background: var(--bg-hover);
+        color: var(--text);
       }
 
-      .scenario-note {
+      .search-clear[hidden] {
+        display: none;
+      }
+
+      .sync-state {
+        display: flex;
+        align-items: center;
+        justify-self: end;
+        gap: 8px;
+        overflow: hidden;
         color: var(--muted);
         font-size: 12px;
-        line-height: 1.45;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
-      .scenario-options {
-        display: flex;
-        flex-wrap: wrap;
-        gap: 6px;
+      .sync-dot {
+        width: 7px;
+        height: 7px;
+        flex: 0 0 auto;
+        border-radius: 50%;
+        background: var(--accent);
+        box-shadow: 0 0 0 3px color-mix(in srgb, var(--accent) 12%, transparent);
       }
 
-      .scenario-button {
-        min-height: 26px;
+      .avatar {
+        width: 34px;
+        height: 34px;
         border: 1px solid var(--line);
-        border-radius: 6px;
-        background: #ffffff;
-        color: #344054;
+        border-radius: 50%;
+        background: var(--bg-elevated);
+        color: var(--text);
         font-size: 12px;
-        font-weight: 650;
-        line-height: 1;
-        padding: 5px 9px;
+        font-weight: 700;
       }
 
-      .scenario-button:hover {
-        border-color: var(--line-strong);
-        background: #fbfcfe;
+      .content {
+        width: 100%;
+        padding: 38px 27px 24px;
       }
 
-      .scenario-button.is-active {
-        border-color: #9cc3f5;
-        background: var(--blue-soft);
-        color: #092b5a;
+      .page-intro h1 {
+        margin: 0;
+        letter-spacing: -0.02em;
+        font-size: 19px;
+        font-weight: 620;
+        line-height: 1.3;
       }
 
-      .summary-grid {
-        display: grid;
-        grid-template-columns: repeat(5, minmax(0, 1fr));
-        gap: 10px;
+      .page-intro p {
+        max-width: 680px;
+        margin: 8px 0 0;
+        color: var(--muted);
+        font-size: 14px;
       }
 
-      .section > .summary-grid {
-        padding: 0 12px 12px;
+      .status-tabs {
+        display: grid;
+        grid-template-columns: repeat(3, 1fr);
+        width: min(580px, 100%);
+        height: 56px;
+        margin-top: 36px;
+        border: 1px solid var(--line-strong);
+        border-radius: 10px;
+        padding: 4px;
       }
 
-      .metric {
-        min-width: 0;
-        border: 1px solid var(--line);
-        border-radius: 8px;
-        background: var(--panel);
-        box-shadow: var(--shadow);
-        padding: 10px;
+      .status-tabs button {
+        position: relative;
+        border: 0;
+        border-radius: 7px;
+        background: transparent;
+        color: var(--muted);
+        font-size: 14px;
+        transition:
+          background 150ms ease,
+          color 150ms ease;
       }
 
-      .metric span {
-        display: block;
-        color: var(--muted);
-        font-size: 10px;
-        letter-spacing: 0.04em;
-        text-transform: uppercase;
+      .status-tabs button + button::before {
+        position: absolute;
+        top: 14px;
+        left: 0;
+        width: 1px;
+        height: 20px;
+        background: var(--line);
+        content: "";
       }
 
-      .metric strong {
-        display: block;
-        overflow: hidden;
-        margin-top: 5px;
-        font-size: 20px;
-        text-overflow: ellipsis;
-        white-space: nowrap;
+      .status-tabs button:hover {
+        color: var(--text);
       }
 
-      .section {
-        overflow: hidden;
-        border: 0;
-        border-radius: 0;
-        background: transparent;
-        box-shadow: none;
+      .status-tabs button[aria-selected="true"] {
+        background: var(--accent-soft);
+        color: var(--text);
+        font-weight: 600;
       }
 
-      .section-header {
-        display: grid;
-        gap: 8px;
-        border-bottom: 0;
-        background: transparent;
-        padding: 0 0 9px;
+      .status-tabs button[aria-selected="true"]::before,
+      .status-tabs button[aria-selected="true"] + button::before {
+        opacity: 0;
       }
 
-      .section-title {
-        min-width: 0;
+      .tab-count {
+        display: inline-grid;
+        min-width: 24px;
+        height: 24px;
+        margin-left: 8px;
+        place-items: center;
+        border-radius: 999px;
+        background: color-mix(in srgb, var(--line) 64%, transparent);
+        color: var(--text);
+        padding: 0 7px;
+        font-size: 12px;
       }
 
-      h1,
-      h2,
-      p {
-        margin: 0;
+      .status-tabs button[aria-selected="true"] .tab-count {
+        background: color-mix(in srgb, var(--accent) 22%, transparent);
       }
 
-      h1 {
-        font-size: 22px;
-        line-height: 28px;
-        overflow-wrap: anywhere;
+      .ledger {
+        margin-top: 36px;
       }
 
-      h2 {
-        color: #344054;
-        font-size: 14px;
+      .ledger-head {
+        display: grid;
+        grid-template-columns: 29% 26% 30% 15%;
+        align-items: center;
+        height: 48px;
+        border-bottom: 1px solid var(--line);
+        color: var(--subtle);
+        padding: 0 48px 0 72px;
+        font-size: 12px;
         letter-spacing: 0.01em;
-        line-height: 18px;
       }
 
-      .subtle {
-        color: var(--muted);
-        font-size: 12px;
+      .account-list {
+        min-height: 160px;
       }
 
-      .pill {
-        display: inline-flex;
+      .account-row {
+        display: grid;
+        grid-template-columns:
+          52px calc(29% - 52px) 26% 30% calc(15% - 42px)
+          42px;
         align-items: center;
-        min-height: 24px;
-        border-radius: 999px;
-        font-size: 12px;
-        font-weight: 800;
-        padding: 3px 9px;
-        white-space: nowrap;
+        min-height: 172px;
+        border-bottom: 1px solid color-mix(in srgb, var(--line) 78%, transparent);
+        transition:
+          background 150ms ease,
+          opacity 150ms ease;
       }
 
-      .status-text {
-        color: var(--muted);
-        font-weight: 700;
+      .account-row:hover {
+        background: var(--bg-hover);
       }
 
-      .status-text.high {
-        color: var(--green);
+      .account-row.is-complete {
+        opacity: 0.52;
       }
 
-      .status-text.medium {
-        color: var(--amber);
+      .account-row.is-complete .action-copy {
+        text-decoration: line-through color-mix(in srgb, var(--accent) 60%, transparent);
       }
 
-      .status-text.low {
-        color: var(--red);
+      .rank {
+        color: var(--accent);
+        padding-left: 8px;
+        font-size: 18px;
+        font-weight: 560;
       }
 
-      .tone-ready,
-      .tone-ready-now,
-      .tone-high {
-        background: var(--green-soft);
-        color: var(--green);
+      .account-cell {
+        display: flex;
+        align-items: center;
+        gap: 18px;
+        min-width: 0;
+        padding-right: 22px;
       }
 
-      .tone-partial,
-      .tone-monitor,
-      .tone-medium {
-        background: var(--amber-soft);
-        color: var(--amber);
+      .company-logo {
+        display: grid;
+        width: 68px;
+        height: 68px;
+        flex: 0 0 auto;
+        place-items: center;
+        border: 1px solid color-mix(in srgb, var(--line) 70%, transparent);
+        border-radius: 14px;
+        background:
+          linear-gradient(
+            145deg,
+            color-mix(in srgb, var(--logo-accent) 30%, var(--bg-elevated)),
+            var(--bg-elevated)
+          );
+        color: var(--logo-accent);
+        font-size: 19px;
+        font-weight: 760;
+        letter-spacing: -0.03em;
+      }
+
+      .account-copy {
+        min-width: 0;
       }
 
-      .tone-blocked,
-      .tone-suppressed,
-      .tone-low {
-        background: var(--red-soft);
-        color: var(--red);
+      .account-copy h2 {
+        overflow: hidden;
+        margin: 0 0 9px;
+        letter-spacing: -0.015em;
+        font-size: 16px;
+        font-weight: 620;
+        line-height: 1.35;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
-      .tone-info {
-        background: var(--blue-soft);
-        color: #092b5a;
+      .account-copy p,
+      .account-copy span {
+        display: block;
+        overflow: hidden;
+        margin: 0;
+        color: var(--muted);
+        font-size: 13px;
+        line-height: 1.65;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
-      .scope-grid {
-        display: grid;
-        grid-template-columns: repeat(3, minmax(0, 1fr));
-        gap: 10px;
-        padding: 12px;
+      .account-copy span {
+        color: var(--subtle);
       }
 
-      .field {
+      .why-cell {
+        align-self: center;
         min-width: 0;
-        border: 1px solid var(--line);
-        border-radius: 7px;
-        background: #ffffff;
-        padding: 9px;
+        padding-right: 34px;
       }
 
-      .field span {
+      .why-cell strong {
         display: block;
-        color: var(--muted);
-        font-size: 10px;
-        letter-spacing: 0.04em;
-        text-transform: uppercase;
+        overflow: hidden;
+        margin-bottom: 9px;
+        color: var(--accent);
+        font-size: 17px;
+        font-weight: 610;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
-      .field strong {
-        display: block;
-        margin-top: 3px;
-        overflow-wrap: anywhere;
+      .why-cell p {
+        display: -webkit-box;
+        overflow: hidden;
+        max-width: 340px;
+        margin: 0;
+        color: var(--muted);
+        font-size: 13px;
+        line-height: 1.5;
+        -webkit-box-orient: vertical;
+        -webkit-line-clamp: 3;
       }
 
-      .table-wrap {
-        display: none;
-        overflow-x: auto;
-        scrollbar-gutter: stable;
-        border: 1px solid var(--line);
-        border-radius: 10px;
-        background: #ffffff;
-        box-shadow: var(--shadow);
+      .action-cell {
+        display: grid;
+        grid-template-columns: 38px 1fr;
+        align-items: start;
+        gap: 14px;
+        min-width: 0;
+        padding-right: 40px;
       }
 
-      table {
-        width: 100%;
-        min-width: 900px;
-        border-collapse: collapse;
-        table-layout: fixed;
+      .complete-button {
+        display: grid;
+        width: 34px;
+        height: 34px;
+        place-items: center;
+        border: 1px solid var(--accent);
+        border-radius: 50%;
+        background: transparent;
+        color: var(--accent);
+        padding: 0;
+        transition:
+          background 150ms ease,
+          color 150ms ease,
+          transform 150ms ease;
       }
 
-      .table-suggested-focus {
-        min-width: 1240px;
+      .complete-button:hover,
+      .complete-button[aria-pressed="true"] {
+        background: var(--accent);
+        color: var(--accent-contrast);
+        transform: translateX(2px);
       }
 
-      .table-monitor {
-        min-width: 1120px;
+      .action-copy {
+        display: -webkit-box;
+        overflow: hidden;
+        border: 0;
+        background: transparent;
+        color: var(--accent);
+        padding: 0;
+        text-align: left;
+        font-size: 15px;
+        line-height: 1.55;
+        -webkit-box-orient: vertical;
+        -webkit-line-clamp: 3;
       }
 
-      .table-suppress-or-block {
-        min-width: 1100px;
+      .action-copy:hover {
+        color: var(--accent-strong);
       }
 
-      th {
-        border-bottom: 1px solid var(--line-strong);
-        background: #f8fafc;
-        color: var(--muted);
-        font-size: 11px;
-        letter-spacing: 0.04em;
-        line-height: 1.25;
-        overflow-wrap: anywhere;
-        padding: 10px 12px;
-        text-align: left;
-        text-transform: uppercase;
-        white-space: nowrap;
+      .owner-cell {
+        display: grid;
+        gap: 14px;
+        color: var(--text);
+        font-size: 12px;
       }
 
-      td {
-        border-bottom: 1px solid var(--line);
-        padding: 12px;
-        vertical-align: top;
-        overflow-wrap: anywhere;
+      .owner-cell span {
+        display: flex;
+        align-items: center;
+        gap: 8px;
+        min-width: 0;
       }
 
-      tbody tr:hover {
-        background: #f8fbff;
+      .owner-cell span > span {
+        display: block;
+        overflow: hidden;
+        text-overflow: ellipsis;
+        white-space: nowrap;
       }
 
-      tr:last-child td {
-        border-bottom: 0;
+      .owner-cell svg {
+        flex: 0 0 auto;
+        color: var(--muted);
       }
 
-      td strong {
-        display: block;
-        overflow-wrap: anywhere;
+      .open-row {
+        display: grid;
+        width: 36px;
+        height: 44px;
+        place-items: center;
+        border: 0;
+        background: transparent;
+        color: var(--subtle);
+        padding: 0;
+        transition:
+          color 150ms ease,
+          transform 150ms ease;
       }
 
-      .col-account {
-        width: 160px;
+      .open-row:hover {
+        color: var(--accent);
+        transform: translateX(2px);
       }
 
-      .col-rank {
-        width: 72px;
+      .result-count {
+        margin: 16px 0 0;
+        color: var(--subtle);
+        text-align: center;
+        font-size: 11px;
       }
 
-      .col-confidence,
-      .col-status {
-        width: 108px;
+      .empty-state {
+        display: grid;
+        min-height: 360px;
+        place-content: center;
+        justify-items: center;
+        color: var(--muted);
+        text-align: center;
       }
 
-      .col-contact {
-        width: 190px;
+      .empty-state h2 {
+        margin: 14px 0 5px;
+        color: var(--text);
+        font-size: 17px;
       }
 
-      .col-why,
-      .col-next {
-        width: 260px;
+      .empty-state p {
+        max-width: 420px;
+        margin: 0;
+        font-size: 13px;
       }
 
-      .col-date {
-        width: 145px;
+      .run-details {
+        width: min(760px, 100%);
+        margin: 22px auto 0;
+        border-top: 1px solid var(--line);
+        color: var(--muted);
+        padding-top: 14px;
+        font-size: 12px;
       }
 
-      .col-cleanup {
-        width: 240px;
+      .run-details summary {
+        width: fit-content;
+        margin: 0 auto;
+        cursor: pointer;
+        color: var(--muted);
+        list-style: none;
       }
 
-      .col-angle {
-        width: 230px;
+      .run-details summary::-webkit-details-marker {
+        display: none;
       }
 
-      .col-opp {
-        width: 200px;
+      .run-details summary:hover {
+        color: var(--text);
       }
 
-      .col-evidence {
-        width: 280px;
+      .run-details-content {
+        display: grid;
+        grid-template-columns: repeat(2, minmax(0, 1fr));
+        gap: 12px 28px;
+        padding: 18px 0 4px;
       }
 
-      .account-link {
-        color: var(--blue);
-        font-weight: 800;
-        text-decoration: none;
+      .run-detail {
+        min-width: 0;
       }
 
-      .account-link:hover {
-        text-decoration: underline;
+      .run-detail span {
+        display: block;
+        margin-bottom: 3px;
+        color: var(--subtle);
+        font-size: 11px;
+        text-transform: uppercase;
+        letter-spacing: 0.08em;
       }
 
-      .cards {
+      .run-detail strong {
         display: block;
-        padding: 0;
+        overflow-wrap: anywhere;
+        color: var(--text);
+        font-weight: 520;
       }
 
-      .card {
-        display: grid;
-        gap: 10px;
-        border: 1px solid var(--line);
-        border-radius: 8px;
-        background: #ffffff;
-        box-shadow: var(--shadow);
-        padding: 12px 14px;
+      .evidence-gaps {
+        grid-column: 1 / -1;
+        margin: 2px 0 0;
+        padding-left: 18px;
       }
 
-      .card + .card {
-        margin-top: 8px;
+      .evidence-gaps li + li {
+        margin-top: 4px;
       }
 
-      .card-top {
-        display: flex;
-        align-items: flex-start;
-        justify-content: space-between;
-        gap: 10px;
+      .drawer-backdrop {
+        position: fixed;
+        z-index: 30;
+        inset: 0;
+        background: var(--backdrop);
+        animation: fade-in 180ms ease-out;
+        -webkit-backdrop-filter: blur(3px);
+        backdrop-filter: blur(3px);
       }
 
-      .card-title {
-        display: grid;
-        gap: 3px;
-        min-width: 0;
+      .drawer-backdrop[hidden] {
+        display: none;
       }
 
-      .card-title > strong {
-        font-size: 15px;
-        line-height: 1.2;
+      .detail-drawer {
+        position: absolute;
+        top: 0;
+        right: 0;
+        bottom: 0;
+        width: min(520px, 92vw);
+        overflow-y: auto;
+        border-left: 1px solid var(--line);
+        background: var(--bg-elevated);
+        box-shadow: var(--shadow);
+        padding: 42px;
+        animation: slide-in 240ms ease-out;
       }
 
-      .card-meta {
+      .drawer-close {
+        position: absolute;
+        top: 24px;
+        right: 24px;
+        display: grid;
+        width: 36px;
+        height: 36px;
+        place-items: center;
+        border: 1px solid var(--line);
+        border-radius: 50%;
+        background: transparent;
         color: var(--muted);
-        font-size: 12px;
-        line-height: 1.35;
+        padding: 0;
       }
 
-      .card-grid {
-        display: grid;
-        grid-template-columns: repeat(2, minmax(0, 1fr));
-        gap: 10px 14px;
+      .drawer-close:hover {
+        background: var(--bg-hover);
+        color: var(--text);
       }
 
-      .card-field {
-        min-width: 0;
+      .drawer-logo {
+        margin-bottom: 28px;
       }
 
-      .card-field span {
-        display: block;
-        color: #344054;
-        font-size: 10px;
-        font-weight: 750;
-        letter-spacing: 0.03em;
+      .eyebrow {
+        margin: 0 0 9px;
+        color: var(--accent);
+        font-size: 11px;
+        letter-spacing: 0.16em;
         text-transform: uppercase;
       }
 
-      .card-field.is-cleanup {
-        border-left: 0;
-        padding-left: 0;
+      .detail-drawer h2 {
+        max-width: calc(100% - 44px);
+        margin: 0;
+        letter-spacing: -0.035em;
+        font-size: 28px;
+        line-height: 1.18;
       }
 
-      .card-field.is-cleanup span,
-      .inline-note strong {
+      .drawer-meta {
+        margin: 8px 0 26px;
         color: var(--muted);
+        font-size: 13px;
       }
 
-      .card-field .card-value {
+      .drawer-section {
+        border-top: 1px solid var(--line);
+        padding: 26px 0;
+      }
+
+      .drawer-section > span,
+      .drawer-action > span {
         display: block;
-        margin-top: 3px;
-        color: var(--text);
-        font-size: 13px;
-        overflow-wrap: anywhere;
-        font-weight: 400;
-        line-height: 1.4;
+        margin-bottom: 10px;
+        color: var(--subtle);
+        font-size: 11px;
+        letter-spacing: 0.12em;
+        text-transform: uppercase;
       }
 
-      .card-note {
-        border-top: 1px solid #eef2f7;
-        color: var(--muted);
-        font-size: 12px;
-        line-height: 1.35;
-        padding-top: 8px;
+      .drawer-section strong {
+        color: var(--accent);
+        font-size: 22px;
       }
 
-      .inline-note {
-        color: var(--muted);
-        font-size: 12px;
-        line-height: 1.4;
-        margin-top: 8px;
+      .drawer-section p,
+      .drawer-action p {
+        margin: 0;
+        color: var(--text);
+        font-size: 15px;
+        line-height: 1.65;
       }
 
-      .inline-note strong {
-        color: #344054;
+      .drawer-action {
+        margin-top: 6px;
+        border-radius: 12px;
+        background: var(--accent-soft);
+        padding: 24px;
       }
 
-      .gaps {
-        display: grid;
-        gap: 6px;
-        border: 1px solid var(--line);
-        border-radius: 10px;
-        background: #ffffff;
-        padding: 14px 16px;
+      .drawer-action p {
+        font-size: 17px;
       }
 
-      .gap {
-        display: grid;
-        grid-template-columns: 10px 1fr;
+      .drawer-link {
+        display: flex;
+        align-items: center;
+        justify-content: center;
         gap: 9px;
-        color: var(--muted);
-        font-size: 13px;
+        width: 100%;
+        height: 44px;
+        margin-top: 24px;
+        border-radius: 8px;
+        background: var(--accent);
+        color: var(--accent-contrast);
+        font-weight: 700;
+        text-decoration: none;
       }
 
-      .dot {
-        width: 5px;
-        height: 5px;
-        margin-top: 7px;
-        border-radius: 999px;
-        background: var(--blue);
+      .drawer-link:hover {
+        background: var(--accent-strong);
       }
 
-      .empty {
-        border: 1px dashed var(--line);
-        border-radius: 10px;
-        background: #ffffff;
-        color: var(--muted);
-        padding: 22px;
-        text-align: center;
+      @keyframes fade-in {
+        from {
+          opacity: 0;
+        }
       }
 
-      .footnote {
-        color: var(--muted);
-        font-size: 12px;
-        padding: 0 2px 4px;
+      @keyframes slide-in {
+        from {
+          opacity: 0.5;
+          transform: translateX(40px);
+        }
       }
 
-      .source-note {
-        color: var(--muted);
-        font-size: 13px;
-        line-height: 1.45;
-        padding: 0 2px 4px;
-      }
+      @media (max-width: 1100px) {
+        .topbar {
+          grid-template-columns: 1fr minmax(280px, 360px) auto;
+        }
 
-      .meta-section {
-        border-top: 1px solid var(--line);
-        padding: 18px 0 0;
-      }
+        .sync-state {
+          display: none;
+        }
 
-      .meta-title {
-        color: #344054;
-        font-size: 14px;
-        font-weight: 650;
-        margin-bottom: 10px;
-      }
+        .ledger-head {
+          grid-template-columns: 34% 27% 39%;
+          padding-right: 32px;
+        }
 
-      .meta-grid {
-        display: grid;
-        grid-template-columns: repeat(3, minmax(0, 1fr));
-        gap: 12px 28px;
-      }
+        .ledger-head span:last-child {
+          display: none;
+        }
 
-      .meta-item {
-        min-width: 0;
-      }
+        .account-row {
+          grid-template-columns: 45px calc(34% - 45px) 27% 39% 34px;
+          min-height: 182px;
+        }
 
-      .meta-item span {
-        display: block;
-        color: var(--muted);
-        font-size: 10px;
-        letter-spacing: 0.04em;
-        text-transform: uppercase;
-      }
+        .owner-cell {
+          display: none;
+        }
 
-      .meta-item strong {
-        display: block;
-        margin-top: 2px;
-        font-size: 13px;
-        line-height: 1.4;
-        font-weight: 400;
+        .action-cell {
+          padding-right: 24px;
+        }
       }
 
-      @media (max-width: 1180px) {
-        .summary-grid,
-        .scope-grid {
-          grid-template-columns: repeat(2, minmax(0, 1fr));
+      @media (min-width: 761px) and (max-height: 980px) {
+        .content {
+          padding-top: 28px;
         }
-      }
 
-      @media (min-width: 1320px) {
-        .main {
-          width: min(1320px, 100%);
+        .status-tabs {
+          margin-top: 26px;
         }
 
-        .table-wrap {
-          display: block;
+        .ledger {
+          margin-top: 24px;
         }
 
-        .cards {
-          display: none;
+        .ledger-head {
+          height: 42px;
         }
 
-        .card-grid {
-          grid-template-columns: minmax(0, 1fr) minmax(0, 1fr);
+        .account-row {
+          min-height: 142px;
+        }
+
+        .company-logo {
+          width: 60px;
+          height: 60px;
+          border-radius: 12px;
+        }
+
+        .account-cell {
+          gap: 16px;
+        }
+
+        .account-copy h2,
+        .why-cell strong {
+          margin-bottom: 6px;
         }
       }
 
-      @media (max-width: 640px) {
+      @media (max-width: 760px) {
         .topbar {
-          position: static;
-          align-items: stretch;
-          flex-direction: column;
+          grid-template-columns: 1fr auto;
+          min-height: 72px;
+          padding: 16px 18px;
         }
 
-        .controls {
-          align-items: stretch;
-          flex-direction: column;
+        .brand-lockup .divider,
+        .current-view,
+        .avatar {
+          display: none;
         }
 
-        .search {
+        .search-box {
+          grid-area: 2 / 1 / auto / -1;
           width: 100%;
         }
 
-        .summary-grid,
-        .scope-grid,
-        .meta-grid,
-        .card-grid {
+        .content {
+          padding: 28px 18px 20px;
+        }
+
+        .status-tabs {
+          margin-top: 28px;
+        }
+
+        .ledger {
+          margin-top: 24px;
+        }
+
+        .ledger-head {
+          display: none;
+        }
+
+        .account-row {
+          grid-template-columns: 32px 1fr 32px;
+          gap: 0;
+          min-height: 0;
+          padding: 24px 0;
+        }
+
+        .rank {
+          grid-area: 1 / 1;
+          align-self: start;
+          padding-top: 4px;
+          padding-left: 0;
+        }
+
+        .account-cell {
+          grid-area: 1 / 2;
+        }
+
+        .company-logo {
+          width: 54px;
+          height: 54px;
+          border-radius: 11px;
+          font-size: 16px;
+        }
+
+        .why-cell {
+          grid-area: 2 / 2;
+          padding: 22px 10px 0 72px;
+        }
+
+        .action-cell {
+          grid-area: 3 / 2;
+          padding: 22px 10px 0 72px;
+        }
+
+        .owner-cell {
+          display: grid;
+          grid-area: 4 / 2;
+          grid-template-columns: repeat(2, minmax(0, 1fr));
+          padding: 22px 10px 0 72px;
+        }
+
+        .open-row {
+          grid-area: 1 / 3 / 5;
+        }
+
+        .account-copy h2 {
+          font-size: 15px;
+        }
+
+        .run-details-content {
           grid-template-columns: 1fr;
         }
 
-        .main {
-          padding: 10px;
+        .evidence-gaps {
+          grid-column: 1;
+        }
+
+        .detail-drawer {
+          padding: 34px 28px;
         }
       }
 
-      @media (forced-colors: active) {
-        .brand-mark {
-          background: Highlight;
-          color: HighlightText;
+      @media (max-width: 480px) {
+        .status-tabs {
+          height: auto;
         }
 
-        .field,
-        .metric,
-        .pill,
-        .section,
-        .card {
-          border: 1px solid CanvasText;
+        .status-tabs button {
+          min-height: 48px;
+          font-size: 13px;
         }
-      }
-    </style>
-  </head>
-  <body>
-    <script id="prioritize-accounts-data" type="application/json">
-__PRIORITIZE_ACCOUNTS_DATA_JSON__
-    </script>
-    <div id="app"></div>
-    <script>
-      const templateToken = ["__PRIORITIZE", "ACCOUNTS", "DATA", "JSON__"].join("_");
-      const rawData = document.getElementById("prioritize-accounts-data").textContent.trim();
-      const data = prepare(parseData(rawData));
-      const scenarios = [
-        ["balanced", "Balanced", "Default view: balances deal value, timing, customer momentum, CRM hygiene, and clear next action."],
-        ["deal-value", "Deal Value", "Favors the largest open opportunities and highest-value accounts first."],
-        ["close-soon", "Close Soon", "Favors overdue or near-term opportunities with enough activity to validate, close, or clean up now."],
-        ["expansion", "Expansion", "Favors existing customers, renewal or expansion motion, partner/co-sell paths, and strategic upside."],
-      ];
-      const state = { q: "", filter: "all", scenario: "balanced" };
-
-      function parseData(raw) {
-        if (!raw || raw === templateToken) {
-          return {
-            title: "Prioritize Accounts",
-            generatedAt: new Date().toISOString(),
-            source: { label: "No source selected", status: "No account ranking loaded" },
-            scope: {},
-            workNow: [],
-            monitor: [],
-            suppressOrBlock: [],
-            evidenceGaps: ["No account ranking has been loaded yet."],
-          };
+
+        .tab-count {
+          min-width: 21px;
+          height: 21px;
+          margin-left: 4px;
+          padding: 0 5px;
+          font-size: 11px;
         }
-        try {
-          return JSON.parse(raw);
-        } catch (error) {
-          return {
-            title: "Prioritize Accounts",
-            generatedAt: new Date().toISOString(),
-            source: { label: "Account ranking", status: "Could not load source results" },
-            scope: {},
-            workNow: [],
-            monitor: [],
-            suppressOrBlock: [],
-            evidenceGaps: ["The account ranking could not be loaded. Regenerate the view from connected source results."],
-          };
+
+        .account-cell {
+          gap: 13px;
         }
-      }
 
-      function prepare(payload) {
-        const workNow = array(payload.workNow || payload.work_now || payload.accounts).map(normalizeWorkNow);
-        const monitor = array(payload.monitor).map(normalizeMonitor);
-        const suppressOrBlock = array(payload.suppressOrBlock || payload.suppress_or_block || payload.suppressions).map(normalizeSuppress);
-        return {
-          title: payload.title || "Prioritize Accounts",
-          generatedAt: payload.generatedAt || payload.generated_at || new Date().toISOString(),
-          source: normalizeSource(payload.source),
-          scope: payload.scope || {},
-          workNow,
-          monitor,
-          suppressOrBlock,
-          evidenceGaps: array(payload.evidenceGaps || payload.evidence_gaps || payload.gaps),
-          nextStep: payload.nextStep || payload.next_step || "",
-        };
-      }
-
-      function normalizeSource(source) {
-        if (!source) return { label: "Connected sources", status: "Source results used" };
-        if (typeof source === "string") return { label: source, status: "Source results used" };
-        return source;
-      }
-
-      function accountUrl(row) {
-        return pick(row, "accountUrl", "account_url", "accountRecordUrl", "account_record_url", "accountSourceUrl", "account_source_url", "sourceUrl", "source_url", "recordUrl", "record_url", "url", "Url", "link", "Link", "account.url", "Account.url");
-      }
-
-      function normalizeWorkNow(row, index) {
-        return {
-          rank: pick(row, "rank", "Rank") || index + 1,
-          account: pick(row, "account", "Account", "name", "Name"),
-          accountUrl: accountUrl(row),
-          motion: pick(row, "motion", "Motion"),
-          whyNow: pick(row, "whyNow", "why_now", "Why Now", "Reason"),
-          crmNextStep: pick(row, "crmNextStep", "crm_next_step", "CRM Next Step", "nextStepFromCrm", "next_step_from_crm"),
-          closeDate: pick(row, "closeDate", "close_date", "Close Date", "dueDate", "due_date", "Due Date"),
-          urgency: pick(row, "urgency", "Urgency", "timing", "Timing"),
-          stageForecast: pick(row, "stageForecast", "stage_forecast", "Stage / Forecast", "stage", "Stage"),
-          lastInteraction: pick(row, "lastInteraction", "last_interaction", "Last Interaction", "lastActivity", "last_activity"),
-          primaryContact: pick(row, "primaryContact", "primary_contact", "Primary Contact"),
-          secondaryContact: pick(row, "secondaryContact", "secondary_contact", "Secondary Contact") || "None",
-          oppRecommendation: pick(row, "oppRecommendation", "opp_recommendation", "Opp Recommendation"),
-          nextStep: pick(row, "nextStep", "next_step", "suggestedNextStep", "suggested_next_step", "Suggested Next Step", "Next Step"),
-          crmCleanup: pick(row, "crmCleanup", "crm_cleanup", "Suggested CRM Cleanup", "cleanup", "Cleanup"),
-          sequenceAngle: pick(row, "sequenceAngle", "sequence_angle", "Sequence Angle"),
-          confidence: pick(row, "confidence", "Confidence"),
-          status: pick(row, "status", "Status") || "ready_now",
-        };
-      }
-
-      function normalizeMonitor(row) {
-        return {
-          account: pick(row, "account", "Account", "name", "Name"),
-          accountUrl: accountUrl(row),
-          motion: pick(row, "motion", "Motion"),
-          reasonToMonitor: pick(row, "reasonToMonitor", "reason_to_monitor", "Reason To Monitor", "Reason"),
-          triggerToRevisit: pick(row, "triggerToRevisit", "trigger_to_revisit", "Trigger To Revisit", "Trigger"),
-          crmCleanup: pick(row, "crmCleanup", "crm_cleanup", "Suggested CRM Cleanup", "cleanup", "Cleanup"),
-          status: pick(row, "status", "Status") || "monitor",
-        };
-      }
-
-      function normalizeSuppress(row) {
-        return {
-          account: pick(row, "account", "Account", "name", "Name"),
-          accountUrl: accountUrl(row),
-          motion: pick(row, "motion", "Motion"),
-          suppressorOrBlocker: pick(row, "suppressorOrBlocker", "suppressor_or_blocker", "Suppressor Or Blocker", "Blocker"),
-          evidence: pick(row, "evidence", "Evidence"),
-          crmCleanup: pick(row, "crmCleanup", "crm_cleanup", "Suggested CRM Cleanup", "cleanup", "Cleanup"),
-          status: pick(row, "status", "Status") || "suppressed",
-        };
-      }
-
-      function pick(record, ...keys) {
-        for (const key of keys) {
-          const value = key.split(".").reduce((current, part) => current?.[part], record);
-          if (value !== undefined && value !== null && String(value).trim() !== "") return value;
+        .why-cell,
+        .action-cell,
+        .owner-cell {
+          padding-left: 67px;
         }
-        return "";
-      }
 
-      function array(value) {
-        return Array.isArray(value) ? value : value ? [value] : [];
+        .owner-cell {
+          grid-template-columns: 1fr;
+        }
       }
 
-      function text(value) {
-        if (value === null || value === undefined || value === "") return "";
-        if (typeof value === "object") {
-          const name = pick(value, "name", "Name");
-          const title = pick(value, "title", "Title", "role", "Role");
-          const email = pick(value, "email", "Email");
-          return [name, title, email].filter(Boolean).join(" - ") || JSON.stringify(value);
+      @media (prefers-reduced-motion: reduce) {
+        *,
+        *::before,
+        *::after {
+          scroll-behavior: auto !important;
+          animation-duration: 0.01ms !important;
+          animation-iteration-count: 1 !important;
+          transition-duration: 0.01ms !important;
         }
-        return String(value);
       }
+    </style>
+  </head>
+  <body>
+    <main class="app-shell">
+      <header class="topbar">
+        <div class="brand-lockup">
+          <span class="brand-mark" aria-hidden="true">
+            <svg width="18" height="18" viewBox="0 0 24 24" fill="none">
+              <path d="M5 12h14M13 6l6 6-6 6" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" />
+            </svg>
+          </span>
+          <strong>Priority Ledger</strong>
+          <span class="divider" aria-hidden="true"></span>
+          <span class="current-view">Sales Focus</span>
+        </div>
+
+        <label class="search-box">
+          <svg width="19" height="19" viewBox="0 0 24 24" fill="none" aria-hidden="true">
+            <circle cx="11" cy="11" r="7" stroke="currentColor" stroke-width="1.8" />
+            <path d="m16.3 16.3 4 4" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" />
+          </svg>
+          <input id="search" type="search" placeholder="Search accounts or actions" aria-label="Search accounts or actions" autocomplete="off" />
+          <button id="search-clear" class="search-clear" type="button" aria-label="Clear search" hidden>
+            <svg width="15" height="15" viewBox="0 0 24 24" fill="none" aria-hidden="true">
+              <path d="m7 7 10 10M17 7 7 17" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" />
+            </svg>
+          </button>
+        </label>
+
+        <div class="sync-state" title="Source status">
+          <span class="sync-dot" aria-hidden="true"></span>
+          <span id="sync-copy">Connected sources used</span>
+        </div>
+
+        <button id="avatar" class="avatar" type="button" aria-label="Sales focus profile">SF</button>
+      </header>
+
+      <section class="content">
+        <div class="page-intro">
+          <h1 id="page-title">Account priorities</h1>
+          <p>Where to spend time, who to involve, and the next best action for each account.</p>
+        </div>
+
+        <nav id="status-tabs" class="status-tabs" aria-label="Account status" role="tablist"></nav>
+
+        <section class="ledger" aria-live="polite">
+          <div class="ledger-head" aria-hidden="true">
+            <span>Account</span>
+            <span>Why it matters</span>
+            <span>Next action</span>
+            <span>Owner &amp; due</span>
+          </div>
+          <div id="account-list" class="account-list"></div>
+          <p id="result-count" class="result-count"></p>
+        </section>
 
-      function esc(value) {
-        return humanizeDates(text(value)).replace(/[&<>"']/g, (char) => ({ "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;" })[char]);
-      }
+        <details class="run-details">
+          <summary>Source &amp; run details</summary>
+          <div id="run-details-content" class="run-details-content"></div>
+        </details>
+      </section>
+    </main>
 
-      function humanizeDates(value) {
-        return String(value).replace(/\b(\d{4})-(\d{2})-(\d{2})(?:[T ][0-9:.]+(?:Z|[+-]\d{2}:?\d{2})?)?\b/g, (_, year, month, day) => humanDate(year, month, day));
-      }
+    <div id="drawer-backdrop" class="drawer-backdrop" hidden>
+      <aside id="detail-drawer" class="detail-drawer" role="dialog" aria-modal="true" aria-labelledby="drawer-title"></aside>
+    </div>
 
-      function humanDate(year, month, day) {
-        const numericYear = Number(year);
-        const date = new Date(Date.UTC(numericYear, Number(month) - 1, Number(day)));
-        if (Number.isNaN(date.valueOf())) return [year, month, day].join("-");
-        const options = { month: "short", day: "numeric", timeZone: "UTC" };
-        if (numericYear !== new Date().getFullYear()) options.year = "numeric";
-        return new Intl.DateTimeFormat(undefined, options).format(date);
-      }
+    <script>
+      const payload = __PRIORITIZE_ACCOUNTS_DATA_JSON__;
+
+      const icons = {
+        arrow: '<svg width="20" height="20" viewBox="0 0 24 24" fill="none" aria-hidden="true"><path d="M5 12h14M13 6l6 6-6 6" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round"/></svg>',
+        check: '<svg width="18" height="18" viewBox="0 0 24 24" fill="none" aria-hidden="true"><path d="m6 12 4 4 8-9" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>',
+        close: '<svg width="18" height="18" viewBox="0 0 24 24" fill="none" aria-hidden="true"><path d="m7 7 10 10M17 7 7 17" stroke="currentColor" stroke-width="1.8" stroke-linecap="round"/></svg>',
+        calendar: '<svg width="17" height="17" viewBox="0 0 24 24" fill="none" aria-hidden="true"><rect x="3" y="5" width="18" height="16" rx="2" stroke="currentColor" stroke-width="1.6"/><path d="M7 3v4M17 3v4M3 10h18" stroke="currentColor" stroke-width="1.6" stroke-linecap="round"/></svg>',
+        external: '<svg width="17" height="17" viewBox="0 0 24 24" fill="none" aria-hidden="true"><path d="M14 5h5v5M19 5l-9 9" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round"/><path d="M19 14v4a1 1 0 0 1-1 1H6a1 1 0 0 1-1-1V6a1 1 0 0 1 1-1h4" stroke="currentColor" stroke-width="1.8" stroke-linecap="round"/></svg>',
+        search: '<svg width="34" height="34" viewBox="0 0 24 24" fill="none" aria-hidden="true"><circle cx="11" cy="11" r="7" stroke="currentColor" stroke-width="1.5"/><path d="m16.3 16.3 4 4" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"/></svg>',
+        user: '<svg width="17" height="17" viewBox="0 0 24 24" fill="none" aria-hidden="true"><circle cx="12" cy="8" r="4" stroke="currentColor" stroke-width="1.6"/><path d="M4.5 21a7.5 7.5 0 0 1 15 0" stroke="currentColor" stroke-width="1.6" stroke-linecap="round"/></svg>'
+      };
+
+      const tabConfig = [
+        { key: "workNow", label: "Work now" },
+        { key: "watch", label: "Watch" },
+        { key: "paused", label: "Paused" }
+      ];
 
-      function safeUrl(value) {
-        const url = String(value || "").trim();
-        return /^https?:\/\//i.test(url) ? url : "";
-      }
+      const groups = Object.fromEntries(
+        tabConfig.map(({ key }) => [key, Array.isArray(payload[key]) ? payload[key] : []])
+      );
+
+      const state = {
+        tab: groups.workNow.length ? "workNow" : tabConfig.find(({ key }) => groups[key].length)?.key || "workNow",
+        query: "",
+        complete: new Set(),
+        selected: null
+      };
+
+      const list = document.getElementById("account-list");
+      const tabs = document.getElementById("status-tabs");
+      const count = document.getElementById("result-count");
+      const search = document.getElementById("search");
+      const searchClear = document.getElementById("search-clear");
+      const backdrop = document.getElementById("drawer-backdrop");
+      const drawer = document.getElementById("detail-drawer");
 
-      function linkedText(label, url, className = "") {
-        const href = safeUrl(url);
-        const classAttr = className ? ` class="${esc(className)}"` : "";
-        return href
-          ? `<a${classAttr} href="${esc(href)}" target="_blank" rel="noopener noreferrer">${esc(label)}</a>`
-          : `<span${classAttr}>${esc(label)}</span>`;
+      function text(value, fallback = "Not available") {
+        const normalized = value == null ? "" : String(value).trim();
+        return normalized || fallback;
       }
 
-      function statusTone(status) {
-        const normalized = String(status || "").toLowerCase().replace(/[^a-z]+/g, "-");
-        if (["high", "ready-now", "ready"].includes(normalized)) return "high";
-        if (["medium", "partial", "monitor", "watch"].includes(normalized)) return "medium";
-        if (["low", "blocked", "suppressed"].includes(normalized)) return "low";
-        return normalized || "info";
+      function element(tag, className, copy) {
+        const node = document.createElement(tag);
+        if (className) node.className = className;
+        if (copy != null) node.textContent = copy;
+        return node;
       }
 
-      function matches(row) {
-        const haystack = searchableText(row).toLowerCase();
-        const queryMatch = !state.q || haystack.includes(state.q.toLowerCase());
-        return queryMatch;
+      function setIcon(node, name) {
+        node.innerHTML = icons[name] || "";
+        return node;
       }
 
-      function searchableText(row) {
-        return text(row.account);
+      function initials(name) {
+        return text(name, "?")
+          .split(/\s+/)
+          .filter(Boolean)
+          .slice(0, 2)
+          .map((part) => part[0])
+          .join("")
+          .toUpperCase();
       }
 
-      function renderMetric(label, value) {
-        return `<div class="metric"><span>${esc(label)}</span><strong>${esc(value)}</strong></div>`;
+      function logoColor(name) {
+        const palette = ["#68d6a7", "#55b8e8", "#a78bfa", "#f29a71", "#e56d9e", "#d7b75b"];
+        const hash = Array.from(text(name, "account")).reduce((sum, character) => sum + character.charCodeAt(0), 0);
+        return palette[hash % palette.length];
       }
 
-      function renderRunDetails(counts) {
-        const scope = data.scope || {};
-        const fields = [
-          ["Source of truth", pick(scope, "sourceOfTruth", "source_of_truth", "sourceScope", "source_scope")],
-          ["Sources checked", array(pick(scope, "sourcesChecked", "sources_checked", "evidenceLanes", "evidence_lanes")).join(", ") || pick(scope, "evidence", "sources")],
-          ["Last updated", dateText(data.generatedAt)],
-          ["Account set", pick(scope, "accountSet", "account_set")],
-          ["Ranking basis", pick(scope, "rankingBasis", "ranking_basis")],
-          ["Assumptions", pick(scope, "assumptions")],
-        ];
-        return `
-          <section class="meta-section" id="run-details">
-            <div class="meta-title">Source & Run Details</div>
-            <div class="meta-grid">
-              ${fields.map(([label, value]) => `<div class="meta-item"><span>${esc(label)}</span><strong>${esc(value || "Not available")}</strong></div>`).join("")}
-            </div>
-          </section>
-        `;
+      function rowKey(row, index) {
+        return `${state.tab}:${text(row.account)}:${row.rank || index + 1}`;
       }
 
-      function activeScenario() {
-        return scenarios.find(([key]) => key === state.scenario) || scenarios[0];
+      function searchableCopy(row) {
+        return [
+          row.account,
+          row.motion,
+          row.stage,
+          row.value,
+          row.whyItMatters,
+          row.nextAction,
+          row.owner,
+          row.dueDate,
+          row.confidence,
+          row.status
+        ]
+          .map((value) => text(value, ""))
+          .join(" ")
+          .toLocaleLowerCase();
+      }
+
+      function filteredRows() {
+        const rows = groups[state.tab];
+        const query = state.query.trim().toLocaleLowerCase();
+        return query ? rows.filter((row) => searchableCopy(row).includes(query)) : rows;
+      }
+
+      function renderTabs() {
+        tabs.replaceChildren();
+        tabConfig.forEach(({ key, label }) => {
+          const button = element("button", "", label);
+          button.type = "button";
+          button.id = `tab-${key}`;
+          button.setAttribute("role", "tab");
+          button.setAttribute("aria-selected", String(state.tab === key));
+          button.setAttribute("aria-controls", "account-list");
+          button.tabIndex = state.tab === key ? 0 : -1;
+
+          const badge = element("span", "tab-count", String(groups[key].length));
+          button.appendChild(badge);
+          button.addEventListener("click", () => {
+            state.tab = key;
+            closeDrawer();
+            render();
+          });
+          button.addEventListener("keydown", (event) => {
+            const currentIndex = tabConfig.findIndex((tab) => tab.key === key);
+            const direction = event.key === "ArrowRight" ? 1 : event.key === "ArrowLeft" ? -1 : 0;
+            if (!direction) return;
+            event.preventDefault();
+            const next = tabConfig[(currentIndex + direction + tabConfig.length) % tabConfig.length];
+            state.tab = next.key;
+            render();
+            document.getElementById(`tab-${next.key}`)?.focus();
+          });
+          tabs.appendChild(button);
+        });
+      }
+
+      function makeAccountRow(row, index) {
+        const key = rowKey(row, index);
+        const article = element("article", "account-row");
+        article.classList.toggle("is-complete", state.complete.has(key));
+
+        article.appendChild(element("div", "rank", String(row.rank || index + 1)));
+
+        const accountCell = element("div", "account-cell");
+        const logo = element("div", "company-logo", initials(row.account));
+        logo.style.setProperty("--logo-accent", logoColor(row.account));
+        logo.setAttribute("aria-hidden", "true");
+
+        const accountCopy = element("div", "account-copy");
+        accountCopy.appendChild(element("h2", "", text(row.account)));
+        accountCopy.appendChild(element("p", "", `${text(row.motion)} · ${text(row.stage)}`));
+        accountCopy.appendChild(element("span", "", `${text(row.confidence)} confidence`));
+        accountCell.append(logo, accountCopy);
+        article.appendChild(accountCell);
+
+        const why = element("div", "why-cell");
+        why.appendChild(element("strong", "", text(row.value, "Value unavailable")));
+        why.appendChild(element("p", "", text(row.whyItMatters)));
+        article.appendChild(why);
+
+        const action = element("div", "action-cell");
+        const complete = setIcon(element("button", "complete-button"), state.complete.has(key) ? "check" : "arrow");
+        complete.type = "button";
+        complete.setAttribute("aria-label", `${state.complete.has(key) ? "Reopen" : "Complete"} ${text(row.account)} action`);
+        complete.setAttribute("aria-pressed", String(state.complete.has(key)));
+        complete.addEventListener("click", () => {
+          if (state.complete.has(key)) state.complete.delete(key);
+          else state.complete.add(key);
+          render();
+        });
+
+        const actionCopy = element("button", "action-copy", text(row.nextAction));
+        actionCopy.type = "button";
+        actionCopy.addEventListener("click", () => openDrawer(row));
+        action.append(complete, actionCopy);
+        article.appendChild(action);
+
+        const owner = element("div", "owner-cell");
+        const ownerLine = element("span");
+        setIcon(ownerLine, "user");
+        ownerLine.appendChild(element("span", "", text(row.owner, "Unassigned")));
+        const dueLine = element("span");
+        setIcon(dueLine, "calendar");
+        dueLine.appendChild(element("span", "", `Due ${text(row.dueDate, "not set")}`));
+        owner.append(ownerLine, dueLine);
+        article.appendChild(owner);
+
+        const open = setIcon(element("button", "open-row"), "arrow");
+        open.type = "button";
+        open.setAttribute("aria-label", `Open ${text(row.account)} details`);
+        open.addEventListener("click", () => openDrawer(row));
+        article.appendChild(open);
+
+        return article;
+      }
+
+      function renderRows() {
+        const rows = filteredRows();
+        list.replaceChildren();
+        rows.forEach((row, index) => list.appendChild(makeAccountRow(row, index)));
+
+        if (!rows.length) {
+          const empty = element("div", "empty-state");
+          setIcon(empty, "search");
+          empty.appendChild(element("h2", "", state.query ? "No matching accounts" : "Nothing in this view"));
+          empty.appendChild(
+            element(
+              "p",
+              "",
+              state.query
+                ? "Try a different account, owner, stage, or action."
+                : "Accounts will appear here when they match this priority status."
+            )
+          );
+          list.appendChild(empty);
+        }
+
+        const label = tabConfig.find(({ key }) => key === state.tab)?.label.toLocaleLowerCase() || "this view";
+        count.textContent = `Showing ${rows.length} of ${groups[state.tab].length} accounts in ${label}`;
       }
 
-      function renderScenarioBar() {
-        const [, , description] = activeScenario();
-        return `
-          <section class="scenario-panel" aria-label="Prioritization scenario">
-            <div class="scenario-options" role="group" aria-label="Choose prioritization scenario">
-              ${scenarios.map(([key, label]) => `
-                <button class="scenario-button ${key === state.scenario ? "is-active" : ""}" type="button" data-scenario="${esc(key)}" aria-pressed="${key === state.scenario ? "true" : "false"}">${esc(label)}</button>
-              `).join("")}
-            </div>
-            <div class="scenario-note">${esc(description)}</div>
-          </section>
-        `;
+      function appendDrawerSection(label, value, emphasize = false) {
+        const section = element("section", "drawer-section");
+        section.appendChild(element("span", "", label));
+        section.appendChild(element(emphasize ? "strong" : "p", "", text(value)));
+        drawer.appendChild(section);
       }
 
-      function scenarioRows(rows) {
-        return rows
-          .map((row, index) => ({ row, index, score: scenarioScore(row) }))
-          .sort((a, b) => (b.score - a.score) || (a.index - b.index))
-          .map(({ row }, index) => ({ ...row, rank: index + 1 }));
+      function safeAccountUrl(value) {
+        try {
+          const url = new URL(value);
+          return ["http:", "https:"].includes(url.protocol) ? url.href : "";
+        } catch {
+          return "";
+        }
       }
 
-      function scenarioScore(row) {
-        if (state.scenario === "balanced" || state.scenario === "deal-value") return Number.MAX_SAFE_INTEGER - Number(row.rank || 0);
-        const body = [
-          row.account,
-          row.motion,
-          row.whyNow,
-          row.closeDate,
-          row.urgency,
-          row.stageForecast,
-          row.lastInteraction,
-          row.oppRecommendation,
-          row.nextStep,
-          row.reasonToMonitor,
-          row.triggerToRevisit,
-          row.suppressorOrBlocker,
-          row.evidence,
-          row.crmCleanup,
-        ].map(text).join(" ").toLowerCase();
-        const weights = {
-          "close-soon": [
-            [/overdue|past target|close date|due soon|near-term|this month|recent meeting|recent customer|active recently/g, 4],
-            [/mutual action|confirm|validate|close plan|signer|decision/g, 2],
-            [/no recent|missing|stale|blank/g, -1],
-          ],
-          "expansion": [
-            [/expansion|renewal|existing|customer|strategic|highest-value|high-value|commit|co-sell|aws|partner|multi-product|deployment|recent meeting|momentum/g, 4],
-            [/\$|value|acv|renewal value|commercial path/g, 2],
-            [/0%|omitted|stale 2024|small/g, -3],
-          ],
-        };
-        return (weights[state.scenario] || []).reduce((score, [pattern, weight]) => {
-          const matches = body.match(pattern);
-          return score + (matches ? matches.length * weight : 0);
-        }, 0);
-      }
-
-      function renderWorkNow() {
-        const rows = scenarioRows(data.workNow.filter(matches));
-        return renderSection(
-          "Suggested Focus",
-          rows.length,
-          workColumns(rows),
-          rows,
-          renderWorkCard,
-          "No suggested focus accounts match the current filter.",
-          renderScenarioBar(),
-        );
+      function openDrawer(row) {
+        state.selected = row;
+        drawer.replaceChildren();
+
+        const close = setIcon(element("button", "drawer-close"), "close");
+        close.type = "button";
+        close.setAttribute("aria-label", "Close account details");
+        close.addEventListener("click", closeDrawer);
+        drawer.appendChild(close);
+
+        const logo = element("div", "company-logo drawer-logo", initials(row.account));
+        logo.style.setProperty("--logo-accent", logoColor(row.account));
+        logo.setAttribute("aria-hidden", "true");
+        drawer.appendChild(logo);
+        drawer.appendChild(element("p", "eyebrow", tabConfig.find(({ key }) => key === state.tab)?.label || "Sales focus"));
+
+        const title = element("h2", "", text(row.account));
+        title.id = "drawer-title";
+        drawer.appendChild(title);
+        drawer.appendChild(element("p", "drawer-meta", `${text(row.motion)} · ${text(row.stage)} · ${text(row.confidence)} confidence`));
+
+        appendDrawerSection("Opportunity value", row.value, true);
+        appendDrawerSection("Why it matters", row.whyItMatters);
+
+        if (row.primaryContact) appendDrawerSection("Primary contact", row.primaryContact);
+        if (row.evidence) appendDrawerSection("Evidence", row.evidence);
+
+        const action = element("section", "drawer-action");
+        action.appendChild(element("span", "", "Next action"));
+        action.appendChild(element("p", "", text(row.nextAction)));
+
+        const accountUrl = safeAccountUrl(row.accountUrl);
+        if (accountUrl) {
+          const link = element("a", "drawer-link", "Open account record");
+          link.href = accountUrl;
+          link.target = "_blank";
+          link.rel = "noopener noreferrer";
+          setIcon(link, "external");
+          link.appendChild(document.createTextNode("Open account record"));
+          action.appendChild(link);
+        }
+        drawer.appendChild(action);
+
+        backdrop.hidden = false;
+        document.body.style.overflow = "hidden";
+        close.focus();
       }
 
-      function renderMonitor() {
-        const rows = scenarioRows(data.monitor.filter(matches));
-        const columns = [
-          ["Account", "account", "col-account"],
-          ["Reason To Monitor", "reasonToMonitor", "col-why"],
-          ["Trigger To Revisit", "triggerToRevisit", "col-next"],
-          ...cleanupColumn(rows),
-        ];
-        return renderSection(
-          "Monitor",
-          rows.length,
-          columns,
-          rows,
-          renderMonitorCard,
-          "No monitor accounts.",
-        );
+      function closeDrawer() {
+        state.selected = null;
+        backdrop.hidden = true;
+        document.body.style.overflow = "";
       }
 
-      function renderSuppress() {
-        const rows = scenarioRows(data.suppressOrBlock.filter(matches));
-        const columns = [
-          ["Account", "account", "col-account"],
-          ["Suppressor Or Blocker", "suppressorOrBlocker", "col-why"],
-          ["Evidence", "evidence", "col-evidence"],
-          ...cleanupColumn(rows),
-        ];
-        return renderSection(
-          "Suppress Or Block",
-          rows.length,
-          columns,
-          rows,
-          renderSuppressCard,
-          "No suppressed or blocked accounts.",
-        );
+      function appendRunDetail(label, value) {
+        const detail = element("div", "run-detail");
+        detail.appendChild(element("span", "", label));
+        detail.appendChild(element("strong", "", text(value)));
+        document.getElementById("run-details-content").appendChild(detail);
       }
 
-      function workColumns(rows) {
-        return [
-          ["Priority", "rank", "col-rank"],
-          ["Account", "account", "col-account"],
-          ["Timing", "closeDate", "col-date"],
-          ["Why", "whyNow", "col-why"],
-          ["Suggested Next Step", "nextStep", "col-next"],
-          ...cleanupColumn(rows),
-          ["Confidence", "confidence", "col-confidence"],
-        ];
-      }
-
-      function cleanupColumn(rows) {
-        return rows.some((row) => text(row.crmCleanup)) ? [["Suggested CRM Cleanup", "crmCleanup", "col-cleanup"]] : [];
-      }
-
-      function renderSection(title, count, columns, rows, cardRenderer, emptyText, headerContent = "") {
-        const sectionId = title.toLowerCase().replace(/[^a-z0-9]+/g, "-").replace(/^-|-$/g, "");
-        return `
-          <section class="section" data-section="${esc(title)}">
-            <div class="section-header">
-              <div class="section-title"><h2>${esc(title)}${count ? ` (${count})` : ""}</h2></div>
-              ${headerContent}
-            </div>
-            ${
-              rows.length
-                ? `<div class="table-wrap"><table class="table-${esc(sectionId)}">
-                    <thead><tr>${columns.map(([label, , className]) => `<th class="${className || ""}">${esc(label)}</th>`).join("")}</tr></thead>
-                    <tbody>${rows.map((row) => renderRow(columns, row)).join("")}</tbody>
-                  </table></div>
-                  <div class="cards">${rows.map(cardRenderer).join("")}</div>`
-                : `<div class="empty">${esc(emptyText)}</div>`
-            }
-          </section>
-        `;
-      }
-
-      function renderRow(columns, row) {
-        return `<tr>${columns
-          .map(([, key]) => {
-            const value = row[key];
-            if (key === "account") return `<td>${linkedText(value, row.accountUrl, "account-link")}</td>`;
-            if (key === "status") return `<td><span class="status-text ${statusTone(value)}">${esc(statusLabel(value))}</span></td>`;
-            if (key === "confidence") return `<td><span class="status-text ${statusTone(value)}">${esc(value)}</span></td>`;
-            return `<td>${esc(value)}</td>`;
-          })
-          .join("")}</tr>`;
-      }
-
-      function renderCard(title, rows, status, titleUrl = "", meta = [], note = "") {
-        const metaText = meta.filter(([, value]) => value).map(([label, value]) => `${label}: ${value}`).join(" · ");
-        return `
-          <article class="card">
-            <div class="card-top">
-              <div class="card-title">
-                <strong>${linkedText(title, titleUrl, "account-link")}</strong>
-                ${metaText ? `<div class="card-meta">${esc(metaText)}</div>` : ""}
-              </div>
-            </div>
-            <div class="card-grid">
-              ${rows.map(([label, value]) => `<div class="card-field ${/cleanup/i.test(label) ? "is-cleanup" : ""}"><span>${esc(label)}</span><div class="card-value">${esc(value)}</div></div>`).join("")}
-            </div>
-            ${note ? `<div class="card-note">${esc(note)}</div>` : ""}
-          </article>
-        `;
-      }
-
-      function renderWorkCard(row) {
-        return renderCard(`#${row.rank} ${row.account}`, [
-          ["Why", row.whyNow],
-          ["Suggested Next Step", row.nextStep],
-          ...(row.crmCleanup ? [["Suggested CRM Cleanup", row.crmCleanup]] : []),
-        ], row.status, row.accountUrl, [
-          ["Timing", row.closeDate],
-          ["Stage", row.stageForecast],
-          ["Confidence", row.confidence],
-        ], [row.urgency, row.lastInteraction].filter(Boolean).join(" "));
-      }
-
-      function statusLabel(status) {
-        const normalized = String(status || "").toLowerCase().replace(/[^a-z]+/g, "-");
-        const labels = {
-          "ready-now": "Ready",
-          "ready": "Ready",
-          "partial": "Needs cleanup",
-          "monitor": "Monitor",
-          "suppressed": "Suppressed",
-          "blocked": "Blocked",
-        };
-        return labels[normalized] || text(status);
-      }
-
-      function renderMonitorCard(row) {
-        return renderCard(row.account, [
-          ["Reason To Monitor", row.reasonToMonitor],
-          ["Trigger To Revisit", row.triggerToRevisit],
-          ...(row.crmCleanup ? [["Suggested CRM Cleanup", row.crmCleanup]] : []),
-        ], row.status, row.accountUrl);
-      }
-
-      function renderSuppressCard(row) {
-        return renderCard(row.account, [
-          ["Suppressor Or Blocker", row.suppressorOrBlocker],
-          ["Evidence", row.evidence],
-          ...(row.crmCleanup ? [["Suggested CRM Cleanup", row.crmCleanup]] : []),
-        ], row.status, row.accountUrl);
-      }
-
-      function renderGaps() {
-        const gaps = data.evidenceGaps.map(text).filter(Boolean);
-        return `
-          <section class="section">
-            <div class="section-header">
-              <div class="section-title"><h2>Evidence Gaps</h2></div>
-            </div>
-            ${gaps.length ? `<div class="gaps">${gaps.map((gap) => `<div class="gap"><span class="dot"></span><span>${esc(gap)}</span></div>`).join("")}</div>` : `<div class="empty">No material evidence gaps called out.</div>`}
-          </section>
-        `;
-      }
-
-      function dateText(value) {
-        const match = String(value || "").match(/^(\d{4})-(\d{2})-(\d{2})/);
-        return match ? humanDate(match[1], match[2], match[3]) : humanizeDates(value || "");
+      function renderRunDetails() {
+        const container = document.getElementById("run-details-content");
+        container.replaceChildren();
+        const scope = payload.scope && typeof payload.scope === "object" ? payload.scope : {};
+        appendRunDetail("Account set", scope.accountSet);
+        appendRunDetail("Ranking basis", scope.rankingBasis);
+        appendRunDetail("Source of truth", scope.sourceOfTruth || payload.source?.label);
+        appendRunDetail("Sources checked", Array.isArray(scope.sourcesChecked) ? scope.sourcesChecked.join(", ") : scope.sourcesChecked);
+
+        if (Array.isArray(payload.evidenceGaps) && payload.evidenceGaps.length) {
+          const gaps = element("ul", "evidence-gaps");
+          payload.evidenceGaps.forEach((gap) => gaps.appendChild(element("li", "", text(gap))));
+          container.appendChild(gaps);
+        }
       }
 
       function render() {
-        const app = document.getElementById("app");
-        app.innerHTML = `
-          <div class="shell">
-            <header class="topbar">
-              <div class="brand">
-                <div class="brand-mark">S</div>
-                <div class="brand-text"><strong>Prioritize Accounts</strong></div>
-              </div>
-              <div class="controls">
-                <input class="control search" id="q" placeholder="Search" value="${esc(state.q)}" />
-              </div>
-            </header>
-            <main class="main" id="results"></main>
-          </div>
-        `;
-        renderResults();
-      }
-
-      function renderResults() {
-        const results = document.getElementById("results");
-        if (!results) return;
-        const visibleWorkNow = data.workNow.filter(matches).length;
-        const visibleMonitor = data.monitor.filter(matches).length;
-        const visibleSuppress = data.suppressOrBlock.filter(matches).length;
-        results.innerHTML = `
-          ${renderWorkNow()}
-          ${renderMonitor()}
-          ${renderSuppress()}
-          ${renderGaps()}
-          ${renderRunDetails({ workNow: visibleWorkNow, monitor: visibleMonitor, suppress: visibleSuppress })}
-        `;
-      }
-
-      document.addEventListener("input", (event) => {
-        if (event.target.id === "q") {
-          state.q = event.target.value;
-          renderResults();
-        }
+        renderTabs();
+        renderRows();
+        renderRunDetails();
+      }
+
+      search.addEventListener("input", () => {
+        state.query = search.value;
+        searchClear.hidden = !state.query;
+        renderRows();
+      });
+
+      searchClear.addEventListener("click", () => {
+        search.value = "";
+        state.query = "";
+        searchClear.hidden = true;
+        renderRows();
+        search.focus();
       });
 
-      document.addEventListener("click", (event) => {
-        const button = event.target.closest("[data-scenario]");
-        if (!button) return;
-        state.scenario = button.getAttribute("data-scenario") || "balanced";
-        renderResults();
+      backdrop.addEventListener("click", (event) => {
+        if (event.target === backdrop) closeDrawer();
       });
 
+      document.addEventListener("keydown", (event) => {
+        if (event.key === "Escape" && !backdrop.hidden) closeDrawer();
+      });
+
+      document.getElementById("page-title").textContent = text(payload.title, "Account priorities");
+      document.getElementById("sync-copy").textContent = text(payload.source?.status, "Connected sources used");
+      document.getElementById("avatar").textContent = text(payload.viewerInitials, "SF").slice(0, 3).toUpperCase();
+      document.title = `${text(payload.title, "Account priorities")} — Sales Focus`;
+
       render();
     </script>
   </body>
diff --git a/plugins/sales/skills/prioritize-accounts/references/request-schema.yaml b/plugins/sales/skills/prioritize-accounts/references/request-schema.yaml
deleted file mode 100644
index cd9f88a..0000000
--- a/plugins/sales/skills/prioritize-accounts/references/request-schema.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-type: object
-description: >
-  Rep-facing request schema for pipeline generation. The workflow prioritizes
-  account selection, suppression, and actionable next steps over broad lead generation.
-properties:
-  owner_scope:
-    type: string
-    description: >
-      Rep identifier, territory, or book-of-business scope when the user wants the
-      workflow to source candidates from an existing book.
-  candidate_accounts:
-    type: array
-    description: >
-      Explicit list of accounts to evaluate. Use this when the user already knows the target set.
-    items:
-      type: string
-  icp_definition:
-    type: string
-    description: >
-      Plain-language ICP definition for discovery-driven runs.
-  segment_description:
-    type: string
-    description: >
-      Segment or territory description when the request is not tied to a fixed account list.
-  motion_goal:
-    type: string
-    enum:
-      - net_new
-      - expansion
-      - mixed
-    description: >
-      Primary pipeline motion. Defaults to mixed when not specified.
-  batch_size:
-    type: integer
-    minimum: 1
-    description: >
-      Maximum number of top accounts to return in the output package.
-  capacity_window:
-    type: integer
-    minimum: 1
-    description: >
-      Practical number of accounts the rep can work now. Prefer this over broad output.
-  signal_preference:
-    type: string
-    enum:
-      - product_usage
-      - intent_or_event
-      - named_accounts
-      - mixed
-    description: >
-      Preference for the main source of why-now evidence.
-  product_focus:
-    type: string
-    description: >
-      Product or workflow focus when the user wants the pipeline constrained to a specific motion.
-  exclude_accounts:
-    type: array
-    items:
-      type: string
-    description: >
-      Accounts to exclude from ranking and drafting.
-  uploaded_examples:
-    type: array
-    items:
-      type: string
-    description: >
-      Optional examples of prior successful outbound or account prioritization.
-required: []
-additionalProperties: true
diff --git a/plugins/sales/skills/prioritize-accounts/references/suppression-and-fallbacks.md b/plugins/sales/skills/prioritize-accounts/references/suppression-and-fallbacks.md
index 90005a2..3709d8c 100644
--- a/plugins/sales/skills/prioritize-accounts/references/suppression-and-fallbacks.md
+++ b/plugins/sales/skills/prioritize-accounts/references/suppression-and-fallbacks.md
@@ -4,9 +4,9 @@ Apply these rules before returning `Work Now`.
 
 ## Hard suppressors
 
-Suppress the account when there is strong evidence of:
+Suppress the account when there is strong evidence that the proposed rep action would duplicate or conflict with motion already in progress, such as:
 
-- an active open opportunity
+- an active open opportunity with already-owned seller, SE, or internal-team motion that makes the proposed action duplicative
 - a recent or upcoming external meeting already in motion
 - clearly active seller, SE, or internal team motion that would make new outreach duplicative
 
diff --git a/plugins/sales/skills/prioritize-accounts/references/template-payload.schema.json b/plugins/sales/skills/prioritize-accounts/references/template-payload.schema.json
new file mode 100644
index 0000000..28020bd
--- /dev/null
+++ b/plugins/sales/skills/prioritize-accounts/references/template-payload.schema.json
@@ -0,0 +1,188 @@
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "title": "Priority Ledger Pane Payload",
+  "description": "Canonical JSON payload to embed in assets/account-priority-pane.template.html.",
+  "type": "object",
+  "required": [
+    "title",
+    "generatedAt",
+    "source",
+    "scope",
+    "workNow",
+    "watch",
+    "paused",
+    "evidenceGaps"
+  ],
+  "properties": {
+    "title": {
+      "type": "string"
+    },
+    "generatedAt": {
+      "type": "string",
+      "format": "date-time"
+    },
+    "viewerInitials": {
+      "description": "Optional initials for the seller avatar in the pane header.",
+      "type": "string",
+      "minLength": 1,
+      "maxLength": 3
+    },
+    "source": {
+      "type": "object",
+      "required": ["label", "status"],
+      "properties": {
+        "label": {
+          "type": "string"
+        },
+        "status": {
+          "type": "string"
+        }
+      },
+      "additionalProperties": true
+    },
+    "scope": {
+      "type": "object",
+      "required": [
+        "sourceOfTruth",
+        "sourcesChecked",
+        "accountSet",
+        "rankingBasis",
+        "assumptions"
+      ],
+      "properties": {
+        "sourceOfTruth": {
+          "type": "string"
+        },
+        "sourcesChecked": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          }
+        },
+        "accountSet": {
+          "type": "string"
+        },
+        "rankingBasis": {
+          "type": "string"
+        },
+        "assumptions": {
+          "type": "string"
+        },
+        "motionGoal": {
+          "type": "string",
+          "enum": ["net_new", "expansion", "mixed"]
+        },
+        "batchSize": {
+          "type": "integer",
+          "minimum": 1
+        },
+        "capacityWindow": {
+          "type": "integer",
+          "minimum": 1
+        }
+      },
+      "additionalProperties": true
+    },
+    "workNow": {
+      "description": "Ranked accounts that deserve seller attention now.",
+      "type": "array",
+      "items": {
+        "$ref": "#/$defs/accountRow"
+      }
+    },
+    "watch": {
+      "description": "Accounts to monitor until a named trigger changes their priority.",
+      "type": "array",
+      "items": {
+        "$ref": "#/$defs/accountRow"
+      }
+    },
+    "paused": {
+      "description": "Suppressed or blocked accounts where no seller action should be taken now.",
+      "type": "array",
+      "items": {
+        "$ref": "#/$defs/accountRow"
+      }
+    },
+    "evidenceGaps": {
+      "type": "array",
+      "items": {
+        "type": "string"
+      }
+    }
+  },
+  "$defs": {
+    "accountRow": {
+      "type": "object",
+      "required": [
+        "rank",
+        "account",
+        "motion",
+        "stage",
+        "value",
+        "whyItMatters",
+        "nextAction",
+        "owner",
+        "dueDate",
+        "confidence",
+        "status"
+      ],
+      "properties": {
+        "rank": {
+          "type": "integer",
+          "minimum": 1
+        },
+        "account": {
+          "type": "string"
+        },
+        "accountUrl": {
+          "type": "string",
+          "format": "uri"
+        },
+        "motion": {
+          "description": "Sales motion such as Renewal, Expansion, New logo, or Cross-sell.",
+          "type": "string"
+        },
+        "stage": {
+          "description": "Seller-friendly opportunity or account stage.",
+          "type": "string"
+        },
+        "value": {
+          "description": "Display-ready opportunity value or Value unavailable when the source does not provide one.",
+          "type": "string"
+        },
+        "whyItMatters": {
+          "description": "Short, source-grounded explanation of the timing, value, momentum, or risk.",
+          "type": "string"
+        },
+        "nextAction": {
+          "description": "Immediate safe action, watch trigger, or unblocking step appropriate to the row's tab.",
+          "type": "string"
+        },
+        "owner": {
+          "description": "Current seller or action owner, or Unassigned when unknown.",
+          "type": "string"
+        },
+        "dueDate": {
+          "description": "Display-ready due date or No date set when unknown.",
+          "type": "string"
+        },
+        "confidence": {
+          "type": "string",
+          "enum": ["High", "Medium", "Low"]
+        },
+        "status": {
+          "type": "string"
+        },
+        "primaryContact": {
+          "type": "string"
+        },
+        "evidence": {
+          "type": "string"
+        }
+      },
+      "additionalProperties": true
+    }
+  },
+  "additionalProperties": true
+}
diff --git a/plugins/sales/skills/prioritize-accounts/references/test-cases.md b/plugins/sales/skills/prioritize-accounts/references/test-cases.md
deleted file mode 100644
index 7513b45..0000000
--- a/plugins/sales/skills/prioritize-accounts/references/test-cases.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Test Cases
-
-Use these cases to judge whether the workflow is aligned with the rep-focused acceptance criteria.
-
-## Net-new trigger case
-
-- Input: named account with a strong external trigger and a reachable executive in `crm`
-- Expectation: account appears in `Work Now` with a source-grounded `Why Now`
-
-## Expansion suppression case
-
-- Input: customer account with product usage but an already-open opportunity
-- Expectation: row is suppressed, not drafted
-
-## Weak contact-path case
-
-- Input: strong-fit account with no usable contact in `crm` or fallback lanes
-- Expectation: row is blocked or monitored, not pushed into `Work Now`
-
-## Mixed batch triage case
-
-- Input: mixed set of accounts with active motion, weak timing, and one high-confidence target
-- Expectation: only the executable account appears in `Work Now`
-
-## Capacity-window case
-
-- Input: ten good candidates, capacity window of three
-- Expectation: no more than three `Work Now` rows
-
-## HTML pane output case
-
-- Input: any substantive ranking with `Scope`, `Work Now`, `Monitor`, `Suppress Or Block`, and `Evidence Gaps`
-- Expectation: response returns a local HTML pane link first, and the pane renders the same table sections and columns as the Prioritize Accounts package
-
-## Existing-motion protection case
-
-- Input: account with recent external meeting and active seller follow-up
-- Expectation: row is suppressed with the motion evidence called out
diff --git a/plugins/sales/skills/review-forecast/SKILL.md b/plugins/sales/skills/review-forecast/SKILL.md
index 96976c1..421cb0c 100644
--- a/plugins/sales/skills/review-forecast/SKILL.md
+++ b/plugins/sales/skills/review-forecast/SKILL.md
@@ -1,305 +1,191 @@
 ---
 name: review-forecast
-description: Generate a forecast review with risk analysis, recommendation posture, and change detection using CRM truth, pasted or exported pipeline context, account notes, and optional meeting, email, document, or internal-message evidence.
+description: Use when the user wants forecast posture, forecast accuracy, commit, upside, forecast-call preparation, or a pipeline or deal-risk review for a seller book, team, period, report, or named deal set. Produce a manager-ready forecast rollup, risk posture, recommendation changes, evidence gaps, and follow-up actions.
 ---
 
 # Review Forecast
 
-Use this skill for forecast review requests such as:
 
-- forecast my book
-- generate a sales forecast with risk analysis
-- show movement between snapshots
-- review these forecast accounts and tell me what should stay in commit
+## Context-Gathering Intake
 
-This skill produces a structured forecast review for one seller book, team, period, pipeline view, or narrowed deal set. Optimize the default path for a fast manager-ready forecast readout: exact scope, open forecast rollup, top risk deals, keep/downgrade recommendations, evidence gaps, and practical next actions. Run deeper forensic history or enrichment only when the user asks for it, supplies comparison snapshots, or a top deal's recommendation materially depends on missing evidence. The initial review is read-only: it may draft manager notes, spreadsheet views, CRM-ready next steps, or deal-risk follow-up. Do not write CRM fields, change forecast categories, update close dates, message owners, or assign tasks unless the user explicitly approves a reviewed downstream action.
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-## Skill Configuration
+Turn CRM or exported pipeline truth into a fast, manager-ready forecast readout. This skill owns the rollup, risk posture, recommendation changes, and follow-up actions; the initial review is read-only and never changes forecast categories, close dates, CRM records, tasks, or messages.
 
-### User Context
+## Common Skill Instructions
 
-Mandatory pre-answer gate:
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the forecast review also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the forecast review.
+## Key Dependency Categories
 
-### Audience And Language
+These categories are particularly important for this workflow; use other sources only when they materially change a top recommendation.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+- [Blocking] ~~CRM for authoritative opportunity, amount, stage, forecast category, close date, owner, next step, activity, report, and snapshot truth. It blocks unless a CRM report/export, pasted snapshot, or linked forecast-truth source is already grounded in context.
+- ~~Knowledge & Files for forecast docs, manager notes, close plans, deal-desk trackers, approval trackers, and category conventions
+- ~~Meeting Transcripts for recent customer evidence that changes timing, stakeholder, urgency, objection, or next-step confidence
+- ~~Email for customer engagement, timing, unanswered asks, and commercial or procurement movement
+- ~~Internal Messaging for manager, deal-desk, legal, product, approval, blocker, and account-team signals
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+Use ~~CRM as the default forecast truth. Use a user-provided/exported pipeline snapshot only as a fallback when CRM cannot be used. Notes, messages, and transcripts are supporting evidence, not substitutes for missing opportunity truth.
 
-### Source Resolution
+## Reference Loading
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+`SKILL.md` owns the normal forecast-review path and output shape. Use [references/request-schema.yaml](references/request-schema.yaml) when structured input, posture/category/amount enums, comparison inputs, or enrichment-mode normalization matters.
 
-### Required Inputs
+## Workflow Guidance
 
-This skill needs enough user-provided, connector-visible, or explicitly inferable context to identify the forecast scope and at least one source of forecast or pipeline truth.
+### 1. Resolve scope and forecast conventions
 
-The skill can produce a limited forecast review from pasted pipeline tables, CSV/exported snapshots, CRM report exports, user-linked docs, account notes, or prior Sales outputs when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
+- If the review scope or forecast truth path is not clear, ask the user via `ask_user_input()`. For a bare invocation such as “review the forecast,” do not assume an owner book or truth source; offer: `My current forecast-period owner book from live CRM`, `A named team, owner, report, or pipeline view`, and `A pasted/exported forecast snapshot or deal set`.
+- If the user names only “my forecast” without defining the source, offer the same explicit choices before building a rollup. Do not retrieve deeper opportunity evidence or render a posture review until the user selects a scope and truth path.
+- Require one scope anchor: owner/book, team, period, named report or pipeline view, focused accounts, focused opportunities, or a supplied deal set.
+- Require one forecast truth source: ~~CRM data, a CRM report/export, pasted pipeline table, current snapshot, or linked forecast context. If none exists, stop and ask for the smallest usable source rather than inferring a forecast from anecdotes.
+- Before rendering a posture review, establish the forecast posture standard, category convention, and amount basis. Find concrete defaults in the supplied data, ~~CRM metadata, or ~~Knowledge & Files first. If any required choice is only inferred, present the sourced default and ask the user to confirm it before producing the posture review; proceed without confirmation only when the active thread or supplied source clearly defines all three.
+- Do not interpret company-specific forecast categories, stage gates, or Commit/Upside norms beyond source-backed thread context, ~~CRM metadata, manager notes or RevOps docs from ~~Knowledge & Files, or user-provided guidance.
+- If scope is ambiguous, make a bounded candidate pass before asking: at most three source reads, only enough to offer up to five concrete choices.
+- Use owner scope only after the user explicitly selects or confirms it. When the user names accounts or opportunities, intentionally bypass owner-book scope; when the user names a report, team, or pipeline view, use that narrower scope and state it.
+- Prefer the canonical ~~CRM report or filter for a named team, segment, book, or pipeline view and state the exact filter. For example, if a Startups forecast is represented by `Sales_Coverage_Opp__c = 'Startups'`, use that first instead of broadening across owner, account, and stamped-segment fields. Broaden only when the user asks, the canonical definition is known to be incomplete, or it returns an implausibly thin result; label the broader filter and do not compare it as if it were canonical.
 
-**Inputs:**
+### 2. Build the forecast truth set
 
-Require one forecast scope anchor:
+- Start with the canonical ~~CRM report/filter or supplied export. State the exact scope, period, source, amount basis, and data freshness in the output.
+- For live ~~CRM, do only the minimum field or schema discovery needed to identify scope, amount, forecast, stage, owner, close-date, next-step, activity, and risk fields. Skip current-user and account-object discovery unless the scope is “my book” or account-level fields are needed.
+- For a normal first pass, gather current open deals in scope and build the rollup first, normally by forecast category and optionally stage: deal count, primary amount, and expected forecast amount.
+- Inspect detail rows only for the highest-value, highest-risk, stale, materially changed, or concentration-driving opportunities.
+- Gather prior-snapshot rows only when the user supplies a comparison snapshot, a named snapshot/report is available, or the user explicitly asks for movement. Do not use broad field history to imply true forecast movement.
+- If current-versus-prior coverage is partial, label that directly.
+- Treat OpportunityHistory or similar field history as field history, not forecast-snapshot movement, unless a true prior snapshot exists.
+- If data is too thin for category recommendations, downgrade to a risk-only readout and say why.
 
-- [required] `owner_name` or `owner_email`
-- [required] `focus_accounts`
-- [required] `focus_opportunities`
-- [required] a named `forecast_period`, team, book, pipeline view, or deal set that can be resolved from available context
+### 3. Add bounded evidence only where it changes the call
 
-Require one forecast truth source:
+- Default to `light` enrichment for the top one to three uncertain or material deals after the rollup exists. Use `none` when the user asks for forecast-data-only review; use `standard` only for a requested deep review or when missing supporting evidence would materially change a high-value recommendation.
+- Use ~~Meeting Transcripts and ~~Email for recent customer engagement, urgency, stakeholder, objection, decision-process, and timing signals.
+- Use ~~Internal Messaging and ~~Knowledge & Files for blockers, approvals, close-plan gaps, legal/procurement risk, or manager context.
+- Do not enrich every deal by default. Stop when the top recommendations are supported; name missing lanes that would materially change confidence.
+- Do not broaden enrichment while a required scope anchor or forecast convention is unresolved; resolve or confirm it first.
+- Do not let supporting lanes override CRM-owned amount, stage, forecast category, owner, close date, or opportunity state.
 
-- [required] `crm` opportunity or forecast data, or sufficient `pipeline_export`, `current_snapshot`, pasted table, CRM report export, or user-linked forecast context
+### 4. Evaluate risk and recommendation posture
 
-Require these seller-facing forecast choices:
+- Evaluate material deals for next-step clarity, timing credibility, stakeholder coverage, decision-process visibility, proof of urgency, recent engagement, and data freshness.
+- Flag supported hygiene signals such as missing next steps, stale activity, unclear close dates, weak stakeholder coverage, and category/amount/stage changes.
+- Flag portfolio concentration when too much posture depends on one deal, owner, stage, segment, product, or timing bucket.
+- Use simple `low`, `moderate`, and `high` risk labels. Separate sourced facts from directional inference.
+- Recommend keep, downgrade, upside, or follow-up-check posture only when evidence supports it. Phrase unsupported changes as questions or `Needs confirmation`.
+- When current and prior snapshots are supplied, add a concise `What Changed` subsection. Without a prior snapshot, say true movement was not evaluated and use current-state examples only when supported, such as booked Closed Won, Commit concentration, stale close dates, missing next steps, or large Closed Lost/churn rows.
 
-- [required] `forecast_posture_standard`: whether the review should optimize for defending Commit, identifying Upside, or producing a conservative manager-ready forecast
-- [required] `forecast_category_convention`: the team or user convention for interpreting Commit, Best Case/Upside, Pipeline, and any other forecast categories used in the source data
-- [required] `amount_basis`: the amount field to use for the forecast review and downstream artifact, such as CARR/ARR, ACV, TCV, weighted amount, or the CRM primary amount
+### Next Step Options
 
-Accept when provided:
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Inspect one high-risk or concentration-driving deal more deeply.
+- Draft a concise manager or forecast-call summary for review.
+- Create a spreadsheet-ready rollup or risk follow-up structure.
+- Draft CRM-ready corrections or next-step text for review.
+- Check whether a matching weekly automation already reruns this `review-forecast` skill for the same scope and forecast convention; if none exists, offer to create one that gives the seller or manager a weekly forecast-risk readout.
 
-- `comparison_snapshot`
-- `enrichment_mode`: `none`, `light`, or `standard`; default to `light` for a current-state forecast review and to `standard` only when the user asks for a deeper review or supporting evidence would materially change a top deal's risk call
-- `include_call_evidence`
-- `include_chat_signals`
-- account notes, manager notes, forecast assumptions, or known deal-risk context
+Next steps to avoid:
+- Automatically changing forecast categories, close dates, amounts, or CRM records.
 
-**Context Rules:**
+### Automation Offer Guard
 
-- If a required scope anchor is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- If `forecast_posture_standard`, `forecast_category_convention`, or `amount_basis` is missing, first check saved Sales context, CRM metadata, manager notes, RevOps docs, forecast docs, or user-provided/exported field names for concrete defaults. If the value is still missing or only inferable, ask the user to confirm or supply it before producing the forecast review.
-- Treat ambiguous company-like proper nouns, partner names, account shorthands, and opportunity shorthands as possible customer/account or deal-set anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup before relying on docs, notes, messages, exports, or public context alone. If CRM returns one high-confidence account/opportunity match, use it as the primary scope candidate; if multiple plausible matches remain, ask the user to choose from concrete candidates.
-- Before asking the user to paste a pipeline table, CRM report export, forecast scope, or forecast convention, first try the workflow source categories that can own those missing inputs: `crm` for opportunity/forecast truth and CRM conventions, `document_store` for manager notes, forecast docs, RevOps docs, and source-of-truth pages, then `meeting_notes`, `external_messaging`, or `internal_messaging` only when they can cheaply clarify a top-deal scope or risk candidate. Manual/exported forecast data is the fallback after those categories are unavailable, empty, or insufficient.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+For current forecast, movement, or risk-review outputs, a weekly forecast-risk brief is the preferred automation offer when the user has a reusable owner book, team, report, pipeline view, period, or deal set plus stable forecast conventions. Frame the value in sales language: spotting commit risk, upside movement, stale next steps, close-date pressure, concentration, and deals that need manager attention before the forecast call.
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+The automation must be a scheduled rerun of this skill, not a separate custom pipeline summary. When creating or describing the automation, make the prompt call this skill directly and preserve the same forecast scope and conventions:
 
-If no usable forecast truth source is available from connectors, exports, pasted context, or linked user context, stop and ask for one. Do not invent forecast posture from account anecdotes alone.
+```text
+Use the Sales `review-forecast` skill.
+Rerun it weekly for the same forecast scope and convention.
+Return the standard Forecast Review output.
 
-**Input Request Format Examples:**
-
-```md
-Which forecast, book, period, or deal set should I review?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
+owner_name: "[owner name when used]"
+owner_email: "[owner email when used]"
+focus_accounts: [same focused accounts when used]
+focus_opportunities: [same focused opportunities when used]
+forecast_period: "[same period or rolling current period]"
+forecast_posture_standard: "[defend_commit, identify_upside, or conservative_manager_ready]"
+forecast_category_convention: "[same sourced or user-confirmed convention]"
+amount_basis: "[carr_arr, acv, tcv, weighted_amount, or crm_primary_amount]"
+enrichment_mode: "[none, light, or standard]"
 ```
 
-```md
-Before I review it, please confirm these forecast choices:
-
-1. **Forecast posture:** defend Commit, identify Upside, or conservative manager-ready?
-2. **Category convention:** how should I interpret Commit, Best Case/Upside, and Pipeline?
-3. **Amount basis:** should I use CARR/ARR, ACV, TCV, weighted amount, or the CRM primary amount?
-
-I found `{source-derived default}` for `{field}`. Correct anything off, or say `use those defaults`.
-```
-
-Proceed with a sensible default only when the active thread, saved context, CRM view, user-provided export, or prior Sales artifact clearly identifies the forecast scope, includes enough forecast truth to review, and resolves the required forecast choices above.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
+For team, report, or pipeline-view scopes that are not expressible as owner, account, or opportunity fields, keep the stable scope in the plain-language prompt text and let this skill resolve the forecast truth set using its normal workflow guidance.
 
-- `crm`: opportunity truth, amount, stage, forecast category, close date, owner, account, next step, activity fields, historical snapshots, current pipeline views, CRM report exports, and user-approved CRM conventions.
-- `meeting_notes`: recent customer-call evidence, stakeholder coverage, decision process, urgency, objections, next steps, and timing signals.
-- `document_store`: account plans, forecast docs, manager notes, close plans, deal-desk trackers, approval trackers, and source-of-truth pages about forecast conventions.
-- `external_messaging`: recent customer email or external-message evidence that materially changes timing, engagement, stakeholder, or risk confidence.
-- `internal_messaging`: blocker, approval, manager, deal-desk, product, legal, procurement, or account-team signals when the user wants internal context.
-- user-provided context: pasted pipeline tables, CSV/exported snapshots, CRM report exports, account notes, manager notes, forecast assumptions, prior Sales outputs, and linked context that can seed or constrain the review.
+The recurring output should follow this skill's Forecast Review format: review scope, overall forecast posture, key movements, highest-risk deals, recommendation changes, evidence gaps, and follow-up actions. Keep it read-only; it may recommend checks, corrections, or CRM-ready text for review, but must not change forecast categories, close dates, amounts, CRM records, tasks, or messages unless the user separately asks and approves.
 
-Source obligations by intent:
+Before offering the weekly forecast brief, check whether the user already has a matching local automation installed. Inspect local automation records under `$CODEX_HOME/automations/*/automation.toml`, or `~/.codex/automations/*/automation.toml` when `CODEX_HOME` is unset, and match by name, prompt, skill name, cadence, owner book, team, report, pipeline view, forecast period, amount basis, category convention, or other stable scope details. Treat active and paused matches as already installed.
 
-- `crm` opportunity/forecast data or a user-provided/exported forecast snapshot is required as forecast truth. When named customer/account/opportunity anchors are supplied, inferred, or confirmed, use CRM when available before optional evidence lanes. Meeting notes, docs, email, and chat are evidence lanes, not substitutes for missing opportunity truth.
-- Use `meeting_notes`, `external_messaging`, `internal_messaging`, and `document_store` only when the user asks for enrichment, the forecast truth is thin, or a top deal's recommendation materially depends on missing customer/account evidence.
-- When optional evidence lanes are checked and produce no relevant result, report `checked/no match` only for lanes that would affect confidence in a top recommendation. Do not prolong the default path to exhaust every lane.
+- If a matching automation exists, do not suggest creating another one. Continue with the next most relevant non-automation follow-up.
+- If no matching automation exists, end with one clear offer to check/create a weekly rerun of `review-forecast` for the same scope. Describe the recurring output as a manager-ready weekly forecast brief covering risk, upside, stale next steps, concentration, and recommended checks. Do not create or update the automation until the user explicitly agrees.
+- If the automation surface is unavailable, do not mention tool details; offer to help set up a recurring forecast-risk brief when automations are available.
 
-Authority and gaps:
+## Modes
 
-- Prefer `crm` or a user-provided/exported pipeline snapshot as the forecast truth source. Treat meeting notes, docs, email, and chat as evidence lanes, not substitutes for missing opportunity truth.
-- Do not let meeting notes, docs, email, chat, public context, or third-party enrichment override CRM-owned forecast, account, or opportunity truth.
-- Treat optional evidence lanes as confidence and risk context only; do not use them to change forecast posture unless the forecast truth source supports the underlying deal state.
-- Continue from sufficient manual or exported context when `crm` is unavailable, and label that the review is based on user-provided/exported forecast data.
-- If both `crm` truth and manual/exported forecast data are unavailable, stop and report the missing source of truth.
-- Treat `meeting_notes`, `document_store`, `external_messaging`, and `internal_messaging` as optional enrichment lanes. If they fail or are unavailable, continue when the forecast truth source is sufficient and label the missing lanes explicitly.
-- Use saved Sales user context for company-specific forecast category interpretation, close-date norms, stage exit criteria, approval rules, and source preferences only when read through `sales:user-context`.
+- `Current Forecast Review` — default; current rollup, top risks, recommendations, and actions.
+- `Movement Review` — use when a prior snapshot or explicit comparison source exists; add true movement.
+- `Risk-Only Review` — use when forecast truth is sufficient for risk but too thin for posture changes.
+- `Focused Deal Review` — use for named accounts or opportunities instead of a full book.
 
-### Context Gathering Principles
+## Output Format
 
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the review-forecast experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+Return these sections in order.
 
 ```md
-## Review Forecast
-This skill helps inspect forecast posture, risk, pipeline quality, and next actions for a book, team, period, or narrowed deal set. It can use CRM, account notes, meeting/call evidence, Slack, docs, and user-provided forecast assumptions. It produces deal risk, movement, evidence gaps, forecast posture, and practical next actions.
-
-Definitions:
-- Commit: deals or revenue expected with high confidence, using the team's sourced forecast convention when available.
-- Hygiene signal: a data or process issue, such as stale close date or missing next step, that may weaken forecast confidence.
-- Concentration risk: too much forecast confidence depending on one deal, owner, stage, segment, product, or timing bucket.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Review a forecast for risk and next actions.`
-- `@Sales review my forecast for this month and flag the biggest risks.`
-- `@Sales look at these committed deals and tell me what could slip.`
-- `@Sales review the enterprise forecast and suggest the next actions by deal.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
+# Forecast Review: [Scope]
 
-### Next Step Guidance
+## Review Scope
 
-This section is the single owner for Review Forecast next-step behavior and action text. Onboarding, spreadsheet creation, draft creation, CRM writeback, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the forecast-review continuation states elsewhere.
+- **Scope:** [Owner, team, report, period, or deal set]
+- **Forecast truth:** [Live CRM, export, pasted data, or linked source]
+- **Amount basis:** [CARR/ARR, ACV, TCV, weighted amount, or CRM primary amount]
+- **Category convention:** [Sourced convention or user-confirmed interpretation]
+- **Data freshness:** [Date/time or limitation]
 
-#### Final Continuation Invariant
+## Overall Forecast Posture
 
-Every final assistant response while `review-forecast` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/writing/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
+[Manager-ready posture summary]
 
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
+| Forecast Category | Deal Count | Primary Amount | Expected Amount | Confidence |
+| --- | ---: | ---: | ---: | --- |
+| [Category] | [Count] | [Amount] | [Amount] | [High/Medium/Low] |
 
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
+## Key Movements
 
-#### Journey States
+- [True movement backed by a prior snapshot, or “True movement not evaluated; no prior snapshot was available.”]
 
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
+## Highest-Risk Deals
 
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the forecast readout** | The first substantive forecast review has just been produced. | `Anything you'd change in the forecast readout before we make it operational?` |
-| **2. Review the revised forecast readout** | The user asked for any change to the review, including a posture adjustment, risk wording, rollup detail, evidence note, source adjustment, or formatting tweak. Make the change in chat first. | `Anything else you'd change? If that looks good, want me to move the forecast, risks, and assumptions into a spreadsheet so it is easier to adjust and build on?` |
-| **3. Prepare the projection artifact** | The user accepts the review, says no changes are needed, says a revision looks good, or asks to operationalize the forecast. | Prefer offering a spreadsheet for projections, risk rollups, assumptions, and scenario adjustments; when a spreadsheet would not help, offer manager notes, CRM hygiene update text, risk follow-up, or an owner/action tracker. |
-| **4. Review the spreadsheet, notes, or update draft** | A spreadsheet, manager note, CRM-ready update, or risk tracker exists, or the user asks to change it. | `Anything you'd change before I save, share, or write the approved forecast updates?` |
-| **5. Share, save, or write approved forecast action** | A reviewed spreadsheet, note, tracker, or update draft exists and does not need edits. | Offer the specific supported action, such as saving the spreadsheet, sharing the manager note, posting the approved risk summary, or writing approved CRM hygiene or next-step updates. Do not post, send, or write CRM changes unless the user explicitly asks for that action or approves the reviewed draft/update. |
+| Deal | Amount | Current Posture | Risk | Why | Recommended Check or Change | Source |
+| --- | ---: | --- | --- | --- | --- | --- |
+| [Deal] | [Amount] | [Category] | [Low/Moderate/High] | [Evidence] | [Action or posture] | [Link/label] |
 
-#### Acceptance Handling
+## Recommendation Changes
 
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the forecast readout, treat a lightweight acknowledgement as acceptance and move to `Prepare the projection artifact`. If the previous continuation offered to prepare a spreadsheet, notes, tracker, or update draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post, send, or write externally. If the previous continuation offered to share, save, or write a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
+- **[Deal]:** [Keep / Downgrade / Upside / Needs confirmation] — [grounded reason and confidence]
 
-## Procedure
+## Evidence Gaps
 
-### 1. Normalize the request
+- [Gap] — [impact on confidence] — [smallest next collection step]
 
-- Use owner scope as the primary path unless the user explicitly names accounts, opportunities, a forecast report, a team, or a pipeline view.
-- If the user names accounts or opportunities without an owner, run a narrowed override scope and say the owner-book lane was bypassed intentionally.
-- Normalize fields from `references/request-schema.yaml` only when structured input validation, YAML normalization, or machine-readable field shape matters.
-- For named teams, segments, books, or pipeline views that map to a canonical CRM report filter or field, use that canonical scope first and state the exact filter in `Review Scope`. For example, if a Startups forecast is represented by `Sales_Coverage_Opp__c = 'Startups'`, use that first instead of broadening across owner, account, and stamped segment fields.
-- Broaden beyond the canonical scope only when the user asks for a broader book definition, saved context says the canonical field is incomplete, or the canonical query returns an implausibly empty/thin result. When broadening, label the broader filter and avoid comparing it as if it were the canonical forecast.
-- Default `enrichment_mode` to `light` for ordinary team/period/current-state reviews; use `standard` only when the user asks for a deep review or when missing supporting evidence would materially change a high-value recommendation; use `none` when the user asks for forecast-data-only review.
-- If neither owner scope nor narrowed deal scope is present, follow `Required Inputs` and ask for the missing scope anchor.
-- If any of `forecast_posture_standard`, `forecast_category_convention`, or `amount_basis` remains unresolved after source/context checks, follow `Required Inputs` and ask for the missing forecast choice before producing the review.
+## Follow-Up Actions
 
-### 2. Resolve forecast truth
+1. [Action] — [Owner or suggested owner] — [Due or TBD] — [Expected outcome] — [Source]
 
-- Prefer `crm` for opportunity truth and forecast comparison.
-- If `crm` is unavailable, continue with sufficient CSV exports, CRM report exports, pasted pipeline tables, user-linked forecast docs, `current_snapshot`, or `pipeline_export`.
-- If the review uses user-provided/exported data rather than live `crm`, state that in `Review Scope`.
-- If current versus prior coverage is partial, label that directly.
-- If data is too thin for posture changes, downgrade to a risk-only readout rather than pretending to know the forecast.
-
-### 3. Collect bounded raw data
-
-Normal CRM fast path:
-
-1. Run only the minimum schema discovery needed to identify scope, amount, forecast, stage, owner, close-date, next-step, activity, and risk fields. Skip current-user and account-object discovery unless the scope is "my book" or account-level fields are needed.
-2. Gather current open deals in the requested period using the canonical scope filter when one exists.
-3. Build the open forecast rollup first, normally by forecast category and optionally stage: deal count, CARR or primary amount, and expected forecast amount.
-4. Inspect detail rows only for the highest-value, highest-risk, stale, or materially changed opportunities.
-5. Gather prior snapshot rows only when the user supplied a comparison snapshot, a named CRM forecast snapshot/report is available, or the user explicitly asks for movement.
-
-Rules:
-
-- Keep the raw evidence set bounded and explicit.
-- Use aggregate rollups as the backbone of the review, then layer risk commentary under the rollup.
-- For a first-pass forecast review, do not run OpportunityHistory or broad historical queries just to create a movement narrative. If no prior snapshot was supplied or resolved, say that true forecast movement is unavailable and use current-state risk and hygiene signals instead.
-- If OpportunityHistory is used without a prior forecast snapshot, label it as CRM field history, not forecast-snapshot movement.
-- When risk coverage depends on unavailable or thin signals, name those missing signals before making a recommendation.
-- Do not interpret company-specific forecast categories, stage gates, or commit/upside norms beyond source-backed user context, CRM metadata, manager notes, RevOps docs, or user-provided guidance.
-
-### 4. Run bounded enrichment
-
-- Use `meeting_notes` for recent customer-call evidence only when it materially changes risk or confidence, especially for the top one to three uncertain deals.
-- Use `external_messaging` for customer engagement, timing, stakeholder, or blocker signals only when it materially changes risk or confidence.
-- Use `internal_messaging` for blocker, approval, manager, deal-desk, or product signals when the user wants internal context or when saved context says internal signals are expected for forecast review.
-- Use `document_store` for account plans, forecast notes, close plans, approval trackers, or process docs when they materially sharpen the review.
-- Do not enrich every deal by default. Complete the CRM or exported-data forecast readout first, then offer or run a targeted enrichment pass for the few deals where support evidence would change the recommendation.
-- Do not broaden enrichment after a required anchor clarification. Resolve the anchor first.
-
-### 5. Evaluate risk and posture
-
-Evaluate each material deal across:
-
-- clear next steps;
-- timing credibility;
-- stakeholder coverage;
-- decision-process visibility;
-- proof of urgency;
-- recent customer engagement;
-- data freshness.
-
-Flag hygiene signals when supported, such as missing next steps, close dates that no longer look credible, stale engagement, and meaningful stage, amount, forecast-category, or timing changes.
-
-Flag portfolio-level concentration risk when too much forecast posture depends on one stage, owner, segment, account, product, or timing bucket without clear next actions.
-
-Use simple labels such as `low`, `moderate`, and `high` risk rather than false precision. Distinguish between sourced risk signals and directional inferences.
-
-### 6. Build recommendation changes
-
-- Compare current and prior snapshots when available.
-- Include a concise `What Changed` subsection when the user supplies both current and prior snapshots.
-- Recommend posture changes only when the evidence supports the change.
-- Phrase unsupported posture changes as questions or follow-up checks, not recommendations.
-- Keep forecast-category, stage, amount, and close-date changes draft-only unless the user explicitly asks for CRM update language; even then, draft only and do not write.
-
-### 7. Render the forecast review
-
-Return a structured forecast review in text or document-ready format. Do not require a custom renderer or docx pipeline.
-
-Required sections:
+---
 
-1. `Review Scope`
-2. `Overall Forecast Posture`
-3. `Key Movements`
-4. `Highest-Risk Deals`
-5. `Recommendation Changes`
-6. `Evidence Gaps`
-7. `Follow-Up Actions`
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-For each important claim, show the supporting source or state that it is an inference from available data.
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-`Overall Forecast Posture` should usually include the open forecast category rollup for the requested scope, with deal count and the forecast's primary amount fields. `Key Movements` should include true movement only when a prior forecast snapshot or explicit comparison source exists. If there is no prior snapshot, keep the section but say that true movement was not evaluated and summarize current-state signals such as Closed Won already booked, open Commit concentration, stale May close dates, missing next steps, or large closed-lost/churn rows.
+## Rules
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+- Do not fabricate opportunities, amounts, stages, close dates, owners, forecast categories, customer engagement, movements, or links.
+- Explain whether the review used live CRM truth, exported/report data, pasted pipeline data, or another user-provided source.
+- Include true movement only with a prior snapshot or explicit comparison source; otherwise keep `Key Movements` and state the limitation.
+- Label uncertain recommendations `Likely`, `Possible`, or `Needs confirmation`; avoid false precision.
+- Keep the initial review read-only. Draft downstream manager notes, CRM-ready text, spreadsheet structure, or risk follow-up only when requested; never write or send without a separate explicit approval step.
 
-## Output Rules
+## Failure Handling
 
-- Keep the review useful for forecast decisions.
-- Keep risk labels simple and defensible.
-- Avoid false precision when the source data is incomplete.
-- If the user supplied comparison snapshots, include what changed without implying certainty that the data does not support.
-- Explain whether the review used live CRM truth, exported CRM/report data, pasted pipeline data, or another user-provided source.
-- Name evidence gaps, source gaps, and confidence limits where they affect recommendations.
-- Keep facts and inference separate. Label uncertain recommendations as `Likely`, `Possible`, or `Needs confirmation`.
-- Do not fabricate opportunities, amounts, stages, close dates, owners, forecast categories, customer engagement, or links.
-- Do not write CRM, send messages, assign owners, or update documents during the initial review. Only perform those actions after a reviewed downstream draft or update is explicitly approved by the user and the available tool supports it.
+If scope or forecast truth cannot be resolved, state the blocker, offer concrete candidates when available, and ask for the smallest missing source or convention. If optional evidence is unavailable, continue from the forecast truth set and make the confidence limit visible.
diff --git a/plugins/sales/skills/review-forecast/agents/openai.yaml b/plugins/sales/skills/review-forecast/agents/openai.yaml
index 1c07ded..0effa02 100644
--- a/plugins/sales/skills/review-forecast/agents/openai.yaml
+++ b/plugins/sales/skills/review-forecast/agents/openai.yaml
@@ -3,4 +3,4 @@ interface:
   short_description: "Forecast reviews with risk analysis and recommendation posture"
   default_prompt: "Review a forecast for risk and next actions."
 policy:
-  allow_implicit_invocation: true
+  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/review-rep-call-trends/SKILL.md b/plugins/sales/skills/review-rep-call-trends/SKILL.md
index eebb83b..1bc6e06 100644
--- a/plugins/sales/skills/review-rep-call-trends/SKILL.md
+++ b/plugins/sales/skills/review-rep-call-trends/SKILL.md
@@ -1,267 +1,160 @@
 ---
 name: review-rep-call-trends
-description: Analyze a sales or customer-facing rep’s recent calls to detect improvement, regression, and stable patterns with objective evidence and practical coaching actions.
+description: Use when the user wants to understand how a sales or customer-facing rep's call behavior is changing over time. Produce an evidence-backed trend readout with improvements, regressions, stable patterns, coaching actions, and calls to re-listen.
 ---
 
-# Rep Trend Coach
+# Review Rep Call Trends
 
-## What this skill does
-Given a target rep, pull a representative sample of their `meeting_notes` calls across the last few months, analyze summaries (and selectively fetch full call content), and produce an evidence-backed coaching report highlighting:
-- what improved
-- what regressed
-- what changed (behaviors, patterns, outcomes)
-- concrete coaching actions tied to specific call moments
 
-## Skill Configuration
+## Context-Gathering Intake
 
-### User Context
+Whenever this skill asks for context, strongly prefer using the `answers-ask-user-input` skill and the `ask_user_input` tool over other tools such as `request_user_input`; otherwise ask directly in the conversation.
 
-Mandatory pre-answer gate:
+Turn a representative time-based call sample into objective coaching guidance. This skill compares older and newer call evidence to identify genuine improvement, regression, and stable patterns. It owns the readout only; it does not create trackers, send feedback, post messages, or save artifacts.
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the output.
+## Common Skill Instructions
 
-### Audience And Language
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Key Dependency Categories
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+These categories are particularly important for this workflow; use other sources only when they materially improve sampling or interpretation.
 
-### Source Resolution
+- [Blocking] ~~Meeting Transcripts for time-sliced call search, summaries, transcript moments, speaker context, dates, companies, and clickable call links. It blocks the default live-source trend path; explicit older/newer call evidence already in context satisfies the need.
+- ~~Knowledge & Files for user-provided or exported transcripts, call summaries, manager-provided call sets, and prior coaching context
 
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
+Call summaries or transcripts are required for trend claims. Manager notes, CRM outcomes, and generic impressions may provide context, but cannot substitute for older-versus-newer call evidence. If the sample is small or skewed, return a limited trend readout rather than overgeneralizing.
 
-### Required Inputs
+## Reference Loading
 
-This skill needs enough user-provided, connector-visible, or explicitly inferable context to identify a target rep and a time window, call set, transcripts, notes, or coaching dimension.
+`SKILL.md` owns the normal time-sliced trend workflow and output format. Load references only when their extra detail matters:
 
-The skill can still produce a limited trend readout from pasted transcripts, uploaded/exported call summaries, manager-provided call sets, or user-linked transcript text when that context is sufficient. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
+- Use [references/request-schema.yaml](references/request-schema.yaml) for structured input normalization.
+- Use [references/rubric.md](references/rubric.md) when the compact behavior categories are insufficient for classifying evidence, especially technical-success or meeting-craft signals.
 
-**Inputs:**
+## Workflow Guidance
 
-Require:
+### 1. Resolve the rep and comparison basis
 
-- [required] target rep or clearly supplied target call set
-- [required] comparison basis: time window, older/newer call sets, transcripts, call summaries, or coaching dimension
+- If the target rep or comparison basis is not clear, ask the user via `ask_user_input()`. For a bare invocation such as “review call trends,” do not assume a rep or time window; offer: `My calls over the default trailing 90 days`, `A named rep over the default trailing 90 days`, and `Supplied older/newer call sets or a custom window`.
+- If the user names only “my call trends” without a comparison basis, offer the same explicit choices before broad retrieval. Do not infer a representative rep, comparison window, or trend-review mode from a fresh request.
+- Require a target rep or clearly supplied target call set and a comparison basis: time window, older/newer sets, transcript set, call summaries, or explicit coaching dimension.
+- After the user selects “my calls,” use the current user when available. For an ambiguous manager-style request, ask for the rep's exact display name or email before broad retrieval.
+- If an attendee-filtered search returns zero results, try small grounded variations: first/last name only, then an obvious nickname when context supports it. If those still return zero, ask for the rep's exact display name in the selected ~~Meeting Transcripts source.
+- After the user selects the default Trend Review path, use the trailing 90 days split into three non-overlapping slices: 0-30 days, 31-60 days, and 61-90 days. Convert every relative window to concrete absolute dates before searching.
+- Preserve user-specified motion, product focus, account, coaching dimension, or audience. If a supplied date phrase could materially change the sample, ask one brief clarification.
+- If the target remains ambiguous after the user selects a lane, make at most three narrow source reads, offer up to five concrete candidates, and confirm any inferred anchor before deeper analysis.
 
-Accept when provided:
+### 2. Collect summaries across time
 
-- call type, focus area, peer examples, manager notes, product focus, date range, and output audience
+- Start with ~~Meeting Transcripts and search each time slice separately. Use the rep's exact email or display name, a limit of about 25 per slice, and `score_threshold=0` when supported—or the lowest practical threshold—so the sample is not biased toward only “strong” calls.
+- When the user names a focus such as discovery, demo, renewal, objections, or technical troubleshooting, use a short matching query. Otherwise use an empty or neutral query to avoid biasing the sample.
+- Aim for 30-60 deduplicated calls when available, with a mix of call types and companies visible in summaries. If a slice is sparse, expand it once by about 30 days and state the adjustment.
+- If there are hundreds of calls, prioritize the most recent calls plus a smaller older baseline while preserving visible call-type and company variety.
+- Build the trend view from summaries first: date, company, title, topics, call link, and the retrieval handle kept only for fetching.
+- Do not claim improvement or regression from a single recent call or from outcomes alone.
 
-**Context Rules:**
+### 3. Compare supported behavior categories
 
-- If a required input is missing or ambiguous, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce useful concrete defaults, then ask the user to confirm or supply that required input before drafting.
-- Before asking the user to paste transcripts, call summaries, rep names, time windows, or coaching dimensions, first try `meeting_notes` for target-rep call candidates, time-window candidates, summaries, metadata, and transcript access. Manual transcript-like material is the fallback after `meeting_notes` is unavailable, empty, or too thin for trend evidence.
-- User confirmation is required for any required input inferred from sources rather than provided by the user.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
+- Compare the same categories across older and newer slices whenever possible.
+- Use the core categories: opening/agenda, discovery depth, qualification/prioritization, positioning/narrative, objection handling, next steps and mutual action plan, technical accuracy, clarity/concision, executive presence, and listening/turn-taking.
+- When the calls are technical or solution-oriented, use the rubric's additional categories for problem framing, diagnosis/troubleshooting, solution design/tradeoffs, and technical-plus-business stakeholder management.
+- Prefer three to six categories with the clearest evidence. Mark mixed evidence as Inconsistent rather than forcing a trend.
+- Call something Improved or Regressed only when the time comparison supports a directional shift. Use Stable for repeated patterns without clear movement.
 
-When a required anchor is ambiguous, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
+### 4. Fetch a small validation set
 
-**Input Request Format Example:**
+- Fetch full context only where summaries are ambiguous, a trend needs validation, or a concrete coaching moment would help.
+- Normally inspect one to two strong recent calls, one to two recent calls with friction, and one to two older baseline calls.
+- Capture two to six brief moments: what happened, why it mattered, category, readable title/date/company context, and live link.
+- Keep quotes short and use the full content to validate—not replace—the summary-level sample.
 
-```md
-Which rep and time window or call set should I review?
-
-a. **{actual candidate}** - {short source-derived context}
-b. **{actual candidate}** - {short source-derived context}
-```
-
-Proceed with a sensible default when the user supplies transcripts or a clear rep/time window.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
+### 5. Turn trends into action
 
-Source categories:
+- Tie each improvement, regression, and stable pattern to evidence from the compared slices.
+- Recommend exactly three coaching actions for the next two weeks. Each action needs a skill to practice, an if/then play, a five-to-ten-minute drill, and an observable check for the next calls.
+- Select three to eight calls to re-listen to, favoring the clearest recent and baseline examples that explain the coaching priorities.
 
-- `meeting_notes`: call search, transcript fetches, summaries, call metadata, call/transcript URLs, speaker labels, participant context, dates, companies, and refetch handles.
-- user-provided context: pasted transcripts, uploaded/exported call summaries, manager notes, rep/call lists, coaching dimensions, and prior feedback artifacts.
+### Next Step Options
 
-Authority and gaps:
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Drill into one improved, regressed, or inconsistent behavior category.
+- Turn the top three actions into a two-week practice plan or coaching note for review.
+- Compare a bounded peer-exemplar set for one coaching priority.
+- Select and summarize the most useful calls to re-listen to.
+- Set up a future trend refresh for the same rep, scope, and comparison basis.
 
-- Use transcript/call-summary evidence for trend claims. Do not infer behavior changes from memory or generic rep impressions.
-- Treat CRM outcomes, manager notes, scorecards, and rep/call lists as context for sampling or interpretation only; do not use them as substitutes for call evidence when claiming behavior trends.
-- Make true change claims only from a comparison basis: older/newer call sets, time slices, repeated summaries, or explicit user-provided comparison context.
-- Prefer live transcript/call URLs over raw connector ids in final output.
-- If the dataset is too small or skewed, produce a limited trend readout and name confidence limits instead of overgeneralizing.
+Next steps to avoid:
+- Making performance judgments from thin evidence or sending feedback automatically.
 
-### Context Gathering Principles
+## Modes
 
-Optimize for marginal value, not absolute speed.
+- Trend Review — default trailing-window analysis.
+- Focused Trend Review — use for a named motion, category, account set, or supplied older/newer calls.
+- Limited Trend Readout — use when one slice, transcript depth, or sample balance is insufficient for confident movement claims.
 
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
+## Output Format
 
-### First-Run Banner
-
-If Sales preflight says the review-rep-call-trends experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
+Return these sections in order.
 
 ```md
-## Review Rep Call Trends
-This skill helps analyze how a rep's call behavior is changing over time. It can use transcripts, call notes, scorecards, manager context, CRM outcomes, or pasted call sets. It produces improving, regressing, and stable patterns with evidence, caveats, and practical coaching actions.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Review a rep's call trends.`
-- `@Sales review Jamie's call trends over the last month.`
-- `@Sales compare this rep's recent discovery calls to their earlier calls and flag changes.`
-- `@Sales find improvement and regression patterns across this rep's demo calls.`
+# Rep Call Trends: [Target Rep]
 
-When invoked by guided Sales onboarding:
+## Coverage
 
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
+- **Target rep:** [Name]
+- **Window analyzed:** [Absolute start] -> [Absolute end]
+- **Time slices:** [Older / middle / recent absolute ranges]
+- **Calls reviewed:** [N summaries], [M full fetches]
+- **Sample shape / limits:** [Mix, sparse slice, or confidence note]
 
-### Next Step Guidance
+## What Improved
 
-This section is the single owner for Review Rep Call Trends next-step behavior and action text. Onboarding, coaching artifact creation, spreadsheet or tracker creation, draft creation, posting or sharing, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the call-trend continuation states elsewhere.
+- **[Behavior change]** — [Evidence from older vs newer readable call context + inline links] — [Why it matters]
 
-#### Final Continuation Invariant
+## What Regressed / Risk Signals
 
-Every final assistant response while `review-rep-call-trends` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/posting/saving/linking an artifact, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
+- **[Behavior change]** — [Evidence from older vs newer readable call context + inline links] — [Why it matters]
 
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation.
+## What Stayed Consistent
 
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
+- **[Stable or inconsistent pattern]** — [Evidence + inline links]
 
-#### Journey States
+## Top 3 Coaching Actions: Next 2 Weeks
 
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, or "done" message while a valid next step remains.
+1. **Practice:** [Skill]
+   - **If / then:** [In-the-moment play]
+   - **Quick drill:** [5-10 minute drill]
+   - **Look for next:** [Observable signal]
 
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the trend readout** | The first substantive call-trend readout has just been produced. | `Anything you'd change in the trend readout before we turn it into a coaching artifact?` |
-| **2. Review the revised trend readout** | The user asked for any change to trend language, evidence, categories, date window, audience, source treatment, or formatting. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to turn it into a coaching tracker, manager-ready summary, rep-facing feedback note, or spreadsheet for tracking change over time?` |
-| **3. Prepare the trend artifact** | The user accepts the readout, says no changes are needed, says a revision looks good, or asks to operationalize it. | Offer to produce the most useful next artifact or draft action, prioritizing coaching tracker, manager-ready summary, rep-facing feedback note, enablement follow-up, or spreadsheet over broad suggestions. |
-| **4. Review the artifact or draft** | A coaching tracker, manager summary, rep-facing note, enablement follow-up, or spreadsheet exists, or the user asks to change it. | `Anything you'd change before I save, post, or share this coaching artifact?` |
-| **5. Save, post, or share approved trend action** | A reviewed artifact or draft exists and does not need edits. | Offer the specific supported action, such as saving the tracker or spreadsheet, sharing the manager summary, or posting the approved feedback note. Do not post or send messages unless the user explicitly asks for that action or approves the reviewed draft. |
+## Calls to Re-Listen
 
-#### Acceptance Handling
+- [Readable date, company, title + inline link] — [Why this call matters]
 
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the trend readout, treat a lightweight acknowledgement as acceptance and move to `Prepare the trend artifact`. If the previous continuation offered to prepare an artifact or draft, produce that artifact or draft for review; do not interpret the acknowledgement as approval to post or send externally. If the previous continuation offered to save, post, or share a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
-
-## Hard rules
-- **Do not make anything up.** If the data is missing, say so.
-- **Be evidence-first.** Every claim about improvement/regression must reference at least one call using readable context in prose plus an inline numbered markdown link to the live transcript/call URL.
-- **Stay objective.** No mind-reading; infer only from what is said/done in the calls.
-- **Quote sparingly.** Keep verbatim quotes short (<=25 words per source).
-- **Do not expose raw connector ids in final user-facing prose.** Raw ids are for retrieval only.
-- Convert all default or user-provided relative date windows to concrete absolute `date_range` values before calling `meeting_notes`. Use the current date from runtime context as the anchor, prefer trailing windows unless the user explicitly requests calendar boundaries, and show the absolute analyzed window in `Coverage`.
-- `SKILL.md` owns the normal trend-analysis path and output format. Use `references/rubric.md` only when the compact category defaults below are insufficient for classifying evidence.
-
-## Workflow
-
-### 1) Resolve the target rep
-1. If the user names a rep (e.g., “analyze Alex Example”), use that exact display name for `attendee`.
-2. If the user clearly asks about their own calls using wording such as `my calls`, `me`, or `how am I doing`, default to the current user name from system context.
-3. If the user asks a manager-style or ambiguous request without naming the rep, ask one brief clarification for the target rep's exact display name or email before searching.
-4. If the attendee-filtered search returns **zero** results, try small, grounded variations:
-   - First/last name only
-   - Common nickname if obvious from context
-   - If still zero, ask the user for the rep's exact display name in the `meeting_notes` system.
-
-### 2) Collect a representative call sample (multiple searches)
-Use `meeting_notes`:
-- search with:
-  - `attendee = <resolved rep display name>`
-  - `score_threshold = 0`
-  - `limit = 25`
-
-Default time slicing (if user doesn’t specify):
-- last ~90 days split into **three** slices: 0–30d, 31–60d, 61–90d (inclusive; use date_range)
-- Run **at least 3 searches** (one per slice). If results are sparse in a slice, expand that slice window by ~30 days and rerun once.
-
-Resolve each slice to absolute start/end dates before searching. For example, if the current date is `YYYY-MM-DD`, derive three non-overlapping absolute windows that cover the trailing 90 days, then pass those exact dates to `meeting_notes`. If the user supplies a natural-language range, convert it first; if the phrase is ambiguous enough to change the sample materially, ask one brief clarification.
+---
 
-Query strategy:
-- If the user specifies focus (e.g., “discovery calls”, “objections”, “renewals”, “technical troubleshooting”), encode that into `query` as a short keyword phrase.
-- If the user does **not** specify focus, use `query = ""` to avoid biasing results.
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-Sampling guidance:
-- Aim for 30–60 total calls if available (dedupe by id).
-- If there are hundreds, keep it manageable by:
-  - prioritizing the most recent calls and a smaller older baseline
-  - ensuring you include a mix of call types/companies when the summaries indicate variety
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
 
-### 3) Build a trends view from call summaries
-From the search results, create a working table (internal to your reasoning) with:
-- date
-- company
-- call title (if present)
-- brief topics from the snippet/summary
-- call url
-- raw call id or file id for internal retrieval only
+## Example Prompts
 
-Then analyze for trends across time slices using the core categories: opening/agenda, discovery depth, qualification/prioritization, positioning/narrative, objection handling, next steps, technical accuracy, clarity/concision, executive presence, and listening/turn-taking.
-- Look for **directional shifts** (e.g., more consistent agenda setting, clearer next steps, better objection handling).
-- Track **recurring misses** (e.g., vague next steps, weak qualification, too much feature-dumping).
+- `Review a rep's call trends.`
+- `Review Jamie's call trends over the last month.`
+- `Compare this rep's recent discovery calls to their earlier calls and flag changes.`
+- `Find improvement and regression patterns across this rep's demo calls.`
 
-### 4) Fetch deep context for a small set of calls
-Use `meeting_notes` fetch for deeper inspection when:
-- summaries are ambiguous
-- you need to validate a claimed trend
-- you want to extract 2–6 concrete “moments” to coach
+## Rules
 
-Pick calls intentionally:
-- 1–2 recent calls that look strong
-- 1–2 recent calls that look weak / had friction
-- 1–2 older calls (baseline)
-
-When reviewing full content:
-- capture brief “moment” notes (what happened + why it mattered)
-- keep quotes short
-- tie back to rubric categories
-- carry forward the live transcript/call URL for user-facing citations
-
-### 5) Produce the coaching report (required format)
-Output must be concise, scannable, and evidence-backed.
-
-#### A) Coverage
-- Target rep: <name>
-- Window analyzed: <start> → <end>
-- Calls reviewed: <N summaries>, <M full fetches>
-
-#### B) What improved (evidence)
-For each improvement:
-- Behavior change
-- Evidence: readable context such as `(date, company, call title)` followed by inline numbered markdown links to supporting transcript/call URLs
-- Why it matters
-
-#### C) What regressed / risk signals (evidence)
-Same structure as above.
-
-#### D) What’s unchanged / consistent pattern
-- One or two bullets with evidence.
-
-#### E) Top 3 coaching actions (next 2 weeks)
-Each action must include:
-- the skill to practice
-- an “if/then” play (what to do in the moment)
-- a quick drill (5–10 minutes)
-- what to look for on the next calls
-
-#### F) Calls to re-listen
-List 3–8 calls with a one-line reason each.
-
-### Citation format
-- Use compact inline numbered markdown links such as `[1](https://example.com/call/123456789)`.
-- Keep the human-readable context in prose, for example: `2026-03-24, Blue Yonder, Pilot & Pricing [1]`.
+- Do not invent calls, behavior, outcomes, metrics, quotes, dates, companies, or links.
+- Every improvement or regression claim must compare older and newer evidence and cite at least one readable call link; do not turn anecdote into trend.
+- Prefer explicit structure, questions, summaries, objections, and next steps over speculative interpretation. Do not infer intent or personality.
+- Cite with readable title/date/company context and compact inline numbered Markdown links, such as `2026-03-24, ExampleCorp, Pilot & Pricing [1](https://example.com/call/123)`. Prefer live transcript or call URLs; never expose raw connector ids.
 - When one statement is supported by multiple calls, append multiple numbered links inline.
-- Prefer the live transcript/call URL from the connector response over raw connector ids.
-- If a result unexpectedly lacks a call URL, cite it by readable title/date/company only and note that a direct link was unavailable. Do not print the raw id.
+- Keep excerpts short, normally no more than 25 quoted words from one source.
+- If a useful call has no stable link, use readable context and say that no direct link was available.
+
+## Failure Handling
 
-## Notes
-- Prefer higher-reliability signals (explicit language, structure, next steps) over speculative ones.
-- When data is limited (few calls, sparse summaries), be explicit about confidence and avoid overgeneralizing.
+If the target or comparison basis cannot be resolved, state the smallest missing input and offer concrete candidates when available. If the sample is sparse or uneven, produce the limited readout, name which movement claims are unsupported, and suggest the smallest additional call set or window that would improve confidence.
diff --git a/plugins/sales/skills/sales-company-research/SKILL.md b/plugins/sales/skills/sales-company-research/SKILL.md
index 5503bfd..e17dc5c 100644
--- a/plugins/sales/skills/sales-company-research/SKILL.md
+++ b/plugins/sales/skills/sales-company-research/SKILL.md
@@ -1,136 +1,82 @@
 ---
 name: sales-company-research
-description: Explicit-only Sales workflow for scheduled or index-routed company research that finds durable internal resources, saves high-confidence Sales plugin memory, and asks focused follow-up questions.
+description: Research companies, partners, and referral channels to shape draft outreach approaches, messages, or partner motions; exclude descriptive company profiling, list enrichment, and internal source finding.
 ---
 
 # Sales Company Research
 
-Run deep company-context research that finds durable, non-obvious resources future Sales workflows should reuse. This skill owns the research workflow: dynamic source discovery, source mapping, candidate ranking, save-versus-review decisions, focused follow-up questions, and the final research response. It uses `sales:user-context` for preflight, memory write policy, state-file structure, and readback.
+Turn external company or partner research into a grounded, draft-only outreach approach. This skill owns research-to-outreach work: company or partner context, fit hypotheses, likely motion, and message drafts. It does not own data-first prospect-list enrichment, internal source-of-truth lookup, sending messages, or CRM writes.
 
-Use this skill only when it is explicitly invoked, routed by the Sales index, or launched by the Sales Company Research automation. It is not an implicit catch-all for ordinary internal-source lookup, customer question routing, or one-off source finding; those usually belong to `find-key-internal-sources` or the focused Sales workflow that needs the source.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to `plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices`.
 
-### User Context
+## Key Dependency Categories
 
-Mandatory pre-answer gate:
+- ~~Sales Intelligence for company, contact, partner, firmographic, similarity, and external-signal context
+- ~~CRM for existing account, ownership, lifecycle, opportunity, and prior-engagement truth
+- ~~Knowledge & Files for approved positioning, proof points, partner criteria, and outreach guardrails
+- Public research for company-controlled facts, reputation, partner programs, and current market context
 
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, saving memory, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. In scheduled research mode, do not append onboarding reminders or onboarding CTAs to the completed research response; this pinned research thread should end with the research result and any `Where you can help` questions. If an onboarding obligation matters, leave it to the onboarding thread or fold the underlying source gap into a research question only when it would improve future Sales research.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the research result.
+Use the user-provided company, domain, partner type, product, or offer as the anchor. If a material anchor is missing, ask only for the smallest input that changes the research path; otherwise proceed with clearly labeled assumptions.
 
-### Audience And Language
+## Workflow
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+1. Resolve the research target and commercial purpose: company fit, partner fit, referral landscape, reputation, or outreach approach.
+2. Gather the narrowest useful evidence from authoritative sources first. Keep sourced company or partner facts distinct from inferred fit, likely buyer need, and recommended motion.
+3. Explain why the target or partner type is promising, uncertain, or a poor fit. Name material evidence gaps instead of filling them with generic claims.
+4. Draft only the outreach artifact the user asked for, such as an approach, short email, message, connection request, or partner script. Do not send, post, create a draft in an external system, or update CRM.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+## Routing Boundaries
 
-### Source Links
+- Use `enrich-company-and-contact-data` when the primary output is a list, missing fields, contact discovery, segmentation, qualification, or firmographic comparison.
+- Use `find-key-internal-sources` when the primary question is which internal owner, document, channel, approver, or source of truth to use.
+- Use `plan-deal-strategy` when the primary job is advancing a defined sale, negotiation, renewal, buying process, or initial sales motion rather than researching a target to shape outreach.
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+### Next Step Options
 
-### Invocation Modes
+After the first output, offer the most relevant follow-up from the options below. Offer one clear transition, not a menu. Suggest ONLY these unless you are very confident another option is more useful:
+- Improve the fit hypothesis, audience, angle, or sequencing using the user's guidance.
+- Draft an alternate outreach version for a different stakeholder or channel.
+- Hand the target to meeting prep or deal strategy when an active conversation or motion exists.
+- Create a concise research brief or internal account-team note after the content is reviewed.
+- Close the smallest material evidence gap that would change the outreach approach.
 
-Use `scheduled research mode` when the request comes from the Sales Company Research automation, heartbeat, or one-time onboarding kickoff. Treat the automation prompt as a thin launcher for this skill; do not require the prompt to repeat source families, ranking rules, save gates, output copy, model, or reasoning instructions.
-
-Use `manual research mode` when the user directly asks Sales to run company research, discover useful Sales resources, fill missing company context, audit available Sales setup sources, or rerun the research after new sources become available.
-
-### Workflow Sources
-
-Resolve source access dynamically from the current tools, available plugins/connectors/apps, `sales_preflight.context.sources`, saved source preferences, current thread context, and any user-provided seeds. The configured Sales source categories are search hints, not an exhaustive provider list and not a readiness claim.
-
-Do not hard-code a customer's connector stack into the workflow or automation prompt. Search across source families that are both available and authorized in the current environment, such as:
-
-- company docs, wikis, knowledge bases, drives, pages, and shared folders;
-- internal messaging channels, pinned posts, files, source-of-truth threads, and approved private-channel searches when the user explicitly authorizes or the workflow policy permits them;
-- CRM, GTM, forecast, pipeline, territory, account-plan, and book-of-business sources;
-- dashboards, BI tools, reporting hubs, data dictionaries, and metric-definition docs;
-- email, calendar, meeting notes, transcripts, call recordings, account rooms, and customer handoff spaces;
-- issue trackers, project trackers, launch hubs, approval trackers, legal/procurement/security paths, and deal governance sources;
-- positioning, competitive, customer-facing asset, customer-story, enablement, and sales-planning libraries.
-
-When a source family is unavailable, unauthorized, rate-limited, or not exposed through tools, turn that gap into a `Where you can help` question only when a pointer or access change would materially improve future Sales workflows.
-
-Use `find-key-internal-sources` as a helper only when ordinary source search is likely to miss the maintained owner, source-of-truth route, channel, or doc family. Keep this skill responsible for final ranking, save decisions, and output.
-
-## Research Workflow
-
-1. Read the Sales preflight payload and current saved user context. Identify unresolved template categories from `../user-context/plugin-author-config/user-context-config.md`, stale or overly generic saved entries, source families already saved, and entries already flagged or saved in the recurring Sales Company Research thread.
-2. Build a source-family map before saving. For each target category, name the most plausible source families, likely owner/team or operating terms, candidate resource types, and any available tools or connectors that can search them. Use saved resources and user-provided links as optional seeds, not as anchors that constrain the search path.
-3. Search broadly enough to compare authority across source families. Follow source trails from promising hits: linked docs, pinned posts, related hubs, dashboards, tracker references, owner pages, account folder indexes, approval pages, and canonical examples. Do not stop after the first few obviously relevant docs; keep going until each core Sales memory area has either a credible candidate, an already-saved source, or a concrete reason it could not be found.
-4. Prefer authoritative interpretation layers over raw connector facts. High-value resources explain how the company operationalizes common Sales concepts: pipeline stage definitions and exit criteria, forecast category usage, close-date norms, new-logo versus expansion distinctions, AE/AM/CSM/SA/overlay responsibilities, approval ownership, territory and segment rules, named-account exceptions, priority-account definitions, deal desk or launch-readiness governance, canonical CRM reports/views, process pages, and tracker definitions.
-5. Check the saved set for coverage across core Sales memory areas before finalizing. At minimum, look for credible candidates or concrete gaps for CRM/forecast process, sales planning/data lineage, account/customer docs, deal governance and approval paths, legal/comms/customer-facing claims guardrails, approved positioning or core narrative, GTM/customer-facing asset and enablement hubs, competitive enablement, and customer-story/use-case libraries. If a category has no high-confidence saved source, do a second-pass search using adjacent terms, linked hubs, owner/team names, and source-family synonyms before asking a focused question. Do not treat one saved source as covering adjacent categories unless its contents actually help that workflow family.
-6. Track every meaningful source as a walkable source record with label, source family, owner or location when known, stable link or connector-visible reference, what it represents, relevant memory category, freshness or access caveat, and why it was found through a non-obvious route when applicable. A bare title, channel name, meeting name, or go-link is not enough when a stable link or connector reference is available.
-7. Rank and dedupe candidates before writing memory. Score candidates against authority, criticality, future Sales workflow impact, company-unique semantics, freshness, non-obviousness beyond ordinary search, durability/revisitability, accessibility, specificity to the user or team, and sensitivity/safety. Keep multiple resources when they serve distinct authority lanes, such as process rules, data lineage, customer-facing assets, positioning, competitive enablement, legal/comms guardrails, customer proof, or deal governance; do not collapse them into one hub merely because titles overlap.
-8. Save high-confidence, source-backed, non-obvious, low-risk resources by default. Do not save broad, sensitive, ambiguous, lower-confidence, unsupported, surprising, account-specific, volatile, or policy-like entries until the user has seen them and explicitly says `save`, `apply`, `update`, `keep these`, or otherwise clearly approves writing those entries.
-9. For broad hubs, save only when they are clearly useful durable starting points. The saved memory and `What was found` bullet must include the safe-use caveat, such as "use as a starting point for account-specific artifacts, not as policy truth" or "refresh before relying on live dashboard values."
-10. Extract concise usage rules, source links, names, aliases, source priorities, and workflow hints. For every saved resource, write a one-sentence utility description that explains what future Sales workflows should use it for. Redact sensitive details and summarize only what future Sales workflows need. If a candidate mostly restates what a connector can retrieve directly, drop it or turn it into a question asking for the missing convention or authoritative source.
-11. When saving, use `../user-context/references/plugin-memory.md` and the `Write` rules in `../user-context/SKILL.md`: update `user-context.md` directly, replace `status: not provided` only in touched scaffold categories, append or merge resources under `## Saved Links And Context`, preserve category descriptions, update `onboarding-state.json` only for status-only onboarding bookkeeping that changed, and run the Sales preflight readback after writing. Do not duplicate saved resource lists, category lists, source URLs, counts, or research-output details into onboarding state. Never run parallel writes against Sales state files.
-12. Identify the five highest-value unresolved asks. Prefer questions that would unlock source authority, book-of-business/account ownership, account-plan sources, approval/governance preferences, customer-facing claims guardrails, customer-story libraries, access gaps, or source freshness. Future automation runs can ask more questions later, so do not exhaustively list every gap.
-
-## Save And Review Rules
-
-Discovery-derived memory may be saved by default only when it is high-confidence, source-backed, non-obvious, low-risk, and useful for future Sales workflows. Discovery authorization is not blanket save authorization.
-
-Do not save:
-
-- raw CRM field metadata, picklist inventories, live values, or ordinary object reads;
-- sensitive deal desk, private legal, commercial, security, account-room, or private-channel details unless the user approves the exact memory entry;
-- generic search results, incidental chatter, duplicate hubs, or easy-to-find account folders without a durable usage reason;
-- dashboard values, tracker statuses, account ownership, or live book facts unless the saved entry is a durable source pointer with a refresh-before-use caveat.
-
-When entries are review-only, ask the concrete question that would make the candidate saveable. Fold review-only candidates into `Where you can help`.
+Next steps to avoid:
+- Sending outreach, creating external drafts, or writing CRM without a separate explicit request.
 
 ## Output
 
-Start every completed research response with exactly one of:
-
-```text
-Sales company research is complete. {N} new resources saved.
-```
-
-```text
-Sales company research is complete. No new resources saved.
-```
-
-Use this output shape when resources were saved:
+Return a compact research-to-outreach package:
 
 ```md
-Sales company research is complete. {N} new resources saved.
+# Sales Research: [Target or Partner Segment]
 
-**What was found**
-- [{Resource title}]({stable URL})
-  {One-sentence user-value description explaining what future Sales workflows should use this for}. {Safe-use caveat when the source is broad, dynamic, account-specific, or not policy truth.}
-- **{Connector-visible resource title}** (no useful link available)
-  {Why this helps future Sales workflows}. Source: {durable connector-visible reference}. {Safe-use caveat when needed.}
+## Sourced Context
+- [Fact + source]
 
-**Where you can help**
-1. {Question that would unlock better future Sales workflows, with why it matters.}
-```
+## Fit And Motion
+- **Fit hypothesis:** [Inference and confidence]
+- **Likely need or partner angle:** [Inference and evidence basis]
+- **Risks / gaps:** [What remains unknown]
 
-Do not include coverage notes or operational research bookkeeping in the completed response. Keep searched source families, source-family depth, connector availability, prior saved-memory state, pinned-thread history, review-only bookkeeping, and reasons non-saved findings were excluded out of the user-facing output unless the user asks for debugging or audit details. Convert material limitations into concrete `Where you can help` questions instead.
+## Recommended Outreach Approach
+- [Audience, angle, sequencing, and safe next step]
 
-Use this output shape when no resources were saved:
+## Draft Outreach
+> [Draft-only message]
 
-```md
-Sales company research is complete. No new resources saved.
+No message was sent and no CRM record was changed.
 
-**What was found**
-{Briefly explain the strongest blockers or why no candidate cleared the high-confidence, low-risk save bar.}
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
 
-**Where you can help**
-1. {Question that would make the next run materially better.}
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
 ```
 
-Output rules:
-
-- `N` counts newly saved resources only, not reviewed candidates or already-saved resources.
-- `What was found` lists only high-confidence saved resources. Do not include review-only candidates there.
-- Include one concise bullet per saved resource. Start each bullet with the resource title as a Markdown link whenever a stable URL exists, then put the user value on the next line in plain language. Do not include category labels such as `saved under`, memory category names, or implementation bookkeeping in the user-facing result.
-- Every saved resource must explain why it helps the user, using concrete downstream Sales value such as better meeting prep, stronger follow-ups, cleaner CRM updates, sharper account prioritization, safer customer-facing claims, faster competitive briefs, or more credible business cases. Do not output bare link bullets. If you cannot explain a resource's utility in one sentence, do not count it as saved until you have enough evidence.
-- Add safe-use caveats for broad hubs, dynamic dashboards, customer/account folders, account rooms, or sources that should not be treated as policy truth.
-- Do not append onboarding reminders, onboarding status, or onboarding CTAs to completed Sales Company Research output. Keep the final visible action inside `Where you can help`.
-- If the saved set lacks legal/comms/customer-facing claims guardrails, approved product positioning/core narrative, or another high-risk customer-facing source category, ask for it in `Where you can help`.
-- Include up to five `Where you can help` questions. Each question should ask for a concrete link, channel, tracker, report, owner, example, preference, access fix, or source-of-truth choice, and say why the answer would be valuable.
-- Use the output shapes above by default. Mention implementation details or readback status only when the user asks for them or when readback failed.
+## Rules
+
+- Cite material sourced claims with useful links when available.
+- Label inference clearly; do not invent company facts, partner terms, contacts, reputation claims, or fit evidence.
+- Keep outreach draft-only. Never send, post, create external drafts, or write CRM in this workflow.
diff --git a/plugins/sales/skills/sales-company-research/agents/openai.yaml b/plugins/sales/skills/sales-company-research/agents/openai.yaml
index b801573..4a219d5 100644
--- a/plugins/sales/skills/sales-company-research/agents/openai.yaml
+++ b/plugins/sales/skills/sales-company-research/agents/openai.yaml
@@ -1,6 +1,6 @@
 interface:
   display_name: "Sales Company Research"
-  short_description: "Find durable company resources that improve Sales workflows"
-  default_prompt: "Run Sales Company Research."
+  short_description: "Research targets and shape draft outreach"
+  default_prompt: "Research this company and draft a grounded outreach approach."
 policy:
   allow_implicit_invocation: false
diff --git a/plugins/sales/skills/salesforce/SKILL.md b/plugins/sales/skills/salesforce/SKILL.md
index b0e7910..bad9d1a 100644
--- a/plugins/sales/skills/salesforce/SKILL.md
+++ b/plugins/sales/skills/salesforce/SKILL.md
@@ -1,122 +1,200 @@
 ---
 name: salesforce
-description: Agentforce Sales connector guide for Sales workflows that use Salesforce for CRM reads, drafts, notes, account plans, Agentforce assignments, or proposed record changes.
+description: Use when a focused Sales workflow needs Salesforce-backed CRM reads, links, drafts, notes, account plans, Agentforce assignments, or explicitly requested Salesforce connector/query guidance. Do not use for generic CRM app construction or when another CRM is authoritative.
 ---
 
 # Salesforce User Guide
 
-Use this as the Salesforce-specific user guide whenever the user or workflow is using Salesforce as the CRM connector, including drafting Salesforce-backed updates, summaries, notes, account plans, Agentforce assignments, or proposed record changes. Keep the surrounding workflow skill authoritative for the sales task itself; this guide adds Agentforce Sales-specific metadata discovery, SOQL query selection, record reads, links, and write safety.
+Prepare a sales person with trustworthy Salesforce context or a safely reviewed Salesforce action, while keeping the surrounding Sales workflow authoritative for the business task.
 
-Do not use this guide when the workflow uses another CRM connector or no CRM connector.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-### User Context
+## Salesforce Operation Ranking
 
-Mandatory pre-answer gate: Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before answering, searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`. Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance. Do not read or reinterpret raw Sales state files unless preflight fails, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+Use this priority order when the requested Salesforce job or record is ambiguous.
 
-Use returned user context as lookup hints and drafting guidance. Always verify Salesforce object and field metadata before querying or writing; saved user context does not replace `describe_global`, `describe_sobject`, record type checks, or write-safety confirmation.
+1. Explicit user request, named record, Salesforce id, account, opportunity, contact, lead, or stated action
+2. Read-only context that helps the active Sales workflow make a decision
+3. Exact record or high-confidence candidate resolved from the user's words and business context
+4. The smallest field set, activity history, account plan, event, or transcript context that answers the request
+5. A reviewed draft of proposed Salesforce changes
+6. An explicitly approved, supported Salesforce write followed by verification
 
-### Audience And Language
+Do not silently pick among plausible records, broaden a read into a write, or treat clarification answers as write approval.
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+## Key Dependency Categories
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
 
-### Source Links
+- ~~CRM, specifically the exposed Agentforce Sales/Salesforce connector, for Salesforce-backed reads, record links, account plans, transcript summaries, Agentforce Lead Nurturing assignments, and approved writes
+- The parent Sales workflow for Calendar, Scheduling, Meeting Transcripts, Email, Internal Messaging, Knowledge & Files, or Sales Intelligence context that Salesforce does not own
+- User-provided record ids, names, exports, field values, and business context when connector access is unavailable or a record needs disambiguation
 
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+Avoid unsupported claims. If Salesforce is missing, unavailable, stale, ambiguous, or limited by the exposed connector surface, state the limitation.
 
-## Connector Boundary
+## Workflow Guidance
+
+Adhere strictly to these workflow steps. These override the default workflow in the index skill.
+
+- 1. Clarify and Gather Context
+    - Resolve the Salesforce task, target record, requested detail, and whether the user wants a read, draft, or approved write.
+    - For an ambiguous record, do the smallest exact or bounded candidate lookup, then ask the user to choose before using the record for a summary or write.
+- 2. First draft
+    - Prefer read-only discovery and the smallest correct record read or query. Use labels in user-facing output and include clickable record links when a trusted URL is available.
+    - For proposed changes, render a reviewed field-level draft with assumptions, source gaps, and the exact approval still needed.
+- 3. Approved action and verification
+    - Write only when the user explicitly asks for the write or approves the reviewed draft/update.
+    - Confirm the target record and supported fields before writing, send only intended changes, inspect the response, and verify with a narrow read when the response does not prove the outcome.
+
+## Overall Rules
 
-The Salesforce CRM app in the Sales plugin is the third-party Agentforce Sales connector, bound in `.app.json` to app id `REPLACE_WITH_SALESFORCE_APP_OR_CONNECTOR_ID`.
+- Always cite sources using hyperlinks when useful links are available.
+- Keep the parent workflow authoritative for the sales task; this guide owns Salesforce-specific lookup, query, links, account-plan actions, assignment, and write safety.
+- Prefer discovery over guessing. Use exact ids or names first, then bounded candidates, then user choice when ambiguity remains.
+- Do not imply support for connector surfaces that are not exposed.
+- Do not delete, upsert by external id, call arbitrary automation, or execute a write without explicit approval.
+
+## Connector Boundary
 
 Use only the Agentforce Sales surfaces that are actually exposed:
 
-- Metadata and identity: `describe_global`, `describe_sobject`, `get_user_info`.
-- Record lookup and reads: `get_record_id_by_name`, `get_record_details`, `get_activity_history`.
-- Querying: `soql_query` only.
-- Sales-specific reads: `query_calendar_events`, `get_account_plan`, `query_agent_type`, `summarize_conversation_transcript`.
-- Writes and mutating sales workflows: `create_record`, `update_record`, `create_account_plan`, `assign_target_to_sdr`.
-
-Do not imply support for SOSL, global text search, query continuation, Bulk API, Composite API, Data 360, Tableau Analytics, Prompt Builder, Flow invocation, arbitrary invocable actions, generic Apex REST calls, delete, or upsert-by-external-id unless a future installed app explicitly exposes those tools. For cross-object keyword search, large exports, high-volume write jobs, delete/upsert needs, or analytics surfaces outside the exposed Agentforce Sales actions, state the missing connector surface and ask for a narrower Salesforce scope, exact object/field, exact identifier, or another approved tool.
-
-## Discovery And Identity
-
-1. Prefer discovery over guessing. Use `describe_global` for object matching and `describe_sobject` for field matching before non-obvious queries or writes.
-2. Use `get_user_info` for requests involving "my", "me", or the current Salesforce owner.
-3. In `describe_sobject`, check field API names, labels, data type, filterability, createability, updateability, nullability, picklist values, references, relationship names, and record type details when those properties affect the task.
-4. Use record type fields when record type changes defaults, picklist choices, required fields, or layout-sensitive behavior.
-5. For relationship queries, do not guess relationship names. Use metadata for relationship field paths and child relationship names.
-6. Use user-facing labels in final answers when known, and API names only when needed for precision or follow-up.
-
-## Record Disambiguation
-
-1. Prefer exact name or id matches before broad text matching when locating records.
-2. Treat broad `LIKE '%...%'` filters as candidate discovery, not proof of the canonical record.
-3. Account names can match regions, subsidiaries, brands, domains, and email-like records. Do not silently pick the first result when multiple plausible records remain.
-4. Use available business context such as country, website, industry, owner, active opportunities, recent tasks, or the user's wording to disambiguate.
-5. If one additional targeted filter will not clearly disambiguate the result, present the candidate records to the user before taking write actions or using the record as the basis for a deal summary.
-
-## Reads And Queries
-
-1. Before authoring non-trivial SOQL, identify the target object, fields needed, filters, sort or limit requirements, whether the query is for display or analysis, and whether selectivity is a concern.
-2. Use `soql_query` for structured reads, relationship fields, ordering, aggregation, semi-joins, anti-joins, and filters on fields Salesforce marks as filterable.
-3. Generate the simplest correct query: select only fields needed for the sales task, include `Id` whenever records may be shown, linked, or used for follow-up, and add a reasonable `LIMIT` unless the user asked for a complete scoped result.
-4. Prefer filtering in SOQL rather than post-filtering. Use aggregates for counts and grouped summaries instead of loading unnecessary records.
-5. Confirm relationship names before querying. Use child-to-parent traversal for parent data from child rows, parent-to-child subqueries for child rows from parent records, and aggregate queries for rollups.
-6. Evaluate wildcard usage carefully. Broad `LIKE '%...%'` filters are candidate discovery, can defeat indexes, and are not proof of the canonical record.
-7. This connector does not support SOSL. If the user asks for keyword search, object-unclear search, cross-object search, or text search in fields that are readable but not SOQL-filterable, say that Agentforce Sales only exposes SOQL and ask for a narrower object, field, or exact identifier.
-8. This connector does not expose a separate query continuation tool. Use a SOQL `LIMIT` when total rows must be capped, tighten filters for high-volume requests, and state the connector limit when the user needs complete export-style coverage.
-
-## Reads And Display Context
-
-- Prefer explicit field projections for business facts and exports.
-- Use `get_record_id_by_name` for exact or near-exact name-to-id lookup when the next step needs a Salesforce id.
-- Use `get_record_details` when the task needs presentation-ready context such as layout fields, optional fields, display values, child relationships, or record-type-sensitive UI metadata. Pass both `Compact` and `Full` layout types unless the user asks for a narrower UI read.
-- Use `get_activity_history` for Account, Contact, Lead, or Opportunity activity summaries.
-- Use `query_calendar_events` for Salesforce calendar or meeting questions.
-- Use `get_account_plan` for account strategy, plan, relationship, challenge, or status context.
-- Use `summarize_conversation_transcript` only after resolving a specific VoiceCall or VideoCall id. If the call is unknown, query VoiceCall and VideoCall separately with `soql_query`, then ask the user which call to summarize.
-- If a requested field is absent from a UI/layout-oriented response, retry with explicit fields before concluding the field is unavailable.
-
-## Query Traps And Recovery
-
-- Do not use `WHERE` filters on fields that Salesforce reports as not filterable.
-- Long text and rich text fields are common traps: they may be readable in `SELECT` but rejected in `WHERE`.
-- A key example is `Task.Description`: do not use `WHERE Description LIKE ...`. Because Agentforce Sales does not expose SOSL, ask for a narrower filterable field, exact record, date/owner/status filter, or another approved search surface.
-- If a field or object is unknown, call `describe_sobject` or use a narrow `FieldDefinition` SOQL query before retrying.
-- If the first result set is too broad, tighten by object, date, owner, status, or another confirmed filterable field rather than adding unsupported text filters.
-- Prefer standard, commonly present fields on `Account`, `Opportunity`, `Contact`, `Task`, and `Event` before optional org-specific fields or relationship-heavy objects.
-- Do not assume optional fields or objects exist, including `CurrencyIsoCode`, `AnnualRevenue`, `OpportunityTeamMember`, `AccountTeamMember`, `Opportunity.Contact`, `OpportunityContactRole.Name`, `PricebookEntry` SOSL, or `FieldDefinition.IsCustom`. Describe the object/field first, or omit the optional enrichment when the standard record data is enough.
-- Cap recovery from invalid schema/query errors. After two focused schema or query-shape failures for the same fact, fall back to safer standard fields, SOSL, exact record reads, or a clear evidence gap instead of continuing exploratory retries.
-- Distinguish connector auth/runtime failures from schema misses. A message that explicitly says reauthentication is required should produce a re-auth CTA; MCP startup, handshake timeout, transport, or availability errors should be described as connector readiness/runtime issues and should not be framed as proof that the user must reconnect.
-
-## Writes
-
-Write only when the user explicitly asks to create or update Salesforce data, create an account plan, or assign a Contact or Lead to Agentforce Lead Nurturing.
-
-- Confirm object API name, record id, and field API names before writes.
-- For `update_record`, send only changed fields in `fields`. For Agentforce AI-generated text updates on core sales objects, only use fields the tool permits, especially `Description` when that is the documented allowed target.
-- For `create_record`, send only intended create fields and include record type fields only when record type affects defaults, picklists, or createability.
-- Use `create_account_plan` for account plan creation instead of generic record creation. Its body must include `AccountId`, `Name`, the challenge, competitive, relationship, and strategy fields, `StartDate`, and `Status`; resolve the account and gather, derive from grounded Salesforce/source evidence, or ask for any missing required values before calling the tool.
-- Before `assign_target_to_sdr`, call `query_agent_type` to list available Agentforce Lead Nurturing or SDR agents, present the choices, and get the user's selection. The target must be a Contact or Lead id.
-- Stop if the user asks to delete, upsert by external id, call Apex REST, invoke Flow, or run arbitrary custom Salesforce automation; those operations are outside this Agentforce Sales connector surface.
-- After any write, inspect the response and, when the response does not prove the outcome, verify with returned fields, `get_record_details`, or a narrow `soql_query` before summarizing the result.
-
-## Agentforce Sales Workflows
-
-- Use `create_account_plan` and `get_account_plan` for account planning requests, but report Salesforce or connector errors plainly if the org does not support the AccountPlan object or the action returns unsupported-object errors. If the user only provides an account name or id, treat that as the target account, not as enough input to create the plan; first resolve `AccountId`, propose or collect the required plan body, and proceed only when the required fields are present.
-- Use `query_agent_type` and `assign_target_to_sdr` for Agentforce Lead Nurturing assignment only when the user asks for that assignment. Treat assignment as a mutating workflow.
-- Use `query_calendar_events` for Salesforce Event records tied to calendar or meeting questions.
-- Use `summarize_conversation_transcript` for voice or video call summary requests after resolving the call id with SOQL or user input.
-
-## Output
-
-Return concise, business-facing results. Include clickable Salesforce record links only when a runtime source provides the org base URL, record URL, or instance URL.
-
-Link rules:
-
-- Prefer a record URL returned by the connector.
-- If connector metadata exposes an org or instance base URL, construct links with that base: `https://<org-or-instance-base>/lightning/r/<ObjectApiName>/<Id>/view`.
-- If only `Id` is known and no trusted base URL is available, show the object label and id instead of inventing an org-specific URL.
+- Metadata and identity: describe_global, describe_sobject, get_user_info
+- Record lookup and reads: get_record_id_by_name, get_record_details, get_activity_history
+- Querying: soql_query only
+- Sales-specific reads: query_calendar_events, get_account_plan, query_agent_type, summarize_conversation_transcript
+- Writes and mutating workflows: create_record, update_record, create_account_plan, assign_target_to_sdr
+
+Do not imply support for SOSL, global text search, query continuation, Bulk API, Composite API, Data 360, Tableau Analytics, Prompt Builder, Flow invocation, arbitrary invocable actions, generic Apex REST, delete, or upsert-by-external-id. For unsupported cross-object keyword search, large exports, high-volume writes, delete/upsert, or analytics requests, state the missing surface and ask for a narrower object, field, exact identifier, or another approved tool.
+
+## Salesforce Query And Write Rules
+
+### Discovery And Reads
+
+- Salesforce is required for Salesforce-backed reads, links, account plans, assignments, transcript summaries, and writes. If it cannot be used, label the answer as not Salesforce-backed.
+- Use get_user_info for my, me, or current-owner requests. Use describe_global for uncertain objects and describe_sobject before non-obvious queries or writes.
+- Confirm API names, labels, data type, filterability, createability, updateability, nullability, picklists, references, relationship names, and record types only when they affect the task.
+- Do not guess relationship names. Treat broad wildcard matches as candidates, use business context to disambiguate, and ask before a summary or write if one targeted filter will not resolve the record.
+- Use get_record_id_by_name when a name must become an id, get_record_details for presentation-ready layout context, get_activity_history for activity summaries, query_calendar_events for Salesforce Events, and get_account_plan for account strategy context.
+- Resolve a specific VoiceCall or VideoCall id before summarize_conversation_transcript. If the call is unknown, query VoiceCall and VideoCall separately, then ask which call to summarize.
+
+### Query Guardrails
+
+- For non-trivial SOQL, identify the target object, needed fields, filters, sort or limit, display-versus-analysis purpose, and selectivity risk.
+- Use the simplest correct query with only needed fields, include Id when rows may be linked or reused, add a reasonable LIMIT, and filter only on fields Salesforce reports as filterable.
+- Do not send aggregate or grouped SOQL, including COUNT, SUM, GROUP BY, or aggregate aliases. Retrieve a bounded id-bearing set and summarize locally, or state that exact aggregate coverage is unavailable.
+- Do not filter on long text, rich text, Task.Description, history OldValue/NewValue, IsPriorityRecord, or other readable-but-unfilterable fields. Bound by parent, date, owner, status, or another filterable field and post-filter locally when bounded.
+- Use INCLUDES or EXCLUDES only for multipicklists. Scope FieldDefinition queries to a known entity, keep each OR disjunction scoped to one field, and split cross-field discovery before merging locally.
+- Prefer standard Account, Opportunity, Contact, Task, and Event fields before optional or custom fields. After two focused schema or query-shape failures for the same fact, fall back to safer standard fields, exact reads, a narrower request, or a clear evidence gap.
+- Distinguish explicit reauthentication errors from runtime readiness failures.
+
+### Writes And Links
+
+- For update_record, send only changed fields. For create_record, send only intended fields and include record type fields only when they affect defaults, picklists, or createability.
+- Do not probe writes to discover validation rules. If Salesforce returns FIELD_CUSTOM_VALIDATION_EXCEPTION, surface the exact message and ask for the missing or corrected business value.
+- Use create_account_plan instead of generic record creation for account plans. Resolve AccountId and gather or derive Name, challenge, competitive, relationship, strategy, StartDate, and Status first; report unsupported AccountPlan errors plainly.
+- Before assign_target_to_sdr, call query_agent_type, present available agents, and get the user's selection. The target must be a Contact or Lead id.
+- Inspect every write response and verify with returned fields, get_record_details, or a narrow soql_query when the response does not prove the outcome.
+- Prefer a connector-returned record URL. If a trusted org or instance base URL is available, construct the Lightning record URL; otherwise show the object label and id instead of inventing a link.
+
+## Modes
+
+### 1. Salesforce Read
+
+- Use when the user or a parent Sales workflow needs account, opportunity, contact, lead, activity, event, account-plan, or transcript context.
+- Resolve the target first, retrieve only the fields and history that affect the sales decision, and call out gaps or ambiguity.
+
+#### Output Format
+
+```md
+# [Record Or Salesforce Context]
+
+## Summary
+
+- [Most important CRM fact with source link]
+- [Current account, opportunity, contact, activity, or plan signal]
+- [Risk, missing field, ambiguity, or source gap]
+
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
+
+### 2. Salesforce Update Draft
+
+- Use when the user asks to prepare a CRM update, note, field change, or create action but has not yet approved the write.
+- Keep proposed values separate from current values and identify the target record, assumptions, and required approval.
+
+#### Output Format
+
+```md
+# Proposed Salesforce Update
+
+**Target:** [Record name, type, and trusted link or id]
+
+| Field | Current Value | Proposed Value | Reason / Source |
+| --- | --- | --- | --- |
+| [Field label] | [Current value or Unknown] | [Proposed value] | [Grounded reason + link] |
+
+## Before I Write
+
+- [Missing business input, validation risk, or approval needed]
+
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
+
+### 3. Approved Salesforce Write
+
+- Use only after the user explicitly asks for the supported write or approves the reviewed draft.
+- Verify the result before reporting completion when the write response is not conclusive.
+
+#### Output Format
+
+```md
+# Salesforce Updated
+
+**Record:** [Record name, type, and trusted link or id]
+
+- **Changed:** [Field or supported action]
+- **Result:** [Verified outcome]
+- **Not changed:** [Any requested item that was unsupported or skipped]
+
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
+
+### 4. Account Plan, SDR Assignment, Or Transcript
+
+- Use when the user explicitly asks for an account plan, Agentforce Lead Nurturing assignment, Salesforce event, or voice/video transcript summary.
+- Resolve the required record or call id first. For an SDR assignment, show available agents and get the user's selection before assigning.
+
+#### Output Format
+
+```md
+# [Account Plan / SDR Assignment / Transcript Summary]
+
+## Target
+
+- [Resolved account, contact, lead, event, or call + trusted link]
+
+## Result
+
+- [Supported read, draft, or verified action]
+- [Important limitation or missing required input]
+
+---
+
+{Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+{Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}
+```
diff --git a/plugins/sales/skills/suggest-sales-next-step/SKILL.md b/plugins/sales/skills/suggest-sales-next-step/SKILL.md
deleted file mode 100644
index fb365c6..0000000
--- a/plugins/sales/skills/suggest-sales-next-step/SKILL.md
+++ /dev/null
@@ -1,252 +0,0 @@
----
-name: suggest-sales-next-step
-description: Run scheduled or manual Sales check-ins that summarize recent Sales work and recommend one next Sales workflow to try.
----
-
-# Suggest Sales Next Step
-
-Run a Sales check-in that turns the user's visible recent Codex conversations, sales-adjacent work, calendar signals, and saved Sales state into one practical next step. This skill owns scheduled check-in behavior so automations can stay thin and durable.
-
-Done well, a Sales check-in is a short natural-language note, not a generic usage report or feature tour. It should answer two questions quickly: what has the user been doing with Sales, if anything, and what is worth trying next. Prefer a timely workflow grounded in a real meeting, account, customer question, deal, or recent Sales output. If no concrete target emerges, default to a Sales workflow the user has not tried yet by reading `skill_experience` and `next_skill_candidates`, excluding helper/provider skills. Keep tips, setup notes, and source gaps subordinate to the one recommended workflow.
-
-## Skill Configuration
-
-### User Context
-
-Mandatory pre-answer gate:
-
-- Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`.
-- Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance.
-- Apply hard `final_obligations` unless the response is clarification-only. If an obligation is an onboarding reminder and the output also needs a skill-owned final continuation, satisfy both in one final natural continuation instead of rendering a standalone onboarding reminder plus a second CTA.
-- Apply `context_gap_note` only when missing setup or inaccessible sources materially limits the output.
-
-### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
-
-### Source Resolution
-
-Use `context.sources` from the `sales_preflight` envelope to resolve each attempted semantic source category to available apps, connectors, and helper skills. Defer connector fallback, source preferences, durable source rules, and helper-skill loading mechanics to `sales:user-context`. Do not prompt about categories this skill does not attempt.
-
-### Required Inputs
-
-This skill needs enough visible context to inspect recent Sales work, saved Sales context, calendar opportunities, or a user-supplied timeframe.
-
-The skill can still produce a limited next-step recommendation from the current thread, pasted context, visible recent Sales work, saved context, or user-provided priorities when connector context is unavailable. Connectors add significant value and reduce manual work, but do not stop solely because connectors are unavailable.
-
-**Inputs:**
-
-Require one anchor:
-
-- [required] recent Sales context, calendar context, saved plugin context, visible thread context, or timeframe
-
-Accept when provided:
-
-- timeframe, preferred workflow family, quiet/notification preference, specific source gaps, and scheduled heartbeat mode
-
-**Context Rules:**
-
-- If a required anchor is missing or ambiguous, use the last 7 days as the default timeframe when the user asks for a next step without a timeframe.
-- If multiple materially different scopes are plausible, use Fast Candidate Resolution: make at most 3 total tool calls before asking the user to choose, including mandatory Sales preflight. Search only to produce concrete defaults, then ask the user to confirm before drafting.
-- Treat ambiguous company-like proper nouns, partner names, and account shorthands as possible customer/account anchors unless the request clearly indicates an internal-only person, product, or topic. When `crm` is available, include a bounded CRM lookup before recommending a customer/account-specific workflow from docs, calendar, messages, notes, or public context alone.
-- Before asking the user to provide priorities, recent context, or a timeframe, first try the source categories that can produce a concrete next-step candidate: visible thread history and saved context, then `calendar` for upcoming/recent meeting opportunities, `meeting_notes` for follow-up opportunities, `crm` for customer/account-specific workflow fit, `data_enrichment` for account/contact research or prioritization gaps, and `document_store`, `external_messaging`, or `internal_messaging` when they can cheaply surface a practical source gap or next action. Manual priorities are the fallback after those categories are unavailable, empty, or too ambiguous.
-- When asking, present up to 5 concrete candidates with one-line rationale. Make lettered options usable as reply targets, but omit a separate reply-with-letter footer when the choices are already clear.
-
-When a required anchor is ambiguous and cannot be safely defaulted, do the minimum source lookup needed to produce concrete choices within the Fast Candidate Resolution budget, then ask immediately. Do not gather enrichment evidence before the user selects the anchor.
-
-### Workflow Sources
-
-When this skill uses a source category, use it for the following information. Do not use indirect or mirrored sources, web search, or Computer Use as substitutes for the authoritative category.
-
-Source categories:
-
-- `calendar`: upcoming and recent customer/account meetings, meeting titles, dates, attendees, external domains, invite descriptions, and first-pass workflow opportunities.
-- `crm`: account or opportunity context that materially changes the recommended Sales workflow.
-- `meeting_notes`: recent call evidence, transcript/notes availability, follow-up opportunities, and customer/account topics.
-- `document_store`: source-of-truth docs, saved team resources, account docs, and onboarding/setup resources that materially affect next-step recommendations.
-- `data_enrichment`: contact, company, fit, and trigger gaps that suggest enrichment, account prioritization, or research workflows.
-- `external_messaging`: recent customer asks, unanswered threads, promised follow-ups, and evidence of needed follow-through.
-- `internal_messaging`: internal blockers, team coordination, account-channel context, and source gaps when relevant to the recommendation.
-- user-provided context: visible thread history, pasted priorities, prior Sales outputs, saved preferences, and explicit next-step constraints.
-
-Authority and gaps:
-
-- Use visible recent Sales work and calendar/meeting evidence for the recommendation; do not invent usage history or upcoming customer work.
-- For customer/account-specific recommendations, use CRM when available for CRM-owned account or opportunity truth before treating other sources as sufficient context.
-- Use `meeting_notes`, `external_messaging`, and `internal_messaging` to identify concrete prep, follow-up, or source-gap opportunities; do not recommend transcript-backed or CRM-backed workflows when the required source is absent unless the prompt is framed as manual/pasted-context.
-- Use `data_enrichment` only to identify account/contact research or prioritization opportunities; do not treat enrichment as proof of CRM state, upcoming meetings, or transcript-backed follow-up work.
-- Treat saved context as a relevance hint, not proof that a workflow has current evidence.
-- If source access is partial, make the recommendation from available context and name what would improve confidence.
-
-### Context Gathering Principles
-
-Optimize for marginal value, not absolute speed.
-
-- Start with the canonical source and narrowest scope.
-- Gather evidence that materially improves the answer.
-- Take the 80/20 path when it gives a useful, grounded result.
-- Broaden only when the first pass is empty, thin, or misleading.
-- Answer once the core artifact is supported; name limitations and offer expansion options.
-
-### First-Run Banner
-
-If Sales preflight says the suggest-sales-next-step experience has not been introduced and this skill is the primary user-facing skill, render a compact first-run intro before the normal output:
-
-```md
-## Suggest Sales Next Step
-This skill runs a short Sales check-in: what you've been doing, what looks quiet or blocked, and one Sales workflow worth trying next. If there is no timely meeting or account signal, it recommends a Sales workflow you have not tried yet.
-
-```
-
-Starter prompts:
-
-- Primary default prompt: `Suggest my next best Sales step.`
-- `@Sales suggest my next best Sales step for today.`
-- `@Sales review my recent Sales work and tell me the highest-leverage thing to do next.`
-- `@Sales check my calendar and recent Sales context for the next useful workflow to run.`
-
-When invoked by guided Sales onboarding:
-
-- Suppress the final CTA unless this skill intro is the active onboarding step.
-- Return status and next-step candidates for onboarding to arbitrate.
-- Mark whether this skill was introduced, tried, skipped, blocked, or accepted.
-
-### Next Step Guidance
-
-This section is the single owner for Suggest Sales Next Step next-step behavior and action text. Onboarding, check-in readbacks, recommended workflow launch, draft creation, automation setup or repair, and follow-up turns may route into this journey or wrap its selected action in their own presentation frame, but they must not redefine the recommendation continuation states elsewhere.
-
-#### Final Continuation Invariant
-
-Every final assistant response while `suggest-sales-next-step` is the active workflow must end with exactly one user-visible next action, phrased as a natural sentence or question in the ordinary prose of the response. This is a final-response gate, not a journey preference. It applies even when the response is only a short answer, local rewrite, one-paragraph summary, confirmation, readback, source-gap note, partial result, status after creating/updating/linking an artifact or automation, or explanation of what changed. Before sending, inspect the final visible assistant-written content: if it does not end with a concrete next action, add the earliest valid continuation below or explicitly choose one of the allowed omission reasons.
-
-Omit the continuation only when the user explicitly asks for no follow-up, the workflow is explicitly closed by the user, a scheduled automation correctly returns `DONT_NOTIFY`, or another active parent flow owns the final CTA. Required-input and clarification responses are not omission cases; make the unresolved question the final natural continuation. In scheduled heartbeat mode, `Worth Trying Next` owns the direct user-facing CTA and the final response must still end with the heartbeat XML block.
-
-If Sales onboarding is active and the `sales_preflight` final obligations include a core-onboarding reminder, fold the reminder into this same final natural continuation. Do not render a separate onboarding reminder plus a second CTA.
-
-#### Journey States
-
-Use the earliest state whose condition matches the current artifact. Do not end with only a bare confirmation, source link, heartbeat readback, or "done" message while a valid next step remains.
-
-| State | Use when | Final continuation |
-| --- | --- | --- |
-| **1. Review the recommendation** | The first substantive recommendation or check-in card has just been produced. | `Want me to run that next step now, or draft the exact message/update so you can review it first?` |
-| **2. Review the revised recommendation** | The user asked for any change to focus, target, evidence, suggested prompt, source-gap wording, or check-in copy. Make the change in chat first. | `Anything else you'd change? If that looks right, want me to run the recommended action or turn it into a reviewed draft?` |
-| **3. Prepare the recommended action** | The user accepts the recommendation, says no changes are needed, says a revision looks good, or asks to act on it. | Prefer running the recommended focused action when enough context exists; otherwise draft the exact prompt, message, update, artifact, or automation setup/repair step that the recommendation calls for. |
-| **4. Review the prepared action** | A draft prompt, message, update, artifact, or automation setup/repair proposal exists, or the user asks to change it. | `Anything you'd change before I run, save, or set this up?` |
-| **5. Run, save, or set up the approved action** | A reviewed action exists and does not need edits. | Offer the specific supported action, such as running the recommended workflow, saving the reusable prompt, setting up or repairing the weekday check-in automation, or creating the reviewed update. Do not post, send, or write externally unless the user explicitly asks for that action or approves the reviewed draft/update. |
-
-#### Acceptance Handling
-
-For follow-up turns after a visible continuation, treat short affirmative replies such as "yes," "ok," "okay," "sure," "sounds good," "looks good," "do it," or "go ahead" as acceptance of the offered journey step unless the user clearly declines, changes topic, or closes the workflow. If the previous continuation asked for review of the recommendation, treat a lightweight acknowledgement as acceptance and move to `Prepare the recommended action`. If the previous continuation offered to prepare a prompt, message, update, artifact, or automation proposal, produce that item for review; do not interpret the acknowledgement as approval to post, send, write, or set up an automation unless that exact action was offered. If the previous continuation offered to run, save, or set up a specific reviewed action, a lightweight affirmative can authorize that specific action when the available tool supports it. Execute the accepted action, then render the next valid natural continuation.
-
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
-
-### Suggested User Prompts
-
-Any suggested user prompt in a Sales check-in must be short, simple, and realistic. When the audit finds a real target, keep the prompt to the invocation only: `Use Sales <workflow> for <actual target>.` When the default fallback is an untried workflow and no concrete target is visible, use a runnable input-lane prompt such as `Use Sales prioritize-accounts with a pasted account list.` Do not include the work the focused workflow already knows how to do, such as `pull together`, `draft`, `give me`, output lists, source instructions, missing-input handling, or step-by-step constraints. Explain expected outputs in the surrounding check-in text, not inside the suggested prompt.
-
-### State
-
-Inspect `$CODEX_HOME/state/plugins/role-specific-plugins/sales/onboarding-state.json` when it exists for guided workflow progress, Sales check-in status, company selling-context learning status, quiet/complete state, onboarding gaps, `skill_experience`, and `next_skill_candidates`. Use `skill_experience` plus `next_skill_candidates` as the default workflow inventory when no timely meeting, account, customer question, or deal signal is available. Exclude `suggest-sales-next-step`, `index`, `user-context`, and provider/helper skills such as `salesforce`, `hubspot`, and `zoominfo` from the default untried-workflow recommendation. Use `user-context.md` for source preferences, source-of-truth links, and "do not use" rules. Do not use `onboarding-state.json` as the weekday sales check-in tip ledger; scheduled weekday sales check-ins run in the same recurring thread, so use the current thread's prior check-in messages and any visible recent check-ins as the primary dedupe source. Do not store private customer details, account facts, transcripts, user-facing sales preferences, previous prompts, or prior tip history in `onboarding-state.json`; those belong in source systems, confirmed `user-context.md` entries, or the recurring thread history.
-
-### Invocation Modes
-
-Use `scheduled mode` when the request comes from an automation, heartbeat, recurring check-in, or weekday sales check-in reminder. Use `manual mode` when the user asks to run a Sales check-in, weekday sales check-in, daily check-in, review usage, inspect adoption, or identify where they could get more value from the plugin.
-
-In scheduled mode, treat the automation prompt as a launcher for this skill. If the automation prompt says to "load and follow the skill" or to avoid duplicating daily check-in rules in the prompt, execute this skill's full workflow; do not interpret thin-prompt language as a request to skip the check-in.
-
-Scheduled mode must re-run the check-in and produce user-facing value even when the last check-in was recent, the user has not used Sales, or no new meeting notes are visible. Treat quiet periods as useful signal: say what did not change, then recommend a workflow the user has not tried yet unless a stronger calendar, source-gap, or recent-work signal exists. Use `DONT_NOTIFY` only for true execution failure or an explicit user preference for quiet/paused check-ins, not merely because there was no new plugin usage, no new meeting notes, no connected-source signal, or a recent prior check-in. Do not return a one-line "no action needed" explanation.
-
-Manual mode should answer directly and can be more explicit about evidence gaps, such as inaccessible thread history or missing onboarding state.
-
-## Workflow
-
-1. Establish the audit window. Use a rolling 7-day window ending at the current run time in the user's timezone. For scheduled runs, state the concrete date range only when it prevents ambiguity. The audit target is the highest-signal accessible recent Sales context in that window, plus calendar and Sales state. Use broader conversation history when it is accessible without a reconstruction pass, but do not spend the ordinary path exhaustively enumerating every surface.
-
-2. Gather recent usage from the highest-signal accessible surfaces first: current-thread context, prior check-in outputs in the recurring thread, Sales state, calendar, recent visible Sales activity, and then Codex thread/history/search tools when available. Look for Sales invocations, adjacent sales-support work, customer/account context, launch or GTM work, saved preferences, connector checks, recent artifacts, onboarding progress, and relevant cases where the user did sales-like work without Sales. Stop once you can say what the user has been doing and recommend one useful next action; name visible-source limits instead of continuing into low-yield history searches. Treat the current thread as the primary source for daily check-in dedupe, but not as the boundary of the broader usage audit when recent history is readily available. If cross-thread history is unavailable or partial, say which usage surfaces were visible and avoid claiming precise adoption counts.
-
-3. Build a conversation-level usage map. Track:
-   - recent conversations that used Sales;
-   - explicit invocation events, such as `@Sales`, `[@sales](plugin://sales@...)`, `$sales`, `sales:<skill>`, or named Sales skill requests;
-   - likely implicit invocations from clear sales-task wording;
-   - recent conversations that were relevant to Sales but did not use the plugin.
-
-4. Interpret usage as a level-set and diagnostic signal. Do not foreground invocation counts in user-facing output. Instead, summarize the recent workflow patterns the user has been doing, such as meeting prep, follow-up, onboarding or saved-context setup, internal navigation, launch or GTM support, forecast review, pipeline work, customer evidence, or account research. Track breadth internally by naming the distinct Sales skills or workflow families used in the audit window, such as `suggest-sales-next-step`, `prepare-for-meeting`, `follow-up-after-call`, `find-key-internal-sources`, `plan-deal-strategy`, `review-forecast`, `prioritize-accounts`, `find-customer-quotes`, `build-business-case`, coaching, or account/competitive intelligence. Also name relevant sales-like conversations that did not use the plugin when visible. If cross-thread history is partial, prefer language such as `from what I could inspect` over a precise total.
-
-5. Search calendar for timely sales work. Use `calendar` to inspect upcoming calls and recent calls, not just static onboarding state. Default to the next 7 days for prep opportunities and the prior 7 days for follow-up opportunities; widen to 14 days only when the next 7 days has no customer/account-relevant calls. Read event titles, dates, attendees, external domains, invite descriptions, and visible agenda text. Prefer external customer, prospect, partner, or account-related meetings; skip obvious 1:1s, interviews, internal staff meetings, and no-account internal planning unless the title or attendees make the sales value concrete. Name specific events in the check-in, such as `Prep Tuesday's Acme security review` or `Follow up on Friday's Contoso implementation sync`. If calendar access is missing or returns no qualifying calls, say that briefly and do not substitute generic calendar advice.
-
-6. Pair calendar events with concrete value. For upcoming calls, suggest the highest-leverage Sales workflow that would help before the meeting, usually `prepare-for-meeting`, `find-key-internal-sources`, `plan-deal-strategy`, `build-competitive-brief`, or `build-business-case`. For recent calls, suggest `follow-up-after-call`, CRM-ready next steps, internal recap drafts, or product-feedback evidence extraction when a transcript, notes provider, or follow-up source is available. When `meeting_notes` is available, search recent call titles, account names, attendee names, and topic keywords to verify whether follow-up evidence exists before recommending a transcript-backed package. Name the specific call and the concrete artifact the user would get; do not say only that Sales "could help with calls."
-
-7. Identify contextual missed leverage moments. Prefer one or two concrete moments from prior workflows or calendar evidence over generic capability lists. Name the workflow moment, the targeted follow-up or improved prompt, and the value unlocked. Useful patterns include:
-   - a prepare-for-meeting draft that could become follow-up scaffolding;
-   - call notes that could become CRM-ready notes plus an internal recap;
-   - a customer or product question that could use find-key-internal-sources;
-   - a prompt that would improve by naming account, source, date, owner, or desired artifact constraints;
-   - a repeated CRM/source convention that should be saved;
-   - product-feedback evidence that should come from transcripts instead of memory;
-   - deal-risk discussion that could become deal strategy or forecast review.
-
-8. Choose the best next usage. Compare recent conversations, calendar opportunities, and onboarding state, then choose the single Sales workflow most likely to create near-term value for the user's actual work. Prioritize an action the user can run right now from available context over a future-only meeting or later follow-up. A future event can still be the topic only when the prompt does useful immediate work, such as drafting prep questions, finding internal sources, creating a notes template, or preparing a follow-up scaffold from already available context. Do not make the best next usage depend on notes, transcripts, meetings, or outcomes the user does not have yet. If there is no new usage or qualifying calendar event, choose the first useful untried workflow from `next_skill_candidates` and `skill_experience`, skipping helper/provider skills and workflows the user dismissed. Use a lightweight prompt that can run from pasted or user-named context when no real target is visible. Explain why this recommendation matters for the user's workflow and what the user would get.
-
-9. Decide whether a source note is warranted. Include `Source Note` only when the audit finds a concrete connector or connected-source gap that affects the recommended workflow, confidence, completeness, or ability to act. Good candidates include connecting, authorizing, or checking `calendar`, `crm`, `meeting_notes`, `document_store`, `external_messaging`, or `internal_messaging`; a connector that appears installed but cannot return useful data; or a missing source category that would unlock the named workflow. Do not use this section for saved preferences, user-context memory, source-of-truth docs that are already reachable, routing advice, broad setup discovery, or general "make the plugin better" coaching. If no clear source improvement is available, omit the section entirely.
-
-10. Decide whether one tip belongs in the check-in. Only include a tip when it directly supports the recommended workflow and is fresh relative to prior visible Sales check-ins. Treat tips as duplicates when they teach the same workflow family, feature, connector/source habit, or prompt tactic even if the wording differs. Good tips are contextual: chaining `prepare-for-meeting` into `follow-up-after-call`, asking `find-key-internal-sources` before drafting, using transcript-backed `find-customer-quotes`, naming the account/date/source when routing is weak, trying an untried workflow family, or asking Sales to remember a reusable format after iterating on an output. Fold the tip into `Worth Trying Next`; do not create a separate feature-tour section.
-
-11. Choose one CTA. The direct action should live in the final `Worth Trying Next` section unless a connector issue is clearly blocking or weakening the recommendation. Make the CTA a short prompt the user can run immediately in the current thread, not a future-dependent offer. Apply the Suggested User Prompts rule: use an actual context target found during the audit when one exists; otherwise use a concrete input lane for the untried workflow, such as a pasted account list, customer question, recent call notes, forecast export, or deal notes. Do not include extra task instructions. Do not emit generic targets such as `latest customer meeting`, `next customer-facing meeting`, `recent external conversation`, or `whatever context is available` when a concrete target was visible. When you include a starter prompt, follow the relevant focused skill's inline first-run and next-step guidance: anchor the prompt in the real meeting, call, account, deal, forecast, account list, customer question, or feedback theme found during the daily check-in audit without overfitting the prompt to incidental details.
-
-## Output
-
-Use these short markdown sections by default. `Worth Trying Next` must be the final user-facing section before the heartbeat XML so the check-in ends on the action the user can take now. Include `Source Note` only when step 9 found a real workflow-time connector or source gap; otherwise omit it. Do not include a separate feature menu, usage report, or standalone `One Tip` section.
-
-```md
-**Check-In**
-...
-
-**Source Note**
-...
-
-**Worth Trying Next**
-...
-```
-
-`Check-In` should read like a short note, not a report. In one compact paragraph or two bullets, say what the user has been doing with Sales or that things look quiet from the visible sources. Mention likely implicit Sales work and distinct workflow families only when that helps explain the recommendation. Do not include invocation counts or lower-bound totals by default; mention count-like evidence only if the user asks for usage metrics or the count materially changes the recommendation. If broader history is partial, add one short caveat such as `From the visible Sales context...` instead of narrating every source searched. Avoid internal or tool-facing language such as `history surfaces`, `bypass`, `artifact`, `guided workflow`, `runtime`, or `state`; use plain language such as `I found`, `missed opportunity`, `follow-up package`, `meeting prep`, `docs`, `notes`, and `next steps`.
-
-`Worth Trying Next` is the decision section: name one Sales workflow, why it is the right next try, and the output the user would get. Prefer a real meeting, account, customer question, deal, forecast, account list, or feedback theme from the audit. If none exists, explicitly frame the recommendation as a workflow they have not tried yet and use a runnable pasted-context prompt. End this section with exactly one runnable prompt that follows the Suggested User Prompts rule. Put expected outputs in the explanatory sentence or short bullets above the prompt, not inside the prompt. Do not overfit the prompt to incidental dates or every source found during the audit. Do not end with a future-only recommendation such as waiting for tomorrow's meeting or drafting from notes that do not yet exist.
-
-`Source Note` should appear only when there is a concrete connector or connected-source issue for the workflow the check-in is recommending. Name the one missing, unauthorized, stale, or failing source, explain the practical sales impact, and say the workflow can still proceed with manual input when true. Keep the suggestion action-sized and user-invokable. If the only available improvement is saved context, a source preference, routing advice, or broad setup discovery, omit this section and handle the coaching inside `Worth Trying Next`.
-
-Avoid usage counts in scheduled check-ins unless the user asks for metrics or a count materially changes the recommendation, confidence, routing advice, or workflow-time connector suggestion. Speak to recent workflow patterns instead.
-
-For proactive suggestions, be specific. Name the event, account or external domain when visible, date, suggested Sales workflow, and potential value, such as `a two-minute meeting brief`, `CRM-ready next steps`, `a customer follow-up email draft`, `an internal recap for the account team`, or `transcript-backed product feedback evidence`. Keep setup recommendations to the next smallest useful action, not the full downstream checklist. Do not present a broad capability menu unless no concrete usage or calendar evidence is available.
-
-Do not include a generic `Practical rule:` paragraph or a broad definition of explicit versus implicit invocation unless the user directly asks for definitions. Teach invocation behavior through the evidence instead.
-
-Avoid generic usage-report and low-effort framing. Prefer plain sales language such as `next value to unlock`, `stronger meeting prep`, `better follow-through`, `cleaner CRM notes`, `the connected source that would help most`, or `a deeper workflow to try`.
-
-End with exactly one direct CTA unless the user explicitly asks for options.
-
-### Scheduled Heartbeat Output
-
-In scheduled mode, preserve the heartbeat automation response format in the final answer. The final response must end with a `<heartbeat>` XML block containing a `NOTIFY` or `DONT_NOTIFY` decision.
-
-When there is a useful check-in, write the markdown sections above as the user-facing notification text, then append:
-
-```xml
-<heartbeat>
-  <decision>NOTIFY</decision>
-  <message>One-sentence summary of the check-in value.</message>
-</heartbeat>
-```
-
-Only use this path for true execution failure or explicit quiet/paused preference. Do not use it just because the last check-in was recent or no new Sales usage, meeting notes, or connected-source signal is visible. When this path applies, do not include markdown prose outside the XML block. Return only:
-
-```xml
-<heartbeat>
-  <decision>DONT_NOTIFY</decision>
-  <message>No useful Sales check-in suggestion right now.</message>
-</heartbeat>
-```
diff --git a/plugins/sales/skills/suggest-sales-next-step/agents/openai.yaml b/plugins/sales/skills/suggest-sales-next-step/agents/openai.yaml
deleted file mode 100644
index 1e9006c..0000000
--- a/plugins/sales/skills/suggest-sales-next-step/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Suggest Sales Next Step"
-  short_description: "7-day sales usage recap with next use, source gaps, and a fresh tip"
-  default_prompt: "Suggest my next best Sales step."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/user-context/SKILL.md b/plugins/sales/skills/user-context/SKILL.md
deleted file mode 100644
index ddb1262..0000000
--- a/plugins/sales/skills/user-context/SKILL.md
+++ /dev/null
@@ -1,237 +0,0 @@
----
-name: user-context
-description: Load or manage the Sales plugin's durable user context, onboarding logic, setup progress, automation metadata, saved preferences, non-obvious CRM conventions, source-of-truth pointers, book-of-business sources, internal team resources, account channels, approval trackers, trusted examples, approved Sales Company Research saves, "please remember" requests, and broad future-facing instructions such as always/never/prefer/next-time feedback after a Sales draft.
----
-
-# User Context
-
-This skill is the Sales plugin's durable context, setup, and onboarding layer. It loads saved preferences, source pointers, CRM conventions, onboarding progress, automation metadata, and reusable sales context for Sales workflows; it also owns direct requests to view, save, update, or reset Sales context and the shared onboarding logic that turns setup state into the next visible action.
-
-## Mandatory Pre-Answer Gate
-
-- For ordinary Sales preflight, use `scripts/sales_preflight.py` as the default state-read path when local shell access is available.
-- Apply the same gate on behalf of other Sales skills that call `sales:user-context` as part of their own mandatory preflight before answering, searching connectors, retrieving evidence, or drafting output.
-- When running the preflight script through a command tool, set the tool's `max_output_tokens` to at least `25000` so the complete payload remains visible as context grows. If the tool output is truncated, warn the user that Sales preflight was truncated and that Sales plugin memory or preflight context may be getting bloated, then rerun with a higher cap and do not use the payload until the complete output is visible.
-- Treat the script payload as satisfying the read requirement only when it reports read status for `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` and renders the compact context it will use in `context.user_context`, `context.sources`, `context.automations`, and `control`.
-- Do not treat listing files, checking that files exist, or saying they should be read as sufficient; saved preferences, profile, context, source preferences, and source-of-truth pointers must be loaded and applied.
-- If the script fails, local shell access is unavailable, or the request is direct context maintenance, actually read the relevant state files manually before answering or writing.
-- If the user-context file is absent or unavailable, follow `references/onboarding.md` for first-run behavior.
-- Read `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json` only when onboarding state, skill experience progress, automations, or progressive nudges are relevant.
-
-## Skill Configuration
-
-### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
-
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
-
-## Purpose
-
-User Context keeps durable, Sales plugin-scoped user context in a local state file instead of mixing private sales facts into reusable plugin instructions. Operational onboarding bookkeeping belongs in a separate JSON state file so `user-context.md` stays feasible for an average user to read, edit, and share. First-run setup should keep the template's unresolved starter categories visible in `user-context.md` as non-authoritative setup scaffolding once setup actually begins, because ordinary preflight reads are intentionally cheap and should not need to reread the bundled template just to know which configured categories are still missing. The first direct onboarding message is the exception: when no Sales state exists yet and the user asks to onboard or learn what Sales can do, render orientation from `references/onboarding.md` immediately and defer scaffolding until the user approves the next setup step or provides context to save.
-
-Use this skill as the fast preflight for every substantive Sales workflow before ordinary work begins. Use it as the primary workflow when the user asks to remember, save, recall, inspect, set up, customize, or maintain Sales preferences, trusted examples, non-obvious CRM conventions, source-priority rules, book-of-business sources, internal team resources, account-channel guidance, approval trackers, account context, or other private context. Route requests to search available company context, fill missing setup entries, or run Sales Company Research to `sales-company-research`; use this skill to apply approved or high-confidence research saves, read/write state, and continue onboarding. Also use it when a follow-up user message after a Sales output gives a reusable instruction, correction, or preference, or when the user accepts an iterated Sales artifact and the accepted change may be reusable plugin memory, even if the user does not say "remember" or explicitly mention Sales.
-
-## Reference Loading
-
-`SKILL.md` owns the normal preflight, direct read, write, onboarding handoff, and first-run setup path. Load references **selectively** based on the user's request, the current state, and the relevant workflow's needs. Do not read every reference file on every invocation:
-
-- Use `references/plugin-memory.md` for direct memory writes, broad future-facing instruction capture, correction capture, Sales Company Research save/review gates, linked-source population, successful-run learning, iteration acceptance learning, and exact save-recap behavior.
-- Use `scripts/sales_preflight.py` as the normal read-only preflight implementation when local shell access is available. Its JSON payload owns Sales state-file reads, compact context, file provenance, and final response obligations for ordinary workflows.
-- Keep first-run state creation automated through `scripts/init_user_context_state.py`; it copies the bundled templates into the user's Sales state directory. Do not run it before the first direct onboarding orientation when no state exists. Run it after the user approves the first setup step, or immediately when the user provides context that should be saved. When running through a sandboxed local shell, request elevated execution by default because the initializer writes under `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`, outside the project workspace. After that initialization, write Sales user context by directly editing `user-context.md` and related JSON state files. Prefer one batched edit for all approved changes over per-entry helper commands.
-- Use `scripts/reset_user_context_state.py` for explicit debugging reset requests that clear local Sales onboarding or user-context state. The helper backs up known state files before removing them from the active state directory.
-- Use `tests/test_state_helpers.py` as the executable regression suite for the user-context preflight contract. Do not read or run this file unless actively needed (e.g. for debugging or to validate new changes to the script).
-- Use `scripts/init_user_context_state.py` for first-run scaffold creation when local state files are missing and a setup/save step needs persistence. Use `plugin-author-config/user-context-config.md` for category names and compact descriptions; the initializer adds the `## Saved Links And Context` placeholder section to every copied category.
-- Use `references/onboarding.md` when first-run orientation, setup completion, environment conflict audit, automation setup, guided step-card continuation, progressive onboarding, agent-journey walkthroughs, or onboarding-state updates are active.
-- Use the primary workflow skill's inline experience guidance whenever a user-facing Sales skill owns the active intent. The focused skill owns first-run intro copy, starter prompts, anchor rules, normal next-step candidates, and onboarding-yield behavior. Onboarding may choose which skill to run next, but it should not duplicate skill-specific intro copy or starter prompt variants.
-- Use `plugin-author-config/source-category-config.json` for source category ids, labels, preferred plugins/apps, and helper skills. Use `references/source-category-runtime.md` for workflow-time connector use, ON_USE auth behavior, fallback rules, and user-approved source preference storage.
-- Use `plugin-author-config/automation-config.md` for default Sales onboarding automation names, frequencies, and instructions. The target thread title is derived from the automation name. Use `references/automation.md` for runtime setup mechanics such as creating heartbeat automations, creating/pinning/renaming target threads, readback, state updates, and cleanup approval.
-- Use `references/onboarding-examples.md` only to audit or explain onboarding traces. Examples do not override the rules above.
-
-## Skill Experience Guidance
-
-Every user-facing Sales skill must define skill-owned experience guidance inline in `SKILL.md`. Loading the relevant skill-owned experience guidance is mandatory whenever that skill is the primary workflow. Read the skill's inline `First-Run Banner` and `Next Step Guidance` sections instead of looking for a separate experience reference.
-
-Use skill-owned experience guidance for first-run intro copy, starter prompts, anchor rules, normal next-step candidates, the final continuation invariant for primary outputs and follow-up turns, and onboarding-yield behavior. Do not put skill-specific intro copy, starter prompt variants, anchor rules, or normal next-step behavior in onboarding. Onboarding may choose which skill to run next, but the skill owns how it introduces itself, what a good first try looks like, and which normal continuation to suggest after output, source gaps, fallback answers, clarification responses, and meta-question turns.
-
-When `sales_preflight.py` reports that the current skill has not been introduced and the skill is the primary user-facing intent, the focused skill must interpret `control.current_skill_experience` immediately after preflight, before deciding whether to show a first-run intro, ask for anchors, gather evidence, draft output, or render the final natural continuation:
-
-- If the user asked what the skill does or has not provided enough task detail, show the first-run intro and offer the primary starter prompt.
-- If the user already provided enough task detail, render the compact first-run intro section from the skill's first-run guidance before normal output. Then proceed without blocking on starter prompts.
-- If the skill is being used as a helper/provider, do not show the intro.
-- If onboarding or another parent workflow owns the final CTA, suppress the skill's final continuation and return next-step candidates for that parent workflow to arbitrate.
-- After sending the first assistant message for a primary user-facing skill, update `onboarding-state.json` so `skill_experience.<skill>.introduced_at` and `first_tried_at` are set when they are still empty.
-
-After a normal primary output, follow-up answer, fallback/source-gap response, clarification answer, or meta-question response, follow the skill's `Next Step Guidance` exactly: end with one concrete user-visible next action phrased as a natural sentence or question in ordinary prose. Clarification questions must be the final natural continuation. Lead with the actual question the user needs to answer. When concrete lettered options are visible, make them usable as reply targets and omit generic one-detail framing or redundant reply-with-letter footers. Exactly one final visible CTA should appear in the response unless onboarding or another parent workflow explicitly owns the CTA; if the immediate workflow is blocked on a clarification, that clarification outranks onboarding and owns the final CTA.
-
-## State Files
-
-Ordinary workflow preflight should read these files through `scripts/sales_preflight.py`; direct context maintenance and fallback paths may read them manually when the script is unavailable or insufficient.
-
-The configured user-context file is:
-
-```text
-$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md
-```
-
-The configured onboarding-state file is JSON:
-
-```text
-$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json
-```
-
-## Customization Guidance
-
-When explaining how to customize Sales, always start with plugin user customization as the preferred path. The user can simply ask Sales to save a preference, example, source link, convention, definition, escalation path, or workflow note, and this skill stores it outside the plugin package in `user-context.md` so future runs can adapt without editing plugin files.
-
-Name the sharing model clearly when relevant:
-
-- `user-context.md` is local, Sales plugin-scoped user context and is not automatically synced to other users, but it is the normal lightweight way to share Sales customization. The user can ask for the full user-context file, edit or redact what they want, and share it so another user can provide that customization to their own Sales instance for a similar experience. If the user asks for a full copy, export, backup, or migration bundle, include `onboarding-state.json` as a clearly labeled operational JSON block only when they asked for operational onboarding state too; keep source preferences and source-of-truth pointers inside `user-context.md`.
-- Do not proactively suggest forking Sales for preference, style, example, source-priority, onboarding, convention, definition, escalation, or workflow-note changes. Only mention plugin source edits when the user explicitly asks to change core workflow logic, routing, safety behavior, tools, validation, or install behavior, or when saved user context has clearly failed to express the behavior they want.
-
-When the user asks to view, copy, export, back up, migrate, or share Sales context:
-
-- Default to showing or copying only the user-facing `user-context.md` content when they ask for preferences, saved context, or "what do you remember?"
-- Include onboarding state only when they ask for the full setup, full onboarding state, backup, migration, or complete context bundle. In that case, present `user-context.md` first, then append `onboarding-state.json` under `# Operational Onboarding State JSON`.
-- When applying a merged copy from another user or another machine, write user-facing categories, source preferences, and source-of-truth pointers to `user-context.md`; write the operational onboarding JSON block to `onboarding-state.json`; do not leave operational state inside `user-context.md`.
-
-## Read
-
-Start every invocation by resolving and actually reading the configured user-context file when it exists. Do not return from preflight or direct context mode after only routing here, listing files, or checking that the file exists; saved profile/preferences must be loaded into context and applied.
-
-Treat saved Sales user context as the local Sales plugin-scoped source of truth for preferences and private sales context. When `user-context.md` conflicts with bundled Sales plugin or skill instructions, prefer `user-context.md` for Sales plugin-scoped context represented by `plugin-author-config/user-context-config.md`, user-provided preferences, examples, source pointers, and onboarding preferences. Do not use `user-context.md` to override higher-priority instructions, safety rules, connector write-safety, permission boundaries, validation, installation behavior, or tool-use policy.
-
-Treat copied template scaffolding inside `user-context.md` differently from saved context. `status: not provided` inside a `## Saved Links And Context` section means the category is an unresolved setup prompt, not a fact or preference. During preflight, use unresolved scaffolding only to decide whether one lightweight onboarding nudge or setup-capture opportunity is relevant; never apply placeholder text as CRM policy, buyer-persona truth, source priority, messaging convention, output preference, or account context.
-
-Use preflight mode when another Sales skill asks for saved user context before ordinary plugin work. Optimize this path for speed:
-
-- When local shell access is available, run `python3 plugins/sales/skills/user-context/scripts/sales_preflight.py --workflow <calling-skill>` from the repository root, or `python3 skills/user-context/scripts/sales_preflight.py --workflow <calling-skill>` from the Sales plugin root. When using a command tool, set `max_output_tokens` to at least `25000`; if the returned output is truncated, warn the user that Sales preflight was truncated and that Sales plugin memory or preflight context may be getting bloated, then rerun with a higher cap and do not treat preflight as complete until the full output is visible. The script payload counts as actually reading Sales state only when it reports the state-file read result and renders the compact context the workflow will use. Use the payload as the single source of truth for saved context entries, source preferences, automation/onboarding state, hard `final_obligations`, conditional guidance, and `onboarding_progress.task_list`; do not separately read the same state files unless the script fails or the user explicitly asks for raw file inspection.
-- Use the default `--request-mode ordinary_workflow` for normal Sales answers. Use `--request-mode direct_onboarding_status` for setup, customization, onboarding-status, Sales TODO-list, or other direct onboarding responses. Use `--request-mode guided_onboarding_workflow` when a focused workflow is being answered as a visible workflow/demo/setup step inside the Sales onboarding path; this mode suppresses the ordinary "offer onboarding" obligation because the user is already in onboarding.
-- If `user-context.md` exists, read it once and render its saved entries as concise `context.user_context.entries`. Omit unresolved scaffold prose except for compact unresolved category names; direct context maintenance can still read the raw file.
-- Use source preferences, "do not use" rules, source-of-truth links, and source-priority guidance from `user-context.md` as connector-selection hints. Do not proactively check connector readiness during preflight. When a focused workflow needs a source, it should try the relevant connector then and handle auth, connection, skip, or manual fallback in that workflow.
-- After reading user context, use the script-driven preflight payload for the calling skill, then immediately let the calling skill continue. The compact payload is the visible/auditable preflight context; do not hide a larger raw-state payload behind it.
-- Also inspect any saved action-orientation preference. Unless the user has asked for quiet behavior or `user-context.md` says action-oriented follow-ups are declined or suppressed, tell the calling skill to end with one concrete next step that advances the current sales workflow, saves a useful preference/source pointer, proposes a follow-up artifact, checks one missing source or connector, or routes to one relevant sibling workflow.
-- Do not browse linked sources, run broad searches, summarize unrelated sections, or ask setup questions during preflight.
-- If `onboarding-state.json` exists, parse only the relevant fields for whether onboarding is active, quiet, complete, whether core onboarding is complete, and any concrete next actions. During ordinary preflight, return at most one concrete guided next step that the calling skill may append after answering the immediate request. If core onboarding is still incomplete, the next step should direct the user back to the next unresolved core setup item: connector setup/confirmation or Sales automation setup. During direct onboarding, setup-status answers, or user requests for the Sales TODO list, use `references/onboarding.md` to render one visible action close, using a compact `Next Step` block only when the step needs the label and letting a self-contained numbered choice set stand on its own. Mirror the compact high-level roadmap into the built-in thread task list when available.
-- If `user-context.md` is missing, read `references/onboarding.md`, return that no saved Sales user context is available, use the script-driven payload or documented manual fallback to provide conditional context-gap guidance, and let the caller continue. Do not run the initializer or scaffold state merely because context is missing. Do not interrupt ordinary plugin work with setup or stop at a setup request. For a high-intent first-run workflow request, tell the caller to answer the user's concrete query first using bundled instructions, user-provided material, and available connectors or sources; if the requested artifact cannot be completed or is meaningfully weaker because saved context or a source is missing, give the best truthful answer from the attempted sources and name the practical gap using `references/onboarding.md#context-gap-note`. Append the `Ordinary Workflow Onboarding CTA` obligation only when preflight returns it because onboarding has not started; do not offer to start onboarding when `onboarding-state.json` already says onboarding is active.
-- If a referenced source is inaccessible, stale, ambiguous, or missing, say that directly only when the missing source affects the current task, then continue with the available context.
-
-### Script-Driven Preflight Payload
-
-Preflight mode must use `scripts/sales_preflight.py` as the authoritative compact payload before ordinary work continues. The script output owns `response_mode`, `control.current_skill_experience`, `context.user_context`, `context.sources`, `context.automations`, hard `final_obligations`, conditional context-gap guidance, and `onboarding_progress.task_list`. Do not hand-author, recreate, or mentally reconstruct the payload when the script is available; use the JSON fields the script returned.
-
-For ordinary Sales workflow requests, honor `final_obligations` exactly as returned by the script. The calling skill must answer the immediate request first, then satisfy any returned onboarding CTA obligation. When the focused skill does not otherwise own a final continuation, append the returned onboarding CTA template with its heading, body, and final reply line intact. When the focused skill has its own final continuation, fold the onboarding reminder or move-on path into that same final natural continuation so the response has one final visible CTA, not a standalone onboarding reminder plus a second skill CTA. Do not downgrade a hard obligation into a `One setup note`, a passive "future prep can get stronger" aside, or a context-gap explanation. Do not use that hard obligation for direct onboarding/setup/status responses, focused workflows launched from the visible onboarding path, or responses where the immediate workflow must ask a required clarification; those callers should use `direct_onboarding_status` or `guided_onboarding_workflow` request mode when appropriate, and clarification-only responses should end with the clarification as the sole final natural continuation.
-
-When the script fails or local shell access is unavailable, fall back to the manual file-read path described above: actually read the relevant state files, use only fields known from those files or the missing-file condition, apply `references/onboarding.md` for direct onboarding/status behavior, and state any missing source or context gap only when it materially affects the current answer. Restore script-driven validation as soon as local shell access is available.
-
-Use direct context mode when the request is primarily about saved context, setup, memory, or context maintenance:
-
-- If `user-context.md` exists, read it before answering or writing.
-- If the requested fact or preference is present, answer from the file.
-- If the requested fact or preference is absent, say it is not saved yet and offer to add it, unless the user already supplied enough information to save it.
-- If the user asks what Sales knows or remembers, summarize `user-context.md` by default. Mention bundled plugin docs only if the user explicitly asks about bundled docs, references, skills, or all available plugin resources.
-- When the request starts, resumes, updates, checks, or completes Sales setup or onboarding, perform the requested sub-step and then apply the step cards in `references/onboarding.md` before answering. The first direct onboarding orientation with no existing Sales state is a response-first fast path: render the orientation immediately, then initialize state only after the user approves the next setup step or supplies context to save. Other setup interactions include customization, onboarding-status checks, Sales TODO-list requests, memory saves while onboarding is active, Sales Company Research save/proposal follow-up, automation setup or cleanup while onboarding is active, and completed or deferred guided workflow updates.
-- Treat onboarding-status questions such as "have we onboarded yet?", "am I set up?", "is Sales configured?", or "what setup is missing?" as direct onboarding interactions, not as ordinary context lookup. Answer the status through the common onboarding finalizer: if onboarding is missing or active, strongly guide the user into the current action close with the shared direct-onboarding structure and built-in thread task list updates when available; if onboarding is complete, say so and offer one useful Sales workflow. Do not end an incomplete onboarding-status answer with only a passive or optional setup suggestion.
-
-Outside explicit Sales Company Research or linked-source refresh work, retrieve linked Google Docs, Drive files, Notion pages, or other resources only when they are accessible through available connectors or explicit local APIs and the user asked to inspect or refresh that source. Do not use browser automation, in-app browser, or Computer Use as fallback retrieval paths for ordinary context-source retrieval.
-
-## Onboarding
-
-Use `references/onboarding.md` as the source of truth when the user asks what Sales can do, asks to set up or customize Sales, invokes onboarding, asks whether onboarding is complete, asks for Sales TODOs, or when `onboarding-state.json` says onboarding is active and a nudge is due. During ordinary preflight, do not show full onboarding; parse only relevant JSON fields and return the hard onboarding-offer obligation with the `Ordinary Workflow Onboarding CTA` template only when onboarding has not started, plus at most one conditional context-gap note when the current answer is materially weakened by missing setup. When onboarding is already active during an ordinary workflow, do not ask whether to start onboarding. If core onboarding is incomplete, answer only urgent or directly requested Sales work and direct the user back to the next unresolved core setup item. If core onboarding is complete, preserve the workflow's own final natural continuation and mention resuming guided onboarding only as a short non-competing note when it is directly useful. During the first direct onboarding message with no state, render the orientation and action close directly from `references/onboarding.md` without creating scaffold files first. During later direct onboarding, setup, customization, memory-save, guided-workflow-update, or Sales TODO-list paths while onboarding is missing or active, follow the current step card in `references/onboarding.md`, render one visible action close, and mirror the compact high-level roadmap into the built-in thread task list when that tool is available. When a focused workflow is run from that visible onboarding path, call preflight with `--request-mode guided_onboarding_workflow` so the workflow can answer normally without adding a redundant onboarding reminder.
-
-Use `plugin-author-config/source-category-config.json` for category definitions and preferred plugins/apps, and `references/source-category-runtime.md` for workflow-time connector use and source preference storage. Use each user-facing skill's inline experience guidance for skill-specific introductions, starter prompts, anchor rules, and next-step candidates. Use `references/onboarding-examples.md` only to audit or explain the onboarding experience across cases.
-
-When direct context work happens while onboarding is missing or active, apply the step-card continuation in `references/onboarding.md` before finalizing. This includes setup, onboarding-status checks, customization, Sales Company Research save/proposal follow-up, proposed user-context updates, memory saves, automation state changes, skill-experience updates, and preference changes. The current step card supplies the next action, and a recap of the just-completed sub-step is not sufficient unless onboarding is complete, quiet, or stopped by the user.
-
-## Action-Oriented Continuation
-
-Default Sales outputs should be useful launchpads, not dead ends. In almost all cases, after answering the user's immediate request, add one concise next step that helps them act on the result. Good next steps continue the same workflow by iterating, drafting or preparing the next artifact, offering a supported post/share/save/write action, or checking one missing source or connector that materially affects the current work. Do not recommend moving to another Sales skill as a generic continuation; only route elsewhere when the user's request has clearly changed or the current skill's own routing rules say another workflow owns the job.
-
-Treat durable memory as a supporting lane, not the default primary CTA, unless the user directly asked to remember/save something or the workflow is in direct memory mode. When a run surfaces a potentially reusable preference, broad instruction, or source shortcut, first apply `references/plugin-memory.md` to decide whether to save now, ask before saving, or only mention it. If it is useful but not the main next action, render it as a short non-competing note before the final natural continuation, then keep the final CTA focused on iterating, operationalizing, or continuing the active sales artifact.
-
-Avoid asking for setup context merely to fill onboarding state. Ask only high-uncertainty, high-impact questions whose answers would significantly improve future outputs, downstream memory triggering, memory usage criteria, or the active workflow. If onboarding is active, the current step card in `references/onboarding.md` governs the next step; otherwise use the ordinary action-oriented continuation rules here.
-
-Show at most one direct CTA during ordinary workflow output unless the user explicitly asks for options, onboarding, setup, or the Sales TODO list. When onboarding has not started in an ordinary workflow, that CTA must be the `## Sales Setup Required` template from preflight, even if the immediate artifact is otherwise complete, unless the immediate workflow is blocked on a required clarification. Clarifications always become the final natural continuation and suppress onboarding CTAs for that response. When onboarding is already active and core onboarding is incomplete, direct the user to finish the next core setup item before continuing non-urgent Sales workflows. When onboarding is active and core onboarding is complete, preserve the workflow-owned final natural continuation and mention resuming guided onboarding only as a short non-competing note when useful. For direct onboarding and setup-status flows, follow `references/onboarding.md` instead: render one visible action close, mirror the compact high-level roadmap into the built-in thread task list when available, and check tasks off as the user completes, approves, declines, or makes them unnecessary. Skip the CTA only when the user asks for quiet behavior, the request is sensitive or high-pressure enough that a follow-up would be distracting, onboarding is complete and the workflow has no reasonable next action, or `user-context.md` records that action-oriented follow-ups are declined or suppressed.
-
-If the user declines action-oriented endings or asks for quieter endings, save that preference under a concise category such as `# Action Orientation Preferences` using `references/plugin-memory.md`, and update `onboarding-state.json` to `quiet` when the request is specifically about onboarding nudges. If the user asks to resume action-oriented guidance, update the same category rather than adding a duplicate.
-
-## Plugin Memory
-
-Use `references/plugin-memory.md` for direct remember/save requests, broad future-facing instructions, correction capture, Sales Company Research save/review gates, linked-source population, and successful-run learning.
-
-That reference is the source of truth for what to save, when to ask a high-leverage clarification, how to write clear memory entries, how to include remote doc/resource links, how to inspect accepted artifact iterations for reusable plugin memory, and how to recap saved memory with the right user-facing save or research-complete copy.
-
-## Validation
-
-After editing `scripts/sales_preflight.py`, state-file templates, onboarding finalization rules, memory write policy, or this skill's state-helper contract, run:
-
-```bash
-python3 -m unittest discover -s plugins/sales/skills/user-context/tests -p "test_*.py"
-```
-
-The tests must use temporary state directories and must not read or write the user's real `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}` files.
-
-## Write
-
-Write only Sales plugin-scoped user context to `user-context.md`: context represented by `plugin-author-config/user-context-config.md`, user-provided preferences, examples, source links, source-of-truth pointers, source priority, "do not use" source rules, definitions, constraints, conventions, setup priorities, and other private context that can improve future Sales workflows. Do not store connector readiness, automatic availability, operational onboarding status, connector-audit bookkeeping, skill-experience progress, or automation metadata in `user-context.md`; store onboarding progress or automation metadata in `onboarding-state.json`. Conversely, do not duplicate saved resources, saved categories, source URLs, artifact text, or saved-memory recaps into `onboarding-state.json`; keep those in `user-context.md`, the pinned research thread, or the live source.
-
-First-run state creation remains automated when persistence is needed: run `python3 plugins/sales/skills/user-context/scripts/init_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/init_user_context_state.py` from the Sales plugin root, to copy `plugin-author-config/user-context-config.md` and seed `onboarding-state.json` from `references/onboarding-state-template.json` plus `plugin-author-config/automation-config.md`. Do not run this command just to show the first direct onboarding orientation. In sandboxed local-shell environments, request elevated execution on the first attempt because the target state directory is under `$CODEX_HOME`, not the project workspace. After that initialization, modify `user-context.md` directly. Do not call a write utility for ordinary memory saves, proposal saves, correction capture, source-link saves, or Sales Company Research approvals. For multi-entry approvals, read the current state once, update every touched category in one coherent edit, update `onboarding-state.json` only for changed operational bookkeeping, then run `scripts/sales_preflight.py --workflow user-context` or the relevant workflow preflight to confirm the saved context reads back cleanly. Never run parallel writes against the same Sales state files.
-
-For discovery-derived, inferred, or not-yet-approved context, present the proposed entries in chat first and save only after the user approves the exact entries. Once approved, write the approved entries directly to `user-context.md`; do not ask the user to approve a helper command or perform one write per entry. If the direct edit is blocked by missing files, run the automated first-run initializer, reread the resulting files, and then apply the approved edit.
-
-For explicit debugging reset requests only, run `python3 plugins/sales/skills/user-context/scripts/reset_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/reset_user_context_state.py` from the Sales plugin root, after the user explicitly asks to clear or reset local Sales plugin state for debugging. The helper moves known Sales state files to a timestamped sibling backup directory and leaves no active Sales state files behind when the state directory becomes empty. Never perform this reset for ordinary onboarding, memory edits, setup retries, source-refresh work, or vague troubleshooting. Prefer a dry-run-style explanation first when the request is ambiguous; report the backup path and mention that restoring means moving the backed-up files back into the state directory. If the helper is unavailable or fails, fall back to a manual backup move only when the user has clearly requested the reset, and report that fallback explicitly.
-
-For write decisions, bias toward saving useful, low-risk Sales memory; infer definition, trigger, usage, source authority, freshness, and exclusions as much as possible; ask only when a clarification would significantly tighten future triggering, usage criteria, source authority, or freshness; read accessible remote sources before deriving memory from them; include stable source links in `user-context.md`; and recap saves using the copy in `references/plugin-memory.md`. Load `references/plugin-memory.md` when the write is ambiguous, source-derived, broad future-facing, correction-driven, or otherwise needs the full policy.
-
-Use this category-block structure:
-
-```md
-# {Category}
-
-- Description: {what this category is or does and when future Sales runs should apply it}
-
-## Saved Links And Context
-
-status: not provided
-```
-
-`user-context.md` is a user-editable surface. The user and agent can edit category names, `Description`, and `Saved Links And Context` directly. The agent may create new categories or revise category descriptions when the existing categories do not fit cleanly, when a clearer category would improve future triggering, or when discovered context changes how the category should be applied.
-
-Put saved resources, linked docs, source pointers, channels, and user-confirmed facts under `## Saved Links And Context` using this shape:
-
-```md
-Resource or fact name (use a real Markdown link when a stable URL is available; otherwise use a connector-visible reference in plain text)
-- Date Added: YYYY-MM-DD.
-- Useful Context: {what this resource or fact represents}
-- Future Use: {how future Sales runs should use it}
-```
-
-When a category has no saved resources yet, use exactly `status: not provided` under `## Saved Links And Context`. Do not add wrapper labels such as `Saved Information`, `Saved Context`, or `User-provided context`. When saving inline context with no external source, make the preference or fact itself the resource name and describe its origin in `Useful Context`; do not add filler bullets such as `user-provided inline`. Use the current date for `Date Added` when the resource is first saved; preserve the original date on later edits unless the old date is clearly wrong. Use `Future Use` for forward-looking retrieval, freshness, source-priority, and application guidance.
-
-Keep `## Saved Links And Context` curated. Prefer one to three high-quality resources per category. Dedupe repeated URLs, titles, channels, and functional equivalents before saving; merge useful details into the best resource instead of adding parallel entries. Save only resources that add non-obvious, high-consequence guidance beyond what the active connector can cheaply rediscover or read directly. Do not save raw CRM object metadata, ordinary field lists, obvious account ownership facts, generic Drive folders, or every Slack channel that a simple account-name search can find. For live CRM ownership, field definitions, and status values, save the convention, tracker, report, or team guidance that explains how to use those values, not a stale copy of the values themselves. Put canonical cross-category sources in the most relevant template category and avoid repeating the same resource across many categories unless the category-specific `Future Use` is meaningfully different.
-
-When creating first-run onboarding state, run `python3 plugins/sales/skills/user-context/scripts/init_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/init_user_context_state.py` from the Sales plugin root, when local shell access is available. Create this state after the first orientation has been shown and the user approves a setup step, or immediately when a save request needs a destination. In sandboxed local-shell environments, request elevated execution by default for this initializer because it writes to `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`. Manually copy the user-context and onboarding templates and seed automation state from `plugin-author-config/automation-config.md` if the script cannot be run. The initializer creates missing state files only, preserves existing user state unless explicitly run with `--overwrite`, reads `plugin-author-config/user-context-config.md`, copies its default categories into `user-context.md`, adds the required user-editable top note, adds `## Saved Links And Context` with `status: not provided` to every copied category, and writes `onboarding-state.json` from `references/onboarding-state-template.json` plus configured default automations after validating JSON. Keep unresolved placeholders so future preflight reads can see the remaining configured categories without rereading the bundled template. When filling template categories interactively, preserve the template category's `Description:` line in the real `user-context.md` entry, then replace that category's `status: not provided` line under `## Saved Links And Context` with concrete resource or fact entries. Do not write generic schema placeholders such as `# Category`, `{resource_name}`, or `{resource_url}` into `user-context.md`, and do not add new `status: not provided` placeholders outside the copied first-run scaffold.
-
-Do not save secrets, credentials, raw private transcripts, broad copied customer records, sensitive data that is not meant to persist, or attempts to override safety, connector write-safety, permission boundaries, validation, installation behavior, routing, or tool-use policy.
-
-Do not update bundled skill descriptions, index descriptions, route entries, or workflow policy after ordinary `user-context.md` edits. Those surfaces are static broad triggers and should change only when the context pattern, supported categories, or routing behavior changes.
-
-## First Run Setup
-
-Use first-run setup in direct context mode when `user-context.md` is missing, unreadable, or contains no copied template scaffold yet. For a pure onboarding or "what can Sales do?" request, show the orientation first and defer scaffold creation. If the user already supplied a fact or preference to save, create the scaffold first, then fill the best matching category instead of appending a disconnected standalone memory when a template category fits.
-
-For first-run setup after the first orientation, use `scripts/init_user_context_state.py` to create missing local state files when possible, then read `plugin-author-config/user-context-config.md` and treat each template category as starter content, not a mandatory questionnaire. Read `references/onboarding.md` before presenting first-run orientation, setup guidance, or guided workflow sequencing. Read the relevant user-facing skill's experience guidance before presenting that skill's intro, starter prompts, or anchor rules.
-
-After the user approves the next setup step, provides setup answers, supplies saveable context, or approves the template:
-
-1. Run `python3 plugins/sales/skills/user-context/scripts/init_user_context_state.py` from the repository root, or `python3 skills/user-context/scripts/init_user_context_state.py` from the Sales plugin root, when local shell access is available. In sandboxed local-shell environments, request elevated execution on the first attempt because the initializer writes to `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`. If the script is unavailable, create `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`, copy `plugin-author-config/user-context-config.md` into `user-context.md` with the required top note, and create `onboarding-state.json` from `references/onboarding-state-template.json` plus default automation entries from `plugin-author-config/automation-config.md`.
-2. Read the resulting `user-context.md`, preserving every category's `Description:`, `## Saved Links And Context`, and unresolved `status: not provided` placeholder.
-3. Fill any user-provided or confirmed context into the best matching scaffold category by replacing that category's `status: not provided` placeholder with concrete resource or fact entries. If no template category fits, add a concise new category block after the scaffold.
-4. Read the resulting `onboarding-state.json`, then update onboarding-state with operational onboarding progress, skill experience progress, and configured automation metadata. Save any source preference, default, source pointer, or "do not use" instruction in `user-context.md` only when the user explicitly provides or approves it.
-5. Continue the user's original request using the newly saved context.
-
-If the user declines setup, continue the original request without saved context. Keep the explanation user-facing; do not describe internal files unless the user asks.
diff --git a/plugins/sales/skills/user-context/agents/openai.yaml b/plugins/sales/skills/user-context/agents/openai.yaml
deleted file mode 100644
index 1d548f6..0000000
--- a/plugins/sales/skills/user-context/agents/openai.yaml
+++ /dev/null
@@ -1,6 +0,0 @@
-interface:
-  display_name: "Sales User Context"
-  short_description: "Manage saved Sales context so it learns how your team sells"
-  default_prompt: "Read Sales user context, apply saved preferences, and look for useful team selling guidance to fill setup gaps."
-policy:
-  allow_implicit_invocation: false
diff --git a/plugins/sales/skills/user-context/plugin-author-config/automation-config.md b/plugins/sales/skills/user-context/plugin-author-config/automation-config.md
deleted file mode 100644
index fe2dfee..0000000
--- a/plugins/sales/skills/user-context/plugin-author-config/automation-config.md
+++ /dev/null
@@ -1,43 +0,0 @@
-# Sales Automation Config
-
-Plugin authors edit this file when adding, removing, renaming, or clarifying default Sales onboarding automations. Keep setup mechanics, thread creation, pinning, readback, duplicate cleanup, failure handling, and automation tool usage in `../references/automation.md`.
-
-The default automations are installed during initial Sales onboarding. Later journey automations are offered only when a focused workflow reaches the right point. This config should stay friendly for plugin authors: name the automation, say how often it should run, and write the instructions. The dedicated thread title is derived from `Name`. Runtime mechanics such as heartbeat kind, RRULE conversion, tool calls, target thread ids, pinning, readback, cleanup, and state metadata belong in `../references/automation.md`.
-
-## Automation Entry Shape
-
-Every automation entry must use this shape:
-
-```md
-`automation_id`
-
-- Name: human-facing automation name.
-- Frequency: seller-facing cadence and local time.
-- Instructions: thin launcher instructions for the scheduled run.
-```
-
-Do not add model, reasoning effort, heartbeat kind, RRULEs, tool names, user-specific target thread ids, canonical automation ids, install status, readback evidence, cleanup state, onboarding notification copy, or per-user preferred times here. Those belong in `../references/automation.md` or `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json`.
-
-## Default Automations
-
-`daily_sales_tips`
-
-- Name: Sales Tips.
-- Frequency: weekdays at 9:00 AM local time.
-- Instructions: Load and follow `$sales:suggest-sales-next-step` in scheduled Sales check-in mode. Produce a short natural-language check-in on recent Sales work and the next Sales workflow worth trying, preserving the heartbeat automation response format.
-
-`weekly_sales_company_research`
-
-- Name: Sales Company Research.
-- Frequency: weekly on Mondays at 9:00 AM local time.
-- Instructions: Load and follow `$sales:sales-company-research` in scheduled research mode. Use the skill's dynamic source discovery, Sales user-context save policy, review gates, coverage-note guidance, and output contract, and compare against entries already flagged or saved from this pinned thread.
-
-## Later Journey Automations
-
-These automations are not installed during initial onboarding. Offer them only from the focused workflow journey that owns the behavior.
-
-`daily_meeting_prep`
-
-- Name: Sales Daily Meeting Prep.
-- Frequency: weekdays at 9:00 AM local time.
-- Instructions: Load and follow `$sales:prepare-for-meeting` in scheduled meeting prep mode. Inspect today's calendar, choose the highest-value meeting where prep would materially help while preferring customer-facing or account-related meetings, prepare only that meeting, and return DONT_NOTIFY when no qualifying meeting needs prep.
diff --git a/plugins/sales/skills/user-context/plugin-author-config/source-category-config.json b/plugins/sales/skills/user-context/plugin-author-config/source-category-config.json
deleted file mode 100644
index f970674..0000000
--- a/plugins/sales/skills/user-context/plugin-author-config/source-category-config.json
+++ /dev/null
@@ -1,111 +0,0 @@
-{
-  "schema_version": "sales_source_category_config.v1",
-  "description": "Author-owned Sales source category catalog. Runtime source resolution, preferred plugin/app routing hints, relevant helper skills, fallback, and durable user-approved preference rules live in ../references/source-category-runtime.md. Skills decide which categories they attempt. User-specific source preferences and source-of-truth pointers live in $CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md.",
-  "categories": {
-    "crm": {
-      "label": "CRM",
-      "preferred_apps": [
-        "Salesforce",
-        "Agentforce Sales",
-        "HubSpot",
-        "Close",
-        "Zoho",
-        "Pipedrive"
-      ],
-      "relevant_skills": [
-        {
-          "app": "Salesforce",
-          "skill": "salesforce"
-        },
-        {
-          "app": "Agentforce Sales",
-          "skill": "salesforce"
-        },
-        {
-          "app": "HubSpot",
-          "skill": "hubspot"
-        }
-      ]
-    },
-    "calendar": {
-      "label": "calendar",
-      "preferred_apps": [
-        "Google Calendar",
-        "Outlook Calendar",
-        "Calendly"
-      ]
-    },
-    "meeting_notes": {
-      "label": "meeting notes or call recordings",
-      "preferred_apps": [
-        "Zoom",
-        "Granola",
-        "Otter.ai",
-        "Fireflies",
-        "Outreach",
-        "Rox"
-      ]
-    },
-    "document_store": {
-      "label": "docs",
-      "preferred_apps": [
-        "Google Drive",
-        "Microsoft SharePoint",
-        "Notion",
-        "Monday.com"
-      ]
-    },
-    "spreadsheets": {
-      "label": "sheets, spreadsheets, Excel, or CSV outputs",
-      "preferred_plugins": [
-        "Spreadsheets",
-        "Microsoft Excel"
-      ],
-      "preferred_apps": [
-        "Google Drive",
-        "Microsoft SharePoint"
-      ]
-    },
-    "data_enrichment": {
-      "label": "data enrichment",
-      "preferred_apps": [
-        "ZoomInfo",
-        "Clay",
-        "HG Insights",
-        "Rox",
-        "Apollo",
-        "Actively",
-        "Meticulate"
-      ],
-      "relevant_skills": [
-        {
-          "app": "ZoomInfo",
-          "skill": "zoominfo"
-        }
-      ]
-    },
-    "external_messaging": {
-      "label": "customer email or external threads",
-      "preferred_apps": [
-        "Gmail",
-        "Outlook Email",
-        "Outreach",
-        "Close",
-        "Rox"
-      ]
-    },
-    "internal_messaging": {
-      "label": "internal messages",
-      "preferred_apps": [
-        "Slack",
-        "Microsoft Teams"
-      ]
-    },
-    "agreements": {
-      "label": "agreements, contracts, signatures, or approval workflows",
-      "preferred_apps": [
-        "Docusign"
-      ]
-    }
-  }
-}
diff --git a/plugins/sales/skills/user-context/plugin-author-config/user-context-config.md b/plugins/sales/skills/user-context/plugin-author-config/user-context-config.md
deleted file mode 100644
index faa1f33..0000000
--- a/plugins/sales/skills/user-context/plugin-author-config/user-context-config.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Sales User Context Config
-
-Plugin authors edit this file when adding, removing, renaming, or clarifying the starter categories Sales should copy into a user's `user-context.md`. Keep save/update policy in `../references/plugin-memory.md`, onboarding mechanics in `../references/onboarding.md`, and placeholder insertion in `../scripts/init_user_context_state.py`.
-
-These categories describe the links, docs, channels, examples, or user-provided information that make future Sales workflows more grounded. Keep each description to one or two sentences that say what the category is or does and when Codex should use it; that gives future Sales runs enough context to apply the saved user context without carrying a separate use-when field. The initializer adds `## Saved Links And Context` with `status: not provided` under every category when it creates the user-facing file. Do not include that saved-context heading, placeholder status, connector setup state, onboarding state, or automation setup details in this config.
-
-## Category Entry Shape
-
-Every category entry must use this shape:
-
-```md
-# Category Name
-
-- Description: What this category is or does and when future Sales runs should apply it.
-```
-
-The entries below are the plugin-author customization surface. Keep each category as an H1 (`# Category Name`) because the initializer copies these headings into the user-facing scaffold.
-
-## Default Categories
-
-# CRM Update Rules And Write Safety
-
-- Description: Rules for interpreting or changing CRM fields such as stage, forecast category, close date, next steps, approval paths, and review boundaries. Use this when drafting CRM-ready notes, interpreting deal health, or deciding whether a CRM write needs review.
-
-# Book Of Business Source
-
-- Description: Sources that explain the user's real customer book, including territory logic, named-account exceptions, account assignments, pod coverage, overlay responsibilities, and ownership refresh paths. Use this when scoping account research, filtering pipeline, or checking which ownership context applies.
-
-# Customer And Account Source Docs
-
-- Description: Trusted account-specific sources such as account plans, mutual action plans, exec briefs, QBRs, implementation plans, security or procurement notes, support summaries, and customer research folders. Use this when preparing customer work, answering account-status questions, or choosing the account team's source of truth.
-
-# Account Rooms And Escalation Channels
-
-- Description: Important rooms or channels and their norms for account collaboration, escalations, posting, tagging, review, and customer-visible boundaries. Use this when finding account history, drafting internal updates, or deciding where and how to ask for help.
-
-# Deal Governance And Approval Trackers
-
-- Description: Approval and governance sources for pricing, discounting, legal, procurement, security, implementation, executive sponsorship, launch readiness, or exceptions. Use this when reviewing deal risk, preparing close plans, or drafting asks for approvers.
-
-# Product Positioning And Value Proposition Sources
-
-- Description: Approved product messaging, positioning, SKU or package language, ICP and persona guidance, solution narratives, and product-specific claims to use or avoid. Use this when drafting customer-facing material, preparing meetings, or explaining product fit.
-
-# Company Brand And Customer-Facing Asset Sources
-
-- Description: Approved brand guidance, templates, asset libraries, examples, screenshots, diagrams, logos, and visual or tone rules for sales deliverables. Use this when creating or reviewing decks, one-pagers, exec briefs, QBRs, event collateral, or other customer-facing assets.
-
-# Build Competitive Brief Sources
-
-- Description: Approved competitive intelligence such as competitor lists, battlecards, win/loss notes, objection handling, displacement narratives, differentiation claims, and competitive do/don't language. Use this when preparing deal strategy, vendor comparisons, customer responses, or competitive risk notes.
-
-# Sales Targets And Planning Sources
-
-- Description: Quota context, targets, segment goals, GTM priorities, campaign focus areas, territory plans, leadership planning docs, and planning trackers that explain current sales focus. Use this when prioritizing accounts, reviewing forecast, selecting outbound motions, or preparing leadership updates.
diff --git a/plugins/sales/skills/user-context/references/automation.md b/plugins/sales/skills/user-context/references/automation.md
deleted file mode 100644
index 390da40..0000000
--- a/plugins/sales/skills/user-context/references/automation.md
+++ /dev/null
@@ -1,84 +0,0 @@
-# Sales Onboarding Automations
-
-Use this reference only when Sales onboarding is setting up, checking, repairing, or later offering recurring Sales automations. Keep this file as the runtime owner for automation setup. Keep ordinary onboarding flow, copy, and workflow choices in `onboarding.md`.
-
-The automation catalog is `../plugin-author-config/automation-config.md`. Always read it before setup and use its `Name`, `Frequency`, and `Instructions`; do not duplicate focused-skill workflow rules in automation prompts.
-
-## Default Setup
-
-Initial Sales onboarding sets up exactly two automations unless the user skips or defers them:
-
-| Automation id | Thread | Model/thinking | Schedule | Prompt | Kickoff |
-| --- | --- | --- | --- | --- | --- |
-| `weekly_sales_company_research` | Dedicated projectless thread titled `Sales Company Research`, pinned | `model="gpt-5.5"`, `thinking="xhigh"` when supported | Weekly Monday 9:00 AM local time | Configured `Instructions` from `automation-config.md` | Send one setup kickoff to the pinned thread |
-| `daily_sales_tips` | Dedicated projectless thread titled `Sales Tips`, pinned | `model="gpt-5.5"`, `thinking="low"` when supported | Weekdays 9:00 AM local time | Configured `Instructions` from `automation-config.md` | No setup kickoff; let the first scheduled check-in run on its own |
-
-Do not install `daily_meeting_prep` during initial onboarding. Offer Sales Daily Meeting Prep later from the Prepare For Meeting journey only, using a dedicated pinned thread titled `Sales Daily Meeting Prep`, `model="gpt-5.5"`, and `thinking="low"` when supported.
-
-If a tool cannot set model or thinking for heartbeat automations, set them on the target thread or Sales Company Research kickoff when supported, record the limitation in onboarding state, and do not fall back to `gpt-5`.
-
-The onboarding thread is never an automation target. Do not rename, pin, unpin, attach a heartbeat to, or send kickoff messages into the current onboarding thread while setting up Sales automations. Only the two dedicated automation target threads may be renamed, pinned, and attached to heartbeat automations.
-
-## Fast Setup Checklist
-
-After the user approves Sales automation setup:
-
-1. Discover `automation_update` and the thread tools: `create_thread`, `set_thread_title`, `set_thread_pinned`, and `send_message_to_thread`. Use `list_threads` only to reuse a clearly matching existing pinned automation thread with the exact configured title; never treat the current onboarding thread as reusable, even if it has been accidentally renamed or pinned.
-2. Read `../plugin-author-config/automation-config.md` and identify the two default automation ids, names, frequencies, and prompts.
-3. For each default automation, create or reuse its dedicated projectless thread, capture the returned or reused thread id, set the exact configured title on only that thread id, pin only that thread id, and keep its thread id.
-4. Create or update the automation with `kind="heartbeat"`, `destination="thread"`, the configured prompt, the configured cadence converted to the narrowest supported RRULE, `status="ACTIVE"`, and the exact `targetThreadId` of the dedicated automation thread.
-5. Read back `$CODEX_HOME/automations/<automation_id>/automation.toml` when available. Confirm `kind = "heartbeat"`, the stored `target_thread_id` matches the pinned thread, the prompt and cadence match the config, and no cron-only workspace fields are present. Use `automation_update mode=view` only when file readback is unavailable.
-6. Send one kickoff message only to the pinned Sales Company Research thread after readback succeeds: tell `$sales:sales-company-research` to run scheduled research mode now. Do not send an immediate Sales Tips kickoff during onboarding; the first Sales Tips check-in should happen on its normal weekday schedule so it does not interrupt guided onboarding.
-7. Update `onboarding-state.json` with only operational metadata: canonical automation id, `kind`, readback status, target thread id/title, target thread pin status, Sales Tips schedule-only disposition when useful, and `initial_resource_discovery` status/thread info. Do not copy tip text, research results, saved resources, source URLs, or customer details into onboarding state.
-8. Stop. Report the concise setup result and move to the first hero workflow chooser. Do not inspect hero workflow skill files or run another guided workflow until the user picks one.
-
-## Readiness Rules
-
-Only say the default automation setup is complete when both default automations are heartbeat-backed, targeted to their dedicated pinned threads, read back successfully, and the Sales Company Research kickoff has started or was explicitly deferred by the user. Sales Tips does not need an immediate kickoff; it is complete when the scheduled automation is installed, read back, and pointed at its pinned thread.
-
-Do not mark setup complete after a plain cron automation, an automation attached to the onboarding thread, missing readback, missing target thread metadata, or a missing first Sales Company Research kickoff.
-
-If `automation_update` cannot persist a dedicated heartbeat thread after one repair attempt, use the fallback path: set up only Sales Tips with the configured prompt and cadence, skip Sales Company Research and the research kickoff, record the limitation as `environment_api_limitations`, and report it as a fallback rather than full setup.
-
-Ask for explicit user approval before deleting automations, pausing automations, unpinning threads, or cleaning up duplicate/stale Sales automation resources.
-
-## Thin Prompts
-
-Automation prompts and kickoff prompts should be thin launchers:
-
-- Sales Company Research: load and follow `$sales:sales-company-research` in scheduled research mode, using that skill's dynamic discovery and output contract.
-- Sales Tips automation prompt: load and follow `$sales:suggest-sales-next-step` in scheduled Sales check-in mode. Do not send this as a setup kickoff during onboarding.
-
-Do not paste source-family lists, ranking rules, save gates, output examples, model names, reasoning effort, or focused-skill workflow contracts into automation prompts or kickoff messages.
-
-## User-Facing Result
-
-When full setup succeeds, use this shape and then stop:
-
-```md
-Automations are set up and read back cleanly.
-
-- **Sales Company Research**: deeply searches across your company context and saves key resources to improve speed and correctness. It runs weekly on Mondays at 9:00 AM local time. I also kicked off the first research run in the pinned **Sales Company Research** thread.
-- **Sales Tips**: looks at how you've been using Codex and gives you one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time, starting with its next scheduled run.
-
-Core onboarding is done. Pick a workflow to try first:
-
-1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-```
-
-When fallback Sales Tips setup succeeds, use this shape:
-
-```md
-The full Sales automation setup hit an environment/API limitation, so I used the fallback path and set up Sales Tips instead.
-
-- **Sales Tips**: looks at how you've been using Codex and gives you one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time, starting with its next scheduled run.
-- **Skipped for now**: Sales Company Research was not set up in this environment, so I skipped the context-gathering kickoff and did not ask you for source links here.
-
-Core onboarding is done with the fallback automation. Pick a workflow to try first:
-
-1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-```
diff --git a/plugins/sales/skills/user-context/references/onboarding-examples.md b/plugins/sales/skills/user-context/references/onboarding-examples.md
deleted file mode 100644
index 32f41cb..0000000
--- a/plugins/sales/skills/user-context/references/onboarding-examples.md
+++ /dev/null
@@ -1,130 +0,0 @@
-# Sales Onboarding Examples
-
-Use these compact cases to audit the core onboarding experience. They are regression anchors, not scripts. Governing rules remain in `onboarding.md`, `plugin-memory.md`, the relevant skill's experience guidance, `../plugin-author-config/source-category-config.json`, `../plugin-author-config/automation-config.md`, `source-category-runtime.md`, and `automation.md`.
-
-## Case 1: Direct Onboarding Orientation
-
-User says:
-
-```text
-@sales help me get started
-```
-
-Expected behavior:
-
-- Open with a compact Sales onboarding orientation that explains what the plugin does, how onboarding works, and that the user can answer `yes`, `skip`, or send a link at each step.
-- Do not initialize state files, create the user-context scaffold, inspect connectors, audit conflicts, run discovery, spawn subagents, create background threads, or install automations before sending the first orientation message.
-- Make `Orientation` the first visible item, then `Connector setup/confirmation`; do not inspect connectors before the first orientation just to populate setup state.
-- Explain that active or obvious one-app sources are informational only and will be tried only when a workflow needs them. Ask the user for help only where multiple apps are plausible, no app was found, or IT/admin action may be needed.
-- Include `Sales tools` only when overlapping broad Sales plugins or sales-specific skills create real or likely conflicts; omit this bullet after a clean audit.
-- Offer one concrete guided next action with real context, never placeholders such as `<paste notes>` or `<company>`, while mirroring the canonical high-level onboarding checklist into the built-in thread task list when available: `Orientation`, `Connector setup/confirmation`, `Sales automation setup`, `First hero prompt`, and `Other hero prompts`.
-- Use `# Sales Onboarding`, then a high-level prose paragraph that does not expose saved state paths, cache paths, or internal onboarding status.
-- Explain guided workflows only when the flow reaches demos or the user asks for the full plan.
-- End with one clear action close and show only the current action the user can approve now. Use a compact `**Next Step**` label only when the step needs the label; a self-contained numbered choice set can stand on its own. If the current step has open questions, make resolving them their own step and include those questions in the action close; if they are non-blocking, say what `okay` will do by default. Do not combine meaningful open questions with a first-time workflow introduction. When the next step introduces a workflow for the first time, put the explanation in primary content before the action close; the action frame should stay small.
-- Do not add a time estimate to meeting-notes/provider connection by default. For Sales Company Research, say it saves high-confidence Sales memory in its pinned thread and asks for review only for uncertain, sensitive, or lower-confidence candidates.
-
-## Case 2: High-Intent First Run
-
-User says:
-
-```text
-@sales prep me for my 3pm customer meeting
-```
-
-Expected behavior:
-
-- Begin the requested Prepare For Meeting workflow first and return a best-effort answer from available sources, even when no saved Sales context exists.
-- Use calendar and source evidence if available rather than stopping at onboarding.
-- Keep the artifact useful even while onboarding is active.
-- Add the `## Sales Setup Required` CTA after the brief only when onboarding has not started, unless the request is already being answered inside a guided onboarding flow. If onboarding is already active, preserve the skill's own final natural continuation and do not ask whether to start onboarding.
-- Add a `**Context Gap**` note only when missing saved context, source access, or setup materially weakens the prepare-for-meeting answer. The note should name the exact missing information and the specific improvement it would unlock.
-- Do not add a generic setup note; the onboarding CTA should say setup is required before Sales can reliably use connected sources and authoritative company context, and end with the exact line: Reply `start` to continue.
-- If the meeting prep counts as the first onboarding guided workflow, start with one short unheaded transition sentence such as `Here's your meeting prep, using {source coverage and assumptions}.`, then the actual output. End with one compact feedback CTA asking what the user would change to make it clearer or more useful, or whether to save any reusable preference and move on.
-
-## Case 3: Durable Preference During Onboarding
-
-User says:
-
-```text
-@sales onboard me, and remember that I prefer concise prep briefs with risks first.
-```
-
-Expected behavior:
-
-- Save the low-risk Sales plugin-scoped preference immediately.
-- Start the recap with `Saved Sales Plugin Memory.`
-- If this is the first plugin-memory mention in onboarding, explain that plugin memory can remember trusted resources and answer/style preferences for future Sales workflows before listing the exact saved preference.
-- Continue onboarding with the saved preference reflected in the next useful guided workflow or setup suggestion.
-
-## Case 4: Sales Automations Are Automatic And Approval-Gated
-
-User says:
-
-```text
-okay
-```
-
-Context: the previous onboarding response explained that recurring Sales workflows can run in the background, listed Sales Company Research and Sales Tips with their configured cadences, and ended with `Should I set those up? They'll run in a different thread and notify you when they have something to review.` The visible step was `Sales automation setup`.
-
-Expected behavior:
-
-- Read `automation.md` and `../plugin-author-config/automation-config.md`.
-- Install or repair `weekly_sales_company_research` and `daily_sales_tips` unless the user has already declined, skipped, or deferred a specific automation.
-- Mention each configured automation once with its cadence: Sales Company Research runs weekly on Mondays at 9:00 AM local time, and Sales Tips runs weekdays at 9:00 AM local time.
-- Use the configured dedicated pinned thread named `Sales Company Research`.
-- Confirm the automation prompt is a thin launcher for `$sales:sales-company-research`; the skill owns dynamic source discovery, source-family coverage, ranking, save gates, and output copy.
-- Explain that Sales Company Research compares against entries already flagged or saved from that pinned thread, saves high-confidence plugin memory by default, and asks for review only for uncertain, sensitive, or lower-confidence candidates.
-- Record automation metadata for all three automations in `onboarding-state.json`.
-- Do not kick off Sales Tips during onboarding; install and read back the automation, then let its first check-in happen on the normal weekday schedule.
-- Immediately kick off one company research search in the pinned `Sales Company Research` thread, record it under `initial_resource_discovery`, and tell the user it is working in the background and visible in the `Pinned` section of the left sidebar.
-- Continue to the next onboarding step after readback succeeds. Mention discovery-thread status as a separate `FYI:` line, then offer the first hero prompt choice card with Prepare For Meeting, Follow Up After Call, and Prioritize Accounts. Load each skill's experience guidance for option wording and include realistic `@Sales` examples when introducing a selected workflow. The three numbered choices can be the action close by themselves; do not add a redundant final line asking the user to pick `1`, `2`, or `3`.
-
-## Case 5: Ordinary Workflow While Onboarding Is Active
-
-User says:
-
-```text
-@sales who knows about enterprise SSO migration?
-```
-
-Expected behavior:
-
-- Complete the requested find-key-internal-sources workflow.
-- Do not interrupt with full onboarding.
-- Do not include the `## Sales Setup Required` start CTA when onboarding is already active. Preserve the skill's own final natural continuation, and optionally add a short non-final note that onboarding is still in progress.
-- If onboarding is quiet or complete, produce the requested Sales artifact directly and end with one useful seller action, not an onboarding recap.
-- If this completed one of the initial guided workflows, offer a beginner-friendly `Agent Journey` walkthrough or the next concrete setup or workflow step.
-
-## Case 6: Onboarding Status Check
-
-User says:
-
-```text
-@sales have we onboarded yet
-```
-
-Expected behavior:
-
-- Answer the status directly from `user-context.md` and `onboarding-state.json`, or say onboarding has not started if the state is missing.
-- Treat missing or active onboarding as a direct onboarding interaction, not a passive status lookup.
-- Explain briefly that setup is required for the best Sales experience because saved preferences, workflow-time source choices, and team context help Sales tailor future prep, follow-up, and other seller-ready work.
-- End with one concrete next setup action that the user can approve now, using a compact `**Next Step**` label only when it improves clarity.
-
-## Case 7: Scheduled Check-In Heartbeat Shape
-
-Context: suggest-sales-next-step automation runs on a weekday at 9:00 AM.
-
-Expected behavior:
-
-- Produce a short check-in card tied to recent usage, calendar/source signals, or visible setup gaps.
-- Suggest one specific prompt grounded in real context, such as `Use Sales follow-up-after-call for today's Acme implementation sync notes.`
-- Preserve heartbeat XML at the end of scheduled-mode output.
-
-No-notification shape:
-
-```text
-<heartbeat>
-  <decision>DONT_NOTIFY</decision>
-  <message>No useful Sales check-in suggestion right now.</message>
-</heartbeat>
-```
diff --git a/plugins/sales/skills/user-context/references/onboarding-state-template.json b/plugins/sales/skills/user-context/references/onboarding-state-template.json
deleted file mode 100644
index 3c28a6e..0000000
--- a/plugins/sales/skills/user-context/references/onboarding-state-template.json
+++ /dev/null
@@ -1,158 +0,0 @@
-{
-  "status": "active",
-  "orientation": {
-    "status": null,
-    "last_shown": null
-  },
-  "last_orientation_shown": null,
-  "last_skill_suggested": null,
-  "connector_confirmation": {},
-  "core_onboarding": {
-    "status": null,
-    "completed_at": null
-  },
-  "hero_prompt_choice": {
-    "status": null,
-    "options": [
-      "prepare-for-meeting",
-      "follow-up-after-call",
-      "prioritize-accounts"
-    ],
-    "last_offered_skills": [],
-    "selected_skill": null,
-    "selected_anchor": null,
-    "last_shown": null,
-    "selected_at": null
-  },
-  "meeting_prep_prompt": {
-    "status": null,
-    "suggested_prompt": null,
-    "selected_prompt": null,
-    "meeting_ref": null
-  },
-  "meeting_prep_run": {
-    "status": null,
-    "completed_at": null
-  },
-  "first_guided_workflow_review": {
-    "status": null,
-    "last_prompted": null
-  },
-  "plugin_memory_intro": {
-    "status": null,
-    "last_shown": null,
-    "shown_at": null
-  },
-  "accepted_preference_memory": {
-    "status": null,
-    "approved_at": null,
-    "saved_at": null,
-    "saved_ref": null
-  },
-  "automations_intro": {
-    "status": null,
-    "last_shown": null
-  },
-  "initial_resource_discovery": {
-    "status": null,
-    "last_run": null,
-    "target_thread_id": null,
-    "target_thread_title": null,
-    "proposal_ref": null
-  },
-  "automations": {},
-  "call_followup_intro": {
-    "status": null,
-    "last_shown": null
-  },
-  "internal_navigation_intro": {
-    "status": null,
-    "last_shown": null
-  },
-  "skill_experience": {
-    "analyze-account-signals": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "build-competitive-brief": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "follow-up-after-call": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "suggest-sales-next-step": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "enrich-company-and-contact-data": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "plan-deal-strategy": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "find-key-internal-sources": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "prepare-for-meeting": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "prioritize-accounts": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "find-customer-quotes": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "review-forecast": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "get-rep-call-feedback": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "review-rep-call-trends": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    },
-    "build-business-case": {
-      "introduced_at": null,
-      "first_tried_at": null,
-      "last_suggested_at": null,
-      "dismissed_at": null
-    }
-  }
-}
diff --git a/plugins/sales/skills/user-context/references/onboarding.md b/plugins/sales/skills/user-context/references/onboarding.md
deleted file mode 100644
index 4dc54c4..0000000
--- a/plugins/sales/skills/user-context/references/onboarding.md
+++ /dev/null
@@ -1,693 +0,0 @@
-# Sales Onboarding Flow
-
-Use this reference when `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` is missing or unreadable, when the user asks what Sales can do, when the user asks to set up or customize Sales, or when `onboarding-state.json` says onboarding is active and a nudge is due.
-
-Onboarding is a mostly linear, nested step-card playbook. It is not a formal graph. Each step owns setup copy, output contract, action close, completion behavior, skip or defer behavior, and any loop or optional branch. Every onboarding message should handle one major user-visible step at most, mirror the overall progress list when the UI is available, and end with one clear action or question.
-
-Store durable personalization and user-approved source preferences in `user-context.md`. Store only operational onboarding progress, including connector-confirmation labels, skill experience progress, automation setup, and discovery proposal status, in `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json`. Do not put saved-resource manifests, saved-category lists, source URLs, research result bodies, or artifact text in onboarding state; keep that content in `user-context.md`, the pinned research thread, or the source system. Do not create, read, or update durable connector-readiness proof as part of onboarding. Sales workflows use plugins, apps, or connectors when the workflow needs the source.
-
-The first direct onboarding response is a response-first fast path. When the user asks to onboard, set up, or learn what Sales can do and no Sales state files exist yet, render Step 1 immediately. Do not run `../scripts/init_user_context_state.py`, write scaffold files, inspect connectors, audit conflicts, create threads, install automations, start discovery, or run workflows before that first message. After the user approves the next visible setup step, or whenever the user supplies context that should be saved, use `../scripts/init_user_context_state.py` when local shell access is available. In sandboxed local-shell environments, request elevated execution by default because it writes to `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`.
-
-Related sources of truth:
-
-- Use `plugin-memory.md` for all plugin memory save/update policy, linked-source population, broad instruction capture, correction capture, Sales Company Research save/review policy, iteration-to-memory review, and the required saved-memory or research-complete recap copy.
-- Use `../scripts/init_user_context_state.py` to create missing first-run state files only after the first orientation message has been shown and the user approves the next setup step, or immediately when the user provides context that should be saved.
-- Use `../plugin-author-config/user-context-config.md` as the source for plugin-specific memory category names and compact descriptions.
-- Use `../plugin-author-config/source-category-config.json` for static source category ids, labels, preferred plugins/apps, and helper skills. Use `source-category-runtime.md` for plugin-first source setup, onboarding connector-confirmation labels, workflow-time source attempts, ON_USE auth behavior, fallback rules, and durable source-preference storage.
-- Use each user-facing skill's inline experience guidance for first-run intro copy, starter prompts, anchor rules, normal next-step candidates, and onboarding-yield behavior.
-- Use `../plugin-author-config/automation-config.md` for the author-owned default automation catalog. Use `automation.md` for runtime setup mechanics: installing configured defaults during onboarding, deriving target thread titles from automation names, creating target threads, pinning and renaming them, kicking off Sales Company Research in the pinned thread, readback checks, duplicate cleanup, and failure reporting.
-- Use `onboarding-examples.md` for illustrative audit traces only.
-
-## Skill Experience Delegation
-
-Onboarding chooses which user-facing skill to try next, but the focused skill owns how it introduces itself and what a good first try looks like.
-
-Before introducing or running a user-facing skill during onboarding, load that skill's inline experience guidance from `SKILL.md`. Use that guidance for:
-
-- first-run intro copy;
-- starter prompts and realistic `@Sales` examples;
-- anchor rules and sensible defaults;
-- normal next-step candidates;
-- onboarding-yield behavior.
-
-Do not duplicate skill-specific intro copy, starter prompt variants, anchor rules, or normal next-step behavior in this file. If the focused skill has enough user intent to run and has not been introduced yet, it should render the compact first-run intro section from its skill-owned guidance before normal workflow output. If onboarding or another parent workflow owns the final CTA, the skill should suppress its own final CTA and return status plus next-step candidates for that parent workflow to arbitrate.
-
-For hero prompts, onboarding owns the choice set and sequencing, but each skill still owns the prompt wording. The first hero chooser offers up to three core hero skills from `prepare-for-meeting`, `follow-up-after-call`, and `prioritize-accounts`. Later hero choosers may include previously tried workflows as repeat options plus at least one untried core workflow while any remain. Build the user-facing option labels and one-sentence descriptions from each skill's experience guidance; do not make preflight carry those descriptions, and do not include sample prompts inside the chooser. Once the user picks an option, treat the numbered choice as consent to try that workflow with its recommended default anchor. Record the selected skill in onboarding state, render that skill's compact first-run intro above the normal workflow output, and run the workflow unless the selected skill truly cannot proceed without one missing anchor. After that hero workflow is accepted, skipped, or deferred, offer the skill-owned next artifact action when useful, then return to the next demo workflow chooser.
-
-## Common Onboarding Message Frame
-
-Every onboarding message that is not complete must include primary content for the current step, then leave the user with one clear way to move forward. A natural final sentence, a compact action frame, or a self-contained numbered choice set can satisfy this requirement. Use the reference copy as the preferred onboarding voice and structure: preserve its core claims, ordering, and plain-language tone wherever the current setup state allows. Adapt only to reflect real source availability, skipped/deferred setup, installed plugins/connectors, or the user's current onboarding step. Do not revert to the older rigid `Next Step` cadence when a natural action question or selectable choice set is clearer, and do not introduce extra setup concepts not present in the reference copy.
-
-Use this compact action frame when the step has multiple questions, a durable approval gate, or a complex choice that needs an explicit label:
-
-```md
-**Next Step**
-{One clear action or question the user can answer now.}
-```
-
-`Next Step` is the action prompt, not the explainer. Put step results, workflow introductions, setup explanations, and teaching content in the primary content immediately before the frame, then keep `Next Step` short and decision-shaped. For simple one-question steps, a natural final sentence such as `Ready to set that up?` or `Should I set those up?` is preferred. For chooser steps where each numbered option is concrete and selectable, the list itself is the action prompt; do not add a redundant `Next Step` heading or final sentence such as `Pick 1, 2, or 3`. Do not let the whole onboarding message be only a next-step frame.
-
-When the current step has open questions, make resolving those questions its own visible onboarding step before introducing the next workflow. Include the questions in `Next Step` instead of leaving them only in the preceding step summary. Use a compact numbered list when there is more than one question. If the questions can be handled by sensible defaults, include the default escape hatch inside the relevant numbered item, including which defaults will be used and which optional gaps will be skipped, deferred, or revisited later. Do not add a separate summary prompt after the numbered list when the list already contains the questions and default path. Do not combine meaningful source, preference, admin, approval, or skip/defer decisions with a first-time workflow introduction.
-
-When introducing a workflow, skill, or new onboarding concept for the first time in the current onboarding flow, introduce it in the primary content before the action close. For skills, use that skill's experience guidance. For onboarding concepts such as plugin memory or automations, briefly explain what it does, what inputs or state it can use, what output or result the user should expect, the recommended default action, and the option for the user to skip or provide a specific anchor instead. Keep the action close to the small action the user can take. Use the New Concept Introduction Pattern below whenever the current response enters a new concept.
-
-When showing user-facing sample prompts during onboarding, teach the preferred invocation pattern: start with `@Sales`, then state the sales job and the real anchor in natural language. Sample prompts should be realistic and seller-shaped, such as `@Sales prepare me for my Acme renewal call tomorrow.`, not placeholder-only prompts. If the prompt is meant to run against the user's actual context, fill it with a real meeting, call, account, or topic before showing it; otherwise label it as an example for later use.
-
-Do not create tiny transition turns whose only purpose is asking whether to introduce the next onboarding item. If the current step has completed and the next item is an intro-only step, add a short transition note such as `Okay, I've saved that. Now let's move on to the next step.` or `Great, moving on.`, then render the next concept introduction or next hero choice card in the same response with one final action close. Preserve real approval gates: first-time plugin memory should introduce `## Plugin Memory` before the first save and then save accepted narrow, low-risk workflow preferences by default; Sales automations should use a short transition sentence and ask whether to set up the recommended automations without a separate `## Sales Plugin Automations` heading; externally visible writes, docs, Slack/email drafts, high-risk memory, and connector setup still need their own approval when the step requires it.
-
-In the hero workflow loop, user replies such as `next workflow`, `move on`, `next demo`, `next guided workflow`, `continue`, or similar mean "show the next demo workflow choices," not "auto-run the next sequential skill." Treat that reply as acceptance or skip/defer of the current artifact as appropriate, save any accepted narrow reusable preference if plugin memory policy allows it, clear the active selected skill for the next choice, and enter `choose_next_hero_prompt`. Do not run another hero workflow until the user picks `1`, `2`, or `3`, names a workflow, or gives a specific meeting, call, or question anchor.
-
-Exactly one final visible CTA or self-contained choice set should appear in the response. FYIs, readbacks, and automation breadcrumbs should be separate from the final action close and should not compete with it.
-
-When onboarding is done, use this terminal frame:
-
-```md
-**Onboarding Complete**
-Congrats, you're set up. Sales now has the context, preferences, automations, and example workflows it needs to be useful in your day-to-day work.
-
-You've tried the three core workflows: Prepare For Meeting, Follow Up After Call, and Prioritize Accounts.
-
-You can keep going through the rest of the Sales skills one by one to get familiar with what else is available. Next up, I recommend **Analyze Account Signals**, which helps find recent account and customer signals for an active account, owner portfolio, or watchlist.
-
-**Next Step**
-Say `okay` and I'll introduce Analyze Account Signals, or start a new thread and include `@Sales` with any real Sales request.
-```
-
-Do not render `Next Step` as an H1/H2/H3 heading. Keep implementation details out of user-facing onboarding. Do not narrate file structures, cache paths, state paths, context-file mechanics, internal probe names, provider taxonomy, or raw state names unless the user explicitly asks for implementation details.
-
-## New Concept Introduction Pattern
-
-Use this pattern when the response enters a new skill or concept. It may appear after a short recap of the just-completed step when the only alternative would be a low-value `Want me to introduce...` transition. Keep exactly one final visible action close for the current actionable decision. Use a `**Next Step**` label only when the decision needs the label.
-
-```md
-## {Concept Name}
-
-{Concept explanation.}
-
-Example prompt: `@Sales {realistic prompt with a real or clearly illustrative anchor}.`
-
-**Next Step**
-{One compact action, approval question, or anchor request.}
-```
-
-For plugin memory, use `## Plugin Memory` and start the body with `Plugin memory is how the Sales plugin remembers approved preferences, source-of-truth links, examples, and team conventions for future Sales workflows.`
-
-For Sales automations, do not render a separate `## Sales Plugin Automations` heading. Start with a simple explanation that these are recurring Codex tasks for company research and tips.
-
-For skill introductions, use the skill display name as the heading, such as `## Prepare For Meeting`, not the machine skill id. Because the heading already names the skill, the body should start with `This skill...`, not `{Skill Name} is a skill in the Sales plugin...`. Do not render user-facing CTAs with code-styled skill names such as `follow-up-after-call`; say `Follow Up After Call`.
-
-User-facing bullets should be polished sentence fragments or full sentences. Do not start bullets with lowercase words unless they are grammatically continuing an introductory sentence. Prefer capitalized fragments such as `A quick call summary`, `Next steps and owners`, and `External follow-up copy when relevant`. Bullets may omit periods when they are fragments; full-sentence bullets should use periods consistently.
-
-## Progress Checklist Contract
-
-Mirror this user-visible roadmap into the built-in thread task list whenever available. The labels should describe the overall onboarding flow, not implementation substeps.
-
-1. Orientation
-2. Connector setup/confirmation
-3. Sales automation setup
-4. First hero prompt
-5. Other hero prompts
-
-Do not replace this checklist with local work-plan labels such as `Load Sales memory policy`, `Search useful resources`, `Draft proposed Sales context`, `Ask for approval to save`, `Probe active connectors`, `Create prep doc`, or `Attach calendar artifact`.
-
-Before every onboarding response, refresh the checklist from current onboarding state after applying actions from that turn. Do not reuse a stale checklist from an earlier thread turn. If the response completed, skipped, or deferred a step, mark that step accordingly and mark the next unresolved visible step as `in_progress` before sending. This is especially important late in onboarding after automation setup, draft creation, call-follow-up iteration, prioritize-accounts handoffs, or completion handoffs.
-
-Treat **core onboarding** as complete after the first two setup items after Orientation are resolved: connector setup/confirmation and Sales automation setup. Until core onboarding is complete, ordinary `@Sales` workflow prompts should answer only when they are urgent or directly requested, then direct the user back to complete the next unresolved core setup item. After core onboarding is complete, Sales can continue through the guided workflows or handle ordinary Sales work without a core-setup warning.
-
-Treat a plain `yes`, `yep`, `ready`, `get started`, or similar reply after a next-step CTA as approval to perform the current visible next step unless the user names a different step. Treat `skip`, `skip for now`, `not now`, or similar as approval to mark the current visible step skipped or deferred when that step allows skipping, then continue to the next step.
-
-## Step 1: Orientation
-
-- ID: `start_sales_onboarding`
-- Type: `linear`
-- Parent: none
-- State field: `orientation`
-- User-facing goal: Introduce the Sales plugin, explain what onboarding will do, and get to the first setup action quickly.
-- Entry condition: Direct onboarding trigger, first-run setup, "what can Sales do?", or resumed onboarding with orientation missing.
-- Stay here when: No state files exist and the first orientation has just been rendered.
-- Exit when: The user approves the next setup step, provides saveable Sales context, or state already records that orientation was shown.
-- Completion: Record orientation as shown only after state exists.
-- Next: Step 2, `confirm_connected_sources`.
-- Output contract: Render the reference copy below as the preferred copy spine. Preserve the core claims, order, and plain-language tone while adapting only to the user's current setup state. Do not add connector results, scaffolding, automation setup, discovery, or workflow output to this first message.
-- Reference copy spine:
-
-```md
-The Sales plugin improves Codex at sales-related work by pulling in relevant business context and leveraging expertly created workflow logic.
-
-**How it works**
-- It automatically improves your experience for a range of sales-related work.
-- You can also explicitly use it by adding `@Sales` to your Codex messages.
-
-**What it can do**
-1. Level up your meetings by building a personalized brief beforehand with exactly what you need to know and what needs to get done, then helping with follow-up.
-2. Help you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-3. Help you win deals by prepping customer-focused competitive briefs, positioning your offerings, and understanding new features so you have the most up-to-date knowledge.
-
-These workflows require access to your CRM, meeting notes, Slack, email, docs, and other context to perform well. Ready to set that up?
-```
-
-- Do not: initialize state, inspect connectors, audit conflicts, create threads, install automations, start discovery, or run workflows before this first message.
-
-## Step 2: Confirm Active/Missing Sources
-
-- ID: `confirm_connected_sources`
-- Type: `linear`
-- Parent: none
-- State field: `connector_confirmation`
-- User-facing goal: Give the user confidence about which source categories look usable and ask only for help where a source is ambiguous, missing, or needs IT/admin enablement.
-- Entry condition: Orientation is complete or the user explicitly asks to continue setup.
-- Stay here when: Source classification has not run or the connected-sources result has not been shown.
-- Exit when: Every source category is recorded as `active`, `missing`, `declined`, `deferred`, `skipped`, or `not_applicable`, or the user chooses to continue with known gaps.
-- Completion: Store onboarding confirmation labels and the resolved source routes in `onboarding-state.json`; do not write connector readiness to `user-context.md`.
-- Next: Step 2A, `resolve_source_questions`, when any source category needs user input; otherwise Step 3A, `introduce_sales_automations`.
-- Output contract: Return a concise standalone connected-sources result with one setup-ready heading line, bold bullets for available sources, one sentence naming remaining source gaps, and one clear setup question. Active connector/app sources are informational fallback routes, not winners, when a related plugin is installed or installable. This setup step, not preflight, inspects the environment, calls `functions.list_available_plugins_to_install` once for the setup pass, and writes resolved routes for each source category. Ask only when there are multiple mutually exclusive installed source tools, an installable related plugin needs approval, no source was found, or the category needs IT/admin help. Do not ask for a docs preference merely because both Google Drive and Notion are available; record both routes and let workflows use both. For missing sources and connector-covered sources, prefer plugin setup, then direct app/connector setup, then manual exports or pasted context. Do not introduce or run the first workflow in the same response when source questions remain. When the common preferred sources match the current session, use this style:
-
-```md
-After some searching, it looks like you're already set up with Google Calendar, Gmail, Slack, Google Drive, Notion, and Spreadsheets.
-
-There are a few more connectors we can add to help Sales work as well as possible:
-
-1. **CRM** provides account status, deal stage, owners, contacts, pipeline risk, and what needs to happen next.
-   Options: Agentforce Sales, HubSpot, Close, Zoho, Pipedrive.
-2. **Meeting notes** improves meeting prep and follow-ups, and grounds CRM updates in real customer context.
-   Options: Zoom, Granola, Otter.ai, Fireflies, Outreach, Rox.
-3. **Data enrichment** fills in company and contact details when account context is thin, so prioritization and outreach are sharper.
-   Options: ZoomInfo, Clay, HG Insights, Rox, Apollo, Actively, Meticulate.
-4. **Contracts** surfaces signature status, approval routing, and deal paperwork blockers before close or renewal conversations.
-   Options: Docusign.
-
-You'll be prompted to install and authenticate plugins or connectors for whichever you select. You can skip anything now; Sales will ask again later only when a workflow would materially benefit from one of these systems.
-```
-
-Do not render separate `Active sources`, `Sources I need your help with`, or `Missing sources and practical impact` headings unless the source result is too unusual for the concise shape.
-- Reference copy: Active and appropriate sources are FYI only. Do not preflight-read installed or active sources just to prove they work. For each category, compare `saved_source_preferences` and `preferred` from preflight, `preferred_plugins`/`preferred_apps` from `source-category-config.json`, `.app.json` ids for preferred apps, the session `Available plugins` and `Available skills` blocks, and the single `functions.list_available_plugins_to_install` result for the setup pass. If one or more related plugins are installed with visible skill/tool surfaces, write them to `connector_confirmation` as active routes under `routes[]` with `source_kind: plugin`, `skill_surface`, plugin details, and evidence fields. If no related plugin is active but a related plugin is installable, highlight it as the recommended setup action even when an app or connector route is already active; explain that Sales should prefer the plugin because it can add dedicated Sales workflow support, and keep the existing app/connector route as fallback if the user defers or install visibility is pending. Rank plugin candidates by saved preferences first, then configured `preferred_plugins`/`preferred_apps`, then `.app.json` connector-id intersections. If no related plugin is installed or installable and exactly one plausible app or connector is installed or available, write it as active with `source_kind`, `skill_surface`, and route details when available, then let the workflow's first real read handle auth, query, or schema issues. If multiple plausible source tools are complementary rather than mutually exclusive, such as Drive and Notion for docs, write multiple active routes instead of asking the user to pick a winner. If multiple plausible source tools are mutually exclusive for the same category, ask the user which one to prefer and write the selected route only after resolving its plugin, app, connector, or manual route. Missing sources should explain practical workflow impact and list specific IT/admin options. Be prescriptive about pilot setup: Salesforce/CRM unlocks account, opportunity, pipeline, owner, forecast, and contact context; Calendar unlocks upcoming meetings, invite details, attendees, timing, and daily prep; Zoom, Granola, Fireflies, or another meeting-notes source unlocks transcripts, customer language, commitments, objections, and follow-up evidence; Slack/internal messaging unlocks account-team coordination and internal blockers; Drive/Docs/Notion unlock account plans, MAPs, discovery notes, source-of-truth docs, and enablement assets; agreement sources unlock contract status, signature progress, approval routing, and deal-paperwork context.
-- Data enrichment copy: Data enrichment adds missing company, contact, account, firmographic, technographic, and market-signal details from trusted third-party or internal data sources. It cleans up account lists, discovers contacts, fills thin CRM context, and scans market signals when the core CRM does not have enough detail. Preferred options are ZoomInfo, Clay, HG Insights, Rox, Apollo, Actively, or Meticulate. If none is available in this Codex environment, say that clearly and ask the user to contact IT or a workspace admin to see whether access can be enabled.
-- Next-step copy: If one or more source categories need user input, make the `Next Step` only about resolving those questions. If no source questions need input, move directly into Step 3A with a short transition note and the Sales automation introduction; do not ask whether to introduce it.
-
-### Step 2A: Resolve Source Questions
-
-- ID: `resolve_source_questions`
-- Type: `optional`
-- Parent: `confirm_connected_sources`
-- State field: `connector_confirmation`; optional user preference saves go to `user-context.md`.
-- User-facing goal: Let the user answer or skip meaningful source choices before Sales introduces the next workflow.
-- Entry condition: Connected-source confirmation found multiple plausible apps, no app, or IT/admin source gaps that need a user choice, preference, or acknowledgement.
-- Stay here when: The user has not answered, skipped, deferred, or accepted defaults for the source questions.
-- Exit when: The user provides the preference/source info, says to continue with defaults, skips, or defers the source questions.
-- Completion: Save any clear low-risk source preference to plugin memory using `plugin-memory.md`; record skipped/deferred/default handling in `onboarding-state.json`. A user-selected source may be recorded as `active` when its plugin, app, or connector is installed, available, surfaced, or active in the current environment with enough evidence for the route kind. The state entry must include the resolved route fields such as `source_kind`, `skill_surface`, and plugin/app/connector/manual details when available. Do not create durable connector-readiness proof.
-- Next: Step 3A, `introduce_sales_automations`.
-- Output contract: Keep primary content focused on the source result and practical impact. Put only the unresolved source questions and skip/defer escape hatch in the action close. Do not ask for a docs preference when multiple docs routes can be used together:
-
-```md
-Please select any connectors you want to set up now:
-
-1. **CRM** provides account status, deal stage, owners, contacts, pipeline risk, and what needs to happen next.
-   Options: Agentforce Sales, HubSpot, Close, Zoho, Pipedrive.
-2. **Meeting notes** improves meeting prep and follow-ups, and grounds CRM updates in real customer context.
-   Options: Zoom, Granola, Otter.ai, Fireflies, Outreach, Rox.
-3. **Data enrichment** fills in company and contact details when account context is thin, so prioritization and outreach are sharper.
-   Options: ZoomInfo, Clay, HG Insights, Rox, Apollo, Actively, Meticulate.
-4. **Contracts** surfaces signature status, approval routing, and deal paperwork blockers before close or renewal conversations.
-   Options: Docusign.
-
-You can skip anything now; Sales will ask again later only when a workflow would materially benefit from one of these systems.
-```
-
-After the user answers, install confirmed plugin or connector candidates one at a time, save any clear source preferences or accepted skips, then move directly into the Sales automation introduction. Do not ask whether to introduce it. For routine source setup preferences in onboarding, do not render the full `Saved Sales Plugin Memory.` recap or `Saved today` list; use only the short transition line below, then render Step 3A in the same response:
-
-If the user confirms an installable plugin or connector candidate returned by `functions.list_available_plugins_to_install`, call `functions.request_plugin_install` with `action_type: "install"`, the returned candidate `tool_type`, the returned candidate `id`, and a concise reason such as `Use Agentforce Sales as the preferred Sales source for CRM.` Pass the returned `tool_type` directly; it may be `plugin` or `connector`. Do not call `request_plugin_install` in parallel with any other tool. If multiple related plugin candidates tie after saved preference order, configured `preferred_plugins`/`preferred_apps` order, and connector-id matching, ask the user to choose; if no related plugin candidate exists, say so and offer admin/app/manual fallback. If plugin install fails, setup fails, or install succeeds but the plugin-owned skills or tools are not visible yet, record the category as `needs_confirmation`, `deferred`, `deferred_environment_api_limitations`, or `skipped_for_now`; do not mark it active and do not treat the failed setup as a durable decline. Keep plugin-first setup eligible for retry in future workflows when that source category matters. If the user explicitly declines a plugin, suppress that plugin in the current workflow and fall back to the next related plugin candidate, existing connector/app route, or manual setup for that category; save a future `do not use` rule only when the user asks for durable avoidance.
-
-If the user selects or confirms an installed or available plugin, app, or connector, treat it as usable for setup purposes only when the route evidence matches `source-category-runtime.md`, write the resolved route to `connector_confirmation`, and transition without a proof read. If the selected source is missing, use the matching workflow source category and native plugin/app/connector path: load any relevant helper skill from `source-category-config.json`, use tool discovery or the exposed app tools if needed, and run the smallest safe read-only action for that app when a workflow needs the source. If the read triggers auth/setup, let that flow complete before continuing and then write the resolved app/connector route. If no read action or setup route is exposed, keep the category unresolved and ask the user whether to install/connect/authorize it, ask IT/admin to enable it, defer/skip the category, or proceed with manual/exported context. Adapt the source list in the transition to the resolved active/default sources, but when the standard onboarding source set applies, use this copy spine:
-
-```md
-Okay, that finishes source setup for now. Sales will use Google Calendar, Gmail, Slack, Google Drive, Notion, and Spreadsheets, and can revisit CRM, meeting notes, enrichment, or contracts when a workflow needs them.
-
-Next, we can set up some simple automations to ensure Sales keeps getting better for you.
-
-1. **Sales Company Research:** Deeply searches across your company context and saves key resources to improve speed and correctness. It runs weekly on Mondays at 9:00 AM local time.
-2. **Sales Tips:** Looks at how you've been using Codex and suggests one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time.
-
-Should I set those up? They'll run in a different thread and notify you when they have something to review.
-```
-
-- Do not: call installed or active connectors during onboarding merely to prove they work, call `request_plugin_install` without a returned install candidate, call `request_plugin_install` in parallel, persist `available`, `verified`, or automatic `blocked` as durable state, expose raw connector ids, or list speculative enrichment vendors beyond the configured preferred plugins/apps.
-
-## Step 3: Sales Automations
-
-Sales plugin automations use a real approval gate: first introduce the concept and recommended onboarding automations, then set them up only after the user approves setup. This is the second core onboarding setup item after connector setup/confirmation. Sales Daily Meeting Prep is intentionally not part of this initial bundle; offer it later after the user has tried and iterated on Prepare For Meeting.
-
-### Step 3A: Introduce Sales Automations
-
-- ID: `introduce_sales_automations`
-- Type: `linear`
-- Parent: `sales_automations`
-- State field: `automations_intro`
-- User-facing goal: Explain recurring Codex Sales tasks before installing them.
-- Entry condition: Connected-source confirmation and any source-question handling are complete, skipped, or deferred.
-- Stay here when: The Sales automation concept has not been introduced.
-- Exit when: The user says `okay`, asks to set up all or some automations, skips, or defers.
-- Completion: Record `automations_intro.status = shown` or equivalent once the overview has been shown.
-- Next: Step 3B, `setup_sales_automations`.
-- Output contract: Read `../plugin-author-config/automation-config.md` before rendering this step and list every default onboarding automation with its configured `Name` and `Frequency`. Never list an automation without its run cadence. Put the automation explanation in primary content and end with the user's setup decision. Use this copy spine:
-
-```md
-Next, we can set up some simple automations to ensure Sales keeps getting better for you.
-
-1. **Sales Company Research:** Deeply searches across your company context and saves key resources to improve speed and correctness. It runs weekly on Mondays at 9:00 AM local time.
-2. **Sales Tips:** Looks at how you've been using Codex and suggests one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time.
-
-Should I set those up? They'll run in a different thread and notify you when they have something to review.
-```
-
-- Do not: install or repair automations in the same response that first introduces automations.
-
-### Step 3B: Set Up Sales Automations
-
-- ID: `setup_sales_automations`
-- Type: `linear`
-- Parent: `sales_automations`
-- State field: `automations.weekly_sales_company_research`, `automations.daily_sales_tips`, and `initial_resource_discovery`
-- User-facing goal: Set up recurring Sales help and keep plugin memory fresh without asking the user to manually pre-populate durable preferences.
-- Entry condition: The user approved Sales automation setup after the automation introduction.
-- Stay here when: Any configured default onboarding automation is not installed or readback is incomplete, the one-time Sales Company Research search has not been kicked off in the pinned thread, research still needs to save high-confidence results or present review-only candidates, or the fallback Sales Tips automation has not been read back after environment/API limitations blocked the full setup.
-- Exit when: `weekly_sales_company_research` and `daily_sales_tips` are installed or explicitly declined/skipped/deferred, and the one-time Sales Company Research search in the pinned automation thread has been started, completed, saved high-confidence eligible entries, presented review-only candidates, found no useful additions, or been skipped/deferred. Also exit when environment/API limitations block the full default setup and the fallback Sales Tips automation has been persisted and read back.
-- Completion: Record canonical automation id/thread metadata under each matching `automations.<automation_id>` object, record the one-time research kickoff/result under `initial_resource_discovery`, and mark core onboarding complete when connector setup/confirmation is also resolved. Do not send or require a one-time Sales Tips kickoff during onboarding; Sales Tips should run on its normal schedule after setup. In the fallback regular tips path, record `weekly_sales_company_research` and `initial_resource_discovery` as skipped or deferred because of `environment_api_limitations`, record the fallback Sales Tips automation metadata, and mark core onboarding complete when connector setup/confirmation is also resolved.
-- Next: Step 4A, `choose_first_hero_prompt`.
-- Output contract: Set up the approved automations by following the short checklist in `automation.md`, then report the result and stop at the first workflow chooser. Do not load hero workflow skill files or run a demo workflow until the user picks one.
-
-Pre-tool gate: before the first automation, thread, kickoff, or onboarding-state mutation in this step, open `automation.md` and `../plugin-author-config/automation-config.md`. Identify only the two default onboarding automations, their configured names, frequencies, prompts, target thread titles, model/thinking values, readback requirements, and kickoff requirements. Then execute the `automation.md#Fast Setup Checklist` directly.
-
-Install or repair only `weekly_sales_company_research` and `daily_sales_tips`. Each needs a dedicated pinned thread, heartbeat automation, readback, and state update. Sales Company Research also needs the one-time setup kickoff in its pinned thread. Sales Tips must not be kicked off immediately during onboarding; let its first check-in happen on the normal weekday schedule. Cron automations, automations without a dedicated pinned target thread, automations without readback, or automations whose thread metadata has not been recorded in onboarding state do not satisfy this step. The current onboarding thread must stay unchanged; only the dedicated automation target threads created or reused by `automation.md` may be renamed, pinned, and attached to heartbeat automations. The initial Sales Company Research kickoff belongs in the pinned `Sales Company Research` thread, not in the main onboarding thread. Tell the user the automation threads are visible in the Pinned section when setup succeeds.
-
-If environment or API limitations block the full default automation bundle, use the fallback daily tips path from `automation.md`: skip Sales Company Research, skip the initial context-gathering kickoff, do not ask the user for context links as part of automation setup, and install or repair only the regular `daily_sales_tips` automation using its configured prompt and cadence. Report this as a fallback rather than a full automation setup. Do not say Sales Company Research is ready; say it was skipped or deferred because this environment could not support the full automation setup.
-
-When setup/readback succeeds, use this shape:
-
-```md
-Automations are set up and read back cleanly.
-
-- **Sales Company Research**: deeply searches across your company context and saves key resources to improve speed and correctness. It runs weekly on Mondays at 9:00 AM local time. I also kicked off the first research run in the pinned **Sales Company Research** thread.
-- **Sales Tips**: looks at how you've been using Codex and gives you one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time, starting with its next scheduled run.
-
-{Optional company research FYI from the rules below.}
-
-Core onboarding is done. Pick a workflow to try first:
-
-1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-
-```
-
-When the fallback Sales Tips setup succeeds, use this shape:
-
-```md
-The full Sales automation setup hit an environment/API limitation, so I used the fallback path and set up Sales Tips instead.
-
-- **Sales Tips**: looks at how you've been using Codex and gives you one practical Sales workflow to try next. It runs weekdays at 9:00 AM local time, starting with its next scheduled run.
-- **Skipped for now**: Sales Company Research was not set up in this environment, so I skipped the context-gathering kickoff and did not ask you for source links here.
-
-Core onboarding is done with the fallback automation. Pick a workflow to try first:
-
-1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-
-```
-
-Do not compress the three hero choices into one inline sentence such as `Pick one to try first: 1 ... 2 ... 3 ...`; each numbered option must be on its own line as `1. **{Name}:** {brief description}`. The numbered choice set is the final action prompt by itself; do not add a `Next Step` heading or a redundant final sentence such as `Pick 1, 2, or 3`.
-
-After automation setup or readback, move directly into the first hero prompt choice. Mention the research thread only when readback confirms saved entries or review-only candidates that need user attention. If state or readback confirms entries were saved, use: `FYI: The pinned thread **"{thread_title}"** saved high-confidence Sales memory resources and listed what changed so you can edit or remove anything later.` If state or readback confirms review-only candidates exist, use: `FYI: The pinned thread **"{thread_title}"** has Sales memory candidates for you to review before saving.` Only say saved entries or proposals exist when state or readback confirms them.
-
-When moving from automation setup to the first hero prompt, render the choice step instead of assuming Prepare For Meeting:
-
-```md
-Automations are set up and read back cleanly.
-
-Core onboarding is done. Pick a workflow to try first:
-
-1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-
-```
-
-Keep automation breadcrumbs, readback cards, and created-card text visually separate from the choice set. Do not put tool-card text in the same paragraph as the chooser. The numbered choice set is the final action prompt; do not add a second CTA.
-
-- Do not: use `multi_agent_v1.spawn_agent`, ad hoc `codex_app.create_thread` calls, ad hoc projectless Codex threads, or repeated background-thread retries for first-run company research outside the dedicated automation target threads created by `automation.md`. Do not pause, delete, unpin, or clean up automations without explicit user approval.
-
-## Step 4: First Hero Prompt
-
-This is the first value demonstration. Onboarding lets the user choose which of the three core hero workflows to try first: `prepare-for-meeting`, `follow-up-after-call`, or `prioritize-accounts`. The onboarding flow owns the choice step and sequencing; each skill's experience guidance owns the skill introduction, starter prompts, anchor rules, and normal next-step candidates.
-
-Hero demos use a repeatable artifact loop: choose a workflow, run it, ask whether to improve the output, optionally take the skill-owned next action after the user accepts, then return to the next demo workflow chooser. Track the active loop with status-only fields such as `hero_workflow.current_skill` and `hero_workflow.current_status` when those fields are available. Do not create growing history arrays or store artifact output in onboarding state. Suggested statuses are `selected`, `running`, `output_delivered`, `reviewing`, `accepted`, `action_offered`, `action_completed`, `skipped`, and `deferred`.
-
-### Step 4A: Choose First Hero Prompt
-
-- ID: `choose_first_hero_prompt`
-- Type: `linear`
-- Parent: `first_hero_prompt`
-- State field: `hero_prompt_choice`; compatibility field: `meeting_prep_prompt`
-- User-facing goal: Let the user choose the first Sales workflow they want to see, with three concrete, realistic options.
-- Entry condition: Core onboarding is complete: connector setup/confirmation is resolved and Sales automation setup is installed, skipped, declined, or deferred.
-- Stay here when: No first hero workflow has been selected, skipped, or deferred.
-- Exit when: The user picks `1`, `2`, or `3`, names a specific meeting/call/question, asks for a specific core workflow, skips, or defers the first hero prompt.
-- Completion: Record `hero_prompt_choice.status = selected|skipped|deferred`, `hero_prompt_choice.selected_skill`, any `hero_prompt_choice.selected_anchor` supplied by the user, and the three offered skills in `hero_prompt_choice.last_offered_skills`.
-- Next: Step 4B, `introduce_selected_first_hero`, only when the user asked to learn about a selected workflow without running it, or Step 4C, `run_selected_first_hero`, when the user picked a numbered option or gave enough intent to try the workflow.
-- Output contract: Load `skills/prepare-for-meeting/SKILL.md`, `skills/follow-up-after-call/SKILL.md`, and `skills/prioritize-accounts/SKILL.md` for inline experience guidance. Use each skill's first-run and starter guidance to render a short choice card with exactly three options. Do not invent a fourth option. Each option should include only the display name and one sentence of value. Do not include sample prompts in the chooser; sample prompts belong in skill introductions, the marketplace prompt list, or the index skill. Use this shape:
-
-```md
-Now that setup is done, pick the first Sales workflow you want to try:
-
-1. **Prepare For Meeting:** Creates a concise brief for an upcoming customer or high-value sales meeting.
-2. **Follow Up After Call:** Turns a recent call or notes into a follow-up package.
-3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.
-```
-
-- Do not: introduce or run one of the three skills in the same response as the choice card unless the user already supplied an unambiguous workflow and anchor before the choice step was shown. Do not render the choices as an inline sentence such as `Pick one to try first: 1 ... 2 ... 3 ...`. Do not include example prompts in the chooser. Do not add a redundant final sentence telling the user to pick a number when the numbered options are already the action prompt. When the user later picks `1`, `2`, or `3`, treat that reply as consent to run the selected workflow with the recommended default anchor, not as a request for another intro-only turn.
-
-### Step 4B: Introduce Selected First Hero Workflow
-
-- ID: `introduce_selected_first_hero`
-- Type: `linear`
-- Parent: `first_hero_prompt`
-- State field: `hero_prompt_choice.selected_skill` and `skill_experience.<selected_skill>.introduced_at`
-- User-facing goal: Explain the selected first hero workflow before asking the user to run it.
-- Entry condition: The user selected or clearly implied one of `prepare-for-meeting`, `follow-up-after-call`, or `prioritize-accounts`.
-- Stay here when: The selected skill has not been introduced.
-- Exit when: The user says `okay`, provides the required anchor, skips, or defers this workflow.
-- Completion: Record `skill_experience.<selected_skill>.introduced_at`.
-- Next: Step 4C, `run_selected_first_hero`.
-- Output contract: Load the selected skill's experience guidance and use its first-run intro, starter prompts, and anchor rules. Introduce the workflow with the New Concept Introduction Pattern: use the skill display heading, start fresh with `This skill...`, include a realistic `@Sales` example, and keep `Next Step` to a compact action prompt using that skill's default anchor behavior. For Prepare For Meeting, ask the user to say `okay` to pick a meeting or name one; make clear that `okay` means the skill will search `calendar` for candidate meetings before asking for manual meeting details. For Follow Up After Call, ask the user to say `okay` so the skill can search `calendar` and `meeting_notes` for recent call candidates, or name one; do not ask for pasted notes/transcripts until those source categories are unavailable or yield no plausible candidates. For Prioritize Accounts, ask the user to say `okay` to prioritize open pipeline by default, or name an account list, territory, ICP, or planning focus.
-- Use this step only when the user explicitly asks to learn about the workflow before running it, or when the selected workflow cannot proceed until the user provides one missing anchor. If the user picked `1`, `2`, or `3` from the hero chooser, skip the intro-only turn and proceed to Step 4C.
-- Do not: run the selected skill in the same response as its introduction when you are explicitly asking for a missing anchor.
-
-### Step 4C: Run Selected First Hero Demo
-
-- ID: `run_selected_first_hero`
-- Type: `linear`
-- Parent: `first_hero_prompt`
-- State field: `skill_experience.<selected_skill>.first_tried_at`; compatibility field: `meeting_prep_run` when the selected skill is `prepare-for-meeting`
-- User-facing goal: Run the selected first hero workflow and show one useful seller-facing output without a separate prompt-confirmation step.
-- Entry condition: The selected skill has been introduced and the user approved running it, the user provided the required anchor, or the user picked `1`, `2`, or `3` from the hero chooser and the selected skill can run with its recommended default anchor.
-- Stay here when: The workflow is running, blocked on source auth, waiting for the smallest manual fallback needed by the selected skill, or no viable anchor exists.
-- Exit when: The output has been delivered or the user skips this workflow.
-- Completion: Mark `skill_experience.<selected_skill>.first_tried_at`; compatibility state may also record `meeting_prep_run` completed when the selected skill is `prepare-for-meeting`.
-- Next: Step 4D, `review_and_iterate_first_hero`.
-- Output contract: When the user picks `1`, `2`, or `3` from the hero chooser, says yes/okay/continue after the selected skill introduction, or names a specific meeting, call, account, or question, apply that skill's Anchor Rules and run the workflow directly. If the selected skill has not been introduced yet, render its compact first-run intro section immediately above the normal workflow output. If the user names a specific anchor, use that anchor. Only ask a follow-up if no good anchor can be found or multiple options are genuinely ambiguous.
-- Demo result contract: The first onboarding hero result should begin with one short unheaded transition sentence, then the actual output, then one compact feedback action close. For Prepare For Meeting, use this shape: `Here's your meeting prep, using {brief context about the selected meeting, source coverage, and any assumptions Sales made}.` For Follow Up After Call, briefly say which call was chosen and whether it is internal or external. For Prioritize Accounts, briefly say which account universe or default planning scope was used and any major source assumption. Keep the preface high-level and beginner-friendly for someone who may have no prior knowledge of what an agent does, but do not use a `What Happened`, `Recap`, or other walkthrough heading by default. If the user asks how Sales produced the result, use the on-request walkthrough guidance below.
-- Ending:
-
-```md
-Is there anything you'd change to make this clearer or more useful, or should we show the next demo workflow choices?
-```
-
-- Do not: offer doc creation, calendar attachment, plugin memory setup, automations, or the next workflow in the same response as the first output. Do not render that feedback CTA as an H2/H3 heading.
-
-### Step 4D: Review And Iterate
-
-- ID: `review_and_iterate_first_hero`
-- Type: `repeatable`
-- Parent: `first_hero_prompt`
-- State field: `first_guided_workflow_review`
-- User-facing goal: Let the user shape the first output before saving preferences or moving on.
-- Entry condition: The first selected hero workflow output has been delivered.
-- Stay here when: The user gives feedback, asks for changes, or has not accepted the result.
-- Exit when: The user says the output looks good, asks to move on, skips, or the iteration is otherwise accepted.
-- Completion: Record review completion and any accepted reusable preference candidate.
-- Next: Step 4E, `save_plugin_memory`, if a reusable preference emerged; otherwise Step 4F, `offer_first_hero_action_or_next_demo`.
-- Output contract: If the user gives feedback, revise the draft and ask only:
-
-```md
-How does that look? Any other changes, or should we show the next demo workflow choices?
-```
-
-Do not ask whether the change applies only to this artifact or future outputs inside the iteration loop. Do not ask to save plugin memory on every iteration. Do not save plugin memory on every iteration. The final action close for an iteration must never be a narrow-preference save approval; offer another tweak, the selected skill's natural artifact action when applicable, or the option to show the next demo workflow choices. When the user accepts the revised output or asks to show the next demo workflow choices, inspect whether the accepted change is reusable. If a reusable preference emerged, introduce plugin memory in Step 4E if needed, summarize the preference, and save it by default when it is narrow, low risk, and clearly accepted. Broad, sensitive, ambiguous, or high-risk preferences always require explicit approval. After any required memory handling, offer the selected skill's natural next action in Step 4F before returning to the next demo workflow chooser.
-
-### Step 4E: Introduce Plugin Memory And Save Preference
-
-- ID: `save_plugin_memory`
-- Type: `optional`
-- Parent: `first_hero_prompt`
-- State field: `plugin_memory_intro`, `accepted_preference_memory`
-- User-facing goal: Teach plugin memory the first time a reusable preference emerges, then save approved memory.
-- Entry condition: The user accepted an iteration and there is a reusable preference candidate, or the user directly asks Sales to remember something during onboarding.
-- Stay here when: The first plugin-memory concept has not been introduced or the first save has not been completed/skipped.
-- Exit when: The preference is saved, declined, skipped, deferred, or judged not reusable.
-- Completion: Record `plugin_memory_intro.status = shown` and `accepted_preference_memory.status = saved|skipped|deferred|not_applicable`.
-- Next: Step 4F, `offer_first_hero_action_or_next_demo`.
-- Output contract: Use `plugin-memory.md` for the first-time save recap. Introduce memory with the New Concept Introduction Pattern: use `## Plugin Memory` and start fresh with `Plugin memory is how the Sales plugin remembers approved preferences, source-of-truth links, examples, and team conventions for future Sales workflows.` Explain that plugin memory stores durable preferences, trusted resources, source preferences, and team conventions. Mention that memory can include both source-of-truth resources and answer/style preferences, and that Sales Company Research can save high-confidence resource memory by default while surfacing uncertain candidates for review. Summarize the accepted preference, save it by default when it is narrow, low risk, and clearly accepted, then recap the exact saved preference.
-
-```md
-## Plugin Memory
-
-Plugin memory is how the Sales plugin remembers approved preferences, source-of-truth links, examples, and team conventions for future Sales workflows. You can ask anytime to save things like "use this Notion page as the source of truth," "prefer inline citations," "always include attendee teams," or "answer exec-facing briefs in this style."
-
-Saved Sales Plugin Memory.
-
-Saved today:
-- {accepted preference}
-```
-
-After the save recap, move directly into Step 4F with a short transition note. Only require explicit yes for broad, sensitive, ambiguous, high-risk, or externally visible preferences. Do not offer to create a prep doc after saving the preference unless the user explicitly asks for one or it is the skill-owned next action being offered in Step 4F.
-
-### Step 4F: Offer First Hero Action Or Next Demo
-
-- ID: `offer_first_hero_action_or_next_demo`
-- Type: `optional`
-- Parent: `first_hero_prompt`
-- State field: `hero_workflow.current_status` and the selected skill's action metadata when available.
-- User-facing goal: After the user accepts the first hero output, offer the most natural next action for that artifact, while keeping the onboarding path moving.
-- Entry condition: The first hero output has been accepted, skipped, or the user asked to move on; any required plugin-memory handling is complete or not applicable.
-- Stay here when: A skill-owned action such as creating a doc, drafting a Slack post, drafting an email, saving a source route, or creating CRM-ready notes has been offered and the user has not accepted, declined, or skipped it.
-- Exit when: The user accepts the action, declines it, skips it, or asks to show the next demo workflow choices.
-- Completion: Record `hero_workflow.current_status = action_offered|action_completed|skipped`.
-- Next: Step 5A, `choose_next_hero_prompt`.
-- Output contract: Load the selected skill's experience guidance and choose one concrete continuation from its normal next-step candidates. The action should be specific to the artifact just produced, not a generic onboarding CTA. Offer exactly one skill-owned action and the option to show the next demo workflow choices. If there is no useful artifact action, skip this step and go straight to Step 5A. Use this shape:
-
-```md
-Okay, I've saved that. Now let's move on to the next step.
-
-Want me to {skill-owned action, e.g. create a shareable prep doc or draft the internal Slack recap}, or should I show the next demo workflow choices?
-```
-
-- Do not: offer multiple artifact actions at once, offer docs/calendar/Slack/email actions before the user accepts the output, or skip the next demo workflow choices option.
-
-- Do not: ask the user to approve the same narrow preference repeatedly after plugin memory has already been introduced. Calendar attachment is explicit-user-request only.
-
-## Step 5: Other Hero Prompts
-
-After a hero workflow is accepted and any skill-owned artifact action is handled, onboarding returns to a chooser loop until `prepare-for-meeting`, `follow-up-after-call`, and `prioritize-accounts` have each been tried, skipped, or deferred. The chooser should show exactly three numbered options when at least three core options are available. Options may include previously tried hero workflows as repeat actions plus one or more untried workflows, so the user can repeat something useful without losing the onboarding path. Prefer at least one untried core workflow while onboarding is incomplete.
-
-### Step 5A: Choose Next Hero Prompt
-
-- ID: `choose_next_hero_prompt`
-- Type: `repeatable`
-- Parent: `other_hero_prompts`
-- State field: `hero_prompt_choice.last_offered_skills`, `hero_prompt_choice.selected_skill`, `hero_workflow.current_status`, and `skill_experience.<skill>.first_tried_at`
-- User-facing goal: Let the user choose the next Sales workflow to try, balancing useful repeats with untried core demo workflows.
-- Entry condition: The first hero workflow is accepted, skipped, or deferred, and at least one core hero skill remains untried, or the user asks to keep exploring hero workflows.
-- Stay here when: No hero workflow has been selected, skipped, or deferred for the current loop.
-- Exit when: The user picks a numbered option, names a specific meeting/call/question, asks for a specific core workflow, skips, or defers the current offer.
-- Completion: Record the offered skills in `hero_prompt_choice.last_offered_skills`, record `hero_prompt_choice.selected_skill`, and record any `hero_prompt_choice.selected_anchor` supplied by the user.
-- Next: Step 5B, `introduce_selected_remaining_hero`, only when the user asked to learn about a selected workflow without running it, Step 5C, `run_selected_remaining_hero`, when the user picked a numbered option or gave enough intent to try the workflow, or Step 6, `complete_onboarding`, when all core hero skills are tried/skipped/deferred and the user does not want another demo.
-- Output contract: Load each core hero skill's experience guidance. Render exactly three choices whenever possible. Choices can include the previous one or two workflows as repeat options, plus at least one untried core workflow while any remain. Preserve the core order `prepare-for-meeting`, `follow-up-after-call`, then `prioritize-accounts` unless the user's last artifact makes a different option clearly more relevant. The model owns the option descriptions by reading the skill experience guidance; preflight should only determine state and ordering. Use this shape:
-
-```md
-Pick the next Sales workflow you want to try:
-
-1. **{Core skill display name}:** {One-sentence value description from that skill's experience guidance}
-2. **{Core skill display name}:** {One-sentence value description from that skill's experience guidance}
-3. **{Core skill display name}:** {One-sentence value description from that skill's experience guidance}
-```
-
-- Do not: hardcode Follow Up After Call or Prioritize Accounts as the next demo when multiple options make sense. Do not offer only previously tried workflows while untried core workflows remain. Do not bury the three choices inside a prose sentence. Do not include example prompts in the chooser. Do not add a redundant final sentence telling the user to pick a number when the numbered options are already the action prompt. When the user later picks a numbered option, treat that reply as consent to run the selected workflow with the recommended default anchor, not as a request for another intro-only turn.
-
-### Step 5B: Introduce Selected Remaining Hero Workflow
-
-- ID: `introduce_selected_remaining_hero`
-- Type: `linear`
-- Parent: `other_hero_prompts`
-- State field: `hero_prompt_choice.selected_skill` and `skill_experience.<selected_skill>.introduced_at`
-- User-facing goal: Explain the selected remaining hero workflow before asking the user to run it.
-- Entry condition: The user selected or clearly implied one of the remaining core hero workflows.
-- Stay here when: The selected skill has not been introduced.
-- Exit when: The user says `okay`, provides the required anchor, skips, or defers this workflow.
-- Completion: Record `skill_experience.<selected_skill>.introduced_at`.
-- Next: Step 5C, `run_selected_remaining_hero`.
-- Output contract: Load the selected skill's experience guidance and use its first-run intro, starter prompts, and anchor rules. Introduce the workflow with the New Concept Introduction Pattern: use the skill display heading, start fresh with `This skill...`, include a realistic `@Sales` example, and keep `Next Step` to a compact action prompt using that skill's default anchor behavior.
-- Use this step only when the user explicitly asks to learn about the workflow before running it, or when the selected workflow cannot proceed until the user provides one missing anchor. If the user picked a numbered option from the hero chooser, skip the intro-only turn and proceed to Step 5C.
-- Do not: run the selected skill in the same response as its introduction when you are explicitly asking for a missing anchor.
-
-### Step 5C: Run Selected Remaining Hero Demo
-
-- ID: `run_selected_remaining_hero`
-- Type: `linear`
-- Parent: `other_hero_prompts`
-- State field: `skill_experience.<selected_skill>.first_tried_at`
-- User-facing goal: Run the selected remaining hero workflow and show one useful seller-facing output.
-- Entry condition: The selected skill has been introduced and the user approved running it, the user provided the required anchor, or the user picked a numbered option from the hero chooser and the selected skill can run with its recommended default anchor.
-- Stay here when: The workflow is running, blocked on source auth, waiting for the smallest manual fallback needed by the selected skill, or no viable anchor exists.
-- Exit when: The output has been delivered or the user skips this workflow.
-- Completion: Mark `skill_experience.<selected_skill>.first_tried_at`; compatibility state may also record `call_followup_intro` or `internal_navigation_intro` where needed by legacy traces.
-- Next: Step 5D, `review_and_continue_remaining_hero`.
-- Output contract: Apply the selected skill's Anchor Rules and run the workflow directly. If the selected skill has not been introduced yet, render its compact first-run intro section immediately above the normal workflow output. If the selected skill is Follow Up After Call, Search the user's calendar for recent calls through the `calendar` source category and search `meeting_notes` for recent call evidence before asking for manual call evidence. Suggest a real call even if it is not a customer call. Prefer customer/prospect/partner/account calls, and fall back to a large, important-looking meeting as recent as possible. If the selected skill is Prioritize Accounts, prioritize open pipeline by default unless the user named an account list, territory, ICP, or planning focus; if no viable account universe is available, ask for the smallest missing scope. If the selected skill is Prepare For Meeting, pick a strong upcoming meeting from `calendar` unless the user named one. Begin the output with one short unheaded transition sentence, then the actual output, then one compact feedback action close. Do not use a `What Happened`, `Recap`, or other walkthrough heading by default.
-- Follow-up CTA: After the output, ask for iteration first and stay in the review loop until the user accepts, asks to show the next demo workflow choices, or skips. Every iteration CTA should offer both paths: change the artifact or show the next demo workflow choices. Once accepted, offer the selected skill's natural next action in Step 5E before returning to Step 5A, or continue to Step 6 when no core demos remain and the user does not want another demo. If the accepted iteration introduced a reusable preference, apply `plugin-memory.md`: narrow, low-risk accepted preferences can be summarized and saved automatically after plugin memory has already been introduced; broad, sensitive, ambiguous, or high-risk preferences require explicit save approval.
-- Do not: show placeholder prompts, keep asking for pasted notes when a suitable default can be chosen, or make the next workflow appear without the chooser loop when more than one untried option remains.
-
-### Step 5D: Review And Continue Remaining Hero
-
-- ID: `review_and_continue_remaining_hero`
-- Type: `repeatable`
-- Parent: `other_hero_prompts`
-- State field: `skill_experience.<selected_skill>.first_tried_at` and any accepted preference memory
-- User-facing goal: Let the user improve the current hero output, then either save reusable preferences, take the skill-owned artifact action, or show the next demo workflow choices.
-- Entry condition: A remaining hero workflow output has been delivered.
-- Stay here when: The user gives feedback, asks for changes, or has not accepted the result.
-- Exit when: The user says the output looks good, asks to move on, skips, or the iteration is otherwise accepted.
-- Completion: Record review completion and any accepted reusable preference candidate; mark the selected skill tried, skipped, or deferred.
-- Next: Step 5E, `offer_remaining_hero_action_or_next_demo`.
-- Output contract: If the user gives feedback, revise the draft and ask:
-
-```md
-How does that look? Any other changes, or should we show the next demo workflow choices?
-```
-
-If a reusable preference clearly emerged and plugin memory has already been introduced, summarize and save narrow, low-risk accepted preferences after the user accepts or asks to show the next demo workflow choices. If plugin memory has not been introduced, use Step 4E's plugin memory introduction and save pattern first, then return to this loop. After memory handling, offer the selected skill's natural next action in Step 5E.
-- Do not: ask a separate follow-up question after the iteration CTA already offers change or move-on paths.
-
-### Step 5E: Offer Remaining Hero Action Or Next Demo
-
-- ID: `offer_remaining_hero_action_or_next_demo`
-- Type: `optional`
-- Parent: `other_hero_prompts`
-- State field: `hero_workflow.current_status` and the selected skill's action metadata when available.
-- User-facing goal: After the user accepts a later hero output, offer the natural skill-owned artifact action without trapping the user before the next demo workflow.
-- Entry condition: The current hero output has been accepted, skipped, or the user asked to move on; any required plugin-memory handling is complete or not applicable.
-- Stay here when: A skill-owned action has been offered and the user has not accepted, declined, skipped, or asked to move on.
-- Exit when: The user accepts the action, declines it, skips it, or asks to show the next demo workflow choices.
-- Completion: Record `hero_workflow.current_status = action_offered|action_completed|skipped`.
-- Next: Step 5A, `choose_next_hero_prompt`, if the user wants another demo or any core hero skills remain; otherwise Step 6, `complete_onboarding`.
-- Output contract: Load the selected skill's experience guidance and choose one concrete continuation from its normal next-step candidates. Offer exactly one skill-owned action and the option to show the next demo workflow choices. If there is no useful artifact action, skip this step and go straight to Step 5A or Step 6.
-
-```md
-Want me to {skill-owned action}, or should I show the next demo workflow choices?
-```
-
-- Do not: offer multiple artifact actions at once, offer docs/calendar/Slack/email actions before the user accepts the output, or omit the option to show the next demo workflow choices.
-
-## Step 6: Completion
-
-- ID: `complete_onboarding`
-- Type: `terminal`
-- Parent: none
-- State field: `status`
-- User-facing goal: Mark onboarding complete only when Sales is meaningfully ready for day-to-day use.
-- Entry condition: Steps 1 through 5 are complete, skipped, or deferred where allowed.
-- Stay here when: Completion criteria are not satisfied and the user has not explicitly asked to quiet onboarding.
-- Exit when: Completion criteria are satisfied, or the user asks to quiet onboarding.
-- Completion: Set `status` to `complete` only when the criteria below are satisfied. Set `status` to `quiet` only when the user asks to stop setup guidance.
-- Next: none.
-- Output contract: Use the `Onboarding Complete` frame from the common message frame. Tell the user they are ready to use Sales productively, name the three completed core workflows, and offer continued guided exploration through the remaining skills. The first recommended post-core skill is `Analyze Account Signals`; end with `Say \`okay\` and I'll introduce Analyze Account Signals, or start a new thread and include \`@Sales\` with any real Sales request.`
-- Do not: say the user is fully onboarded while `status` is `active`.
-
-## Completion Criteria
-
-Core onboarding is complete when criteria 1 and 2 are resolved. Full guided onboarding remains `Status: active` until all criteria are satisfied:
-
-1. Connector/source confirmation has covered every source category as active, missing with options explained, skipped, declined, deferred, or not applicable. Do not require active sources to prove readable during onboarding.
-2. Sales Company Research and Sales Tips have each been installed, declined, skipped, or deferred, and the one-time company research kickoff in the pinned automation thread has run, started, saved high-confidence eligible memory, presented review-only candidates, found no useful additions, skipped, or deferred. Sales Tips does not need an immediate onboarding kickoff; its first check-in should run on the normal weekday schedule. This criterion is also resolved when environment/API limitations block the full default setup and the fallback Sales Tips automation has been persisted and read back while Sales Company Research and `initial_resource_discovery` are recorded as skipped or deferred for `environment_api_limitations`.
-3. `prepare-for-meeting` has been tried, skipped, or deferred, and review/iteration/plugin-memory handling is resolved.
-4. `follow-up-after-call` and `prioritize-accounts` have each been tried, skipped, or deferred.
-
-Source behavior is configured during setup and consumed at workflow time: Sales reads the configured `connector_confirmation` route, tries that source when a workflow needs it, continues when it works, and asks for connect/auth, skip, or manual fallback when setup or runtime reveals a gap.
-
-## Continued Guided Exploration
-
-After core onboarding is complete, users can keep exploring by saying `okay`, `continue`, or naming another Sales workflow. Use each skill's experience guidance for intro/starter logic and continue in this order unless the user chooses a different workflow. When offering continued exploration, remind the user that they can also start a new thread and include `@Sales` with any real Sales request.
-
-When introducing `Analyze Account Signals` during continued guided exploration, do not make skip the visible default. Use the skill's Anchor Rules and end with `Say \`okay\` and I'll pick a suitable account to analyze, or name an account, owner portfolio, or watchlist.` If no suitable account is visible after the user says `okay`, ask one concise clarification.
-
-1. `analyze-account-signals`
-2. `plan-deal-strategy`
-3. `review-forecast`
-4. `build-competitive-brief`
-5. `find-customer-quotes`
-6. `build-business-case`
-7. `enrich-company-and-contact-data`
-8. `find-key-internal-sources`
-9. `get-rep-call-feedback`
-10. `review-rep-call-trends`
-11. `suggest-sales-next-step`
-
-## CTA Arbitration
-
-Invariant: exactly one final visible CTA per response.
-
-- Ordinary skill run: the skill renders its normal next step from its experience guidance and its own workflow policy.
-- Guided onboarding run: onboarding renders the final CTA; the skill suppresses its final CTA and returns next-step candidates.
-- Onboarding review loop: final CTA is review, accept, continue, save, skip, or defer, not a skill continuation.
-- Helper/provider skill: no intro or CTA unless explicitly requested.
-- Ordinary skill run with an onboarding reminder obligation: merge the reminder into the single final action close or use the onboarding setup CTA as the sole final CTA. Do not render a standalone onboarding reminder that asks the user to continue onboarding and then render a separate skill-owned CTA.
-- Automation setup/readback: keep automation cards and breadcrumbs visually separate from the action close.
-
-## Workflow Walkthrough On Request
-
-Do not render `What Happened`, `Recap`, or another walkthrough heading by default in onboarding demo outputs, ordinary direct skill runs, post-completion guided exploration, automation readbacks, follow-up turns, or source-gap replies. For the first three onboarding demo outputs, use only a short unheaded transition sentence before the artifact body. If the user explicitly asks what happened, asks how Sales produced the result, or says yes to a walkthrough offer, then explain the observable steps.
-
-When a walkthrough is requested, explain the observable tool/app calls, retrievals, source gaps, and artifact assembly at a beginner-friendly level without revealing hidden reasoning. Keep it grounded in visible activity: sources attempted, what worked, what did not, useful facts found, and how those facts shaped the artifact.
-
-End the walkthrough by offering one valuable next action: continue to the next uncompleted guided workflow, fix the highest-value missing source, save a useful preference discovered during the run, or take a practical seller action from the artifact. Preserve the one-final-CTA rule.
-
-## Ordinary Workflow Onboarding CTA
-
-Use this template only after answering an ordinary Sales workflow request while onboarding is missing or has not started. Do not use it for direct onboarding/status/setup responses, and do not use it when `onboarding-state.json` says onboarding is already active.
-
-Do not use this template when the immediate workflow is blocked on a required clarification. Clarification questions always become the single final action close and suppress onboarding CTAs for that response.
-
-```md
-## Sales Setup Required
-
-This is required before Sales can reliably use your connected sources and source and use your authoritative company context. It also gives you a useful overview of the plugin's key functionality.
-
-Reply `start` to continue.
-```
-
-## Active Onboarding Reminder
-
-Use this only after an ordinary Sales workflow when onboarding is already active and a reminder would be useful. Do not use the `## Sales Setup Required` start CTA, do not ask whether to start onboarding, and do not compete with the skill's final action close. Keep the note short and non-final, for example:
-
-```md
-Sales onboarding is still in progress; when you want to resume it, say `continue onboarding` in this thread.
-```
-
-## Core Onboarding Reminder
-
-Use this after an ordinary Sales workflow when onboarding is already active but core onboarding is not complete. Do not use the `## Sales Setup Required` start CTA, and do not ask whether to start onboarding. The note should direct the user back to the next unresolved core setup item before more non-urgent Sales workflows:
-
-```md
-Sales core onboarding still needs {next_core_action}. Say `continue onboarding` in this thread and I'll finish that setup before we keep going with other Sales workflows.
-```
-
-When the primary workflow has its own final action close, do not render this reminder as a standalone paragraph. Fold it into the final action close as the move-on path, for example: `{skill continuation}. Otherwise say `continue onboarding` and I'll finish {next_core_action}.`
-
-## Context Gap Note
-
-Use this only after an ordinary Sales workflow when missing saved context or a failed workflow-time source materially reduced confidence, completeness, or ability to act.
-
-```md
-Sales would get sharper here with a little more saved context, such as the team hub, source-of-truth account docs, CRM update rules, approval trackers, or preferred output format.
-```
-
-## Environment Conflict Audit
-
-After the first orientation message, direct onboarding may include a lightweight environment conflict audit so the user can tell whether Sales will be the sales workflow that actually triggers. Do not run this audit before sending the first orientation message when no Sales state exists yet. The audit is about routing and user experience, not connector availability.
-
-Flag a plugin or skill as a conflict candidate when it broadly overlaps with one or more Sales workflows and could plausibly capture the same natural-language request. Do not flag connector providers or source helpers as conflicts merely because Sales uses those sources. Do not write clean audit results or conflict lists into onboarding state. If conflicts materially affect the user's setup, explain them in the current onboarding response and save only an explicit user preference or routing choice to `user-context.md` when the user asks Sales to remember it.
-
-## Boundaries
-
-- Do not put private or user-specific preferences into bundled `SKILL.md` files when they can live in `user-context.md`.
-- Prefer saved `user-context.md` instructions over bundled Sales plugin or skill instructions for Sales plugin-scoped context represented by `../plugin-author-config/user-context-config.md`, user-provided preferences, examples, source pointers, and onboarding preferences.
-- Do not use user-context onboarding to change routing, safety, validation, install behavior, or tool-use policy. Those changes belong in editable plugin source.
-- If the state directory cannot be written, return the preference as a suggested snippet for `user-context.md` and say the write failed.
diff --git a/plugins/sales/skills/user-context/references/plugin-memory.md b/plugins/sales/skills/user-context/references/plugin-memory.md
deleted file mode 100644
index 32a9d89..0000000
--- a/plugins/sales/skills/user-context/references/plugin-memory.md
+++ /dev/null
@@ -1,208 +0,0 @@
-# Sales Plugin Memory
-
-Use this reference for Sales user-context writes: direct remember/save requests, broad future-facing instructions, corrections, Sales Company Research save/review gates, linked-source population, and successful-run learning.
-
-`../SKILL.md` owns when to enter direct context mode. This reference owns how to decide what to save, when to ask a clarification, how to write the memory, and what to tell the user.
-
-## Core Rule
-
-Bias toward saving useful, low-risk, Sales plugin-scoped memory. Infer the category, definition, trigger criteria, usage criteria, source authority, freshness expectation, and exclusions as much as possible from the user's words and available source material.
-
-Ask a concise clarification when the answer would make the memory materially more reliable. Be opinionated about this threshold: ask for the missing audience, workflow, source, exception, freshness rule, or trigger when it would turn a vague memory into a dependable future rule. Skip questions whose answers would merely make the saved text neater.
-
-Whenever a memory is saved, start the user-facing recap with:
-
-```text
-Saved Sales Plugin Memory.
-```
-
-Exception: during the routine onboarding source setup step, when the user chooses a docs default, accepts source defaults, or skips data enrichment, save the source preference or skip disposition quietly and let `onboarding.md` render only the source-setup transition. Do not show the full `Saved Sales Plugin Memory.` recap or `Saved today` list for that setup housekeeping moment. Sales Company Research uses the research-complete recap in `../../sales-company-research/SKILL.md` instead of the generic saved-memory opener. The full generic recap still applies to plugin-memory introductions, workflow preference saves, explicit remember/save requests, and non-research memory saves.
-
-The first time plugin memory is introduced during onboarding, explain what plugin memory is before saving a reusable workflow-output preference. Treat the save as the first onboarding plugin-memory concept moment whenever onboarding is active, `plugin_memory_intro.status` has not already been recorded as `shown`, `introduced`, or `completed`, and the candidate came from an accepted workflow artifact or user feedback. Earlier source preferences or setup facts can be saved in their owning setup step without teaching the plugin-memory model, and they do not count as `plugin_memory_intro`. When the user has accepted the current artifact, says it looks good, or asks to move on, that acceptance is enough to save a narrow, low-risk workflow preference by default after the intro. Ask before saving only when the candidate is broad, sensitive, ambiguous, high-risk, or likely to affect externally visible write behavior. Use this pattern for the first onboarding workflow-preference memory write:
-
-```md
-## Plugin Memory
-
-Plugin memory is how the Sales plugin remembers approved preferences, source-of-truth links, examples, and team conventions for future Sales workflows. You can ask anytime to save things like "use this Notion page as the source of truth," "prefer inline citations," "always include attendee teams," or "answer exec-facing briefs in this style."
-
-Saved Sales Plugin Memory.
-
-Saved today:
-- {accepted preference}
-```
-
-For later narrow, low-risk Sales-scoped memory saves, use a shorter recap and do not ask for a separate confirmation when the user already accepted the iteration or asked to move on:
-
-```md
-Saved Sales Plugin Memory.
-
-Saved today:
-- {preference 1}
-- {preference 2}
-```
-
-Every save recap must name the exact preferences, resources, or source rules that were saved. Plugin memory can include both source-of-truth resources and answer/style preferences. Discovery-derived memory may be saved by default only when it is high-confidence, source-backed, durable, non-obvious, Sales-scoped, and low risk; anything ambiguous, sensitive, broad, lower-confidence, or potentially surprising must be proposed for user review before saving.
-
-First-run state creation remains automated through `../scripts/init_user_context_state.py` when memory needs a destination. The first pure onboarding orientation should not run this initializer just to show the welcome message; `onboarding.md` owns that response-first fast path. For actual saves, the initializer copies the bundled user-context and onboarding-state templates into the user's Sales state directory. In sandboxed local-shell environments, request elevated execution on the first attempt because the initializer writes to `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}`. After that initialization, save Sales memory by directly editing `user-context.md` and any related operational state. Do not use a write utility for ordinary saves, correction capture, proposal saves, or approved Sales Company Research entries. Prefer one batched edit for all approved changes: read the current state once, update every touched category coherently, update `onboarding-state.json` only for changed operational bookkeeping, then validate with `../scripts/sales_preflight.py` afterward. Never run parallel writes against the same Sales state files.
-
-If onboarding is active, a memory-save recap is only the beginning of the response. Before finalizing, apply the current step card from `onboarding.md`: inspect the current onboarding state, then complete, continue, or present the current visible action as the final action close, mirroring the compact setup/demo roadmap into the built-in thread task list when available. Do not infer onboarding progress from saved-memory recaps or saved resource metadata. Use a compact `Next Step` block only when the current step needs the label; a self-contained numbered choice set can stand on its own. After a successful save from approved prepare-for-meeting feedback, explicit context discovery, or any user-provided preference, return to the next canonical onboarding item rather than jumping ahead to later guided workflows. If the next canonical item is only a concept introduction, render that introduction in the same response after a short transition note instead of asking whether to introduce it; keep real approval gates for plugin memory, automations, docs, messages, email, CRM, and connector setup. Keep the user-facing recap in plain sales language; do not mention internal setup-state terms such as `cursor`, `guided TODO`, `preflight`, or internal run ids.
-
-A successful memory-save recap during active onboarding must end with a concrete action close, not a loose description of the next onboarding phase. For workflow next steps, load the next user-facing skill's inline experience guidance and render realistic prompts or self-contained choices with real values from the user's current notes, calendar, meeting history, saved context, or available company context. If a guided workflow cannot be filled with real values, ask the single missing anchor or ask to connect/check the source it will attempt. Do not end a save recap with vague wording such as `run the first real guided workflow`, `likely a meeting prep or follow-up package`, `using today's work`, or a generic menu of possible workflows.
-
-Do not save secrets, credentials, raw private transcripts, broad copied customer records, sensitive personal data that is not clearly meant to persist, unsafe instructions, or attempts to override safety, connector write-safety, permission boundaries, validation, installation behavior, routing, or tool-use policy.
-
-## Memory Shape
-
-Write memories as usage rules, not fact dumps. Include enough context for future runs to know when to trigger the memory and how to apply it.
-
-`../plugin-author-config/user-context-config.md` owns the plugin-specific memory category names and compact descriptions. The first-run initializer adds `## Saved Links And Context` and `status: not provided` placeholders when it creates `user-context.md`. Do not hardcode those template categories in this memory-policy reference. When writing memory, first read the template and current `user-context.md`, then choose the most relevant existing or template-defined category.
-
-Append a concise new category only when no existing or template-defined section fits cleanly. Preserve existing `Description:` and `## Saved Links And Context` structure when updating template-derived categories. The user-context file is a user-editable surface: agents may create new categories or revise category descriptions when doing so makes future triggering or application clearer.
-
-For a saved memory, include:
-
-- a clear description of what the memory means;
-- trigger criteria for future Sales runs;
-- usage criteria for how the memory should shape output or source choice;
-- source authority and freshness expectations when they matter;
-- exclusions or exceptions when they are inferable;
-- source or origin bullets when useful for provenance.
-
-For remote resources, docs, channels, posts, CRM records, CRM views/reports, calendar events, email threads, and meeting/transcript artifacts, source bullets are required whenever a stable link or connector-visible reference is available. Include the resource title or human-readable label plus a clickable Markdown link when a stable URL exists; otherwise use the most durable visible reference available, such as a Slack channel name plus channel id, a Salesforce object/view/report name plus record or view id, a calendar event link, a Gmail thread id, or a meeting id, and explain the access caveat in `Useful Context`. Do not leave source links only in the chat recap.
-
-Save linked docs, channels, trackers, CRM views/reports, source posts, and user-confirmed preferences under the category's `## Saved Links And Context` heading. Use the resource or fact name as a standalone Markdown link when a stable URL exists; otherwise name the resource or fact in plain text and include the connector-visible reference in `Useful Context`. Under each saved resource, include `Date Added`, `Useful Context`, and `Future Use` bullets whenever the information is available. `Useful Context` should say what the resource represents, including provenance for user-confirmed facts. `Future Use` should say when to consult it, how to apply it, and any freshness or source-priority caveat. Prefer storing lightweight pointers and usage guidance over copying rich source facts into memory: if a source can be revisited, save the link/reference and a concise description of when to read it rather than a long summary. For user-provided preferences with no external link, use a named standalone fact such as `Meeting Notes Provider Preference` and put the confirmation/source note in `Useful Context`. Do not use wrapper labels such as `Saved Information`, `Saved Context`, or `User-provided context`.
-
-Prefer a small, high-quality set of saved links or facts over comprehensive capture. Before saving or proposing context, dedupe by stable URL, canonical title, channel identity, and functional purpose. Keep the most authoritative, freshest, easiest-to-revisit, and least-obvious resource; merge useful context from weaker duplicates into that resource's `Useful Context` or `Future Use` only when it materially improves future runs. As a default target, keep one to three saved entries per category and put canonical cross-category sources in the most relevant template category instead of repeating the same link in many categories. Repeat a resource in multiple categories only when the category-specific `Future Use` is genuinely different and worth the extra surface area. Drop incidental, redundant, stale, inaccessible, obvious, or merely corroborating sources from saved context, while mentioning them in the chat recap if they helped the current discovery.
-
-Apply a strict non-obviousness and authority bar before proposing or saving any resource. User context should preserve source shortcuts and conventions that future Sales runs would not reliably infer from normal connector reads: CRM update ownership rules, manager or RevOps conventions, approval paths, source-priority rules, hard-to-find account rooms, channel posting norms, tracker semantics, trusted examples, and stale-source warnings. Do not propose or save raw CRM object metadata, ordinary field names, picklist inventories, obvious account owner/book facts, generic search results, account channels that are easy to find by account name, or live status values that should be refreshed from the connector. Random notes, bug-bash docs, feedback threads, incidental meetings, or isolated Slack chatter do not become authoritative resources merely because they mention a workflow; use them only as discovery clues unless the source is explicitly a canonical operating doc, trusted example, tracker, owner guidance, or user-confirmed preference. When the connector can retrieve the fact directly, save only the non-obvious resource that explains how to interpret or act on that fact.
-
-Give an explicit ranking boost to company-unique semantics: sources that explain how this company interprets or operationalizes a common sales concept differently than a generic CRM, sales methodology, or public playbook would. Save authoritative interpretation layers, not raw CRM facts. High-value examples include pipeline stage definitions and exit criteria, forecast category usage, close-date norms, new-logo versus expansion distinctions, AE/AM/CSM/SA/overlay responsibilities, approval ownership for discounting/security/legal/procurement, territory and segment rules, named-account exceptions, priority-account definitions, deal desk or launch-readiness governance, canonical CRM reports/views, and process docs that explain how to interpret CRM values. For CRM-related discovery, prefer the convention, RevOps note, manager guidance, canonical report, process page, or tracker definition that teaches future Sales runs how to interpret values over copied field metadata or live record contents.
-
-For volatile tracker fields, live ownership, status, launch readiness, channels, SMEs, and other values likely to change, prefer saving the tracker/resource and the kinds of facts it contains instead of copying exact values into memory. If exact values are useful enough to save, mark them with an observed date and a refresh-before-use expectation.
-
-## Company-Context Discovery
-
-Route company-context discovery to `sales-company-research` when the user asks Sales to fill in, enrich, audit, infer, propose, or discover missing `user-context.md` setup from available company context. Typical prompts include:
-
-- "search company context for my Sales setup"
-- "fill the missing user-context template items"
-- "propose the missing Sales setup entries"
-- "audit what context you can infer"
-- "look through available sources and suggest updates"
-
-During onboarding, there is no separate durable-preferences questionnaire. When the user accepts an iteration and a narrow, low-risk reusable preference emerged, summarize the preference, introduce plugin memory if needed, save the preference by default, and continue the canonical onboarding flow. Broader setup context is handled by the Sales Company Research automation step. When onboarding reaches that step, Sales may kick off one immediate research run in the dedicated pinned `Sales Company Research` automation thread after the Sales automation setup is installed or repaired. If the user explicitly asks Sales to search or discover context outside that Sales automation step, route to `sales-company-research` as a separate user-requested research workflow and continue onboarding only after the save/proposal is resolved or deferred.
-
-This reference owns the save and review gates that `sales-company-research` applies after it finds candidate resources. The research skill owns source discovery, dynamic source-family coverage, candidate ranking, coverage-note wording, output shape, and capped `Where you can help` questions. Keep the automation prompt thin by invoking `sales-company-research` instead of copying those rules into the prompt.
-
-Discovery-derived memory may be saved by default only when it is high-confidence, source-backed, non-obvious, low-risk, and useful for future Sales workflows. Discovery authorization is not blanket save authorization: even when the user says "fill", "apply", "do it", or similar language, do not save candidates that are broad, sensitive, ambiguous, lower-confidence, unsupported by an authoritative source, or likely to surprise the user. Do not write review-only candidates to `user-context.md` until the user has seen them and explicitly says `save`, `apply`, `update`, `keep these`, or otherwise clearly approves writing those entries.
-
-When research saves high-confidence entries or the user approves review-only entries, save all eligible or approved entries with one direct state edit whenever practical. Update `user-context.md` by replacing `status: not provided` only in touched scaffold categories, appending or merging resources under `## Saved Links And Context`, and preserving each category's `Description:` metadata. Update `onboarding-state.json` only for operational bookkeeping that changed, such as discovery status, timestamps, or pinned-thread references. Do not copy saved resource lists, saved category lists, source URLs, or save-count manifests into onboarding state; `user-context.md` is the source of truth for saved memory. After the edit, run `sales_preflight.py --workflow onboarding` or the relevant workflow preflight to confirm the saved context and onboarding state read back cleanly.
-
-When reporting only unsaved review candidates, start with:
-
-```text
-Proposed plugin memory.
-```
-
-Then use this user-facing output shape by default:
-
-```text
-Proposed plugin memory.
-
-I found a few useful setup entries that would make future Sales runs sharper. I have not saved these yet.
-
-## What I Found
-
-**{Target Category}**
-
-{Source title}
-Source: {stable URL or connector-visible reference}
-
-Why useful: {what would change in future Sales behavior, including limits such as "do not treat this as a customer-facing escalation path" when needed}
-Why this surfaced: {non-obvious route, authority signal, or why this beat more obvious alternatives}
-Rank: {Top candidate | Also useful}, Confidence: {High | Medium}
-
-## What I Need From You
-
-1. **{Unresolved Category}**
-   I did not find {specific source category or convention}.
-   What would help: {specific link, channel, tracker, report, owner, or example}. This matters because {short downstream risk or value}.
-
-Say "save these" to save only the "What I Found" entries, or reply with numbers like "2 is go/team-book" and I will fold those into the proposal.
-```
-
-For exact-save previews, anchor source links or connector-visible references inside the candidate memory entries, not only in a separate evidence list. Render the candidate entries as normal Markdown in the chat response; do not wrap the proposed memory content in fenced code blocks or inline backticks. Each candidate should look like the content that would be saved to `user-context.md`, using the target template category and preserving useful template metadata:
-
-# {Target Category}
-
-- Description: {what this category is or does and when future Sales runs should apply it}
-
-## Saved Links And Context
-
-{source title} (use a real Markdown link when a stable URL is available; otherwise use a connector-visible reference in plain text)
-- Date Added: YYYY-MM-DD.
-- Useful Context: {what this source represents, plus owner/location and access caveat when useful}
-- Future Use: {specific saved rule, source priority, workflow hint, owner, channel, or convention inferred from it; prefer telling future runs when to reread the source over copying volatile details}
-
-{user-provided fact or preference name, when no external source exists}
-- Date Added: YYYY-MM-DD.
-- Useful Context: {what the preference means and who confirmed it}
-- Future Use: {when future Sales runs should apply it}
-
-If several sources support one memory, save only the tightest set of resources needed to make the memory reliable. Usually this means one canonical resource, occasionally two or three when they cover distinct authority lanes such as workflow semantics, live status, and support routing. If the candidate includes user-provided context with no external source, add a named standalone fact or preference instead of a fake source link. After the proposed save block, add `Confidence:` and `Open question:` lines only when they help the user decide whether to save. A top-level `Source Links` section may still be included as a navigation aid, but it must not be the only place links appear. If template categories still need user-provided sources, include them as numbered `What I Need From You` asks rather than an unnumbered pointer list.
-
-End unsaved review-candidate results with one direct approval prompt. When there are good review-only entries and no `What I Need From You` asks, use wording such as `Say "save these", or tell me which entries to edit or remove.` When `What I Need From You` asks remain, ask for the missing numbered pointers before or alongside save approval, such as `Send any of those numbered pointers, or say "save these" to keep the current proposals as-is.` Do not use `Saved Sales Plugin Memory.` until a write actually succeeds.
-
-## Iteration Acceptance Learning
-
-When the user iterates on any Sales artifact and then accepts it, says it looks good, asks to move on, or asks to create the next artifact from the revised version, inspect the accepted change for reusable plugin memory. This applies across Sales workflows, not just onboarding.
-
-Reusable iteration signals include recurring output structure, tone, audience, artifact naming, section order, source priority, internal versus external messaging conventions, CRM-update rules, evidence standards, next-step style, doc/share preferences, or channel/email drafting preferences. Do not save one-off content changes, private transcript details, temporary deal facts, or corrections that only apply to the current artifact.
-
-When the accepted preference is narrow, low-risk, and Sales plugin-scoped, summarize it back to the user, save it to plugin memory without a separate confirmation question, and say it was saved for future use. During onboarding, if plugin memory has not already been introduced, first render the `## Plugin Memory` explanation from this reference, then save the accepted narrow preference in the same response. Ask before saving only when the accepted preference is broad, sensitive, ambiguous, high-risk, or likely to affect externally visible write behavior. If the user declines a save prompt, continue the workflow without saving and do not keep asking about the same preference in that session.
-
-When a preference or source shortcut is only a possible learning from the run, do not let the memory question replace the workflow's main next step. Use a brief save hint before the final action close, such as `_Save hint: if this format or source route should be reused, I can remember it after you confirm._` The hint should be one sentence, name the specific possible memory when helpful, and avoid asking broad setup questions. Do not show this hint when a save already happened, when the user asked for no next step, when the answer is clarification-only, or when it would distract from a sensitive or high-pressure response.
-
-Ask only questions whose answers would materially improve the saved context. Number every question and tie it to an unresolved setup category or missing source authority. Include every unresolved template category from `../plugin-author-config/user-context-config.md` unless the user scoped discovery more narrowly; group adjacent categories only when one concrete pointer would answer the same question for all of them. If the user opts out, explicitly move forward with the current confidence level and avoid repeatedly asking the same setup question.
-
-## Broad Instruction Capture
-
-When the user provides a broad future-facing instruction during or immediately after a Sales interaction, identify whether it is reusable Sales user context before ending the turn.
-
-Treat a follow-up instruction as Sales plugin-scoped when it refers to the current sales workflow, customer-facing output, source choice, process convention, internal or external messaging, evidence source, review path, audience definition, operating norm, output format, or connector use.
-
-Common signals include `always`, `never`, `for customer`, `for security`, `for future`, `from now on`, `next time`, `before drafting`, `double check`, `use this source`, `prefer`, `don't`, and `please make sure`.
-
-Reusable drafting preferences are especially likely to be saveable after Sales has produced a message draft: preferred signoffs, greetings, casing, tone, structure, evidence labels, subject-line style, and customer/internal audience conventions.
-
-If the likely scope is Sales but the exact trigger, audience, or output surface is unclear, infer the narrowest reasonable scope and save unless one missing detail would materially improve future triggering or usage.
-
-## Correction Capture
-
-When the user corrects a Sales answer, first apply the correction in the current response. Then decide whether the correction is reusable Sales user context.
-
-Treat a correction as saveable when it expresses a future-facing preference, convention, source priority, terminology rule, audience nuance, workflow-specific rule, messaging norm, escalation path, output format preference, or operating context.
-
-Common signals include `actually`, `not X, Y`, `we call that`, `for us`, `next time`, `prefer`, `always`, `don't`, `that's not how we`, or repeated correction of the same pattern.
-
-Do not save one-off factual corrections that only fix the current artifact. For ambiguous corrections, infer the narrowest reasonable scope and save unless a clarification would significantly improve triggering or usage.
-
-Save correction-captured entries only in `user-context.md`, not `onboarding-state.json`, unless the user explicitly asks to update onboarding state too.
-
-## Successful-Run Learning
-
-Even when Sales successfully answers the user's request, inspect whether the workflow would have been faster, more complete, or more accurate if a durable source hint had been known earlier.
-
-Good candidates include an authoritative channel, internal doc, document folder, system view, saved report, mapping, workspace, source-of-truth owner, search query pattern, or preferred evidence route discovered during the run.
-
-Save successful-run learning when the source is clearly authoritative or repeatedly useful, when finding it consumed meaningful effort, when it materially improved answer confidence, or when future runs are likely to need the same route. Include when the source should be checked, what it should and should not override, and any freshness or permission caveat that matters.
-
-Do not save transient search results, raw private records, broad customer dumps, sensitive transcript content, secrets, or sources the user did not have permission to use.
-
-## Linked Resources
-
-If the user provides a linked resource as a context source, or Sales Company Research finds a remote resource or doc, read it through an available connector or explicit local API before writing derived context.
-
-Infer the memory definition, trigger criteria, usage criteria, source authority, and freshness expectation from the source material as much as possible before saving. Add the remote resource or doc link to the saved memory entry whenever a stable link is available, and write a clear description of what the source establishes and how Sales should use it.
-
-If the resource is inaccessible, save only user-provided metadata or a user-supplied summary and say the source was not read.
diff --git a/plugins/sales/skills/user-context/references/source-category-runtime.md b/plugins/sales/skills/user-context/references/source-category-runtime.md
deleted file mode 100644
index 106edff..0000000
--- a/plugins/sales/skills/user-context/references/source-category-runtime.md
+++ /dev/null
@@ -1,274 +0,0 @@
-# Sales Source Category Runtime
-
-This reference owns how Sales uses source categories at workflow time. Plugin-author editable category ids, labels, preferred plugins/apps, and helper skills live in `../plugin-author-config/source-category-config.json`.
-
-A source category is the kind of sales evidence a workflow may need, such as CRM, calendar, docs, meeting notes, email, Slack, or enrichment. The catalog is a routing aid, not a record of plugin, app, or connector setup.
-
-## Ownership Map
-
-- `.app.json` declares app and connector dependencies the plugin can request through the install/connect/auth flow. It is not proof that an app is installed, authorized, preferred, or readable for the current user.
-- `../plugin-author-config/source-category-config.json` owns static source category ids, seller-facing labels, preferred plugin/app order, and category-level `relevant_skills`.
-- `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md` owns durable user-approved source preferences, source-of-truth links, team conventions, examples, formatting preferences, "do not use" rules, and operating notes.
-- `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/onboarding-state.json` owns onboarding progress, connector-confirmation labels, guided workflow progress, and automation metadata.
-- `$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/category-state.json` is legacy-only. Normal onboarding and preflight should not create it, read it, or update it. If an older copy exists, do not treat it as durable connector proof. Migrate any explicit user choices that still matter into `user-context.md` as source preferences or decline rules, then leave automatic readiness fields behind.
-
-## Setup-Owned Source Routes
-
-Source route selection happens during onboarding, an explicit source-setup turn, or workflow-time repair for a missing/broken source, not during ordinary preflight. The setup/search step is responsible for inspecting the current environment, preferring related plugins before raw app/connector routes, asking for installation or setup when needed, and writing resolved routes to `onboarding-state.json` under `connector_confirmation`.
-
-Preflight is a reader. It should return the configured routes that setup wrote, plus unresolved setup gaps, but it must not choose a plugin, app, connector, or manual route on its own. Runtime workflows consume the configured routes.
-
-During setup, prefer source routes in this order:
-
-1. Installed/enabled related plugin from the session `Available plugins` block when it has visible plugin-owned skill/tool surface.
-2. Installable related plugin returned by `functions.list_available_plugins_to_install`, including a plugin that contains or declares a connector/app id from the category's preferred list.
-3. Installed or installable app/connector returned by the same install listing or exposed in the session/tool surface.
-4. Manual, uploaded, pasted, or exported context.
-5. Missing/unavailable with practical IT/admin options.
-
-At the start of onboarding source resolution or explicit source setup, call `functions.list_available_plugins_to_install` once and reuse those results for all source categories in that pass. Also read the session `Available plugins` block to identify installed plugins, and read the session `Available skills` block when deciding whether an installed plugin has plugin-owned skills exposed. Load `../plugin-author-config/source-category-config.json` for category `preferred_plugins` and `preferred_apps`, and load `.app.json` to map preferred app names to configured app/connector ids.
-
-Installed source tools are treated as usable for setup purposes only when their surface is visible enough to use later. If setup finds one or more installed related plugins with visible plugin-owned skill/tool surfaces for the same category, write all useful routes as active under `routes` and let workflows choose among them by evidence need. Do not force a preference question just because several non-conflicting sources are available, such as Google Drive and Notion for docs; the agent can use both. If setup finds an installed app or connector and also finds a related plugin candidate, prefer the plugin anyway because Sales workflows should use plugin-owned skills and tools when available; keep the app/connector route as fallback if the user defers or install visibility is pending. If setup finds one installed app or connector and no related plugin candidate, write the app/connector route as active and let the workflow try the actual read only when it needs that source. Do not perform a read merely to prove an installed source works during onboarding.
-
-Do not mark a plugin route as active unless there is enough evidence: the plugin is listed in `Available plugins` or was just installed successfully with `request_plugin_install`, the relevant app/connector/tool surface is callable or exposed, and plugin-owned skills are visible in `Available skills` when that plugin is expected to provide skills. If plugin install succeeds but skills/tools are not visible until the next turn or session refresh, record the route as `needs_confirmation` or deferred/pending and ask the user to continue after refresh. Do not silently mark it `plugin_owned`.
-
-Use only these setup evidence strings unless this contract is intentionally updated:
-
-- `plugin_install_evidence`: `plugin_in_available_plugins`, `request_plugin_install_completed`, `installed_plugin_skill_visible`, `plugin_owned_skill_visible`, or `user_confirmed_installed_plugin`.
-- `skill_surface_evidence`: `plugin_skills_visible`, `plugin_owned_skill_visible`, or `plugin_owned_skills_visible`.
-- `app_surface_evidence` or `tool_surface_evidence`: `callable_app_tools_visible`, `callable_plugin_tools_visible`, or `plugin_tools_visible`.
-- `connector_surface_evidence`: `request_plugin_install_completed_or_callable_surface_visible` or another explicit connector-surface proof returned by setup.
-
-Recommended active plugin route state:
-
-```json
-{
-  "connector_confirmation": {
-    "crm": {
-      "status": "active",
-      "preferred": "HubSpot",
-      "source_kind": "plugin",
-      "skill_surface": "plugin_owned",
-      "plugin": {
-        "id": "hubspot@openai-curated",
-        "name": "HubSpot"
-      },
-      "plugin_install_evidence": "request_plugin_install_completed",
-      "app_surface_evidence": "callable_app_tools_visible",
-      "skill_surface_evidence": "plugin_skills_visible",
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Recommended active multi-route state:
-
-```json
-{
-  "connector_confirmation": {
-    "document_store": {
-      "status": "active",
-      "routes": [
-        {
-          "preferred": "Google Drive",
-          "source_kind": "plugin",
-          "skill_surface": "plugin_owned",
-          "plugin": {
-            "id": "google-drive@openai-curated",
-            "name": "Google Drive"
-          },
-          "plugin_install_evidence": "plugin_in_available_plugins",
-          "skill_surface_evidence": "plugin_skills_visible",
-          "setup_action": "none_try_on_use"
-        },
-        {
-          "preferred": "Notion",
-          "source_kind": "plugin",
-          "skill_surface": "plugin_owned",
-          "plugin": {
-            "id": "notion@openai-curated",
-            "name": "Notion"
-          },
-          "plugin_install_evidence": "plugin_in_available_plugins",
-          "skill_surface_evidence": "plugin_skills_visible",
-          "setup_action": "none_try_on_use"
-        }
-      ],
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Recommended active app/connector route state:
-
-```json
-{
-  "connector_confirmation": {
-    "crm": {
-      "status": "active",
-      "preferred": "Salesforce",
-      "source_kind": "connector",
-      "skill_surface": "sales_vendored_helper",
-      "connector": {
-        "id": "REPLACE_WITH_SALESFORCE_APP_OR_CONNECTOR_ID",
-        "name": "Salesforce"
-      },
-      "connector_surface_evidence": "request_plugin_install_completed_or_callable_surface_visible",
-      "setup_action": "none_try_on_use"
-    }
-  }
-}
-```
-
-Use `source_kind: manual` and `skill_surface: manual` only when the user chooses manual, uploaded, pasted, or exported context as the configured route for that source category.
-
-## Runtime Source Resolution
-
-Use sources only when the active workflow needs them. Do not proactively read every category during onboarding, and do not surface installed connectors as durable readiness state.
-
-Choose sources by the fact they own, not by connector availability or convenience. When a workflow asks for stable ownership, approval, routing, source-of-truth, or "who/where should I ask" information, prefer the most canonical ownership or routing source available for that fact, such as a people directory, team directory, company knowledge base, maintained wiki page, verified routing doc, go-link index, source-of-truth hub, or saved source pointer. Use broad document search, chat/message search, public research, or mirrored sources as supporting evidence only when the canonical source is missing, stale, thin, contradicted, or the workflow explicitly needs recent discussion.
-
-For quick ordinary workflow runs, prefer a bounded first pass over exhaustive triangulation. A useful default is one canonical source attempt, one narrow fallback when the first pass is empty/thin/misleading, and then a user-facing answer with source limitations and an offer to continue. If a connector times out, rate-limits, or consumes roughly the time of two slow source calls for a non-required enrichment lane, return the best available partial result instead of continuing low-yield retrieval.
-
-Do not spend more than roughly three to five minutes of active retrieval on a user-facing workflow without yielding back to the user. If that budget is reached before the answer is strong, stop searching and give a concise checkpoint: what was checked, the best current candidates or partial answer, the limiting gap, and one recommended next step. Choose that next step by usefulness: ask the user to clarify or steer when the search space is ambiguous, suggest continuing deeper when results are promising but incomplete, or suggest acting on the current best answer when it is good enough. Do not continue silent low-yield retrieval merely because more sources or query variants remain.
-
-For each source category a workflow actually attempts:
-
-1. Read saved source preferences, source-of-truth pointers, and "do not use" rules from `user-context.md`.
-2. Read the configured `connector_confirmation` route or `routes` from preflight. If the category is active and one route has `source_kind: plugin`, use the plugin-owned skill or callable tool surface for the active task. If several routes are active, choose by fact ownership and likely authority; use multiple routes when doing so would materially improve confidence, such as searching both Drive-native assets and Notion source-of-truth pages.
-3. If the route is missing, incomplete, broken, manual-only, or connector/app-based, first run the plugin-first setup branch below before using connector fallback. Ask before installing unless the user explicitly requested installation. When a connector route is already active, still prefer an available related plugin because the plugin can add dedicated Sales workflow support.
-4. For `source_kind: app` or `source_kind: connector`, attempt the smallest useful app/connector read for the active task. For apps covered by marketplace `policy.authentication: ON_USE`, this attempted use should trigger setup or authorization when needed. Load Sales' vendored helper skill only when the configured route has `skill_surface: sales_vendored_helper`.
-5. For `source_kind: manual`, use pasted, uploaded, exported, or otherwise user-provided context.
-6. If the read works, continue the workflow with citations or source notes as appropriate. A successful read is evidence for this run only; do not write connector readiness state.
-7. If the configured route fails, explain the practical blocker and offer the next best path: install or reconnect a related plugin when available, connect or authorize the app, install the connector when exposed, ask IT/admin, skip that source for this run, or use a manual/exported fallback. Preferred and active routes are always retryable in future workflows unless the user explicitly saves a durable `do not use` rule; when a future workflow needs a source and the route is missing, unauthorized, stale, or broken, try it again and prompt the user to install, connect, reauthorize, or choose a fallback.
-8. Save a default, source preference, source-of-truth pointer, decline, or "do not use this source" rule only when the user explicitly asks to remember it, chooses it as a future default, provides a link or fact to save, or declines a source for future use.
-
-## Plugin-First Setup For Missing Sources
-
-When setup finds that a needed source category is not already covered by a proven active plugin route, prefer related plugin setup before raw app/connector setup or manual fallback. Preflight surfaces this as `setup_action: run_plugin_first_source_setup`, `plugin_preference_order`, and `setup_recovery.type: plugin_first_source_setup`.
-
-1. Build the plugin preference order from `saved_source_preferences` when preflight provides them first, then the source category's configured `preferred_plugins`, then `preferred_apps`. Use that order to rank plugins, but do not require the user to have saved a preference before plugin setup is attempted.
-2. Check the session `Available plugins` block for an installed plugin whose name, id, description, or declared app/connector ids match a preferred plugin or app for the category. If it has visible plugin-owned skill/tool surface, write it as the active route.
-3. If no installed related plugin exists, call `functions.list_available_plugins_to_install` if it has not already been called in this setup pass, then look for related plugin candidates.
-4. A candidate is related when its `name` or `display_name` matches a preferred plugin or app, its `id` clearly names the preferred plugin/app, its `app_connector_ids` intersects the `.app.json` ids for any category preferred app, or its description clearly names the same provider/category and no better preferred plugin/app match exists.
-5. Prefer plugin candidates over connector candidates even when the app/connector route is already callable or active. If one related plugin candidate is clearly best by saved preference order, configured `preferred_plugins`/`preferred_apps` order, or connector-id intersection, ask one clear install/defer question unless the user explicitly requested installation. If the user confirms, call `functions.request_plugin_install` with the exact returned candidate `tool_type` and `id`. Do not call `request_plugin_install` in parallel with any other tool.
-6. If multiple related plugin candidates tie after saved preference order, configured `preferred_plugins`/`preferred_apps` order, and connector-id matching, ask the user to choose among only those tied relevant candidates.
-7. If installation or setup fails, or install succeeds but plugin skills/tools are not visible yet, keep the plugin setup path retryable. Record `needs_confirmation`, `deferred`, `deferred_environment_api_limitations`, or `skipped_for_now` for operational state, not `declined`, and surface plugin-first setup again in a future workflow when that source category is material. Use connector/app or manual fallback for the current run when useful.
-8. If the user declines a plugin, suppress that plugin for the current workflow and continue with the next related plugin candidate, the existing app/connector route, another user-selected source, or manual/exported context. Save a durable `do not use` or `do not offer` rule only when the user explicitly asks for that future behavior.
-9. If no related plugin candidate exists, use the same install-list results and exposed session tools to find an installed or installable app/connector candidate.
-10. If one plausible app or connector is available and no plugin route is chosen, ask the user to confirm it if needed, then record the app/connector route in `connector_confirmation` without a proof read.
-11. If multiple plausible apps or connectors are available and no plugin route is chosen, ask the user which one to prefer and record that route after selection.
-12. If no matching plugin, app, or connector is available, explain the practical workflow impact and ask the user to install/connect/authorize a preferred source, ask IT/admin to enable one, defer/skip the category, or use manual/exported context.
-13. If no setup route is available or the user skips setup, write the manual/skipped/deferred route and use pasted, uploaded, exported, or manual context for the current run when that can satisfy the evidence need.
-
-Example install-list candidate shape:
-
-```json
-{
-  "tool_type": "plugin",
-  "id": "hubspot@openai-curated",
-  "name": "HubSpot",
-  "display_name": "HubSpot",
-  "app_connector_ids": ["REPLACE_WITH_HUBSPOT_APP_OR_CONNECTOR_ID"],
-  "description": "Use HubSpot CRM records and sales workflows from Codex."
-}
-```
-
-Do not proactively suggest every installable plugin; show only plugins related to the current Sales source category. Do not use `tool_search` as proof that a plugin is installed. Installation evidence must come from the session `Available plugins` block or a successful `request_plugin_install` call. Do not guess plugin ids; call `request_plugin_install` only with an exact candidate returned by `list_available_plugins_to_install`.
-
-When an attempted source fails, distinguish failure class before giving the user a setup CTA:
-
-- `needs re-auth`: the connector explicitly says reauthentication or reauthorization is required. Ask the user to reconnect or authorize that app.
-- `not ready / startup issue`: the app or MCP server times out during startup, handshake, client creation, or transport setup. Say the connector did not become ready, suggest retrying shortly or checking app/runtime status, and do not tell the user to reconnect unless the app later returns a re-auth error.
-- `missing or unsupported`: the connector/app is not installed, not exposed, or lacks the needed action. Offer install/admin enablement, skip, or manual/exported fallback.
-- `query/schema issue`: the source is reachable but the requested object, field, filter, or query shape failed. Use the relevant helper skill's recovery rules before asking the user.
-
-## Relevant Skills
-
-If an attempted category's plan includes `relevant_skills`, load the matching helper skill only when the configured source route is `source_kind: app` or `source_kind: connector` and `skill_surface: sales_vendored_helper`. The helper skill adds provider-specific connector rules only; the focused Sales workflow remains authoritative for the seller task and output.
-
-## Missing Sources And Fallbacks
-
-When a plugin, connector, or app cannot be used, keep the message seller-facing. Say what source is missing and why it matters for the current job, such as `meeting notes or call recordings are needed for transcript-backed follow-up`. Avoid implementation-facing labels like `category-state`, `preflight`, or raw category ids unless the user asks.
-
-Preferred apps are source-category hints, not the only route. During setup, if a preferred source is missing, try an appropriate app/connector alternative only when it can genuinely satisfy the same evidence need. Do not satisfy a source through a related connector that merely mirrors or imports the native source's content unless the user has saved that mirrored source as the source of truth.
-
-Docs preferences are light source-selection hints, not provider lockouts or strong ranking overrides. A saved preference such as `look in Notion first for Sales docs` means give Notion a small first-look bias when it fits the current evidence need, but still actively consider Google Drive, SharePoint, Slack-linked docs, email attachments, CRM-linked files, and other available sources. For broad discovery tasks, especially Sales Company Research, do not let a preferred docs provider or the static preferred-app catalog dominate the search plan: search across all available and authorized source families early enough to compare candidates by authority, artifact type, freshness, and future workflow value. Include non-doc knowledge surfaces such as company knowledge bases, wikis, internal messaging, CRM, email, calendar, meetings, issue trackers, dashboards/reporting tools, and customer-facing asset libraries whenever they can plausibly contain a better Sales source. Use Google Drive whenever the artifact type points there, including Drive-native files, Docs, Sheets, decks, folders, customer-shareable collateral, enablement libraries, or linked assets. Use multiple docs sources when doing so would materially improve confidence or source authority.
-
-Manual input, pasted notes, and exports are valid fallbacks after a real connector attempt fails, the connected route is unsupported in the environment, the user declines connector use, or the current need is urgent enough to bypass setup. Treat manual fallbacks as one-run inputs unless the user asks Sales to remember a preference or source pointer.
-
-## Durable Source Preferences
-
-Save durable source choices in `user-context.md`, not connector readiness JSON. Good saved entries include:
-
-- preferred CRM or meeting-notes source;
-- first-look docs preference such as `look in Notion first for internal source-of-truth pages, but still use Google Drive for Drive-native files, Docs, Sheets, decks, shareable collateral, linked folders, or evidence likely to live there`;
-- source-of-truth docs, hubs, trackers, reports, or channels;
-- source priority such as `prefer Zoom over Notion meeting notes`;
-- write-safety rules such as `never write CRM without review`;
-- explicit avoidance such as `I do not have ZoomInfo; do not try enrichment connectors unless I say access changed`;
-- team-specific conventions for when a source should win over another source.
-
-Use the normal memory shape from `plugin-memory.md`: resource or fact name, `Date Added`, `Useful Context`, and `Future Use`. Include freshness and fallback expectations when they affect future workflow behavior.
-
-## Onboarding Connector Confirmation
-
-Onboarding may classify each source category so the user understands what Sales can use, what needs setup, and what may require IT/admin help. These labels and route metadata are operational onboarding state, not durable connector-readiness proof, and they belong under `connector_confirmation` in `onboarding-state.json`.
-
-When explaining source coverage during onboarding, make the practical workflow value concrete:
-
-- `crm`: account, opportunity, owner, pipeline, contact, activity, forecast, and account-status context for meeting prep, follow-up, deal strategy, prioritization, account signals, and forecast review.
-- `calendar`: upcoming meetings, invite details, attendee lists, timing, and daily/scheduled meeting selection.
-- `meeting_notes`: transcripts, recordings, customer language, commitments, objections, decisions, and follow-up evidence.
-- `document_store`: account plans, mutual action plans, discovery notes, enablement assets, business-case sources, and source-of-truth docs.
-- `spreadsheets`: Sheets, spreadsheet models, Excel files, CSV exports, owner trackers, account lists, and forecast or planning tables.
-- `external_messaging`: customer asks, promised follow-ups, scheduling context, attachments, and engagement signals.
-- `internal_messaging`: account-team coordination, blockers, source routing, internal follow-up drafts, and escalation context.
-- `data_enrichment`: company, contact, firmographic, technographic, and market-signal details when CRM or user-provided lists are thin.
-- `agreements`: contracts, e-signature status, approval routing, agreement lifecycle context, and deal paperwork signals.
-
-Use exactly these statuses:
-
-- `active`: setup found or installed one or more source routes and wrote either route-level `routes[]` entries or backward-compatible `source_kind`, `skill_surface`, and route details when available. This is FYI only. Do not perform an eager read just to prove an installed source works; attempt active sources only when a workflow needs them.
-- `needs_confirmation`: setup found multiple plausible source tools, an installable related plugin that needs approval, or a selected source preference that still needs a route recorded. Ask the user which source to prefer or whether to install the plugin when needed. If the selected source is an installed plugin, app, or connector, write the chosen route and let the workflow's first real read handle auth, query, or schema failures. If the selected source is missing, use plugin setup before connector/app setup and manual fallback.
-- `missing`: setup found no matching installed plugin, connector, or app. Explain what Sales workflows will be weaker without it and list concrete IT/admin options, such as installing the plugin/connector, authorizing the app for Codex, or using a manual/exported fallback for now.
-- `deferred`, `skipped`, `declined`, or `unavailable`: setup did not choose an active route. Include practical impact and the next best plugin/app/connector/manual path when useful. Treat failed setup, pending plugin visibility, temporary unavailability, and `skipped_for_now` as retryable while related plugins may become available later. Treat `declined` as a current-workflow suppression unless the user explicitly saves a future `do not use` rule.
-
-Recommended state shape:
-
-```json
-{
-  "connector_confirmation": {
-    "calendar": {
-      "status": "active",
-      "preferred": "Google Calendar",
-      "confirmed_at": "2026-05-23T00:00:00Z"
-    },
-    "document_store": {
-      "status": "active",
-      "routes": [
-        {"preferred": "Google Drive", "source_kind": "plugin"},
-        {"preferred": "Notion", "source_kind": "plugin"}
-      ]
-    },
-    "crm": {
-      "status": "needs_confirmation",
-      "candidates": ["Salesforce", "Agentforce Sales", "HubSpot"]
-    },
-    "meeting_notes": {
-      "status": "missing",
-      "options": ["Zoom", "Granola", "Otter.ai", "Fireflies", "Outreach", "Rox"]
-    }
-  }
-}
-```
-
-Do not write `available`, `verified`, `checked`, `installed`, or automatic `blocked` as durable source status. If the user chooses a default source or says to avoid one in the future, save that user-approved preference in `user-context.md`.
-
-## Onboarding Behavior
-
-First-run onboarding should not perform connector reads before the first orientation message. After the user approves setup, run source setup/search, call `functions.list_available_plugins_to_install` once, prefer related plugins over raw app/connector routes, and write resolved routes for each category to `connector_confirmation`. Do not preflight-read installed or active connectors merely to prove they work. Active sources are informational only; do not ask the user to confirm every source when setup finds clear active installed plugins, apps, or connectors that can coexist in the same category. Ask for help only when there are multiple mutually exclusive plausible source tools, an installable related plugin needs approval, no source was found, or IT/admin action may be needed. For missing or connector-covered sources, use plugin setup before connector/app setup and manual fallback. Introduce Sales, confirm active/missing sources, set up Sales Company Research and Sales Tips in the initial automation step, then let the user choose the first guided hero workflow from Prepare For Meeting, Follow Up After Call, and Prioritize Accounts. Offer Sales Daily Meeting Prep later after the user has tried and iterated on Prepare For Meeting. When a guided workflow needs a source, the focused workflow tries the configured source route at that moment and handles plugin install, auth, connection, skip, or manual fallback in context.
-
-During onboarding, explain at a high level that Sales works best when it has active workflow sources plus saved source pointers and preferences. Ask for memory/resource pointers after the first useful output, not before the user has seen value.
diff --git a/plugins/sales/skills/user-context/scripts/init_user_context_state.py b/plugins/sales/skills/user-context/scripts/init_user_context_state.py
deleted file mode 100644
index 026fad3..0000000
--- a/plugins/sales/skills/user-context/scripts/init_user_context_state.py
+++ /dev/null
@@ -1,228 +0,0 @@
-#!/usr/bin/env python3
-"""Initialize local Sales user-context and onboarding-state files."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import STATE_DIR_HELP, resolve_state_dir
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-USER_CONTEXT_TEMPLATE = SKILL_ROOT / "plugin-author-config/user-context-config.md"
-AUTOMATION_CONFIG = SKILL_ROOT / "plugin-author-config/automation-config.md"
-ONBOARDING_STATE_TEMPLATE = SKILL_ROOT / "references/onboarding-state-template.json"
-
-USER_CONTEXT_NOTE = """<!--
-Sales user-context scaffold. This file is user-editable.
-Unresolved `status: not provided` entries are setup prompts, not saved facts.
-Agents may create or revise categories when helpful. Saved links, docs, and
-user-provided context should include Date Added, Useful Context, and Future Use
-when available.
--->
-
-"""
-
-DEFAULT_CATEGORIES_MARKER = "## Default Categories"
-SAVED_CONTEXT_HEADING = "## Saved Links And Context"
-SAVED_CONTEXT_PLACEHOLDER = "status: not provided"
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Create missing Sales local state files from bundled config/templates. "
-            "Existing files are preserved unless --overwrite is passed."
-        )
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument(
-        "--state-dir",
-        type=Path,
-        default=None,
-        help=STATE_DIR_HELP,
-    )
-    parser.add_argument(
-        "--overwrite",
-        action="store_true",
-        help="Overwrite existing user-context.md and onboarding-state.json.",
-    )
-    return parser.parse_args()
-
-
-def validate_json(path: Path) -> None:
-    with path.open("r", encoding="utf-8") as handle:
-        json.load(handle)
-
-
-def build_user_context_scaffold() -> str:
-    template_text = USER_CONTEXT_TEMPLATE.read_text(encoding="utf-8")
-    if DEFAULT_CATEGORIES_MARKER not in template_text:
-        raise ValueError("user-context-config.md must include a Default Categories section")
-    category_text = template_text.split(DEFAULT_CATEGORIES_MARKER, 1)[1].strip()
-    sections = [
-        section.strip() for section in re.split(r"(?m)(?=^# .+$)", category_text) if section.strip()
-    ]
-    if not sections:
-        raise ValueError("user-context-config.md must define at least one category")
-
-    rendered_sections: list[str] = []
-    for section in sections:
-        if not section.startswith("# "):
-            raise ValueError("user-context-config.md category entries must start with H1 headings")
-        if "- Priority:" in section:
-            raise ValueError("user-context-config.md must not define Priority fields")
-        if "## User Resources" in section or SAVED_CONTEXT_HEADING in section:
-            raise ValueError(
-                "user-context-config.md must not include saved-context subsections; "
-                "the initializer adds them"
-            )
-        rendered_sections.append(
-            f"{section.rstrip()}\n\n{SAVED_CONTEXT_HEADING}\n\n{SAVED_CONTEXT_PLACEHOLDER}"
-        )
-
-    return "\n\n".join(rendered_sections) + "\n"
-
-
-def load_automation_config() -> dict[str, dict[str, object]]:
-    text = AUTOMATION_CONFIG.read_text(encoding="utf-8")
-    if "## Default Automations" in text:
-        text = text.split("## Default Automations", 1)[1]
-    if "## Later Journey Automations" in text:
-        text = text.split("## Later Journey Automations", 1)[0]
-    matches = list(re.finditer(r"(?m)^`([a-z0-9_]+)`\s*$", text))
-    automations: dict[str, dict[str, object]] = {}
-    for index, match in enumerate(matches):
-        automation_id = match.group(1)
-        end = matches[index + 1].start() if index + 1 < len(matches) else len(text)
-        block = text[match.end() : end]
-        metadata: dict[str, object] = {}
-        for field, key in (
-            ("Name", "name"),
-            ("Frequency", "frequency"),
-            ("Instructions", "instructions"),
-        ):
-            field_match = re.search(rf"(?m)^- {re.escape(field)}:\s*(.+?)\s*$", block)
-            if not field_match:
-                raise ValueError(
-                    f"automation-config.md entry {automation_id} missing field: {field}"
-                )
-            value = field_match.group(1).strip()
-            if key in {"name", "target_thread_title", "frequency"}:
-                value = value.rstrip(".")
-            metadata[key] = value
-        metadata["target_thread_title"] = metadata["name"]
-        automations[automation_id] = metadata
-    if not automations:
-        raise ValueError("automation-config.md must define at least one automation entry")
-    return automations
-
-
-def build_automation_state_entry(metadata: dict[str, object]) -> dict[str, object]:
-    return {
-        "status": None,
-        "canonical_id": None,
-        "target_thread_id": None,
-        "target_thread_title": metadata["target_thread_title"],
-        "target_thread_status": None,
-        "last_installed": None,
-        "frequency": metadata["frequency"],
-        "name": metadata["name"],
-        "instructions": metadata["instructions"],
-    }
-
-
-def build_onboarding_state(automations: dict[str, dict[str, object]]) -> dict[str, object]:
-    with ONBOARDING_STATE_TEMPLATE.open("r", encoding="utf-8") as handle:
-        state = json.load(handle)
-    state["automations"] = {
-        automation_id: build_automation_state_entry(metadata)
-        for automation_id, metadata in automations.items()
-    }
-    return state
-
-
-def write_text_if_needed(path: Path, text: str, overwrite: bool) -> str:
-    existed = path.exists()
-    if existed and not overwrite:
-        return "preserved"
-    path.write_text(text, encoding="utf-8")
-    return "overwritten" if existed else "created"
-
-
-def main() -> int:
-    args = parse_args()
-
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-
-    missing_templates = [
-        str(path.relative_to(SKILL_ROOT))
-        for path in (
-            USER_CONTEXT_TEMPLATE,
-            AUTOMATION_CONFIG,
-            ONBOARDING_STATE_TEMPLATE,
-        )
-        if not path.exists()
-    ]
-    if missing_templates:
-        print("Missing Sales bundled state source(s):", file=sys.stderr)
-        for missing in missing_templates:
-            print(f"- {missing}", file=sys.stderr)
-        return 1
-
-    try:
-        user_context_scaffold = build_user_context_scaffold()
-        automations = load_automation_config()
-        validate_json(ONBOARDING_STATE_TEMPLATE)
-    except (json.JSONDecodeError, ValueError) as exc:
-        print(
-            f"Invalid Sales bundled state source: {exc}",
-            file=sys.stderr,
-        )
-        return 1
-
-    state_dir.mkdir(parents=True, exist_ok=True)
-
-    user_context_path = state_dir / "user-context.md"
-    onboarding_state_path = state_dir / "onboarding-state.json"
-
-    user_context_text = USER_CONTEXT_NOTE + user_context_scaffold
-    user_context_result = write_text_if_needed(
-        user_context_path,
-        user_context_text,
-        args.overwrite,
-    )
-
-    onboarding_state_text = json.dumps(build_onboarding_state(automations), indent=2) + "\n"
-    onboarding_state_result = write_text_if_needed(
-        onboarding_state_path,
-        onboarding_state_text,
-        args.overwrite,
-    )
-
-    try:
-        validate_json(onboarding_state_path)
-    except json.JSONDecodeError as exc:
-        print(
-            f"Invalid written Sales state JSON: {exc}",
-            file=sys.stderr,
-        )
-        return 1
-
-    print(f"Sales state directory: {state_dir}")
-    print(f"user-context.md: {user_context_result}")
-    print(f"onboarding-state.json: {onboarding_state_result}")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/sales/skills/user-context/scripts/reset_user_context_state.py b/plugins/sales/skills/user-context/scripts/reset_user_context_state.py
deleted file mode 100755
index 78a5614..0000000
--- a/plugins/sales/skills/user-context/scripts/reset_user_context_state.py
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/usr/bin/env python3
-"""Back up and clear local Sales state files."""
-
-from __future__ import annotations
-
-import argparse
-import shutil
-import sys
-from datetime import datetime
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import STATE_DIR_HELP, STATE_FILENAMES, resolve_state_dir
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Move active Sales local state files into a timestamped sibling backup directory. "
-            "This leaves Sales ready for fresh onboarding without deleting the prior state."
-        )
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument(
-        "--state-dir",
-        type=Path,
-        default=None,
-        help=STATE_DIR_HELP,
-    )
-    parser.add_argument(
-        "--backup-dir",
-        type=Path,
-        default=None,
-        help=(
-            "Backup directory to create. Defaults to a timestamped sibling of the state "
-            "directory, such as <state-parent>/sales-backup-YYYYMMDD-HHMMSS."
-        ),
-    )
-    parser.add_argument(
-        "--dry-run",
-        action="store_true",
-        help="Print what would move without changing files.",
-    )
-    return parser.parse_args()
-
-
-def resolve_paths(args: argparse.Namespace) -> tuple[Path, Path]:
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-    if args.backup_dir:
-        backup_dir = args.backup_dir.expanduser()
-    else:
-        timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-        backup_dir = state_dir.parent / f"{state_dir.name}-backup-{timestamp}"
-    return state_dir, backup_dir
-
-
-def main() -> int:
-    args = parse_args()
-    state_dir, backup_dir = resolve_paths(args)
-
-    state_files = [state_dir / filename for filename in STATE_FILENAMES]
-    existing_state_files = [path for path in state_files if path.exists()]
-
-    if not existing_state_files:
-        print(f"No active Sales state files found in: {state_dir}")
-        return 0
-
-    if backup_dir.exists():
-        print(f"Backup directory already exists: {backup_dir}", file=sys.stderr)
-        return 1
-
-    if args.dry_run:
-        print(f"Sales state directory: {state_dir}")
-        print(f"Backup directory: {backup_dir}")
-        for path in existing_state_files:
-            print(f"would move: {path.name}")
-        print("Dry run only; no files were moved.")
-        return 0
-
-    backup_dir.mkdir(parents=True)
-
-    moved = []
-    for path in existing_state_files:
-        shutil.move(str(path), str(backup_dir / path.name))
-        moved.append(path.name)
-
-    removed_state_dir = False
-    try:
-        state_dir.rmdir()
-        removed_state_dir = True
-    except OSError:
-        removed_state_dir = False
-
-    print(f"Sales state directory: {state_dir}")
-    print(f"Backup directory: {backup_dir}")
-    for filename in moved:
-        print(f"moved: {filename}")
-    if removed_state_dir:
-        print("state directory: removed because it was empty")
-    else:
-        print("state directory: kept because it still contains other files")
-    print("Restore by moving the backed-up files back into the Sales state directory.")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/sales/skills/user-context/scripts/sales_preflight.py b/plugins/sales/skills/user-context/scripts/sales_preflight.py
deleted file mode 100644
index 6d01ea1..0000000
--- a/plugins/sales/skills/user-context/scripts/sales_preflight.py
+++ /dev/null
@@ -1,2301 +0,0 @@
-#!/usr/bin/env python3
-"""Read Sales local state and emit a compact deterministic preflight payload."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import re
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-from state_paths import STATE_DIR_HELP, resolve_state_dir
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[3]
-ONBOARDING_REFERENCE = PLUGIN_ROOT / "skills/user-context/references/onboarding.md"
-SOURCE_CATEGORY_CONFIG_REFERENCE = (
-    PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.json"
-)
-AUTOMATION_CONFIG_REFERENCE = (
-    PLUGIN_ROOT / "skills/user-context/plugin-author-config/automation-config.md"
-)
-
-DEFAULT_MAX_CONTEXT_BYTES = 2_000_000
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description=(
-            "Read Sales user-context and onboarding-state files "
-            "and return context plus workflow control obligations as JSON."
-        )
-    )
-    parser.add_argument(
-        "--workflow",
-        default="ordinary",
-        help="Calling Sales workflow name, for example prepare-for-meeting.",
-    )
-    parser.add_argument(
-        "--request-mode",
-        choices=("ordinary_workflow", "direct_onboarding_status", "guided_onboarding_workflow"),
-        default="ordinary_workflow",
-        help=(
-            "Whether the caller is doing ordinary work, direct setup/onboarding, "
-            "or a focused workflow launched from the onboarding path."
-        ),
-    )
-    parser.add_argument(
-        "--codex-home",
-        type=Path,
-        default=None,
-        help="Codex home directory. Defaults to $CODEX_HOME or ~/.codex.",
-    )
-    parser.add_argument(
-        "--state-dir",
-        type=Path,
-        default=None,
-        help=STATE_DIR_HELP,
-    )
-    parser.add_argument(
-        "--max-context-bytes",
-        type=int,
-        default=DEFAULT_MAX_CONTEXT_BYTES,
-        help=(
-            "Compatibility limit for raw file reads. Compact preflight still renders a "
-            "derived summary instead of raw file contents."
-        ),
-    )
-    parser.add_argument(
-        "--section",
-        action="append",
-        default=[],
-        help=(
-            "Markdown section heading to return even when full context is omitted. Can be repeated."
-        ),
-    )
-    return parser.parse_args()
-
-
-def sha256_text(text: str) -> str:
-    return hashlib.sha256(text.encode("utf-8")).hexdigest()
-
-
-def file_mtime(path: Path) -> str | None:
-    try:
-        return datetime.fromtimestamp(path.stat().st_mtime, tz=timezone.utc).isoformat()
-    except OSError:
-        return None
-
-
-def section_bounds(markdown: str) -> dict[str, tuple[int, int, str]]:
-    matches = list(re.finditer(r"(?m)^(#{1,6})\s+(.+?)\s*$", markdown))
-    bounds: dict[str, tuple[int, int, str]] = {}
-    for index, match in enumerate(matches):
-        start = match.start()
-        level = len(match.group(1))
-        end = len(markdown)
-        for next_match in matches[index + 1 :]:
-            next_level = len(next_match.group(1))
-            if next_level <= level:
-                end = next_match.start()
-                break
-        heading = match.group(2).strip()
-        bounds[heading.casefold()] = (start, end, heading)
-    return bounds
-
-
-def extract_sections(markdown: str, requested: list[str]) -> list[dict[str, str]]:
-    if not requested:
-        return []
-    bounds = section_bounds(markdown)
-    sections: list[dict[str, str]] = []
-    for heading in requested:
-        bound = bounds.get(heading.casefold())
-        if not bound:
-            sections.append({"heading": heading, "status": "missing", "content": ""})
-            continue
-        start, end, canonical_heading = bound
-        sections.append(
-            {
-                "heading": canonical_heading,
-                "status": "present",
-                "content": markdown[start:end].strip(),
-            }
-        )
-    return sections
-
-
-def parse_saved_context_heading(line: str) -> dict[str, str]:
-    match = re.match(r"^\[(.+)\]\(([^)]+)\)\s*$", line)
-    if match:
-        return {"name": match.group(1).strip(), "url": match.group(2).strip()}
-    return {"name": line.strip()}
-
-
-def guardrail_sentences(text: str) -> list[str]:
-    sentences = re.split(r"(?<=[.!?])\s+", text.strip())
-    return [
-        sentence
-        for sentence in sentences
-        if re.search(
-            r"\b(avoid|approved|current|currentness|do not|fresh|higher authority|"
-            r"human review|keep|unless|verify|review|sensitive|treat)\b",
-            sentence,
-            flags=re.IGNORECASE,
-        )
-    ]
-
-
-def summarize_user_context(markdown: str | None) -> dict[str, Any]:
-    """Return a compact complete rendering of saved Sales user context."""
-    if not markdown:
-        return {"entries": [], "unresolved_categories": []}
-
-    entries: list[str] = []
-    unresolved_categories: list[str] = []
-    category: str | None = None
-    in_saved_context = False
-    current_entry: dict[str, Any] | None = None
-
-    def flush_entry() -> None:
-        nonlocal current_entry
-        if current_entry:
-            parts = [f"{current_entry['category']}: {current_entry['name']}"]
-            if current_entry.get("url"):
-                parts.append(f"<{current_entry['url']}>")
-            if current_entry.get("context"):
-                parts.append(current_entry["context"])
-            guardrails = guardrail_sentences(str(current_entry.get("use") or ""))
-            if guardrails:
-                parts.append(" ".join(guardrails))
-            notes = current_entry.get("notes")
-            if notes:
-                parts.extend(str(note) for note in notes)
-            entries.append(" | ".join(parts))
-            current_entry = None
-
-    for raw_line in markdown.splitlines():
-        line = raw_line.strip()
-        if not line or line.startswith("<!--") or line.startswith("-->"):
-            continue
-        heading = re.match(r"^# ([^#].*?)\s*$", line)
-        if heading:
-            flush_entry()
-            category = heading.group(1).strip()
-            in_saved_context = False
-            continue
-        if line == "## Saved Links And Context":
-            flush_entry()
-            in_saved_context = True
-            continue
-        if not category or not in_saved_context:
-            continue
-        if line == "Status: Not Provided":
-            flush_entry()
-            unresolved_categories.append(category)
-            continue
-        if not line.startswith("- "):
-            flush_entry()
-            current_entry = {"category": category, **parse_saved_context_heading(line)}
-            continue
-        if not current_entry:
-            continue
-        bullet = line[2:].strip()
-        for prefix, key in (
-            ("Date Added:", "date_added"),
-            ("Useful Context:", "context"),
-            ("Future Use:", "use"),
-        ):
-            if bullet.startswith(prefix):
-                current_entry[key] = bullet[len(prefix) :].strip().rstrip(".")
-                break
-        else:
-            current_entry.setdefault("notes", []).append(bullet)
-    flush_entry()
-    return {"entries": entries, "unresolved_categories": unresolved_categories}
-
-
-NEGATIVE_SOURCE_PREFERENCE_RE = re.compile(
-    r"\b(do not use|don't use|never use|declin(?:e|ed)|skip|not preferred|avoid)\b",
-    flags=re.IGNORECASE,
-)
-
-SOURCE_PREFERENCE_SECTION_HINTS = {
-    "source",
-    "sources",
-    "preference",
-    "preferences",
-    "preferred",
-    "default",
-    "defaults",
-    "connector",
-    "connectors",
-    "crm",
-    "calendar",
-    "meeting",
-    "notes",
-    "docs",
-    "documents",
-    "email",
-    "messaging",
-    "messages",
-    "enrichment",
-    "agreement",
-    "agreements",
-    "contract",
-    "contracts",
-    "signature",
-    "signatures",
-}
-
-SOURCE_CATEGORY_ALIASES = {
-    "crm": {"crm", "account", "accounts", "opportunity", "opportunities", "pipeline"},
-    "calendar": {"calendar", "meetings"},
-    "meeting_notes": {"meeting", "meetings", "notes", "transcript", "transcripts", "recordings"},
-    "document_store": {"docs", "documents", "drive", "sharepoint", "notion"},
-    "data_enrichment": {"enrichment", "firmographic", "technographic", "market", "signals"},
-    "external_messaging": {"external", "customer", "customers", "email", "thread", "threads"},
-    "internal_messaging": {"internal", "slack", "teams", "messages", "messaging"},
-    "agreements": {
-        "agreement",
-        "agreements",
-        "approval",
-        "approvals",
-        "contract",
-        "contracts",
-        "docusign",
-        "esignature",
-        "signature",
-        "signatures",
-    },
-}
-
-
-def source_preference_section_name(category_name: str) -> bool:
-    tokens = {
-        token.casefold()
-        for token in re.findall(r"[a-z0-9]+", category_name, flags=re.IGNORECASE)
-    }
-    return bool(tokens & SOURCE_PREFERENCE_SECTION_HINTS)
-
-
-def entry_mentions_source_category(entry: str, category_id: str, label: str) -> bool:
-    entry_tokens = {
-        token.casefold()
-        for token in re.findall(r"[a-z0-9]+", entry, flags=re.IGNORECASE)
-    }
-    aliases = {
-        token.casefold()
-        for token in re.findall(
-            r"[a-z0-9]+",
-            f"{category_id.replace('_', ' ')} {label}",
-            flags=re.IGNORECASE,
-        )
-        if len(token) > 2
-    }
-    aliases.update(SOURCE_CATEGORY_ALIASES.get(category_id, set()))
-    return bool(entry_tokens & aliases)
-
-
-def extract_saved_source_preferences(
-    markdown: str | None,
-    source_category_config: dict[str, dict[str, Any]],
-) -> dict[str, list[str]]:
-    """Find exact configured source preferences that the user saved in Sales memory."""
-    if not markdown:
-        return {}
-    preferences: dict[str, list[str]] = {}
-    for entry in summarize_user_context(markdown).get("entries", []):
-        if not isinstance(entry, str) or ":" not in entry:
-            continue
-        section_name, _ = entry.split(":", 1)
-        section_has_source_hint = source_preference_section_name(section_name)
-        if NEGATIVE_SOURCE_PREFERENCE_RE.search(entry):
-            continue
-        normalized_entry = normalize_app_name(entry)
-        for category_id, metadata in source_category_config.items():
-            label = str(metadata.get("label") or category_id)
-            if not section_has_source_hint and not entry_mentions_source_category(
-                entry,
-                category_id,
-                label,
-            ):
-                continue
-            for preferred_app in preferred_source_routes(metadata):
-                preferred_app_name = str(preferred_app)
-                if normalize_app_name(preferred_app_name) not in normalized_entry:
-                    continue
-                category_preferences = preferences.setdefault(category_id, [])
-                if preferred_app_name not in category_preferences:
-                    category_preferences.append(preferred_app_name)
-    return preferences
-
-
-def read_text_payload(path: Path, max_context_bytes: int, sections: list[str]) -> dict[str, Any]:
-    payload: dict[str, Any] = {
-        "path": str(path),
-        "status": "missing",
-        "bytes": 0,
-        "sha256": None,
-        "mtime": None,
-        "content": None,
-        "sections": [],
-        "omitted": False,
-        "error": None,
-    }
-    if not path.exists():
-        return payload
-    try:
-        text = path.read_text(encoding="utf-8")
-    except OSError as exc:
-        payload["status"] = "unreadable"
-        payload["error"] = str(exc)
-        return payload
-    except UnicodeDecodeError as exc:
-        payload["status"] = "unreadable"
-        payload["error"] = f"utf-8 decode failed: {exc}"
-        return payload
-
-    encoded_len = len(text.encode("utf-8"))
-    payload.update(
-        {
-            "status": "present",
-            "bytes": encoded_len,
-            "sha256": sha256_text(text),
-            "mtime": file_mtime(path),
-            "sections": extract_sections(text, sections),
-        }
-    )
-    payload["content"] = text
-    if encoded_len > max_context_bytes:
-        payload["raw_content_exceeds_compat_limit"] = (
-            f"{encoded_len} > {max_context_bytes}"
-        )
-    return payload
-
-
-def read_json_payload(path: Path, max_context_bytes: int) -> dict[str, Any]:
-    payload = read_text_payload(path, max_context_bytes=max_context_bytes, sections=[])
-    payload["data"] = None
-    if payload["status"] != "present":
-        return payload
-    try:
-        payload["data"] = json.loads(payload["content"] or path.read_text(encoding="utf-8"))
-    except json.JSONDecodeError as exc:
-        payload["status"] = "unreadable"
-        payload["error"] = f"invalid json: {exc}"
-    return payload
-
-
-def markdown_template_from_onboarding_reference(heading_name: str) -> str:
-    text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-    heading = re.search(rf"(?m)^## {re.escape(heading_name)}\s*$", text)
-    if not heading:
-        raise RuntimeError(f"{heading_name} heading not found")
-    following = text[heading.end() :]
-    code_block = re.search(r"```md\n(.*?)\n```", following, flags=re.DOTALL)
-    if not code_block:
-        raise RuntimeError(f"{heading_name} markdown block not found")
-    return code_block.group(1).strip()
-
-
-def context_gap_note_template() -> str:
-    return markdown_template_from_onboarding_reference("Context Gap Note")
-
-
-ONBOARDING_STEP_DEFINITIONS = (
-    {
-        "id": "start_sales_onboarding",
-        "label": "Orientation",
-        "phrase": "orienting you to Sales onboarding",
-    },
-    {
-        "id": "connector_setup_confirmation",
-        "label": "Connector setup/confirmation",
-        "phrase": "completing Sales connector setup and source confirmation",
-    },
-    {
-        "id": "sales_automation_setup",
-        "label": "Sales automation setup",
-        "phrase": "setting up Sales automations and context discovery",
-        "parallel_continuation": True,
-    },
-    {
-        "id": "first_hero_prompt",
-        "label": "First hero prompt",
-        "phrase": "choosing and trying the first Sales hero prompt",
-    },
-    {
-        "id": "other_hero_prompts",
-        "label": "Other hero prompts",
-        "phrase": "trying the remaining guided Sales workflows",
-    },
-)
-
-NEXT_ONBOARDING_ACTION_PHRASES = {
-    step["id"]: step["phrase"] for step in ONBOARDING_STEP_DEFINITIONS
-}
-WEEKLY_DISCOVERY_AUTOMATION_ID = "weekly_sales_company_research"
-DAILY_TIPS_AUTOMATION_ID = "daily_sales_tips"
-SCHEDULED_HELP_AUTOMATION_IDS = [DAILY_TIPS_AUTOMATION_ID]
-ONBOARDING_AUTOMATION_IDS = [
-    WEEKLY_DISCOVERY_AUTOMATION_ID,
-    *SCHEDULED_HELP_AUTOMATION_IDS,
-]
-USER_FACING_SKILLS = (
-    "analyze-account-signals",
-    "build-competitive-brief",
-    "follow-up-after-call",
-    "suggest-sales-next-step",
-    "enrich-company-and-contact-data",
-    "plan-deal-strategy",
-    "find-key-internal-sources",
-    "prepare-for-meeting",
-    "prioritize-accounts",
-    "find-customer-quotes",
-    "review-forecast",
-    "get-rep-call-feedback",
-    "review-rep-call-trends",
-    "build-business-case",
-)
-HELPER_OR_ROUTER_SKILLS = {"salesforce", "hubspot", "zoominfo", "user-context", "index"}
-CORE_ONBOARDING_SKILLS = (
-    "prepare-for-meeting",
-    "follow-up-after-call",
-    "prioritize-accounts",
-)
-POST_COMPLETION_GUIDED_SKILLS = (
-    "analyze-account-signals",
-    "plan-deal-strategy",
-    "review-forecast",
-    "build-competitive-brief",
-    "find-customer-quotes",
-    "build-business-case",
-    "enrich-company-and-contact-data",
-    "find-key-internal-sources",
-    "get-rep-call-feedback",
-    "review-rep-call-trends",
-    "suggest-sales-next-step",
-)
-RESOLVED_TASK_STATUSES = {
-    "accepted_fallback_installed",
-    "complete",
-    "complete_with_fallback_automation",
-    "completed",
-    "completed_with_defaults",
-    "declined",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "done",
-    "fallback_weekday_check_in_installed",
-    "installed_fallback_current_thread",
-    "not_applicable",
-    "skipped",
-    "skipped_for_now",
-}
-
-ONBOARDING_RESOLVED_SETUP_DISPOSITIONS = {
-    "declined",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "not_applicable",
-    "skipped",
-    "skipped_for_now",
-    "unavailable",
-}
-
-FULL_AUTOMATION_READY_STATUSES = {"active", "installed", "ready", "verified"}
-FALLBACK_AUTOMATION_READY_STATUSES = {
-    "accepted_fallback_installed",
-    "complete_with_fallback_automation",
-    "fallback_daily_sales_tips_installed",
-    "fallback_weekday_check_in_installed",
-    "installed_fallback_current_thread",
-}
-AUTOMATION_READBACK_READY_STATUSES = {
-    "clean",
-    "read_back",
-    "readback_verified",
-    "verified",
-}
-TARGET_THREAD_READY_STATUSES = {
-    "pinned",
-    "ready",
-    "verified",
-    "verified_pinned",
-}
-
-
-def next_onboarding_action(onboarding_progress: dict[str, Any]) -> dict[str, str | None]:
-    task_list = onboarding_progress.get("task_list")
-    if not isinstance(task_list, list):
-        return {
-            "id": None,
-            "label": "Finish Sales onboarding",
-            "phrase": "finishing Sales onboarding",
-        }
-    for task in task_list:
-        if not isinstance(task, dict):
-            continue
-        status = str(task.get("status") or "").casefold()
-        if status in RESOLVED_TASK_STATUSES:
-            continue
-        if (
-            task.get("parallel_continuation")
-            and status == "in_progress"
-            and not task.get("action_required")
-        ):
-            continue
-        task_id = str(task.get("id") or "")
-        label = str(task.get("label") or "Finish Sales onboarding")
-        task_phrase = task.get("phrase")
-        return {
-            "id": task_id or None,
-            "label": label,
-            "phrase": (
-                task_phrase
-                if isinstance(task_phrase, str) and task_phrase
-                else NEXT_ONBOARDING_ACTION_PHRASES.get(
-                    task_id,
-                    label[:1].lower() + label[1:] if label else "finishing Sales onboarding",
-                )
-            ),
-        }
-    return {
-        "id": None,
-        "label": "Finish Sales onboarding",
-        "phrase": "finishing Sales onboarding",
-    }
-
-
-def ordinary_onboarding_cta_template(next_action: dict[str, str | None]) -> str:
-    template = markdown_template_from_onboarding_reference("Ordinary Workflow Onboarding CTA")
-    return template
-
-
-def core_onboarding_reminder_template(next_action: dict[str, str | None]) -> str:
-    template = markdown_template_from_onboarding_reference("Core Onboarding Reminder")
-    phrase = next_action.get("phrase") or next_action.get("label") or "finishing Sales core setup"
-    return template.replace("{next_core_action}", str(phrase))
-
-
-def parse_source_category_config() -> dict[str, dict[str, Any]]:
-    if not SOURCE_CATEGORY_CONFIG_REFERENCE.exists():
-        return {}
-    config = json.loads(SOURCE_CATEGORY_CONFIG_REFERENCE.read_text(encoding="utf-8"))
-    categories = config.get("categories") if isinstance(config, dict) else None
-    if not isinstance(categories, dict):
-        return {}
-    catalog: dict[str, dict[str, Any]] = {}
-    for category_id, raw_metadata in categories.items():
-        if not isinstance(raw_metadata, dict):
-            continue
-        preferred_apps = raw_metadata.get("preferred_apps")
-        preferred_plugins = raw_metadata.get("preferred_plugins")
-        relevant_skills = raw_metadata.get("relevant_skills")
-        metadata: dict[str, Any] = {
-            "label": raw_metadata.get("label") or category_id,
-            "preferred_plugins": preferred_plugins
-            if isinstance(preferred_plugins, list)
-            else [],
-            "preferred_apps": preferred_apps if isinstance(preferred_apps, list) else [],
-            "relevant_skills": relevant_skills if isinstance(relevant_skills, list) else [],
-        }
-        catalog[category_id] = metadata
-    return catalog
-
-
-def normalize_app_name(value: Any) -> str:
-    return re.sub(r"[^a-z0-9]+", "", str(value or "").casefold())
-
-
-def preferred_source_routes(metadata: dict[str, Any]) -> list[str]:
-    routes = [
-        *[str(plugin) for plugin in metadata.get("preferred_plugins") or []],
-        *[str(app) for app in metadata.get("preferred_apps") or []],
-    ]
-    return list(dict.fromkeys(route for route in routes if route))
-
-
-def normalize_source_kind(value: Any) -> str | None:
-    source_kind = str(value or "").casefold()
-    if source_kind in {"plugin", "app", "connector", "manual"}:
-        return source_kind
-    return None
-
-
-PLUGIN_INSTALL_EVIDENCE = {
-    "installed_plugin_skill_visible",
-    "plugin_in_available_plugins",
-    "plugin_owned_skill_visible",
-    "request_plugin_install_completed",
-    "user_confirmed_installed_plugin",
-}
-
-PLUGIN_SKILL_SURFACE_EVIDENCE = {
-    "plugin_skills_visible",
-    "plugin_owned_skill_visible",
-    "plugin_owned_skills_visible",
-}
-
-PLUGIN_TOOL_SURFACE_EVIDENCE = {
-    "callable_app_tools_visible",
-    "callable_plugin_tools_visible",
-    "plugin_tools_visible",
-}
-
-PLUGIN_FIRST_SETUP_STATUSES = {
-    "active",
-    "missing",
-    "needs_confirmation",
-    "unavailable",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "skipped_for_now",
-}
-
-
-def compact_mapping(value: Any) -> dict[str, Any] | None:
-    if not isinstance(value, dict):
-        return None
-    compact = {
-        str(key): item
-        for key, item in value.items()
-        if isinstance(key, str) and (isinstance(item, (str, int, float, bool)) or item is None)
-    }
-    return compact or None
-
-
-def default_skill_surface(source_kind: str | None, metadata: dict[str, Any]) -> str | None:
-    if source_kind == "plugin":
-        return "plugin_owned"
-    if source_kind in {"app", "connector"}:
-        if metadata.get("relevant_skills"):
-            return "sales_vendored_helper"
-        return "direct_tool"
-    if source_kind == "manual":
-        return "manual"
-    return None
-
-
-def default_runtime_action(source_kind: str | None, skill_surface: str | None) -> str | None:
-    if source_kind == "plugin":
-        return "use_plugin_owned_skill_or_tools"
-    if source_kind in {"app", "connector"}:
-        if skill_surface == "sales_vendored_helper":
-            return "use_connector_or_app_with_sales_helper_guidance"
-        return "use_direct_tool_when_workflow_needs_source"
-    if source_kind == "manual":
-        return "use_manual_or_exported_context"
-    return None
-
-
-def copy_configured_route_metadata(
-    entry: dict[str, Any],
-    raw_entry: dict[str, Any],
-    metadata: dict[str, Any],
-) -> None:
-    source_kind = normalize_source_kind(raw_entry.get("source_kind"))
-    if source_kind is None:
-        if isinstance(raw_entry.get("plugin"), dict):
-            source_kind = "plugin"
-        elif isinstance(raw_entry.get("connector"), dict):
-            source_kind = "connector"
-        elif isinstance(raw_entry.get("app"), dict):
-            source_kind = "app"
-        elif isinstance(raw_entry.get("manual"), dict):
-            source_kind = "manual"
-        elif entry.get("status") == "active" and entry.get("preferred"):
-            source_kind = "connector"
-
-    if source_kind:
-        entry["source_kind"] = source_kind
-
-    for key in ("plugin", "app", "connector", "manual"):
-        compact = compact_mapping(raw_entry.get(key))
-        if compact is not None:
-            entry[key] = compact
-
-    skill_surface = str(raw_entry.get("skill_surface") or "") or default_skill_surface(
-        source_kind,
-        metadata,
-    )
-    if skill_surface:
-        entry["skill_surface"] = skill_surface
-
-    runtime_action = str(raw_entry.get("runtime_action") or "") or default_runtime_action(
-        source_kind,
-        skill_surface,
-    )
-    if runtime_action:
-        entry["runtime_action"] = runtime_action
-
-    for evidence_key in (
-        "installed_evidence",
-        "plugin_install_evidence",
-        "app_surface_evidence",
-        "connector_surface_evidence",
-        "skill_surface_evidence",
-        "tool_surface_evidence",
-    ):
-        evidence_value = raw_entry.get(evidence_key)
-        if isinstance(evidence_value, str) and evidence_value:
-            entry[evidence_key] = evidence_value
-
-
-def compact_source_route(raw_route: Any, metadata: dict[str, Any]) -> dict[str, Any] | None:
-    if not isinstance(raw_route, dict):
-        return None
-    route: dict[str, Any] = {}
-    for key in ("status", "preferred", "name", "label", "fallback", "setup_action"):
-        value = raw_route.get(key)
-        if isinstance(value, str) and value:
-            route[key] = value
-    copy_configured_route_metadata(route, raw_route, metadata)
-    if "preferred" not in route:
-        for route_key in ("plugin", "app", "connector", "manual"):
-            raw_value = raw_route.get(route_key)
-            if isinstance(raw_value, str) and raw_value:
-                route["preferred"] = raw_value
-                break
-            if isinstance(raw_value, dict):
-                for name_key in ("name", "display_name", "id"):
-                    name_value = raw_value.get(name_key)
-                    if isinstance(name_value, str) and name_value:
-                        route["preferred"] = name_value
-                        break
-                if "preferred" in route:
-                    break
-    if "runtime_action" not in route and route.get("source_kind"):
-        runtime_action = default_runtime_action(
-            str(route.get("source_kind") or ""),
-            str(route.get("skill_surface") or ""),
-        )
-        if runtime_action:
-            route["runtime_action"] = runtime_action
-    return route or None
-
-
-def active_plugin_route_present(confirmation: dict[str, Any]) -> bool:
-    if confirmation.get("source_kind") == "plugin":
-        return True
-    routes = confirmation.get("routes")
-    if not isinstance(routes, list):
-        return False
-    return any(isinstance(route, dict) and route.get("source_kind") == "plugin" for route in routes)
-
-
-def unproven_plugin_route(entry: dict[str, Any]) -> bool:
-    if entry.get("source_kind") != "plugin":
-        return False
-    installed_evidence = str(entry.get("installed_evidence") or "")
-    plugin_install_evidence = str(entry.get("plugin_install_evidence") or "")
-    if installed_evidence in PLUGIN_INSTALL_EVIDENCE:
-        return False
-    if plugin_install_evidence not in PLUGIN_INSTALL_EVIDENCE:
-        return True
-    if entry.get("skill_surface") == "plugin_owned":
-        skill_surface_evidence = str(entry.get("skill_surface_evidence") or "")
-        if skill_surface_evidence not in PLUGIN_SKILL_SURFACE_EVIDENCE:
-            return True
-    surface_evidence = {
-        str(entry.get("app_surface_evidence") or ""),
-        str(entry.get("tool_surface_evidence") or ""),
-        str(entry.get("skill_surface_evidence") or ""),
-    }
-    return not surface_evidence.intersection(
-        PLUGIN_TOOL_SURFACE_EVIDENCE | PLUGIN_SKILL_SURFACE_EVIDENCE
-    )
-
-
-def plugin_route_candidate(raw_entry: dict[str, Any], entry: dict[str, Any]) -> str | None:
-    for value in (
-        entry.get("preferred"),
-        raw_entry.get("preferred"),
-        raw_entry.get("label"),
-    ):
-        if isinstance(value, str) and value:
-            return value
-    plugin = raw_entry.get("plugin")
-    if isinstance(plugin, str) and plugin:
-        return plugin
-    if isinstance(plugin, dict):
-        for key in ("name", "id"):
-            value = plugin.get(key)
-            if isinstance(value, str) and value:
-                return value
-    return None
-
-
-def list_from_inventory(inventory: dict[str, Any], *keys: str) -> list[str]:
-    values: list[str] = []
-    for key in keys:
-        raw = inventory.get(key)
-        if isinstance(raw, list):
-            values.extend(str(item) for item in raw if isinstance(item, str) and item)
-    return values
-
-
-def first_matching_app(preferred_apps: list[str], candidates: set[str]) -> str | None:
-    for app in preferred_apps:
-        if normalize_app_name(app) in candidates:
-            return app
-    return None
-
-
-def matching_apps(preferred_apps: list[str], candidates: set[str]) -> list[str]:
-    return [app for app in preferred_apps if normalize_app_name(app) in candidates]
-
-
-def missing_source_it_options(preferred_apps: list[str], category_id: str = "") -> list[str]:
-    source_list = ", ".join(preferred_apps) if preferred_apps else "a matching source"
-    if category_id == "data_enrichment":
-        return [
-            (
-                "Ask IT or a workspace admin whether Codex access can be enabled for "
-                f"one of: {source_list}."
-            )
-        ]
-    return [
-        f"Ask IT or a workspace admin to install or enable one of: {source_list}.",
-        "Use exported, pasted, or manually supplied context for the current workflow.",
-    ]
-
-
-def normalize_connector_confirmation_entry(
-    category_id: str,
-    metadata: dict[str, Any],
-    raw_entry: Any,
-    inventory: dict[str, Any],
-) -> dict[str, Any]:
-    label = metadata.get("label") or category_id
-    preferred_apps = [str(app) for app in metadata.get("preferred_apps") or []]
-    preferred_plugins = [str(plugin) for plugin in metadata.get("preferred_plugins") or []]
-    preferred_routes = preferred_source_routes(metadata)
-    base: dict[str, Any] = {
-        "label": label,
-        "preferred_apps": preferred_apps,
-        "state_scope": "onboarding_only_not_durable_connector_readiness",
-        "workflow_time_behavior": "attempt_actual_reads_only_when_a_workflow_needs_the_source",
-        "eager_read": False,
-    }
-    if preferred_plugins:
-        base["preferred_plugins"] = preferred_plugins
-
-    if isinstance(raw_entry, dict):
-        status = str(raw_entry.get("status") or "").casefold()
-        if status == "available":
-            status = "needs_confirmation"
-        if status in {"selected", "selected_for_verification", "needs_setup_probe"}:
-            status = "needs_confirmation"
-        if status == "not_available":
-            status = "missing"
-        if status in ONBOARDING_RESOLVED_SETUP_DISPOSITIONS:
-            entry = dict(base)
-            entry["status"] = status
-            entry["setup_action"] = "none_try_on_use"
-            copy_configured_route_metadata(entry, raw_entry, metadata)
-            return entry
-        if status in {"active", "needs_confirmation", "missing"}:
-            entry = dict(base)
-            entry["status"] = status
-            if status == "active":
-                preferred = raw_entry.get("preferred") or raw_entry.get("app")
-                if preferred:
-                    entry["preferred"] = str(preferred)
-                fallback = raw_entry.get("fallback") or raw_entry.get("backup")
-                if fallback:
-                    entry["fallback"] = str(fallback)
-                raw_routes = raw_entry.get("routes")
-                if isinstance(raw_routes, list):
-                    compacted_routes = [
-                        route
-                        for route in (
-                            compact_source_route(raw_route, metadata) for raw_route in raw_routes
-                        )
-                        if route is not None
-                    ]
-                    routes = [
-                        route
-                        for route in compacted_routes
-                        if not unproven_plugin_route(route)
-                    ]
-                    if routes:
-                        entry["routes"] = routes
-                        if "preferred" not in entry:
-                            first_preferred = routes[0].get("preferred")
-                            if isinstance(first_preferred, str) and first_preferred:
-                                entry["preferred"] = first_preferred
-                    elif compacted_routes:
-                        entry = dict(base)
-                        entry["status"] = "needs_confirmation"
-                        entry["candidates"] = preferred_routes
-                        entry["setup_action"] = "run_plugin_first_source_setup"
-                        entry["setup_note"] = (
-                            "plugin_route_requires_plugin_install_and_surface_evidence_before_active"
-                        )
-                if entry.get("status") == "active":
-                    if raw_entry.get("confirmed_at"):
-                        entry["confirmed_at"] = raw_entry["confirmed_at"]
-                    entry["setup_action"] = "none_try_on_use"
-                    copy_configured_route_metadata(entry, raw_entry, metadata)
-                    if unproven_plugin_route(entry):
-                        entry = dict(base)
-                        entry["status"] = "needs_confirmation"
-                        entry["candidates"] = preferred_routes
-                        entry["setup_action"] = "run_plugin_first_source_setup"
-                        entry["setup_note"] = (
-                            "plugin_route_requires_plugin_install_and_surface_evidence_before_active"
-                        )
-            elif status == "needs_confirmation":
-                candidates = raw_entry.get("candidates")
-                if not isinstance(candidates, list) or not candidates:
-                    selected = raw_entry.get("preferred") or raw_entry.get("app")
-                    if selected:
-                        candidates = [str(selected)]
-                    else:
-                        candidates = preferred_routes
-                entry["candidates"] = [str(candidate) for candidate in candidates]
-                selected = raw_entry.get("preferred") or raw_entry.get("app")
-                if selected:
-                    entry["preferred"] = str(selected)
-                    entry["setup_action"] = "verify_selected_app_with_simple_read"
-                else:
-                    entry["setup_action"] = "ask_user_to_choose_preferred_app"
-                copy_configured_route_metadata(entry, raw_entry, metadata)
-            else:
-                options = raw_entry.get("options")
-                if not isinstance(options, list) or not options:
-                    options = preferred_routes
-                entry["options"] = [str(option) for option in options]
-                entry["it_admin_options"] = raw_entry.get(
-                    "it_admin_options"
-                ) or missing_source_it_options(entry["options"], category_id)
-                entry["setup_action"] = "explain_workflow_impact_and_it_admin_options"
-                copy_configured_route_metadata(entry, raw_entry, metadata)
-            return entry
-
-    configured_apps = {
-        normalize_app_name(app)
-        for app in list_from_inventory(
-            inventory,
-            "active_apps",
-            "active",
-            "installed_and_active",
-            "connected_apps",
-            "available_apps",
-            "available",
-            "installed_apps",
-            "surfaced_apps",
-        )
-    }
-    available_matches = matching_apps(preferred_apps, configured_apps)
-    if available_matches:
-        entry = dict(base)
-        entry.update(
-            {
-                "status": "needs_confirmation",
-                "candidates": available_matches,
-                "setup_action": "run_plugin_first_source_setup",
-            }
-        )
-        return entry
-
-    if inventory:
-        entry = dict(base)
-        entry.update(
-            {
-                "status": "missing",
-                "options": preferred_routes,
-                "it_admin_options": missing_source_it_options(preferred_routes, category_id),
-                "setup_action": "run_plugin_first_source_setup",
-            }
-        )
-        return entry
-
-    entry = dict(base)
-    entry.update(
-        {
-            "status": "needs_confirmation",
-            "candidates": preferred_routes,
-            "setup_action": "run_plugin_first_source_setup",
-        }
-    )
-    return entry
-
-
-def build_connector_confirmation(
-    source_category_config: dict[str, dict[str, Any]],
-    onboarding_data: Any,
-) -> dict[str, dict[str, Any]]:
-    raw_confirmation = (
-        onboarding_data.get("connector_confirmation") if isinstance(onboarding_data, dict) else None
-    )
-    if not isinstance(raw_confirmation, dict):
-        raw_confirmation = {}
-    inventory = (
-        onboarding_data.get("connector_inventory") if isinstance(onboarding_data, dict) else {}
-    )
-    if not isinstance(inventory, dict):
-        inventory = {}
-
-    confirmation: dict[str, dict[str, Any]] = {}
-    raw_preferences = raw_confirmation.get("preferences")
-    if not isinstance(raw_preferences, dict):
-        raw_preferences = {}
-    for category_id, metadata in source_category_config.items():
-        raw_entry = raw_confirmation.get(category_id)
-        if raw_entry is None:
-            raw_entry = raw_preferences.get(category_id)
-        confirmation[category_id] = normalize_connector_confirmation_entry(
-            category_id,
-            metadata,
-            raw_entry,
-            inventory,
-        )
-    return confirmation
-
-
-def compact_source_categories(
-    source_category_config: dict[str, dict[str, Any]],
-    connector_confirmation: dict[str, dict[str, Any]],
-    saved_source_preferences: dict[str, list[str]] | None = None,
-) -> dict[str, Any]:
-    categories: dict[str, dict[str, Any]] = {}
-    for category_id, metadata in source_category_config.items():
-        confirmation = connector_confirmation.get(category_id, {})
-        status = str(confirmation.get("status") or "unknown").casefold()
-        preferred_apps = [str(app) for app in metadata.get("preferred_apps") or []]
-        preferred_plugins = [str(plugin) for plugin in metadata.get("preferred_plugins") or []]
-        preferred_routes = preferred_source_routes(metadata)
-        entry: dict[str, Any] = {
-            "label": metadata.get("label") or category_id,
-            "status": status,
-        }
-        if status != "active":
-            if preferred_plugins:
-                entry["preferred_plugins"] = preferred_plugins
-            entry["preferred_apps"] = preferred_apps
-        if confirmation.get("preferred") is not None:
-            entry["preferred"] = confirmation.get("preferred")
-        if confirmation.get("fallback") is not None:
-            entry["fallback"] = confirmation.get("fallback")
-        if confirmation.get("candidates"):
-            entry["candidates"] = confirmation.get("candidates")
-        if confirmation.get("options"):
-            entry["options"] = confirmation.get("options")
-        for key in (
-            "source_kind",
-            "skill_surface",
-            "plugin",
-            "app",
-            "connector",
-            "manual",
-            "installed_evidence",
-            "plugin_install_evidence",
-            "app_surface_evidence",
-            "connector_surface_evidence",
-            "skill_surface_evidence",
-            "tool_surface_evidence",
-            "runtime_action",
-            "routes",
-            "setup_note",
-        ):
-            if confirmation.get(key) is not None:
-                entry[key] = confirmation.get(key)
-        helper_skills = metadata.get("relevant_skills") or []
-        if helper_skills and confirmation.get("source_kind") != "plugin":
-            entry["helper_skills"] = helper_skills
-            entry["helper_skills_apply_when"] = ["app", "connector"]
-        if status != "active" or confirmation.get("setup_action") != "none_try_on_use":
-            entry["setup_action"] = confirmation.get("setup_action")
-        if confirmation.get("it_admin_options"):
-            entry["it_admin_options"] = confirmation.get("it_admin_options")
-
-        saved_preferences = list((saved_source_preferences or {}).get(category_id, []))
-        explicit_preference = entry.get("preferred")
-        if isinstance(explicit_preference, str) and explicit_preference in preferred_routes:
-            saved_preferences.append(explicit_preference)
-        saved_preferences = list(dict.fromkeys(saved_preferences))
-        plugin_preference_order = list(dict.fromkeys([*saved_preferences, *preferred_routes]))
-        active_plugin_route = status == "active" and active_plugin_route_present(confirmation)
-        if plugin_preference_order and status in PLUGIN_FIRST_SETUP_STATUSES and not active_plugin_route:
-            previous_setup_action = entry.get("setup_action")
-            entry["plugin_preference_order"] = plugin_preference_order
-            if saved_preferences:
-                entry["saved_source_preferences"] = saved_preferences
-            entry["setup_action"] = "run_plugin_first_source_setup"
-            if previous_setup_action and previous_setup_action != entry["setup_action"]:
-                entry["fallback_setup_action"] = previous_setup_action
-            entry["setup_recovery"] = {
-                "type": "plugin_first_source_setup",
-                "preferred_sources": plugin_preference_order,
-                "plugin_preferred_over": "active_or_available_app_connector_routes",
-                "candidate_match": "candidate_name_display_id_or_app_connector_id_matches_preferred_source",
-                "candidate_lookup": "functions.list_available_plugins_to_install",
-                "install_request": "functions.request_plugin_install_after_user_approval",
-                "retry_policy": "retry_on_future_workflows_after_failed_setup_or_pending_visibility",
-                "decline_scope": "suppress_current_workflow_only_unless_user_saves_do_not_use",
-                "fallback": "use_existing_app_connector_route_or_manual_context_when_no_plugin_candidate_or_user_declines",
-            }
-        categories[category_id] = entry
-    return {
-        "resolution": "plugin_or_app_or_connector_then_manual_context",
-        "has_setup_gaps": any(
-            str(entry.get("status") or "").casefold() != "active"
-            or entry.get("setup_action") not in {None, "none_try_on_use"}
-            for entry in categories.values()
-        ),
-        "categories": categories,
-    }
-
-
-def parse_automation_config() -> dict[str, dict[str, Any]]:
-    if not AUTOMATION_CONFIG_REFERENCE.exists():
-        return {}
-    text = AUTOMATION_CONFIG_REFERENCE.read_text(encoding="utf-8")
-    if "## Default Automations" in text:
-        text = text.split("## Default Automations", 1)[1]
-    matches = list(re.finditer(r"(?m)^`([a-z0-9_]+)`\s*$", text))
-    catalog: dict[str, dict[str, Any]] = {}
-    for index, match in enumerate(matches):
-        automation_id = match.group(1)
-        end = matches[index + 1].start() if index + 1 < len(matches) else len(text)
-        block = text[match.end() : end]
-        entry: dict[str, Any] = {"id": automation_id}
-        for field, key in (
-            ("Name", "name"),
-            ("Frequency", "frequency"),
-            ("Instructions", "instructions"),
-        ):
-            field_match = re.search(rf"(?m)^- {re.escape(field)}:\s*(.+?)\s*$", block)
-            if field_match:
-                value = field_match.group(1).strip()
-                if key in {"name", "target_thread_title", "frequency"}:
-                    value = value.rstrip(".")
-                entry[key] = value
-        if "name" in entry:
-            entry["target_thread_title"] = entry["name"]
-        if all(
-            key in entry for key in ("name", "target_thread_title", "frequency", "instructions")
-        ):
-            catalog[automation_id] = entry
-    return catalog
-
-
-def compact_automation_state(
-    automation_config: dict[str, dict[str, Any]],
-    onboarding_data: Any,
-) -> dict[str, dict[str, Any]]:
-    raw_automations = (
-        onboarding_data.get("automations") if isinstance(onboarding_data, dict) else {}
-    )
-    if not isinstance(raw_automations, dict):
-        raw_automations = {}
-    automations: dict[str, dict[str, Any]] = {}
-    for automation_id, config in automation_config.items():
-        raw_state = raw_automations.get(automation_id)
-        if not isinstance(raw_state, dict):
-            raw_state = {}
-        automations[automation_id] = {
-            "status": raw_state.get("status") or "not_configured",
-            "frequency": config.get("frequency"),
-        }
-        if raw_state.get("initial_run_status") is not None:
-            automations[automation_id]["initial_run_status"] = raw_state.get(
-                "initial_run_status"
-            )
-    return automations
-
-
-def workflow_keys(records: Any) -> set[str]:
-    keys: set[str] = set()
-    if not isinstance(records, list):
-        return keys
-    for record in records:
-        if not isinstance(record, dict):
-            continue
-        for field in ("workflow", "key"):
-            value = record.get(field)
-            if isinstance(value, str) and value:
-                keys.add(value)
-    return keys
-
-
-def skill_experience_state(onboarding_data: Any) -> dict[str, dict[str, Any]]:
-    if not isinstance(onboarding_data, dict):
-        return {}
-    raw_state = onboarding_data.get("skill_experience")
-    if not isinstance(raw_state, dict):
-        return {}
-    state: dict[str, dict[str, Any]] = {}
-    for skill, raw_entry in raw_state.items():
-        if isinstance(skill, str) and isinstance(raw_entry, dict):
-            state[skill] = raw_entry
-    return state
-
-
-def skill_experience_entry(onboarding_data: Any, skill: str) -> dict[str, Any]:
-    return skill_experience_state(onboarding_data).get(skill, {})
-
-
-def skill_has_been_introduced(onboarding_data: Any, skill: str) -> bool:
-    entry = skill_experience_entry(onboarding_data, skill)
-    if entry.get("introduced_at"):
-        return True
-    return str(entry.get("status") or "").casefold() in {"introduced", "tried", "accepted"}
-
-
-def skill_has_been_tried(onboarding_data: Any, skill: str) -> bool:
-    entry = skill_experience_entry(onboarding_data, skill)
-    if entry.get("first_tried_at"):
-        return True
-    return str(entry.get("status") or "").casefold() in {"tried", "accepted", "completed"}
-
-
-def skill_has_been_dismissed(onboarding_data: Any, skill: str) -> bool:
-    entry = skill_experience_entry(onboarding_data, skill)
-    if entry.get("dismissed_at"):
-        return True
-    return str(entry.get("status") or "").casefold() in {"declined", "deferred", "skipped"}
-
-
-def completed_skill_experience_keys(onboarding_data: Any) -> set[str]:
-    return {
-        skill
-        for skill in skill_experience_state(onboarding_data)
-        if skill_has_been_tried(onboarding_data, skill)
-    }
-
-
-def deferred_skill_experience_keys(onboarding_data: Any) -> set[str]:
-    return {
-        skill
-        for skill in skill_experience_state(onboarding_data)
-        if skill_has_been_dismissed(onboarding_data, skill)
-    }
-
-
-def build_current_skill_experience(
-    workflow: str,
-    request_mode: str,
-    onboarding_data: Any,
-) -> dict[str, Any]:
-    skill = workflow or "ordinary"
-    entry = skill_experience_entry(onboarding_data, skill)
-    user_facing = skill in USER_FACING_SKILLS
-    introduced = skill_has_been_introduced(onboarding_data, skill)
-    tried = skill_has_been_tried(onboarding_data, skill)
-    if request_mode in {"direct_onboarding_status", "guided_onboarding_workflow"}:
-        cta_owner = "onboarding"
-    elif user_facing:
-        cta_owner = "skill"
-    else:
-        cta_owner = "none"
-    intro_eligible = (
-        user_facing
-        and not introduced
-        and request_mode in {"ordinary_workflow", "guided_onboarding_workflow"}
-    )
-    return {
-        "skill": skill,
-        "user_facing": user_facing,
-        "helper_or_router": skill in HELPER_OR_ROUTER_SKILLS,
-        "introduced": introduced,
-        "tried": tried,
-        "intro_eligible": intro_eligible,
-        "cta_owner": cta_owner,
-    }
-
-
-def onboarding_step_block(onboarding_data: Any, key: str) -> dict[str, Any]:
-    if not isinstance(onboarding_data, dict):
-        return {}
-    block = onboarding_data.get(key)
-    return block if isinstance(block, dict) else {}
-
-
-def raw_step_status(onboarding_data: Any, key: str) -> str:
-    block = onboarding_step_block(onboarding_data, key)
-    return str(block.get("status") or "").casefold()
-
-
-def standard_block_status(onboarding_data: Any, key: str) -> str:
-    status = raw_step_status(onboarding_data, key)
-    if status in RESOLVED_TASK_STATUSES | {
-        "attached",
-        "confirmed",
-        "introduced",
-        "saved",
-        "selected",
-        "shown",
-    }:
-        return "completed"
-    if status in {
-        "active",
-        "drafted",
-        "in_progress",
-        "offered",
-        "pending_approval",
-        "prompted",
-        "ready_for_review",
-        "reviewing",
-        "running",
-        "started",
-        "suggested",
-    }:
-        return "in_progress"
-    return "pending"
-
-
-def orientation_progress_status(onboarding_data: Any) -> str:
-    if not isinstance(onboarding_data, dict):
-        return "pending"
-    status = raw_step_status(onboarding_data, "orientation")
-    if status in {"complete", "completed", "shown"}:
-        return "completed"
-    if onboarding_data.get("last_orientation_shown"):
-        return "completed"
-    if status in {"active", "in_progress", "started"}:
-        return "in_progress"
-    return "pending"
-
-
-def meeting_prep_demo_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    if skill_has_been_tried(onboarding_data, "prepare-for-meeting"):
-        return "completed"
-    if "prepare-for-meeting" in resolved_workflows:
-        return "completed"
-    run_status = standard_block_status(onboarding_data, "meeting_prep_run")
-    if run_status != "pending":
-        return run_status
-    prompt_status = standard_block_status(onboarding_data, "meeting_prep_prompt")
-    if prompt_status != "pending":
-        return "in_progress"
-    last_suggested = onboarding_step_block(onboarding_data, "last_skill_suggested")
-    key = str(last_suggested.get("key") or last_suggested.get("workflow") or "").casefold()
-    if key == "prepare-for-meeting":
-        return "in_progress"
-    return "pending"
-
-
-def prep_review_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    status = standard_block_status(onboarding_data, "first_guided_workflow_review")
-    if status != "pending":
-        return status
-    if "prepare-for-meeting" in resolved_workflows:
-        return "in_progress"
-    return "pending"
-
-
-def plugin_memory_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    explicit_status = standard_block_status(onboarding_data, "accepted_preference_memory")
-    if explicit_status != "pending":
-        return explicit_status
-    intro_status = standard_block_status(onboarding_data, "plugin_memory_intro")
-    if intro_status == "completed":
-        return "in_progress"
-    if prep_review_status(onboarding_data, resolved_workflows) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def sales_automations_intro_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    resolved_workflows: set[str],
-) -> str:
-    if standard_block_status(onboarding_data, "automations_intro") == "completed":
-        return "completed"
-    automation_status = sales_automations_status(onboarding_data, automation_config)
-    if automation_status != "pending":
-        return "completed"
-    if plugin_memory_status(onboarding_data, resolved_workflows) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def sales_automations_setup_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    resolved_workflows: set[str],
-) -> str:
-    automation_status = sales_automations_status(onboarding_data, automation_config)
-    if automation_status != "pending":
-        return automation_status
-    if (
-        sales_automations_intro_status(
-            onboarding_data,
-            automation_config,
-            resolved_workflows,
-        )
-        == "completed"
-    ):
-        return "in_progress"
-    return "pending"
-
-
-def call_followup_intro_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    if skill_has_been_introduced(onboarding_data, "follow-up-after-call"):
-        return "completed"
-    if "follow-up-after-call" in resolved_workflows:
-        return "completed"
-    if standard_block_status(onboarding_data, "call_followup_intro") == "completed":
-        return "completed"
-    return "pending"
-
-
-def call_followup_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    if skill_has_been_tried(onboarding_data, "follow-up-after-call"):
-        return "completed"
-    if "follow-up-after-call" in resolved_workflows:
-        return "completed"
-    if call_followup_intro_status(onboarding_data, resolved_workflows) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def internal_navigation_intro_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    if skill_has_been_introduced(onboarding_data, "find-key-internal-sources"):
-        return "completed"
-    if "find-key-internal-sources" in resolved_workflows:
-        return "completed"
-    if standard_block_status(onboarding_data, "internal_navigation_intro") == "completed":
-        return "completed"
-    return "pending"
-
-
-def internal_navigation_status(onboarding_data: Any, resolved_workflows: set[str]) -> str:
-    if skill_has_been_tried(onboarding_data, "find-key-internal-sources"):
-        return "completed"
-    if "find-key-internal-sources" in resolved_workflows:
-        return "completed"
-    if internal_navigation_intro_status(onboarding_data, resolved_workflows) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def connector_confirmation_status(
-    onboarding_data: Any,
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    if not isinstance(onboarding_data, dict):
-        return "pending"
-    raw_confirmation = onboarding_data.get("connector_confirmation")
-    inventory = onboarding_data.get("connector_inventory")
-    has_classification_input = (
-        isinstance(raw_confirmation, dict)
-        and bool(raw_confirmation)
-        or isinstance(inventory, dict)
-        and bool(inventory)
-    )
-    if not has_classification_input:
-        return "pending"
-    normalized_confirmation = build_connector_confirmation(source_category_config, onboarding_data)
-    statuses: list[str] = []
-    for category_id in source_category_config:
-        status = ""
-        normalized_entry = normalized_confirmation.get(category_id)
-        if isinstance(normalized_entry, dict):
-            status = str(normalized_entry.get("status") or "").casefold()
-        if status == "available":
-            status = "needs_confirmation"
-        if status == "not_available":
-            status = "missing"
-        statuses.append(status)
-    if statuses and all(
-        status
-        in {
-            "active",
-            "needs_confirmation",
-            "missing",
-            "declined",
-            "deferred",
-            "deferred_environment_api_limitations",
-            "skipped",
-            "not_applicable",
-            "skipped_for_now",
-            "unavailable",
-        }
-        for status in statuses
-    ):
-        return "completed"
-    if any(status for status in statuses):
-        return "in_progress"
-    return "pending"
-
-
-def connector_confirmation_action_required(
-    onboarding_data: Any,
-    source_category_config: dict[str, dict[str, Any]] | None = None,
-) -> bool:
-    if not isinstance(onboarding_data, dict):
-        return False
-    if source_category_config:
-        if connector_confirmation_status(onboarding_data, source_category_config) == "pending":
-            return False
-        entries = build_connector_confirmation(source_category_config, onboarding_data).values()
-    else:
-        raw_confirmation = onboarding_data.get("connector_confirmation")
-        if not isinstance(raw_confirmation, dict):
-            return False
-        entries = raw_confirmation.values()
-    for entry in entries:
-        if not isinstance(entry, dict):
-            continue
-        status = str(entry.get("status") or "").casefold()
-        if status in {"available", "needs_confirmation", "missing", "not_available"}:
-            return True
-    return False
-
-
-def source_questions_status(
-    onboarding_data: Any,
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    confirmation_status = connector_confirmation_status(onboarding_data, source_category_config)
-    if confirmation_status == "pending":
-        return "pending"
-    if connector_confirmation_action_required(onboarding_data, source_category_config):
-        return "in_progress"
-    return "completed"
-
-
-def introduce_meeting_prep_status(
-    onboarding_data: Any,
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    if skill_has_been_introduced(onboarding_data, "prepare-for-meeting"):
-        return "completed"
-    if standard_block_status(onboarding_data, "meeting_prep_intro") == "completed":
-        return "completed"
-    if standard_block_status(onboarding_data, "meeting_prep_prompt") != "pending":
-        return "completed"
-    if standard_block_status(onboarding_data, "meeting_prep_run") != "pending":
-        return "completed"
-    if source_questions_status(onboarding_data, source_category_config) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def hero_prompt_choice_status(onboarding_data: Any) -> str:
-    status = standard_block_status(onboarding_data, "hero_prompt_choice")
-    if status != "pending":
-        return status
-    choice = onboarding_step_block(onboarding_data, "hero_prompt_choice")
-    if choice.get("selected_skill") or choice.get("selected_anchor") or choice.get("last_shown"):
-        return "in_progress"
-    if standard_block_status(onboarding_data, "meeting_prep_prompt") != "pending":
-        return "completed"
-    return "pending"
-
-
-def selected_first_hero_skill(onboarding_data: Any) -> str | None:
-    choice = onboarding_step_block(onboarding_data, "hero_prompt_choice")
-    selected = choice.get("selected_skill")
-    if isinstance(selected, str) and selected in CORE_ONBOARDING_SKILLS:
-        return selected
-    prompt = onboarding_step_block(onboarding_data, "meeting_prep_prompt")
-    if prompt.get("selected_prompt") or prompt.get("meeting_ref"):
-        return "prepare-for-meeting"
-    return None
-
-
-def core_hero_skill_is_resolved(onboarding_data: Any, skill: str) -> bool:
-    return skill_has_been_tried(onboarding_data, skill) or skill_has_been_dismissed(
-        onboarding_data, skill
-    )
-
-
-def remaining_core_hero_skills(onboarding_data: Any) -> list[str]:
-    return [
-        skill
-        for skill in CORE_ONBOARDING_SKILLS
-        if not core_hero_skill_is_resolved(onboarding_data, skill)
-    ]
-
-
-def hero_prompt_choice_summary(onboarding_data: Any) -> dict[str, Any]:
-    remaining = remaining_core_hero_skills(onboarding_data)
-    resolved = [skill for skill in CORE_ONBOARDING_SKILLS if skill not in remaining]
-    choice = onboarding_step_block(onboarding_data, "hero_prompt_choice")
-    return {
-        "core_skill_order": list(CORE_ONBOARDING_SKILLS),
-        "resolved_core_skills": resolved,
-        "remaining_core_skills": remaining,
-        "suggested_options": remaining[:3],
-        "current_offer_label": (
-            "Choose Your First Sales Demo"
-            if len(remaining) == len(CORE_ONBOARDING_SKILLS)
-            else "Choose Your Next Sales Demo"
-        ),
-        "selection_state": choice,
-    }
-
-
-def compact_hero_prompt_choice_summary(onboarding_data: Any) -> dict[str, Any]:
-    summary = hero_prompt_choice_summary(onboarding_data)
-    choice = summary.get("selection_state")
-    compact: dict[str, Any] = {
-        "current_offer_label": summary.get("current_offer_label"),
-        "resolved_core_skills": summary.get("resolved_core_skills") or [],
-        "remaining_core_skills": summary.get("remaining_core_skills") or [],
-        "suggested_options": summary.get("suggested_options") or [],
-    }
-    if isinstance(choice, dict):
-        for key in ("status", "selected_skill", "selected_anchor"):
-            if choice.get(key) is not None:
-                compact[key] = choice[key]
-    return compact
-
-
-def discovery_state(onboarding_data: Any) -> dict[str, Any]:
-    initial_discovery = onboarding_step_block(onboarding_data, "initial_resource_discovery")
-    if initial_discovery:
-        return initial_discovery
-    background_discovery = onboarding_step_block(onboarding_data, "background_context_discovery")
-    if background_discovery:
-        return background_discovery
-    return onboarding_step_block(onboarding_data, "company_context_discovery")
-
-
-def resource_discovery_status(onboarding_data: Any) -> str:
-    if not isinstance(onboarding_data, dict):
-        return "pending"
-    status = str(discovery_state(onboarding_data).get("status") or "").casefold()
-    if status in {
-        "complete",
-        "completed",
-        "declined",
-        "deferred",
-        "deferred_environment_api_limitations",
-        "no_useful_results",
-        "not_useful",
-        "saved",
-        "skipped",
-        "skipped_for_now",
-        "unavailable",
-    }:
-        return "completed"
-    if status in {
-        "active",
-        "approved",
-        "drafted",
-        "drafting",
-        "in_progress",
-        "kicked_off",
-        "pending_approval",
-        "proposed",
-        "proposal_ready",
-        "ready_for_review",
-        "running",
-        "running_in_pinned_thread",
-        "sent_to_thread",
-        "started",
-    }:
-        return "in_progress"
-    return "pending"
-
-
-def resource_discovery_action_required(onboarding_data: Any) -> bool:
-    status = str(discovery_state(onboarding_data).get("status") or "").casefold()
-    return status in {"pending_approval", "proposed", "proposal_ready", "ready_for_review"}
-
-
-def sales_automations_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-) -> str:
-    configured_ids = [
-        automation_id
-        for automation_id in ONBOARDING_AUTOMATION_IDS
-        if automation_id in automation_config
-    ]
-    if not configured_ids:
-        return "completed"
-    automation_status = automation_progress_status(
-        onboarding_data,
-        automation_config,
-        automation_ids=configured_ids,
-    )
-    if automation_status != "completed":
-        return automation_status
-    if WEEKLY_DISCOVERY_AUTOMATION_ID not in configured_ids:
-        return "completed"
-    discovery_status = resource_discovery_status(onboarding_data)
-    if discovery_status in {"completed", "in_progress"}:
-        return "completed"
-    return "pending"
-
-
-def connector_setup_confirmation_status(
-    onboarding_data: Any,
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    confirmation_status = connector_confirmation_status(onboarding_data, source_category_config)
-    if confirmation_status == "pending":
-        return "pending"
-    if source_questions_status(onboarding_data, source_category_config) == "completed":
-        return "completed"
-    return "in_progress"
-
-
-def sales_automation_setup_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    automation_status = sales_automations_status(onboarding_data, automation_config)
-    if automation_status == "completed":
-        return "completed"
-    if automation_status == "in_progress":
-        return "in_progress"
-    if standard_block_status(onboarding_data, "automations_intro") != "pending":
-        return "in_progress"
-    if connector_setup_confirmation_status(onboarding_data, source_category_config) == "completed":
-        return "in_progress"
-    return "pending"
-
-
-def core_onboarding_progress_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-) -> str:
-    explicit_status = standard_block_status(onboarding_data, "core_onboarding")
-    connector_status = connector_setup_confirmation_status(onboarding_data, source_category_config)
-    automation_status = sales_automation_setup_status(
-        onboarding_data,
-        automation_config,
-        source_category_config,
-    )
-    if explicit_status == "completed":
-        if connector_status == "completed" and automation_status == "completed":
-            return "completed"
-        return "in_progress"
-    if connector_status == "completed" and automation_status == "completed":
-        return "completed"
-    if connector_status != "pending" or automation_status != "pending":
-        return "in_progress"
-    return "pending"
-
-
-def first_hero_prompt_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-    resolved_workflows: set[str],
-) -> str:
-    resolved_core_workflows = set(CORE_ONBOARDING_SKILLS) & resolved_workflows
-    if resolved_core_workflows and all(
-        skill_has_been_dismissed(onboarding_data, skill) for skill in resolved_core_workflows
-    ):
-        return "completed"
-
-    if "prepare-for-meeting" in resolved_core_workflows:
-        run_status = meeting_prep_demo_status(onboarding_data, resolved_workflows)
-    elif resolved_core_workflows:
-        run_status = "completed"
-    else:
-        run_status = "pending"
-
-    if run_status != "pending":
-        if run_status != "completed":
-            return run_status
-
-        review_status = standard_block_status(onboarding_data, "first_guided_workflow_review")
-        if review_status == "pending":
-            return "in_progress"
-        if review_status != "completed":
-            return review_status
-
-        memory_block = onboarding_step_block(onboarding_data, "accepted_preference_memory")
-        has_memory_candidate = any(
-            memory_block.get(field)
-            for field in ("candidate", "proposed_preference", "preference", "summary")
-        )
-        memory_status = standard_block_status(onboarding_data, "accepted_preference_memory")
-        if has_memory_candidate and memory_status != "completed":
-            return "in_progress"
-        return "completed"
-
-    if (
-        core_onboarding_progress_status(
-            onboarding_data,
-            automation_config,
-            source_category_config,
-        )
-        != "completed"
-    ):
-        return "pending"
-
-    choice_status = hero_prompt_choice_status(onboarding_data)
-    if choice_status == "completed":
-        if raw_step_status(onboarding_data, "hero_prompt_choice") in RESOLVED_TASK_STATUSES:
-            return "completed"
-        return "in_progress"
-    if choice_status == "in_progress":
-        return "in_progress"
-    return "in_progress"
-
-
-def other_hero_prompts_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-    resolved_workflows: set[str],
-) -> str:
-    first_status = first_hero_prompt_status(
-        onboarding_data,
-        automation_config,
-        source_category_config,
-        resolved_workflows,
-    )
-    if first_status != "completed":
-        return "pending"
-    resolved_core_workflows = set(CORE_ONBOARDING_SKILLS) & resolved_workflows
-    if set(CORE_ONBOARDING_SKILLS).issubset(resolved_core_workflows):
-        return "completed"
-    if any(
-        skill_has_been_introduced(onboarding_data, skill)
-        or skill_has_been_tried(onboarding_data, skill)
-        or skill_has_been_dismissed(onboarding_data, skill)
-        for skill in CORE_ONBOARDING_SKILLS
-        if skill not in resolved_core_workflows
-    ):
-        return "in_progress"
-    return "in_progress"
-
-
-def onboarding_step_status(
-    step_id: str,
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-    completed_workflows: set[str],
-    resolved_workflows: set[str],
-) -> str:
-    if step_id == "start_sales_onboarding":
-        return orientation_progress_status(onboarding_data)
-    if step_id == "connector_setup_confirmation":
-        return connector_setup_confirmation_status(onboarding_data, source_category_config)
-    if step_id == "sales_automation_setup":
-        return sales_automation_setup_status(
-            onboarding_data,
-            automation_config,
-            source_category_config,
-        )
-    if step_id == "first_hero_prompt":
-        return first_hero_prompt_status(
-            onboarding_data,
-            automation_config,
-            source_category_config,
-            resolved_workflows,
-        )
-    if step_id == "other_hero_prompts":
-        return other_hero_prompts_status(
-            onboarding_data,
-            automation_config,
-            source_category_config,
-            resolved_workflows,
-        )
-    return "pending"
-
-
-def build_onboarding_step(
-    definition: dict[str, Any],
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-    completed_workflows: set[str],
-    resolved_workflows: set[str],
-) -> dict[str, Any]:
-    step_id = str(definition["id"])
-    step = {
-        "id": step_id,
-        "label": definition["label"],
-        "status": onboarding_step_status(
-            step_id,
-            onboarding_data,
-            automation_config,
-            source_category_config,
-            completed_workflows,
-            resolved_workflows,
-        ),
-    }
-    if definition.get("parallel_continuation"):
-        step["parallel_continuation"] = True
-    if step_id == "connector_setup_confirmation" and connector_confirmation_action_required(
-        onboarding_data,
-        source_category_config,
-    ):
-        step["action_required"] = True
-    if (
-        step_id
-        in {
-            "sales_automation_setup",
-            "first_hero_prompt",
-            "other_hero_prompts",
-        }
-        and step["status"] == "in_progress"
-    ):
-        step["action_required"] = True
-    if step_id == "sales_automation_setup" and resource_discovery_action_required(onboarding_data):
-        step["action_required"] = True
-    return step
-
-
-def automation_entry_is_ready(entry: Any) -> bool:
-    if not isinstance(entry, dict):
-        return False
-    status = str(entry.get("status") or "").casefold()
-    if status in ONBOARDING_RESOLVED_SETUP_DISPOSITIONS:
-        return True
-    if status in FALLBACK_AUTOMATION_READY_STATUSES:
-        return bool(entry.get("canonical_id") and entry.get("target_thread_id"))
-    if status not in FULL_AUTOMATION_READY_STATUSES:
-        return False
-    if str(entry.get("kind") or "").casefold() != "heartbeat":
-        return False
-    if str(entry.get("readback_status") or "").casefold() not in AUTOMATION_READBACK_READY_STATUSES:
-        return False
-    if not all(
-        entry.get(field)
-        for field in ("canonical_id", "target_thread_id", "target_thread_title")
-    ):
-        return False
-    target_status = str(entry.get("target_thread_status") or "").casefold()
-    return target_status in TARGET_THREAD_READY_STATUSES or entry.get("target_thread_pinned") is True
-
-
-def automation_entry_has_started(entry: Any) -> bool:
-    if not isinstance(entry, dict):
-        return False
-    return any(
-        entry.get(field)
-        for field in (
-            "status",
-            "canonical_id",
-            "target_thread_id",
-            "target_thread_status",
-            "last_installed",
-            "last_offered",
-            "cadence",
-            "preferred_time",
-        )
-    )
-
-
-def automation_progress_status(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    automation_ids: list[str] | None = None,
-) -> str:
-    if not automation_config:
-        return "pending"
-    automations = onboarding_data.get("automations") if isinstance(onboarding_data, dict) else None
-    if not isinstance(automations, dict):
-        return "pending"
-    configured_ids = automation_ids if automation_ids is not None else list(automation_config)
-    configured_entries = [automations.get(automation_id) for automation_id in configured_ids]
-    if configured_entries and all(automation_entry_is_ready(entry) for entry in configured_entries):
-        return "completed"
-    if any(automation_entry_has_started(entry) for entry in configured_entries):
-        return "in_progress"
-    return "pending"
-
-
-def onboarding_is_complete_or_quiet(onboarding_status: str) -> bool:
-    return onboarding_status.casefold() in {"complete", "completed", "quiet"}
-
-
-def build_onboarding_progress(
-    onboarding_data: Any,
-    automation_config: dict[str, dict[str, Any]],
-    source_category_config: dict[str, dict[str, Any]],
-) -> dict[str, Any]:
-    completed_workflows = completed_skill_experience_keys(onboarding_data)
-    deferred_workflows = deferred_skill_experience_keys(onboarding_data)
-    resolved_workflows = completed_workflows | deferred_workflows
-
-    task_list = [
-        build_onboarding_step(
-            definition,
-            onboarding_data,
-            automation_config,
-            source_category_config,
-            completed_workflows,
-            resolved_workflows,
-        )
-        for definition in ONBOARDING_STEP_DEFINITIONS
-    ]
-    core_step_ids = ["connector_setup_confirmation", "sales_automation_setup"]
-    core_steps = [task for task in task_list if task.get("id") in core_step_ids]
-    core_remaining = [
-        task
-        for task in core_steps
-        if str(task.get("status") or "").casefold() not in RESOLVED_TASK_STATUSES
-    ]
-    core_status = core_onboarding_progress_status(
-        onboarding_data,
-        automation_config,
-        source_category_config,
-    )
-    if core_status == "completed":
-        for task in core_steps:
-            task["status"] = "completed"
-            task.pop("action_required", None)
-        core_remaining = []
-    elif len(core_remaining) == len(core_steps) and all(
-        str(task.get("status") or "").casefold() == "pending" for task in core_remaining
-    ):
-        core_status = "pending"
-
-    return {
-        "flow_id": "standard_sales_onboarding",
-        "core_onboarding": {
-            "status": core_status,
-            "complete": core_status == "completed",
-            "required_step_ids": core_step_ids,
-            "remaining_step_ids": [str(task.get("id") or "") for task in core_remaining],
-            "remaining_labels": [str(task.get("label") or "") for task in core_remaining],
-            "next_action": next_onboarding_action({"task_list": core_steps}),
-        },
-        "core_onboarding_skills": list(CORE_ONBOARDING_SKILLS),
-        "hero_prompt_choices": compact_hero_prompt_choice_summary(onboarding_data),
-        "task_list": task_list,
-    }
-
-
-def compact_status(file_payload: dict[str, Any]) -> str:
-    return str(file_payload.get("status") or "missing")
-
-
-def build_payload(args: argparse.Namespace) -> dict[str, Any]:
-    state_dir = resolve_state_dir(args.codex_home, args.state_dir)
-    user_context = read_text_payload(
-        state_dir / "user-context.md",
-        max_context_bytes=args.max_context_bytes,
-        sections=args.section,
-    )
-    onboarding_state = read_json_payload(
-        state_dir / "onboarding-state.json",
-        max_context_bytes=args.max_context_bytes,
-    )
-
-    user_context_status = compact_status(user_context)
-    onboarding_state_status = compact_status(onboarding_state)
-
-    onboarding_data = onboarding_state.get("data") if onboarding_state_status == "present" else None
-    onboarding_status = "missing"
-    if isinstance(onboarding_data, dict):
-        onboarding_status = str(onboarding_data.get("status") or "active")
-    elif onboarding_state_status == "unreadable":
-        onboarding_status = "unreadable"
-
-    source_category_config = parse_source_category_config()
-    automation_config = parse_automation_config()
-    saved_source_preferences = extract_saved_source_preferences(
-        user_context.get("content"),
-        source_category_config,
-    )
-    connector_confirmation = build_connector_confirmation(
-        source_category_config,
-        onboarding_data,
-    )
-    sources = compact_source_categories(
-        source_category_config,
-        connector_confirmation,
-        saved_source_preferences,
-    )
-    automations = compact_automation_state(automation_config, onboarding_data)
-    onboarding_progress = build_onboarding_progress(
-        onboarding_data,
-        automation_config,
-        source_category_config,
-    )
-    current_skill_experience = build_current_skill_experience(
-        args.workflow,
-        args.request_mode,
-        onboarding_data,
-    )
-
-    quiet = onboarding_status == "quiet"
-    potential_context_gaps: list[str] = []
-    if user_context_status != "present":
-        potential_context_gaps.append("saved Sales user context is missing or unreadable")
-
-    final_obligations: list[dict[str, Any]] = []
-    final_response_checks: list[dict[str, Any]] = []
-    conditional_guidance: list[dict[str, Any]] = []
-    if args.request_mode == "ordinary_workflow" and onboarding_status.casefold() == "missing":
-        onboarding_cta_action = next_onboarding_action(onboarding_progress)
-        final_obligations.append(
-            {
-                "id": "offer_sales_onboarding_next_step",
-                "timing": "append_after_main_answer",
-                "text_ref": (
-                    "skills/user-context/references/onboarding.md#ordinary-workflow-onboarding-cta"
-                ),
-                "template": ordinary_onboarding_cta_template(onboarding_cta_action),
-                "next_action": onboarding_cta_action,
-                "requirement": (
-                    "After answering the user's immediate Sales request, append the provided "
-                    "Sales Setup Required CTA because onboarding has not started, unless the "
-                    "immediate workflow is blocked on a required clarification. Keep the exact "
-                    "heading, body, and final reply line. Do not add the next onboarding "
-                    "action, full roadmap, source-status details, setup diagnostics, or a time "
-                    "estimate. Do not downgrade this to a context-gap note, setup aside, or "
-                    "passive future-improvement note. When a required clarification is needed, "
-                    "the clarification must be the sole final natural continuation and this onboarding "
-                    "CTA is deferred."
-                ),
-                "skip_when": (
-                    "Skip only when the response is direct onboarding/setup/status work, "
-                    "a focused workflow launched from the onboarding path, onboarding is "
-                    "complete, onboarding is quiet, or the user explicitly asked to stop "
-                    "or quiet onboarding guidance, or the immediate workflow requires a "
-                    "clarification before it can proceed."
-                ),
-                "onboarding_status": onboarding_status,
-            }
-        )
-    elif args.request_mode == "ordinary_workflow" and onboarding_status.casefold() in {
-        "active",
-        "in_progress",
-        "started",
-    }:
-        core_onboarding = onboarding_progress.get("core_onboarding")
-        core_complete = (
-            isinstance(core_onboarding, dict) and core_onboarding.get("complete") is True
-        )
-        if not core_complete:
-            next_core_action = (
-                core_onboarding.get("next_action")
-                if isinstance(core_onboarding, dict)
-                else next_onboarding_action(onboarding_progress)
-            )
-            if not isinstance(next_core_action, dict):
-                next_core_action = next_onboarding_action(onboarding_progress)
-            final_obligations.append(
-                {
-                    "id": "complete_sales_core_onboarding",
-                    "timing": "append_after_main_answer",
-                    "text_ref": (
-                        "skills/user-context/references/onboarding.md#core-onboarding-reminder"
-                    ),
-                    "template": core_onboarding_reminder_template(next_core_action),
-                    "next_action": next_core_action,
-                    "requirement": (
-                        "Sales onboarding is already active, but core onboarding is not "
-                        "complete. After answering only urgent or directly requested Sales "
-                        "work, direct the user to resume the next unresolved core setup item. "
-                        "Do not append the Sales Onboarding start CTA or ask whether to start "
-                        "onboarding. If the primary workflow has a skill-owned final "
-                        "continuation, fold this reminder into that same final natural "
-                        "continuation as the move-on path instead of rendering a standalone onboarding "
-                        "reminder plus another CTA."
-                    ),
-                    "skip_when": (
-                        "Skip when the response is direct onboarding/setup/status work, a "
-                        "focused workflow launched from the onboarding path, onboarding is "
-                        "complete or quiet, core onboarding is complete, or the user explicitly "
-                        "asked to stop or quiet onboarding guidance."
-                    ),
-                    "onboarding_status": onboarding_status,
-                }
-            )
-        else:
-            final_response_checks.append(
-                {
-                    "id": "active_onboarding_does_not_start_over",
-                    "text_ref": (
-                        "skills/user-context/references/onboarding.md#active-onboarding-reminder"
-                    ),
-                    "onboarding_status": onboarding_status,
-                }
-            )
-    if args.request_mode == "ordinary_workflow" and not quiet and potential_context_gaps:
-        conditional_guidance.append(
-            {
-                "id": "context_gap_note",
-                "timing": "append_after_main_answer_only_if_material",
-                "text_ref": "skills/user-context/references/onboarding.md#context-gap-note",
-                "template": context_gap_note_template(),
-                "apply_when": (
-                    "append only when the completed workflow answer has a material gap "
-                    "that saved context or a source category the workflow attempted would improve"
-                ),
-                "omit_when": (
-                    "omit when available evidence was sufficient or the gap did not affect "
-                    "confidence, completeness, or ability to act"
-                ),
-                "potential_gaps": potential_context_gaps,
-            }
-        )
-
-    return {
-        "schema_version": "sales_preflight.v2",
-        "workflow": args.workflow,
-        "read_only": True,
-        "state": {
-            "state_dir": str(state_dir),
-            "user_context_status": user_context_status,
-            "onboarding_state_status": onboarding_state_status,
-            "onboarding_status": onboarding_status,
-            "quiet": quiet,
-        },
-        "context": {
-            "user_context": summarize_user_context(user_context.get("content")),
-            "sources": sources,
-            "automations": automations,
-        },
-        "control": {
-            "response_mode": args.request_mode,
-            "current_skill_experience": current_skill_experience,
-            "final_obligations": final_obligations,
-            "final_response_checks": final_response_checks,
-            "conditional_guidance": conditional_guidance,
-            "onboarding_progress": onboarding_progress,
-            "manual_file_read_fallback_allowed_when": [
-                "this script fails",
-                "the user explicitly asks for raw file inspection",
-            ],
-        },
-        "provenance": {
-            "user_context": "read" if user_context_status == "present" else user_context_status,
-            "onboarding_state": (
-                "read" if onboarding_state_status == "present" else onboarding_state_status
-            ),
-            "generated_at": datetime.now(timezone.utc).isoformat(),
-        },
-    }
-
-
-def main() -> int:
-    args = parse_args()
-    try:
-        payload = build_payload(args)
-    except Exception as exc:  # pragma: no cover - last-resort CLI guard
-        print(
-            json.dumps(
-                {
-                    "schema_version": "sales_preflight.v2",
-                    "status": "error",
-                    "error": str(exc),
-                },
-                indent=2,
-                sort_keys=True,
-            )
-        )
-        return 1
-    print(json.dumps(payload, indent=2, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/sales/skills/user-context/scripts/state_paths.py b/plugins/sales/skills/user-context/scripts/state_paths.py
deleted file mode 100644
index 2305182..0000000
--- a/plugins/sales/skills/user-context/scripts/state_paths.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/usr/bin/env python3
-"""Shared Sales state path resolution."""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-MARKETPLACE_ID = "role-specific-plugins"
-PLUGIN_ID = "sales"
-STATE_FILENAMES = (
-    "user-context.md",
-    # Legacy readiness state is no longer created by onboarding, but reset should clear it.
-    "category-state.json",
-    "onboarding-state.json",
-)
-
-STATE_DIR_HELP = (
-    f"Sales state directory. Defaults to <codex-home>/state/plugins/{MARKETPLACE_ID}/"
-    f"{PLUGIN_ID}."
-)
-
-
-def default_codex_home() -> Path:
-    env_home = os.environ.get("CODEX_HOME")
-    if env_home:
-        return Path(env_home).expanduser()
-    return Path.home() / ".codex"
-
-
-def configured_state_dir(codex_home: Path) -> Path:
-    return codex_home / "state/plugins" / MARKETPLACE_ID / PLUGIN_ID
-
-
-def resolve_state_dir(codex_home: Path | None, state_dir: Path | None) -> Path:
-    if state_dir:
-        return state_dir.expanduser()
-
-    resolved_home = codex_home.expanduser() if codex_home else default_codex_home()
-    return configured_state_dir(resolved_home)
diff --git a/plugins/sales/skills/user-context/scripts/validate_sales_automation_setup.py b/plugins/sales/skills/user-context/scripts/validate_sales_automation_setup.py
deleted file mode 100755
index 16dd1ab..0000000
--- a/plugins/sales/skills/user-context/scripts/validate_sales_automation_setup.py
+++ /dev/null
@@ -1,230 +0,0 @@
-#!/usr/bin/env python3
-"""Validate persisted Sales automation setup against onboarding requirements."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import re
-import sys
-import tomllib
-from pathlib import Path
-from typing import Any
-
-MARKETPLACE_ID = "role-specific-plugins"
-PLUGIN_ID = "sales"
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-AUTOMATION_CONFIG = SKILL_ROOT / "plugin-author-config/automation-config.md"
-DEFAULT_AUTOMATION_IDS = ("weekly_sales_company_research", "daily_sales_tips")
-LATER_AUTOMATION_IDS = ("daily_meeting_prep",)
-READY_READBACK_STATUSES = {"clean", "confirmed", "read", "read_back", "readback_verified", "ready", "verified"}
-READY_TARGET_THREAD_STATUSES = {"pinned", "ready", "verified"}
-RESOLVED_SKIP_STATUSES = {
-    "declined",
-    "deferred",
-    "deferred_environment_api_limitations",
-    "not_applicable",
-    "skipped",
-    "skipped_for_now",
-}
-WORKSPACE_ONLY_TOML_KEYS = {
-    "cwds",
-    "execution_environment",
-    "executionEnvironment",
-    "local_environment_config_path",
-    "localEnvironmentConfigPath",
-}
-
-
-def default_codex_home() -> Path:
-    return Path(os.environ.get("CODEX_HOME", Path.home() / ".codex")).expanduser()
-
-
-def default_state_dir() -> Path:
-    return default_codex_home() / "state/plugins" / MARKETPLACE_ID / PLUGIN_ID
-
-
-def default_automations_dir() -> Path:
-    return default_codex_home() / "automations"
-
-
-def parse_automation_config() -> dict[str, dict[str, str]]:
-    text = AUTOMATION_CONFIG.read_text(encoding="utf-8")
-    entries: dict[str, dict[str, str]] = {}
-    for match in re.finditer(r"^`([^`]+)`\n\n(?P<body>(?:- .+(?:\n|$))+)", text, re.M):
-        automation_id = match.group(1)
-        body = match.group("body")
-        entry: dict[str, str] = {"id": automation_id}
-        for line in body.splitlines():
-            field_match = re.match(r"^- ([^:]+):\s*(.*)$", line)
-            if field_match:
-                key = field_match.group(1).casefold()
-                value = field_match.group(2).strip()
-                if key in {"name", "frequency"}:
-                    value = value.rstrip(".")
-                entry[key] = value
-        entries[automation_id] = entry
-    return entries
-
-
-def slugify_name(value: str) -> str:
-    return re.sub(r"[^a-z0-9]+", "-", value.casefold()).strip("-")
-
-
-def expected_rrule(frequency: str) -> str | None:
-    if frequency == "weekdays at 9:00 AM local time":
-        return "FREQ=WEEKLY;BYDAY=MO,TU,WE,TH,FR;BYHOUR=9;BYMINUTE=0;BYSECOND=0"
-    if frequency == "weekly on Mondays at 9:00 AM local time":
-        return "FREQ=WEEKLY;BYDAY=MO;BYHOUR=9;BYMINUTE=0;BYSECOND=0"
-    return None
-
-
-def read_json(path: Path) -> dict[str, Any]:
-    if not path.exists():
-        return {}
-    payload = json.loads(path.read_text(encoding="utf-8"))
-    return payload if isinstance(payload, dict) else {}
-
-
-def read_toml(path: Path) -> dict[str, Any]:
-    if not path.exists():
-        return {}
-    return tomllib.loads(path.read_text(encoding="utf-8"))
-
-
-def state_entry_is_resolved_skip(entry: dict[str, Any]) -> bool:
-    return str(entry.get("status") or "").casefold() in RESOLVED_SKIP_STATUSES
-
-
-def validate_automation(
-    *,
-    automation_id: str,
-    config: dict[str, str],
-    onboarding_state: dict[str, Any],
-    automations_dir: Path,
-    require: bool,
-    failures: list[str],
-) -> bool:
-    state_entry = (
-        onboarding_state.get("automations", {}).get(automation_id)
-        if isinstance(onboarding_state.get("automations"), dict)
-        else None
-    )
-    if not isinstance(state_entry, dict):
-        if require:
-            failures.append(f"{automation_id}: missing onboarding-state automations entry")
-        return False
-    if state_entry_is_resolved_skip(state_entry):
-        return False
-
-    name = config.get("name")
-    prompt = config.get("instructions")
-    frequency = config.get("frequency")
-    if not name or not prompt or not frequency:
-        failures.append(f"{automation_id}: automation-config.md entry is missing Name, Frequency, or Instructions")
-        return False
-
-    canonical_id = state_entry.get("canonical_id") or slugify_name(name)
-    automation_toml = automations_dir / str(canonical_id) / "automation.toml"
-    persisted = read_toml(automation_toml)
-    if not persisted:
-        failures.append(f"{automation_id}: missing automation readback at {automation_toml}")
-        return False
-
-    expected_schedule = expected_rrule(frequency)
-    persisted_target_thread_id = persisted.get("target_thread_id") or persisted.get("targetThreadId")
-
-    checks = {
-        "stored kind is heartbeat": str(persisted.get("kind") or "").casefold() == "heartbeat",
-        "stored prompt matches automation-config.md Instructions": persisted.get("prompt") == prompt,
-        "stored target_thread_id is present": bool(persisted_target_thread_id),
-        "stored target_thread_id matches onboarding state": (
-            not state_entry.get("target_thread_id")
-            or persisted_target_thread_id == state_entry.get("target_thread_id")
-        ),
-        "onboarding state records canonical_id": bool(state_entry.get("canonical_id")),
-        "onboarding state records kind heartbeat": str(state_entry.get("kind") or "").casefold() == "heartbeat",
-        "onboarding state records readback": str(state_entry.get("readback_status") or "").casefold()
-        in READY_READBACK_STATUSES,
-        "onboarding state records target thread id": bool(state_entry.get("target_thread_id")),
-        "onboarding state records exact target thread title": state_entry.get("target_thread_title") == name,
-        "onboarding state records pinned target thread": (
-            str(state_entry.get("target_thread_status") or "").casefold() in READY_TARGET_THREAD_STATUSES
-            or state_entry.get("target_thread_pinned") is True
-        ),
-        "stored automation has no workspace-only fields": not (set(persisted) & WORKSPACE_ONLY_TOML_KEYS),
-    }
-    if expected_schedule:
-        checks["stored rrule matches configured cadence"] = persisted.get("rrule") == expected_schedule
-    for label, ok in checks.items():
-        if not ok:
-            failures.append(f"{automation_id}: {label}")
-
-    if automation_id == "weekly_sales_company_research":
-        discovery = onboarding_state.get("initial_resource_discovery")
-        if not isinstance(discovery, dict) or not discovery.get("status"):
-            failures.append("weekly_sales_company_research: missing initial_resource_discovery kickoff status")
-        elif discovery.get("target_thread_id") and discovery.get("target_thread_id") != state_entry.get("target_thread_id"):
-            failures.append(
-                "weekly_sales_company_research: initial_resource_discovery target_thread_id does not match automation target"
-            )
-
-    return True
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--state-dir", type=Path, default=default_state_dir())
-    parser.add_argument("--automations-dir", type=Path, default=default_automations_dir())
-    parser.add_argument(
-        "--require-onboarding-defaults",
-        action="store_true",
-        help="Require the default Sales onboarding automations to be installed and ready.",
-    )
-    parser.add_argument(
-        "--include-daily-meeting-prep",
-        action="store_true",
-        help="Also validate the later Sales Daily Meeting Prep automation when it is recorded.",
-    )
-    args = parser.parse_args()
-
-    failures: list[str] = []
-    config = parse_automation_config()
-    onboarding_state = read_json(args.state_dir / "onboarding-state.json")
-    automation_ids = list(DEFAULT_AUTOMATION_IDS)
-    if args.include_daily_meeting_prep or (
-        isinstance(onboarding_state.get("automations"), dict)
-        and "daily_meeting_prep" in onboarding_state["automations"]
-    ):
-        automation_ids.extend(LATER_AUTOMATION_IDS)
-
-    validated: list[str] = []
-    for automation_id in automation_ids:
-        if automation_id not in config:
-            failures.append(f"{automation_id}: missing automation-config.md entry")
-            continue
-        validated_current = validate_automation(
-            automation_id=automation_id,
-            config=config[automation_id],
-            onboarding_state=onboarding_state,
-            automations_dir=args.automations_dir,
-            require=args.require_onboarding_defaults or automation_id not in LATER_AUTOMATION_IDS,
-            failures=failures,
-        )
-        if validated_current:
-            validated.append(automation_id)
-
-    payload = {
-        "ok": not failures,
-        "validated": validated,
-        "failures": failures,
-        "state_dir": str(args.state_dir),
-        "automations_dir": str(args.automations_dir),
-    }
-    print(json.dumps(payload, indent=2))
-    return 0 if not failures else 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/plugins/sales/skills/user-context/scripts/validate_user_context_preflight.py b/plugins/sales/skills/user-context/scripts/validate_user_context_preflight.py
deleted file mode 100644
index 5dc87cf..0000000
--- a/plugins/sales/skills/user-context/scripts/validate_user_context_preflight.py
+++ /dev/null
@@ -1,1156 +0,0 @@
-#!/usr/bin/env python3
-"""Validate the Sales user-context preflight invariant."""
-
-from __future__ import annotations
-
-import json
-import re
-import sys
-from pathlib import Path
-
-PLUGIN_ROOT = Path(__file__).resolve().parents[3]
-SKILLS_ROOT = PLUGIN_ROOT / "skills"
-REQUIRED_PATH = "`$CODEX_HOME/state/plugins/{marketplace_id}/{plugin_id}/user-context.md`"
-SIBLING_REQUIRED_PHRASES = (
-    "Mandatory pre-answer gate:",
-    f"Actually read {REQUIRED_PATH}",
-    "Do not treat routing to the context skill, listing files, or checking that the file exists as sufficient",
-    "saved profile/preferences must be loaded into context and applied",
-    "trigger onboarding from `skills/user-context/references/onboarding.md`",
-)
-SIBLING_PREFLIGHT_HANDOFF_PHRASES = (
-    "Mandatory pre-answer gate:",
-    "Invoke `sales:user-context` in preflight mode",
-    "Use the returned `sales_preflight` envelope as authoritative",
-)
-USER_CONTEXT_REQUIRED_PHRASES = (
-    "## Mandatory Pre-Answer Gate",
-    "use `scripts/sales_preflight.py` as the default state-read path",
-    "on behalf of other Sales skills that call `sales:user-context`",
-    "set the tool's `max_output_tokens` to at least `25000`",
-    "warn the user that Sales preflight was truncated and that Sales plugin memory or preflight context may be getting bloated",
-    f"reports read status for {REQUIRED_PATH}",
-    "Do not treat listing files, checking that files exist, or saying they should be read as sufficient",
-    "saved preferences, profile, context, source preferences, and source-of-truth pointers must be loaded and applied",
-    "actually read the relevant state files manually",
-    "follow `references/onboarding.md`",
-    "Ordinary workflow preflight should read these files through `scripts/sales_preflight.py`",
-    "sales_preflight.py",
-    "modify `user-context.md` directly",
-    "Never run parallel writes against the same Sales state files",
-    "Script-Driven Preflight Payload",
-    "set `max_output_tokens` to at least `25000`",
-    "do not treat preflight as complete until the full output is visible",
-    "Loading the relevant skill-owned experience guidance is mandatory whenever that skill is the primary workflow",
-    "the final continuation invariant for primary outputs and follow-up turns",
-    "end with one concrete user-visible next action phrased as a natural sentence or question",
-    "Lead with the actual question the user needs to answer",
-)
-SOFT_ONLY_PHRASES = (
-    "Invoke `user-context` in preflight mode",
-    "Do not treat routing to `user-context`",
-    "follow `skills/user-context/references/onboarding.md`",
-    "mental checklist / scratchpad",
-)
-FORMULAIC_CLARIFICATION_PROMPT_PHRASES = (
-    "**I need one detail to continue.**",
-    "**I need a few forecast choices to continue.**",
-    "Reply with a letter, or send",
-    "allow the user to reply with a letter or provide a different answer",
-)
-CLICKABLE_INLINE_SOURCE_PHRASE = "When referencing sources inline, prefer clickable Markdown links"
-AUDIENCE_AND_LANGUAGE_SECTION = """### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details."""
-LEGACY_AUDIENCE_LANGUAGE_PHRASES = (
-    "Use practical sales and business language",
-    "Prefer terms like accounts",
-    "sales-adjacent operators",
-    "preflight, cursor, probe",
-)
-USER_FACING_DEFAULT_PROMPTS = {
-    "analyze-account-signals": "Analyze signals for one of my active accounts.",
-    "build-competitive-brief": "Build a competitive brief for a competitor set.",
-    "follow-up-after-call": "Help me follow up from a recent call.",
-    "suggest-sales-next-step": "Suggest my next best Sales step.",
-    "enrich-company-and-contact-data": "Enrich a company or contact list.",
-    "plan-deal-strategy": "Build a deal strategy for an active account.",
-    "find-key-internal-sources": "Find the key internal sources for a customer question.",
-    "prepare-for-meeting": "Prepare me for an upcoming customer meeting.",
-    "prioritize-accounts": "Prioritize accounts for pipeline focus.",
-    "find-customer-quotes": "Find customer quotes about a feedback theme.",
-    "review-forecast": "Review a forecast for risk and next actions.",
-    "get-rep-call-feedback": "Give feedback on a rep's calls.",
-    "review-rep-call-trends": "Review a rep's call trends.",
-    "build-business-case": "Build a business case for a customer.",
-}
-HELPER_OR_ROUTER_SKILLS = {"salesforce", "hubspot", "zoominfo", "user-context", "index"}
-
-
-def app_name_aliases(value: str) -> set[str]:
-    normalized = re.sub(r"[^a-z0-9]+", "", value.casefold())
-    aliases = {normalized}
-    if normalized.endswith("com"):
-        aliases.add(normalized[:-3])
-    if normalized == "agentforcesales":
-        aliases.add("salesforce")
-    return aliases
-
-
-def has_all_nearby_terms(
-    text: str,
-    anchor: str,
-    required_terms: tuple[str | tuple[str, ...], ...],
-    *,
-    window: int = 1200,
-) -> bool:
-    anchor_index = text.find(anchor)
-    if anchor_index < 0:
-        return False
-    nearby = text[anchor_index : anchor_index + window].casefold()
-    for term in required_terms:
-        if isinstance(term, tuple):
-            if not any(option.casefold() in nearby for option in term):
-                return False
-        elif term.casefold() not in nearby:
-            return False
-    return True
-
-
-def json_path_exists(value: object, path: tuple[str, ...]) -> bool:
-    current = value
-    for part in path:
-        if not isinstance(current, dict) or part not in current:
-            return False
-        current = current[part]
-    return True
-
-
-def main() -> int:
-    failures: list[str] = []
-    skill_files = sorted(SKILLS_ROOT.glob("*/SKILL.md"))
-    required_scripts = (
-        PLUGIN_ROOT / "skills/user-context/scripts/sales_preflight.py",
-        PLUGIN_ROOT / "skills/user-context/scripts/init_user_context_state.py",
-        PLUGIN_ROOT / "skills/user-context/scripts/reset_user_context_state.py",
-        PLUGIN_ROOT / "skills/user-context/scripts/validate_sales_automation_setup.py",
-    )
-    required_tests = (PLUGIN_ROOT / "skills/user-context/tests/test_state_helpers.py",)
-    source_category_config = (
-        PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.json"
-    )
-    app_manifest = PLUGIN_ROOT / ".app.json"
-    user_context_config = (
-        PLUGIN_ROOT / "skills/user-context/plugin-author-config/user-context-config.md"
-    )
-    automation_config = (
-        PLUGIN_ROOT / "skills/user-context/plugin-author-config/automation-config.md"
-    )
-    automation_reference = PLUGIN_ROOT / "skills/user-context/references/automation.md"
-    legacy_source_category_config_md = (
-        PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.md"
-    )
-    legacy_category_state_template = (
-        PLUGIN_ROOT / "skills/user-context/references/category-state-template.json"
-    )
-    source_category_runtime = (
-        PLUGIN_ROOT / "skills/user-context/references/source-category-runtime.md"
-    )
-    plugin_memory_reference = PLUGIN_ROOT / "skills/user-context/references/plugin-memory.md"
-    legacy_source_categories = PLUGIN_ROOT / "references/source-categories.md"
-    legacy_source_category_catalog = PLUGIN_ROOT / "references/source-category-catalog.md"
-    legacy_audience_reference = PLUGIN_ROOT / "references/audience-and-language.md"
-    legacy_top_level_references = PLUGIN_ROOT / "references"
-    legacy_top_level_scripts = PLUGIN_ROOT / "scripts"
-    legacy_root_author_config = PLUGIN_ROOT / "plugin-author-config"
-    hero_prompts = PLUGIN_ROOT / "skills/user-context/references/hero-prompts.md"
-    onboarding_reference = PLUGIN_ROOT / "skills/user-context/references/onboarding.md"
-    onboarding_state_template = (
-        PLUGIN_ROOT / "skills/user-context/references/onboarding-state-template.json"
-    )
-    meeting_prep_skill = PLUGIN_ROOT / "skills/prepare-for-meeting/SKILL.md"
-    preflight_script = PLUGIN_ROOT / "skills/user-context/scripts/sales_preflight.py"
-    call_followup_skill = PLUGIN_ROOT / "skills/follow-up-after-call/SKILL.md"
-    internal_navigation_skill = PLUGIN_ROOT / "skills/find-key-internal-sources/SKILL.md"
-    sales_company_research_skill = PLUGIN_ROOT / "skills/sales-company-research/SKILL.md"
-    sales_company_research_agent = (
-        PLUGIN_ROOT / "skills/sales-company-research/agents/openai.yaml"
-    )
-
-    if not skill_files:
-        failures.append(f"No skill files found under {SKILLS_ROOT}")
-    for script_file in required_scripts:
-        if not script_file.exists():
-            failures.append(
-                f"Missing required Sales state helper script: {script_file.relative_to(PLUGIN_ROOT)}"
-            )
-    for test_file in required_tests:
-        if not test_file.exists():
-            failures.append(
-                f"Missing required Sales state helper test: {test_file.relative_to(PLUGIN_ROOT)}"
-            )
-
-    if onboarding_reference.exists():
-        onboarding_text = onboarding_reference.read_text(encoding="utf-8")
-        for phrase in (
-            "# Sales Onboarding Flow",
-            "## Common Onboarding Message Frame",
-            "**Onboarding Complete**",
-            "## Progress Checklist Contract",
-            "## Step 1: Orientation",
-            "## Step 2: Confirm Active/Missing Sources",
-            "### Step 2A: Resolve Source Questions",
-            "## Step 3: Sales Automations",
-            "### Step 3A: Introduce Sales Automations",
-            "### Step 3B: Set Up Sales Automations",
-            "## Step 4: First Hero Prompt",
-            "### Step 4A: Choose First Hero Prompt",
-            "### Step 4B: Introduce Selected First Hero Workflow",
-            "### Step 4C: Run Selected First Hero Demo",
-            "### Step 4D: Review And Iterate",
-            "### Step 4E: Introduce Plugin Memory And Save Preference",
-            "## Step 5: Other Hero Prompts",
-            "### Step 5A: Choose Next Hero Prompt",
-            "### Step 5B: Introduce Selected Remaining Hero Workflow",
-            "### Step 5C: Run Selected Remaining Hero Demo",
-            "### Step 5D: Review And Continue Remaining Hero",
-            "## Step 6: Completion",
-            "## Ordinary Workflow Onboarding CTA",
-            "The Sales plugin improves Codex at sales-related work",
-            "**How it works**",
-            "**What it can do**",
-            "Ready to set that up?",
-            "After some searching, it looks like you're already set up",
-            "There are a few more connectors we can add to help Sales work as well as possible",
-            "**Meeting notes** improves meeting prep and follow-ups",
-            "You'll be prompted to install and authenticate plugins or connectors",
-            "When the current step has open questions",
-            "Use a compact numbered list when there is more than one question",
-            "include the default escape hatch inside the relevant numbered item",
-            "Do not add a separate summary prompt after the numbered list",
-            "`Next Step` is the action prompt, not the explainer",
-            "Do not let the whole onboarding message be only a next-step frame",
-            "When introducing a workflow, skill, or new onboarding concept for the first time",
-            "Use the New Concept Introduction Pattern",
-            "Do not create tiny transition turns",
-            "Okay, I've saved that. Now let's move on to the next step.",
-            "Okay, that finishes source setup for now. Sales will use",
-            "do not render the full `Saved Sales Plugin Memory.` recap or `Saved today` list",
-            "Preserve real approval gates",
-            "## New Concept Introduction Pattern",
-            "## {Concept Name}",
-            "This skill...",
-            "Plugin memory is how the Sales plugin remembers approved preferences",
-            "simple automations to ensure Sales keeps getting better for you",
-            "the body should start with `This skill...`",
-            "Do not render user-facing CTAs with code-styled skill names",
-            "User-facing bullets should be polished sentence fragments",
-            "Do not start bullets with lowercase words",
-            "teach the preferred invocation pattern",
-            "start with `@Sales`",
-            "@Sales prepare me for my Acme renewal call tomorrow",
-            "The numbered choice set is the final action prompt by itself",
-            "do not add a `Next Step` heading",
-            "Keep automation breadcrumbs, readback cards, and created-card text visually separate from the choice set",
-            "Do not reuse a stale checklist from an earlier thread turn",
-            "mark the next unresolved visible step as `in_progress`",
-            "Reply `start` to continue.",
-            "start a new thread and include `@Sales`",
-            "## Active Onboarding Reminder",
-            "## Core Onboarding Reminder",
-            "You can keep going through the rest of the Sales skills one by one",
-            "Say `okay` and I'll introduce Analyze Account Signals",
-            "ID: `start_sales_onboarding`",
-            "Orientation",
-            "Connector setup/confirmation",
-            "Sales automation setup",
-            "First hero prompt",
-            "Other hero prompts",
-            "core onboarding is complete",
-            "automation-config.md",
-            "Do not preflight-read installed or active sources",
-            "calls `functions.list_available_plugins_to_install` once for the setup pass",
-            "If the user confirms an installable plugin or connector candidate",
-            "installable related plugin needs approval",
-            "add dedicated Sales workflow support",
-            "Keep plugin-first setup eligible for retry in future workflows",
-            "Do not call `request_plugin_install` in parallel",
-            "Data enrichment adds missing company, contact, account",
-            "Preferred options are ZoomInfo, Clay, HG Insights, Rox, Apollo, Actively, or Meticulate",
-            "You can skip anything now; Sales will ask again later only when a workflow would materially benefit",
-            "move directly into Step 3A",
-            "## Step 4: First Hero Prompt",
-            "Do not run another hero workflow until the user picks `1`, `2`, or `3`",
-            "treat the numbered choice as consent to try that workflow",
-            "render that skill's compact first-run intro above the normal workflow output",
-            "hero_prompt_choice",
-            "choose_first_hero_prompt",
-            "Later hero choosers may include previously tried workflows as repeat options",
-            "skills/prepare-for-meeting/SKILL.md",
-            "inline experience guidance",
-            "## Prepare For Meeting",
-            "start fresh with `This skill...`",
-            "@Sales prepare me for my Acme renewal call tomorrow",
-            "Here's your meeting prep, using",
-            "## Workflow Walkthrough On Request",
-            "Do not render `What Happened`, `Recap`, or another walkthrough heading by default",
-            "Do not render that feedback CTA as an H2/H3 heading",
-            "How does that look? Any other changes, or should we show the next demo workflow choices?",
-            "Only require explicit yes for broad, sensitive, ambiguous, high-risk, or externally visible preferences",
-            "Do not save plugin memory on every iteration",
-            "must never be a narrow-preference save approval",
-            "Calendar attachment is explicit-user-request only",
-            "Should I set those up? They'll run in a different thread and notify you when they have something to review.",
-            "Do not load hero workflow skill files or run a demo workflow until the user picks one",
-            "automation.md#Fast Setup Checklist",
-            "Automations are set up and read back cleanly",
-            "I also kicked off the first research run in the pinned **Sales Company Research** thread",
-            "starting with its next scheduled run",
-            "Do not compress the three hero choices into one inline sentence",
-            "each numbered option must be on its own line",
-            "Core onboarding is done. Pick a workflow to try first:",
-            "1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.",
-            "2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.",
-            "3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.",
-            "Sales Company Research",
-            "Sales Tips",
-            "one practical Sales workflow to try next",
-            "improve speed and correctness",
-            "weekly on Mondays at 9:00 AM local time",
-            "runs weekdays at 9:00 AM local time",
-            "Sales Daily Meeting Prep",
-            "not in the main onboarding thread",
-            'FYI: The pinned thread **"{thread_title}"**',
-            "Only say saved entries or proposals exist when state or readback confirms them",
-            "Pinned",
-            "initial_resource_discovery",
-            "## Step 5: Other Hero Prompts",
-            "choose_next_hero_prompt",
-            "Hero demos use a repeatable artifact loop",
-            "hero_workflow.current_status",
-            "offer_first_hero_action_or_next_demo",
-            "offer_remaining_hero_action_or_next_demo",
-            "normal next-step candidates",
-            "show the next demo workflow choices",
-            "Choices can include the previous one or two workflows as repeat options",
-            "Do not offer only previously tried workflows",
-            "skills/follow-up-after-call/SKILL.md",
-            "Suggest a real call even if it is not a customer call",
-            "large, important-looking meeting as recent as possible",
-            "ask for iteration first",
-            "prioritize open pipeline by default",
-            "skills/prioritize-accounts/SKILL.md",
-        ):
-            if phrase not in onboarding_text:
-                failures.append(
-                    f"skills/user-context/references/onboarding.md missing progress checklist contract wording: {phrase}"
-                )
-        if not has_all_nearby_terms(
-            onboarding_text,
-            "For Prepare For Meeting",
-            (
-                "`okay`",
-                "`calendar`",
-                ("manual meeting details", "manual fallback", "provide attendee/agenda details"),
-            ),
-        ):
-            failures.append(
-                "skills/user-context/references/onboarding.md missing Prepare For Meeting behavior: okay should search calendar before manual meeting-detail fallback"
-            )
-        if not has_all_nearby_terms(
-            onboarding_text,
-            "For Follow Up After Call",
-            (
-                "`okay`",
-                "`calendar`",
-                "`meeting_notes`",
-                ("pasted notes/transcripts", "manual call evidence", "manual fallback"),
-            ),
-        ):
-            failures.append(
-                "skills/user-context/references/onboarding.md missing Follow Up After Call intro behavior: okay should search calendar and meeting_notes before manual evidence fallback"
-            )
-        if not has_all_nearby_terms(
-            onboarding_text,
-            "If the selected skill is Follow Up After Call",
-            (
-                "`calendar`",
-                "`meeting_notes`",
-                (
-                    "before asking for manual call evidence",
-                    "before manual call evidence",
-                    "manual fallback",
-                ),
-            ),
-        ):
-            failures.append(
-                "skills/user-context/references/onboarding.md missing Follow Up After Call run behavior: search calendar and meeting_notes before manual call evidence"
-            )
-        for stale_phrase in (
-            "Guided Setup Continuation Rule",
-            "Prepare For Meeting Demo Report Contract",
-            "Connector Confirmation Copy Contract",
-            "Plugin Memory Contract",
-            "Weekly Discovery Contract",
-            "Choose prepare-for-meeting prompt",
-            "Explain plugin memory",
-            "Capture key context",
-            "Set up weekly discovery and run initial search",
-            "Set up weekly Sales Resource Discovery",
-            "Set up daily meeting prep and weekday check-in",
-            "## Step 7: Scheduled Help",
-            "## Step 7: Completion",
-            "Reply with a docs preference or enrichment info",
-            "walk how to customize",
-            "## Welcome To Sales",
-            "**What it can help with:**",
-            "Sales source setup looks mostly ready",
-            "Should I set up the recommended Sales automations?",
-            "Sales Weekday Check-In",
-        ):
-            if stale_phrase in onboarding_text:
-                failures.append(
-                    f"skills/user-context/references/onboarding.md still contains stale onboarding phrase: {stale_phrase}"
-                )
-    else:
-        failures.append(
-            "Missing onboarding reference: skills/user-context/references/onboarding.md"
-        )
-
-    if not onboarding_state_template.exists():
-        failures.append(
-            "Missing onboarding state template: skills/user-context/references/onboarding-state-template.json"
-        )
-    else:
-        try:
-            onboarding_state_data = json.loads(
-                onboarding_state_template.read_text(encoding="utf-8")
-            )
-        except json.JSONDecodeError as exc:
-            failures.append(
-                f"skills/user-context/references/onboarding-state-template.json is invalid JSON: {exc}"
-            )
-            onboarding_state_data = {}
-        skill_experience = onboarding_state_data.get("skill_experience")
-        hero_prompt_choice = onboarding_state_data.get("hero_prompt_choice")
-        if not isinstance(hero_prompt_choice, dict):
-            failures.append(
-                "skills/user-context/references/onboarding-state-template.json missing hero_prompt_choice"
-            )
-        else:
-            expected_options = [
-                "prepare-for-meeting",
-                "follow-up-after-call",
-                "prioritize-accounts",
-            ]
-            if hero_prompt_choice.get("options") != expected_options:
-                failures.append(
-                    "onboarding-state-template hero_prompt_choice.options must list the three core hero skills"
-                )
-            for field in (
-                "status",
-                "selected_skill",
-                "selected_anchor",
-                "last_shown",
-                "selected_at",
-                "last_offered_skills",
-            ):
-                if field not in hero_prompt_choice:
-                    failures.append(f"onboarding-state-template hero_prompt_choice missing {field}")
-        if not isinstance(skill_experience, dict):
-            failures.append(
-                "skills/user-context/references/onboarding-state-template.json missing skill_experience"
-            )
-        else:
-            for skill_name in USER_FACING_DEFAULT_PROMPTS:
-                entry = skill_experience.get(skill_name)
-                if not isinstance(entry, dict):
-                    failures.append(
-                        f"onboarding-state-template skill_experience missing {skill_name}"
-                    )
-                    continue
-                for field in (
-                    "introduced_at",
-                    "first_tried_at",
-                    "last_suggested_at",
-                    "dismissed_at",
-                ):
-                    if field not in entry:
-                        failures.append(
-                            f"onboarding-state-template skill_experience.{skill_name} missing {field}"
-                        )
-            for helper_name in HELPER_OR_ROUTER_SKILLS:
-                if helper_name in skill_experience:
-                    failures.append(
-                        f"onboarding-state-template must not track helper/router skill experience: {helper_name}"
-                    )
-    if preflight_script.exists():
-        preflight_text = preflight_script.read_text(encoding="utf-8")
-        for phrase in (
-            "build_onboarding_progress",
-            "ordinary_onboarding_cta_template",
-            "offer_sales_onboarding_next_step",
-            "#ordinary-workflow-onboarding-cta",
-            "guided_onboarding_workflow",
-            "ONBOARDING_STEP_DEFINITIONS",
-            "start_sales_onboarding",
-            "Orientation",
-            "connector_setup_confirmation",
-            "Connector setup/confirmation",
-            "sales_automation_setup",
-            "Sales automation setup",
-            "first_hero_prompt",
-            "First hero prompt",
-            "hero_prompt_choice_status",
-            "selected_first_hero_skill",
-            "hero_prompt_choice_summary",
-            "remaining_core_hero_skills",
-            "hero_prompt_choices",
-            "choosing and trying the first Sales hero prompt",
-            "other_hero_prompts",
-            "Other hero prompts",
-            "core_onboarding",
-            "complete_sales_core_onboarding",
-            "#core-onboarding-reminder",
-            "initial_resource_discovery",
-            "DAILY_TIPS_AUTOMATION_ID",
-            "SCHEDULED_HELP_AUTOMATION_IDS",
-            "ONBOARDING_AUTOMATION_IDS",
-            "sales_automation_setup_status",
-            "compact_source_categories",
-            "build_connector_confirmation",
-            "plugin_or_app_or_connector_then_manual_context",
-            "parse_automation_config",
-            "compact_automation_state",
-            "summarize_user_context",
-            "weekly_sales_company_research",
-            "USER_FACING_SKILLS",
-            "CORE_ONBOARDING_SKILLS",
-            "POST_COMPLETION_GUIDED_SKILLS",
-            "build_current_skill_experience",
-            "current_skill_experience",
-        ):
-            if phrase not in preflight_text:
-                failures.append(
-                    f"skills/user-context/scripts/sales_preflight.py missing progress helper wording: {phrase}"
-                )
-
-    if legacy_source_categories.exists():
-        failures.append(
-            "Legacy source category reference still exists: references/source-categories.md"
-        )
-    if legacy_source_category_catalog.exists():
-        failures.append(
-            "Legacy source category catalog still exists: references/source-category-catalog.md"
-        )
-    if legacy_audience_reference.exists():
-        failures.append(
-            "Legacy audience reference still exists: references/audience-and-language.md"
-        )
-    if legacy_top_level_references.exists():
-        failures.append("Legacy top-level references directory still exists: references")
-    if legacy_top_level_scripts.exists():
-        failures.append("Legacy top-level scripts directory still exists: scripts")
-    if legacy_root_author_config.exists():
-        failures.append("Legacy root author config directory still exists: plugin-author-config")
-    if legacy_source_category_config_md.exists():
-        failures.append(
-            "Legacy Markdown source category config still exists: skills/user-context/plugin-author-config/source-category-config.md"
-        )
-    if legacy_category_state_template.exists():
-        failures.append(
-            "Duplicate category-state template still exists: skills/user-context/references/category-state-template.json"
-        )
-
-    declared_app_aliases: set[str] = set()
-    if not app_manifest.exists():
-        failures.append("Missing app manifest: .app.json")
-    else:
-        try:
-            app_manifest_config = json.loads(app_manifest.read_text(encoding="utf-8"))
-        except json.JSONDecodeError as exc:
-            failures.append(f".app.json is invalid JSON: {exc}")
-            app_manifest_config = {}
-        declared_apps = app_manifest_config.get("apps")
-        if not isinstance(declared_apps, dict) or not declared_apps:
-            failures.append(".app.json must define a non-empty apps object")
-        else:
-            for app_key in declared_apps:
-                if isinstance(app_key, str):
-                    declared_app_aliases.update(app_name_aliases(app_key))
-
-    if not source_category_config.exists():
-        failures.append(
-            "Missing source category config: skills/user-context/plugin-author-config/source-category-config.json"
-        )
-    else:
-        try:
-            source_config = json.loads(source_category_config.read_text(encoding="utf-8"))
-        except json.JSONDecodeError as exc:
-            failures.append(
-                f"skills/user-context/plugin-author-config/source-category-config.json is invalid JSON: {exc}"
-            )
-            source_config = {}
-        source_text = json.dumps(source_config, sort_keys=True)
-        if source_config.get("schema_version") != "sales_source_category_config.v1":
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json missing schema_version sales_source_category_config.v1"
-            )
-        categories = source_config.get("categories")
-        if not isinstance(categories, dict) or not categories:
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json must define a non-empty categories object"
-            )
-        for phrase in (
-            "## Category Authoring Schema",
-            "Required for:",
-            "Recommended for:",
-            "## Workflow Posture",
-            "## Setup Strength",
-            "## Runtime Resolution",
-            "## State Shape",
-            "native connector/app MCP tool",
-            "adjacent connector",
-            "Do not use Computer Use",
-            "browser automation",
-            "- Typical apps:",
-            "Use these importance values:",
-            "`recommended`:",
-        ):
-            if phrase in source_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/source-category-config.json contains deprecated source-category wording: {phrase}"
-                )
-        if "user_importance" in source_text:
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json must not use user_importance"
-            )
-        if "- Priority:" in source_text:
-            failures.append(
-                "skills/user-context/plugin-author-config/source-category-config.json must not use source-category Priority fields"
-            )
-
-        for phrase in (
-            "Importance:",
-            "- Priority:",
-            "Required for:",
-            "Recommended for:",
-            "## Workflow Posture",
-            "user_importance",
-        ):
-            if phrase in source_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/source-category-config.json must not define workflow-specific or user-specific source posture: {phrase}"
-                )
-        if isinstance(categories, dict):
-            for category_id, metadata in categories.items():
-                if not isinstance(category_id, str) or not category_id:
-                    failures.append(
-                        "skills/user-context/plugin-author-config/source-category-config.json has an invalid category id"
-                    )
-                    continue
-                if not isinstance(metadata, dict):
-                    failures.append(
-                        f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} must be an object"
-                    )
-                    continue
-                required_category_fields = (
-                    "label",
-                    "preferred_apps",
-                )
-                for field in required_category_fields:
-                    if field not in metadata:
-                        failures.append(
-                            f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} missing: {field}"
-                        )
-                if not isinstance(metadata.get("preferred_apps"), list):
-                    failures.append(
-                        f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} preferred_apps must be a list"
-                    )
-                elif declared_app_aliases:
-                    for preferred_app in metadata["preferred_apps"]:
-                        if not isinstance(preferred_app, str) or not preferred_app:
-                            failures.append(
-                                f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} preferred_apps must contain non-empty strings"
-                            )
-                            continue
-                        if not app_name_aliases(preferred_app) & declared_app_aliases:
-                            failures.append(
-                                f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} preferred app {preferred_app} is not declared in .app.json"
-                            )
-                if "preferred_plugins" in metadata:
-                    preferred_plugins = metadata.get("preferred_plugins")
-                    if not isinstance(preferred_plugins, list):
-                        failures.append(
-                            f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} preferred_plugins must be a list"
-                        )
-                    else:
-                        for preferred_plugin in preferred_plugins:
-                            if not isinstance(preferred_plugin, str) or not preferred_plugin:
-                                failures.append(
-                                    f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} preferred_plugins must contain non-empty strings"
-                                )
-                relevant_skills = metadata.get("relevant_skills", [])
-                if "relevant_skills" in metadata:
-                    if not isinstance(relevant_skills, list):
-                        failures.append(
-                            f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} relevant_skills must be a list"
-                        )
-                    else:
-                        for index, entry in enumerate(relevant_skills):
-                            if not isinstance(entry, dict):
-                                failures.append(
-                                    f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} relevant_skills[{index}] must be an object"
-                                )
-                                continue
-                            if not isinstance(entry.get("app"), str) or not entry.get("app"):
-                                failures.append(
-                                    f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} relevant_skills[{index}] missing app"
-                                )
-                            if not isinstance(entry.get("skill"), str) or not entry.get("skill"):
-                                failures.append(
-                                    f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} relevant_skills[{index}] missing skill"
-                                )
-                for removed_field in ("why_it_matters", "most_useful_for", "verification_note"):
-                    if removed_field in metadata:
-                        failures.append(
-                            f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} should not define {removed_field}; keep category config to label, preferred_plugins, preferred_apps, and relevant_skills"
-                        )
-                if "typical_apps" in metadata:
-                    failures.append(
-                        f"skills/user-context/plugin-author-config/source-category-config.json category {category_id} must use preferred_apps, not typical_apps"
-                    )
-
-    if not user_context_config.exists():
-        failures.append(
-            "Missing user context config: skills/user-context/plugin-author-config/user-context-config.md"
-        )
-    else:
-        user_context_config_text = user_context_config.read_text(encoding="utf-8")
-        for phrase in (
-            "# Sales User Context Config",
-            "## Category Entry Shape",
-            "## Default Categories",
-            "- Description:",
-        ):
-            if phrase not in user_context_config_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/user-context-config.md missing author-friendly category field: {phrase}"
-                )
-        for deprecated_phrase in (
-            "\n## User Resources",
-            "\n## Saved Links And Context",
-            "\nStatus: Not Provided",
-            "\nstatus: not provided",
-            "\n- Priority:",
-            "\n- Use When:",
-        ):
-            if deprecated_phrase in user_context_config_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/user-context-config.md contains generated user-context detail that belongs in init_user_context_state.py: {deprecated_phrase.strip()}"
-                )
-
-    if not automation_config.exists():
-        failures.append(
-            "Missing automation config: skills/user-context/plugin-author-config/automation-config.md"
-        )
-    else:
-        automation_text = automation_config.read_text(encoding="utf-8")
-        for phrase in (
-            "## Default Automations",
-            "## Later Journey Automations",
-            "- Name:",
-            "- Frequency:",
-            "- Instructions:",
-            "daily_sales_tips",
-            "daily_meeting_prep",
-            "weekly_sales_company_research",
-            "Load and follow `$sales:suggest-sales-next-step`",
-            "scheduled Sales check-in mode",
-            "short natural-language check-in",
-            "next Sales workflow worth trying",
-            "Load and follow `$sales:sales-company-research`",
-            "scheduled research mode",
-            "dynamic source discovery",
-            "Sales user-context save policy",
-            "coverage-note guidance",
-            "compare against entries already flagged or saved from this pinned thread",
-            "Load and follow `$sales:prepare-for-meeting`",
-        ):
-            if phrase not in automation_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/automation-config.md missing author-friendly automation field: {phrase}"
-                )
-        for deprecated_phrase in (
-            "Use GPT-5.5 with",
-            "- Target thread title:",
-            "- Kind:",
-            "- RRULE:",
-            "- Skill:",
-            "- Scheduled mode:",
-            "- Prompt:",
-            "Onboarding notification:",
-            "Sales company research is complete. {N} new resources saved.",
-        ):
-            if deprecated_phrase in automation_text:
-                failures.append(
-                    f"skills/user-context/plugin-author-config/automation-config.md contains runtime/setup detail that belongs in automation.md or onboarding state: {deprecated_phrase}"
-                )
-
-    if not automation_reference.exists():
-        failures.append(
-            "Missing automation reference: skills/user-context/references/automation.md"
-        )
-    else:
-        automation_reference_text = automation_reference.read_text(encoding="utf-8")
-        for phrase in (
-            '`model="gpt-5.5"`',
-            '`thinking="low"`',
-            '`thinking="xhigh"`',
-            "do not fall back to `gpt-5`",
-            "## Default Setup",
-            "## Fast Setup Checklist",
-            "Dedicated projectless thread titled `Sales Company Research`",
-            "Dedicated projectless thread titled `Sales Tips`",
-            "dedicated pinned thread titled `Sales Daily Meeting Prep`",
-            "Discover `automation_update` and the thread tools",
-            "Create or update the automation with `kind=\"heartbeat\"`",
-            "the exact `targetThreadId`",
-            "stored `target_thread_id` matches the pinned thread",
-            "Update `onboarding-state.json` with only operational metadata",
-            "Stop. Report the concise setup result",
-            "No setup kickoff; let the first scheduled check-in run on its own",
-            "Do not send an immediate Sales Tips kickoff during onboarding",
-            "missing first Sales Company Research kickoff",
-            "Do not mark setup complete after a plain cron automation",
-            "Sales Company Research: load and follow `$sales:sales-company-research`",
-            "Do not paste source-family lists, ranking rules, save gates, output examples, model names, reasoning effort, or focused-skill workflow contracts",
-        ):
-            if phrase not in automation_reference_text:
-                failures.append(
-                    f"skills/user-context/references/automation.md missing runtime automation wording: {phrase}"
-                )
-
-    if not source_category_runtime.exists():
-        failures.append(
-            "Missing source category runtime reference: skills/user-context/references/source-category-runtime.md"
-        )
-    else:
-        runtime_text = source_category_runtime.read_text(encoding="utf-8")
-        for phrase in (
-            "## Runtime Source Resolution",
-            "## Onboarding Connector Confirmation",
-            "## Relevant Skills",
-            "## Durable Source Preferences",
-            "## Onboarding Behavior",
-            "## Plugin-First Setup For Missing Sources",
-            "source-category-config.json",
-            "relevant_skills",
-            "workflow needs them",
-            "user-context.md",
-            "do not write connector readiness state",
-            "needs_confirmation",
-            "functions.list_available_plugins_to_install",
-            "functions.request_plugin_install",
-            "Available plugins",
-            "Available skills",
-            "plugin_install_evidence",
-            "skill_surface_evidence",
-            "app_surface_evidence",
-            "run_plugin_first_source_setup",
-            "saved_source_preferences",
-            "plugin_preference_order",
-            "plugin_first_source_setup",
-            "Prefer plugin candidates over connector candidates",
-            "including a plugin that contains or declares a connector/app id",
-            "add dedicated Sales workflow support",
-            "Do not perform an eager read",
-        ):
-            if phrase not in runtime_text:
-                failures.append(
-                    f"skills/user-context/references/source-category-runtime.md missing: {phrase}"
-                )
-
-    if hero_prompts.exists():
-        failures.append(
-            "skills/user-context/references/hero-prompts.md should be removed; user-facing skill experience now lives with each skill"
-        )
-
-    for skill_name, default_prompt in USER_FACING_DEFAULT_PROMPTS.items():
-        skill_dir = SKILLS_ROOT / skill_name
-        if not skill_dir.exists():
-            failures.append(f"Missing user-facing Sales skill directory: skills/{skill_name}")
-            continue
-        experience_file = skill_dir / "references/experience.md"
-        if experience_file.exists():
-            failures.append(f"skills/{skill_name}/references/experience.md should be moved inline")
-        skill_file = skill_dir / "SKILL.md"
-        if not skill_file.exists():
-            failures.append(f"skills/{skill_name}/SKILL.md is required")
-        else:
-            skill_text = skill_file.read_text(encoding="utf-8")
-            experience_phrases = (
-                "### First-Run Banner",
-                f"If Sales preflight says the {skill_name} experience has not been introduced",
-                "### Next Step Guidance",
-                "#### Final Continuation Invariant",
-                "another active parent flow owns the final CTA",
-            )
-            for phrase in experience_phrases:
-                if phrase not in skill_text:
-                    failures.append(f"skills/{skill_name}/SKILL.md missing: {phrase}")
-            if skill_name == "prioritize-accounts":
-                for phrase in (
-                    "### Input Parameter Confirmation",
-                    "ask at most three high-impact, high-uncertainty questions",
-                    "before searching connectors, reading sources, or drafting an answer",
-                    "Do not treat connector/source availability",
-                    "state assumptions in `Scope` instead of blocking",
-                    "If the default or assumed source returns no owned open pipeline",
-                    "label the result as assumption-based",
-                    "Suppress any extra skill-owned final continuation when the clarification questions are already the next step",
-                ):
-                    if phrase not in skill_text:
-                        failures.append(
-                            f"skills/prioritize-accounts/SKILL.md missing input confirmation phrase: {phrase}"
-                        )
-        agent_file = skill_dir / "agents/openai.yaml"
-        if not agent_file.exists():
-            failures.append(f"skills/{skill_name}/agents/openai.yaml is required")
-        elif f'default_prompt: "{default_prompt}"' not in agent_file.read_text(encoding="utf-8"):
-            failures.append(
-                f"skills/{skill_name}/agents/openai.yaml must use short default_prompt: {default_prompt}"
-            )
-
-    for helper_name in HELPER_OR_ROUTER_SKILLS:
-        helper_experience = SKILLS_ROOT / helper_name / "references/experience.md"
-        if helper_experience.exists():
-            failures.append(
-                f"Helper/router skill should not expose a first-run experience: skills/{helper_name}/references/experience.md"
-            )
-
-    if not meeting_prep_skill.exists():
-        failures.append("Missing prepare-for-meeting skill: skills/prepare-for-meeting/SKILL.md")
-    else:
-        meeting_prep_skill_text = meeting_prep_skill.read_text(encoding="utf-8")
-        for phrase in (
-            "### Required Inputs",
-            "Source access can suggest concrete candidates",
-            "The skill can still produce a limited brief from user-provided details alone",
-            "### First-Run Banner",
-            "### Next Step Guidance",
-            "### Workflow Sources",
-            "## Output Shapes",
-            "Clarification Stop",
-            "Daily Meeting Prep is the primary accepted-brief continuation owned by this skill",
-            "Do not offer meeting doc creation as the accepted-brief default while Daily Meeting Prep remains eligible",
-            "if the visible continuation already offered Sales Daily Meeting Prep, treat the acknowledgement as approval",
-            "record that disposition under `automations.daily_meeting_prep`",
-        ):
-            if phrase not in meeting_prep_skill_text:
-                failures.append(f"skills/prepare-for-meeting/SKILL.md missing: {phrase}")
-        if "Summary" not in meeting_prep_skill_text:
-            failures.append("skills/prepare-for-meeting/SKILL.md missing summary section guidance")
-        if not (
-            "compact bold labels, not Markdown H1/H2/H3 headings" in meeting_prep_skill_text
-            or all(
-                phrase in meeting_prep_skill_text
-                for phrase in (
-                    "## Summary",
-                    "## Goal",
-                    "## Open Questions",
-                    "## Proposed Agenda",
-                    "## Background Context",
-                )
-            )
-        ):
-            failures.append(
-                "skills/prepare-for-meeting/SKILL.md missing recognizable single-meeting output structure"
-            )
-
-    if not plugin_memory_reference.exists():
-        failures.append(
-            "Missing plugin memory reference: skills/user-context/references/plugin-memory.md"
-        )
-    else:
-        plugin_memory_text = plugin_memory_reference.read_text(encoding="utf-8")
-        for phrase in (
-            "Plugin memory is how the Sales plugin remembers approved preferences",
-            "Saved today:",
-            "source-of-truth resources and answer/style preferences",
-            "Discovery-derived memory may be saved by default only when it is high-confidence",
-            "Do not copy saved resource lists",
-            "Route company-context discovery to `sales-company-research`",
-            "This reference owns the save and review gates",
-            "Keep the automation prompt thin by invoking `sales-company-research`",
-        ):
-            if phrase not in plugin_memory_text:
-                failures.append(
-                    f"skills/user-context/references/plugin-memory.md missing: {phrase}"
-                )
-
-    if not sales_company_research_skill.exists():
-        failures.append(
-            "Missing sales-company-research skill: skills/sales-company-research/SKILL.md"
-        )
-    else:
-        sales_company_research_text = sales_company_research_skill.read_text(encoding="utf-8")
-        for phrase in (
-            "name: sales-company-research",
-            "Explicit-only Sales workflow",
-            "Mandatory pre-answer gate:",
-            "Invoke `sales:user-context` in preflight mode",
-            "dynamic source discovery",
-            "Do not hard-code a customer's connector stack",
-            "do not append onboarding reminders or onboarding CTAs",
-            "Do not stop after the first few obviously relevant docs",
-            "second-pass search",
-            "Keep multiple resources when they serve distinct authority lanes",
-            "one-sentence utility description",
-            "legal/comms/customer-facing claims guardrails",
-            "Sales company research is complete. {N} new resources saved.",
-            "Sales company research is complete. No new resources saved.",
-            "Do not include coverage notes or operational research bookkeeping",
-            "safe-use caveat",
-            "Convert material limitations into concrete `Where you can help` questions",
-            "What was found",
-            "Where you can help",
-            "up to five `Where you can help` questions",
-            "Start each bullet with the resource title as a Markdown link",
-            "Do not include category labels such as `saved under`",
-            "Every saved resource must explain why it helps the user",
-            "Do not output bare link bullets",
-            "Use the output shapes above by default",
-        ):
-            if phrase not in sales_company_research_text:
-                failures.append(
-                    f"skills/sales-company-research/SKILL.md missing research workflow phrase: {phrase}"
-                )
-    if not sales_company_research_agent.exists():
-        failures.append(
-            "Missing sales-company-research agents metadata: skills/sales-company-research/agents/openai.yaml"
-        )
-    else:
-        sales_company_research_agent_text = sales_company_research_agent.read_text(
-            encoding="utf-8"
-        )
-        for phrase in (
-            'display_name: "Sales Company Research"',
-            "allow_implicit_invocation: false",
-        ):
-            if phrase not in sales_company_research_agent_text:
-                failures.append(
-                    f"skills/sales-company-research/agents/openai.yaml missing: {phrase}"
-                )
-
-    if not call_followup_skill.exists():
-        failures.append("Missing call-followup skill: skills/follow-up-after-call/SKILL.md")
-    else:
-        call_output_text = call_followup_skill.read_text(encoding="utf-8")
-        for phrase in (
-            "Primary evidence` should name the source lane and source handle",
-            "clickable Markdown link when the connector exposes a useful URL",
-            "email body formatted as block quote text",
-            "Format the verbatim email body as a Markdown block quote",
-            "### External Comms",
-            "Not applicable: this was an internal call.",
-            "### Internal Follow-Up",
-            "Format the verbatim internal follow-up draft as a Markdown block quote",
-            "internal follow-up draft is a team meeting recap",
-            "Include a brief summary, next steps, owners or TBDs",
-            "Call notes: no useful link available",
-            "standard bold section labels",
-            "`**Next Steps**`",
-            "narrow, low-risk accepted preferences can be summarized and saved automatically",
-        ):
-            if phrase not in call_output_text:
-                failures.append(
-                    f"skills/follow-up-after-call/SKILL.md missing call-followup output contract phrase: {phrase}"
-                )
-
-    if not internal_navigation_skill.exists():
-        failures.append(
-            "Missing find-key-internal-sources skill: skills/find-key-internal-sources/SKILL.md"
-        )
-    else:
-        internal_navigation_text = internal_navigation_skill.read_text(encoding="utf-8")
-        for phrase in (
-            "### Internal Ownership",
-            "DRIs, approvers, SMEs, maintainers",
-            "recurring decision forums",
-            "canonical docs, escalation channels",
-        ):
-            if phrase not in internal_navigation_text:
-                failures.append(
-                    f"skills/find-key-internal-sources/SKILL.md missing ownership guidance: {phrase}"
-                )
-
-    for skill_file in skill_files:
-        text = skill_file.read_text(encoding="utf-8")
-        if text.count(AUDIENCE_AND_LANGUAGE_SECTION) != 1:
-            failures.append(
-                f"{skill_file.relative_to(PLUGIN_ROOT)} missing standardized Audience And Language guidance"
-            )
-        for phrase in LEGACY_AUDIENCE_LANGUAGE_PHRASES:
-            if phrase in text:
-                failures.append(
-                    f"{skill_file.relative_to(PLUGIN_ROOT)} still has legacy audience wording: {phrase}"
-                )
-        if CLICKABLE_INLINE_SOURCE_PHRASE not in text:
-            failures.append(
-                f"{skill_file.relative_to(PLUGIN_ROOT)} missing clickable inline source guidance"
-            )
-        required_phrases = (
-            USER_CONTEXT_REQUIRED_PHRASES
-            if skill_file.parent.name == "user-context"
-            else SIBLING_REQUIRED_PHRASES
-        )
-        if skill_file.parent.name == "user-context":
-            missing = [phrase for phrase in required_phrases if phrase not in text]
-        else:
-            explicit_missing = [phrase for phrase in SIBLING_REQUIRED_PHRASES if phrase not in text]
-            handoff_missing = [
-                phrase for phrase in SIBLING_PREFLIGHT_HANDOFF_PHRASES if phrase not in text
-            ]
-            missing = [] if not explicit_missing or not handoff_missing else handoff_missing
-        if missing:
-            failures.append(f"{skill_file.relative_to(PLUGIN_ROOT)} missing: {', '.join(missing)}")
-        for phrase in SOFT_ONLY_PHRASES:
-            if phrase in text:
-                failures.append(
-                    f"{skill_file.relative_to(PLUGIN_ROOT)} still uses soft preflight wording: {phrase}"
-                )
-        for phrase in FORMULAIC_CLARIFICATION_PROMPT_PHRASES:
-            if phrase in text:
-                failures.append(
-                    f"{skill_file.relative_to(PLUGIN_ROOT)} still uses formulaic clarification prompt wording: {phrase}"
-                )
-        if "### Source Categories" in text:
-            if "This skill includes these Sales source categories:" not in text:
-                failures.append(
-                    f"{skill_file.relative_to(PLUGIN_ROOT)} Source Categories section must name included categories"
-                )
-            for phrase in (
-                "Use the `source_category_plan` returned by Sales preflight for source-category labels",
-                "At runtime, use the `source_category_plan` returned by Sales preflight when available",
-                "### Provider Guides\n\n- If `crm` resolves to HubSpot",
-            ):
-                if phrase in text:
-                    failures.append(
-                        f"{skill_file.relative_to(PLUGIN_ROOT)} still has legacy source-category boilerplate: {phrase}"
-                    )
-
-    if failures:
-        print("Sales user-context preflight validation failed:")
-        for failure in failures:
-            print(f"- {failure}")
-        return 1
-
-    print(f"Sales user-context preflight validation passed for {len(skill_files)} skills.")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/plugins/sales/skills/user-context/tests/test_state_helpers.py b/plugins/sales/skills/user-context/tests/test_state_helpers.py
deleted file mode 100644
index d60f901..0000000
--- a/plugins/sales/skills/user-context/tests/test_state_helpers.py
+++ /dev/null
@@ -1,2642 +0,0 @@
-#!/usr/bin/env python3
-"""Regression tests for the Sales user-context preflight helper."""
-
-from __future__ import annotations
-
-import json
-import subprocess
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-
-SKILL_ROOT = Path(__file__).resolve().parents[1]
-PLUGIN_ROOT = SKILL_ROOT.parents[1]
-SCRIPT_DIR = SKILL_ROOT / "scripts"
-INIT_SCRIPT = SCRIPT_DIR / "init_user_context_state.py"
-RESET_SCRIPT = SCRIPT_DIR / "reset_user_context_state.py"
-VALIDATE_AUTOMATION_SCRIPT = SCRIPT_DIR / "validate_sales_automation_setup.py"
-STATE_MARKETPLACE_ID = "role-specific-plugins"
-STATE_PLUGIN_ID = "sales"
-ONBOARDING_REFERENCE = SKILL_ROOT / "references/onboarding.md"
-AUTOMATION_REFERENCE = SKILL_ROOT / "references/automation.md"
-SOURCE_RUNTIME_REFERENCE = SKILL_ROOT / "references/source-category-runtime.md"
-CALL_FOLLOWUP_SKILL = PLUGIN_ROOT / "skills/follow-up-after-call/SKILL.md"
-MEETING_PREP_SKILL = PLUGIN_ROOT / "skills/prepare-for-meeting/SKILL.md"
-INTERNAL_NAVIGATION_SKILL = PLUGIN_ROOT / "skills/find-key-internal-sources/SKILL.md"
-SALES_COMPANY_RESEARCH_SKILL = PLUGIN_ROOT / "skills/sales-company-research/SKILL.md"
-CONNECTOR_CONFIRMATION_COPY = "## Step 2: Confirm Active/Missing Sources"
-MEETING_PROMPT_COPY = "### Step 4A: Choose First Hero Prompt"
-HERO_OUTPUT_COPY = "### Step 4C: Run Selected First Hero Demo"
-USER_FACING_SKILLS = (
-    "analyze-account-signals",
-    "build-competitive-brief",
-    "follow-up-after-call",
-    "suggest-sales-next-step",
-    "enrich-company-and-contact-data",
-    "plan-deal-strategy",
-    "find-key-internal-sources",
-    "prepare-for-meeting",
-    "prioritize-accounts",
-    "find-customer-quotes",
-    "review-forecast",
-    "get-rep-call-feedback",
-    "review-rep-call-trends",
-    "build-business-case",
-)
-
-
-class SalesStateHelperTests(unittest.TestCase):
-    def setUp(self) -> None:
-        self._tmp = tempfile.TemporaryDirectory(
-            prefix="sales-user-context-tests-",
-            dir="/private/tmp",
-        )
-        self.tmp_path = Path(self._tmp.name)
-        self.state_dir = self.tmp_path / "state"
-        self.maxDiff = None
-
-    def tearDown(self) -> None:
-        self._tmp.cleanup()
-
-    def run_json(self, args: list[str], *, expect: int = 0) -> dict:
-        proc = subprocess.run(args, text=True, capture_output=True)
-        self.assertEqual(
-            proc.returncode,
-            expect,
-            msg=(
-                f"command failed: {' '.join(args)}\nSTDOUT:\n{proc.stdout}\nSTDERR:\n{proc.stderr}"
-            ),
-        )
-        return json.loads(proc.stdout)
-
-    def preflight(self, *extra: str, state_dir: Path | None = None) -> dict:
-        return self.run_json(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "sales_preflight.py"),
-                "--workflow",
-                "prepare-for-meeting",
-                "--state-dir",
-                str(state_dir or self.state_dir),
-                *extra,
-            ]
-        )
-
-    def namespaced_state_dir(self, codex_home: Path) -> Path:
-        return codex_home / "state/plugins" / STATE_MARKETPLACE_ID / STATE_PLUGIN_ID
-
-    def all_connector_confirmation(self, status: str = "active") -> dict[str, dict[str, str]]:
-        config = json.loads(
-            (
-                PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.json"
-            ).read_text(encoding="utf-8")
-        )
-        return {
-            category_id: {
-                "status": status,
-                "preferred": metadata["preferred_apps"][0],
-            }
-            for category_id, metadata in config["categories"].items()
-        }
-
-    def skill_experience_state(
-        self,
-        *,
-        introduced: set[str] | None = None,
-        tried: set[str] | None = None,
-        dismissed: set[str] | None = None,
-    ) -> dict[str, dict[str, str | None]]:
-        introduced = introduced or set()
-        tried = tried or set()
-        dismissed = dismissed or set()
-        state = {}
-        for skill in USER_FACING_SKILLS:
-            state[skill] = {
-                "introduced_at": "2026-05-25T00:00:00Z"
-                if skill in introduced or skill in tried
-                else None,
-                "first_tried_at": "2026-05-25T00:01:00Z" if skill in tried else None,
-                "last_suggested_at": None,
-                "dismissed_at": "2026-05-25T00:02:00Z" if skill in dismissed else None,
-            }
-        return state
-
-    def ready_automation(
-        self,
-        automation_id: str,
-        thread_id: str,
-        thread_title: str,
-        *,
-        status: str = "active",
-    ) -> dict[str, str]:
-        return {
-            "status": status,
-            "canonical_id": automation_id,
-            "kind": "heartbeat",
-            "readback_status": "verified",
-            "target_thread_id": thread_id,
-            "target_thread_title": thread_title,
-            "target_thread_status": "pinned",
-        }
-
-    def ready_automations(self) -> dict[str, dict[str, str]]:
-        return {
-            "weekly_sales_company_research": self.ready_automation(
-                "auto_weekly",
-                "thread_weekly",
-                "Sales Company Research",
-            ),
-            "daily_sales_tips": self.ready_automation(
-                "auto_tips",
-                "thread_tips",
-                "Sales Tips",
-            ),
-        }
-
-    def write_automation_toml(
-        self,
-        automations_dir: Path,
-        canonical_id: str,
-        *,
-        kind: str,
-        name: str,
-        prompt: str,
-        rrule: str,
-        target_thread_id: str | None,
-    ) -> None:
-        automation_dir = automations_dir / canonical_id
-        automation_dir.mkdir(parents=True)
-        target_line = (
-            f'target_thread_id = "{target_thread_id}"\n' if target_thread_id else ""
-        )
-        (automation_dir / "automation.toml").write_text(
-            (
-                "version = 1\n"
-                f'id = "{canonical_id}"\n'
-                f'kind = "{kind}"\n'
-                f'name = "{name}"\n'
-                f'prompt = {json.dumps(prompt)}\n'
-                'status = "ACTIVE"\n'
-                f'rrule = "{rrule}"\n'
-                f"{target_line}"
-            ),
-            encoding="utf-8",
-        )
-
-    def test_missing_preflight_returns_onboarding_obligation_and_context_gap_guidance(self) -> None:
-        payload = self.preflight(state_dir=self.tmp_path / "missing")
-
-        self.assertTrue(payload["read_only"])
-        self.assertEqual(payload["state"]["user_context_status"], "missing")
-        self.assertEqual(
-            payload["control"]["current_skill_experience"]["skill"],
-            "prepare-for-meeting",
-        )
-        self.assertFalse(payload["control"]["current_skill_experience"]["introduced"])
-        self.assertTrue(payload["control"]["current_skill_experience"]["intro_eligible"])
-        self.assertEqual(payload["control"]["current_skill_experience"]["cta_owner"], "skill")
-        self.assertNotIn("category_state_status", payload["state"])
-        self.assertEqual(payload["state"]["onboarding_state_status"], "missing")
-        obligation = payload["control"]["final_obligations"][0]
-        self.assertEqual(obligation["id"], "offer_sales_onboarding_next_step")
-        self.assertEqual(obligation["timing"], "append_after_main_answer")
-        self.assertTrue(obligation["template"].startswith("## Sales Setup Required"))
-        self.assertIn(
-            "This is required before Sales can reliably use your connected sources",
-            obligation["template"],
-        )
-        self.assertIn(
-            "source and use your authoritative company context",
-            obligation["template"],
-        )
-        self.assertIn("Reply `start` to continue.", obligation["template"])
-        self.assertNotIn("**Next Step**", obligation["template"])
-        self.assertEqual(obligation["next_action"]["id"], "start_sales_onboarding")
-        self.assertIn("requires a clarification", obligation["skip_when"])
-        self.assertEqual(payload["control"]["final_response_checks"], [])
-        self.assertEqual(
-            payload["control"]["conditional_guidance"][0]["id"],
-            "context_gap_note",
-        )
-        self.assertEqual(
-            payload["control"]["conditional_guidance"][0]["potential_gaps"],
-            ["saved Sales user context is missing or unreadable"],
-        )
-        sources = payload["context"]["sources"]
-        self.assertTrue(sources["has_setup_gaps"])
-        self.assertEqual(
-            {
-                category_id
-                for category_id, entry in sources["categories"].items()
-                if entry["status"] == "needs_confirmation"
-            },
-            set(sources["categories"]),
-        )
-        first_source = next(iter(sources["categories"].values()))
-        self.assertIn("preferred_apps", first_source)
-        self.assertEqual(
-            sources["resolution"],
-            "plugin_or_app_or_connector_then_manual_context",
-        )
-        self.assertNotIn("impact", first_source)
-
-    def test_default_preflight_reads_namespaced_state_dir(self) -> None:
-        codex_home = self.tmp_path / "codex-home"
-        state_dir = self.namespaced_state_dir(codex_home)
-        state_dir.mkdir(parents=True)
-        (state_dir / "user-context.md").write_text(
-            "# Output Preferences\n\n## Saved Links And Context\n\nKeep it short.\n",
-            encoding="utf-8",
-        )
-        (state_dir / "onboarding-state.json").write_text(
-            json.dumps({"status": "complete"}),
-            encoding="utf-8",
-        )
-
-        payload = self.run_json(
-            [
-                sys.executable,
-                str(SCRIPT_DIR / "sales_preflight.py"),
-                "--workflow",
-                "prepare-for-meeting",
-                "--codex-home",
-                str(codex_home),
-            ]
-        )
-
-        self.assertEqual(payload["state"]["state_dir"], str(state_dir))
-        self.assertEqual(payload["state"]["user_context_status"], "present")
-        self.assertIn("Output Preferences: Keep it short.", payload["context"]["user_context"]["entries"])
-
-    def test_direct_onboarding_mode_suppresses_ordinary_final_obligation(self) -> None:
-        payload = self.preflight(
-            "--request-mode",
-            "direct_onboarding_status",
-            state_dir=self.tmp_path / "missing",
-        )
-
-        self.assertEqual(payload["control"]["response_mode"], "direct_onboarding_status")
-        self.assertEqual(payload["control"]["final_obligations"], [])
-        self.assertEqual(payload["control"]["conditional_guidance"], [])
-
-    def test_guided_onboarding_workflow_suppresses_ordinary_onboarding_obligation(self) -> None:
-        payload = self.preflight(
-            "--request-mode",
-            "guided_onboarding_workflow",
-            state_dir=self.tmp_path / "missing",
-        )
-
-        self.assertEqual(payload["control"]["response_mode"], "guided_onboarding_workflow")
-        self.assertEqual(payload["control"]["final_obligations"], [])
-        self.assertEqual(payload["control"]["conditional_guidance"], [])
-
-    def test_active_onboarding_does_not_offer_to_start_over(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text(
-            "# Output Preferences\n\n## Saved Links And Context\n\nstatus: not provided\n",
-            encoding="utf-8",
-        )
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps({"status": "active"}),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight()
-
-        obligation = payload["control"]["final_obligations"][0]
-        self.assertEqual(obligation["id"], "complete_sales_core_onboarding")
-        self.assertIn("core onboarding is not complete", obligation["requirement"])
-        self.assertEqual(payload["control"]["final_response_checks"], [])
-        self.assertEqual(payload["control"]["conditional_guidance"], [])
-
-    def test_complete_onboarding_suppresses_ordinary_onboarding_obligation(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps({"status": "complete"}),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight()
-
-        self.assertEqual(payload["control"]["final_obligations"], [])
-
-    def test_explicit_core_onboarding_completion_does_not_override_missing_automation_readback(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "core_onboarding": {"status": "completed"},
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        status_payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = status_payload["control"]["onboarding_progress"]
-        task_statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-
-        self.assertFalse(progress["core_onboarding"]["complete"])
-        self.assertEqual(progress["core_onboarding"]["remaining_step_ids"], ["sales_automation_setup"])
-        self.assertEqual(task_statuses["connector_setup_confirmation"], "completed")
-        self.assertEqual(task_statuses["sales_automation_setup"], "in_progress")
-
-        ordinary_payload = self.preflight()
-        self.assertIn(
-            "complete_sales_core_onboarding",
-            [item["id"] for item in ordinary_payload["control"]["final_obligations"]],
-        )
-
-    def test_fallback_core_completion_and_default_connector_preferences_are_authoritative(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": {
-                        "status": "completed_with_defaults",
-                        "preferences": {
-                            "calendar": {"preferred": "Google Calendar", "status": "active"},
-                            "crm": {"preferred": "Salesforce", "status": "active"},
-                            "document_store": {
-                                "backup": "Notion",
-                                "preferred": "Google Drive",
-                                "status": "active",
-                            },
-                            "spreadsheets": {
-                                "preferred": "Google Drive",
-                                "status": "active",
-                            },
-                            "external_messaging": {"preferred": "Gmail", "status": "active"},
-                            "internal_messaging": {"preferred": "Slack", "status": "active"},
-                            "meeting_notes": {
-                                "preferred": "Zoom",
-                                "status": "active",
-                            },
-                            "data_enrichment": {
-                                "preferred": None,
-                                "status": "skipped_for_now",
-                            },
-                            "agreements": {
-                                "preferred": None,
-                                "status": "skipped_for_now",
-                            },
-                        },
-                    },
-                    "core_onboarding": {
-                        "completed_at": "2026-05-27T19:15:04.064136+00:00",
-                        "remaining_steps": [],
-                        "status": "complete_with_fallback_automation",
-                    },
-                    "automations": {
-                        "weekly_sales_company_research": {
-                            "status": "deferred_environment_api_limitations",
-                        },
-                        "daily_sales_tips": {
-                            "status": "installed_fallback_current_thread",
-                            "canonical_id": "daily-sales-tips",
-                            "target_thread_id": "thread_tips",
-                        },
-                    },
-                    "initial_resource_discovery": {
-                        "status": "deferred_environment_api_limitations",
-                    },
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        status_payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = status_payload["control"]["onboarding_progress"]
-        task_statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-        sources = status_payload["context"]["sources"]
-        confirmation = sources["categories"]
-
-        self.assertTrue(progress["core_onboarding"]["complete"])
-        self.assertEqual(progress["core_onboarding"]["remaining_step_ids"], [])
-        self.assertEqual(task_statuses["connector_setup_confirmation"], "completed")
-        self.assertEqual(task_statuses["sales_automation_setup"], "completed")
-        self.assertEqual(confirmation["crm"]["status"], "active")
-        self.assertEqual(confirmation["crm"]["preferred"], "Salesforce")
-        self.assertEqual(confirmation["document_store"]["preferred"], "Google Drive")
-        self.assertEqual(confirmation["spreadsheets"]["preferred"], "Google Drive")
-        self.assertEqual(confirmation["data_enrichment"]["status"], "skipped_for_now")
-        self.assertEqual(confirmation["agreements"]["status"], "skipped_for_now")
-        self.assertEqual(
-            confirmation["data_enrichment"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertTrue(sources["has_setup_gaps"])
-        self.assertEqual(
-            {
-                category_id
-                for category_id, entry in confirmation.items()
-                if entry["status"] == "active"
-            },
-            {
-                "calendar",
-                "crm",
-                "document_store",
-                "external_messaging",
-                "internal_messaging",
-                "meeting_notes",
-                "spreadsheets",
-            },
-        )
-        self.assertEqual(
-            [
-                category_id
-                for category_id, entry in confirmation.items()
-                if entry["status"] != "active"
-            ],
-            ["agreements", "data_enrichment"],
-        )
-        self.assertNotIn("impact", confirmation["data_enrichment"])
-
-        ordinary_payload = self.preflight()
-        self.assertNotIn(
-            "complete_sales_core_onboarding",
-            [item["id"] for item in ordinary_payload["control"]["final_obligations"]],
-        )
-
-    def test_connector_defaults_and_fallback_automations_derive_core_completion(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": {
-                        "status": "completed_with_defaults",
-                        "preferences": {
-                            "calendar": {"preferred": "Google Calendar", "status": "active"},
-                            "crm": {"preferred": "Salesforce", "status": "active"},
-                            "document_store": {
-                                "backup": "Notion",
-                                "preferred": "Google Drive",
-                                "status": "active",
-                            },
-                            "spreadsheets": {
-                                "preferred": "Google Drive",
-                                "status": "active",
-                            },
-                            "external_messaging": {"preferred": "Gmail", "status": "active"},
-                            "internal_messaging": {"preferred": "Slack", "status": "active"},
-                            "meeting_notes": {
-                                "preferred": "Zoom",
-                                "status": "active",
-                            },
-                            "data_enrichment": {
-                                "preferred": None,
-                                "status": "skipped_for_now",
-                            },
-                            "agreements": {
-                                "preferred": None,
-                                "status": "skipped_for_now",
-                            },
-                        },
-                    },
-                    "automations_intro": {"status": "accepted_fallback_installed"},
-                    "automations": {
-                        "weekly_sales_company_research": {
-                            "status": "deferred_environment_api_limitations",
-                        },
-                        "daily_sales_tips": {
-                            "status": "installed_fallback_current_thread",
-                            "canonical_id": "daily-sales-tips",
-                            "target_thread_id": "thread_tips",
-                        },
-                    },
-                    "initial_resource_discovery": {
-                        "status": "deferred_environment_api_limitations",
-                    },
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        status_payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = status_payload["control"]["onboarding_progress"]
-        task_statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-
-        self.assertTrue(progress["core_onboarding"]["complete"])
-        self.assertEqual(progress["core_onboarding"]["remaining_step_ids"], [])
-        self.assertEqual(task_statuses["connector_setup_confirmation"], "completed")
-        self.assertEqual(task_statuses["sales_automation_setup"], "completed")
-
-        ordinary_payload = self.preflight()
-        self.assertEqual(ordinary_payload["control"]["final_obligations"], [])
-
-    def test_onboarding_progress_uses_canonical_overall_steps(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "meeting_prep_prompt": {
-                        "status": "confirmed",
-                        "selected_prompt": "Prep me for the Acme QBR tomorrow.",
-                    },
-                    "meeting_prep_run": {"status": "completed"},
-                    "first_guided_workflow_review": {"status": "completed"},
-                    "plugin_memory_intro": {"status": "shown"},
-                    "accepted_preference_memory": {"status": "saved"},
-                    "automations": self.ready_automations(),
-                    "initial_resource_discovery": {
-                        "status": "started",
-                        "target_thread_title": "Sales Company Research",
-                    },
-                    "skill_experience": self.skill_experience_state(
-                        tried={
-                            "prepare-for-meeting",
-                            "follow-up-after-call",
-                            "prioritize-accounts",
-                        }
-                    ),
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]["task_list"]
-
-        self.assertEqual(
-            [item["label"] for item in progress],
-            [
-                "Orientation",
-                "Connector setup/confirmation",
-                "Sales automation setup",
-                "First hero prompt",
-                "Other hero prompts",
-            ],
-        )
-        self.assertEqual(
-            [item["status"] for item in progress],
-            ["completed", "completed", "completed", "completed", "completed"],
-        )
-        self.assertTrue(payload["control"]["onboarding_progress"]["core_onboarding"]["complete"])
-        self.assertNotIn("Add starter context", [item["label"] for item in progress])
-        self.assertNotIn("Search company context", [item["label"] for item in progress])
-
-    def test_first_hero_prompt_can_be_any_core_hero_skill(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "automations": self.ready_automations(),
-                    "initial_resource_discovery": {"status": "started"},
-                    "hero_prompt_choice": {
-                        "status": "selected",
-                        "selected_skill": "follow-up-after-call",
-                    },
-                    "first_guided_workflow_review": {"status": "completed"},
-                    "accepted_preference_memory": {"status": "skipped"},
-                    "skill_experience": self.skill_experience_state(tried={"follow-up-after-call"}),
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = {
-            item["label"]: item["status"]
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-        }
-
-        self.assertEqual(progress["First hero prompt"], "completed")
-        self.assertEqual(progress["Other hero prompts"], "in_progress")
-
-    def test_skipped_first_hero_prompt_is_resolved(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "automations": self.ready_automations(),
-                    "initial_resource_discovery": {"status": "started"},
-                    "core_onboarding": {"status": "completed"},
-                    "hero_prompt_choice": {"status": "skipped"},
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = {
-            item["label"]: item["status"]
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-        }
-
-        self.assertEqual(progress["First hero prompt"], "completed")
-
-    def test_hero_prompt_choices_offer_only_untried_core_skills(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "automations": self.ready_automations(),
-                    "initial_resource_discovery": {"status": "started"},
-                    "first_guided_workflow_review": {"status": "completed"},
-                    "accepted_preference_memory": {"status": "skipped"},
-                    "skill_experience": self.skill_experience_state(
-                        tried={"prepare-for-meeting"},
-                        dismissed={"prioritize-accounts"},
-                    ),
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        choices = payload["control"]["onboarding_progress"]["hero_prompt_choices"]
-
-        self.assertEqual(choices["current_offer_label"], "Choose Your Next Sales Demo")
-        self.assertEqual(choices["suggested_options"], ["follow-up-after-call"])
-        self.assertEqual(
-            choices["resolved_core_skills"],
-            ["prepare-for-meeting", "prioritize-accounts"],
-        )
-
-    def test_onboarding_progress_marks_only_uncertain_sources_action_required(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": {
-                        "calendar": {
-                            "status": "active",
-                            "preferred": "Google Calendar",
-                        },
-                        "crm": {
-                            "status": "needs_confirmation",
-                            "candidates": ["Salesforce", "HubSpot"],
-                        },
-                    },
-                    "skill_experience": self.skill_experience_state(),
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = {
-            item["label"]: item for item in payload["control"]["onboarding_progress"]["task_list"]
-        }
-
-        self.assertEqual(progress["Connector setup/confirmation"]["status"], "in_progress")
-        self.assertTrue(progress["Connector setup/confirmation"]["action_required"])
-        self.assertNotIn("Draft proposed Sales context", progress)
-
-    def test_init_script_creates_first_run_state_from_templates_without_category_state(
-        self,
-    ) -> None:
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(INIT_SCRIPT),
-                "--state-dir",
-                str(self.state_dir),
-            ],
-            text=True,
-            capture_output=True,
-        )
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn("user-context.md: created", proc.stdout)
-        self.assertNotIn("category-state.json", proc.stdout)
-        user_context = self.state_dir / "user-context.md"
-        category_state = self.state_dir / "category-state.json"
-        onboarding_state = self.state_dir / "onboarding-state.json"
-        author_config_text = (
-            PLUGIN_ROOT / "skills/user-context/plugin-author-config/user-context-config.md"
-        ).read_text(encoding="utf-8")
-        self.assertTrue(user_context.exists())
-        self.assertFalse(category_state.exists())
-        self.assertTrue(onboarding_state.exists())
-        self.assertIn("# Sales User Context Config", author_config_text)
-        self.assertIn("## Category Entry Shape", author_config_text)
-        self.assertIn("## Default Categories", author_config_text)
-        self.assertNotIn("## User Resources", author_config_text)
-        self.assertNotIn("\n## Saved Links And Context", author_config_text)
-        self.assertNotIn("- Priority:", author_config_text)
-        self.assertNotIn("- Use When:", author_config_text)
-        user_context_text = user_context.read_text(encoding="utf-8")
-        self.assertIn(
-            "Unresolved `status: not provided` entries are setup prompts",
-            user_context_text,
-        )
-        self.assertIn("## Saved Links And Context\n\nstatus: not provided", user_context_text)
-        self.assertNotIn("# Sales User Context Config", user_context_text)
-        self.assertNotIn("- Priority:", user_context_text)
-        self.assertNotIn("- Use When:", user_context_text)
-        onboarding_state_data = json.loads(onboarding_state.read_text(encoding="utf-8"))
-        self.assertEqual(
-            set(onboarding_state_data["automations"]),
-            {
-                "daily_sales_tips",
-                "weekly_sales_company_research",
-            },
-        )
-        self.assertEqual(
-            onboarding_state_data["automations"]["daily_sales_tips"]["frequency"],
-            "weekdays at 9:00 AM local time",
-        )
-        self.assertNotIn(
-            "initial_run_status",
-            onboarding_state_data["automations"]["daily_sales_tips"],
-        )
-        self.assertEqual(
-            onboarding_state_data["automations"]["weekly_sales_company_research"][
-                "target_thread_title"
-            ],
-            "Sales Company Research",
-        )
-        self.assertIn("meeting_prep_prompt", onboarding_state_data)
-        self.assertIn("hero_prompt_choice", onboarding_state_data)
-        self.assertEqual(
-            onboarding_state_data["hero_prompt_choice"]["options"],
-            [
-                "prepare-for-meeting",
-                "follow-up-after-call",
-                "prioritize-accounts",
-            ],
-        )
-        self.assertIn("last_offered_skills", onboarding_state_data["hero_prompt_choice"])
-        self.assertIn("plugin_memory_intro", onboarding_state_data)
-        self.assertIn("accepted_preference_memory", onboarding_state_data)
-        self.assertIn("shown_at", onboarding_state_data["plugin_memory_intro"])
-        self.assertIn("approved_at", onboarding_state_data["accepted_preference_memory"])
-        self.assertIn("saved_at", onboarding_state_data["accepted_preference_memory"])
-        self.assertNotIn("prep_doc", onboarding_state_data)
-        self.assertIn("automations_intro", onboarding_state_data)
-        self.assertIn("initial_resource_discovery", onboarding_state_data)
-        self.assertIn("target_thread_title", onboarding_state_data["initial_resource_discovery"])
-        self.assertIn("call_followup_intro", onboarding_state_data)
-        self.assertIn("internal_navigation_intro", onboarding_state_data)
-        self.assertIn("skill_experience", onboarding_state_data)
-        self.assertIn("prepare-for-meeting", onboarding_state_data["skill_experience"])
-        self.assertNotIn("starter_context", onboarding_state_data)
-        self.assertNotIn("plugin_memory_orientation", onboarding_state_data)
-        self.assertNotIn("key_team_docs", onboarding_state_data)
-        self.assertNotIn("background_context_discovery", onboarding_state_data)
-        self.assertNotIn("company_context_discovery", onboarding_state_data)
-
-    def test_reset_script_backs_up_known_and_legacy_state_files(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        for filename in (
-            "user-context.md",
-            "category-state.json",
-            "onboarding-state.json",
-        ):
-            (self.state_dir / filename).write_text(filename, encoding="utf-8")
-        backup_dir = self.tmp_path / "sales-backup-fixed"
-
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(RESET_SCRIPT),
-                "--state-dir",
-                str(self.state_dir),
-                "--backup-dir",
-                str(backup_dir),
-            ],
-            text=True,
-            capture_output=True,
-        )
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn(f"Backup directory: {backup_dir}", proc.stdout)
-        self.assertFalse(self.state_dir.exists())
-        for filename in (
-            "user-context.md",
-            "category-state.json",
-            "onboarding-state.json",
-        ):
-            self.assertFalse((self.state_dir / filename).exists())
-            self.assertEqual(
-                (backup_dir / filename).read_text(encoding="utf-8"),
-                filename,
-            )
-
-    def test_reset_script_dry_run_does_not_move_files(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text("memory", encoding="utf-8")
-        backup_dir = self.tmp_path / "sales-backup-dry-run"
-
-        proc = subprocess.run(
-            [
-                sys.executable,
-                str(RESET_SCRIPT),
-                "--state-dir",
-                str(self.state_dir),
-                "--backup-dir",
-                str(backup_dir),
-                "--dry-run",
-            ],
-            text=True,
-            capture_output=True,
-        )
-
-        self.assertEqual(proc.returncode, 0, msg=proc.stderr)
-        self.assertIn("Dry run only; no files were moved.", proc.stdout)
-        self.assertTrue((self.state_dir / "user-context.md").exists())
-        self.assertFalse(backup_dir.exists())
-
-    def test_automation_setup_validator_accepts_heartbeat_threads_and_research_kickoff(self) -> None:
-        automations_dir = self.tmp_path / "automations"
-        self.state_dir.mkdir(parents=True)
-        research_prompt = (
-            "Load and follow `$sales:sales-company-research` in scheduled research mode. "
-            "Use the skill's dynamic source discovery, Sales user-context save policy, "
-            "review gates, coverage-note guidance, and output contract, and compare against "
-            "entries already flagged or saved from this pinned thread."
-        )
-        tips_prompt = (
-            "Load and follow `$sales:suggest-sales-next-step` in scheduled Sales check-in mode. "
-            "Produce a short natural-language check-in on recent Sales work and the next Sales "
-            "workflow worth trying, preserving the heartbeat automation response format."
-        )
-        self.write_automation_toml(
-            automations_dir,
-            "sales-company-research",
-            kind="heartbeat",
-            name="Sales Company Research",
-            prompt=research_prompt,
-            rrule="FREQ=WEEKLY;BYDAY=MO;BYHOUR=9;BYMINUTE=0;BYSECOND=0",
-            target_thread_id="thread_research",
-        )
-        self.write_automation_toml(
-            automations_dir,
-            "sales-tips",
-            kind="heartbeat",
-            name="Sales Tips",
-            prompt=tips_prompt,
-            rrule="FREQ=WEEKLY;BYDAY=MO,TU,WE,TH,FR;BYHOUR=9;BYMINUTE=0;BYSECOND=0",
-            target_thread_id="thread_tips",
-        )
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "automations": {
-                        "weekly_sales_company_research": {
-                            "status": "configured",
-                            "canonical_id": "sales-company-research",
-                            "kind": "heartbeat",
-                            "readback_status": "confirmed",
-                            "target_thread_id": "thread_research",
-                            "target_thread_title": "Sales Company Research",
-                            "target_thread_status": "pinned",
-                        },
-                        "daily_sales_tips": {
-                            "status": "configured",
-                            "canonical_id": "sales-tips",
-                            "kind": "heartbeat",
-                            "readback_status": "confirmed",
-                            "target_thread_id": "thread_tips",
-                            "target_thread_title": "Sales Tips",
-                            "target_thread_status": "pinned",
-                        },
-                    },
-                    "initial_resource_discovery": {
-                        "status": "started",
-                        "target_thread_id": "thread_research",
-                        "target_thread_title": "Sales Company Research",
-                    },
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.run_json(
-            [
-                sys.executable,
-                str(VALIDATE_AUTOMATION_SCRIPT),
-                "--state-dir",
-                str(self.state_dir),
-                "--automations-dir",
-                str(automations_dir),
-                "--require-onboarding-defaults",
-            ]
-        )
-
-        self.assertTrue(payload["ok"])
-        self.assertEqual(
-            payload["validated"],
-            ["weekly_sales_company_research", "daily_sales_tips"],
-        )
-
-    def test_automation_setup_validator_rejects_cron_and_missing_research_kickoff(self) -> None:
-        automations_dir = self.tmp_path / "automations"
-        self.state_dir.mkdir(parents=True)
-        self.write_automation_toml(
-            automations_dir,
-            "sales-company-research",
-            kind="cron",
-            name="Sales Company Research",
-            prompt="expanded prompt",
-            rrule="FREQ=WEEKLY;BYDAY=MO;BYHOUR=9;BYMINUTE=0;BYSECOND=0",
-            target_thread_id=None,
-        )
-        self.write_automation_toml(
-            automations_dir,
-            "sales-tips",
-            kind="heartbeat",
-            name="Sales Tips",
-            prompt="expanded prompt",
-            rrule="FREQ=WEEKLY;BYDAY=MO,TU,WE,TH,FR;BYHOUR=9;BYMINUTE=0;BYSECOND=0",
-            target_thread_id="thread_tips",
-        )
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "automations": {
-                        "weekly_sales_company_research": {
-                            "status": "configured",
-                            "canonical_id": "sales-company-research",
-                            "target_thread_title": "Sales Company Research",
-                        },
-                        "daily_sales_tips": {
-                            "status": "configured",
-                            "canonical_id": "sales-tips",
-                            "kind": "heartbeat",
-                            "readback_status": "confirmed",
-                            "target_thread_id": "thread_tips",
-                            "target_thread_title": "Sales Tips",
-                            "target_thread_status": "pinned",
-                        },
-                    }
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.run_json(
-            [
-                sys.executable,
-                str(VALIDATE_AUTOMATION_SCRIPT),
-                "--state-dir",
-                str(self.state_dir),
-                "--automations-dir",
-                str(automations_dir),
-                "--require-onboarding-defaults",
-            ],
-            expect=1,
-        )
-        failures = "\n".join(payload["failures"])
-
-        self.assertFalse(payload["ok"])
-        self.assertIn("weekly_sales_company_research: stored kind is heartbeat", failures)
-        self.assertIn(
-            "weekly_sales_company_research: stored prompt matches automation-config.md Instructions",
-            failures,
-        )
-
-    def test_onboarding_reference_preserves_connector_and_guided_output_copy(self) -> None:
-        text = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("# Sales Onboarding Flow", text)
-        self.assertIn("Common Onboarding Message Frame", text)
-        self.assertIn(CONNECTOR_CONFIRMATION_COPY, text)
-        self.assertIn(MEETING_PROMPT_COPY, text)
-        self.assertIn(HERO_OUTPUT_COPY, text)
-        self.assertIn("### Step 4B: Introduce Selected First Hero Workflow", text)
-        self.assertIn("### Step 4D: Review And Iterate", text)
-        self.assertIn("### Step 4E: Introduce Plugin Memory And Save Preference", text)
-        self.assertIn("Do not run another hero workflow until the user picks", text)
-        self.assertIn("hero_prompt_choice", text)
-        self.assertNotIn("### Step 3E: Optional Save Prep Doc", text)
-        self.assertIn("## Step 3: Sales Automations", text)
-        self.assertIn("### Step 3A: Introduce Sales Automations", text)
-        self.assertIn("### Step 3B: Set Up Sales Automations", text)
-        self.assertIn("## Step 5: Other Hero Prompts", text)
-        self.assertIn("### Step 5A: Choose Next Hero Prompt", text)
-        self.assertIn("### Step 5B: Introduce Selected Remaining Hero Workflow", text)
-        self.assertIn("### Step 5C: Run Selected Remaining Hero Demo", text)
-        self.assertIn("### Step 5D: Review And Continue Remaining Hero", text)
-        self.assertIn("## Step 6: Completion", text)
-        self.assertIn(
-            "Here's your meeting prep, using",
-            text,
-        )
-        self.assertIn(
-            "Do not render `What Happened`, `Recap`, or another walkthrough heading by default",
-            text,
-        )
-        self.assertIn("render that feedback CTA as an H2/H3 heading", text)
-        self.assertNotIn("Recommended Next Steps", text)
-        self.assertIn(
-            "How does that look? Any other changes, or should we show the next demo workflow choices?",
-            text,
-        )
-        self.assertNotIn("should I save any reusable preference and move on?", text)
-        self.assertIn("must never be a narrow-preference save approval", text)
-        self.assertIn(
-            "Only require explicit yes for broad, sensitive, ambiguous, high-risk, or externally visible preferences",
-            text,
-        )
-        self.assertIn("Do not save plugin memory on every iteration", text)
-        self.assertIn("Calendar attachment is explicit-user-request only", text)
-        self.assertIn("initial_resource_discovery", text)
-        self.assertIn("Pre-tool gate: before the first automation", text)
-        self.assertIn("outside the dedicated automation target threads created by `automation.md`", text)
-        self.assertIn("prefer plugin setup", text)
-        self.assertIn("direct app/connector setup", text)
-        self.assertIn("asks for connect/auth, skip, or manual fallback", text)
-
-    def test_inventory_requires_setup_to_write_resolved_source_routes(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_inventory": {
-                        "active_apps": ["Google Calendar"],
-                        "available_apps": ["Salesforce", "HubSpot"],
-                    },
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["calendar"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["calendar"]["candidates"], ["Google Calendar"])
-        self.assertEqual(
-            confirmation["calendar"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(confirmation["crm"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["crm"]["candidates"], ["Salesforce", "HubSpot"])
-        self.assertEqual(
-            confirmation["crm"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(confirmation["meeting_notes"]["status"], "missing")
-        self.assertEqual(
-            confirmation["meeting_notes"]["options"],
-            ["Zoom", "Granola", "Otter.ai", "Fireflies", "Outreach", "Rox"],
-        )
-        self.assertTrue(confirmation["meeting_notes"]["it_admin_options"])
-        self.assertEqual(
-            confirmation["meeting_notes"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(confirmation["data_enrichment"]["status"], "missing")
-        self.assertEqual(
-            confirmation["data_enrichment"]["it_admin_options"],
-            [
-                "Ask IT or a workspace admin whether Codex access can be enabled for one of: ZoomInfo, Clay, HG Insights, Rox, Apollo, Actively, Meticulate."
-            ],
-        )
-
-    def test_configured_connector_route_is_returned_for_runtime_use(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "active",
-            "preferred": "Salesforce",
-            "source_kind": "connector",
-            "skill_surface": "sales_vendored_helper",
-            "connector": {"name": "Salesforce"},
-            "installed_evidence": "connector_inventory",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["crm"]["status"], "active")
-        self.assertEqual(confirmation["crm"]["preferred"], "Salesforce")
-        self.assertEqual(confirmation["crm"]["source_kind"], "connector")
-        self.assertEqual(confirmation["crm"]["skill_surface"], "sales_vendored_helper")
-        self.assertEqual(confirmation["crm"]["connector"], {"name": "Salesforce"})
-        self.assertEqual(confirmation["crm"]["installed_evidence"], "connector_inventory")
-        self.assertEqual(
-            confirmation["crm"]["runtime_action"],
-            "use_connector_or_app_with_sales_helper_guidance",
-        )
-        self.assertEqual(confirmation["crm"]["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(
-            confirmation["crm"]["plugin_preference_order"],
-            ["Salesforce", "Agentforce Sales", "HubSpot", "Close", "Zoho", "Pipedrive"],
-        )
-        self.assertEqual(
-            confirmation["crm"]["setup_recovery"]["plugin_preferred_over"],
-            "active_or_available_app_connector_routes",
-        )
-        self.assertIn("helper_skills", confirmation["crm"])
-        self.assertEqual(confirmation["crm"]["helper_skills_apply_when"], ["app", "connector"])
-        source_step = next(
-            item
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-            if item["id"] == "connector_setup_confirmation"
-        )
-        self.assertEqual(source_step["status"], "completed")
-        self.assertFalse(source_step.get("action_required", False))
-
-    def test_configured_plugin_route_uses_plugin_surface_without_sales_helper(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "active",
-            "preferred": "Salesforce",
-            "source_kind": "plugin",
-            "skill_surface": "plugin_owned",
-            "plugin": {
-                "id": "salesforce@example-marketplace",
-                "name": "salesforce",
-            },
-            "installed_evidence": "installed_plugin_skill_visible",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["crm"]["status"], "active")
-        self.assertEqual(confirmation["crm"]["preferred"], "Salesforce")
-        self.assertEqual(confirmation["crm"]["source_kind"], "plugin")
-        self.assertEqual(confirmation["crm"]["skill_surface"], "plugin_owned")
-        self.assertEqual(
-            confirmation["crm"]["plugin"],
-            {"id": "salesforce@example-marketplace", "name": "salesforce"},
-        )
-        self.assertEqual(
-            confirmation["crm"]["installed_evidence"],
-            "installed_plugin_skill_visible",
-        )
-        self.assertEqual(
-            confirmation["crm"]["runtime_action"],
-            "use_plugin_owned_skill_or_tools",
-        )
-        self.assertNotIn("helper_skills", confirmation["crm"])
-
-    def test_configured_plugin_route_uses_new_install_and_surface_evidence(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "active",
-            "preferred": "HubSpot",
-            "source_kind": "plugin",
-            "skill_surface": "plugin_owned",
-            "plugin": {
-                "id": "hubspot@openai-curated",
-                "name": "HubSpot",
-            },
-            "plugin_install_evidence": "request_plugin_install_completed",
-            "app_surface_evidence": "callable_app_tools_visible",
-            "skill_surface_evidence": "plugin_skills_visible",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["crm"]["status"], "active")
-        self.assertEqual(confirmation["crm"]["preferred"], "HubSpot")
-        self.assertEqual(confirmation["crm"]["source_kind"], "plugin")
-        self.assertEqual(confirmation["crm"]["skill_surface"], "plugin_owned")
-        self.assertEqual(
-            confirmation["crm"]["plugin"],
-            {"id": "hubspot@openai-curated", "name": "HubSpot"},
-        )
-        self.assertEqual(
-            confirmation["crm"]["plugin_install_evidence"],
-            "request_plugin_install_completed",
-        )
-        self.assertEqual(
-            confirmation["crm"]["app_surface_evidence"],
-            "callable_app_tools_visible",
-        )
-        self.assertEqual(
-            confirmation["crm"]["skill_surface_evidence"],
-            "plugin_skills_visible",
-        )
-        self.assertEqual(
-            confirmation["crm"]["runtime_action"],
-            "use_plugin_owned_skill_or_tools",
-        )
-        self.assertNotIn("helper_skills", confirmation["crm"])
-
-    def test_configured_multi_route_source_category_is_returned_for_runtime_use(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["document_store"] = {
-            "status": "active",
-            "routes": [
-                {
-                    "preferred": "Google Drive",
-                    "source_kind": "plugin",
-                    "skill_surface": "plugin_owned",
-                    "plugin": {
-                        "id": "google-drive@openai-curated",
-                        "name": "Google Drive",
-                    },
-                    "plugin_install_evidence": "plugin_in_available_plugins",
-                    "skill_surface_evidence": "plugin_skills_visible",
-                },
-                {
-                    "preferred": "Notion",
-                    "source_kind": "plugin",
-                    "skill_surface": "plugin_owned",
-                    "plugin": {
-                        "id": "notion@openai-curated",
-                        "name": "Notion",
-                    },
-                    "plugin_install_evidence": "plugin_in_available_plugins",
-                    "skill_surface_evidence": "plugin_skills_visible",
-                },
-            ],
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        document_store = payload["context"]["sources"]["categories"]["document_store"]
-
-        self.assertEqual(document_store["status"], "active")
-        self.assertNotIn("setup_action", document_store)
-        self.assertEqual(
-            [route["preferred"] for route in document_store["routes"]],
-            ["Google Drive", "Notion"],
-        )
-        for route in document_store["routes"]:
-            self.assertEqual(route["source_kind"], "plugin")
-            self.assertEqual(route["skill_surface"], "plugin_owned")
-            self.assertEqual(route["runtime_action"], "use_plugin_owned_skill_or_tools")
-        self.assertNotIn("setup_recovery", document_store)
-
-    def test_plugin_route_without_visible_skill_surface_stays_unresolved(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "active",
-            "preferred": "HubSpot",
-            "source_kind": "plugin",
-            "skill_surface": "plugin_owned",
-            "plugin": {
-                "id": "hubspot@openai-curated",
-                "name": "HubSpot",
-            },
-            "plugin_install_evidence": "request_plugin_install_completed",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["crm"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["crm"]["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(
-            confirmation["crm"]["setup_note"],
-            "plugin_route_requires_plugin_install_and_surface_evidence_before_active",
-        )
-
-    def test_saved_preferred_source_leads_plugin_preference_order(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text(
-            "# Source Preferences And Defaults\n\n"
-            "## Saved Links And Context\n\n"
-            "Agentforce Sales CRM preference\n"
-            "- Date Added: 2026-05-29.\n"
-            "- Useful Context: Agentforce Sales is the preferred CRM source.\n"
-            "- Future Use: Use Agentforce Sales when Sales needs CRM account or pipeline context.\n",
-            encoding="utf-8",
-        )
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "missing",
-            "options": ["Salesforce", "Agentforce Sales", "HubSpot"],
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        crm = payload["context"]["sources"]["categories"]["crm"]
-
-        self.assertEqual(crm["status"], "missing")
-        self.assertEqual(crm["saved_source_preferences"], ["Agentforce Sales"])
-        self.assertEqual(
-            crm["plugin_preference_order"],
-            ["Agentforce Sales", "Salesforce", "HubSpot", "Close", "Zoho", "Pipedrive"],
-        )
-        self.assertEqual(crm["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(
-            crm["fallback_setup_action"],
-            "explain_workflow_impact_and_it_admin_options",
-        )
-        self.assertEqual(
-            crm["setup_recovery"],
-            {
-                "type": "plugin_first_source_setup",
-                "preferred_sources": [
-                    "Agentforce Sales",
-                    "Salesforce",
-                    "HubSpot",
-                    "Close",
-                    "Zoho",
-                    "Pipedrive",
-                ],
-                "plugin_preferred_over": "active_or_available_app_connector_routes",
-                "candidate_match": (
-                    "candidate_name_display_id_or_app_connector_id_matches_preferred_source"
-                ),
-                "candidate_lookup": "functions.list_available_plugins_to_install",
-                "install_request": "functions.request_plugin_install_after_user_approval",
-                "retry_policy": (
-                    "retry_on_future_workflows_after_failed_setup_or_pending_visibility"
-                ),
-                "decline_scope": (
-                    "suppress_current_workflow_only_unless_user_saves_do_not_use"
-                ),
-                "fallback": (
-                    "use_existing_app_connector_route_or_manual_context_when_no_plugin_candidate_or_user_declines"
-                ),
-            },
-        )
-
-    def test_failed_preferred_plugin_setup_stays_retryable_until_user_declines(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text(
-            "# Source Preferences And Defaults\n\n"
-            "## Saved Links And Context\n\n"
-            "Agentforce Sales CRM preference\n"
-            "- Date Added: 2026-05-29.\n"
-            "- Useful Context: Agentforce Sales is the preferred CRM source.\n"
-            "- Future Use: Use Agentforce Sales when Sales needs CRM account or pipeline context.\n",
-            encoding="utf-8",
-        )
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "deferred",
-            "preferred": "Agentforce Sales",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        crm = payload["context"]["sources"]["categories"]["crm"]
-
-        self.assertEqual(crm["status"], "deferred")
-        self.assertEqual(crm["setup_action"], "run_plugin_first_source_setup")
-        self.assertEqual(
-            crm["setup_recovery"]["retry_policy"],
-            "retry_on_future_workflows_after_failed_setup_or_pending_visibility",
-        )
-
-        confirmation_state["crm"] = {
-            "status": "declined",
-            "preferred": "Agentforce Sales",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        crm = payload["context"]["sources"]["categories"]["crm"]
-
-        self.assertEqual(crm["status"], "declined")
-        self.assertNotIn("setup_recovery", crm)
-
-    def test_unproven_plugin_route_is_not_returned_as_active(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["meeting_notes"] = {
-            "status": "active",
-            "label": "Zoom",
-            "source_kind": "plugin",
-            "skill_surface": "plugin_owned",
-            "plugin": "Zoom",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["meeting_notes"]["status"], "needs_confirmation")
-        self.assertEqual(
-            confirmation["meeting_notes"]["candidates"],
-            ["Zoom", "Granola", "Otter.ai", "Fireflies", "Outreach", "Rox"],
-        )
-        self.assertEqual(
-            confirmation["meeting_notes"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(
-            confirmation["meeting_notes"]["setup_note"],
-            "plugin_route_requires_plugin_install_and_surface_evidence_before_active",
-        )
-
-    def test_user_selected_available_source_stays_unresolved_until_read_probe(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        confirmation_state = self.all_connector_confirmation()
-        confirmation_state["crm"] = {
-            "status": "selected",
-            "preferred": "Salesforce",
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "connector_confirmation": confirmation_state,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        confirmation = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(confirmation["crm"]["status"], "needs_confirmation")
-        self.assertEqual(confirmation["crm"]["preferred"], "Salesforce")
-        self.assertEqual(confirmation["crm"]["candidates"], ["Salesforce"])
-        self.assertEqual(
-            confirmation["crm"]["setup_action"],
-            "run_plugin_first_source_setup",
-        )
-        self.assertEqual(
-            confirmation["crm"]["fallback_setup_action"],
-            "verify_selected_app_with_simple_read",
-        )
-        self.assertEqual(confirmation["crm"]["saved_source_preferences"], ["Salesforce"])
-        self.assertEqual(
-            confirmation["crm"]["plugin_preference_order"],
-            ["Salesforce", "Agentforce Sales", "HubSpot", "Close", "Zoho", "Pipedrive"],
-        )
-
-    def test_connector_confirmation_progress_requires_confirmation_before_first_workflow(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        connector_confirmation = self.all_connector_confirmation()
-        connector_confirmation["crm"] = {
-            "status": "needs_confirmation",
-            "candidates": ["Salesforce"],
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": connector_confirmation,
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]["task_list"]
-        labels = [item["label"] for item in progress]
-        connector_step = progress[labels.index("Connector setup/confirmation")]
-        automation_step = progress[labels.index("Sales automation setup")]
-        first_hero_step = progress[labels.index("First hero prompt")]
-
-        self.assertLess(
-            labels.index("Orientation"),
-            labels.index("Connector setup/confirmation"),
-        )
-        self.assertLess(
-            labels.index("Connector setup/confirmation"),
-            labels.index("Sales automation setup"),
-        )
-        self.assertLess(
-            labels.index("Sales automation setup"),
-            labels.index("First hero prompt"),
-        )
-        self.assertEqual(connector_step["status"], "in_progress")
-        self.assertTrue(connector_step["action_required"])
-        self.assertEqual(automation_step["status"], "pending")
-        self.assertEqual(first_hero_step["status"], "pending")
-
-    def test_review_loop_stays_before_optional_doc_branch(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "meeting_prep_prompt": {"status": "confirmed"},
-                    "meeting_prep_run": {"status": "completed"},
-                    "first_guided_workflow_review": {"status": "in_progress"},
-                    "skill_experience": self.skill_experience_state(tried={"prepare-for-meeting"}),
-                    "personalization_captured": [],
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]["task_list"]
-        by_label = {item["label"]: item for item in progress}
-
-        self.assertEqual(by_label["First hero prompt"]["status"], "in_progress")
-        self.assertEqual(by_label["Sales automation setup"]["status"], "in_progress")
-        self.assertNotIn("Save prep doc", by_label)
-
-    def test_plugin_memory_resolution_unblocks_sales_automations(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "meeting_prep_prompt": {"status": "confirmed"},
-                    "meeting_prep_run": {"status": "completed"},
-                    "first_guided_workflow_review": {"status": "completed"},
-                    "accepted_preference_memory": {"status": "skipped"},
-                    "skill_experience": self.skill_experience_state(tried={"prepare-for-meeting"}),
-                    "personalization_captured": [],
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = {
-            item["label"]: item["status"]
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-        }
-
-        self.assertNotIn("Save prep doc", progress)
-        self.assertEqual(progress["Sales automation setup"], "in_progress")
-        self.assertEqual(progress["First hero prompt"], "completed")
-
-    def test_source_preferences_do_not_count_as_plugin_memory_intro(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "meeting_prep_prompt": {"status": "confirmed"},
-                    "meeting_prep_run": {"status": "completed"},
-                    "first_guided_workflow_review": {"status": "completed"},
-                    "skill_experience": self.skill_experience_state(tried={"prepare-for-meeting"}),
-                    "personalization_captured": [
-                        {
-                            "category": "Source Preferences",
-                            "summary": "Prefer Notion for Sales docs.",
-                        }
-                    ],
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = {
-            item["label"]: item["status"]
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-        }
-
-        self.assertEqual(progress["Sales automation setup"], "in_progress")
-        self.assertEqual(progress["First hero prompt"], "completed")
-        self.assertNotIn("Save prep doc", progress)
-
-    def test_sales_automations_require_immediate_discovery_proposal_search(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        ready_automation = self.ready_automation(
-            "auto_123",
-            "thread_123",
-            "Sales Company Research",
-        )
-        ready_scheduled_automations = {
-            key: value
-            for key, value in self.ready_automations().items()
-            if key != "weekly_sales_company_research"
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "automations_intro": {"status": "started"},
-                    "automations": {
-                        "weekly_sales_company_research": ready_automation,
-                        **{
-                            key: value
-                            for key, value in self.ready_automations().items()
-                            if key != "weekly_sales_company_research"
-                        },
-                    },
-                    "initial_resource_discovery": {
-                        "status": "started",
-                        "target_thread_title": "Sales Company Research",
-                    },
-                    "skill_experience": self.skill_experience_state(),
-                    "personalization_captured": [],
-                }
-            ),
-            encoding="utf-8",
-        )
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        missing_tip_step = next(
-            item
-            for item in payload["control"]["onboarding_progress"]["task_list"]
-            if item["id"] == "sales_automation_setup"
-        )
-        self.assertEqual(missing_tip_step["status"], "completed")
-
-        for discovery_state, expected_status, expected_action_required in (
-            ({}, "pending", False),
-            (
-                {"status": "started", "target_thread_title": "Sales Company Research"},
-                "completed",
-                False,
-            ),
-            ({"status": "proposal_ready"}, "completed", True),
-            ({"status": "completed"}, "completed", False),
-        ):
-            with self.subTest(discovery_state=discovery_state):
-                (self.state_dir / "onboarding-state.json").write_text(
-                    json.dumps(
-                        {
-                            "status": "active",
-                            "orientation": {"status": "shown"},
-                            "automations": {
-                                "weekly_sales_company_research": ready_automation,
-                                **ready_scheduled_automations,
-                            },
-                            "initial_resource_discovery": discovery_state,
-                            "skill_experience": self.skill_experience_state(),
-                            "personalization_captured": [],
-                        }
-                    ),
-                    encoding="utf-8",
-                )
-
-                payload = self.preflight("--request-mode", "direct_onboarding_status")
-                weekly_step = next(
-                    item
-                    for item in payload["control"]["onboarding_progress"]["task_list"]
-                    if item["id"] == "sales_automation_setup"
-                )
-
-                self.assertEqual(weekly_step["status"], expected_status)
-                self.assertEqual(
-                    bool(weekly_step.get("action_required")),
-                    expected_action_required,
-                )
-
-    def test_cron_automations_do_not_complete_default_sales_automation_setup(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        cron_automations = {
-            key: {
-                "status": "installed",
-                "canonical_id": value["canonical_id"],
-                "kind": "cron",
-                "target_thread_id": value["target_thread_id"],
-                "target_thread_title": value["target_thread_title"],
-                "target_thread_status": "pinned",
-                "readback_status": "verified",
-            }
-            for key, value in self.ready_automations().items()
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "automations": cron_automations,
-                    "initial_resource_discovery": {
-                        "status": "started",
-                        "target_thread_title": "Sales Company Research",
-                    },
-                    "core_onboarding": {"status": "completed"},
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]
-        statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-
-        self.assertFalse(progress["core_onboarding"]["complete"])
-        self.assertEqual(statuses["sales_automation_setup"], "in_progress")
-        self.assertEqual(progress["core_onboarding"]["remaining_step_ids"], ["sales_automation_setup"])
-
-    def test_automations_without_readback_and_pinned_thread_metadata_are_not_ready(
-        self,
-    ) -> None:
-        self.state_dir.mkdir(parents=True)
-        incomplete_automations = {
-            key: {
-                "status": "installed",
-                "canonical_id": value["canonical_id"],
-                "kind": "heartbeat",
-                "target_thread_id": value["target_thread_id"],
-            }
-            for key, value in self.ready_automations().items()
-        }
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps(
-                {
-                    "status": "active",
-                    "orientation": {"status": "shown"},
-                    "connector_confirmation": self.all_connector_confirmation(),
-                    "automations": incomplete_automations,
-                    "initial_resource_discovery": {
-                        "status": "started",
-                        "target_thread_title": "Sales Company Research",
-                    },
-                    "core_onboarding": {"status": "completed"},
-                }
-            ),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        progress = payload["control"]["onboarding_progress"]
-        statuses = {item["id"]: item["status"] for item in progress["task_list"]}
-
-        self.assertFalse(progress["core_onboarding"]["complete"])
-        self.assertEqual(statuses["sales_automation_setup"], "in_progress")
-
-    def test_call_followup_drafts_are_blockquoted_team_recaps(self) -> None:
-        contract = CALL_FOLLOWUP_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn("email body formatted as block quote text", contract)
-        self.assertIn("Format the verbatim email body as a Markdown block quote", contract)
-        self.assertIn("### External Comms", contract)
-        self.assertIn("Not applicable: this was an internal call.", contract)
-        self.assertIn("### Internal Follow-Up", contract)
-        self.assertIn("standard bold section labels", contract)
-        self.assertIn("`**Next Steps**`", contract)
-        self.assertIn(
-            "Format the verbatim internal follow-up draft as a Markdown block quote",
-            contract,
-        )
-        self.assertIn("internal follow-up draft is a team meeting recap", contract)
-        self.assertIn("Include a brief summary, next steps, owners or TBDs", contract)
-        self.assertIn("Call notes: no useful link available", contract)
-        self.assertIn(
-            "clickable Markdown link when the connector exposes a useful URL",
-            contract,
-        )
-        self.assertIn("clickable Markdown call-notes link", contract)
-        self.assertIn(
-            "narrow, low-risk accepted preferences can be summarized and saved automatically",
-            contract,
-        )
-
-    def test_sales_skills_prefer_clickable_inline_sources(self) -> None:
-        required_phrase = "When referencing sources inline, prefer clickable Markdown links"
-        for skill_path in sorted((PLUGIN_ROOT / "skills").glob("*/SKILL.md")):
-            with self.subTest(skill=skill_path.parent.name):
-                self.assertIn(required_phrase, skill_path.read_text(encoding="utf-8"))
-
-    def test_sales_skills_have_standard_audience_language(self) -> None:
-        required_section = """### Audience And Language
-
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
-
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details."""
-        legacy_phrases = (
-            "Use practical sales and business language",
-            "Prefer terms like accounts",
-            "sales-adjacent operators",
-            "preflight, cursor, probe",
-        )
-        for skill_path in sorted((PLUGIN_ROOT / "skills").glob("*/SKILL.md")):
-            skill_text = skill_path.read_text(encoding="utf-8")
-            with self.subTest(skill=skill_path.parent.name):
-                self.assertEqual(1, skill_text.count(required_section))
-                for phrase in legacy_phrases:
-                    self.assertNotIn(phrase, skill_text)
-
-    def test_sales_skills_use_natural_required_input_prompts(self) -> None:
-        forbidden_phrases = (
-            "**I need one detail to continue.**",
-            "**I need a few forecast choices to continue.**",
-            "Reply with a letter, or send",
-            "allow the user to reply with a letter or provide a different answer",
-        )
-        for skill_path in sorted((PLUGIN_ROOT / "skills").glob("*/SKILL.md")):
-            skill_text = skill_path.read_text(encoding="utf-8")
-            with self.subTest(skill=skill_path.parent.name):
-                for phrase in forbidden_phrases:
-                    self.assertNotIn(phrase, skill_text)
-
-    def test_meeting_prep_inner_sections_use_compact_labels(self) -> None:
-        skill = MEETING_PREP_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn("## Output Shapes", skill)
-        self.assertIn("Summary", skill)
-        self.assertTrue(
-            "compact bold labels, not Markdown H1/H2/H3 headings" in skill
-            or all(
-                phrase in skill
-                for phrase in (
-                    "## Summary",
-                    "## Goal",
-                    "## Open Questions",
-                    "## Proposed Agenda",
-                    "## Background Context",
-                )
-            )
-        )
-
-    def test_meeting_prep_has_simple_required_anchor_gate(self) -> None:
-        skill = MEETING_PREP_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn("### Required Inputs", skill)
-        self.assertIn("Source access can suggest concrete candidates", skill)
-        self.assertIn("specific meeting/account anchor is selected or clearly inferable", skill)
-        self.assertIn("bounded candidate pass", skill)
-        self.assertIn("up to 5 concrete lettered options", skill)
-        self.assertIn("Never present a generic instruction", skill)
-        self.assertIn("{Natural clarification question", skill)
-        self.assertIn(
-            "The skill can still produce a limited brief from user-provided details alone",
-            skill,
-        )
-        self.assertIn(
-            "Do not stop solely because connectors are unavailable",
-            skill,
-        )
-        self.assertNotIn("## Clarification Needed", skill)
-
-    def test_internal_navigation_defines_internal_ownership(self) -> None:
-        skill = INTERNAL_NAVIGATION_SKILL.read_text(encoding="utf-8")
-
-        self.assertIn("### Internal Ownership", skill)
-        self.assertIn("DRIs, approvers, SMEs, maintainers", skill)
-        self.assertIn("recurring decision forums", skill)
-        self.assertIn("canonical docs, escalation channels", skill)
-
-    def test_preflight_reads_saved_state_and_ignores_legacy_category_state(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text(
-            "# Source Preferences And Defaults\n\n"
-            "## Saved Links And Context\n\n"
-            "Territory report preference\n"
-            "- Date Added: 2026-05-22.\n"
-            "- Useful Context: Validation-only Sales memory entry in a temporary state dir.\n"
-            "- Future Use: Use this source before broad CRM searching for account scoping.\n",
-            encoding="utf-8",
-        )
-        (self.state_dir / "category-state.json").write_text("{not-json", encoding="utf-8")
-        (self.state_dir / "onboarding-state.json").write_text(
-            json.dumps({"status": "quiet"}),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight()
-
-        self.assertEqual(payload["state"]["user_context_status"], "present")
-        self.assertTrue(payload["state"]["quiet"])
-        self.assertEqual(payload["control"]["final_obligations"], [])
-        self.assertNotIn("category_state", payload["context"])
-        self.assertNotIn("category_readiness", payload["context"])
-        self.assertNotIn("files_read", payload["provenance"])
-        crm_source = payload["context"]["sources"]["categories"]["crm"]
-        self.assertEqual(crm_source["status"], "needs_confirmation")
-        self.assertNotIn("verified_app", crm_source)
-        self.assertNotIn("installed", crm_source)
-        self.assertEqual(
-            payload["context"]["sources"]["resolution"],
-            "plugin_or_app_or_connector_then_manual_context",
-        )
-        self.assertEqual(
-            payload["context"]["user_context"]["entries"][0],
-            (
-                "Source Preferences And Defaults: Territory report preference | "
-                "Validation-only Sales memory entry in a temporary state dir"
-            ),
-        )
-
-    def test_sources_are_compact_runtime_preferences(self) -> None:
-        payload = self.preflight("--workflow", "plan-deal-strategy")
-        sources = payload["context"]["sources"]["categories"]
-
-        self.assertEqual(
-            sources["crm"]["helper_skills"],
-            [
-                {"app": "Salesforce", "skill": "salesforce"},
-                {"app": "Agentforce Sales", "skill": "salesforce"},
-                {"app": "HubSpot", "skill": "hubspot"},
-            ],
-        )
-        self.assertEqual(
-            payload["context"]["sources"]["resolution"],
-            "plugin_or_app_or_connector_then_manual_context",
-        )
-        for entry in sources.values():
-            self.assertNotIn("evidence", entry)
-            self.assertNotIn("checked", entry)
-            self.assertNotIn("importance", entry)
-            self.assertNotIn("setup_preference_order", entry)
-
-    def test_source_categories_have_no_author_configurable_importance_or_readiness_state(
-        self,
-    ) -> None:
-        source_category_config_path = (
-            PLUGIN_ROOT / "skills/user-context/plugin-author-config/source-category-config.json"
-        )
-        source_category_config = json.loads(source_category_config_path.read_text(encoding="utf-8"))
-        source_category_config_text = json.dumps(source_category_config, sort_keys=True)
-        source_category_runtime = SOURCE_RUNTIME_REFERENCE.read_text(encoding="utf-8")
-        app_manifest = json.loads((PLUGIN_ROOT / ".app.json").read_text(encoding="utf-8"))
-        declared_apps = {
-            "".join(ch for ch in app_key.casefold() if ch.isalnum())
-            for app_key in app_manifest["apps"]
-        }
-
-        self.assertEqual(
-            source_category_config["schema_version"],
-            "sales_source_category_config.v1",
-        )
-        self.assertIn("crm", source_category_config["categories"])
-        self.assertIn("meeting_notes", source_category_config["categories"])
-        self.assertIn("spreadsheets", source_category_config["categories"])
-        for metadata in source_category_config["categories"].values():
-            self.assertIn("label", metadata)
-            self.assertIsInstance(metadata.get("preferred_apps"), list)
-            self.assertLessEqual(
-                set(metadata),
-                {"label", "preferred_apps", "preferred_plugins", "relevant_skills"},
-            )
-            if "preferred_plugins" in metadata:
-                self.assertIsInstance(metadata["preferred_plugins"], list)
-                for preferred_plugin in metadata["preferred_plugins"]:
-                    self.assertIsInstance(preferred_plugin, str)
-                    self.assertTrue(preferred_plugin)
-            if "relevant_skills" in metadata:
-                self.assertIsInstance(metadata["relevant_skills"], list)
-                for entry in metadata["relevant_skills"]:
-                    self.assertIsInstance(entry, dict)
-                    self.assertIsInstance(entry.get("app"), str)
-                    self.assertIsInstance(entry.get("skill"), str)
-            for preferred_app in metadata["preferred_apps"]:
-                normalized = "".join(ch for ch in preferred_app.casefold() if ch.isalnum())
-                aliases = {normalized}
-                if normalized.endswith("com"):
-                    aliases.add(normalized[:-3])
-                if normalized == "agentforcesales":
-                    aliases.add("salesforce")
-                self.assertTrue(
-                    aliases & declared_apps,
-                    msg=f"{preferred_app} is not declared in .app.json",
-                )
-        self.assertIn(
-            "Outlook Calendar",
-            source_category_config["categories"]["calendar"]["preferred_apps"],
-        )
-        self.assertIn("salesforce", declared_apps)
-        self.assertIn(
-            "Outlook Email",
-            source_category_config["categories"]["external_messaging"]["preferred_apps"],
-        )
-        self.assertIn(
-            "Microsoft Teams",
-            source_category_config["categories"]["internal_messaging"]["preferred_apps"],
-        )
-        self.assertIn(
-            "Microsoft SharePoint",
-            source_category_config["categories"]["document_store"]["preferred_apps"],
-        )
-        self.assertEqual(
-            source_category_config["categories"]["spreadsheets"]["preferred_plugins"],
-            ["Spreadsheets", "Microsoft Excel"],
-        )
-        self.assertEqual(
-            source_category_config["categories"]["spreadsheets"]["preferred_apps"],
-            ["Google Drive", "Microsoft SharePoint"],
-        )
-        self.assertEqual(
-            source_category_config["categories"]["meeting_notes"]["preferred_apps"],
-            ["Zoom", "Granola", "Otter.ai", "Fireflies", "Outreach", "Rox"],
-        )
-        self.assertIn(
-            '"options": ["Zoom", "Granola", "Otter.ai", "Fireflies", "Outreach", "Rox"]',
-            source_category_runtime,
-        )
-        self.assertIn("Plugin-First Setup For Missing Sources", source_category_runtime)
-        self.assertIn("functions.list_available_plugins_to_install", source_category_runtime)
-        self.assertIn("functions.request_plugin_install", source_category_runtime)
-        self.assertIn("Available plugins", source_category_runtime)
-        self.assertIn("Available skills", source_category_runtime)
-        self.assertIn("plugin_install_evidence", source_category_runtime)
-        self.assertIn("skill_surface_evidence", source_category_runtime)
-        self.assertIn("app_surface_evidence", source_category_runtime)
-        self.assertIn("run_plugin_first_source_setup", source_category_runtime)
-        self.assertIn("saved_source_preferences", source_category_runtime)
-        self.assertIn("plugin_preference_order", source_category_runtime)
-        self.assertIn("plugin_first_source_setup", source_category_runtime)
-        self.assertIn("connector-id intersection", source_category_runtime)
-        self.assertIn("setup fails", source_category_runtime)
-        self.assertIn("surface plugin-first setup again in a future workflow", source_category_runtime)
-        self.assertIn("Prefer plugin candidates over connector candidates", source_category_runtime)
-        self.assertIn(
-            "including a plugin that contains or declares a connector/app id",
-            source_category_runtime,
-        )
-        self.assertIn(
-            "add dedicated Sales workflow support",
-            source_category_runtime,
-        )
-        self.assertIn("Do not use `tool_search` as proof", source_category_runtime)
-        self.assertIn("## Runtime Source Resolution", source_category_runtime)
-        self.assertIn("A successful read is evidence for this run only", source_category_runtime)
-        self.assertIn("## Durable Source Preferences", source_category_runtime)
-        self.assertIn("I do not have ZoomInfo", source_category_runtime)
-        self.assertNotIn("Importance:", source_category_config_text)
-        self.assertNotIn("user_importance", source_category_config_text)
-        self.assertNotIn("- Priority:", source_category_config_text)
-        self.assertNotIn("- Typical apps:", source_category_config_text)
-        self.assertNotIn("Required for:", source_category_config_text)
-        self.assertNotIn("Recommended for:", source_category_config_text)
-        self.assertNotIn("why_it_matters", source_category_config_text)
-        self.assertNotIn("most_useful_for", source_category_config_text)
-        self.assertNotIn("verification_note", source_category_config_text)
-        self.assertFalse((SKILL_ROOT / "references/category-state-template.json").exists())
-        self.assertFalse((SKILL_ROOT / "references/hero-prompts.md").exists())
-
-    def test_automation_config_is_author_friendly_and_preflight_summarizes_it(self) -> None:
-        automation_config = (
-            PLUGIN_ROOT / "skills/user-context/plugin-author-config/automation-config.md"
-        ).read_text(encoding="utf-8")
-
-        for phrase in (
-            "- Name:",
-            "- Frequency:",
-            "- Instructions:",
-            "daily_sales_tips",
-            "daily_meeting_prep",
-            "weekly_sales_company_research",
-            "## Later Journey Automations",
-            "Load and follow `$sales:suggest-sales-next-step`",
-            "scheduled Sales check-in mode",
-            "short natural-language check-in",
-            "next Sales workflow worth trying",
-            "Load and follow `$sales:sales-company-research`",
-            "scheduled research mode",
-            "dynamic source discovery",
-            "Sales user-context save policy",
-            "coverage-note guidance",
-            "compare against entries already flagged or saved from this pinned thread",
-            "Load and follow `$sales:prepare-for-meeting`",
-        ):
-            self.assertIn(phrase, automation_config)
-        for runtime_detail in (
-            "Use GPT-5.5 with",
-            "- Kind:",
-            "- RRULE:",
-            "- Skill:",
-            "- Scheduled mode:",
-            "- Prompt:",
-            "Onboarding notification:",
-            "- Target thread title:",
-            "Sales company research is complete. {N} new resources saved.",
-        ):
-            self.assertNotIn(runtime_detail, automation_config)
-
-        payload = self.preflight("--request-mode", "direct_onboarding_status")
-        automation_plan = payload["context"]["automations"]
-        self.assertEqual(
-            automation_plan["daily_sales_tips"]["frequency"],
-            "weekdays at 9:00 AM local time",
-        )
-        self.assertEqual(
-            automation_plan["daily_meeting_prep"]["status"],
-            "not_configured",
-        )
-        self.assertNotIn("instructions", automation_plan["daily_meeting_prep"])
-        self.assertNotIn("target_thread_title", automation_plan["weekly_sales_company_research"])
-        self.assertNotIn("instructions", automation_plan["weekly_sales_company_research"])
-
-    def test_automation_runtime_specifies_supported_model_and_reasoning(self) -> None:
-        automation_reference = AUTOMATION_REFERENCE.read_text(encoding="utf-8")
-
-        for phrase in (
-            '`model="gpt-5.5"`',
-            '`thinking="low"`',
-            '`thinking="xhigh"`',
-            "do not fall back to `gpt-5`",
-            "## Fast Setup Checklist",
-            "Discover `automation_update` and the thread tools",
-            "Create or update the automation with `kind=\"heartbeat\"`",
-            "the exact `targetThreadId`",
-            "stored `target_thread_id` matches the pinned thread",
-            "Update `onboarding-state.json` with only operational metadata",
-            "Stop. Report the concise setup result",
-            "No setup kickoff; let the first scheduled check-in run on its own",
-            "Do not send an immediate Sales Tips kickoff during onboarding",
-            "Do not mark setup complete after a plain cron automation",
-            "Sales Company Research: load and follow `$sales:sales-company-research`",
-            "Do not paste source-family lists, ranking rules, save gates, output examples, model names, reasoning effort, or focused-skill workflow contracts",
-        ):
-            self.assertIn(phrase, automation_reference)
-
-    def test_large_context_still_renders_compact_context(self) -> None:
-        self.state_dir.mkdir(parents=True)
-        (self.state_dir / "user-context.md").write_text(
-            "# Action Orientation Preferences\n\n"
-            "## Saved Links And Context\n\n"
-            "Concise prep briefs\n"
-            "- Date Added: 2026-05-22.\n"
-            "- Useful Context: Section extraction validation.\n"
-            "- Future Use: Preserve nested subsection content.\n\n"
-            "# Other Category\n\n" + ("x" * 500),
-            encoding="utf-8",
-        )
-
-        payload = self.preflight(
-            "--max-context-bytes",
-            "80",
-            "--section",
-            "Action Orientation Preferences",
-        )
-
-        self.assertNotIn("user_context_markdown", payload["context"])
-        self.assertNotIn("omitted", payload["provenance"])
-        self.assertEqual(payload["provenance"]["user_context"], "read")
-        entry = payload["context"]["user_context"]["entries"][0]
-        self.assertEqual(
-            entry,
-            "Action Orientation Preferences: Concise prep briefs | Section extraction validation",
-        )
-
-    def test_onboarding_copy_keeps_visible_action_close_small_and_plugin_first(self) -> None:
-        onboarding = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        plugin_memory = (SKILL_ROOT / "references/plugin-memory.md").read_text(encoding="utf-8")
-        automation_config = (
-            PLUGIN_ROOT / "skills/user-context/plugin-author-config/automation-config.md"
-        ).read_text(encoding="utf-8")
-
-        self.assertIn("# Sales Onboarding Flow", onboarding)
-        self.assertIn("Step 1: Orientation", onboarding)
-        self.assertIn("Ordinary Workflow Onboarding CTA", onboarding)
-        self.assertIn("## Sales Setup Required", onboarding)
-        self.assertIn("Reply `start` to continue.", onboarding)
-        self.assertIn(
-            "This is required before Sales can reliably use your connected sources",
-            onboarding,
-        )
-        self.assertIn(
-            "Every onboarding message that is not complete must include primary content",
-            onboarding,
-        )
-        self.assertIn("`Next Step` is the action prompt, not the explainer", onboarding)
-        self.assertIn(
-            "A natural final sentence, a compact action frame, or a self-contained numbered choice set can satisfy this requirement",
-            onboarding,
-        )
-        self.assertIn(
-            "Do not let the whole onboarding message be only a next-step frame", onboarding
-        )
-        self.assertIn("When the current step has open questions", onboarding)
-        self.assertIn(
-            "Use a compact numbered list when there is more than one question", onboarding
-        )
-        self.assertIn(
-            "include the default escape hatch inside the relevant numbered item",
-            onboarding,
-        )
-        self.assertIn("Do not add a separate summary prompt after the numbered list", onboarding)
-        self.assertIn(
-            "When introducing a workflow, skill, or new onboarding concept for the first time",
-            onboarding,
-        )
-        self.assertIn("## New Concept Introduction Pattern", onboarding)
-        self.assertIn("Do not create tiny transition turns", onboarding)
-        self.assertIn("Okay, I've saved that. Now let's move on to the next step.", onboarding)
-        self.assertIn("Okay, that finishes source setup for now. Sales will use", onboarding)
-        self.assertIn(
-            "do not render the full `Saved Sales Plugin Memory.` recap or `Saved today` list",
-            onboarding,
-        )
-        self.assertIn(
-            "Preserve real approval gates",
-            onboarding,
-        )
-        self.assertIn("## {Concept Name}", onboarding)
-        self.assertIn("the body should start with `This skill...`", onboarding)
-        self.assertIn(
-            "Plugin memory is how the Sales plugin remembers approved preferences",
-            onboarding,
-        )
-        self.assertIn("simple automations to ensure Sales keeps getting better for you", onboarding)
-        self.assertIn("Do not render user-facing CTAs with code-styled skill names", onboarding)
-        self.assertIn("User-facing bullets should be polished sentence fragments", onboarding)
-        self.assertIn("Do not start bullets with lowercase words", onboarding)
-        self.assertIn("teach the preferred invocation pattern", onboarding)
-        self.assertIn("The Sales plugin improves Codex at sales-related work", onboarding)
-        self.assertIn("**How it works**", onboarding)
-        self.assertIn("**What it can do**", onboarding)
-        self.assertIn("It automatically improves your experience", onboarding)
-        self.assertIn("start with `@Sales`", onboarding)
-        self.assertIn("start a new thread and include `@Sales`", onboarding)
-        self.assertIn("Ready to set that up?", onboarding)
-        self.assertIn("After some searching, it looks like you're already set up", onboarding)
-        self.assertIn("calls `functions.list_available_plugins_to_install` once for the setup pass", onboarding)
-        self.assertIn("If the user confirms an installable plugin or connector candidate", onboarding)
-        self.assertIn("installable related plugin needs approval", onboarding)
-        self.assertIn("saved_source_preferences", onboarding)
-        self.assertIn("add dedicated Sales workflow support", onboarding)
-        self.assertIn("Keep plugin-first setup eligible for retry in future workflows", onboarding)
-        self.assertIn("Do not call `request_plugin_install` in parallel", onboarding)
-        self.assertIn("## Active Onboarding Reminder", onboarding)
-        self.assertIn("Do not use the `## Sales Setup Required` start CTA", onboarding)
-        self.assertIn("@Sales prepare me for my Acme renewal call tomorrow", onboarding)
-        self.assertIn(
-            "The numbered choice set is the final action prompt by itself",
-            onboarding,
-        )
-        self.assertIn("do not add a `Next Step` heading", onboarding)
-        self.assertIn(
-            "Keep automation breadcrumbs, readback cards, and created-card text visually separate from the choice set",
-            onboarding,
-        )
-        self.assertIn("Do not reuse a stale checklist from an earlier thread turn", onboarding)
-        self.assertIn("mark the next unresolved visible step as `in_progress`", onboarding)
-        self.assertIn("Do not render `Next Step` as an H1/H2/H3 heading", onboarding)
-        self.assertIn("**Onboarding Complete**", onboarding)
-        self.assertIn("You can keep going through the rest of the Sales skills", onboarding)
-        self.assertIn("Say `okay` and I'll introduce Analyze Account Signals", onboarding)
-        self.assertIn("Progress Checklist Contract", onboarding)
-        self.assertIn("Orientation", onboarding)
-        self.assertIn("Connector setup/confirmation", onboarding)
-        self.assertIn("Sales automation setup", onboarding)
-        self.assertIn("First hero prompt", onboarding)
-        self.assertIn("Other hero prompts", onboarding)
-        self.assertIn("core onboarding is complete", onboarding)
-        self.assertNotIn("Choose prepare-for-meeting prompt", onboarding)
-        self.assertNotIn("Save prep doc", onboarding)
-        self.assertNotIn("Explain plugin memory", onboarding)
-        self.assertNotIn("Capture key context", onboarding)
-        self.assertIn("Choose Next Hero Prompt", onboarding)
-        self.assertIn("Introduce Selected Remaining Hero Workflow", onboarding)
-        self.assertIn("Run Selected Remaining Hero Demo", onboarding)
-        self.assertIn("Review And Continue Remaining Hero", onboarding)
-        self.assertIn("Sales Daily Meeting Prep", onboarding)
-        self.assertNotIn("Sales Weekday Check-In", onboarding)
-        self.assertNotIn("**Next Step: Resolve Sources, Then Try Prepare For Meeting**", onboarding)
-        self.assertNotIn("Two source notes before we continue", onboarding)
-        self.assertNotIn("Reply with a docs preference or enrichment info", onboarding)
-        self.assertIn("You can skip anything now; Sales will ask again later", onboarding)
-        self.assertIn("## Step 4: First Hero Prompt", onboarding)
-        self.assertIn("Do not run another hero workflow until the user picks", onboarding)
-        self.assertIn("treat the numbered choice as consent to try that workflow", onboarding)
-        self.assertIn(
-            "render that skill's compact first-run intro above the normal workflow output",
-            onboarding,
-        )
-        self.assertIn("choose_first_hero_prompt", onboarding)
-        self.assertIn("Introduce Selected First Hero Workflow", onboarding)
-        self.assertIn("## Prepare For Meeting", onboarding)
-        self.assertIn("start fresh with `This skill...`", onboarding)
-        prepare_intro = onboarding.split("For Prepare For Meeting", 1)[1][:700]
-        self.assertIn("`okay`", prepare_intro)
-        self.assertIn("`calendar`", prepare_intro)
-        self.assertIn("manual meeting details", prepare_intro)
-        self.assertIn("skills/prepare-for-meeting/SKILL.md", onboarding)
-        self.assertIn("inline experience guidance", onboarding)
-        self.assertIn("move directly into Step 3A", onboarding)
-        self.assertNotIn("Want me to introduce Prepare For Meeting next?", onboarding)
-        self.assertIn("Sales will ask again later only when a workflow would materially benefit", onboarding)
-        self.assertNotIn("**Next Step: Set Up Sales Automations**", onboarding)
-        self.assertNotIn("\n## Sales Plugin Automations\n", onboarding)
-        self.assertIn(
-            "Should I set those up? They'll run in a different thread and notify you when they have something to review.",
-            onboarding,
-        )
-        self.assertIn("Sales Tips", onboarding)
-        self.assertIn(
-            "Do not load hero workflow skill files or run a demo workflow until the user picks one",
-            onboarding,
-        )
-        self.assertIn("automation.md#Fast Setup Checklist", onboarding)
-        self.assertIn("Automations are set up and read back cleanly", onboarding)
-        self.assertIn(
-            "I also kicked off the first research run in the pinned **Sales Company Research** thread",
-            onboarding,
-        )
-        self.assertIn(
-            "starting with its next scheduled run",
-            onboarding,
-        )
-        self.assertIn("one practical Sales workflow to try next", onboarding)
-        self.assertIn("Do not compress the three hero choices into one inline sentence", onboarding)
-        self.assertIn("each numbered option must be on its own line", onboarding)
-        self.assertIn("Core onboarding is done. Pick a workflow to try first:", onboarding)
-        self.assertIn(
-            "1. **Prepare For Meeting:** Builds a concise brief for an upcoming customer or high-value sales meeting.",
-            onboarding,
-        )
-        self.assertIn(
-            "2. **Follow Up After Call:** Turns a recent call or notes into a recap, next steps, email draft, CRM-ready update, and internal recap.",
-            onboarding,
-        )
-        self.assertIn(
-            "3. **Prioritize Accounts:** Helps you understand what needs to be done with your accounts by flagging anomalies, building forecasts, and prioritizing accounts.",
-            onboarding,
-        )
-        self.assertIn("1. **{Core skill display name}:** {One-sentence value description", onboarding)
-        self.assertNotIn("1. Prepare For Meeting\n   {One-sentence value description", onboarding)
-        self.assertNotIn("2. Follow Up After Call\n   {One-sentence value description", onboarding)
-        self.assertIn("3. **{Core skill display name}:** {One-sentence value description", onboarding)
-        self.assertNotIn(
-            "Example prompt: `@Sales {realistic prompt from that skill's experience file}`",
-            onboarding,
-        )
-        self.assertNotIn("Choose one to try first: `1`", onboarding)
-        self.assertNotIn("Pick `1`, `2`, or `3`, or name a real meeting", onboarding)
-        self.assertIn("weekly on Mondays at 9:00 AM local time", onboarding)
-        self.assertIn("runs weekdays at 9:00 AM local time", onboarding)
-        self.assertIn('FYI: The pinned thread **"{thread_title}"**', onboarding)
-        self.assertIn(
-            "Only say saved entries or proposals exist when state or readback confirms them",
-            onboarding,
-        )
-        self.assertNotIn("**Next Step: Try Call Follow-Up**", onboarding)
-        self.assertIn("## Step 5: Other Hero Prompts", onboarding)
-        self.assertIn("choose_next_hero_prompt", onboarding)
-        self.assertIn(
-            "Hero demos use a repeatable artifact loop",
-            onboarding,
-        )
-        self.assertIn("hero_workflow.current_status", onboarding)
-        self.assertIn("offer_first_hero_action_or_next_demo", onboarding)
-        self.assertIn("offer_remaining_hero_action_or_next_demo", onboarding)
-        self.assertIn("normal next-step candidates", onboarding)
-        self.assertIn("show the next demo workflow choices", onboarding)
-        self.assertIn("start fresh with `This skill...`", onboarding)
-        self.assertIn(
-            "Choices can include the previous one or two workflows as repeat options",
-            onboarding,
-        )
-        self.assertIn("Do not offer only previously tried workflows", onboarding)
-        self.assertIn("skills/follow-up-after-call/SKILL.md", onboarding)
-        self.assertNotIn("**Next Step: Try Prioritize Accounts**", onboarding)
-        self.assertIn("## Step 6: Completion", onboarding)
-        self.assertIn(
-            "prioritize open pipeline by default",
-            onboarding,
-        )
-        self.assertIn("skills/prioritize-accounts/SKILL.md", onboarding)
-        self.assertNotIn("Add starter context", onboarding)
-        self.assertNotIn("Connected Source Check", onboarding)
-        self.assertIn("Do not preflight-read installed or active sources", onboarding)
-        self.assertIn("Calendar attachment is explicit-user-request only", onboarding)
-        self.assertIn("response-first fast path", onboarding)
-        self.assertIn("Do not run `../scripts/init_user_context_state.py`", onboarding)
-        self.assertIn("not in the main onboarding thread", onboarding)
-        self.assertIn("Install or repair only `weekly_sales_company_research` and `daily_sales_tips`", onboarding)
-        self.assertIn("`weekly_sales_company_research` and `daily_sales_tips`", onboarding)
-        self.assertNotIn("Want me to introduce Follow Up After Call next?", onboarding)
-        self.assertIn("return to the next demo workflow chooser", onboarding)
-        self.assertIn("The initial Sales Company Research kickoff belongs in the pinned", onboarding)
-        sales_company_research = SALES_COMPANY_RESEARCH_SKILL.read_text(encoding="utf-8")
-        self.assertIn("pinned `Sales Company Research` automation thread", plugin_memory)
-        self.assertIn("Exception: during the routine onboarding source setup step", plugin_memory)
-        self.assertIn(
-            "Plugin memory is how the Sales plugin remembers approved preferences",
-            plugin_memory,
-        )
-        self.assertIn("Do not copy saved resource lists", plugin_memory)
-        self.assertIn("Saved today:", plugin_memory)
-        self.assertIn("save the accepted narrow preference in the same response", plugin_memory)
-        self.assertIn("source-of-truth resources and answer/style preferences", plugin_memory)
-        self.assertIn(
-            "Discovery-derived memory may be saved by default only when it is high-confidence",
-            plugin_memory,
-        )
-        self.assertIn("Route company-context discovery to `sales-company-research`", plugin_memory)
-        self.assertIn("This reference owns the save and review gates", plugin_memory)
-        self.assertIn("Keep the automation prompt thin by invoking `sales-company-research`", plugin_memory)
-        self.assertIn("company-unique semantics", sales_company_research)
-        self.assertIn("authoritative interpretation layers", sales_company_research)
-        self.assertIn("pipeline stage definitions and exit criteria", sales_company_research)
-        self.assertIn("do not append onboarding reminders or onboarding CTAs", sales_company_research)
-        self.assertIn("Do not stop after the first few obviously relevant docs", sales_company_research)
-        self.assertIn("second-pass search", sales_company_research)
-        self.assertIn("Keep multiple resources when they serve distinct authority lanes", sales_company_research)
-        self.assertIn("one-sentence utility description", sales_company_research)
-        self.assertIn("Sales company research is complete. {N} new resources saved.", sales_company_research)
-        self.assertIn("What was found", sales_company_research)
-        self.assertIn("Where you can help", sales_company_research)
-        self.assertIn("Do not include coverage notes or operational research bookkeeping", sales_company_research)
-        self.assertIn("safe-use caveat", sales_company_research)
-        self.assertIn("legal/comms/customer-facing claims guardrails", sales_company_research)
-        self.assertIn("up to five `Where you can help` questions", sales_company_research)
-        self.assertIn("Start each bullet with the resource title as a Markdown link", sales_company_research)
-        self.assertIn("Do not include category labels such as `saved under`", sales_company_research)
-        self.assertIn("Every saved resource must explain why it helps the user", sales_company_research)
-        self.assertIn("Do not output bare link bullets", sales_company_research)
-        self.assertIn("Convert material limitations into concrete `Where you can help` questions", sales_company_research)
-        self.assertIn(
-            "Load and follow `$sales:sales-company-research` in scheduled research mode",
-            automation_config,
-        )
-        self.assertIn(
-            "dynamic source discovery",
-            automation_config,
-        )
-        self.assertNotIn("Use GPT-5.5 with", automation_config)
-        self.assertIn("Iteration Acceptance Learning", plugin_memory)
-        self.assertNotIn("3-5 minutes", onboarding)
-        self.assertNotIn("setup/check usually takes 3-5 minutes", onboarding)
-        self.assertNotIn("Saved setup state", onboarding)
-        self.assertNotIn("Onboarding status", onboarding)
-
-    def test_skill_experience_examples_teach_sales_invocation_pattern(self) -> None:
-        self.assertFalse((SKILL_ROOT / "references/hero-prompts.md").exists())
-        prepare_skill = MEETING_PREP_SKILL.read_text(encoding="utf-8")
-        followup_skill = CALL_FOLLOWUP_SKILL.read_text(encoding="utf-8")
-        internal_sources_skill = INTERNAL_NAVIGATION_SKILL.read_text(encoding="utf-8")
-        index_skill = (PLUGIN_ROOT / "skills/index/SKILL.md").read_text(encoding="utf-8")
-        onboarding = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-        user_context_skill = (PLUGIN_ROOT / "skills/user-context/SKILL.md").read_text(
-            encoding="utf-8"
-        )
-
-        self.assertIn("### First-Run Banner", prepare_skill)
-        self.assertIn("## Prepare For Meeting", prepare_skill)
-        self.assertIn("This skill creates a concise brief", prepare_skill)
-        self.assertNotIn("Prepare For Meeting is a skill in the Sales plugin", prepare_skill)
-        self.assertIn("### First-Run Banner", followup_skill)
-        self.assertIn("## Follow Up After Call", followup_skill)
-        self.assertIn("This skill turns a recent customer", followup_skill)
-        self.assertNotIn("Follow Up After Call is a skill in the Sales plugin", followup_skill)
-        self.assertIn("A quick call summary", onboarding)
-        self.assertIn(
-            "@Sales find who owns the Enterprise SSO rollout path for Acme",
-            internal_sources_skill,
-        )
-        self.assertIn("### First-Run Banner", internal_sources_skill)
-        self.assertIn("## Find Key Internal Sources", internal_sources_skill)
-        self.assertIn(
-            "This skill helps locate the people",
-            internal_sources_skill,
-        )
-
-        self.assertIn(
-            "start with `@Sales`, name the sales job, and use a realistic anchor", index_skill
-        )
-        self.assertIn("@Sales prepare me for my next customer meeting", index_skill)
-        self.assertIn("@Sales follow up from my latest customer call", index_skill)
-        self.assertIn("@Sales prioritize my accounts for pipeline focus this week", index_skill)
-        self.assertIn(
-            "Loading the relevant skill-owned experience guidance is mandatory whenever that skill is the primary workflow",
-            user_context_skill,
-        )
-        self.assertIn(
-            "the final continuation invariant for primary outputs and follow-up turns",
-            user_context_skill,
-        )
-        self.assertIn(
-            "end with one concrete user-visible next action phrased as a natural sentence or question",
-            user_context_skill,
-        )
-        self.assertIn(
-            "Clarification questions must be the final natural continuation",
-            user_context_skill,
-        )
-        self.assertIn(
-            "render the compact first-run intro section",
-            user_context_skill,
-        )
-
-        for skill_name in USER_FACING_SKILLS:
-            skill_text = (PLUGIN_ROOT / f"skills/{skill_name}/SKILL.md").read_text(encoding="utf-8")
-            self.assertFalse(
-                (PLUGIN_ROOT / f"skills/{skill_name}/references/experience.md").exists()
-            )
-            self.assertIn("### First-Run Banner", skill_text)
-            self.assertIn("### Next Step Guidance", skill_text)
-            self.assertIn(
-                "#### Final Continuation Invariant",
-                skill_text,
-            )
-            self.assertIn("another active parent flow owns the final CTA", skill_text)
-
-    def test_prepare_for_meeting_offers_daily_prep_before_meeting_doc(self) -> None:
-        skill_text = MEETING_PREP_SKILL.read_text(encoding="utf-8")
-
-        self.assertLess(
-            skill_text.index("| **Offer Daily Meeting Prep**"),
-            skill_text.index("| **Create a meeting doc**"),
-        )
-        self.assertIn(
-            "Daily Meeting Prep is the primary accepted-brief continuation owned by this skill",
-            skill_text,
-        )
-        self.assertIn(
-            "Do not offer meeting doc creation as the accepted-brief default while Daily Meeting Prep remains eligible",
-            skill_text,
-        )
-        self.assertIn(
-            "if Daily Meeting Prep is eligible, move to `Offer Daily Meeting Prep`",
-            skill_text,
-        )
-        self.assertIn(
-            "If the previous continuation was `Offer Daily Meeting Prep`, treat a lightweight acknowledgement as approval",
-            skill_text,
-        )
-
-    def test_onboarding_has_no_eager_active_connector_reads(self) -> None:
-        onboarding = ONBOARDING_REFERENCE.read_text(encoding="utf-8")
-
-        self.assertIn("Do not preflight-read installed or active sources just to prove they work", onboarding)
-        self.assertIn("prefer plugin setup", onboarding)
-        self.assertIn("direct app/connector setup", onboarding)
-        self.assertIn("without a proof read", onboarding)
-        self.assertNotIn("verified source", onboarding.casefold())
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/plugins/sales/skills/zoominfo/SKILL.md b/plugins/sales/skills/zoominfo/SKILL.md
index 782c666..af32153 100644
--- a/plugins/sales/skills/zoominfo/SKILL.md
+++ b/plugins/sales/skills/zoominfo/SKILL.md
@@ -1,31 +1,37 @@
 ---
 name: zoominfo
-description: ZoomInfo connector guide for Sales workflows or explicit ZoomInfo requests involving company search, contact search, enrichment, intent signals, similar-account discovery, contact recommendations, and company or contact research.
+description: Use only when a focused Sales workflow has selected a present and connected ZoomInfo connector, or the user explicitly asks for ZoomInfo company search, contact search, enrichment, intent, similarity, recommendations, or research. Do not use when another enrichment source is selected.
 ---
 
 # ZoomInfo User Guide
 
-Use this as the ZoomInfo-specific guide whenever the user or workflow is using ZoomInfo for market intelligence, data enrichment, prospect discovery, company/contact research, intent signals, lookalikes, or recommendation workflows. Keep the surrounding Sales workflow authoritative for the seller task itself; this guide adds ZoomInfo-specific routing, identifier resolution, query hygiene, enrichment sequencing, and result-quality guardrails.
+Use ZoomInfo for focused Sales Intelligence work after the parent Sales workflow selects it. Keep the parent workflow authoritative for the seller's task; this guide owns ZoomInfo-specific lookup, search, identity, enrichment, credit, and result-quality rules.
 
-Do not use this guide when the workflow uses another enrichment provider or no ZoomInfo connector.
+## Common Skill Instructions
 
-## Skill Configuration
+MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
 
-### User Context
+## ZoomInfo Operation Ranking
 
-Mandatory pre-answer gate: Invoke `sales:user-context` in preflight mode by loading `[$sales:user-context](../user-context/SKILL.md)` and running its preflight script before answering, searching connectors, retrieving evidence, or drafting output. Do not look for a callable MCP tool named `sales:user-context`. Use the returned `sales_preflight` envelope as authoritative for saved context, source-category mapping, final obligations, and conditional guidance. Do not read or reinterpret raw Sales state files unless preflight fails, local shell access is unavailable, or the user explicitly asks for raw state inspection.
+1. Explicit user request, named company or person, domain, email, ZoomInfo ID, or stated constraint
+2. The narrowest ZoomInfo lane needed by the active Sales workflow
+3. Canonical company or contact identity resolved from strong identifiers
+4. Search and shortlist before enrichment, research, similarity, or recommendations
+5. The smallest credit-consuming step that materially improves the answer
+6. A result that clearly separates qualified matches, near matches, and gaps
 
-Use returned user context only as a relevance hint for ICP, territory, persona, terminology, and preferred output style. Do not let saved context replace ZoomInfo identifiers, supported lookup values, connector-returned facts, or explicit user constraints.
+Do not silently relax hard filters, guess identities, or present ZoomInfo as CRM ground truth.
 
-### Audience And Language
+## Key Dependency Categories
 
-Write for Sales users, not plugin maintainers. This applies to final answers, setup/status readbacks, failure explanations, tool preambles, and mid-turn progress narration.
+These are particularly important for this workflow; use your best judgment to potentially include other data sources to improve quality.
 
-Translate implementation work into practical Sales impact: what Sales is checking, setting up, saving, or preparing, and why it matters. Avoid implementation terms such as preflight, state file, cache, raw connector id, heartbeat, targetThreadId, schema, API, runtime, metadata, and provider taxonomy unless the user asks for debugging details.
+- ~~Sales Intelligence, specifically a present and connected ZoomInfo connector, for ZoomInfo search, enrichment, intent, similarity, recommendations, and research
+- ~~CRM for account ownership, customer status, opportunity facts, and forecast truth
+- The parent Sales workflow for Calendar, Meeting Transcripts, Email, Internal Messaging, or Knowledge & Files context
+- User-provided names, domains, emails, IDs, ICP constraints, and business context when connector access or identity is incomplete
 
-### Source Links
-
-When referencing sources inline, prefer clickable Markdown links over plain bracket labels whenever the source exposes a useful URL. Use the source title, record name, channel/thread, or meeting/date as the link text, for example a clickable Markdown link whose visible text is `Meeting notes: May 19` or `Slack thread: May 15-21`. Use plain text labels only when no useful URL or stable connector-visible link is available, and say `(no useful link available)` when that absence matters.
+If ZoomInfo is missing, unavailable, ambiguous, or limited by the exposed connector surface, state the limitation instead of substituting unsupported claims.
 
 ## Connector Boundary
 
@@ -44,7 +50,7 @@ Do not blur these lanes:
 - Use `account_research` or `contact_research` when the user wants a narrative brief, meeting prep, or broader situational readout.
 - Use similarity or recommendation actions for "more like this", account expansion, or stakeholder discovery, not as a substitute for precise search filters.
 
-Do not present ZoomInfo as CRM ground truth. If a research response includes relationship or engagement context, label it as connector-surfaced context unless it is independently grounded by the surrounding workflow.
+Do not present ZoomInfo as CRM ground truth. If a research response includes relationship or engagement context, label it as connector-surfaced context unless it is independently grounded by ~~CRM or the surrounding workflow.
 
 ## Default Resolution Pattern
 
@@ -185,6 +191,7 @@ Do not add friction for a small, clearly requested enrichment task. Do be explic
 ## Output Rules
 
 - Prefer compact tables for candidate lists, enrichment outputs, similar-company lists, and contact recommendations.
+- Prefer clickable connector-returned links when available; never construct guessed links from opaque IDs.
 - Separate:
   - `Qualified matches`
   - `Near matches` when useful
@@ -193,3 +200,27 @@ Do not add friction for a small, clearly requested enrichment task. Do be explic
 - Do not guess emails, phone numbers, exact intent, or confidence levels not surfaced by the connector.
 - When the answer depends on connector-reported fields or recommendation metadata, say so directly.
 - If the user's request asked for a ranked list, rank by explicit connector signal or stated criteria, not by invisible model preference.
+
+#### Output Format
+
+    # [ZoomInfo Search / Enrichment / Research]
+
+    ## Qualified Matches
+
+    | Company / Person | Key Match Evidence | Useful Detail | Source |
+    | --- | --- | --- | --- |
+    | [Result] | [Hard filters satisfied] | [Returned field] | [Link or connector source] |
+
+    ## Near Matches
+
+    - [Result + exact mismatch, when useful]
+
+    ## Gaps / Connector Limitations
+
+    - [Missing field, unsupported filter, bounded coverage, ambiguity, or credit-aware next step]
+
+    ---
+
+    {Follow the instructions and output format/conditions in [Limitations and Improvements](../index/SKILL.md#limitations-and-improvements)}
+
+    {Follow the instructions and output format/conditions in [Next Steps](../index/SKILL.md#4-next-steps)}